Bioethanol Fuel Production Processes. II: Biomass Hydrolysis, Fermentation, and Bioethanol Fuel Separation [1 ed.] 1032127503, 9781032127507

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Bioethanol Fuel Production Processes. II This book provides an overview of the research on production processes for bioethanol fuels in general, hydrolysis of the pretreated biomass for bioethanol production, microbial fermentation of hydrolysates and substrates with yeasts for bioethanol production, and separation and distillation of bioethanol fuels from the fermentation broth, complementing the research on biomass pretreatments presented in the first volume. It presents an overview of the research on biomass hydrolysis in general, wood hydrolysis, straw hydrolysis, and cellulose hydrolysis for bioethanol fuel production in the first section for biomass hydrolysis. It provides an overview of the research on microbial hydrolysate fermentation for bioethanol production in general, alternative fermentation processes for bioethanol fuel production such as simultaneous saccharification and fermentation (SSF) and consolidated biomass processing (CBP) compared with the separate hydrolysis and fermentation (SHF) process, metabolic engineering of microorganisms and substrates for bioethanol fuel production, and utilization of Saccharomyces cerevisiae for microbial fermentation of hydrolysates for bioethanol fuel production in the second section for hydrolysate fermentation. It provides an overview of the research on the bioethanol fuel separation from the fermentation broth in the last section. This book is a valuable resource for the stakeholders primarily in the research fields of energy and fuels, chemical engineering, environmental science and engineering, biotechnology, microbiology, chemistry, physics, mechanical engineering, agricultural sciences, food science and e­ ngineering, materials science, biochemistry, genetics, molecular biology, plant sciences, water resources, economics, business, management, transportations science and technology, ecology, public, environmental and occupational health, social sciences, toxicology, multidisciplinary sciences, and humanities among others.

Bioethanol Fuel Production Processes. II Biomass Hydrolysis, Fermentation, and Bioethanol Fuel Separation 

Edited by

Ozcan Konur

Designed cover image: © Shutterstock First edition published 2024 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2024 selection and editorial matter, Ozcan Konur; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 978-1-032-12750-7 (hbk) ISBN: 978-1-032-12855-9 (pbk) ISBN: 978-1-003-22649-9 (ebk) DOI: 10.1201/9781003226499 Typeset in Times by codeMantra

Contents Preface.............................................................................................................................................xxi Acknowledgments......................................................................................................................... xxiii Editor.............................................................................................................................................xxiv Contributors....................................................................................................................................xxv

PART 3  I ntroduction to Production Processes for Bioethanol Fuels Chapter 19 Production Processes for Bioethanol Fuels: Scientometric Study................................3 Ozcan Konur 19.1 Introduction........................................................................................................3 19.2 Materials and Methods.......................................................................................4 19.3 Results................................................................................................................4 19.3.1 The Most Prolific Documents in the Production Processes for Bioethanol Fuels..............................................................4 19.3.2 The Most Prolific Authors in the Production Processes for Bioethanol Fuels..............................................................5 19.3.3 The Most Prolific Research Output by Years in the Production Processes for Bioethanol Fuels.....................................5 19.3.4 The Most Prolific Institutions in the Production Processes for Bioethanol Fuels..............................................................7 19.3.5 The Most Prolific Funding Bodies in the Production Processes for Bioethanol Fuels...........................................8 19.3.6 The Most Prolific Source Titles in the Production Processes for Bioethanol Fuels......................................... 10 19.3.7 The Most Prolific Countries in the Production Processes for Bioethanol Fuels............................................................ 11 19.3.8 The Most Prolific Scopus Subject Categories in the Production Processes for Bioethanol Fuels................................... 12 19.3.9 The Most Prolific Keywords in the Production Processes for Bioethanol Fuels............................................................ 12 19.3.10 The Most Prolific Research Fronts in the Production Processes for Bioethanol Fuels......................................... 14 19.4 Discussion......................................................................................................... 15 19.4.1 Introduction......................................................................................... 15 19.4.2 The Most Prolific Documents in the Production Processes for Bioethanol Fuels............................................................ 17 19.4.3 The Most Prolific Authors in the Production Processes for Bioethanol Fuels............................................................ 17 19.4.4 The Most Prolific Research Output by Years in the Production Processes for Bioethanol Fuels................................... 19 19.4.5 The Most Prolific Institutions in the Production Processes for Bioethanol Fuels............................................................ 19 v

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19.4.6 The Most Prolific Funding Bodies in the Production Processes for Bioethanol Fuels......................................... 19 19.4.7 The Most Prolific Source Titles in the Production Processes for Bioethanol Fuels.........................................20 19.4.8 The Most Prolific Countries in the Production Processes for Bioethanol Fuels.........................................20 19.4.9 The Most Prolific Scopus Subject Categories in the Production Processes for Bioethanol Fuels............................... 21 19.4.10 The Most Prolific Keywords in the Production Processes for Bioethanol Fuels............................................................ 21 19.4.11 The Most Prolific Research Fronts in the Production Processes for Bioethanol Fuels......................................... 22 19.5 Conclusion and Future Research...................................................................... 23 Acknowledgments.......................................................................................................24 References...................................................................................................................24 Chapter 20 Production Processes for Bioethanol Fuels: Review................................................... 30 Ozcan Konur 20.1 Introduction...................................................................................................... 30 20.2 Materials and Methods..................................................................................... 31 20.3 Results.............................................................................................................. 31 20.3.1 Feedstock Pretreatment....................................................................... 31 20.3.2 Hydrolysis of the Feedstocks...............................................................34 20.3.3 Bioethanol Production......................................................................... 37 20.4 Discussion......................................................................................................... 39 20.4.1 Introduction......................................................................................... 39 20.4.2 Feedstock Pretreatment....................................................................... 41 20.4.3 Hydrolysis of the Feedstocks............................................................... 42 20.4.4 Bioethanol Production......................................................................... 43 20.5 Conclusion and Future Research...................................................................... 43 Acknowledgments.......................................................................................................44 References...................................................................................................................44

PART 4  Biomass Hydrolysis for Bioethanol Production Chapter 21 Biomass Hydrolysis: Scientometric Study................................................................... 51 Ozcan Konur 21.1 Introduction...................................................................................................... 51 21.2 Materials and Methods..................................................................................... 51 21.3 Results.............................................................................................................. 52 21.3.1 The Most-Prolific Documents in the Biomass Hydrolysis....................................................................... 52 21.3.2 The Most-Prolific Authors in the Biomass Hydrolysis............................................................................. 52 21.3.3 The Most-Prolific Research Output by Years in Biomass Hydrolysis......................................................................... 54

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21.3.4 The Most-Prolific Institutions in the Biomass Hydrolysis............................................................................. 54 21.3.5 The Most-Prolific Funding Bodies in the Biomass Hydrolysis............................................................................. 56 21.3.6 The Most-Prolific Source Titles in the Biomass Hydrolysis............................................................................. 57 21.3.7 The Most-Prolific Countries in the Biomass Hydrolysis............................................................................................ 58 21.3.8 The Most-Prolific Scopus Subject Categories in the Biomass Hydrolysis................................................................... 59 21.3.9 The Most-Prolific Scopus Keywords in the Biomass Hydrolysis............................................................................. 59 21.3.10 The Most-Prolific Research Fronts in Biomass Hydrolysis............................................................................. 62 21.4 Discussion......................................................................................................... 62 21.4.1 Introduction......................................................................................... 62 21.4.2 The Most-Prolific Documents in the Biomass Hydrolysis............................................................................. 63 21.4.3 The Most-Prolific Authors in the Biomass Hydrolysis............................................................................................64 21.4.4 The Most-Prolific Research Output by Years in the Biomass Hydrolysis................................................................... 65 21.4.5 The Most-Prolific Institutions in the Biomass Hydrolysis............................................................................. 65 21.4.6 The Most-Prolific Funding Bodies in the Biomass Hydrolysis.............................................................................66 21.4.7 The Most-Prolific Source Titles in Biomass Hydrolysis.............................................................................66 21.4.8 The Most-Prolific Countries in the Biomass Hydrolysis............................................................................. 67 21.4.9 The Most-Prolific Scopus Subject Categories in Biomass Hydrolysis......................................................................... 67 21.4.10 The Most-Prolific Scopus Keywords in Biomass Hydrolysis............................................................................. 67 21.4.11 The Most-Prolific Research Fronts in Biomass Hydrolysis............................................................................. 68 21.5 Conclusion and Future Research...................................................................... 68 Acknowledgments....................................................................................................... 69 Appendix: The Keyword Set for Biomass Hydrolysis................................................ 69 References................................................................................................................... 70 Chapter 22 Biomass Hydrolysis: Review....................................................................................... 75 Ozcan Konur 22.1 Introduction...................................................................................................... 75 22.2 Materials and Methods..................................................................................... 75 22.3 Results.............................................................................................................. 76 22.3.1 The Hydrolysis of the Biomass Constituents....................................... 76 22.3.2 The Hydrolysis of the Agricultural Residues...................................... 77 22.3.3 The Hydrolysis of the Wood................................................................ 81

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22.3.4 The Hydrolysis of the Grass................................................................ 83 22.4 Discussion.........................................................................................................84 22.4.1 Introduction.........................................................................................84 22.4.2 The Hydrolysis of the Biomass Constituents....................................... 85 22.4.3 The Hydrolysis of the Agricultural Residues...................................... 86 22.4.4 The Hydrolysis of the Wood................................................................ 86 22.4.5 The Hydrolysis of the Grass................................................................ 87 22.5 Conclusion and Future Research...................................................................... 87 Acknowledgments....................................................................................................... 88 References................................................................................................................... 88 Chapter 23 Wood Hydrolysis: Scientometric Study....................................................................... 91 Ozcan Konur 23.1 Introduction...................................................................................................... 91 23.2 Materials and Methods..................................................................................... 91 23.3 Results..............................................................................................................92 23.3.1 The Most Prolific Documents in Wood Hydrolysis.................................................................................92 23.3.2 The Most Prolific Authors in Wood Hydrolysis..................................92 23.3.3 The Most Prolific Research Output by Years in Wood Hydrolysis...................................................................94 23.3.4 The Most Prolific Institutions in Wood Hydrolysis.................................................................................94 23.3.5 The Most Prolific Funding Bodies in Wood Hydrolysis.................................................................................96 23.3.6 The Most Prolific Source Titles in Cellulose Hydrolysis............................................................................97 23.3.7 The Most Prolific Countries in Wood Hydrolysis................................................................................. 98 23.3.8 The Most Prolific Scopus Subject Categories in Wood Hydrolysis.............................................................................99 23.3.9 The Most Prolific Scopus Keywords in Wood Hydrolysis.............................................................................99 23.3.10 The Most Prolific Research Fronts in Wood Hydrolysis........................................................................... 102 23.4 Discussion....................................................................................................... 103 23.4.1 Introduction....................................................................................... 103 23.4.2 The Most Prolific Documents in Wood Hydrolysis............................................................................... 104 23.4.3 The Most Prolific Authors in Wood Hydrolysis............................................................................... 104 23.4.4 The Most Prolific Research Output by Years in Wood Hydrolysis................................................................. 106 23.4.5 The Most Prolific Institutions in Wood Hydrolysis............................................................................... 106 23.4.6 The Most Prolific Funding Bodies in Wood Hydrolysis............................................................................... 106 23.4.7 The Most Prolific Source Titles in Wood Hydrolysis............................................................................... 107

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23.4.8 The Most Prolific Countries in Wood Hydrolysis............................................................................... 107 23.4.9 The Most Prolific Scopus Subject Categories in Wood Hydrolysis......................................................... 108 23.4.10 The Most Prolific Scopus Keywords in Wood Hydrolysis........................................................................... 108 23.4.11 The Most Prolific Research Fronts in Wood Hydrolysis........................................................................... 108 23.5 Conclusion and Future Research.................................................................... 109 Acknowledgments..................................................................................................... 110 Appendix: The Keyword Set for Wood Hydrolysis................................................... 110 References................................................................................................................. 111 Chapter 24 Wood Hydrolysis: Review......................................................................................... 115 Ozcan Konur 24.1 Introduction.................................................................................................... 115 24.2 Materials and Methods................................................................................... 115 24.3 Results............................................................................................................ 116 24.3.1 Enzymatic Hydrolysis of Wood Combined with Other Pretreatments................................................. 116 24.3.2 Other Issues Regarding Wood Hydrolysis........................................ 121 24.4 Discussion....................................................................................................... 124 24.4.1 Introduction....................................................................................... 124 24.4.2 Enzymatic Hydrolysis of Wood Combined with Other Pretreatments.................................................................. 126 24.4.3 Other Issues Regarding Wood Hydrolysis........................................ 128 24.5 Conclusion and Future Research.................................................................... 128 Acknowledgments..................................................................................................... 129 References................................................................................................................. 129 Chapter 25 Straw Hydrolysis: Scientometric Study..................................................................... 133 Ozcan Konur 25.1 Introduction.................................................................................................... 133 25.2 Materials and Methods................................................................................... 134 25.3 Results............................................................................................................ 134 25.3.1 The Most Prolific Documents in the Straw Hydrolysis..................................................................... 134 25.3.2 The Most Prolific Authors in the Straw Hydrolysis......................................................................... 134 25.3.3 The Most Prolific Research Output by Years in Straw Hydrolysis................................................................. 136 25.3.4 The Most Prolific Institutions in the Straw Hydrolysis............................................................................... 137 25.3.5 The Most Prolific Funding Bodies in the Straw Hydrolysis............................................................................... 138 25.3.6 The Most Prolific Source Titles in the Straw Hydrolysis............................................................................... 139 25.3.7 The Most Prolific Countries in the Straw Hydrolysis....................... 140

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25.3.8 The Most Prolific Scopus Subject Categories in the Straw Hydrolysis..................................................................... 140 25.3.9 The Most Prolific Scopus Keywords in the Straw Hydrolysis......................................................................... 141 25.3.10 The Most Prolific Research Fronts in Straw Hydrolysis............................................................................... 143 25.4 Discussion....................................................................................................... 143 25.4.1 Introduction....................................................................................... 143 25.4.2 The Most Prolific Documents in the Straw Hydrolysis............................................................................... 145 25.4.3 The Most Prolific Authors in the Straw Hydrolysis............................................................................... 146 25.4.4 The Most Prolific Research Output by Years in the Straw Hydrolysis........................................................... 147 25.4.5 The Most Prolific Institutions in the Straw Hydrolysis............................................................................... 148 25.4.6 The Most Prolific Funding Bodies in the Straw Hydrolysis............................................................................... 148 25.4.7 The Most Prolific Source Titles in Straw Hydrolysis............................................................................... 148 25.4.8 The Most Prolific Countries in the Straw Hydrolysis............................................................................... 149 25.4.9 The Most Prolific Scopus Subject Categories in Straw Hydrolysis............................................................................... 149 25.4.10 The Most Prolific Scopus Keywords in Straw Hydrolysis........................................................................... 149 25.4.11 The Most Prolific Research Fronts in Straw Hydrolysis........................................................................... 150 25.5 Conclusion and Future Research.................................................................... 150 Acknowledgments..................................................................................................... 151 Appendix: The Keyword Set For Straw Hydrolysis.................................................. 152 References................................................................................................................. 152 Chapter 26 Straw Hydrolysis: Review......................................................................................... 156 Ozcan Konur 26.1 Introduction.................................................................................................... 156 26.2 Materials and Methods................................................................................... 156 26.3 Results............................................................................................................ 157 26.3.1 The Enzymatic Hydrolysis of Straw Combined with Other Pretreatments................................................. 157 26.3.2 The Sole Acid and Enzymatic Hydrolysis of Straw............................................................................................. 164 26.4 Discussion....................................................................................................... 167 26.4.1 Introduction....................................................................................... 167 26.4.2 The Enzymatic Hydrolysis of Straw Combined with Other Pretreatments................................................. 169 26.4.3 The Sole Acid and Enzymatic Hydrolysis of Straw.......................... 172 26.5 Conclusion and Future Research.................................................................... 172 Acknowledgments..................................................................................................... 173 References................................................................................................................. 173

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Chapter 27 Cellulose Hydrolysis: Scientometric Study............................................................... 176 Ozcan Konur 27.1 Introduction.................................................................................................... 176 27.2 Materials and Methods................................................................................... 176 27.3 Results............................................................................................................ 177 27.3.1 The Most-Prolific Documents in the Cellulose Hydrolysis.......................................................................... 177 27.3.2 The Most-Prolific Authors in the Cellulose Hydrolysis......................................................................... 177 27.3.3 The Most-Prolific Research Output by Years in Cellulose Hydrolysis........................................................... 179 27.3.4 The Most-Prolific Institutions in the Cellulose Hydrolysis.......................................................................... 180 27.3.5 The Most-Prolific Funding Bodies in the Cellulose Hydrolysis.......................................................................... 181 27.3.6 The Most-Prolific Source Titles in the Cellulose Hydrolysis.......................................................................... 182 27.3.7 The Most-Prolific Countries in the Cellulose Hydrolysis.................................................................... 182 27.3.8 The Most-Prolific Scopus Subject Categories in the Cellulose Hydrolysis.................................................................... 183 27.3.9 The Most-Prolific Scopus Keywords in the Cellulose Hydrolysis.......................................................................... 184 27.3.10 The Most-Prolific Research Fronts in Cellulose Hydrolysis..................................................................... 184 27.4 Discussion....................................................................................................... 187 27.4.1 Introduction....................................................................................... 187 27.4.2 The Most-Prolific Documents in the Cellulose Hydrolysis.......................................................................... 187 27.4.3 The Most-Prolific Authors in the Cellulose Hydrolysis.................................................................... 188 27.4.4 The Most-Prolific Research Output by Years in the Cellulose Hydrolysis.................................................................... 189 27.4.5 The Most-Prolific Institutions in the Cellulose Hydrolysis.......................................................................... 190 27.4.6 The Most-Prolific Funding Bodies in the Cellulose Hydrolysis.................................................................... 190 27.4.7 The Most-Prolific Source Titles in Cellulose Hydrolysis.......................................................................... 190 27.4.8 The Most-Prolific Countries in the Cellulose Hydrolysis.......................................................................... 191 27.4.9 The Most-Prolific Scopus Subject Categories in Cellulose Hydrolysis..................................................................... 191 27.4.10 The Most-Prolific Scopus Keywords in Cellulose Hydrolysis.......................................................................... 191 27.4.11 The Most-Prolific Research Fronts in Cellulose Hydrolysis.......................................................................... 192 27.5 Conclusion and Future Research.................................................................... 192 Acknowledgments..................................................................................................... 193 Appendix: The Keyword Set for Cellulose Hydrolysis............................................. 193 References................................................................................................................. 194

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Chapter 28 Cellulose Hydrolysis: Review.................................................................................... 198 Ozcan Konur 28.1 Introduction.................................................................................................... 198 28.2 Materials and Methods................................................................................... 198 28.3 Results............................................................................................................ 199 28.3.1 The Enzymatic Hydrolysis of Cellulose............................................ 199 28.3.2 The Chemical Hydrolysis of Cellulose..............................................202 28.4 Discussion.......................................................................................................207 28.4.1 Introduction.......................................................................................207 28.4.2 The Enzymatic Hydrolysis of Cellulose............................................208 28.4.3 The Chemical Hydrolysis of Cellulose..............................................209 28.5 Conclusion and Future Research.................................................................... 210 Acknowledgments..................................................................................................... 211 References................................................................................................................. 211

PART 5  Hydrolysate Fermentation for Bioethanol Production Chapter 29 Hydrolysate and Substrate Fermentation: Scientometric Study................................ 217 Ozcan Konur 29.1 Introduction.................................................................................................... 217 29.2 Materials and Methods................................................................................... 218 29.3 Results............................................................................................................ 218 29.3.1 The Most Prolific Documents in the Hydrolysate and Substrate Fermentation.......................................... 218 29.3.2 The Most Prolific Authors in the Hydrolysate and Substrate Fermentation............................................................... 219 29.3.3 The Most Prolific Research Output by Years in Hydrolysate and Substrate Fermentation.......................................... 219 29.3.4 The Most Prolific Institutions in the Hydrolysate and Substrate Fermentation...................................................................... 221 29.3.5 The Most Prolific Funding Bodies in the Hydrolysate and Substrate Fermentation.......................................... 222 29.3.6 The Most Prolific Source Titles in the Hydrolysate and Substrate Fermentation...................................................................... 223 29.3.7 The Most Prolific Countries in the Hydrolysate and Substrate Fermentation......................................................................224 29.3.8 The Most Prolific Scopus Subject Categories in the Hydrolysate and Substrate Fermentation.......................................... 225 29.3.9 The Most Prolific Keywords in the Hydrolysate and Substrate Fermentation............................................................... 225 29.3.10 The Most Prolific Research Fronts in Hydrolysate and Substrate Fermentation...................................................................... 227 29.4 Discussion........................................................................................................ 229 29.4.1 Introduction........................................................................................ 229

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29.4.2 The Most Prolific Documents in the Hydrolysate and Substrate Fermentation...................................................................... 229 29.4.3 The Most Prolific Authors in the Hydrolysate and Substrate Fermentation...................................................................... 230 29.4.4 The Most Prolific Research Output by Years in the Hydrolysate and Substrate Fermentation..................................... 232 29.4.5 The Most Prolific Institutions in the Hydrolysate and Substrate Fermentation...................................................................... 232 29.4.6 The Most Prolific Funding Bodies in the Hydrolysate and Substrate Fermentation............................................................... 232 29.4.7 The Most Prolific Source Titles in Hydrolysate and Substrate Fermentation...................................................................... 233 29.4.8 The Most Prolific Countries in the Hydrolysate and Substrate Fermentation...................................................................... 233 29.4.9 The Most Prolific Scopus Subject Categories in Hydrolysate and Substrate Fermentation.......................................... 234 29.4.10 The Most Prolific Keywords in Hydrolysate and Substrate Fermentation...................................................................... 234 29.4.11 The Most Prolific Research Fronts in Hydrolysate and Substrate Fermentation...................................................................... 234 29.5 Conclusion and Future Research.................................................................... 235 Acknowledgments..................................................................................................... 236 Appendix: The Keyword Set For Hydrolysate and Substrate Fermentation............................................................................................................. 236 References................................................................................................................. 238 Chapter 30 Hydrolysate and Substrate Fermentation: Review.................................................... 242 Ozcan Konur 30.1 Introduction.................................................................................................... 242 30.2 Materials and Methods................................................................................... 242 30.3 Results............................................................................................................ 243 30.3.1 The Hydrolysate and Substrate Fermentation................................... 243 30.3.2 The Fermentation Inhibitors.............................................................. 245 30.3.3 The Hydrolysate Detoxification........................................................ 247 30.3.4 The Microorganism and Substrate Metabolic Engineering....................................................................................... 249 30.4 Discussion....................................................................................................... 252 30.4.1 Introduction....................................................................................... 252 30.4.2 The Hydrolysate and Substrate Fermentation................................... 253 30.4.3 The Fermentation Inhibitors.............................................................. 255 30.4.4 The Hydrolysate Detoxification........................................................ 255 30.4.5 The Microorganism and Substrate Metabolic Engineering....................................................................................... 256 30.5 Conclusion and Future Research.................................................................... 256 Acknowledgments..................................................................................................... 257 References................................................................................................................. 257

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Chapter 31 The Alternative Fermentation Processes for the Bioethanol Production: Scientometric Study...............................................................................260 Ozcan Konur 31.1 Introduction....................................................................................................260 31.2 Materials and Methods................................................................................... 261 31.3 Results............................................................................................................ 261 31.3.1 The Most Prolific Documents in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 261 31.3.2 The Most Prolific Authors in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 261 31.3.3 The Most Prolific Research Output by Years in Alternative Fermentation Processes for Bioethanol Production................................................................. 262 31.3.4 The Most Prolific Institutions in the Alternative Fermentation Processes For Bioethanol Production.......................................................................264 31.3.5 The Most Prolific Funding Bodies in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 265 31.3.6 The Most Prolific Source Titles in the Alternative Fermentation Processes for Bioethanol Production.......................................................................266 31.3.7 The Most Prolific Countries in the Alternative Fermentation Processes for Bioethanol Production................................................................. 267 31.3.8 The Most Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production................................................................. 268 31.3.9 The Most Prolific Keywords in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 268 31.3.10  The Most Prolific Research Fronts in Alternative Fermentation Processes for Bioethanol Production....................................................................... 271 31.4 Discussion....................................................................................................... 271 31.4.1 Introduction....................................................................................... 271 31.4.2 The Most Prolific Documents in the Alternative Fermentation Processes for Bioethanol Production................................................................. 273 31.4.3 The Most Prolific Authors in the Alternative Fermentation Processes for Bioethanol Production.......................... 274 31.4.4 The Most Prolific Research Output by Years in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 275 31.4.5 The Most Prolific Institutions in the Alternative Fermentation Processes for Bioethanol Production.......................... 275

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31.4.6 The Most Prolific Funding Bodies in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 276 31.4.7 The Most Prolific Source Titles in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 276 31.4.8 The Most Prolific Countries in the Alternative Fermentation Processes For Bioethanol Production....................................................................... 276 31.4.9 The Most Prolific Scopus Subject Categories in the Alternative Fermentation Processes for Bioethanol Production....................................................................... 277 31.4.10 The Most Prolific Keywords in the Alternative Fermentation Processes for Bioethanol Production.......................... 277 31.4.11 The Most Prolific Research Fronts in the Alternative Fermentation Processes For Bioethanol Production......................... 278 31.5  Conclusion and Future Research...................................................................... 278 Acknowledgments.....................................................................................................280 Appendix: The Keyword Set for Alternative Fermentation Processes for Bioethanol Production.........................................................................................280 References.................................................................................................................280 Chapter 32 The Alternative Fermentation Processes for the Bioethanol Production: Review.................................................................................284 Ozcan Konur 32.1 Introduction....................................................................................................284 32.2 Materials and Methods................................................................................... 285 32.3 Results............................................................................................................ 285 32.3.1 The Alternative Fermentation Processes for the Straw and Corn Stover........................................................... 285 32.3.2 The Alternative Fermentation Processes for the Wood.................... 288 32.3.3 The Alternative Fermentation Processes for the Other Biomass........................................................................ 291 32.4 Discussion....................................................................................................... 293 32.4.1 Introduction....................................................................................... 293 32.4.2 The Alternative Fermentation Processes for the Straw and Corn Stover................................................................. 295 32.4.3 The Alternative Fermentation Processes for the Wood...................................................................................... 296 32.4.4 The Alternative Fermentation Processes for the Other Biomass...... 297 32.5 Conclusion and Future Research.................................................................... 297 Acknowledgments..................................................................................................... 298 References................................................................................................................. 298 Chapter 33 Metabolic Engineering for the Bioethanol Production: Scientometric Study...........302 Ozcan Konur 33.1 Introduction....................................................................................................302 33.2 Materials and Methods................................................................................... 303

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33.3 Results............................................................................................................ 303 33.3.1 The Most-Prolific Documents in the Metabolic Engineering for Bioethanol Production......................................................................................... 303 33.3.2 The Most-Prolific Authors in the Metabolic Engineering for Bioethanol Production............................................ 303 33.3.3 The Most-Prolific Research Output by Years in Metabolic Engineering for Bioethanol Production.........................................................................................304 33.3.4 The Most-Prolific Institutions in the Metabolic Engineering for Bioethanol Production............................................306 33.3.5 The Most-Prolific Funding Bodies in the Metabolic Engineering for Bioethanol Production...........................307 33.3.6 The Most-Prolific Source Titles in the Metabolic Engineering for Bioethanol Production...........................308 33.3.7 The Most-Prolific Countries in the Metabolic Engineering for Bioethanol Production............................................309 33.3.8 The Most-Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production.........................................................................................309 33.3.9 The Most-Prolific Keywords in the Metabolic Engineering for Bioethanol Production............................................ 311 33.3.10 The Most-Prolific Research Fronts in Metabolic Engineering for Bioethanol Production............................................ 311 33.4 Discussion....................................................................................................... 313 33.4.1 Introduction....................................................................................... 313 33.4.2 The Most-Prolific Documents in the Metabolic Engineering for Bioethanol Production......................................................................................... 315 33.4.3 The Most-Prolific Authors in the Metabolic Engineering for Bioethanol Production............................................ 316 33.4.4 The Most-Prolific Research Output by Years in the Metabolic Engineering for Bioethanol Production......................................................................................... 317 33.4.5 The Most-Prolific Institutions in the Metabolic Engineering for Bioethanol Production............................................ 318 33.4.6 The Most-Prolific Funding Bodies in the Metabolic Engineering for Bioethanol Production........................... 318 33.4.7 The Most-Prolific Source Titles in the Metabolic Engineering for Bioethanol Production........................... 318 33.4.8 The Most-Prolific Countries in the Metabolic Engineering for Bioethanol Production............................................ 319 33.4.9 The Most-Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production..................... 319 33.4.10 The Most-Prolific Keywords in the Metabolic Engineering for Bioethanol Production....................................................................... 320 33.4.11 The Most-Prolific Research Fronts in the Metabolic Engineering for Bioethanol Production............................................ 320 33.5 Conclusion and Future Research.................................................................... 321 Acknowledgments..................................................................................................... 322

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Appendix: The Keyword Set for Metabolic Engineering for Bioethanol Production......................................................................................... 322 References................................................................................................................. 323 Chapter 34 Metabolic Engineering for the Bioethanol Production: Review............................... 327 Ozcan Konur 34.1 Introduction.................................................................................................... 327 34.2 Materials and Methods................................................................................... 327 34.3 Results............................................................................................................ 328 34.3.1 The Metabolic Engineering of Saccharomyces cerevisiae........................................................................................... 328 34.3.2 The Metabolic Engineering of Other Microorganisms..................... 332 34.3.3 The Metabolic Engineering of the Substrates................................... 335 34.4 Discussion....................................................................................................... 336 34.4.1 Introduction....................................................................................... 336 34.4.2 The Metabolic Engineering of Saccharomyces cerevisiae........................................................................................... 337 34.4.3 The Metabolic Engineering of Other Microorganisms................................................................................ 339 34.4.4 The Metabolic Engineering of the Substrates...................................340 34.5 Conclusion and Future Research....................................................................340 Acknowledgments..................................................................................................... 341 References................................................................................................................. 341 Chapter 35 The Utilization of the Saccharomyces cerevisiae for the Bioethanol Production: Scientometric Study............................................................................... 345 Ozcan Konur 35.1 Introduction.................................................................................................... 345 35.2 Materials and Methods...................................................................................346 35.3 Results............................................................................................................346 35.3.1 The Most Prolific Documents in the Utilization of the S. cerevisiae for Bioethanol Production..................................346 35.3.2 The Most Prolific Authors in the Utilization of S. cerevisiae for Bioethanol Production............................................ 347 35.3.3 The Most Prolific Research Output by Years in Utilization of the S. cerevisiae for Bioethanol Production.......................................................................348 35.3.4 The Most Prolific Institutions in the Utilization of the S. cerevisiae for Bioethanol Production.......................................................................348 35.3.5 The Most Prolific Funding Bodies in the Utilization of the S. cerevisiae for Bioethanol Production......................................................................................... 350 35.3.6  The Most Prolific Source Titles in the Utilization of the S. cerevisiae for Bioethanol Production....................................................................... 351 35.3.7 The Most Prolific Countries in the Utilization of the S. cerevisiae for Bioethanol Production................................................................. 352

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35.3.8 The Most Prolific Scopus Subject Categories in the Utilization of the S. cerevisiae for Bioethanol Production............................................ 353 35.3.9 The Most Prolific Keywords in the Utilization of the S. cerevisiae for Bioethanol Production............................................ 353 35.3.10 The Most Prolific Research Fronts in Utilization of the S. cerevisiae for Bioethanol Production.................................. 355 35.4 Discussion....................................................................................................... 357 35.4.1 Introduction....................................................................................... 357 35.4.2 The Most Prolific Documents in the Utilization of the S. cerevisiae for Bioethanol Production...................................... 358 35.4.3 The Most Prolific Authors in the Utilization of the S. cerevisiae for Bioethanol Production.................................. 358 35.4.4 The Most Prolific Research Output by Years in the Utilization of the S. cerevisiae for Bioethanol Production.........................................................................................360 35.4.5 The Most Prolific Institutions in the Utilization of the S. cerevisiae for Bioethanol Production..................................360 35.4.6 The Most Prolific Funding Bodies in the Utilization of the S. cerevisiae for Bioethanol Production..................................360 35.4.7 The Most Prolific Source Titles in the Utilization of the S. cerevisiae for Bioethanol Production.................................. 361 35.4.8 The Most Prolific Countries in the Utilization of the S. cerevisiae for Bioethanol Production...................................... 361 35.4.9 The Most Prolific Scopus Subject Categories in the Utilization of the S. cerevisiae for Bioethanol Production....................................................................... 362 35.4.10 The Most Prolific Keywords in the Utilization of the S. cerevisiae for Bioethanol Production.................................. 362 35.4.11 The Most Prolific Research Fronts in the Utilization of the S. cerevisiae for Bioethanol Production.................................. 362 35.5 Conclusion and Future Research.................................................................... 363 Acknowledgments.....................................................................................................364 Appendix: The Keyword Set for the Utilization of S. cerevisiae for Bioethanol Production.........................................................................................364 References................................................................................................................. 365 Chapter 36 The Utilization of the Saccharomyces cerevisiae for the Bioethanol Production: Review........................................................................... 369 Ozcan Konur 36.1 Introduction.................................................................................................... 369 36.2 Materials and Methods................................................................................... 370 36.3 Results............................................................................................................ 370 36.3.1 The Utilization of the S. cerevisiae for the Fermentation of Xylose..................................................................... 370 36.3.2 The Utilization of the S. cerevisiae for the Fermentation of the Other Feedstocks.............................................. 375 36.4 Discussion....................................................................................................... 382 36.4.1 Introduction....................................................................................... 382 36.4.2 The Utilization of the S. cerevisiae for the Fermentation of Xylose............................................................... 384

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36.4.3 The Utilization of the S. cerevisiae for the Fermentation of the Other Feedstocks.............................................. 385 36.5 Conclusion and Future Research.................................................................... 387 Acknowledgments..................................................................................................... 387 References................................................................................................................. 388

PART 6  Bioethanol Fuel Separation Chapter 37 Distillation Separation of Bioethanol: The Current Advances................................. 393 Huidong Chen, Di Cai, Jieyi Wen, Changsheng Su, and Jianghong Wang 37.1 Introduction.................................................................................................... 393 37.2 Chemical Components in Feed and the Basic Properties.............................. 394 37.3 The Basic Bioethanol Distillation Processes.................................................. 396 37.4 Alternative Bioethanol Distillation Processes................................................ 397 37.4.1 Dehydration Directions..................................................................... 397 37.4.2 Energy-Saving Directions.................................................................400 37.5 Methods for the Evaluation of the Bioethanol Distillation Process.........................................................................................402 37.6 Conclusions.....................................................................................................403 References.................................................................................................................403 Chapter 38 In situ Bioethanol Separation from Fermentation Broth...........................................409 Di Cai, Zhihao Si, Hanzhu Wu, Yan Zhuang, and Peiyong Qin 38.1 Introduction....................................................................................................409 38.2 ISPR based on Vapor Liquid Equilibrium...................................................... 410 38.2.1 Gas Stripping..................................................................................... 410 38.2.2 Vacuum Fermentation....................................................................... 412 38.3 ISPR based on Phase Transfer........................................................................ 412 38.3.1 Adsorption......................................................................................... 412 38.3.2 Liquid-Liquid Extraction................................................................... 413 38.4 ISPR based on Membrane-based Techniques................................................ 414 38.4.1 Pervaporation.................................................................................... 414 38.4.2 Perstraction........................................................................................ 416 38.4.3 Membrane Distillation...................................................................... 416 38.5 Multistage ISPR Processes............................................................................. 417 38.6 Conclusions..................................................................................................... 418 References................................................................................................................. 419 Chapter 39 Advances and Challenges in the Production and Purification of Bioethanol Using Intensified Processes............................................ 426 Fernando Israel Gómez-Castro, Juan Gabriel Segovia-Hernández, Ricardo Morales Rodríguez, and Carolina Conde Mejía 39.1 Introduction.................................................................................................... 426

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39.2 Process Intensification.................................................................................... 427 39.3 Process Intensification in the Conversion from Biomass to Bioethanol.................................................................................... 428 39.4 Process Intensification in the Purification of Bioethanol.................................................................................................. 430 39.5 Challenges and Future Trends........................................................................ 432 39.6 Conclusions..................................................................................................... 433 References................................................................................................................. 434 Index............................................................................................................................................... 437

Preface The recent supply shocks caused first by the COVID-19 pandemic and later by the Ukrainian war have shown that biofuels such as bioethanol, biohydrogen, biogas, biosyngas, and biodiesel fuels could play a vital role in maintaining the energy security and indirectly food security at the global scale. These shocks have also resulted in the need for further setup of incentive structures for the production and consumption of bioethanol fuels in blends with crude oil-based gasoline, petrodiesel, or liquefied natural gas (LNG) in gasoline and diesel engines, their direct utilization in direct ethanol fuel cells (DEFC), and in the production of biohydrogen fuels for fuel cells and valuable biochemicals from bioethanol fuels. Thus, it is essential to assess the research on the production, evaluation, and utilization of bioethanol fuels from a wide range of biomass including first generation starch and sugar feedstocks, wood, grass, second generation lignocellulosic biomass including waste biomass and agricultural residues such as starch feedstock residues and sugar feedstock residues, and third generation algal biomass. Thus, this six-volume Handbook of Bioethanol Fuels assesses the research on the production, evaluation, and utilization of bioethanol fuels and presents a representative sample of this interdisciplinary research population with a collection of 110 chapters (Table 1.1). The first two volumes provide an overview of the research on the fundamental processes for bioethanol fuel production with a collection of 39 chapters: Pretreatments of the biomass, hydrolysis of the pretreated biomass, microbial fermentation of hydrolysates with yeasts, and separation and distillation of bioethanol fuels from the fermentation broth. They also provide an overview of the research on bioethanol fuels and production processes for bioethanol fuels (Tables 1.2 and 1.3). The third and fourth volumes provide an overview of the research on the production of bioethanol fuels from the non-waste and waste biomass, respectively, with a collection of 36 chapters. In this context, the third volume covers the production of bioethanol fuels from first generation starch feedstocks and sugar feedstocks, grass biomass, wood biomass, cellulose, biosyngas, and third generation algae (Table 1.4) while the fourth volume covers the production of second generation bioethanol fuels from residual sugar feedstocks, residual starch feedstocks, food waste, industrial waste, urban waste, forestry waste, and lignocellulosic biomass at large (Table 1.5). They also provide an overview of the research on feedstock-based bioethanol fuels, non-waste feedstock-based bioethanol fuels, and second generation waste biomass-based bioethanol fuels (Tables 1.4 and 1.5). Finally, the fifth and sixth volumes provide an overview of the research on the evaluation and utilization of bioethanol fuels with a collection of 37 chapters. In this context, the fifth volume covers the evaluation and utilization of bioethanol fuels in general, gasoline fuels, nanotechnology applications in bioethanol fuels, utilization of bioethanol fuels in transport engines, evaluation of bioethanol fuels, utilization of bioethanol fuels, and development and utilization of bioethanol fuel sensors (Table 1.6). Furthermore, the sixth volume of this handbook provides an overview of the research on the country-based experience of bioethanol fuels at large, Chinese, US, and European experience of bioethanol fuels, production of bioethanol fuel-based biohydrogen fuels for fuel cells, bioethanol fuel cells, and bioethanol fuel-based biochemicals with a collection of 19 chapters (Table 1.7). Thus, the second volume of this handbook provides an overview of the research on production processes for bioethanol fuels in general, hydrolysis of the pretreated biomass for bioethanol production, microbial fermentation of hydrolysates and substrates with yeasts for bioethanol production, and separation and distillation of bioethanol fuels from the fermentation broth, complementing the research on biomass pretreatments presented in the first volume (Table 1.3). The second volume of this handbook presents an overview of the research on biomass hydrolysis in general, wood hydrolysis, straw hydrolysis, and cellulose hydrolysis for bioethanol fuel production in the first section for biomass hydrolysis. xxi

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It continues to provide an overview of the research on microbial hydrolysate fermentation for bioethanol production in general, alternative fermentation processes for bioethanol fuel production such as simultaneous saccharification and fermentation (SSF) and consolidated biomass processing (CBP) compared with the separate hydrolysis and fermentation (SHF) process, metabolic engineering of microorganisms and substrates for bioethanol fuel production, and utilization of Saccharomyces cerevisiae for microbial fermentation of hydrolysates for bioethanol fuel production in the second section for hydrolysate fermentation. It continues to provide an overview of the research on the bioethanol fuel separation from the fermentation broth in the last section. Hence, the second volume indicates that bioethanol fuel research has intensified in recent years to become a major part of the bioenergy and biofuels research together primarily with biodiesel, biohydrogen, and biogas research as a sustainable alternative to crude oil-based gasoline and petrodiesel fuels as well as LNG. The second volume also indicates that hydrolysis of the biomass, microbial hydrolysate fermentation, and separation and distillation of bioethanol fuels from fermentation broth together with the biomass pretreatments are the fundamental production processes for bioethanol fuel production, making bioethanol fuels more competitive in relation to crude oil- and natural gas-based fuels. The second volume also indicates that a small number of documents, authors, institutions, publication years, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the research on the production processes for bioethanol fuels. The second volume also indicates that the level of funding for the research on the production processes for bioethanol fuels has not been sufficient with the resulting loss of momentum in the research output in recent years. Thus, there is a crucial need to improve the incentive structures for the major stakeholders such as researchers and their institutions as well as source titles and academic databases to improve both the volume and quality of the research output in these fields. This is a crucial need to maintain the energy security and indirectly food security at a global scale in light of the recent supply shocks caused by the COVID-19 pandemic and the Ukrainian war. The second volume also indicates that the contribution of the social sciences and humanities to the research in these fields has been minimal, due to in part by the restrictive editorial policies of the source titles in these fields toward social science- and humanities-based interdisciplinary studies. Thus, there is ample room to improve incentive structures for the inclusion of social sciences and humanities into these fields. The second volume also indicates that China, Europe as a whole, and the USA have been major producers of the research in these research fields, and there has been heavy competition among them in terms of both volume and citation impact of the research output. The USA and Europe as a whole have had a higher citation impact in relation to China, benefiting from their first-mover advantage starting their research in the 1970s. On the other hand, China as a late mover and rising mega star have had more intensive research funding initiatives in relation to the USA and Europe, improving its both research output and citation impact through the provision of efficient incentive structures for its major stakeholders in the last two decades. In this way, China might also overtake both the USA and Europe in terms of citation impact of the research output in addition to the volume of the research output in the future. This handbook at large and its second volume are a valuable resource for the stakeholders primarily in the research fields of energy and fuels, chemical engineering, environmental science and engineering, biotechnology, microbiology, chemistry, physics, mechanical engineering, agricultural sciences, food science and engineering, materials science, biochemistry, genetics, molecular biology, plant sciences, water resources, economics, business, management, transportations science and technology, ecology, public, environmental and occupational health, social sciences, toxicology, multidisciplinary sciences, and humanities among others. Ozcan Konur

Acknowledgments This handbook has been a multi-stakeholder project from its conception to its publication. CRC Press and Taylor and Francis Group have been the major stakeholders in financing and executing it. Marc Gutierrez has been the executive director of the project. A large number of teams from the Publisher have contributed immensely to the production of the handbook. However, only a limited number of authors have participated in this project due to the low level of incentives, compared to journals at a global scale. Furthermore, a small number of highly cited scholars have shaped the research on bioethanol fuels. The contribution of all these and other stakeholders to this handbook has been greatly acknowledged.

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Editor The Editor has interdisciplinary research interests and has published primarily in the areas of bioenergy and biofuels, algal bioenergy and biofuels, nanoenergy and nanofuels, nanobiomedicine, algal biomedicine, disability studies, higher education, biodiesel fuels, algal biomass, lignocellulosic biomass, scientometrics, and bioethanol fuels. He has edited a book titled Bioenergy and Biofuels (CRC Press, 2018), a handbook titled Handbook of Algal Science, Technology, and Medicine (Elsevier, 2020), and a handbook titled Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment (CRC Press, 2021) in three volumes.

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Contributors Di Cai National Energy R&D Center for Biorefinery Beijing University of Chemical Technology Beijing, China Huidong Chen National Energy R&D Center for Biorefinery and Center for Process Simulation & Optimization Beijing University of Chemical Technology Beijing, China

Zhihao Si National Energy R&D Center for Biorefinery and College of Life Science and Technology Beijing University of Chemical Technology Beijing, China Changsheng Su National Energy R&D Center for Biorefinery Beijing University of Chemical Technology Beijing, China

Fernando Israel Gómez-Castro Department of Chemical Engineering University of Guanajuato Guanajuato, Mexico

Jianghong Wang Center for Process Simulation & Optimization Beijing University of Chemical Technology Beijing, China

Ozcan Konur Department of Materials Engineering (Formerly) Ankara Yildirim Beyazit University Ankara, Turkey

Jieyi Wen National Energy R&D Center for Biorefinery Beijing University of Chemical Technology Beijing, China

Carolina Conde Mejía Multidisciplinary Academic Division Juarez Autonomous University of Tabasco Tabasco, Mexico

Hanzhu Wu National Energy R&D Center for Biorefinery and College of Life Science and Technology Beijing University of Chemical Technology Beijing, China

Peiyong Qin College of Life Science and Technology Beijing University of Chemical Technology Beijing, China Ricardo Morales Rodríguez Department of Chemical Engineering University of Guanajuato Guanajuato, Mexico.

Yan Zhuang National Energy R&D Center for Biorefinery and College of Life Science and Technology Beijing University of Chemical Technology Beijing, China

Juan Gabriel Segovia-Hernández Department of Chemical Engineering University of Guanajuato Guanajuato, Mexico

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Part 3 Introduction to Production Processes for Bioethanol Fuels

19

Production Processes for Bioethanol Fuels Scientometric Study Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

19.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomassbased bioethanol fuels (Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining the energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 1983, 2003), COVID-19 pandemics (Fauci et al., 2020; Li et al., 2020), or wars (Hamilton, 1983; Jones, 2012) in the aftermath of the Russian invasion of Ukraine (Reeves, 2014). However, it is necessary to pretreat the biomass (Taherzadeh and Karimi, 2008; Yang and Wyman, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and the resulting hydrolysates, respectively. The research in the field of the production processes for bioethanol fuels has intensified in this context in the key research fronts of the pretreatment (Alvira et al., 2010; Hendriks and Zeeman, 2009; Mosier et al., 2005) and hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Zhang and Lynd, 2004) of the feedstock, fermentation (Jonsson and Martin, 2016; Lin and Tanaka, 2006; Palmqvist and HahnHagerdal, 2000) of the hydrolysates, and bioethanol fuel production in general (Balat, 2011; Limayem and Ricke, 2012, Lin and Tanaka, 2006) and to a lesser extent evaluation (Hamelinck et al., 2005; Pimentel and Patzek, 2005; Schmer et al., 2008), utilization (Macedo et al., 2008; Sheehan et al., 2003) and distillation (Sano et al., 1994; Vane, 2005) of bioethanol fuels. The research in this field has also intensified for the feedstocks of lignocellulosic biomass at large (Mosier et al., 2005; Sun and Cheng, 2002), cellulose (Pinkert et al., 2009; Zhang and Lynd, 2004), wood biomass (Galbe and Zacchi, 2002; Zhu and Pan, 2010), starch feedstock residues (Binod et al., 2010; Talebnia et al., 2010), and to a lesser extent lignin (Bourbonnais and Paice, 1990; Kirk and Farrell, 1987), grass biomass (Keshwani and Cheng, 2009; Pimentel and Patzek, 2005), industrial waste (Cardona et al., 2010; Prasad et al., 2007), sugar feedstocks (Bai et al., 2008; Canilha et al., 2012), starch feedstocks (Bai et al., 2008; Bothast and Schlicher, 2005), sugar feedstock residues (Cardona et al., 2010; Laser et al., 2002), algal biomass (Ho et al., 2013; John et al., 2011), biosyngas (Henstra et al., 2007; Munasinghe and Khanal, 2010), urban waste (Prasad et al., 2007; Ravindran and Jaiswal, 2016), food waste (Guimaraes et al., 2010; Ravindran and Jaiswal, 2016), and forestry waste (Duff and Murray, 1996). Thus, it complements the research on the feedstocks for the bioethanol fuels and evaluation and utilization of bioethanol fuels at large. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). DOI: 10.1201/9781003226499-26

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Bioethanol Fuel Production Processes. II

The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As there have been no published current scientometric studies in this field, this book chapter presents a scientometric study of the research in the production processes for bioethanol fuels. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts.

19.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in October 2022. As a first step for the search of the relevant literature, the keywords were selected using the mostcited first 300 population papers for each feedstock. The selected keyword list was then optimized to obtain a representative sample of papers for each research field. These five keyword lists were then integrated to obtain the keyword list for this research field (Konur, 2023a,b,c). As a second step, two sets of data were used for this study. First, a population sample of 50,108 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 251 most-cited papers, corresponding to 0.5% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the production processes for bioethanol fuels. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

19.3 RESULTS 19.3.1 The Most Prolific Documents in the Production Processes for Bioethanol Fuels The information on the types of documents for both datasets is given in Table 19.1. The articles and conference papers, published in journals, dominate both the sample (72%) and population (95%)

TABLE 19.1 Documents in the Production Processes for Bioethanol Fuels Documents Article Review Short Survey Conference paper Book chapter Letter Note Book Editorial Sample size

Sample Dataset (%) 68.5 23.9 4.8 2.8 0.0 0.0 0.0 0.0 0.0 251

Population Dataset (%) 92.3 2.5 0.2 2.8 1.4 0.4 0.3 0.1 0.1 50,108

Surplus (%) −23.8 21.4 4.6 0.0 −1.4 −0.4 −0.3 −0.1 −0.1

Population dataset, The number of papers (%) in the set of the 50,108 population papers; Sample dataset, The number of papers (%) in the set of 251 highly cited papers.

Production Processes for Bioethanol Fuels: Scientometric Study

5

papers with 23% deficit. Further, review papers and short surveys have a 26% surplus as they are over-represented in the sample papers as they constitute 29% and 3% of the sample and population papers, respectively. It is further notable that 97%, 2%, and 1% of the population papers were published in journals, books, and book series, respectively. Similarly, 99%, 1%, and 0% of the sample papers were published in the journals, books, and book series, respectively.

19.3.2 The Most Prolific Authors in the Production Processes for Bioethanol Fuels The information about the most prolific 25 authors with at least 1.5% of sample papers each is given in Table 19.2. The most prolific authors are John N. Saddler and Barbel Hahn-Hagerdal with 3.6% of the sample papers each, followed by Charles E. Wyman, Bernard Henrissat, and Igor V. Grigoriev with 2.8%, 2.8%, and 2.5% of the sample papers, respectively. The other prolific authors are Bruce E. Dale, Lee R. Lynd, Michael E. Himmel, Mark T. Holtzapple, Dan Cullen, and Pedro M. Coutinho with 2% of the sample papers each. On the other hand, the most influential author is Barbel Hahn-Hagerdal with 3.4% surplus, followed by John N. Saddler, Bernard Henrissat, and Charles E. Wyman with 3.2%, 2.8%, and 2.6% surplus, respectively. The other influential authors are Igor V. Grigoriev, Pedro M. Coutinho, Lee R. Lynd, Michael E. Himmel, Mark T. Holtzapple, and Dan Cullen, with 1.9%– 2.4% surplus each. The most prolific institution for the sample dataset is the Joint Genome Institute with four authors, followed by Lund University with two authors. On the other hand, the most prolific country for the sample dataset is the USA with 17 authors, followed by Sweden with three authors. In total, only seven countries house these top authors. The most prolific research front for these top authors is the pretreatments of the feedstocks with 24 authors followed by the hydrolysis of the feedstocks with 23 authors. The other prolific research fronts are the fermentation of the hydrolysates and the bioethanol production in general with nine and eight authors, respectively. On the other hand, there is significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only three of these top researchers are female with a representation rate of 12%. Additionally, there are other authors with the relatively low citation impact and with 0.1%–0.3% of the population papers each: Runcang Sun, Akihiko Kondo, Arthur J. Ragauskas, Qiang Yong, Ashok Pandey, Lonnie O. Ingram, Mohammad J. Taherzadeh, Venkatesh Balan, Mats Galbe, Yong-Su Jin, Lisbeth Olsson, Keikhosro Karimi, Mercedes Ballesteros, Juan C. Parajo, Feng Xu, Jie Bao, Ying Xu, Zhenhong Yuan, Ye Ni, Junyong Zhu, Bruce S. Dien, Tao Shao, Carlos R. Soccol, Hasan Jameel, Angel T. Martinez, Hongzhang Chen, Caoxing Huang, Gunnar Liden, Yunqiao Pu, Chiaki Ogino, Jinming Zhang, Fengwu Bai, Eulogio Castro, Michael A. Cotta, Kyoung Heon Kim, Chenhuan Lai, Wei Qi, Herbert Sixta, Tomohisa Hasunuma, Liangcai Peng, Ignacio Ballesteros, Jose A. Teixeira, Yong Y. Lee, Xinshu Zhuang, Verawat Champreda, Paul Christakopoulos, Guang Yang, Jinguang Hu, Yongcan Jin, Jin-Ho Seo, Michael R. Ladisch, Carlos A. Rosa, Tianwei Tan, Tae-Hyun Kim, Anne S. Meyer, Xueqing Qiu, and Qiang Yu.

19.3.3 The Most Prolific Research Output by Years in the Production Processes for Bioethanol Fuels Information about papers published between 1970 and 2022 is given in Figure 19.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s and the early 2020s with 48% and 19% of the population dataset, respectively. Similarly, the publication rates for the 2000s, 1990s, 1980s, and 1970s were 14%, 9%, 6%, and 2%, respectively. Further, the rate for the pre-1970s was 2%. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 55% and 19% of the sample dataset, respectively. Similarly, the publication rates

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TABLE 19.2 Most Prolific Authors in the Production Processes for Bioethanol Fuels No.

Author name Saddler, John N.

 2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hahn-Hagerdal, Barbel* Wyman, Charles E. Henrissat, Bernard Grigoriev, Igor V. Dale, Bruce E. Lynd, Lee R. Himmel, Michael E. Holtzapple, Mark T. Cullen, Dan Coutinho, Pedro M Simmons, Blake A. Zacchi, Guido Singh, Seema* Jonsson, Leif J. Jeffries, Thomas W. Gold, Michael H. Yang, Bin Baker, Scott E. Larrondo, Luis F. Lindquist, Erika E.* Martinez, Diego Nilvebrant, Nils-Olof Salamov, Asaf A. Schmutz, Jeremy

Author code 7005297559 57202481615 7005389381 7004396809 7005911606 25027225800 7201511969 35586183800 7007125552 7004167004 7202109135 7006153340 7102183263 7006727748 35264950300 7102349315 7005806269 7402444296 7404473046 35232609200 6603826074 34571433600 7202958664 57209815309 7003657563 56549573500

Sample Papers %

Population Papers %

Surplus

Country

HI

N

Res. Front

3.6

0.4

3.2

Univ. British Columbia

Institution

Canada

99

420

P, H, F, R

3.6 2.8 2.8 2.4 2.0 2.0 2.0 2.0 2.0 2.0 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6

0.2 0.2 0.0 0.0 0.2 0.1 0.1 0.1 0.1 0.0 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.4 2.6 2.8 2.4 1.8 1.9 1.9 1.9 1.9 2.0 1.4 1.4 1.4 1.5 1.5 1.5 1.5 1.6 1.6 1.6 1.6 1.6 1.6 1.6

Lund Univ. Univ. Calf. Riverside King Abdulaziz Univ. Jnt. Genome Inst. Michigan State Univ. Dartmouth Coll. Natl. Renew. Ener. Lab. Texas A&M Univ. USDA Forest Serv. Aix Marseille Univ. Lawrence Berkeley Natl. Lab. Lund Univ. Sandia Natl. Lab. Umea Univ. Xylome Inc. Oregon Hlth. Sci. Univ. Washington State Univ. Pacific NW Natl. Lab. Pont. Cath. Univ. Chile Jnt. Genome Inst. Massachusetts Inst. Technol. Borregaard Jnt. Genome Inst. Jnt. Genome Inst.

Sweden USA S. Arabia USA USA USA USA USA USA France USA Sweden USA Sweden USA USA USA USA Chile USA USA Norway USA USA

76 80 136 116 92 75 74 47 46 73 76 68 59 41 59 57 41 51 29 70 29 22 79 92

258 287 598 354 430 287 423 199 120 145 446 204 186 148 156 148 98 149 76 116 35 43 126 273

P, H, F, R P, H, F, R P, H P, H P, H, F, R P, H, F, R P, H P P, H P, H P, H P, H, F, R P, H P, H, F, R P, F, R P, H P, H P, H P, H P, H P, H H, F P, H P, H

Author code: the unique code given by Scopus to the authors. Sample papers: the number of papers authored in the sample dataset. Population papers: the number of papers authored in the population dataset. *, Female; F, Fermentation of the hydrolysates; H, Hydrolysis of the feedstock; HI, H-index; N, Number of papers published by each author; P, Pretreatment of the feedstock; R, Bioethanol fuel production.

Bioethanol Fuel Production Processes. II

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Production Processes for Bioethanol Fuels: Scientometric Study

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10 9

Number of papers (%)

8

Population papers Sample papers

7 6 5 4 3 2 1 0

FIGURE 19.1  The research output by years regarding the production processes for bioethanol fuels.

for 1990s, 1980s, and 1970s were 13%, 4%, and 1% of the sample papers, respectively. Further, the rate for the pre-1970s was 0%. The most prolific publication years for the population dataset were 2021, 2020, and 2022 with 6.6%, 5.9%, and 6% of the dataset, respectively, whilst 70% of the population papers were published between 2008 and 2022. Similarly, 85% of the sample papers were published between 1998 and 2016, whilst the most prolific publication years were 2007, 2009, and 2011 with 9.2%, 8.4%, and 7.6% of the sample papers, respectively.

19.3.4 The Most Prolific Institutions in the Production Processes for Bioethanol Fuels Information about the most prolific 31 institutions publishing papers on the production processes for bioethanol fuels with at least 1.6% of the sample papers each is given in Table 19.3. The most prolific institutions are the Lund University and USDA Forest Service with 5.6% and 5.2% of the sample papers, respectively, followed by the National Renewable Energy Laboratory (NREL), University of British Columbia, University of Wisconsin Madison, and Dartmouth College with 4%–4.8% of the sample papers each. The other prolific institutions are Oak Ridge National Laboratory, Technical University of Denmark, NC State University, Joint Genome Institute, Pacific Northwest National Laboratory, and Novozymes Biotech Inc. with 2.8%–3.6% of the sample papers each. Similarly, the top country for these most prolific institutions is the USA with 18 institutions. The other prolific countries are Denmark, Finland, France, and Sweden with two institutions each. In total, only nine countries house these top institutions. On the other hand, the institutions with the most citation impact are the Lund University and USDA Forest Service with 4.7% and 4.5% surplus, respectively, followed by the NREL, University of British Columbia, Dartmouth College, and University of Wisconsin Madison with 4.1%, 3.8%, 3.7%, and 3.3% surplus, respectively. The other influential institutions are Oak Ridge National Laboratory, Novozymes Biotech Inc., Joint Genome Institute, Pacific Northwest National Laboratory, and Technical University of Denmark with 2.5%–2.9% surplus each. Additionally, there are other institutions with the relatively low citation impact and with 0.4%–2.3% of the population papers each: the Chinese Academy of Sciences, University of Sao Paulo, South China

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Bioethanol Fuel Production Processes. II

TABLE 19.3 The Most Prolific Institutions in the Production Processes for Bioethanol Fuels No.  1  2  3  4  5  6  7  8  9 10 11 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Institutions Lund Univ. USDA Forest Serv. Natl. Renew. Ener. Lab. Univ. British Columbia Univ. Wisconsin Madison Dartmouth Coll. Oak Ridge Natl. Lab. Tech. Univ. Denmark NC State Univ. Joint Genome Inst. Pacific NW Natl. Lab. Novozymes Biotech Inc. USDA Agr. Res. Serv. Michigan State Univ. Aix Marseille Univ. CNRS Texas A&M Univ. Univ. Tokyo CSIC Wageningen Univ. Res. Purdue Univ. VTT Tech. Res. Ctr. Univ. Calif. Berkeley Univ. Minnesota Helsinki Univ. Univ. Copenhagen Jnt. Bioener. Inst. Georgia Inst. Technol. Umea Univ. Massachusetts Inst. Technol.

Country Sweden USA USA Canada USA USA USA Denmark USA USA USA USA USA USA France France USA Japan Spain Netherlands USA Finland USA USA Finland Denmark USA USA Sweden USA

Sample Papers (%)

Population Papers (%)

5.6 5.2 4.8 4.4 4.0 4.0 3.6 3.2 2.8 2.8 2.8 2.8 2.4 2.4 2.4 2.0 2.0 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6

0.9 0.7 0.7 0.6 0.7 0.3 0.7 0.7 0.7 0.1 0.1 0.0 0.9 0.6 0.3 0.7 0.3 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.2 0.1

Surplus (%) 4.7 4.5 4.1 3.8 3.3 3.7 2.9 2.5 2.1 2.7 2.7 2.8 1.5 1.8 2.1 1.3 1.7 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.3 1.3 1.3 1.4 1.5

University of Technology, United States Department of Agriculture, Nanjing Forestry University, State University of Campinas, Beijing Forestry University, State Key Laboratory of Pulp and Paper Engineering, Kyoto University, China Agricultural University, Russian Academy of Sciences, University of Illinois Urbana-Champaign, Tianjin University, Jiangnan University, Ministry of Agriculture of China, Tianjin University of Science and Technology, Beijing University of Chemical Technology, State University of Paulista, Tsinghua University, Zhejiang University, Qilu University of Technology, University of Putra Malaysia, University of Florida, East China University of Science and Technology, Korea University, Aalto University, Chalmers University of Technology, Seoul National University, and Nanjing Tech University.

19.3.5 The Most Prolific Funding Bodies in the Production Processes for Bioethanol Fuels Information about the most prolific 13 funding bodies funding at least 1.2% of the sample papers each is given in Table 19.4. Further, only 21% and 44% of the sample and population papers each were funded, respectively.

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Production Processes for Bioethanol Fuels: Scientometric Study

TABLE 19.4 The Most Prolific Funding Bodies in the Production Processes for Bioethanol Fuels No.  1  2  3  4  5  6  7  8  9 10 11 12 13

Funding Bodies U.S. Department of Energy National Science Foundation European Commission Office of Science National Institute of General Medical Sciences Swedish National Board for Industrial and Technical Development Natural Sciences and Engineering Research Council of Canada U.S. Department of Agriculture Swedish Research Council National Renewable Energy Laboratory Carl Tryggers Foundation for Scientific Research Knut and Alice Wallenberg Foundation Lawrence Livermore National Laboratory

Country

Sample Paper No. (%)

Population Paper No. (%)

USA USA EU USA USA Sweden

4.0 2.0 2.0 2.0 2.0 2.0

1.5 1.3 0.8 0.7 0.4 0.1

2.5 0.7 1.2 1.3 1.6 1.9

Canada

1.6

0.9

0.7

USA Sweden USA Sweden Sweden USA

1.6 1.6 1.2 1.2 1.2 1.2

0.6 0.2 0.2 0.0 0.0 0.0

1.0 1.4 1.0 1.2 1.2 1.2

Surplus (%)

The most prolific funding body is the U.S. Department of Energy with 4% of the sample papers. The other funding bodies are the National Science Foundation, European Commission, Office of Science, National Institute of General Medical Sciences, and Swedish National Board for Industrial and Technical Development with 2% of the sample papers each. On the other hand, the most prolific countries for these top funding bodies are the USA and Sweden with seven and four funding bodies, respectively. In total, only three countries and the EU house these top funding bodies. The funding body with the most citation impact is the U.S. Department of Energy with 2.5% surplus, followed by the Swedish National Board for Industrial and Technical Development and National Institute of General Medical Sciences with 1.9% and 1.6% surplus, respectively. The other influential funding bodies are the Swedish Research Council, Office of Science, European Commission, Carl Tryggers Foundation for Scientific Research, Knut and Alice Wallenberg Foundation, and Lawrence Livermore National Laboratory with 1.2%–1.4% surplus each. Further, the funding bodies with the least citation impact are the National Science Foundation and Natural Sciences and Engineering Research Council of Canada with 0.7% surplus each. The other funding bodies with the relatively low citation impact and with 0.4%–9.1% of the population papers each are the National Natural Science Foundation of China, National Council for Scientific and Technological Development, National Key Research and Development Program of China, Higher Education Personnel Improvement Coordination, Research Support Foundation of the State of Sao Paulo, Fundamental Research Funds for the Central Universities, Japan Society for the Promotion of Science, National Research Foundation of Korea, European Regional Development Fund, China Postdoctoral Science Foundation, Chinese Academy of Sciences, Priority Academic Program Development of Jiangsu Higher Education Institutions, Ministry of Education, Culture, Sports, Science and Technology, Natural Science Foundation of Jiangsu Province, National Council of Science and Technology, China Scholarship Council, National High-tech Research and Development Program, National Basic Research Program of China (973 Program), Ministry of Science and Technology, India, Seventh Framework Program, Ministry of Education, Biological and Environmental Research, Ministry of Economy and Competitiveness, Foundation for Science and Technology, and National Institute of Food and Agriculture. It is notable that National Natural Science Foundation of China funds 9.1% of the population papers.

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Bioethanol Fuel Production Processes. II

19.3.6 The Most Prolific Source Titles in the Production Processes for Bioethanol Fuels Information about the most prolific 20 source titles publishing at least 1.2% of the sample papers each in the production processes for bioethanol fuels is given in Table 19.5. The most prolific source title is the Bioresource Technology with 15.1% of the sample papers, followed by Biotechnology and Bioengineering, Applied Microbiology and Biotechnology, and Green Chemistry with 9.2%, 5.6%, and 5.6% of the sample papers, respectively. The other prolific titles are the Applied and Environmental Microbiology, Enzyme and Microbial Technology, Science, Biotechnology for Biofuels, Current Opinion in Biotechnology, and Proceedings of the National Academy of Sciences of the United States of America with 2.4%–3.6% of the sample papers each. On the other hand, the source title with the most impact is the Bioresource Technology with 8.5% surplus, followed by Biotechnology and Bioengineering and Green Chemistry with 7.3% and 5.0% surplus, respectively. The other influential titles are the Applied Microbiology and Biotechnology, Science, Current Opinion in Biotechnology, and Enzyme and Microbial Technology with 2.6%– 3.9% surplus each. The other source titles with the relatively low citation impact with 0.4%–1.6% of the population papers each are Applied Biochemistry and Biotechnology, Bioresources, Industrial Crops and Products, Biotechnology Letters, Process Biochemistry, Biomass Conversion and Biorefinery, Cellulose, Journal of Agricultural and Food Chemistry, ACS Sustainable Chemistry and Engineering, Renewable Energy, Holzforschung, Biochemical Engineering Journal, Advanced Materials Research, Journal of Industrial Microbiology and Biotechnology, RSC Advances, Waste and Biomass Valorization, Bioprocess and Biosystems Engineering, Fuel, World Journal of Microbiology and Biotechnology, Journal of Bioscience and Bioengineering, International Journal of Biological Macromolecules, Journal of Membrane Science, International Biodeterioration and TABLE 19.5 The Most Prolific Source Titles in the Production Processes for Bioethanol Fuels No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

Source Titles Bioresource Technology Biotechnology and Bioengineering Applied Microbiology and Biotechnology Green Chemistry Applied and Environmental Microbiology Enzyme and Microbial Technology Science Biotechnology for Biofuels Current Opinion in Biotechnology Proceedings of the National Academy of Sciences of the United States of America Biotechnology Advances Nature Biotechnology Biomass and Bioenergy Journal of Chemical Technology and Biotechnology Applied Biochemistry and Biotechnology Part A Carbohydrate Polymers Industrial and Engineering Chemistry Research Journal of Biotechnology Biotechnology Progress Biomacromolecules

Sample Papers (%)

Population Papers (%)

15.1 9.2 5.6 5.6 3.6 3.6 3.6 2.8 2.8 2.4

6.6 1.9 1.7 0.6 1.3 1.0 0.1 1.5 0.1 0.1

8.5 7.3 3.9 5.0 2.3 2.6 3.5 1.3 2.7 2.3

2.0 2.0 1.6 1.6 1.6 1.2 1.2 1.2 1.2 1.2

0.1 0.1 1.2 0.6 0.6 0.8 0.7 0.5 0.5 0.1

1.9 1.9 0.4 1.0 1.0 0.4 0.5 0.7 0.7 1.1

Surplus (%)

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Production Processes for Bioethanol Fuels: Scientometric Study

Biodegradation, Plos One, Bioenergy Research, Journal of Bacteriology, Agricultural and Biological Chemistry, Separation and Purification Technology, Scientific Reports, Journal of Biological Chemistry, Journal of Cleaner Production, and Chemical Engineering Journal.

19.3.7 The Most Prolific Countries in the Production Processes for Bioethanol Fuels Information about the most prolific 20 countries publishing at least 1.2% of sample papers each in the production processes for bioethanol fuels is given in Table 19.6. The most prolific country is the USA with 48% of the sample papers, followed by Canada and Sweden with 11% and 10% of the sample papers, respectively. The other prolific countries are China, Denmark, the UK, Japan, Spain, and Germany with 5%–6% of the sample papers each. On the other hand, nine European countries listed in Table 19.6 produce 45% and 21% of the sample and population papers, respectively, with 24% surplus, making it second largest prolific producer of the research in this field. On the other hand, the country with the most citation impact is the USA with 31% surplus, followed by Sweden, Canada, and Denmark with 8%, 7%, and 5% surplus, respectively. The other prolific countries are Netherlands, the UK, Germany, Finland, and Spain with 2% surplus each. Similarly, the country with the least citation impact is China with 14% deficit, followed by Brazil, S. Korea, and Japan with 2%–4% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.4%–2.1% of the sample papers each: Malaysia, Italy, Thailand, Taiwan, Iran, Portugal, Russian Federation, Indonesia, Turkey, Poland, South Africa, Egypt, Belgium, Nigeria, Pakistan, Greece, Argentina, Switzerland, Czech Republic, New Zealand, Israel, Saudi Arabia, Colombia, Singapore, and Norway.

TABLE 19.6 The Most Prolific Countries in the Production Processes for Bioethanol Fuels No.

Countries

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

USA Canada Sweden China Denmark UK Japan Spain Germany India France Finland Netherlands Australia Brazil S. Korea Austria Chile Mexico S. Africa

Sample Papers (%) 48.2 10.8 10.4 6.0 6.0 5.6 5.2 5.2 5.2 4.0 4.0 3.6 3.6 2.8 2.0 2.0 1.6 1.6 1.2 1.2

Population Papers (%) 17.4 4.0 2.7 20.3 1.3 3.6 7.1 3.6 3.2 3.1 2.9 1.7 1.5 1.7 5.5 4.0 0.7 0.4 1.6 1.0

Surplus (%) 30.8 6.8 7.7 −14.3 4.7 2.0 −1.9 1.6 2.0 0.9 1.1 1.9 2.1 1.1 −3.5 −2.0 0.9 1.2 −0.4 0.2

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Bioethanol Fuel Production Processes. II

19.3.8 The Most Prolific Scopus Subject Categories in the Production Processes for Bioethanol Fuels Information about the most prolific ten Scopus subject categories indexing at least 6% of the sample papers each is given in Table 19.7. The most prolific Scopus subject categories in the production processes for bioethanol fuels are the Chemical Engineering and Biochemistry, Genetics and Molecular Biology with 51% of the sample papers each, followed by Immunology and Microbiology, Environmental Science, and Energy with 39%, 36%, and 26% of the sample papers, respectively. It is notable that Social Sciences including Economics and Business account for only 1% of the sample and population studies each. On the other hand, the Scopus subject category with the most citation impact is the Chemical Engineering with 15% surplus. The other influential subject areas are Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology, and Environmental Science with 14%, 13%, and 11% surplus, respectively. Similarly, the least influential subject category is the Agricultural and Biological Sciences with 15% deficit. The other least influential subjects are Chemistry, Materials Science, and Engineering with 8%, 4%, and 3% deficits, respectively.

19.3.9 The Most Prolific Keywords in the Production Processes for Bioethanol Fuels Information about the Scopus keywords used with at least 6.4% or 3.0% of the sample or population papers, respectively, is given in Table 19.8. For this purpose, keywords related to the keyword set given in the appendix of the related papers are selected from a list of the most prolific keyword set provided by Scopus database. These keywords are grouped under the five headings: feedstocks, pretreatments, fermentation, hydrolysis and hydrolysates, and products. The most prolific keyword related to the biomass and biomass constituents is cellulose with 54% of the sample papers, followed by lignin, biomass, and lignocellulose with 28%–49% of the sample papers, respectively. The other prolific keywords are hemicellulose, wood, lignocellulosic biomass, and carbohydrate with 11%–16% of the sample papers each. Further, the most prolific keyword related to the pretreatments is enzymes with 26% of the sample papers, followed by pretreatment, degradation, temperature, and biodegradation with 10%–12% TABLE 19.7 The Most Prolific Scopus Subject Categories in the Production Processes for Bioethanol Fuels No.  1  2  3  4  5  6  7  8  9 10

Scopus Subject Categories Chemical Engineering Biochemistry, Genetics and Molecular Biology Immunology and Microbiology Environmental Science Energy Chemistry Agricultural and Biological Sciences Engineering Multidisciplinary Materials Science

Sample Papers (%)

Population Papers (%)

Surplus (%)

51.0 50.6

36.1 37.5

14.9 13.1

39.4 35.9 25.5 10.4 8.8

25.5 25.4 19.4 18.5 23.6

13.9 10.5 6.1 −8.1 −14.8

8.0 6.4 6.0

10.7 2.1 10.2

−2.7 4.3 −4.2

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Production Processes for Bioethanol Fuels: Scientometric Study

TABLE 19.8 The Most Prolific Keywords in the Production Processes for Bioethanol Fuels No. 1

Keywords

Sample Papers (%)

3

Surplus (%)

Cellulose

54.2

20.6

33.6

Lignin

48.6

17.2

31.4

Biomass

39.0

15.3

23.7

Lignocellulose

27.9

6.9

21.0

Hemicellulose

15.9

4.1

11.8

Wood

13.9

4.9

9.0

Lignocellulosic biomass

12.0

4.8

7.2

Carbohydrate

10.8

5.7

5.1

Zea

10.4

4.1

Corn

6.4

Straw

5.2

Bagasse 2

Population Papers (%)

Feedstocks

6.3 6.4

3.1

2.1

3.3

−3.3

Pretreatments Enzymes

25.9

10.9

15.0

Pretreatment

12.4

6.1

6.3

Degradation

10.4

4.7

5.7

Temperature

10.0

5.7

4.3

Biodegradation

10.0

5.2

4.8

Pre-treatment

8.0

6.1

1.9

Hypocrea

8.0

Dissolution

7.6

1.7

5.9

Sulfuric acid

7.2

2.4

4.8

Trichoderma

7.2

ILs

6.4

2.5

Genetic Engineering

6.4

1.5

4.9

pH

5.6

6.5

−0.9

Water

4.8

3.6

1.2

Genetics

5.2

−5.2

Delignification

3.0

−3.0

8.0

7.2 3.9

Fermentation Fungi

34.7

11.9

22.8

Fermentation

33.1

28.9

4.2

Bacteria

17.9

12.8

5.1

Saccharomyces

12.4

9.1

3.3

Yeast

11.2

9.6

1.6

Basidiomycota

6.4

Escherichia

4.8

3.7

6.4 1.1

Bioreactors

4.4

6.5

−2.1

Acetic Acid

4.4

3.9

0.5 (Continued )

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Bioethanol Fuel Production Processes. II

TABLE 19.8 (Continued ) The Most Prolific Keywords in the Production Processes for Bioethanol Fuels No. 4

Keywords Hydrolysis and hydrolysates Hydrolysis

Population Papers (%)

Surplus (%)

47.4

19.6

27.8

Sugar

28.7

9.0

19.7

Enzyme activity

25.5

12.2

13.3

Cellulases

21.1

7.1

14.0

Glucose

19.5

13.7

5.8

Enzymatic hydrolysis

15.1

10.3

4.8

Xylose

13.5

5.8

7.7

Saccharification

12.7

8.1

4.6 0.1

Enzymology 5

Sample Papers (%)

3.2

3.1

Products Ethanol

27.5

21.5

6.0

Biofuels

21.1

9.1

12.0

Biofuel production

9.2

1.5

7.7

Bioethanol

7.6

7.1

0.5

Ethanol production

5.2

3.5

1.7

of the sample papers each. The other prolific keywords are the pre-treatment, hypocrea, dissolution, sulfuric acid, and trichoderma with 7%–8% of the sample papers each. The most prolific keyword related to the fermentation is fungi with 35% of the sample papers, followed by fermentation with 33% of the sample papers. The other prolific keywords are bacteria, saccharomyces, and yeast with 11%–18% of the sample papers each. Further, the most prolific keyword related to the hydrolysis and hydrolysates is hydrolysis with 47% of the sample papers. The other prolific keywords are sugar, enzyme activity, cellulases, glucose, enzymatic hydrolysis, xylose, and saccharification with 13%–29% of the sample papers each. Finally, the most prolific keyword related to the products is ethanol with 28% of the sample papers, followed by biofuels with 21% of the sample papers. On the other hand, the most prolific keywords across all of the research fronts are cellulose, lignin, hydrolysis, biomass, fungi, fermentation, sugar, lignocellulose, ethanol, enzymes, enzyme activity, biofuels, cellulases, and glucose with 20%–54% of the sample papers each. The other prolific keywords are bacteria, hemicellulose, enzymatic hydrolysis, wood, xylose, saccharification, saccharomyces, pretreatment, and lignocellulosic biomass with 12%–18% of the sample papers each. Similarly, the most influential keywords are cellulose, lignin, hydrolysis, biomass, fungi, lignocellulose, and sugar with 20%–34% surplus each. The other influential keywords are enzymes, cellulases, enzyme activity, biofuels, and hemicellulose with 11%–18% surplus each.

19.3.10 The Most Prolific Research Fronts in the Production Processes for Bioethanol Fuels Information about the research fronts for the sample papers in the production processes for bioethanol fuels is given in Table 19.9. As this table shows, the most prolific research front for this field is the lignocellulosic biomass-based bioethanol fuels with 28% of the sample papers. The other prolific research fronts are the cellulose-based bioethanol fuels, wood-based bioethanol fuels, and starch feedstock residues-based bioethanol fuels with 18%, 13%, and 12% of the sample papers, respectively.

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Production Processes for Bioethanol Fuels: Scientometric Study

TABLE 19.9 The Most Prolific Research Fronts for the Production Processes for Bioethanol Fuels No. 1 2 3 4 5

6

Research Fronts Lignocellulosic biomass-based bioethanol fuels Cellulose-based bioethanol fuels Wood-based bioethanol fuels Starch feedstock residues-based bioethanol fuels Minor research fronts Lignin-based bioethanol fuels

N Paper (%) Sample 28.3 18.3 12.7 12.4 5.6

Grass-based bioethanol fuels

4.0

Industrial waste-based bioethanol fuels

3.2

Sugar feedstock-based bioethanol fuels

3.2

Starch feedstock-based for bioethanol fuels

2.4

Sugar feedstock residues-based bioethanol fuels

2.0

Highly minor research fronts Algal biomass-based bioethanol fuels

0.8

Biosyngas-based bioethanol fuels

0.8

Urban waste-based bioethanol fuels

0.8

Food waste-based bioethanol fuels

0.4

Forestry waste-based bioethanol fuels Sample size

0.4 251

N paper (%) sample, The number of papers in the population sample of 251 papers.

The other prolific research fronts are the lignin-based bioethanol fuels, grass-based bioethanol fuels, industrial waste-based bioethanol fuels, sugar feedstock-based bioethanol fuels, starch feedstock-based for bioethanol fuels, and sugar feedstock residues-based bioethanol fuels with 2%–6% of the sample papers each. Further, the other minor research fronts are algal biomass-based bioethanol fuels, syngasbased bioethanol fuels, urban waste-based bioethanol fuels, forestry waste-based bioethanol fuels, and food waste-based bioethanol fuels with 0.4%–0.8% of the sample papers each. Information about the thematic research fronts for the sample papers in the production processes for bioethanol fuels is given in Table 19.10. As this table shows, the most prolific research front is the pretreatments of the biomass with 79% of the sample papers, followed by the hydrolysis of the feedstock with 44% of the sample papers. The other prolific research fronts are the hydrolysate fermentation and bioethanol production in general with 24% and 12% of the sample papers, respectively. Further, utilization, evaluation, and distillation of bioethanol fuels are the minor research fronts with only 0.4%, 0.4%, and 2.4% of the sample papers, respectively.

19.4 DISCUSSION 19.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation. The research in the field of the

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Bioethanol Fuel Production Processes. II

TABLE 19.10 The Most Prolific Thematic Research Fronts for the Production Processes for Bioethanol Fuels No. 1 2 3 4 5 6 7

Research Fronts Biomass pretreatments Biomass hydrolysis Hydrolysate fermentation Bioethanol production Bioethanol fuel evaluation Bioethanol fuel distillation Bioethanol fuel utilization

N Paper (%) Sample 79.3 44.2 23.5 12.0 2.4 0.4 0.4

N paper (%) sample, The number of papers in the population sample of 251 papers.

production processes for bioethanol fuels has intensified in this context in the key research fronts of the pretreatment and hydrolysis of the feedstocks, fermentation of the hydrolysates, production of bioethanol fuels in general, and to a lesser extent evaluation, utilization, and distillation of bioethanol fuels. The research in this field has also intensified for the feedstocks of lignocellulosic biomass at large, cellulose, wood biomass, starch feedstock residues, and to a lesser extent lignin, grass biomass, industrial waste, sugar feedstocks, starch feedstocks, sugar feedstock residues, algal biomass, syngas, urban waste, food waste, and forestry waste. Thus, it complements the research on the feedstocks for the bioethanol fuels and evaluation and utilization of bioethanol fuels at large. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, war-related chocks as in the case of Russian invasion of Ukraine, or COVID-19 shocks. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As there has been no current scientometric study in this field, this book chapter presents a scientometric study of the research in the production processes for bioethanol fuels. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the mostcited first 300 population papers for each feedstock. The selected keyword list was then optimized to obtain a representative sample of papers for each research field. These keyword lists were then integrated to obtain the keyword list for this research field (Konur, 2023a,b,c). As a second step, two sets of data were used for this study. First, a population sample of 50,108 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 251 most-cited papers, corresponding to 0.5% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for production processes for bioethanol fuels. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

Production Processes for Bioethanol Fuels: Scientometric Study

17

19.4.2 The Most Prolific Documents in the Production Processes for Bioethanol Fuels Articles (together with conference papers) dominate both the sample (72%) and population (95%) papers with 23% deficit (Table 19.1). Further, review papers have a surplus (26%) and the representation of the reviews in the sample papers is quite extraordinary (29%). Scopus differs from the Web of Science database in differentiating and showing articles (69%) and conference papers (3%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared to those published only in the conference proceedings. Hence, the total number of articles and review papers in the sample dataset are 72% and 29%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus as well as in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of the reviews in the sample data set was manually found as nearly 36% compared to 29% as indexed by Scopus, decreasing the number of articles and conference papers to 64% for the sample dataset. It is notable that many techno-economic and life cycle studies were often indexed as reviews by the Scopus database. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance following the good practice in the publishing sector. It would also be helpful to use the document types listed in Table 19.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from the experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as well as it is done in the Web of Science database.

19.4.3 The Most Prolific Authors in the Production Processes for Bioethanol Fuels There have been most prolific 25 authors with at least 1.5% of the sample papers each as given in Table 19.2. These authors have shaped the development of the research in this field. The most prolific authors are John N. Saddler, Barbel Hahn-Hagerdal, and to a lesser extent Charles E. Wyman, Bernard Henrissat, Igor V. Grigoriev, Bruce E. Dale, Lee R. Lynd, Michael E. Himmel, Mark T. Holtzapple, Dan Cullen, and Pedro M. Coutinho. Further, the most influential authors are Barbel Hahn-Hagerdal, John N. Saddler, and to a lesser extent Bernard Henrissat, Charles E. Wyman, Igor V. Grigoriev, Pedro M. Coutinho, Lee R. Lynd, Michael E. Himmel, Mark T. Holtzapple, and Dan Cullen. It is notable that all of these top authors, except one, are male. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for the names with more than two components such as ‘Blake Sam de Hyun Cullen’. The probable outcomes are ‘Cullen, B.S.D.H.’, ‘de Hyun Cullen, B.S.’, or ‘Hyun Cullen, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative whilst the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name by a hyphen: ‘Hyun-Cullen, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening Chinese names. For example. ‘Runcang Yong’ is often shortened as ‘Yong, R.’, ‘Yong, R.-C.’, and ‘Yong, R.C.’ as it is done in the Web of Science

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database as well. However, the gold standard in this case is ‘Yong, R’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PUBMED and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the second option to differentiate Chinese names efficiently: Yong, R.C.’. Therefore, there have been difficulties in locating papers for the Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors especially for the authors with common names such as ‘Yong, R.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies as well as allocating credit to the authors as there are the same author entries for different authors with the same name, for example, ‘Yong, R.’ in the databases. In this context, the coding of authors in Scopus database is a welcome innovation compared to the other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system especially for common names. For example, some of the papers for a certain author maybe allocated to another researcher with a different author code. It is possible that Scopus uses a number of software programs to differentiate the author names and the program may not be false-proof (Kim, 2018). In this context, it does not help that author names are not given in full in some journals and books. This makes it difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in full in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Cullen, H.J.’, ‘Cullen, H.’, or ‘Cullen, J.’ instead of ‘Cullen, Hyun Jae’. There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example. ‘Cullen, A.P.C.’ might be given as ‘Cullen, A.’ or ‘Cullen, A.C.’ or ‘Cullen, A.P.’ or ‘Cullen, C’ in journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Özğümüş, Sütçiğdem) rather than in the English spellings (e.g., Ozgumus, Sutcigdem) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in the English spellings. It is observed that national spellings of the author names do not help much in conducting scientometric studies as well in allocating credits to the authors as sometimes there are often the different author entries for the English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are Joint Genome Institute and to a lesser extent Lund University. Further, the most prolific countries for the sample dataset are the USA and to a lesser extent Sweden. These findings confirm the dominance of the USA and to a lesser extent Europe in this field. On the other hand, pretreatments and hydrolysis of the feedstocks and to a lesser extent the fermentation of the hydrolysates and the bioethanol fuels in general are the key research fronts studied by these top authors. It is also notable that there is significant gender deficit for the sample dataset as surprisingly with a representation rate of 12%. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from the minority groups in science on the basis of race, sexuality, age, and disability, besides the gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

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19.4.4 The Most Prolific Research Output by Years in the Production Processes for Bioethanol Fuels The research output observed between 1970 and 2022 is illustrated in Figure 19.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s and early 2020s. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s. These findings suggest that the most prolific sample and population papers were primarily published in the 2010s. Further, a significant portion of the sample and population papers were published in the early 2020s and 2000s, respectively. These are the thought-provoking findings as there has been significant research boom in since 2009 and 2002 for the population and sample papers, respectively. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field since 2007. Furthermore, the recent supply shocks experienced due to the COVID-19 pandemics and the Ukrainian war might also be behind the research boom in this field since 2019, especially the sharp rise in the research output in 2021. Based on these findings, the size of the population papers likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming, as well as the supply shocks are translated efficiently to the research funding in this field.

19.4.5 The Most Prolific Institutions in the Production Processes for Bioethanol Fuels The most prolific 31 institutions publishing papers on the production processes for bioethanol fuels with at least 1.6% of the sample papers each given in Table 19.3 have shaped the development of the research in this field. The most prolific institutions are the Lund University, USDA Forest Service, NREL, and to a lesser extent University of British Columbia, University of Wisconsin Madison, Dartmouth College, Oak Ridge National Laboratory, Technical University of Denmark, NC State University, Joint Genome Institute, Pacific Northwest National Laboratory, and Novozymes Biotech Inc. Similarly, the top countries for these most prolific institutions are the USA, and to a lesser extent Denmark, Finland, France, and Sweden. In total, nine countries house these top institutions. On the other hand, the institutions with the most impact are the Lund University, USDA Forest Service, and to a lesser extent NREL, University of British Columbia, Dartmouth College, University of Wisconsin Madison, Oak Ridge National Laboratory, Novozymes Biotech Inc., Joint Genome Institute, Pacific Northwest National Laboratory, and Technical University of Denmark. These findings confirm the dominance of the institutions from the USA, Europe, and to a lesser extent Canada.

19.4.6 The Most Prolific Funding Bodies in the Production Processes for Bioethanol Fuels The most prolific 13 funding bodies funding at least 1.2% of the sample papers each is given in Table 19.4. It is notable that only 21% and 44% of the sample and population papers were funded, respectively. The most prolific funding bodies are the U.S. Department of Energy, and to a lesser extent National Science Foundation, European Commission, Office of Science, National Institute of General Medical Sciences, and Swedish National Board for Industrial and Technical Development.

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On the other hand, the most prolific countries for these top funding bodies are the USA and Sweden. In total, only three countries and the EU house these top funding bodies. The funding bodies with the most citation impact are the U.S. Department of Energy, Swedish National Board for Industrial and Technical Development, and to a lesser extent National Institute of General Medical Sciences, Swedish Research Council, Office of Science, European Commission, Carl Tryggers Foundation for Scientific Research, Knut and Alice Wallenberg Foundation, and Lawrence Livermore National Laboratory. Further, the funding bodies with the least citation impact are the National Science Foundation and Natural Sciences and Engineering Research Council of Canada. On the other hand, National Natural Science Foundation of China has a spectacular 8.3% deficit. These findings on the funding of the research in this field suggest that the level of the funding for the population papers, mostly since 2008, is moderately intensive and it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, the funding rate for the sample papers since 1998 is low. It is also notable that the funding rate in this field is relatively modest compared to those in the other research fronts of the bioethanol fuels such as algal bioethanol fuels. Further, it is expected that this funding rate would improve in light of the recent supply shocks. Further, it emerges that China, the USA, and Sweden have heavily funded the research on the production processes for bioethanol fuels.

19.4.7 The Most Prolific Source Titles in the Production Processes for Bioethanol Fuels The most prolific 20 source titles publishing at least 1.1% of the sample papers each in the production processes for bioethanol fuels have shaped the development of the research in this field (Table 19.5). The most prolific source titles are the Bioresource Technology, Biotechnology and Bioengineering, and to a lesser extent Applied Microbiology and Biotechnology, Green Chemistry, Applied and Environmental Microbiology, Enzyme and Microbial Technology, Science, Biotechnology for Biofuels, Current Opinion in Biotechnology, and Proceedings of the National Academy of Sciences of the United States of America. On the other hand, the source titles with the most citation impact are the Bioresource Technology, Biotechnology and Bioengineering, and to a lesser extent Green Chemistry, Applied Microbiology and Biotechnology, Science, Current Opinion in Biotechnology, and Enzyme and Microbial Technology. It is notable that these top source titles are primarily related to the bioresources, biotechnology, and microbiology. This finding suggests that Bioresource Technology and the other prolific journals in these fields have significantly shaped the development of the research in this field as they focus primarily on the production processes for bioethanol fuels with a high yield. In this context, the influence of two top journals is quite extraordinary.

19.4.8 The Most Prolific Countries in the Production Processes for Bioethanol Fuels The most prolific 20 countries publishing at least 1.2% of the sample papers each have significantly shaped the development of the research in this field (Table 19.6). The most prolific countries are the USA and to a lesser extent Canada, Sweden, China, Denmark, the UK, Japan, Spain, and Germany. On the other hand, nine European countries listed in Table 19.6 produce 45% and 21% of the sample and population papers, respectively, with 24% surplus, making Europe as a whole second largest prolific producer of the research in this field. Further, the countries with the most citation impact are the USA and to a lesser extent Sweden, Canada, Denmark, Netherlands, the UK, Germany, Finland, and Spain. Similarly, the countries with the least impact are China and to a lesser extent Brazil, S. Korea, and Japan.

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The close examination of these findings suggests that the USA, Europe, and to a lesser extent China, Canada, India, and Japan are the major producers of the research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009). The USA has further developed a strong research infrastructure to support its corn- and grass-based bioethanol industry (Gillon, 2010). However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Fang et al., 2010). Next, Europe has been a persistent player in the scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010). Further, Canada (Tahmooresnejad et al., 2015), Japan (Negishi et al., 2004), and India (Karpagam et al., 2011) are the other countries with substantial research activities in bioethanol fuels.

19.4.9 The Most Prolific Scopus Subject Categories in the Production Processes for Bioethanol Fuels The most prolific ten Scopus subject categories indexing at least 6% of the sample papers each, given in Table 19.7, have shaped the development of the research in this field. The most prolific Scopus subject categories in the production processes for bioethanol fuels are Chemical Engineering, Biochemistry, Genetics and Molecular Biology, and to a lesser extent Immunology and Microbiology, Environmental Science, and Energy. It is also notable that Social Sciences including Economics and Business have a minimal presence in both sample and population studies. On the other hand, the Scopus subject categories with the most citation impact are Chemical Engineering, and to a lesser extent Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology, and Environmental Science. Similarly, the least influential subject categories are Agricultural and Biological Sciences and to a lesser extent Chemistry, Materials Science, and Engineering. These findings are thought-provoking suggesting that the primary subject categories are related to chemical engineering, biochemistry, and to a lesser extent microbiology, environmental science, and energy as the core of the research in this field concerns with the production processes for bioethanol fuels. The other finding is that social sciences are not well represented in both the sample and population papers as in line with the most fields in bioethanol fuels. The social, environmental, and economics studies account for the field of social sciences. It is notable that the editorial policies of the most journals in this field exclude social sciencebased interdisciplinary studies such as scientometric, user, or policy-related studies. This development has been in contrast to the interdisciplinarity (Jacobs and Frickel, 2009; Nissani, 1997) of this field in light of the pressures for increasing incentives for the primary stakeholders (North, 1991). Thus, for the healthy development of this research field, social science- and humanities-based interdisciplinary studies have a lot to contribute to this field.

19.4.10 The Most Prolific Keywords in the Production Processes for Bioethanol Fuels A limited number of keywords have shaped the development of the research in this field as shown in Table 19.8. These keywords are grouped under the five headings: feedstocks, pretreatments, fermentation, hydrolysis and hydrolysates, and products. The most prolific keywords across all of the research fronts are cellulose, lignin, hydrolysis, biomass, fungi, fermentation, sugar, lignocellulose, ethanol, enzymes, enzyme activity, biofuels, cellulases, glucose, and to a lesser extent bacteria, hemicellulose, enzymatic hydrolysis, wood, xylose, saccharification, saccharomyces, pretreatment, and lignocellulosic biomass.

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Similarly, the most influential keywords are cellulose, lignin, hydrolysis, biomass, fungi, lignocellulose, sugar, and to a lesser extent enzymes, cellulases, enzyme activity, biofuels, and hemicellulose. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas for both sample and population datasets. These findings also highlight different spelling of some strategic keywords such as pretreatment v. pre-treatment v. treatment and bioethanol v. ethanol v. bio-ethanol, etc. However, there is tendency toward the use of the connected keywords without using a hyphen: Bioethanol or pretreatment. It is particularly notable that the use of treatment and ethanol instead of pretreatment and bioethanol in the paper titles, respectively, makes the literature search less efficient and time-consuming.

19.4.11 The Most Prolific Research Fronts in the Production Processes for Bioethanol Fuels Information about the research fronts for the sample papers in the production processes for bioethanol fuels is given in Table 19.9. As this table shows, the most prolific research front for this field is the lignocellulosic biomass-based bioethanol fuels, followed by cellulose-based bioethanol fuels, wood-based bioethanol fuels, and starch feedstock residues-based bioethanol fuels. The other prolific research fronts are the lignin-based bioethanol fuels, grass-based bioethanol fuels, industrial wastebased bioethanol fuels, sugar feedstock-based bioethanol fuels, starch feedstock-based for bioethanol fuels, and sugar feedstock residues-based bioethanol fuels. Further, the other minor research fronts are algal biomass-based bioethanol fuels, syngas-based bioethanol fuels, urban waste-based bioethanol fuels, food waste-based bioethanol fuels, and forestry waste-based bioethanol fuels. Thus, the first four research fields have substantial importance, complementing the remaining bioethanol fuel research fields. It is important to note that the research on the production of the first generation bioethanol fuels from sugar and starch feedstocks for bioethanol fuels comprises only 5.6% of the sample papers in total. These first generation bioethanol fuels are not much desirable as they undermine the food security (Makenete et al., 2008; Wu et al., 2012). Table 19.10 shows that the most prolific research fronts are the pretreatments of the biomass, followed by the hydrolysis of the feedstocks. The other prolific research fronts are the hydrolysate fermentation and bioethanol production in general. Further, bioethanol fuel evaluation, utilization, and distillation are the minor research fronts. These findings are thought-provoking in seeking ways to increase feedstock-based bioethanol yield at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Further, it is notable that production processes for bioethanol fuels have become a core unit of the bioethanol research to make it more competitive with the crude oil-based gasoline and diesel fuels, especially for the USA, Europe, and China. It is notable that the pretreatment and hydrolysis of the feedstocks emerge as primary research fronts for this field. These processes are required to improve the ethanol yield. However, the research fronts of the fermentation of the hydrolysates and the bioethanol production from the hydrolysates are also important. Further, the field of the evaluation, utilization, and distillation of bioethanol fuels is a neglected area. This suggests that the primary stakeholders have been primarily interested in these key processes of the bioethanol production. It is also notable that evaluation of the bioethanol fuels such as techno-economics, life cycle, economics, social, land use, labor, and environmental impact-related studies emerges as a case study for the bioethanol fuels. Similarly, the utilization of these biofuels in the gasoline or diesel engines is also an important research field from a societal perspective. In this context, the USA and Brazil have been the global leaders in the production and use of the corn- and sugarcane-based bioethanol fuels since the 1970s in the aftermath of the global crude oil crisis in the early 1970s. Separation and distillation of the bioethanol fuels is also an important research field.

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In the end, these most-cited papers in this field hint that the production processes for bioethanol fuels could be optimized using the structure, processing, and property relationships of these feedstocks in the fronts of the feedstock pretreatment and hydrolysis, and hydrolysate fermentation (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

19.5  CONCLUSION AND FUTURE RESEARCH The research on the production processes for bioethanol fuels has been mapped through a scientometric study of both sample (251 papers) and population (50,108 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the appendix of the related studies, and the relevant keywords are presented in Table 19.8. However, it should be noted that it has been very difficult to compile a representative keyword set since this research field has been connected closely with many other fields. Therefore, it has been necessary to compile a keyword list to exclude papers concerned with the other research fields. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose, Scopus database has been selected. The journal coverage of this database has been notably wider than that of Web of Science and other multisubject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts as well as highly cited papers have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all the academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed since 2009 and 2002 for the population and sample papers, respectively, possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Furthermore, the recent COVID-19 pandemics and Russian invasion of Ukraine have resulted in a global supply shocks shifting the recent focus of the stakeholders from the crude oil- and natural gas-based fuels to biomass-based fuels such as bioethanol fuels. It is expected that there would be further incentives for the key stakeholders to carry out the research for the production processes for bioethanol fuels to increase the ethanol yield and to make it more competitive with the crude oil-based gasoline and diesel fuels. This might be truer for the crude oil- and foreign exchangedeficient countries to maintain the energy and food security at the face of the global supply shocks. The relatively modest funding rate of 21% and 44% for the sample and population papers, respectively, suggests that funding for the population papers in this field significantly enhanced the research in this field primarily since 2009, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further. The institutions from the USA, and to a lesser extent Denmark, Finland, France, and Sweden have mostly shaped the research in this field. Further, USA and to a lesser extent Canada, Sweden, China, Denmark, the UK, Japan, Spain, and Germany have been the major producers of the research in this field as the major producers and users of bioethanol fuels. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives.

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It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. On the other hand, the Scopus keywords are grouped under the five headings: biomass, pretreatments, fermentation, hydrolysis and hydrolysates, and products. These prolific keywords highlight the major fields of the research in this field for both sample and population papers. Table 19.9 shows that the most prolific research fronts for this field are the lignocellulosic biomass-based bioethanol fuels, cellulose-based bioethanol fuels, wood biomass-based bioethanol fuels, starch feedstock residues-based bioethanol fuels, and to a lesser extent lignin-based bioethanol fuels, grass biomass-based bioethanol fuels, industrial waste-based bioethanol fuels, sugar feedstock-based bioethanol fuels, starch feedstock-based for bioethanol fuels, sugar feedstock residues-based bioethanol fuels, algal biomass-based bioethanol fuels, syngas-based bioethanol fuels, urban waste-based bioethanol fuels, food waste-based bioethanol fuels, and forestry waste-based bioethanol fuels. It is important to note that the research on the production processes for the first generation bioethanol fuels from sugar and starch feedstocks for bioethanol fuels comprise only a small part of the sample papers in total. These first generation bioethanol fuels are not much desirable as they undermine the food security. Further, Table 19.10 shows that most prolific research fronts are the pretreatments and hydrolysis of the biomass, hydrolysate fermentation, bioethanol production in general, and to a lesser extent evaluation, utilization, and distillation of bioethanol fuels. The first four research fronts dominate the research in this field whilst the field of the utilization, evaluation, and distillation of the bioethanol fuels is relatively a neglected research field. In this context, it is notable that there is ample room for the improvement of the research on social science and humanitarian aspects of the research on the production processes for the bioethanol fuels such as scientometric and user studies. It is important to include social studies besides technical studies in the journals and books for the healthy and sustainable development of this research field in line with the increasing societal concerns. These findings are thought-provoking in seeking ways to increase the bioethanol yield at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Further, it is notable that production processes for bioethanol fuels have become a core unit of the bioethanol research to make it more competitive with the crude oilbased gasoline and diesel fuels, especially for the USA, Europe, Brazil, and China. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields especially in the aftermath of the significant global supply shocks such as COVID-19 pandemics and the Russian invasion of Ukraine. It is recommended that further scientometric studies are carried out for the primary research fronts. It is further recommended that reviews of the most-cited papers are carried out for each primary research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the production processes for bioethanol fuels has been gratefully acknowledged.

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Henstra, A. M., J. Sipma, A. Rinzema and A. J. Stams. 2007. Microbiology of synthesis gas fermentation for biofuel production. Current Opinion in Biotechnology 18:200–206. Hill, J., E. Nelson, D. Tilman, S. Polasky and D. Tiffany. 2006. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proceedings of the National Academy of Sciences of the United States of America 103:11206–11210. Hill, J., S. Polasky and E. Nelson, et al. 2009. Climate change and health costs of air emissions from biofuels and gasoline. Proceedings of the National Academy of Sciences of the United States of America 106:2077–2082. Ho, S. H., S. W. Huang and C. Y. Chen, et al. 2013. Bioethanol production using carbohydrate-rich microalgae biomass as feedstock. Bioresource Technology 135:191–198. Hsieh, W. D., R. H. Chen, T. L. Wu and T. H. Lin. 2002. Engine performance and pollutant emission of an SI engine using ethanol-gasoline blended fuels. Atmospheric Environment 36:403–410. Huang, H. J., S. Ramaswamy, U. W. Tschirner and B. V. Ramarao. 2008. A review of separation technologies in current and future biorefineries. Separation and Purification Technology 62:1–21. Jacobs, J. A. and S. Frickel. 2009. Interdisciplinarity: A critical assessment. Annual Review of Sociology 35:43–65. John, R. P., G. S. Anisha, K. M. Nampoothiri and A. Pandey. 2011. Micro and macroalgal biomass: A renewable source for bioethanol. Bioresource Technology 102:186–193. Jones, T. C. 2012. America, oil, and war in the Middle East. Journal of American History 99:208–218. Jonsson, L. J. and C. Martin. 2016. Pretreatment of lignocellulose: Formation of inhibitory by-products and strategies for minimizing their effects. Bioresource Technology 199:103–112. Karpagam, R., S. Gopalakrishnan, M. Natarajan and B. R. Babu. 2011. Mapping of nanoscience and nanotechnology research in India: A scientometric analysis, 1990-2009. Scientometrics 89:501–522. Kerr, R. A. 2007. Global warming is changing the world. Science 316:188–190. Keshwani, D. R. and J. J. Cheng. 2009. Switchgrass for bioethanol and other value-added applications: A review. Bioresource Technology 100:1515–1523. Kilian, L. 2008. Exogenous oil supply shocks: How big are they and how much do they matter for the US economy? Review of Economics and Statistics 90:216–240. Kilian, L. 2009. Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review 99:1053–1069. Kim, J. 2018. Evaluating author name disambiguation for digital libraries: A case of DBLP. Scientometrics 116:1867–1886. Kirk, T. K. and R. L. Farrell. 1987. Enzymatic “combustion”: The microbial degradation of lignin. Annual Review of Microbiology 41:465–505. Konur, O. 2000. Creating enforceable civil rights for disabled students in higher education: An institutional theory perspective. Disability & Society 15:1041–1063. Konur, O. 2002a. Access to nursing education by disabled students: Rights and duties of nursing programs. Nurse Education Today 22:364–374. Konur, O. 2002b. Assessment of disabled students in higher education: Current public policy issues. Assessment and Evaluation in Higher Education 27:131–52. Konur, O. 2002c. Access to employment by disabled people in the UK: Is the disability discrimination act working? International Journal of Discrimination and the Law 5:247–279. Konur, O. 2006a. Participation of children with dyslexia in compulsory education: Current public policy issues. Dyslexia 12:51–67. Konur, O. 2006b. Teaching disabled students in higher education. Teaching in Higher Education 11:351–363. Konur, O. 2007a. A judicial outcome analysis of the Disability Discrimination Act: A windfall for the employers? Disability & Society 22:187–204. Konur, O. 2007b. Computer-assisted teaching and assessment of disabled students in higher education: The interface between academic standards and disability rights. Journal of Computer Assisted Learning 23:207–219. Konur, O. 2011. The scientometric evaluation of the research on the algae and bio-energy. Applied Energy 88:3532–3540. Konur, O. 2012a. The evaluation of the biogas research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:1277–1292. Konur, O. 2012b. The evaluation of the educational research: A scientometric approach. Energy Education Science and Technology Part B: Social and Educational Studies 4:1935–1948. Konur, O. 2012c. The evaluation of the global energy and fuels research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 30:613–628.

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Konur, O. 2012d. The evaluation of the research on the biodiesel: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1003–1014. Konur, O. 2012e. The evaluation of the research on the bioethanol: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1051–1064. Konur, O. 2012f. The evaluation of the research on the biofuels: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:903–916. Konur, O. 2012g. The evaluation of the research on the biohydrogen: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:323–338. Konur, O. 2012h. The evaluation of the research on the microbial fuel cells: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:309–322. Konur, O. 2012i. The scientometric evaluation of the research on the production of bioenergy from biomass. Biomass and Bioenergy 47:504–515. Konur, O. 2015. Current state of research on algal bioethanol. In Marine Bioenergy: Trends and Developments, Ed. S. K. Kim and C. G. Lee, pp. 217–244. Boca Raton, FL: CRC Press. Konur, O., Ed. 2018a. Bioenergy and Biofuels. Boca Raton, FL: CRC Press. Konur, O. 2018b. Bioenergy and biofuels science and technology: Scientometric overview and citation classics. In Bioenergy and Biofuels, Ed. O. Konur, pp. 3–63. Boca Raton: CRC Press. Konur, O. 2019. Cyanobacterial bioenergy and biofuels science and technology: A scientometric overview. In Cyanobacteria: From Basic Science to Applications, Ed. A. K. Mishra, D. N. Tiwari and A. N. Rai, pp. 419–442. Amsterdam: Elsevier. Konur, O. 2020a. The scientometric analysis of the research on the bioethanol production from green macroalgae. In Handbook of Algal Science, Technology and Medicine, Ed. O. Konur, pp. 385–401. London: Academic Press. Konur, O., Ed. 2020b. Handbook of Algal Science, Technology and Medicine. London: Academic Press. Konur, O., Ed. 2021a. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021b. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 1. Biodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021c. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 2. Biodiesel Fuels based on the Edible and Nonedible Feedstocks, Wastes, and Algae: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021d. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 3. Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O. 2023a. Biomass pretreatments: Scientometric study. In Bioethanol Fuel Production Processes. I: Biomass Pretreatments. Handbook of Bioethanol Fuels Volume 1, Ed. O. Konur. Boca Raton, FL: CRC Press. Konur, O. 2023b. Biomass hydrolysis: Scientometric study. In Bioethanol Fuel Production Processes. II: Biomass Hydrolysis, Fermentation, and Bioethanol Fuel Separation. Handbook of Bioethanol Fuels Volume 2, Ed. O. Konur. Boca Raton, FL: CRC Press. Konur, O. 2023c. Hydrolysate and substrate fermentation: Scientometric study. In Bioethanol Fuel Production Processes. II: Biomass Hydrolysis, Fermentation, and Bioethanol Fuel Separation. Handbook of Bioethanol Fuels Volume 2, Ed. O. Konur. Boca Raton, FL: CRC Press. Konur, O. and F. L. Matthews. 1989. Effect of the properties of the constituents on the fatigue performance of composites: A review. Composites 20:317–328. Kruyt, B., D. P. van Vuuren, H. J. de Vries and H. Groenenberg. 2009. Indicators for energy security. Energy Policy 37:2166–2181. Laser, M., D. Schulman and S. G. Allen, et al. 2002. A comparison of liquid hot water and steam pretreatments of sugar cane bagasse for bioconversion to ethanol. Bioresource Technology 81:33–44. Leydesdorff, L. 2000. Is the European Union becoming a single publication system? Scientometrics 47:265–280. Leydesdorff, L. and C. Wagner. 2009. Is the United States losing ground in science? A global perspective on the world science system. Scientometrics 78:23–36. Leydesdorff, L. and P. Zhou. 2005. Are the contributions of China and Korea upsetting the world system of science? Scientometrics 63:617–630. Li, H., S. M. Liu, X. H. Yu, S. L. Tang and C. K. Tang. 2020. Coronavirus disease 2019 (COVID-19): Current status and future perspectives. International Journal of Antimicrobial Agents 55:105951. Limayem, A. and S. C. Ricke. 2012. Lignocellulosic biomass for bioethanol production: Current perspectives, potential issues and future prospects. Progress in Energy and Combustion Science, 38:449–467.

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Lin, Y. and S. Tanaka. 2006. Ethanol fermentation from biomass resources: Current state and prospects. Applied Microbiology and Biotechnology 69:627–642. Ma, X., L. Sun and C. Song. 2002. A new approach to deep desulfurization of gasoline, diesel fuel and jet fuel by selective adsorption for ultra-clean fuels and for fuel cell applications. Catalysis Today 77:107–116. Macedo, I. C., J. E. A. Seabra and J. E. A. R. Silva. 2008. Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy 32:582–595. Makenete, A. L., W. J. Lemmer and J. Kupka. 2008. The impact of biofuel production on food security: A briefing paper with a particular emphasis on maize-to-ethanol production. International Food and Agribusiness Management Review 11:101–110. Morschbacker, A. 2009. Bio-ethanol based ethylene. Polymer Reviews 49:79–84. Mosier, N., C. Wyman and B. Dale, et al. 2005. Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology 96:673–686. Munasinghe, P. C. and S. K. Khanal. 2010. Biomass-derived syngas fermentation into biofuels: Opportunities and challenges. Bioresource Technology 101:5013–5022. Najafi, G., B. Ghobadian and T. Tavakoli, et al. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86:630–639. Negishi, M., Y. Sun and K. Shigi. 2004. Citation database for Japanese papers: A new bibliometric tool for Japanese academic society. Scientometrics 60:333–351. Newman, P. W. G. and J. R. Kenworthy. 1989. Gasoline consumption and cities: A comparison of U.S. cities with a global survey. Journal of the American Planning Association 55:24–37. Nissani, M. 1997. Ten cheers for interdisciplinarity: The case for interdisciplinary knowledge and research. Social Science Journal 34:201–216. North, D. C. 1991. Institutions. Journal of Economic Perspectives 5:97–112. Olsson, L. and B. Hahn-Hagerdal. 1996. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme and Microbial Technology 18:312–331. Palmqvist, E. and B. Hahn-Hagerdal. 2000. Fermentation of lignocellulosic hydrolysates. II: Inhibitors and mechanisms of inhibition. Bioresource Technology 74:25–33. Pimentel, D. and T. W. Patzek. 2005. Ethanol production using corn, switchgrass, and wood; biodiesel production using soybean and sunflower. Natural Resources Research 14:65–76. Pinkert, A., K. N. Marsh, S. Pang and M. P. Staiger. 2009. Ionic liquids and their interaction with cellulose. Chemical Reviews 109:6712–6728. Prasad, S., A. Singh and H. C. Joshi. 2007. Ethanol as an alternative fuel from agricultural, industrial and urban residues. Resources, Conservation and Recycling 50:1–39. Ravindran, R. and A. K. Jaiswal. 2016. A comprehensive review on pre-treatment strategy for lignocellulosic food industry waste: Challenges and opportunities. Bioresource Technology 199:92–102. Reeves, S. 2014. To Russia with love: How moral arguments for a humanitarian intervention in Syria opened the door for an invasion of the Ukraine. Michigan State University International Law Review 23:199. Sanchez, O. J. and C. A. Cardona. 2008. Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource Technology 99:5270–5295. Sano, T., H. Yanagishita, Y. Kiyozumi, F. Mizukami and K. Haraya. 1994. Separation of ethanol/water mixture by silicalite membrane on pervaporation. Journal of Membrane Science 95:221–228. Schmer, M. R., K. P. Vogel, R. B. Mitchell and R. K. Perrin. 2008. Net energy of cellulosic ethanol from switchgrass. Proceedings of the National Academy of Sciences of the United States of America 105:464–469. Sheehan, J., A. Aden and K. Paustian, et al. 2003. Energy and environmental aspects of using corn stover for fuel ethanol. Journal of Industrial Ecology 7:117–146. Sun, Y. and J. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: A review. Bioresource Technology 83:1–11. Taherzadeh, M. J. and K. Karimi. 2007. Enzyme-based hydrolysis processes for ethanol from lignocellulosic materials: A review. Bioresources 2:707–738. Taherzadeh, M. J. and K. Karimi. 2008. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: A review. International Journal of Molecular Sciences 9:1621–1651. Tahmooresnejad, L., C. Beaudry, C and A. Schiffauerova. 2015. The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States. Scientometrics 102:753–787. Talebnia, F., D. Karakashev and I. Angelidaki. 2010. Production of bioethanol from wheat straw: An overview on pretreatment, hydrolysis and fermentation. Bioresource Technology 101:4744–4753.

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Vane, L. M. 2005. A review of pervaporation for product recovery from biomass fermentation processes. Journal of Chemical Technology and Biotechnology 80:603–629. Winzer, C. 2012. Conceptualizing energy security. Energy Policy 46:36–48. Wu, F., D. Zhang and J. Zhang. 2012. Will the development of bioenergy in China create a food security problem? Modeling with fuel ethanol as an example. Renewable Energy 47:127–134. Yang, B. and C. E. Wyman. 2008. Pretreatment: The key to unlocking low-cost cellulosic ethanol. Biofuels, Bioproducts and Biorefining 2:26–40. Zhang, Y. H. P. and L. R. Lynd. 2004. Toward an aggregated understanding of enzymatic hydrolysis of cellulose: Noncomplexed cellulase systems. Biotechnology and Bioengineering 88:797–824. Zhu, J. Y. and X. J. Pan. 2010. Woody biomass pretreatment for cellulosic ethanol production: Technology and energy consumption evaluation. Bioresource Technology 101:4992–5002.

20

Production Processes for Bioethanol Fuels Review Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

20.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Hill et al., 2006; Konur, 2012, 2015, 2020) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol fuel production from the feedstocks through the hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of the production processes for bioethanol fuels has intensified in this context in the key research fronts of the pretreatment of the biomass (Alvira et al., 2010; Hendriks and Zeeman, 2009; Mosier et al., 2005) and hydrolysis of the biomass (Alvira et al., 2010; Sun and Cheng, 2002; Zhang and Lynd, 2004), fermentation of hydrolysates (Jonsson and Martin, 2016; Lin and Tanaka, 2006; Palmqvist and Hahn-Hagerdal, 2000), and bioethanol fuel production in general (Balat, 2011; Limayem and Ricke, 2012, Lin and Tanaka, 2006) and to a lesser extent evaluation (Hamelinck et al., 2005; Pimentel and Patzek, 2005; Schmer et al., 2008), utilization (Macedo et al., 2008; Sheehan et al., 2003) and distillation (Sano et al., 1994; Vane, 2005) of bioethanol fuels. The research in this field has also intensified for the feedstocks of lignocellulosic biomass at large (Mosier et al., 2005; Sun and Cheng, 2002), cellulose (Pinkert et al., 2009; Zhang and Lynd, 2004), wood biomass (Galbe and Zacchi, 2002; Zhu and Pan, 2010), starch feedstock residues (Binod et al., 2010; Talebnia et al., 2010), and to a lesser extent lignin (Bourbonnais and Paice, 1990; Kirk and Farrell, 1987), grass biomass (Keshwani and Cheng, 2009; Pimentel and Patzek, 2005), industrial waste (Cardona et al., 2010; Prasad et al., 2007), sugar feedstocks (Bai et al., 2008; Canilha et al., 2012), starch feedstocks (Bai et al., 2008; Bothast and Schlicher, 2005), sugar feedstock residues (Cardona et al., 2010; Laser et al., 2002), algal biomass (Ho et al., 2013; John et al., 2011), biosyngas (Henstra et al., 2007; Munasinghe and Khanal, 2010), urban waste (Prasad et al., 2007; Ravindran and Jaiswal, 2016), food waste (Guimaraes et al., 2010; Ravindran and Jaiswal, 2016), and forestry waste (Duff and Murray, 1996). Thus, it complements the research on the feedstocks for the bioethanol fuels and evaluation and utilization of bioethanol fuels at large. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

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DOI: 10.1201/9781003226499-27

Production Processes for Bioethanol Fuels: Review

31

Although there has been a large number of review papers on the bioethanol fuels (Hendriks and Zeeman, 2009; Mosier et al., 2005; Sun and Cheng, 2002), there has been no review of the mostcited 25 papers in this field. Thus, this book chapter presents a review of the most-cited 25 articles in the field of the bioethanol fuels. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field.

20.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in October 2022. As a first step for the search of the relevant literature, the keywords were selected using the mostcited first 300 population papers for each feedstock. The selected keyword list was then optimized to obtain a representative sample of papers for each research field. These seven keyword lists were then integrated to obtain the keyword list for this research field (Konur, 2023a,b,c). As a second step, a sample dataset was used for this study. The first 25 articles with at least 577 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

20.3 RESULTS The brief information about 25 most-cited papers with at least 577 citations each on the bioethanol fuels is given below. The primary research fronts are the pretreatments and hydrolysis of the feedstocks with 11 HCPs each whilst the other research front is the bioethanol fuel production with three HCPs.

20.3.1 Feedstock Pretreatment The brief information about 11 most-cited papers on the pretreatments of feedstocks with at least 657 citations each is given below (Table 20.1). Floudas et al. (2012) mapped the detailed evolution of wood-degrading enzymes in a paper with 1,098 citations. They noted that wood was highly resistant to decay, largely due to the presence of lignin, and the only organisms capable of substantial lignin decay were white-rot fungi in the agaricomycetes, which also contained non–lignin-degrading brown-rot and ectomycorrhizal species. They analyzed 31 fungal genomes and found that lignin-degrading peroxidases expanded in the lineage leading to the ancestor of the agaricomycetes, which was reconstructed as a white-rot species, and then contracted in parallel lineages leading to brown-rot and mycorrhizal species. Thus, the origin of lignin degradation might have coincided with the sharp decrease in the rate of organic carbon burial around the end of the carboniferous period. Tien and Kirk (1983) determined the lignin-degrading enzyme from the wood-decomposing basidiomycete Phanerochaete chrysosporium Burds in a paper with 976 citations. They found that the extracellular fluid of ligninolytic cultures of this basidiomycete contained an enzyme that degraded lignin substructure model compounds as well as spruce and birch lignins. It had a molecular size of 42,000 daltons and required hydrogen peroxide for activity. Martinez et al. (2008) performed the genome sequencing and analysis of the biomass-degrading fungus Trichoderma reesei in a paper with 896 citations. They assembled 89 scaffolds to generate 34 Mbp of nearly contiguous T. reesei genome sequence comprising 9,129 predicted gene models. They found that its genome encoded fewer cellulases and hemicellulases than any other sequenced fungus able to hydrolyze plant cell wall polysaccharides. Further, many T. reesei genes encoding

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TABLE 20.1 The Pretreatment of Feedstocks No.

Papers

Biomass

Prts.

Floudas et al. (2012)

Wood

Enzymes

 3

Tien and Kirk (1983)

Wood

Enzymes

 4

Martinez et al. (2008)

Lignocellulosic biomass

 8

Pandey and Pitman (2003)

 9

Sun et al. (2009)

13

Cai and Zhang (2005)

Wood Pine, sapwood, beech Wood Pine, oak Cellulose

14

Martinez et al. (2004)

15

Parameters

Keywords

Lead Authors

Wood degradation, wood-degrading enzyme evolution, genome, peroxidases Lignin-degrading enzymes, fungi

Lignin, decomposition Lignin, degrading, enzyme

Grigoriev, Igor V. 25027225800 Tien, Ming 7006146774

Enzymes

Biomass-degrading fungi, genome, enzymes

Biomass, fungus, degrading

Martinez, Diego 7202958664

Enzymes

Wood decay, fungi, lignin and carbohydrate content, fungi types, wood types, FTIR spectra IL types, dissolution rates, wood types, delignification, cellulose recovery Cellulose dissolution, alkaline solvents, cellulose dissolution behavior, solubility, hydrogen bonding Fungal genome, wood degradation, hydrolytic enzymes, lignocellulose degradation Cellulose dissolution, ILs, anions, cellulose solubilization

Wood, decay, fungi

Pandey, Krishna K. 56252805800

Wood, delignification, IL

Cellulose degradation, enzymes, oxidases, cellodextrin, GH61 glycoside hydrolases Lignin depolymerization and repolymerization, delignification Cellulose dissolution mechanism, IL, hydrogen bonding, carbohydrate hydroxyl protons

IL IL

Enzymes

Fukaya et al. (2008)

Lignocellulosic biomass wood Cellulose

16

Quinlan et al. (2011)

Cellulose

Enzymes

18

Li et al. (2007)

Steam

20

Remsing et al. (2006)

Wood Aspen Cellulose

ILs

IL

Affil.

Cits 1098

Rogers, Robin D. 35474829200 Zhang, Lina* 55917992100

Jnt. Genome Inst. USA Pennsylvania State Univ. USA Massachusetts Inst. Technol. USA Inst. Wood Sci. Technol. India Univ. Alabama USA Wuhan Univ. China

Lignocellulose, degrading, fungus

Cullen, Dan 7202109135

USDA Forest Serv. USA

685

Cellulose dissolution

Ohno, Hiroyuki 7403244652

672

Cellulose, degradation, biomass Wood, delignification

Johansen, Katja S.* 36473579400 Li, Jiebing 7410069979 Rogers, Robin D. 35474829200

Tokyo Univ. Agr. Technol. Japan Univ. Copenhagen Denmark Rise Res. Inst. Sweden Univ. Alabama USA

Cellulose, dissolution

Cellulose, IL

*, Female; Cits., Number of citations received for each paper; Na, not available; Prt: Biomass pretreatments.

976

896

843

833 751

670 665 657

Bioethanol Fuel Production Processes. II

 1

Production Processes for Bioethanol Fuels: Review

33

carbohydrate-active enzymes were distributed non-randomly in clusters that lied between regions of synteny with other sordariomycetes. Hence, numerous genes encoding biosynthetic pathways for secondary metabolites might promote survival of T. reesei in its competitive soil habitat. Pandey and Pitman (2003) performed the Fourier transform infrared (FTIR) studies of the changes in wood chemistry following decay by brown-rot and white-rot fungi in a paper with 843 citations. They used Scots pine, sapwood, and beech decayed by Coniophora puteana ((Schumacher) Karsten), Coriolus versicolor ((L.) Quelet), and Phanerochaete chrysosporium (Burdsall). They exposed wood to these fungi for different durations up to 12 weeks. They found that in wood decayed by C. puteana there was a progressive increase in lignin content relative to carbohydrate evident from increases in the relative height of lignin associated bands and a corresponding decrease in the intensities of carbohydrate bands. At higher weight losses, spectra for wood decayed by C. puteana had many similarities with that of Klason lignin isolated from wood. In contrast, wood decayed by P. chrysosporium showed selective type decay with a reduction in peak heights associated with lignin relative to carbohydrates. Although weight losses in samples exposed to C. versicolor were high, simultaneous decay resulted in little change in the relative intensities of the lignin and carbohydrate bands, with only a slight preference for lignin. Sun et al. (2009) dissolved and partially delignified yellow pine and red oak in 1-ethyl-3-methylimidazolium acetate ([C2mim]OAc) after mild grinding in a paper with 833 citations. They showed that [C2mim]OAc was a better solvent for wood than 1-butyl-3-methylimidazolium chloride ([C4mim]Cl) and that type of wood, initial wood load, and particle size, etc. affected dissolution and dissolution rates. For example, red oak dissolved better and faster than yellow pine. They obtained carbohydrate-free lignin and cellulose-rich materials by using the proper reconstitution solvents (e.g., acetone/water 1:1 v/v) and they achieved approximately 26.1% and 34.9% reductions of lignin content in the reconstituted cellulose-rich materials from pine and oak, respectively, in one dissolution/reconstitution cycle. Further, for pine, they recovered 59% of the holocellulose in the original wood in the cellulose-rich reconstituted material, whilst they recovered 31% and 38% of the original lignin, respectively, as carbohydrate-free lignin and as carbohydrate-bonded lignin in the celluloserich materials. Cai and Zhang (2005) dissolved cellulose in LiOH/urea and NaOH/urea aqueous solutions in a paper with 751 citations. They evaluated the dissolution behavior and solubility of cellulose. They found that cellulose having viscosity-average molecular weight of 11.4 × 104 and 37.2 × 104 could be dissolved, respectively, in 7% NaOH/12% urea and 4.2% LiOH/12% urea aqueous solutions precooled to −10°C within 2 min, whereas all of them could not be dissolved in KOH/urea aqueous solution. Further, the dissolution power of the solvent systems was in the order of LiOH/ urea  > NaOH/urea  ≫ KOH/urea aqueous solution. LiOH/urea and NaOH/urea aqueous solutions as non-derivatizing solvents broke the intra- and intermolecular hydrogen bonding of cellulose and prevented the approach toward each other of the cellulose molecules, leading to the good dispersion of cellulose to form an actual solution. Martinez et al. (2004) sequenced the 30-million base-pair genome of Phanerochaete chrysosporium strain RP78 using a whole-genome shotgun approach in a paper with 685 citations. They found that the P. chrysosporium genome had an impressive array of genes encoding secreted oxidases, peroxidases, and hydrolytic enzymes that cooperated in wood decay. Analysis of the genome data would enhance the understanding of lignocellulose degradation and provide a framework for further development of bioprocesses for biomass utilization. Fukaya et al. (2008) dissolved cellulose with polar ionic liquids (ILs) under mild conditions in a paper with 672 citations. They obtained a series of alkylimidazolium salts containing dimethyl phosphate, methyl methylphosphonate, or methyl phosphonate prepared by a facile, one-pot procedure as room temperature ILs, which had the potential to solubilize cellulose under mild conditions. Especially, they found that N-ethyl-N′-methylimidazolium methylphosphonate ([C2mim] MeO) enabled the preparation of 10 wt% cellulose solution by keeping it at 45°C for 30 min with stirring and rendered soluble 2–4 wt% cellulose without pretreatments and heating.

34

Bioethanol Fuel Production Processes. II

Quinlan et al. (2011) studied the oxidative degradation of cellulose by a copper metalloenzyme that exploited biomass components in a paper with 670 citations. They showed that glycoside hydrolase (CAZy) GH61 enzymes were a unique family of copper-dependent oxidases and copper was needed for GH61 maximal activity. Further, the formation of cellodextrin and oxidized cellodextrin products by GH61 was enhanced in the presence of small molecule redox-active cofactors such as ascorbate and gallate. The active site of GH61 contained a type II copper and, uniquely, a methylated histidine in the copper’s coordination sphere, thus providing an innovative paradigm in bioinorganic enzymatic catalysis. Li et al. (2007) studied the lignin depolymerization and repolymerization and its critical role for delignification of aspen wood by steam explosion pretreatment in a paper with 665 citations. They analyzed the lignin portion and observed the competition between lignin depolymerization and repolymerization and identified the conditions required for these two types of reaction. Further, the addition of 2-naphthol inhibited the repolymerization reaction strongly, resulting in a highly improved delignification by subsequent solvent extraction and an extracted lignin of uniform structure. Remsing et al. (2006) explored the mechanism of cellulose dissolution in 1-n-butyl-3- methylimidazolium chloride ([C4mim]Cl) in a paper with 657 citations. Through13C and35/37Cl NMR relaxation measurements on several model systems, they found that the solvation of cellulose by this IL involved hydrogen bonding between the carbohydrate hydroxyl protons and the IL chloride ions in a 1∶1 stoichiometry.

20.3.2 Hydrolysis of the Feedstocks There are 11 HCPs for the hydrolysis of feedstocks with at least 577 citations (Table 20.2). Chen and Dixon (2007) showed that lignin modification improved fermentable sugar yields for biofuel production in a paper with 1,028 citations. They found that in stems of transgenic alfalfa lines independently downregulated in each of six lignin biosynthetic enzymes, recalcitrance to both acid pretreatment and enzymatic digestion was directly proportional to lignin content. Further, some transgenics yield nearly twice as much sugar from cell walls as wild-type plants. Thus, lignin modification could bypass the need for acid pretreatment and thereby facilitate bioprocess consolidation. Suganuma et al. (2008) hydrolyzed cellulose by amorphous carbon-bearing SO3H, COOH, and OH groups in a paper with 885 citations. They found that crystalline pure cellulose was not hydrolyzed by conventional strong solid Brønsted acid catalysts such as niobic acid, H-mordenite, Nafion, and Amberlyst-15, whilst amorphous carbon-bearing solid catalysts functioned as an efficient catalyst for the reaction. The apparent activation energy for the hydrolysis of cellulose into glucose using the carbon catalyst was 110 kJ/mol, smaller than that for sulfuric acid under optimal conditions (170 kJ/mol). Further, this carbon catalyst could be readily separated from the saccharide solution after reaction for reuse in the reaction without loss of activity. They attributed the catalytic performance of the carbon catalyst to the ability of the material to adsorb β-1,4 glucan, which did not adsorb to other solid acids. Li et al. (2010) compared the efficiency of dilute acid hydrolysis and dissolution in an IL pretreatment with switchgrass in a paper with 860 citations. When subject to IL pretreatment, they observed that switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. Further, the IL pretreatment enabled a significant enhancement in the rate of enzymatic hydrolysis of the cellulose component of switchgrass, with a rate increase of 16.7-fold, and a glucan yield of 96.0% obtained in 24 h. In conclusion, the IL pretreatment offered unique advantages compared to the dilute acid pretreatment process for switchgrass. Kilpelainen et al. (2007) dissolved wood in ILs in a paper with 832 citations. They found that 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) and 1-allyl-3-methylimidazolium chloride ([Amim]Cl) had good solvating power for Norway spruce sawdust and Norway spruce and Southern pine thermomechanical pulp fibers as these ILs provided solutions which permitted the complete

No.  2

 6  7

10 11

12

17

Papers

Biomass

Prts.

Parameters

Chen and Dixon (2007)

Lignocellulosic Acids, enzymes Biomass engineering, pretreatment, hydrolysis, biomass lignin content, sugar yield Alfalfa Suganuma et al. Cellulose C catalysts Hydrolysis, solid carbon catalysts, crystalline (2008) cellulose, activation energy, catalyst recycling Li et al. (2010) Switchgrass Acids, IL, Pretreatment types, acid hydrolysis, delignification, enzymes saccharification, sugar yields, crystallinity, surface area, lignin content, enzymatic hydrolysis Kilpelainen et al. Wood IL, enzymes IL pretreatment, enzymatic hydrolysis, IL types, (2007) Spruce, pine wood types Lee et al. (2009) Wood IL, enzymes Enzymatic hydrolysis, IL pretreatment, delignification, cellulose crystallinity index, enzyme recycling Eriksson et al. Lignocellulose Enzymes, Enzymatic hydrolysis, pretreatment, mechanisms, (2002) Spruce surfactants enzyme adsorption, cellulase stability, surfactantlignin interactions Pretreatments, enzymatic hydrolysis, lignin Silverstein et al. Cotton stalks Acids, alkali, degradation, cellulose conversion rate (2007) ozone, H2O2

22

Lloyd and Wyman (2005)

Corn stover

Acids, enzymes Enzymatic hydrolysis, xylose and glucose yields, total potential sugar yield

23

Sasaki et al. (2000)

Cellulose

Water

24

Sasaki et al. (1998)

Cellulose

Water

25

Harris et al. (2010)

Lignocellulosic Enzymes biomass

Keywords

Lead Authors

Affil.

Lignin, sugar

Chen, Fang 57188570847

Univ. N. Texas USA

Cellulose, hydrolysis

Hara, Michkazu 7403345875

Tokyo Inst. Technol. Japan Sandia Natl. Lab. USA

Switchgrass, pretreatment, Singh, Seema* saccharification, 35264950300 recalcitrance, IL Wood, IL, dissolution Kilpelainen, Ilkka 7006830888 Wood, IL, hydrolysis Dordick, Jonathan S. 7102545507 Lignocellulose, hydrolysis

Tjerneld, Folke 7006446969

Stalks, pretreatment, saccharification

Sharma-Shivappa, Ratna R. 16231216900 Wyman, Charles E. 7004396809

Corn stover, pretreatment, hydrolysis

Cellulose dissolution and hydrolysis, cellulose, Cellulose, dissolution, glucose, and cellobiose decomposition rates, hydrolysis hydrogen linkages Cellulose decomposition, pretreatments, hydrolysis Cellulose, hydrolysis product yields, cellulose hydrolysis, decomposition rate Enzymatic hydrolysis, GH61 enzymes, hydrolytic Lignocellulosic biomass, activity, divalent metal ions, protein loading hydrolysis

885 860

832 816

754

NC State Univ. USA

669

626

Arai, Kunio 7403965625

Univ. Calif. Riverside USA Tohoku Univ. Japan

Arai, Kunio 7403965625

Tohoku Univ. Japan

579

Harris, Paul V. 56305899400

Novozymes Biotech. USA

577

608

35

*, Female; Cits., Number of citations received for each paper; Na, not available; Prt, Biomass pretreatments.

Univ. Helsinki Finland Rensselaer Polytech Inst. USA Lund Univ. Sweden

Cits 1028

Production Processes for Bioethanol Fuels: Review

TABLE 20.2 The Hydrolysis of Feedstocks

36

Bioethanol Fuel Production Processes. II

acetylation of the wood. Alternatively, they obtained transparent amber solutions of wood when the dissolution of the same lignocellulosic samples was attempted in 1-benzyl-3-methylimidazolium chloride ([BzMim]Cl). They then digested the cellulose of the regenerated wood to glucose by a cellulase enzymatic hydrolysis pretreatment. Lee et al. (2009) used 1-ethyl-3-methylimidazolium acetate ([Emim]CH3COO) to extract lignin from wood flour in a paper with 816 citations. They observed that the cellulose in the pretreated wood flour became far less crystalline without undergoing solubilization. When 40% of the lignin was removed, the cellulose crystallinity index dropped below 45, resulting in >90% of the cellulose in wood flour to be hydrolyzed by Trichoderma viride cellulase. They then reused this IL, thereby resulting in a highly concentrated solution of chemically unmodified lignin. Eriksson et al. (2002) explored the mechanism of surfactant effect in enzymatic hydrolysis of lignocellulose in a paper with 754 citations. They screened a number of surfactants for their ability to improve enzymatic hydrolysis of steam-pretreated spruce (SPS). They found that the non-ionic surfactants were the most effective. Studies of adsorption of the dominating cellulase of Trichoderma reesei, Cel7A (CBHI), during hydrolysis showed that the anionic and non-ionic surfactants reduced enzyme adsorption to the lignocellulose substrate. The approximate reduction of enzyme adsorption was from 90% adsorbed enzyme to 80% with surfactant addition. Surfactants had only a weak effect on cellulase temperature stability. They explained the improved conversion of lignocellulose with surfactant by the reduction of the unproductive enzyme adsorption to the lignin part of the substrate. This was due to hydrophobic interaction of surfactant with lignin on the lignocellulose surface, which released unspecifically bound enzyme. Silverstein et al. (2007) compared chemical pretreatment methods for the hydrolysis of cotton stalks in a paper with 669 citations. They pretreated ground cotton stalks at a solid loading of 10% (w/v) with H2SO4, NaOH, and H2O2 at concentrations of 0.5%, 1%, and 2% (w/v). Treatment temperatures were 90°C and 121°C at 15 psi for residence times of 30, 60, and 90 min. Further they performed ozone pretreatment at 4°C with constant sparging of stalks in water. They found that solids from H2SO4, NaOH, and H2O2 pretreatments (at 2%, 60 min, 121°C/15 psi) showed significant lignin degradation and/or high sugar availability and hence they hydrolyzed them by Celluclast 1.5 L and Novozym 188 at 50°C. Sulfuric acid pretreatment resulted in the highest xylan reduction (95.23% for 2% acid, 90 min, 121°C/15 psi) but the lowest cellulose to glucose conversion during hydrolysis (23.85%). Sodium hydroxide pretreatment resulted in the highest level of delignification (65.63% for 2% NaOH, 90 min, 121°C/15 psi) and cellulose conversion (60.8%). Hydrogen peroxide pretreatment resulted in significantly lower delignification (maximum of 29.51% for 2%, 30 min, 121°C/15 psi) and cellulose conversion (49.8%) than sodium hydroxide pretreatment, but had a higher cellulose conversion than sulfuric acid pretreatment. Ozone did not cause any significant changes in lignin, xylan, or glucan contents over time. Lloyd and Wyman (2005) performed the dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis of the remaining solids in a paper with 625 citations. They reported the individual xylose and glucose yields as a percentage of the total potential yield of both sugars over a range of sulfuric acid concentrations of 0.22%, 0.49%, and 0.98% w/w at 140°C, 160°C, 180°C, and 200°C. They found that up to 15% of the total potential sugar in the substrate could be released as glucose during pretreatment and between 15% and 90+% of the xylose remaining in the solid residue could be recovered in subsequent enzymatic hydrolysis, depending on the enzyme loading. Glucose yields increased from as high as 56% of total maximum potential glucose plus xylose for just enzymatic digestion to 60% when glucose released in pretreatment was included. Xylose yields similarly increased from as high as 34% of total potential sugars for pretreatment alone to between 35% and 37% when credit was taken for xylose released in digestion. Yields were much lower if no acid was used. Conditions that maximized individual sugar yields were often not the same as those that maximized total sugar yields. Overall, they obtained up to about 92.5% of the total sugars originally available in the corn stover used or coupled dilute acid pretreatment and enzymatic hydrolysis.

Production Processes for Bioethanol Fuels: Review

37

Sasaki et al. (2000) dissolved and hydrolyzed cellulose in subcritical and supercritical water in a paper with 608 citations. They performed the decomposition experiments of microcrystalline cellulose in subcritical and supercritical water (25 MPa, 320°C−400°C, and 0.05−10.0 s). At 400°C, they obtained mainly hydrolysis products, while in 320°C−350°C water, aqueous decomposition products of glucose were the main products. Further, below 350°C the cellulose decomposition rate was slower than the glucose and cellobiose decomposition rates, while above 350°C, the cellulose hydrolysis rate drastically increased and became higher than the glucose and cellobiose decomposition rates. On the other hand, below 280°C, cellulose particles became gradually smaller with increasing reaction time but, at high temperatures (300°C−320°C), cellulose particles disappeared with increasing transparency and much more rapidly than expected from the lower temperature results. In conclusion, cellulose hydrolysis at high temperature took place with dissolution in water. This was probably because of the cleavage of intra- and intermolecular hydrogen linkages in the cellulose crystal. Thus, a homogeneous atmosphere was formed in supercritical water, and this resulted in the drastic increase of the cellulose decomposition rate above 350°C. Sasaki et al. (1998) hydrolyzed cellulose rapidly in subcritical and supercritical water (SCW) to recover glucose, fructose, and oligomers in a paper with 579 citations. They performed the cellulose decomposition experiments with a flow type reactor in the range of temperature from 290°C to 400°C at 25 MPa and developed a high pressure slurry feeder to feed the cellulose–water slurries. They found that hydrolysis product yields (around 75%) in SCW were much higher than those in subcritical water. At a low temperature region, the glucose or oligomer conversion rate was much faster than the hydrolysis rate of cellulose. Thus, even if the hydrolysis products were formed, their further decomposition rapidly took place and thus high yields of hydrolysis products could be obtained. However, around the critical point, the hydrolysis rate jumped to more than an order of magnitude higher level and became faster than the glucose or oligomer decomposition rate. For this reason, they obtained a high yield of hydrolysis products in SCW. Harris et al. (2010) stimulated lignocellulosic biomass hydrolysis by proteins of glycoside hydrolase family 61 (GH61) in a paper with 577 citations. They showed that certain GH61 proteins lacked measurable hydrolytic activity by themselves but in the presence of various divalent metal ions could significantly reduce the total protein loading required to hydrolyze lignocellulosic biomass. They also solved the structure of one highly active GH61 protein and found that it was devoid of conserved, closely juxtaposed acidic side chains that could serve as general proton donor and nucleophile/base in a canonical hydrolytic reaction. They concluded that the GH61 proteins were unlikely to be glycoside hydrolases. Structure-based mutagenesis showed the importance of several conserved residues for GH61 function. By incorporating the gene for one GH61 protein into a commercial Trichoderma reesei strain producing high levels of cellulolytic enzymes, they reduced by 2-fold the total protein loading (and hence the cost) required to hydrolyze lignocellulosic biomass.

20.3.3  Bioethanol Production There are only three HCPs for the production of the bioethanol fuels in general with at least 648 citations each (Table 20.3). Further, there are no HCPs for the evaluation, utilization, and distillation of the bioethanol fuels. As the pretreatment and hydrolysis of the feedstock are the fundamental parts of the bioethanol production, these narrated papers often cover these processes too. Larsson et al. (1999) studied the effect of the combined severity (SC) of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by Saccharomyces cerevisiae in a paper with 889 citations. When the CS of the hydrolysis conditions increased, they observed that the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose, and 3.0 and 3.4 for glucose above which it decreased. Further, the decrease in the yield of monosaccharides coincided with the maximum concentrations of furfural and 5-HMF. With the further increase in CS, the concentrations of furfural and 5-HMF decreased while the formation of formic acid and levulinic acid increased. The yield of ethanol decreased at approximately CS 3, whilst the volumetric

38

TABLE 20.3 The Production of Bioethanol Fuels No.

Papers

Biomass

Larsson et al. Softwood (1999) Spruce

19

Saha et al. (2005)

21

Alper et al. (2006)

Prts. Acids

Yeasts

Parameters

S. cerevisiae Acid hydrolysis severity, fermentation, sugar and ethanol yield, fermentation inhibitors, volumetric productivity Wheat straw Acids, enzymes E. coli Ethanol production, pretreatments, hydrolysis, fermentation, ethanol yield, detoxification, SHF, SSF Lignocellulosic Na S. cerevisiae Yeast engineering, ethanol biomass tolerance, glucose conversion

Keywords

Lead Authors

Affil.

Cits

Softwood, fermentation, hydrolysis

Hahn-Hagerdal, Barbel* 7005389381

Lund Univ. Sweden

889

Straw, saccharification, fermentation, pretreatment, ethanol

Saha, Badal C. 7202946302

USDA Agr. Res. Serv. USA

664

Ethanol, yeast

Stephanopoulos, Gregory 24527470500

Massachusetts Inst. Technol. USA

648

*, Female; Cits., Number of citations received for each paper; Na, Not available; Prt, Biomass pretreatments.

Bioethanol Fuel Production Processes. II

 5

Production Processes for Bioethanol Fuels: Review

39

productivity decreased at lower CS. They then assayed the effect of acetic acid, formic acid, levulinic acid, furfural, and 5-HMF on fermentability in model fermentations. Ethanol yield and volumetric productivity decreased with increasing concentrations of acetic acid, formic acid, and levulinic acid. However, furfural and 5-HMF decreased the volumetric productivity but did not influence the final yield of ethanol. The decrease in volumetric productivity was more pronounced when 5-HMF was added to the fermentation, and this compound was depleted at a lower rate than furfural. Further, the inhibition observed in hydrolysates produced in higher CS could not be fully explained by the effect of the furfural, 5-HMF, acetic acid, formic acid, and levulinic acid. Saha et al. (2005) performed the dilute acid pretreatment, enzymatic saccharification, and fermentation of wheat straw to produce ethanol in a paper with 664 citations. They found that the maximum yield of monomeric sugars from wheat straw (7.83%, w/v, DS) by dilute H2SO4 (0.75%, v/v) pretreatment and enzymatic saccharification (45°C, pH 5.0, 72 h) using cellulase, β-glucosidase, xylanase, and esterase was 565 mg/g. Under this condition, no measurable quantities of furfural and hydroxymethylfurfural (HMF) were produced. The yield of ethanol (per liter) from acid pretreated enzyme saccharified wheat straw (78.3 g) hydrolysate by recombinant Escherichia coli strain FBR5 was 19 g with a yield of 0.24 g/g DS. Detoxification of the acid- and enzyme-treated wheat straw hydrolysate by overliming reduced the fermentation time from 118 to 39 h in the case of separate hydrolysis and fermentation (SHF) (35°C, pH 6.5), and increased the ethanol yield from 13 to 17 g/L and decreased the fermentation time from 136 to 112 h in the case of simultaneous saccharification and fermentation (SSF) (35°C, pH 6.0). Alper et al. (2006) applied global transcription machinery engineering (gTME) to Saccharomyces cerevisiae for improved glucose/ethanol tolerance in a paper with 648 citations. They found that mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype resulted from the combined effect of three separate mutations in the SPT15 gene [serine substituted for phenylalanine (Phe177Ser) and, similarly, Tyr195His, and Lys218Arg].

20.4 DISCUSSION 20.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol fuel production from the feedstocks through the hydrolysis and fermentation of the biomass and hydrolysates, respectively. The research in the field of the production processes for bioethanol fuels has intensified in this context in the key research fronts of the pretreatment and hydrolysis of the feedstocks, fermentation of the hydrolysates, production of bioethanol fuels in general, and to a lesser extent evaluation, utilization, and distillation of bioethanol fuels. The research in this field has also intensified for the feedstocks of lignocellulosic biomass at large, cellulose, wood biomass, starch feedstock residues, and to a lesser extent lignin, grass biomass, industrial waste, sugar feedstocks, starch feedstocks, sugar feedstock residues, algal biomass, biosyngas, urban waste, food waste, and forestry waste. Thus, it complements the research on the feedstocks for the bioethanol fuels and evaluation and utilization of bioethanol fuels at large. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there has been a number of review papers for this field, there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents

40

Bioethanol Fuel Production Processes. II

a review of the most-cited 25 articles on the bioethanol fuel production processes. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 300 population papers for each research front. The selected keyword list was then optimized to obtain a representative sample of papers for each research field. These seven keyword lists were then integrated to obtain the keyword list for this research field (Konur, 2023a,b,c). As a second step, a sample dataset was used for this study. The first 25 articles with at least 577 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in the bioethanol fuels is given in Table 20.4. As this table shows, the most prolific research front for this field is the wood biomass such as poplar and pine with 40% of the HCPs, followed by the cellulose with 28% of these HCPs. The other prolific feedstocks are the lignocellulosic biomass at large, starch feedstock residues, and grass biomass with 24%, 12%, and 8% of the HCPs, respectively. On the other hand, it is notable that there are no HCPs for industrial waste, sugar feedstocks, starch feedstocks, sugar feedstock residues, algal biomass, syngas, urban waste, food waste, and forestry waste. Thus, the first five feedstocks dominate the HCPs in this field where only major waste biomass is starch feedstock residues such as corn stover or wheat straw and cellulose is the key constituent of the lignocellulosic biomass. It is also notable that the research on the sugar and starch bioethanol fuels is not substantial for all of the samples studied, reflecting the adverse effect of undermining the food security (Bentivoglio et al., 2016; Elobeid and Hart, 2007).

TABLE 20.4 The Most Prolific Research Fronts for the Bioethanol Fuels No.

N Paper % Review

N Paper % Sample

Surplus %

Wood-based bioethanol fuels

Research Fronts

40

12.7

27.3

Cellulose-based bioethanol fuels

28

18.3

9.7

Lignocellulosic biomass-based bioethanol fuels

24

28.3

−4.3

Starch feedstock residues-based bioethanol fuels

12

12.4

−0.4

Grass-based bioethanol fuels

8

4.0

4

Lignin-based bioethanol fuels

0

5.6

−5.6

Industrial waste-based bioethanol fuels

0

3.2

−3.2

Sugar feedstock-based bioethanol fuels

0

3.2

−3.2

Starch feedstock-based for bioethanol fuels

0

2.4

−2.4

Sugar feedstock residues-based bioethanol fuels

0

2.0

−2

Algal biomass-based bioethanol fuels

0

0.8

−0.8

Syngas-based bioethanol fuels

0

0.8

−0.8

Urban waste-based bioethanol fuels

0

0.8

−0.8

Food waste-based bioethanol fuels

0

0.4

−0.4

Forestry waste-based bioethanol fuels

0

0.4

−0.4

Sample size

25

251

N Paper (%) review, The number of papers in the sample of 25 reviewed papers; N paper (%) sample, The number of papers in the population sample of 251 papers.

41

Production Processes for Bioethanol Fuels: Review

TABLE 20.5 The Most Prolific Thematic Research Fronts for the Bioethanol Fuels No. 1 2 3 4 5 6 7

Research Fronts

N Paper % Review

N Paper % Sample

Biomass pretreatments Biomass hydrolysis Hydrolysate fermentation Bioethanol production Bioethanol fuel evaluation Bioethanol fuel distillation Bioethanol fuel utilization

96 52 12 12 0 0 0

79.3 44.2 23.5 12.0 2.4 0.4 0.4

Surplus % 16.7 7.8 −11.5 0 −2.4 −0.4 −0.4

N Paper (%) review, The number of papers in the sample of 25 reviewed papers; N paper (%) sample, The number of papers in the population sample of 251 papers.

Further, the most influential research fronts are the wood biomass with 27% surplus, followed by cellulose and grass biomass with 10% and 4% surplus, respectively. Further, the lignin is the least influential feedstock with 6% deficit, followed by lignocellulosic biomass, industrial waste, sugar feedstocks, starch feedstocks, and sugar feedstock residues with 2%–4% deficit each. It is also notable that a significant part of the papers for the lignocellulosic biomass-based bioethanol fuels are the reviews and short surveys. Information about the thematic research fronts for the sample papers in the bioethanol fuels is given in Table 20.5. As this table shows, the most prolific research fronts for this field are the pretreatment and hydrolysis of the feedstocks with 96% and 52% of the HCPs, respectively. The other prolific research fronts are the bioethanol production in general and hydrolysate fermentation with 12% of the HCPs each. Further, there are no HCPs for the evaluation, utilization, and distillation of bioethanol fuels. On the other hand, biomass pretreatments and hydrolysis are the most influential research fronts with 17% and 8% surplus, respectively, whilst hydrolysate fermentation is the least influential research front with 12% deficit, followed by bioethanol fuel evaluation with 2% deficit.

20.4.2 Feedstock Pretreatment The brief information about 11 most-cited papers on the pretreatments of feedstocks with at least 657 citations each is given below (Table 20.1). On the other hand, it is notable that as the Table 20.5 shows, 100% of these HCPs are related to the pretreatments of the feedstocks, respectively. These findings show that both pretreatments and hydrolysis of the feedstock are the fundamental processes for the bioethanol production from the feedstock. These narrated studies highlight the importance of the pretreatment and hydrolysis processes for the production of the bioethanol fuels from the feedstock with a high ethanol yield. These pretreatments, primarily enzymatic and chemical pretreatments, fractionate the feedstock and enhance the enzymatic digestibility of the biomass. Floudas et al. (2012) mapped the detailed evolution of wood-degrading enzymes and found that the origin of lignin degradation might have coincided with the sharp decrease in the rate of organic carbon burial around the end of the carboniferous period. Further, Tien and Kirk (1983) determined the lignin-degrading enzyme from the wood-decomposing basidiomycete P. chrysosporium Burds and found that the extracellular fluid of ligninolytic cultures of this basidiomycete contained an enzyme that degraded lignin substructure model compounds as well as spruce and birch lignins. Martinez et al. (2008) performed the genome sequencing and analysis of the biomass-degrading fungus T. reesei and found that its genome encoded fewer cellulases and hemicellulases than any other sequenced fungus able to hydrolyze plant cell wall polysaccharides. Further, Pandey and Pitman (2003) performed the FTIR studies of the changes in wood chemistry following decay by brown-rot

42

Bioethanol Fuel Production Processes. II

and white-rot fungi and found that in wood decayed by C. puteana there was a progressive increase in lignin content relative to carbohydrate evident from increases in the relative height of lignin associated bands and a corresponding decrease in the intensities of carbohydrate bands. Sun et al. (2009) dissolved and partially delignified yellow pine and red oak in [C2mim]OAc after mild grinding and showed this IL was a better solvent for wood than [C4mim]Cl and that type of wood, initial wood load, particle size, etc. affected dissolution and dissolution rates. Further, Cai and Zhang (2005) dissolved cellulose in LiOH/urea and NaOH/urea aqueous solutions and found that the dissolution power of the solvent systems was in the order of LiOH/urea  > NaOH/ urea  ≫ KOH/urea aqueous solution. Martinez et al. (2004) sequenced the 30-million base-pair genome of P. chrysosporium strain RP78 using a whole-genome shotgun approach and found that the P. chrysosporium genome had an impressive array of genes encoding secreted oxidases, peroxidases, and hydrolytic enzymes that cooperated in wood decay. Further, Fukaya et al. (2008) dissolved cellulose with polar ILs under mild conditions and found that [C2mim]MeO enabled the preparation of 10 wt% cellulose solution by keeping it at 45°C for 30 min with stirring. Quinlan et al. (2011) studied the oxidative degradation of cellulose by a copper metalloenzyme that exploited biomass components and showed that GH61 enzymes were a unique family of copperdependent oxidases and copper was needed for GH61 maximal activity. Further, Li et al. (2007) studied the lignin depolymerization and repolymerization and its critical role for delignification of aspen wood by steam explosion pretreatment and observed the competition between lignin depolymerization and repolymerization. Finally, Remsing et al. (2006) explored the mechanism of cellulose dissolution in [C4mim]Cl and found that the solvation of cellulose by this IL involved hydrogen bonding between the carbohydrate hydroxyl protons and the IL chloride ions in a 1∶1 stoichiometry.

20.4.3 Hydrolysis of the Feedstocks There are 11 HCPs for the hydrolysis of feedstock with at least 577 citations (Table 20.2). On the other hand, it is notable that as the Table 20.5 shows, 52% of these HCPs are related to the hydrolysis of the feedstocks, respectively. These findings show that both pretreatments and hydrolysis of the feedstock are the fundamental processes for the bioethanol production from the feedstock. These narrated studies highlight the importance of the pretreatment and hydrolysis processes for the production of the bioethanol fuels from the feedstock with a high ethanol yield. These pretreatments, primarily enzymatic and chemical pretreatments, fractionate the feedstock and enhance the enzymatic digestibility of the biomass. Chen and Dixon (2007) showed that lignin modification improved fermentable sugar yields for biofuel production. Further, Suganuma et al. (2008) hydrolyzed cellulose by amorphous carbonbearing SO3H, COOH, and OH groups and found that crystalline pure cellulose was not hydrolyzed by conventional strong solid Brønsted acid amorphous carbon-bearing solid catalysts functioned as an efficient catalyst for the reaction. Li et al. (2010) compared the efficiency of dilute acid hydrolysis and dissolution in an IL with switchgrass and observed that switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. Further, Kilpelainen et al. (2007) dissolved wood in ILs and found that [Bmim]Cl and [Amim]Cl had good solvating power for wood samples. Lee et al. (2009) used [Emim]CH3COO to extract lignin from wood flour and observed that the cellulose in the pretreated wood flour became far less crystalline without undergoing solubilization. Further, Eriksson et al. (2002) explored the mechanism of surfactant effect in enzymatic hydrolysis of lignocellulose and found that the non-ionic surfactants were the most effective. Silverstein et al. (2007) compared chemical pretreatment methods for the hydrolysis of cotton stalks and found that sulfuric acid pretreatment resulted in the highest xylan reduction but the lowest cellulose to glucose conversion during hydrolysis. Further, Lloyd and Wyman (2005) performed the

Production Processes for Bioethanol Fuels: Review

43

dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis of the remaining solids and found that up to 15% of the total potential sugar in the substrate could be released as glucose during pretreatment. Sasaki et al. (2000) dissolved and hydrolyzed cellulose in subcritical and supercritical water, and at 400°C, they obtained mainly hydrolysis products, while in 320°C−350°C water, aqueous decomposition products of glucose were the main products. Further, Sasaki et al. (1998) hydrolyzed cellulose rapidly in subcritical and supercritical water and found that hydrolysis product yields (around 75%) in SCW were much higher than those in subcritical water. Finally, Harris et al. (2010) stimulated lignocellulosic biomass hydrolysis by proteins of GH61 and showed that certain GH61 proteins lacked measurable hydrolytic activity by themselves but in the presence of various divalent metal ions could significantly reduce the total protein loading required to hydrolyze lignocellulosic biomass.

20.4.4  Bioethanol Production There are only three HCPs for the production of the bioethanol fuels in general with at least 648 citations each (Table 20.3). Further, there are no HCPs for the evaluation, utilization, and distillation of bioethanol fuels. As the pretreatment and hydrolysis of the feedstock are the fundamental parts of the bioethanol production, these narrated papers often cover these processes too. It is also notable that as the Table 20.5 shows, 12% of these HCPs are related to the production bioethanol fuels from the feedstocks. These narrated studies highlight the importance of the pretreatment (primarily chemical, enzymatic, or hydrothermal) and hydrolysis (primarily enzymatic or acid) processes as well as of the fermentation processes (SSF or SHF) on the production of the bioethanol fuels from the feedstocks with a high ethanol yield. Further, some fermentation studies focus on the detoxification of the lignocellulosic hydrolysates to improve the ethanol yield. Larsson et al. (1999) studied the effect of the combined severity (SC) of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by S. cerevisiae and observed that yield of ethanol decreased at approximately CS 3, whilst the volumetric productivity decreased at lower CS. Further, Saha et al. (2005) performed the dilute acid pretreatment, enzymatic saccharification, and fermentation of wheat straw to produce ethanol and found that the yield of ethanol (per liter) from acid pretreated enzyme saccharified wheat straw (78.3 g) hydrolysate by recombinant E. coli strain FBR5 was 19 g with a yield of 0.24 g/g DS. Finally, Alper et al. (2006) applied global transcription machinery engineering (gTME) to S. cerevisiae for improved glucose/ethanol tolerance and found that mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol.

20.5  CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 577 citations each is given under three primary headings: The pretreatments and hydrolysis of the feedstocks and production of the bioethanol fuels in general. The usual characteristics of these HCPs are that the pretreatments and hydrolysis of the feedstocks and fermentation of the resulting hydrolysates are the primary processes for the bioethanol fuel production from various feedstocks to improve the ethanol yield. The key findings on these research fronts should be read in light of the increasing public concerns about climate change, GHG emissions, and global warming as these concerns have been certainly behind the boom in the research on the bioethanol fuels as an alternative to crude oil-based gasoline and diesel fuels in the last decades. It is also a sustainable alternative to food crop-based bioethanol fuels such as corn grain-based bioethanol fuels. The recent supply shocks caused by the COVID-19 pandemics and the Russian invasion of Ukraine also highlight the importance of the production and utilization of the bioethanol fuels as an alternative to the crude oil-based gasoline and diesel fuels.

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As Table 20.4 shows, the most prolific research fronts for this field are the wood biomass, cellulose, lignocellulosic biomass, starch feedstock residues, and grass biomass. On the other hand, it is notable that there are no HCPs for the industrial waste, sugar feedstocks, starch feedstocks, sugar feedstock residues, algal biomass, syngas, urban waste, food waste, and forestry waste. Thus, the first five feedstocks dominate the HCPs in this field where only major waste biomass is starch feedstock residues such as corn stover or wheat straw and cellulose is the key constituent of the lignocellulosic biomass. It is also notable that the research on the sugar and starch bioethanol fuels is not substantial for all of the samples studied, reflecting the adverse effect of undermining the food security. As Table 20.5 shows the most prolific thematic research fronts for this field are the pretreatment and hydrolysis of the feedstocks, and to a lesser extent hydrolysate fermentation and bioethanol production in general. Further, there is no HCP for the evaluation, utilization, and distillation of bioethanol fuels. These studies emphasize the importance of proper incentive structures for the efficient production of bioethanol fuels in the light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the USA and Brazil with vast forests and farmlands have developed strong incentive structures for the efficient bioethanol fuels. In light of the recent supply shocks caused primarily by the COVID-19 pandemics and Russian invasion of Ukraine, it is expected that the incentive structures such as public funding would be enhanced to increase the share of bioethanol fuels in the global fuel portfolio as a strong alternative to crude oil-based gasoline and diesel fuels. In this context, it is expected that the most prolific researchers, institutions, countries, funding bodies, and journals in this field would have a first-mover advantage to benefit from such potential incentives. This is especially true for the US stakeholders as the USA has become the global leader in both the production and utilization of second generation bioethanol fuels from the feedstocks. It is expected the research would focus more on the algal, wood, and grass biomass-based bioethanol as well as the agricultural residues- and waste biomass-based bioethanol fuels at the expense of the starch and sugar-based bioethanol fuels due to the large societal concerns about the food security in the future. It is recommended that further review studies are performed for the primary research fronts of the bioethanol fuels.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the bioethanol fuels has been gratefully acknowledged.

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Macedo, I. C., J. E. A. Seabra and J. E. A. R. Silva. 2008. Green house gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy 32:582–595. Martinez, D., R. M. Berka and B. Henrissat, et al. 2008. Genome sequencing and analysis of the biomassdegrading fungus Trichoderma reesei (syn. Hypocrea jecorina). Nature Biotechnology 26:553–560. Martinez, D., L. F. Larrondo and N. Putnam, et al. 2004. Genome sequence of the lignocellulose degrading fungus Phanerochaete chrysosporium strain RP78. Nature Biotechnology 22:695–700. Morschbacker, A. 2009. Bio-ethanol based ethylene. Polymer Reviews 49:79–84. Mosier, N., C. Wyman and B. Dale, et al. 2005. Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology 96:673–686. Munasinghe, P. C. and S. K. Khanal. 2010. Biomass-derived syngas fermentation into biofuels: Opportunities and challenges. 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Part 4 Biomass Hydrolysis for Bioethanol Production

21 Scientometric Study

Biomass Hydrolysis Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

21.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009; Zhang, 2008) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining the energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), COVID-19 pandemics (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2001) in the aftermath of Russian invasion of Ukraine starting in February 2022 (Reeves, 2014) and COVID-19 pandemics. However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Dwivedi et al., 2009; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of biomass hydrolysis (Alvira et al, 2010; Hendriks and Zeeman, 2009; Kumar et al., 2009; Sun and Cheng, 2002) has thus been intensified in recent years. The hydrolysis of the agricultural residues (Lloyd and Wyman, 2005; Saha et al., 2005), wood (Larsson et al., 1999a,b; Lee et al., 2009), biomass constituents (Huang and Fu, 2013; Mansfield et al., 1999), and grass (Hu and Wen, 2008; Li et al., 2010) have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955, 1972, 1979; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As there have been no scientometric studies on the biomass hydrolysis as on May 2022, this book chapter presents a scientometric study of the research in biomass hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts.

21.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. DOI: 10.1201/9781003226499-29

51

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As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 300 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This extended keyword list was provided in the appendix for future replication studies. As a second step, two sets of data were used for this study. First, a population sample of around 11,739 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 235 most-cited papers, corresponding to 2% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the biomass hydrolysis. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

21.3 RESULTS 21.3.1  The Most-Prolific Documents in the Biomass Hydrolysis The information on the types of documents for both datasets is given in Table 21.1. The articles and conference papers dominate both the sample (86%) and population (96%) papers as they are underrepresented in the sample papers by 10%. Further, review papers and short surveys have a surplus as they are over-represented in the sample papers by 13% as they constitute 15% and 2% of the sample and population papers, respectively. It is further notable that 98% of the population papers were published in journals, while 1% each of them were published in book series and books. On the contrary, 99% of the sample papers were published in the journals.

21.3.2  The Most-Prolific Authors in the Biomass Hydrolysis The information about the most-prolific 26 authors with at least 1.7% of sample papers each is given in Table 21.2. TABLE 21.1 Documents in the Biomass Hydrolysis Documents Article Review Conference paper Short Survey Book chapter Letter Note Book Editorial Sample size

Sample Dataset (%)

Population Dataset (%)

Surplus (%)

80.9 13.6 4.7 0.9 0.0 0.0 0.0 0.0 0.0 235

92.6 2.1 3.3 0.1 1.0 0.4 0.3 0.1 0.0 11,739

−11.7 11.5 1.4 0.8 −1.0 −0.4 −0.3 −0.1 0.0

Population dataset, the number of papers (%) in the set of the 11,739 population papers; sample dataset, the number of papers (%) in the set of 235 highly cited papers.

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TABLE 21.2 Most-Prolific Authors in the Biomass Hydrolysis

No.

Author Name

Sample Population Papers Papers Author Code (%) (%) Surplus

 1

Saddler, John N.

7005297559

7.7

1.1

6.6

 2

Wyman, Charles E

7004396809

5.5

0.6

4.9

 3  4

Zacchi, Guido Hahn-Hagerdal, Barbel* Ladisch, Michael R. Jonsson, Leif J. Dale, Bruce E.

7006727748 7005389381

3.0 3.0

0.5 0.2

2.5 2.8

7005670397 7102349315 7201511969

3.0 2.6 2.1

0.2 0.4 0.5

2.8 2.2 1.6

 5  6  7  8  9

7003788758 6701407496

2.1 2.1

0.4 0.4

1.7 1.7

10 11

Galbe, Mats Taherzadeh, Mohammad J. Garrote, Gil Holtzapple, Mark T.

6603849654 7004167004

2.1 2.1

0.2 0.2

1.9 1.9

12

Arantes, Valdeir

56344492500

2.1

0.1

2.0

13

Lynd, Lee R.

35586183800

2.1

0.1

2.0

14

Yang, Bin

7404473046

2.0

0.1

1.9

15

7006110611

1.7

0.3

1.4

16

Ballesteros, Mercedes* Zhu, Junyong

7405692678

1.7

0.3

1.4

17

Karimi, Keikhosro

10046195700

1.7

0.2

1.5

18

6602643634

1.7

0.2

1.5

19 20

Mussatto, Solange I.* Negro, Maria J.* Roberto, Ines C.*

6701512649 7003893391

1.7 1.7

0.2 0.2

1.5 1.5

21 22

Tjerneld, Folke Zhang, Yi-Heng P.

7006446969 34876090400

1.7 1.7

0.2 0.2

1.5 1.5

23

Bura, Renata*

6602335655

1.7

0.1

1.6

Nilvebrant, Nils. O. 57209815309

1.7

0.1

1.6

8523577500 6603821896

1.7 1.7

0.1 0.1

1.6 1.6

24 25 26

Ohgren, Karin* Palmqvist, Eva*

Institution Univ. British Columbia Univ. Calif. Riverside Lund Univ. Lund Univ.

Country HI Canada USA Sweden Sweden

N

96 403

Res. Front Wood

80 286 Agr. Res. 67 204 75 258

Wood Wood

Purdue Univ. USA Umea Univ. Sweden Michigan USA State Univ. Lund Univ. Sweden Univ. Boras Sweden

59 290 Agr. Res. 39 147 Wood 90 446 Chem. biol. 50 131 Wood 64 405 Agr. Res.

Univ. Vigo Texas A&M Univ. Univ. Sao Paulo Dartmouth Coll. Washington State Univ. CIMAT

Spain USA

42 98 Agr. Res. 46 195 Agr. Res.

Brazil

26

56

Wood

USA

74 286

Cell.

USA

39

Spain

49 134 Agr. Res.

USA

62 303

Iran

53 211 Agr. Res.

USDA Forest Serv. Isfahan Univ. Technol. Tech. Univ. Denmark CIEMAT Univ. Sao Paulo Lund Univ. Chinese Acad. Sci. Univ. Washington Borregaard Inc. Lund Univ. Lund Univ.

93 Agr. Res.

Wood

Denmark 52 196 Agr. Res. Spain Brazil Sweden China

37 73 Agr. Res. 41 122 Agr. Res. 50 170 56 179

Wood Cell.

USA

22

50

Agr. res.

Norway

22

43

Wood

Sweden Sweden

6 19

6 Agr. Res. 31 Wood

*, Female; Agr. Res., Agricultural residues; Author code, the unique code given by Scopus to the authors; Cell., Cellulose; HI, H-inde21; N, number of papers published by each author; population papers, the number of papers authored in the population dataset; sample papers, the number of papers authored in the sample dataset.

54

Bioethanol Fuel Production Processes. II

The most-prolific author is John N. Saddler of University of British Columbia of Canada with 7.7% of the sample papers working primarily on the wood hydrolysis. Next, Charles E. Wyman publishes 5.5% of the sample papers, while Guido Zacchi, Barbel Hahn-Hagerdal, Michael R. Ladisch, and Leif J. Jonsson publish around 3% of the sample papers each. The most influential author is John N. Saddler with 6.6% surplus, followed by Charles E. Wyman with 4.9% surplus. The other influential authors are Barbel Hahn-Hagerdal, Michael R. Ladisch, and Guido Zacchi with around 3% surplus each. The most-prolific institution for the sample dataset is the Lund University with six authors, while the Center for Energy, Environmental and Technological Research (CIEMAT), and University of Sao Paulo house two authors each. On the other hand, the most-prolific countries for the sample dataset are the USA and Sweden with eight authors each, while Spain and Brazil house three and two authors, respectively. The most-prolific research front is the hydrolysis of the agricultural residues with 13 authors, closely followed by the hydrolysis of the wood biomass with ten authors. Additionally, two authors primarily study the hydrolysis of the cellulose. On the other hand, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only eight of these top researchers are female with a representation rate of 32%. Additionally, there are other authors with the relatively low citation impact and with 0.3%–0.6% of the population papers each: Qiang Yong, Hasan Jameel, Juan C. Parajo, Runcang Sun, Zhenhong Yuan, Liangcai Peng, Wei Qi, Yongcan Ji, Venkatesh Balan, Yonghao Ni, Yong Xu, Caoxing Huang, Lisbeth Olsson, Ashok Pandey, Feng Xu, Xinshu Zhuang, Chenhuan Lai, Qiang Yu, Jiang Zhang, and Pedram Fatehi.

21.3.3  The Most-Prolific Research Output by Years in Biomass Hydrolysis Information about papers published between 1970 and 2022 is given in Figure 21.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s with 56% of the population dataset. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 9%, 15%, 7%, 7%, and 3%, respectively. Additionally, 1% of the population papers were published between 1885 and 1969. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 55% and 29% of the sample dataset, respectively. The publication rates for the 1990s, 1980s, and 1970s were 10%, 4%, and 2% of the sample papers, respectively. The most-prolific publication year for the population dataset was 2016 with 6.2% of the dataset, while 62% of the population papers were published between 2010 and 2020. Similarly, 79% of the sample papers were published between 2000 and 2013, while the most-prolific publication years were 2007 and 2009 with 10.2% of the sample papers each. The other prolific years were 2010 and 2011 with 9% and 7% of the sample papers, respectively.

21.3.4  The Most-Prolific Institutions in the Biomass Hydrolysis Information about the most-prolific 23 institutions publishing papers on the biomass hydrolysis with at least 1.7% of the sample papers each is given in Table 21.3. The most-prolific institution is the Lund University with 8.5% of the sample papers, followed by University of British Columbia with 8.1% of the sample papers. The other prolific institutions are the National Renewable Energy Laboratory, Dartmouth College, University of Wisconsin-Madison, Purdue University, and Technical University of Denmark with 3%–6% of the sample papers each. The top country for these most-prolific institutions is the USA with 13 institutions, while Spain and Sweden house two institutions each. In total, only nine countries house these top institutions.

55

Biomass Hydrolysis: Scientometric Study 12

Number of papers (%)

10

Population papers Sample papers

8

6

4

2

0

FIGURE 21.1  The research output by years regarding the biomass hydrolysis.

TABLE 21.3 The Most-Prolific Institutions in Biomass Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Institutions Lund Univ. Univ. British Columbia Natl. Renew. Ener. Lab. Dartmouth Coll. Univ. Wisconsin-Madison Purdue Univ. Tech. Univ. Denmark Chinese Acad. Sci. Univ. Sao Paulo USDA Agr. Res. Serv. USDA Forest Serv. VTT Tech. Res. Ctr. CIEMAT Univ. Calif. Riverside Texas A&M Univ. Michigan State Univ. Univ. Vigo Oak Ridge Natl. Lab. Georgia Inst. Technol. North Carolina State Univ. Univ. Boras Virginia Polytech. State Univ. Isfahan Univ. Technol.

Country

Sample Papers (%)

Population Papers (%)

Surplus (%)

Sweden Canada USA USA USA USA Denmark China Brazil USA USA Finland Spain USA USA USA Spain USA USA USA Sweden USA

8.5 8.1 6.0 6.0 3.4 3.4 3.4 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.1 2.1 2.1 2.1 1.7 1.7 1.7

1.4 1.5 1.4 0.5 0.9 0.7 0.7 3.0 2.0 1.2 0.9 0.6 0.5 0.4 0.4 0.8 0.7 0.6 0.4 1.1 0.3 0.3

7.1 6.6 4.6 5.5 2.5 2.7 2.7 -0.4 0.6 1.4 1.7 2.0 2.1 2.2 2.2 1.3 1.4 1.5 1.7 0.6 1.4 1.4

Iran

1.7

0.3

1.4

56

Bioethanol Fuel Production Processes. II

On the other hand, the institution with the most citation impact is the Lund University with 7.1% surplus, followed by University of British Columbia, Dartmouth College, and National Renewable Energy Laboratory with 4.6%–6.6% surplus each. The other prolific institutions are Purdue University, Technical University of Denmark, and University of Wisconsin-Madison with 2.5%–2.7% surplus each. Additionally, there are other institutions with the relatively low citation impact and with 0.6%–2.0% of the population papers each: South China University of Technology, Nanjing Forestry University, Beijing Forestry University, State University of Campinas, Qilu University of Technology, Kyoto University, Tianjin University of Science & Technology, Tianjin University, Chalmers University of Technology, Korea University, Paulista State University, Russian Academy of Sciences, Huazhong Agricultural University, CNRS, Aalto University, Tsinghua University, China Agricultural University, University of Illinois Urbana-Champaign, and the US DOE Bioenergy Research Centers.

21.3.5  The Most-Prolific Funding Bodies in the Biomass Hydrolysis Information about the most-prolific 12 funding bodies funding at least 1.7% of the sample papers each is given in Table 21.4. Only 31% and 46% of the sample and population papers were funded, respectively. The most-prolific funding body is the U.S. Department of Energy with 3.8% of the sample papers, closely followed by the European Commission with 3.0% of the sample papers. On the other hand, the most-prolific country for these top funding bodies is the USA with four funding bodies, while Brazil, Canada, and Sweden house two funding bodies each. In total, five countries and the EU house these top funding bodies. The funding body with the most citation impact is the Swedish National Board of Industrial and Technical Development with 1.9% surplus, closely followed by Natural Resources of Canada with 1.8% surplus. The other influential funding bodies are the US Department of Energy, Laboratory

TABLE 21.4 The Most-Prolific Funding Bodies in Biomass Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12

Funding Bodies US Dept. Ener. Eur. Commis. Res. Supp. Found. State Sao Paulo Natrl. Sci. Eng. Res. Counc. Canada US Dept. Agric. Lab. Dir. Res. Devnt. Natl. Nucl. Sec. Admn. Natrl. Resourc. Canada Swedish Nat. Board Ind. Tech. Devnt. Natl. Natr. Sci. Found. China Natl. Counc. Sci. Technol. Devnt. Swedish Res. Counc.

Country

Sample Paper No. (%)

Population Paper No. (%)

Surplus (%)

USA EU Brazil

3.8 3.0 2.1

2.2 1.7 1.8

1.6 1.3 0.3

Canada

2.1

1.1

1.0

USA USA USA Canada Sweden

2.1 2.1 2.1 2.1 2.1

1.0 0.5 0.5 0.2 0.1

1.1 1.6 1.6 1.9 2.0

China

1.7

9.9

–8.2

Brazil

1.7

3.1

–1.4

Sweden

1.7

0.3

1.4

57

Biomass Hydrolysis: Scientometric Study

Directed Research and Development, and National Nuclear Security Administration with 1.6% surplus each. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 8% deficit. Additionally, the National Council for Scientific and Technological Development of Brazil has 1.4% deficit. The other funding bodies with the relatively low citation impact and with 0.7%–2.7% of the population papers each are Ministry of Science and Technology of China, Coordination of Superior Level Appearance of Brazil, National Key Research and Development Program of China, Ministry of Education of China, National Science Foundation, Ministry of Science, Technology and Innovation, Ministry of Education, Culture, Sports, Science and Technology, Fundamental Research Funds for the Central Universities, National Research Foundation of Korea, Japan Society for the Promotion of Science, European Regional Development Fund, Chinese Academy of Sciences, Government of Canada, Priority Academic Program Development of Jiangsu Higher Education Institutions, National Basic Research Program of China (973 Program), Ministry of Finance of Japan, Natural Science Foundation of Jiangsu Province, Department of Biotechnology, Ministry of Science and Technology of India, and China Scholarship Council.

21.3.6  The Most-Prolific Source Titles in the Biomass Hydrolysis Information about the most-prolific 15 source titles publishing at least 1.7% of the sample papers each in biomass hydrolysis is given in Table 21.5. The most-prolific source title is Bioresource Technology with over 20% of the sample papers, followed by Biotechnology and Bioengineering with 14% of the sample papers. Enzyme and Microbial Technology, Biotechnology for Biofuels, Biotechnology Progress, and Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology are the other prolific journals with 3.4%–6.0% of the sample papers each. TABLE 21.5 The Most-Prolific Source Titles in Biomass Hydrolysis No. 1 2 3 4 5 7

6 8 9 10 11 12 13 14

15

Source Titles

Sample Papers (%)

Population Papers (%)

Surplus (%)

Bioresource Technology Biotechnology and Bioengineering Enzyme and Microbial Technology Biotechnology for Biofuels Biotechnology Progress Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Biomass and Bioenergy Green Chemistry Process Biochemistry Carbohydrate Polymers Industrial and Engineering Chemistry Research Applied Microbiology and Biotechnology Journal of Biotechnology Proceedings of the National Academy of Sciences of the United States of America Journal of Food Engineering

20.4 14.0 6.0 5.5 4.3 3.4

11.6 2.5 1.2 2.4 0.7 1.0

8.8 11.5 4.8 3.1 3.6 2.4

3.0 3.0 2.6 1.7 1.7

1.7 0.7 1.1 1.4 1.0

1.3 2.3 1.5 0.3 0.7

1.7

0.8

0.9

1.7 1.7

0.5 0.1

1.2 1.6

1.7

0.1

1.6

58

Bioethanol Fuel Production Processes. II

On the other hand, the source title with the most citation impact is the Biotechnology and Bioengineering with 12% surplus, closely followed by Bioresource Technology with 9% surplus. The other influential journals are Enzyme and Microbial Technology, Biotechnology Progress, and Biotechnology for Biofuels with 3%–5% surplus each. Similarly, the source titles with the least impact are the Carbohydrate Polymers, Industrial and Engineering Chemistry Research, and Applied Microbiology and Biotechnology with less than 1% surplus each. The other source titles with the relatively low citation impact with 0.6%–2.6% of the population paper each are Applied Biochemistry and Biotechnology, Industrial Crops and Products, Bioresources, Cellulose, Biomass Conversion and Biorefinery, Renewable Energy, ACS Sustainable Chemistry and Engineering, Animal Feed Science and Technology, Journal of Chemical Technology and Biotechnology, Journal of Agricultural and Food Chemistry, Biotechnology Letters, Bioenergy Research, RSC Advances, Journal of Animal Science, Waste and Biomass Valorization, Bioprocess and Biosystems Engineering, Journal of Dairy Science, Advanced Materials Research, Biochemical Engineering Journal, Cellulose Chemistry and Technology, Fuel, and International Journal of Biological Macromolecules.

21.3.7  The Most-Prolific Countries in the Biomass Hydrolysis Information about the most-prolific 13 countries publishing at least 2.1% of sample papers each in biomass hydrolysis is given in Table 21.6. The most-prolific country is the USA with 40% of the sample papers. Sweden, Canada, China, Spain, Denmark, and Japan are the other prolific countries with 5%–13% of the sample papers each. Further, five European countries listed in Table 21.6 produce as a whole 29% and 13% of the sample and population papers, respectively, with 16% surplus. On the other hand, the country with the most citation impact is the USA with 20% surplus, while Sweden, Canada, and Denmark are the other influential countries with 4%–9% surplus each. Similarly, the country with the least citation impact is China with 13% deficit, while India, Brazil, and Japan have 1%–3% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.6%–4.4% of the sample papers each: South Korea, the UK, Germany, Malaysia, Thailand, Italy, Russia, Portugal, Indonesia, Taiwan, Netherlands, South Africa, Iran, Belgium, Turkey, Poland, Pakistan, Austria, Egypt, and New Zealand. TABLE 21.6 The Most-Prolific Countries in the Biomass Hydrolysis No.

Countries

Sample Papers (%)

Population Papers (%)

Surplus (%)

 1  2  3  4  5  6  7  8  9 10 11 12 13

USA Sweden Canada China Spain Denmark Japan India Brazil Finland France Australia Mexico

40.0 12.8 10.2 8.5 6.0 5.5 5.1 3.8 3.4 2.6 2.1 2.1 2.1

20.0 3.6 4.9 21.3 3.7 1.5 6.3 7.0 6.2 1.9 2.4 1.9 1.4

20.0 9.2 5.3 −12.8 2.3 4.0 −1.2 −3.2 −2.8 0.7 −0.3 0.2 0.7

59

Biomass Hydrolysis: Scientometric Study

TABLE 21.7 The Most-Prolific Scopus Subject Categories in the Biomass Hydrolysis No.

Scopus Subject Categories

 1  2

Chemical Engineering Biochemistry, Genetics, and Molecular Biology Immunology and Microbiology Environmental Science Energy Chemistry Agricultural and Biological Sciences Materials Science Engineering Multidisciplinary

 3  4  5  6  7  8  9 10

Sample Papers (%)

Population Papers (%)

Surplus (%)

64.7 51.5

43.4 34.8

21.3 16.7

42.1

19.8

22.3

39.1 35.3 8.9 8.5

29.0 28.1 18.9 21.8

10.1 7.2 −10.0 −13.3

6.8 6.0 2.1

10.5 9.3 1.5

−3.7 −3.3 0.6

21.3.8  The Most-Prolific Scopus Subject Categories in the Biomass Hydrolysis Information about the most-prolific ten Scopus subject categories indexing at least 2.1% of the sample papers each is given in Table 21.7. The most-prolific Scopus subject category in the biomass hydrolysis is Chemical Engineering with 65% of sample papers, closely followed by Biochemistry; Genetics and Molecular Biology with 52% of the sample papers. The other prolific subject categories are Environmental Science, Immunology and Microbiology, and Energy with 35%–42% of the sample papers each. It is notable that Social Sciences including Economics and Business account for only 1.5% of the population studies. On the other hand, the Scopus subject category with the most citation impact is the Immunology and Microbiology with 22% surplus, closely followed by Chemical Engineering and Biochemistry; Genetics and Molecular Biology with 21% and 17% surplus, respectively. Similarly, the Scopus subject category with the least citation impact is Agricultural and Biological Sciences with 13% deficit, closely followed by Chemistry with 10% deficit.

21.3.9  The Most-Prolific Scopus Keywords in the Biomass Hydrolysis Information about the keywords used with at least 6.0% or 3.5% of the sample or population papers, respectively, is given in Table 21.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most-prolific keyword set provided by Scopus database. These keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. There are 17 keywords selected related to the biomass and biomass constituents: cellulose, lignin, biomass, lignocellulose, Zea mays (corn. maize), hemicellulose, carbohydrates, wood, lignocellulosic biomass, Triticum aestivum, and corn stover with 10%–71% of the sample papers each. The prolific keywords related to the biomass hydrolysis are hydrolysis, enzymatic hydrolysis, and saccharification with 24%–79% of the sample papers each, while those related to the biomass pretreatments are cellulases, enzymes, enzyme activity, pretreatment, alcohol, Trichoderma reesei, temperature, ionic liquids, enzymolysis, pretreatment, Hypocrea jecorina, and sulfuric acids with 10%–41% of the sample papers each.

60

Bioethanol Fuel Production Processes. II

TABLE 21.8 The Most-Prolific Keywords in Biomass Hydrolysis No. 1

Keywords

Sample Papers (%)

Cellulose

71.1

38.9

32.2

Lignin

59.1

27.2

31.9

40.9

22.3

18.6

Lignocellulose

31.9

12.4

19.5

Zea mays (corn, maize)

27.2

10.3

16.9

Hemicellulose

19.1

8.0

11.1

Carbohydrates

17.4

10.3

7.1

Wood

13.2

5.8

7.4

Lignocellulosic biomass

12.8

8.6

4.2

Triticum aestivum

10.6

5.2

5.4

9.8

2.9

6.9

Microcrystalline cellulose

8.1

2.0

6.1

Straw

6.8

5.0

1.8

Xylan

6.4

3.9

2.5

Bagasse

6.0

6.4

−0.4

Sugarcane

4.7

5.4

−0.7

5.8

−5.8

Sugarcane bagasse Hydrolysis Hydrolysis

79.1

49.5

29.6

Enzymatic hydrolysis

33.2

26.3

6.9

Saccharification

24.3

19.5

4.8

6.0

3.2

2.8

Enzymatic digestibility

3

Surplus (%)

Biomass

Corn stover

2

Population Papers (%)

Biomass

Cellulose hydrolysis

6.0

3.1

2.9

Digestibility

3.8

4.1

−0.3

Pretreatments Cellulases

41.3

18.6

22.7

Enzymes

35.3

17.2

18.1

Enzyme activity

33.2

18.7

14.5

Pretreatment

20.9

9.7

11.2

Alcohol

20.0

8.7

11.3

Trichoderma reesei

17.9

4.8

13.1

Temperature

15.3

7.5

7.8

Ionic liquids

13.6

5.6

8

Enzymolysis

13.2

6.1

7.1

Pretreatment

11.5

9.3

2.2

Hypocrea jecorina

11.5

2.6

8.9

Sulfuric acids

10.6

5.3

5.3

Fungi

8.9

6.5

2.4

Enzyme inhibition

8.9

2.7

6.2

Enzyme kinetics

8.9

2.3

6.6

Beta glucosidase

8.1

3.6

4.5 (Continued )

61

Biomass Hydrolysis: Scientometric Study

TABLE 21.8 (Continued ) The Most-Prolific Keywords in Biomass Hydrolysis No.

Keywords

Sample Papers (%)

5

Surplus (%)

7.7

3.0

4.7

Acids

7.2

2.4

4.8

Acetic acids

6.8

4.4

2.4

Water

6.8

4.2

2.6

Ammonia

6.0

1.9

4.1

Simultaneous saccharification and fermentation pH

3.8

3.1

0.7

6.4

−3

3.8

−3.8

Sodium hydroxide

3.6

−3.6

Xylan endo 1,3 beta xylosidase

2.5

−2.5

3.4

Enzymology

4

Population Papers (%)

Detoxification

Other processes Fermentation

34.9

20.5

14.4

Saccharomyces cerevisiae

12.8

6.2

6.6

Yeast

10.2

7.5

2.7

Adsorption

8.9

4.1

4.8

Delignification

7.7

4.8

2.9

Bioconversion

7.2

2.5

4.7

Biotransformation

7.2

2.4

4.8

Degradation

6.8

4.0

2.8

Glucose

31.9

21.7

10.2

Ethanol

31.1

19.1

12

Sugar

26.8

19.9

6.9

Xylose

14.0

8.0

6

Biofuel

11.5

13.3

−1.8

Polysaccharides

11.1

7.4

3.7

Furfural

7.2

3.7

3.5

Cellulose derivatives

7.2

0.0

7.2

Ethanol production

6.8

4.1

2.7

Bioethanol

6.4

9.7

−3.3

Fermentable sugars

3.8

3.9

−0.1

5.1

−5.1

Hydrolysis products

Bioethanol production

The prolific keywords related to the other processes are fermentation, Saccharomyces cerevisiae, yeasts, and adsorption with 10%–12% of the sample papers each. Further, those related to the hydrolysis products are glucose, ethanol, sugar, xylose, biofuel, and polysaccharides with 11%–32% of the sample papers each. It is notable that only 6.4% of the indexed papers employ bioethanol keyword. Further, the most-prolific keywords are cellulose, lignin, hydrolysis, cellulases, lignocellulose, biomass, enzymes, Zea mays (corn. maize), enzyme activity, fermentation, Trichoderma reesei, ethanol, alcohol, pretreatment, and hemicellulose with 11%–32% surplus each.

62

Bioethanol Fuel Production Processes. II

TABLE 21.9 The Most-Prolific Research Fronts the Biomass Hydrolysis No. 1

Research Fronts Hydrolysis of biomass constituents Cellulose

8.5

Lignocellulose

5.5

Hemicellulose

3.4

Hydrolysis of agricultural residues Corn stover

2.6 32.3 12.8

Wheat straw

6.4

Other residues

4.7

Rice straw

4.3

Sugarcane bagasse 3 4 5 6 7 8

53.6 33.6

Lignin

Other constituents 2

N Paper (%) Sample

Wood hydrolysis Hydrolysis of biomass in general Grass hydrolysis Food waste hydrolysis Plant hydrolysis Hydrolysates

4.3 15.7 9.8 3.4 1.7 0.4 14.9

N paper (%) sample, the number of papers in the population sample of 235 papers.

21.3.10  The Most-Prolific Research Fronts in Biomass Hydrolysis Information about the research fronts for the sample papers in biomass hydrolysis with regard to the biomass used in these pretreatments is given in Table 21.9. As Table 21.9 shows, there are two primary research fronts for this field: the hydrolysis of the biomass constituents and agricultural residues with 54% and 32% of the sample papers, respectively. The other prolific research fronts are the hydrolysis of the wood and lignocellulosic biomass, and hydrolysates with 16%, 10%, and 15% of the sample papers, respectively. Next, the hydrolysis of the grass, food waste, and plants are the other minor research fronts with 3%, 2%, and 1% of the sample papers, respectively. The most-prolific biomass constituent is cellulose with 34% of the sample papers, while corn stover is the most-prolific agricultural residue with 13% of the sample papers.

21.4 DISCUSSION 21.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass. The research in the

Biomass Hydrolysis: Scientometric Study

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fields of biomass hydrolysis has thus intensified in recent years. The hydrolysis of the agricultural residues, wood, biomass constituents, and grass has been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, COVID-19 shocks, or war-related shocks as in the case of Russian invasion of Ukraine. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As there have been no scientometric studies on the biomass hydrolysis, this book chapter presents a scientometric study of the research in the biomass hydrolysis as on May 2022. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of both these datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 300 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replication studies. Further, a selected list of the keywords was presented in Table 21.7. As a second step, two sets of data were used for this study. First, a population sample of over 11,729 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 235 most-cited papers, corresponding to 2% of the population dataset, was used to examine the scientometric characteristics of these citation classics. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the biomass hydrolysis. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

21.4.2  The Most-Prolific Documents in the Biomass Hydrolysis Articles (together with conference papers) dominate both the sample (85%) and population (96%) papers (Table 21.1). Further, review papers and articles have a surplus (13%) and deficit (10%), respectively. The representation of the reviews and short surveys in the sample papers is extraordinarily high (15%). Scopus differs from the Web of Science database in differentiating and showing articles (81%) and conference papers (5%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared to those published only in the conference proceedings. Similarly, Scopus differs from Web of Science database in introducing short surveys (1%). Hence, the total number of articles and review papers in the sample dataset are 85% and 15%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus as well as in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of the reviews in the sample dataset was manually found as 20% compared to 15% as indexed by Scopus, reducing the number of articles and conference papers to 80% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 21.1 for this purpose. Book chapters may also be classified as articles or reviews as an

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additional classification to differentiate review chapters from the experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as well as it is done in the Web of Science database.

21.4.3  The Most-Prolific Authors in the Biomass Hydrolysis There have been most-prolific 26 authors with at least 1.7% of the sample papers each as given in Table 21.2. These authors have shaped the development of the research in this field. The most-prolific authors are John N. Saddler and to a lesser extent Charles E. Wyman, Guido Zacchi, Barbel Hahn-Hagerdal, Michael R. Ladisch, and Leif J. Jonsson. It is notable that these top researchers are from the USA and Europe. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for the names with more than two components such as ‘Blake Sam de Hyun Saddler’. The probable outcomes are ‘Saddler, B.S.D.H.’, ‘de Hyun Saddler’, B.S.’, or ‘Hyun Saddler, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative, while the last choice is an undesired outcome as two last words are taken as the last name. It is a good practice to combine the words of the last name by a hyphen: ‘Hyun-Saddler, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in shortening Chinese names. For example, ‘Yuoyong Wang is often shortened as ‘Wang, Y.’, ‘Wang, Y.-Y.’, and ‘Wang, Y.Y’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhang Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PUBMED and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Wang, Y.Y’. Therefore, there have been difficulties to locate papers for the Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors especially for the authors with common names such as ‘Zhang, X’ or ‘Huang, X’ or ‘Zhu, X’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies as well as allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Zhang, X’ in the databases. In this context, the coding of authors in Scopus database is a welcome innovation compared to the other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system especially for common names. For example, some of the papers for a certain author maybe allocated to another researcher with a different author code. It is possible that Scopus uses a number of software programs to differentiate the author names and the program may not be false-proof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Saddler, H.J.’ or just ‘Saddler’ instead of ‘Saddler, Hyun Jae’. There are also inconsistencies in the naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example. ‘Saddler, A.P.C.’ might be given as ‘Saddler, A.’, ‘Saddler, A.P.’, ‘Saddler, C.’, or ‘Saddler, A.C.’ in the journals and books. This

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also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Çampaçöl, Özökçe) rather than in the English spellings (e.g., Campacol, Ozokce) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in the English spellings. It is observed that national spellings of the author names do not help in conducting scientometric studies as well in allocating credits to the authors as sometimes there are the different author entries for the English and National spellings in the Scopus database. The most-prolific institutions for the sample dataset are the Lund University and to a lesser extent CIEMAT and University of Sao Paulo. The most-prolific countries for the sample dataset are the USA and Sweden and to a lesser extent Spain and Brazil. These findings confirm the dominance of the USA and Europe and to a lesser extent of Brazil in this field. The most-prolific research fronts are the hydrolysis of agricultural residues and wood and to a lesser extent cellulose. These findings hint that agricultural residues and wood are the primary sources of ethanol through their hydrolysis, followed up by the fermentation of their hydrolysates. It is also notable that there is a significant gender deficit for the sample dataset as surprisingly only eight of these top researchers are female with 32% representation rate. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from the minority groups in science on the basis of race, sexuality, age, and disability, besides the gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

21.4.4  The Most-Prolific Research Output by Years in the Biomass Hydrolysis The research output observed between 1970 and 2022 is illustrated in Figure 21.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. These findings suggest that the most-prolific sample and population papers were primarily published in the last two decades. These are the thought-provoking findings as there has been no significant research in this field in the pre-2000s, but there has been a significant research boom in the last two decades. However, it is notable that the research output lost its momentum after 2013. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field in the last two decades. Based on these findings, the size of the population papers is likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming are translated efficiently to the research funding in this field. Furthermore, there have been additional incentives for the research on the biomass hydrolysis due to the current supply shocks due to the COVID-19 pandemics and Russian invasion of Ukraine as there have been public pressures for the replacement of crude oil-based gasoline and diesel fuels by bioethanol fuels and biodiesel fuels.

21.4.5  The Most-Prolific Institutions in the Biomass Hydrolysis The most-prolific 23 institutions publishing papers on the biomass hydrolysis with at least 1.7% of the sample papers each given in Table 21.3 have shaped the development of the research in this field.

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The most-prolific institutions are the Lund University and University of British Columbia and, to a lesser extent, the National Renewable Energy Laboratory, Dartmouth College, University of Wisconsin-Madison, Purdue University, and Technical University of Denmark. Further, the top countries for these most-prolific institutions are the USA and, to a lesser extent, Spain and Sweden. In total, only nine countries house these top institutions. On the other hand, the institutions with the most citation impact are Lund University and, to a lesser extent, University of British Columbia, Dartmouth College and National Renewable Energy Laboratory, Purdue University, Technical University of Denmark, and University of WisconsinMadison. These findings confirm the dominance of the US, Canadian, and European institutions in this research field. The absence of China in this top institution list is remarkable.

21.4.6  The Most-Prolific Funding Bodies in the Biomass Hydrolysis The most-prolific 12 funding bodies funding at least 1.7% of the sample papers each is given in Table 21.4. It is notable that only 31% and 46% of the sample and population papers were funded, respectively. The most-prolific funding bodies are the U.S. Department of Energy and European Commission. The most-prolific countries for these top funding bodies are the USA and, to a lesser extent, Brazil, Canada, and Sweden. In total, five countries and the EU house these top funding bodies. The funding bodies with the most citation impact are Swedish National Board of Industrial and Technical Development and Natural Resources of Canada and, to a lesser extent, the US Department of Energy, Laboratory Directed Research and Development, and National Nuclear Security Administration. Further, the funding bodies with the least impact are National Natural Science Foundation of China and National Council for Scientific and Technological Development of Brazil. These findings on the funding of the research in this field suggest that the level of the funding for the population papers, mostly in the last two decades, is modest and it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in the light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding, especially for the sample papers, there is ample room to enhance funding in this field. With the current supply shocks, it is expected that funding for the biomass hydrolysis would increase substantially. It is also remarkable that the USA, Europe, Canada, China, and Brazil dominate the research funding in this field. However, there are questions about the level of funding after 2013 where the research output lost its momentum highly.

21.4.7  The Most-Prolific Source Titles in Biomass Hydrolysis The most-prolific 15 source titles publishing at least 1.7% of the sample papers each in biomass hydrolysis have shaped the development of the research in this field (Table 21.5). The most-prolific source titles are Bioresource Technology and to a lesser extent Biotechnology and Bioengineering, Enzyme and Microbial Technology, Biotechnology for Biofuels, Biotechnology Progress, and Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology. Further, the source titles with the most citation impact are the Biotechnology and Bioengineering and Bioresource Technology and, to a lesser extent, Enzyme and Microbial Technology, Biotechnology Progress, and Biotechnology for Biofuels. It is notable that these top source titles are primarily related to the biomass, bioresources, biotechnology, and bioenergy. This finding suggests that the journals in this field have significantly shaped the development of the research in this field as they focus on the biomass hydrolysis.

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21.4.8  The Most-Prolific Countries in the Biomass Hydrolysis The most-prolific 13 countries publishing at least 2.1% of the sample papers each have significantly shaped the development of the research in this field (Table 21.6). The most-prolific countries are the USA and, to a lesser extent, Sweden, Canada, China, Spain, Denmark, and Japan. Further, five European countries listed in Table 21.6 produce as a whole 29% and 13% of the sample and population papers, respectively, with 16% surplus. On the other hand, the countries with the most citation impact are the USA and, to a lesser extent, Sweden, Canada, and Denmark while China and, to a lesser extent, India, Brazil, and Japan are the countries with the least impact. The close examination of these findings suggests that the USA, Europe, China, Canada, and Japan are the major producers of the research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The USA has further developed a strong research infrastructure to support its corn- and grass-based bioethanol industry (Gillon, 2010). However, China has been a rising mega star in the scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009). Next, Europe has been a persistent player in the scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010). Additionally, Brazil has also been a persistent player in the scientific research at a moderate level (Glanzel et al., 2006). Brazil has also developed a strong research infrastructure to support its biomass-based bioethanol industry (Macedo et al., 2008).

21.4.9  The Most-Prolific Scopus Subject Categories in Biomass Hydrolysis The most-prolific ten Scopus subject categories indexing at least 2.1% of the sample papers each, given in Table 21.7, have shaped the development of the research in this field. The most-prolific Scopus subject categories in the biomass hydrolysis are Chemical Engineering and to a lesser extent Biochemistry, Genetics and Molecular Biology, Environmental Science, Immunology and Microbiology, and Energy. These findings are thought-provoking suggesting that the primary subject categories are related to chemical engineering, molecular biology, microbiology, and environmental sciences. The other key finding is that social sciences are not well represented in both the sample and population papers, as in the most fields in bioethanol fuels. These findings are not surprising as the key research fronts in this field relate to the development and applications of biomass hydrolysis.

21.4.10  The Most-Prolific Scopus Keywords in Biomass Hydrolysis A limited number of keywords have shaped the development of the research in this field as shown in Table 21.8 and the Appendix. These keywords are grouped under five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. The prolific keywords related to the biomass and biomass constituents are cellulose, lignin, biomass, lignocellulose, Zea mays (corn. maize), hemicellulose, carbohydrates, wood, lignocellulosic biomass, Triticum aestivum, and corn stover, while those related to the pretreatment are cellulases, enzymes, enzyme activity, pretreatment, alcohol, Trichoderma reesei, temperature, ionic liquids, enzymolysis, pretreatment, Hypocrea jecorina, and sulfuric acids. The prolific keywords related to the biomass hydrolysis are hydrolysis, enzymatic hydrolysis, and saccharification, while those related to the other processes are fermentation, Saccharomyces

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cerevisiae, and adsorption. Further, those related to the hydrolysis products are glucose, ethanol, sugar, xylose, biofuel, and polysaccharides, and it is notable that only 6.4% of the indexed papers employ bioethanol keyword. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas. The relevant keywords are presented in Table 21.8 as well as in the Appendix.

21.4.11  The Most-Prolific Research Fronts in Biomass Hydrolysis As Table 21.9 shows, there are two primary research fronts for this field: Hydrolysis of biomass constituents and agricultural residues, while the other research fronts are the hydrolysis of the wood and lignocellulosic biomass and hydrolysates. Further, the most-prolific biomass constituent is cellulose, while corn stover is the most-prolific agricultural residue. These findings are thought-provoking in seeking ways to increase bioethanol yield through the biomass hydrolysis at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. In the end, these most-cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of the acid and enzymatic hydrolysis (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

21.5  CONCLUSION AND FUTURE RESEARCH The research on the biomass hydrolysis has been mapped through a scientometric study of both sample (235 papers) and population (11,739 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the Appendix, and the relevant keywords are presented in Table 21.8. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose, Scopus database has been selected. The journal coverage of this database has been notably wider than that of Web of Science and other multisubject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication periods, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all the academic databases. In this respect, it seems that authors, Journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality also need to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change, although it lost its momentum after 2013. The institutions from the USA, Europe, Brazil, and Canada have mostly shaped the research in this field. The relatively modest funding rate of 46% for the population papers suggests that funding in this field significantly enhanced the research in this field primarily in the last two decades, possibly

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more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further as only 31% of the sample papers declared any funding. It is expected that the current supply shocks such as the COVID-19 shocks and the shocks due to Russian invasion of Ukraine would increase the funding rate in this research field as bioethanol fuels are a green alternative to crude oil-based gasoline and diesel fuels, although there has been no sharp rise in the research output in 2020 and 2021. The USA, Europe, Canada, China, Brazil, and Japan have been the major producers of the research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass as well as other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives. The primary Scopus subject categories have been Chemical Engineering and, to a lesser extent, Biochemistry. Genetics and Molecular Biology, Environmental Science, Immunology and Microbiology, and Energy as the focus of the research has been on the biomass hydrolysis to increase the sugar and bioethanol yield at large. Further, social sciences are not well represented in both the sample and population papers as in the most fields in bioethanol fuels. These findings are not surprising. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. These groups of keywords highlight the potential primary research fronts for these fields. There are two primary research fronts for this field: hydrolysis of biomass constituents and agricultural residues. The other research fronts are the hydrolysis of wood, lignocellulosic biomass, and hydrolysates. These findings are thought-provoking. The focus of these most-cited 235 papers as well as 11,739 population papers is the biomass hydrolysis to increase the sugar and bioethanol yield. These studies highlight strong structure–processing–property relationships for biomass hydrolysis for bioethanol fuels and their derivatives. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields especially in the aftermath of the significant global supply shocks such as Russian invasion of Ukraine and the COVID-19 shocks. It is recommended that further scientometric studies are carried out for the primary types of the hydrolysis. It is further recommended that reviews of the most-cited papers are carried out for each research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the biomass hydrolysis has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR BIOMASS HYDROLYSIS ((TITLE (hydroly* OR saccharif* OR prehydroly* OR posthydroly* OR *hydrolysis OR digestibili* OR digestible OR ssf OR shf OR accessibility OR “sugar recovery” OR “fermentable sugars” OR “reducing sugars” OR “sugar yield*” OR “sugar production” OR “sugar release” OR “sugar extraction”) AND TITLE (*mannan OR waste OR wastes OR *grass OR biomass* OR lignocellul* OR bagasse* OR *cellulose OR *cellulosic OR avicel OR *wood OR woody OR *woods OR *algae OR *algal OR *alga OR “olive tree*” OR chlamydomonas OR straw OR straws OR stover* OR eucalyptus OR miscanthus OR pine OR chlorella OR lignin OR cellulolytic OR

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xylan OR porphyridium OR nannochloropsis OR poplar* OR cedar OR pulp* OR “corn cob*” OR spruce OR beech OR oak OR pinus OR residue* OR willow* OR cypress OR sawdust OR prosopis OR “rice hull*” OR “*bean hull*” OR husk* OR birch OR cynara OR “*cane tops” OR “cotton stalk*” OR “corn stalk*” OR alfalfa OR “maize stem*” OR cornstalk* OR hyacinth OR salix OR corncob* OR “fruit bunch*” OR populus OR sage OR “*weed stem*” OR bamboo* OR birch OR “*flower stalk*” OR “barley hull*” OR “wheat bran” OR pseudostem* OR bluestem* OR reed OR arundo OR cellobiose* OR sawmill OR ulva OR seaweed* OR rhizome OR agave OR “coffee grounds” OR molass* OR triticale OR arborera OR saccharum OR bioresource* OR eucheuma OR laminaria OR bode OR taro OR szarvasi OR eichhornia OR sago OR kenaf OR scrap* OR fir OR chestnut OR cistus OR “distillers’ grains” OR “grape marc” OR medicago OR “shea meal” OR brassica OR “flax shives” OR gelidium OR saccharina OR “olive stones” OR lantana OR “corn meal” OR “spent grain*” OR “almond shells”)) OR (TITLE (*ethanol AND hydroly*) OR (TITLE (inhibit* OR “degradation products” OR “degradation compounds” OR xylose OR pentose* OR hexose* OR glucose OR detoxif* OR toxicity) AND TITLE (hydroly* OR saccharif* OR prehydroly* OR posthydroly* OR *hydrolysis OR ssf OR shf OR lignocellulos* OR cellulos*)))) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR neur OR dent OR heal OR psyc OR eart) OR TITLE (litter OR nano* OR *protein OR thermochemical OR soil* OR phar* OR anaerobic OR sludge OR furan* OR antioxida* OR wastewater* OR *furfural OR *lipid OR “hydrothermal carbon*” OR butanol OR dye* OR seed* OR electrochem* OR levulinic OR mariculture OR atmosph* OR tissue* OR electr* OR diet* OR pyroly* OR lactic OR *hydrogen OR “oil prod*” OR collagen OR *gas OR xylitol OR supercapacitor* OR *methane OR adhesive* OR *ester OR acetoin OR succinic OR *butyrate OR juice OR alcohol* OR *diesel OR *methanol OR lipase OR synthes* OR proline OR neural OR lysine OR beet OR ash OR “essential oil*” OR organic OR “thermal degradation” OR “biological degradation” OR peptide* OR cyclase* OR receptor* OR urea OR ketone OR corrosion OR “non digestible” OR oxidative OR atp OR “non-hydroly*” OR lead OR casein OR zein OR “value added” OR synthase OR acetobacter OR ethanolamide OR *amide OR fescue* OR antibacterial OR tannin* OR muscle* OR amylase OR ginsenoside* OR trout OR meat* OR hydrolyticus OR vitro OR hydrolytic OR edible OR acetal)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “bk”) OR LIMIT-TO (DOCTYPE, “sh”) OR LIMIT-TO (DOCTYPE, “ed”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”))

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Konur, O. 2002b. Assessment of disabled students in higher education: Current public policy issues. Assessment and Evaluation in Higher Education 27:131–152. Konur, O. 2002c. Access to employment by disabled people in the UK: Is the Disability Discrimination Act working? International Journal of Discrimination and the Law 5:247–279. Konur, O. 2006a. Participation of children with dyslexia in compulsory education: Current public policy issues. Dyslexia 12:51–67. Konur, O. 2006b. Teaching disabled students in Higher Education. Teaching in Higher Education 11:351–363. Konur, O. 2007a. A judicial outcome analysis of the Disability Discrimination Act: A windfall for the employers? Disability & Society 22:187–204. Konur, O. 2007b. Computer-assisted teaching and assessment of disabled students in higher education: The interface between academic standards and disability rights. Journal of Computer Assisted Learning 23:207–219. Konur, O. 2011. The scientometric evaluation of the research on the algae and bio-energy. Applied Energy 88:3532–3540. Konur, O. 2012a. Prof. Dr. Ayhan Demirbasʼ scientometric biography. Energy Education Science and Technology Part A: Energy Science and Research 28:727–738. Konur, O. 2012b. The evaluation of the biogas research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:1277–1292. Konur, O. 2012c. The evaluation of the global energy and fuels research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 30:613–628. Konur, O. 2012d. The evaluation of the research on the biodiesel: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1003–1014. Konur, O. 2012e. The evaluation of the research on the bioethanol: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1051–1064. Konur, O. 2012f. The evaluation of the research on the biofuels: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:903–916. Konur, O. 2012g. The evaluation of the research on the biohydrogen: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:323–338. Konur, O. 2012h. The evaluation of the research on the microbial fuel cells: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:309–322. Konur, O. 2012i. The scientometric evaluation of the research on the production of bioenergy from biomass. Biomass and Bioenergy 47:504–515. Konur, O. 2015. Current state of research on algal bioethanol. In Marine Bioenergy: Trends and Developments, Ed. S. K. Kim and C. G. Lee, pp. 217–244. Boca Raton, FL: CRC Press. Konur, O., Ed. 2018a. Bioenergy and Biofuels. Boca Raton, FL: CRC Press. Konur, O. 2018b. Bioenergy and biofuels science and technology: Scientometric overview and citation classics. In Bioenergy and Biofuels, Ed. O. Konur, pp. 3–63. Boca Raton: CRC Press. Konur, O. 2019. Cyanobacterial bioenergy and biofuels science and technology: A scientometric overview. In Cyanobacteria: From Basic Science to Applications, Ed. A. K. Mishra, D. N. Tiwari and A. N. Rai, pp. 419–442. Amsterdam: Elsevier. Konur, O. 2020a. The scientometric analysis of the research on the bioethanol production from green macroalgae. In Handbook of Algal Science, Technology and Medicine, Ed. O. Konur, pp. 385–401. London: Academic Press. Konur, O., Ed. 2020b. Handbook of Algal Science, Technology and Medicine. London: Academic Press. Konur, O., Ed. 2021a. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021b. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 1. Biodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021c. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 2. Biodiesel Fuels based on the Edible and Nonedible Feedstocks, Wastes, and Algae: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021d. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 3. Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press.

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22 Review

Biomass Hydrolysis Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

22.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Hill et al., 2006; Konur, 2012, 2015, 2019, 2020) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Taherzadeh and Karimi, 2008; Yang and Wyman, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of biomass hydrolysis (Sun and Cheng, 2002; Zhang and Lynd, 2004) has thus intensified in recent years. They hydrolysis of the agricultural residues (Lloyd and Wyman, 2005; Saha et al., 2005), wood (Larsson et al., 1999a,b), biomass constituents (Huang and Fu, 2013; Mansfield et al., 1999), and grass has been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there has been a number of review papers on the biomass hydrolysis (Sun and Cheng, 2002; Zhang and Lynd, 2004), there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents a review of the most-cited 25 articles in the field of the biomass hydrolysis. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field.

22.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May, 2022. As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 300 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 422 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape. DOI: 10.1201/9781003226499-30

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22.3 RESULTS The brief information about 25 most-cited papers with at least 422 citations each on the biomass hydrolysis is given below. The primary research fronts are the hydrolysis of the agricultural residues, wood, and biomass constituents with ten, seven, and six highly cited papers (HCPs), respectively. Further, two HCPS papers are related to the hydrolysis of the grass.

22.3.1  The Hydrolysis of the Biomass Constituents There are six HCPs for the research front of the hydrolysis of the biomass constituents (Table 22.1). Suganuma et al. (2008) studied the hydrolysis of cellulose by solid acid catalysts in a paper with 864 citations. They observed that amorphous carbon bearing SO3H, COOH, and OH performed as an efficient catalyst for the cellulose hydrolysis. The apparent activation energy for the hydrolysis of cellulose into glucose using the carbon catalyst was 110 kJ/mol, smaller than that for sulfuric acid under optimal conditions (170 kJ/mol). Further, this carbon catalyst could be readily separated from the saccharide solution after reaction for reuse in the reaction without loss of activity. The superb catalytic performance of the carbon catalyst was due to the ability of the material to adsorb β-1,4glucan, which did not adsorb to other solid acids. Sasaki et al. (2000) studied the dissolution and hydrolysis of cellulose in subcritical and supercritical water in a paper with 599 citations. At 400°C, they mainly obtained hydrolysis products, while in 320°C–350°C water, aqueous decomposition products of glucose were the main products. Further, below 350°C, the cellulose decomposition rate was slower than the glucose and cellobiose decomposition rates, while above 350°C, the cellulose hydrolysis rate drastically increased and became higher than the glucose and cellobiose decomposition rates. Further, below 280°C, cellulose particles

TABLE 22.1 The Hydrolysis of the Biomass Constituents No. 1

2

3

4 5

6

Papers Suganuma et al. (2008) Sasaki et al. (2000) Sasaki et al. (1998)

Biomass Cellulose

Prt. Solid acids

Parameters

Keywords

Cellulose hydrolysis, Cellulose, solid acid catalysts hydrolysis

Cellulose, cellobiose

Lead Author

Affil.

Hara, Tokyo Inst. 864 Michikazu Technol. 7403345875 Japan Arai, Kunio Tohoku Univ. 599 7403965625 Japan

Sub- and Cellulose hydrolysis Cellulose, supercritical and dissolution, hydrolysis water temperature effect Cellulose Sub- and Cellulose hydrolysis Cellulose, Arai, Kunio Tohoku Univ. supercritical and dissolution, hydrolysis 7403965625 Japan water temperature effect Onda et al. Cellulose Solid acids Cellulose hydrolysis, Cellulose Onda, Ayumu Kochi Univ. (2008) solid acids hydrolysis 56689677300 Japan Dadi et al. Cellulose Ionic liquids, Cellulose hydrolysis, Cellulose, Schall, Univ. Toledo (2006) enzymes pretreatment effect saccharification Constance USA A.* 6603671396 Kristensen Lignocellulose Enzymes Lignocellulosic Lignocellulose, Jorgensen, Univ. et al. biomass hydrolysis, hydrolysis Henning Copenhagen (2009) sugar yield 7202554496 Denmark decrease, enzyme adsorption to cellulose

*, Female; cits., number of citations received for each paper; prt, biomass pretreatments.

Cits

577

510 489

464

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became gradually smaller with increasing reaction time, but at high temperatures (300°C–320°C), cellulose particles disappeared with increasing transparency and much more rapidly than expected from the lower temperature results. Cellulose hydrolysis at high temperature took place with dissolution in water due to the cleavage of intra- and intermolecular hydrogen linkages in the cellulose crystal. Thus, a homogeneous atmosphere was formed in supercritical water, and this resulted in a drastic increase of the cellulose decomposition rate above 350°C. Sasaki et al. (1998) proposed a new method to hydrolyze cellulose rapidly in subcritical and supercritical water to recover glucose, fructose, and oligomers in a paper with 577 citations. They observed that hydrolysis product yields (around 75%) in supercritical water were much higher than those in subcritical water. At a low-temperature region, the glucose or oligomer conversion rate was much faster than the hydrolysis rate of cellulose. Thus, even if the hydrolysis products, such as glucose or oligomers, were formed, their further decomposition rapidly took place and thus high yields of hydrolysis products could not be obtained. However, around the critical point, the hydrolysis rate jumped to more than an order of magnitude higher level and became faster than the glucose or oligomer decomposition rate. This is the reason why they obtained a high yield of hydrolysis products in supercritical water. Onda et al. (2008) investigated the selective hydrolysis of cellulose over solid acid catalysts at temperatures higher than 90°C in a paper with 510 citations. Among the solid acid catalysts tested, such as the H-form zeolite catalysts and the sulfated and sulfonated catalysts, they observed that a sulfonated activated-carbon catalyst showed a remarkably high yield of glucose, which was due to the high hydrothermal stability and the excellent catalytic property attributed to the strong acid sites of SO3H functional groups and the hydrophobic planes. Dadi et al. (2006) studied the cellulose hydrolysis kinetics using an ionic liquid (IL) pretreatment in a paper with 489 citations. They used 1-n-butyl-3-methylimidazolium chloride. They observed that the initial enzymatic hydrolysis rates were approximately 50-fold higher for regenerated cellulose as compared to untreated cellulose (Avicel PH-101) as measured by a soluble reducing sugar assay. Kristensen et al. (2009) investigated the determinants of the sugar yield in the enzymatic hydrolysis of lignocellulosic substrates with high concentration in a paper with 464 citations. They observed that the decreasing biomass conversion at increasing biomass concentrations was a generic or intrinsic effect, describing a linear correlation from 5% to 30% initial total solids content (w/w). Product inhibition by glucose and in particular cellobiose at the increased concentrations at high solid loading played a role but could not completely account for the decreasing conversion. However, adsorption of cellulases decreased at increasing solids concentrations. Hence, there was a strong correlation between the decreasing cellulose adsorption and biomass conversion, indicating that the inhibition of cellulase adsorption to cellulose was causing the decrease in the sugar yield. They concluded that the inhibition of cellulase adsorption by hydrolysis products was the main cause of the decreasing sugar yields at increasing substrate concentrations in the enzymatic decomposition of cellulosic biomass.

22.3.2  The Hydrolysis of the Agricultural Residues There are 10 HCPs for the research front of the hydrolysis of the agricultural residues (Table 22.2). Silverstein et al. (2007) compared four chemical pretreatment methods for the hydrolysis of cotton stalks in a paper with 652 citations. They pretreated ground cotton stalks at a solid loading of 10% (w/v) with sulfuric acid (H2SO4), sodium hydroxide (NaOH), and hydrogen peroxide (H2O2) at concentrations of 0.5%, 1%, and 2% (w/v). Treatment temperatures were 90°C and 121°C at 15 psi for residence times of 30, 60, and 90 min. Further, they performed ozone pretreatment at 4°C with constant sparging of stalks in water. They observed that solids from the first three pretreatments (at 2%, 60 min, 121°C/15 psi) showed significant lignin degradation and/or high sugar availability and hence were hydrolyzed by Celluclast 1.5 L and Novozym 188 at 50°C. Sulfuric acid pretreatment

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TABLE 22.2 The Hydrolysis of the Agricultural Residues No.  1

Papers

Biomass

Prt.

Hsu et al. (2010)

Rice straw

H2SO4, NaOH, H2O2, O3, enzymes

Parameters

Lignocellulose hydrolysis, chemical treatment efficiency  2 Saha et al. Wheat straw Acids, enzymes Cellulose, (2005) cellulose, hemicellulose hemicellulose hydrolysis, sugar and ethanol yield  3 Lloyd and Corn stover Acids, enzymes Total sugar Wyman yield (glucose (2005) and xylose)  4 Yang and Corn stover Water, acids Cellulose Wyman cellulose, hydrolysis, (2004) xylan, lignin xylan and lignin removal, reactors  5 Luterbacher Corn stover, γ-Valerolactone, Hydrolysis, et al. (2014) softwood, solvent water, acids hardwood mixture  6

Silverstein et Cotton stalks al. (2007) cellulose, lignin, xylan

Keywords Cotton stalks, hydrolysis

Affil.

Cits

SharmaNC State Shivappa, Univ. Ratna R* USA 16231216900

Lead Author

652

Wheat straw, Saha, Badal C. USDA Agr. 646 saccharification 7202946302 Res. Serv. USA

Corn stover, hydrolysis

Wyman, Charles E.

Corn stover, cellulose, digestibility

Wyman, Charles E.

Univ. Calif. 620 Riverside USA Univ. Calif. 556 Riverside USA

Biomass, sugar Dumesic, JA Univ. 509 production 56873834000 Wisconsin Madison USA Rice straw, Guo, Gia L Inst. Nucl. 479 hydrolysis 7402768046 Ener. Res. Taiwan Corn stover, Zacchi, Guido Lund Univ. 466 hydrolysis, 7006727748 Sweden lignin, hemicellulose

Acids, enzymes Hydrolysis, solid residue properties  7 Ohgren et al. Corn stover, Steam, acids Corn stover (2007) lignin, hydrolysis, hemicellulose lignin and hemicellulose removal, xylanase effect  8 Wyman Corn stover Ammonia, Cellulose, Corn stover, Wyman, Univ. Calif. 447 et al. (2005) cellulose, acids, alkali, hemicellulose sugar recovery Charles E. Riverside hemicellulose flow-through, hydrolysis, USA controlled pH, pretreatment enzymes type effect  9 Yang and Corn stover, BSA, enzymes Cellulose Cellulose, Wyman, Univ. Calif. 426 Wyman cellulose, hydrolysis, lignin, Charles E. Riverside (2006) lignin BSA effect hydrolysis USA 10 Teymouri Corn stover Ammonia, Hydrolysis Corn stover, Dale, Bruce E. Michigan 422 et al. (2005) enzymes optimization, hydrolysis 7201511969 State sugar and Univ. ethanol yield USA *, Female; cits., number of citations received for each paper; prt, biomass pretreatments.

resulted in the highest xylan reduction (95.23% for 2% acid, 90 min, 121°C/15 psi) but the lowest cellulose-to-glucose conversion during hydrolysis (23.85%). NaOH pretreatment resulted in the highest level of delignification (65.63% for 2% NaOH, 90 min, 121°C/15 psi) and cellulose conversion (60.8%). H2O2 pretreatment resulted in significantly lower delignification (maximum of 29.51%

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for 2%, 30 min, 121°C/15 psi) and cellulose conversion (49.8%) than NaOH pretreatment but had a higher cellulose conversion than H2SO4 pretreatment. Ozone did not cause any significant changes in lignin, xylan, or glucan contents over time. A modified severity parameter (log M0) explained most of the variation in xylan or lignin reduction through simple linear regressions. Saha et al. (2005) studied the dilute acid pretreatment, enzymatic hydrolysis, and fermentation of wheat straw with 49% and 28% cellulose and hemicellulose, respectively, to ethanol in a paper with 646 citations. They observed that the maximum yield of monomeric sugars from wheat straw (7.8%, w/v, DS) by dilute H2SO4 (0.8%, v/v) pretreatment and enzymatic saccharification (45°C, pH 5.0, 72 h) using cellulase, β-glucosidase, xylanase, and esterase was 565 10 mg/g. Under this condition, no measurable quantities of furfural and hydroxymethylfurfural were produced. The yield of ethanol (per liter) from acid pretreated enzyme saccharified wheat straw (78 g) hydrolysate by recombinant Escherichia coli strain FBR5 was 19 g with a yield of 0.24 g/g DS. Detoxification of the acid- and enzyme-treated wheat straw hydrolysate by overliming reduced the fermentation time from 118 to 39 h in the case of separate hydrolysis and fermentation (SHF) (35°C, pH 6.5), and increased the ethanol yield from 13 to 17 g/L and decreased the fermentation time from 136 to 112 h in the case of simultaneous saccharification and fermentation (SSF) (35°C, pH 6.0). Lloyd and Wyman (2005) studied the combined sugar (xylose and glucose) yields for dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis of the remaining solids in a paper with 620 citations. They observed that up to 15% of the total potential sugar in the substrate could be released as glucose during pretreatment and between 15% and 90+% of the xylose remaining in the solid residue could be recovered in subsequent enzymatic hydrolysis, depending on the enzyme loading. Further, glucose yields increased from as high as 56% of total maximum potential glucose plus xylose for just enzymatic digestion to 60% when glucose released in pretreatment was included. Xylose yields similarly increased from as high as 34% of total potential sugars for pretreatment alone to between 35% and 37% when credit was taken for xylose released in digestion. However, sugar yields were much lower if no acid was used. Overall, up to about 92.5% of the total sugars originally available in the corn stover used could be recovered for coupled dilute acid pretreatment and enzymatic hydrolysis. Further, enhanced hemicellulase activity could further improve xylose yields, particularly for low cellulase loadings. Yang and Wyman (2004) studied the effect of xylan and lignin removal by batch and flowthrough pretreatment on the enzymatic digestibility of corn stover cellulose in a paper with 556 citations. They used these reactors over a range of flow rates between 160°C and 220°C, with water only and also with 0.1 wt% sulfuric acid. They observed that increasing flow with just water enhanced the xylan dissolution rate, more than doubled total lignin removal, and increased cellulose digestibility. Furthermore, adding dilute sulfuric acid increased the rate of xylan removal for both batch and flow-through systems. Interestingly, adding acid also increased the lignin removal rate with flow, but less lignin was left in solution when acid was added in batch. Although the enzymatic hydrolysis of pretreated cellulose was related to xylan removal, the digestibility was much better for flow-through compared with batch systems, for the same degree of xylan removal. Cellulose digestibility for flow-through reactors was related to lignin removal as well. They asserted that altering lignin also affected the enzymatic digestibility of corn stover. Luterbacher et al. (2014) investigated the sugar production from corn stover, hardwood, and softwood using biomass-derived γ-valerolactone in a solvent mixture in a paper with 509 citations. They used a solvent mixture of biomass-derived γ-valerolactone (GVL), water, and dilute acid (0.05 weight percent H2SO4). They observed that GVL promoted thermocatalytic hydrolysis through complete solubilization of the biomass, including the lignin fraction. The carbohydrates could be recovered and concentrated (up to 127 g/L) by extraction from GVL into an aqueous phase by addition of NaCl or liquid CO2. This strategy was well suited for fermentative upgrading to ethanol at high titers and near theoretical yield. They asserted that the overall process could be cost-competitive for ethanol production, with biomass pretreatment followed by enzymatic hydrolysis.

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Hsu et al. (2010) investigated the effect of the structural properties of the solid residues on the enzymatic hydrolysis of the dilute-acid-pretreated rice straw in a paper with 479 citations. They obtained a maximal sugar yield of 83% when the rice straw was pretreated with 1% (w/w) sulfuric acid with a reaction time of 1–5 min at 160°C or 180°C, followed by enzymatic hydrolysis. Further, the complete release of sugar (xylose and glucose) increased the pore volume of the pretreated solid residues resulted in an efficiency of 70% for the enzymatic hydrolysis. The extra pore volume was generated by the release of acid-soluble lignin, and this resulted in the enzymatic hydrolysis being enhanced by nearly 10%. However, the increase in the crystallinity index of the pretreated rice straw was limited. Ohgren et al. (2007) investigated the effect of hemicellulose and lignin removal on enzymatic hydrolysis of steam pretreated corn stover in a paper with 466 citations. They obtained a near-theoretical glucose yield (96%–104%) from acid-catalyzed and steam-pretreated corn stover when xylanases were used to supplement cellulases during hydrolysis. These xylanases hydrolyzed residual hemicellulose, thereby improved the access of enzymes to cellulose. Under these conditions, xylose yields reached 70%–74%. Further, when pretreatment severity was reduced by using autocatalysis instead of acid-catalyzed steam pretreatment, xylose yields were increased to 80%–86%. The overall glucose yield increased slightly due to delignification, but the overall xylose yield decreased due to hemicellulose loss in the delignification step. Wyman et al. (2005) compared the sugar recovery from corn stover using ammonia explosion, aqueous ammonia recycle, controlled pH, dilute acid, flow-through, and lime pretreatments with the same cellulose in a paper with 447 citations. They obtained high yields of glucose from cellulose by cellulase enzymes for each pretreatment and observed that the cellulase formulations used were effective in solubilizing residual xylan left in the solids after each pretreatment. Thus, overall sugar yields from hemicellulose and cellulose in the coupled pretreatment and enzymatic hydrolysis operations were high for all of the pretreatments with corn stover. In addition, high-pH methods reduced cellulase use. However, they asserted that the substantial differences in sugar release patterns in the pretreatment and enzymatic hydrolysis operations had important implications for the choice of process, enzymes, and fermentative organisms. Yang and Wyman (2006) introduced bovine serum albumin (BSA) pretreatment to enhance enzymatic hydrolysis of cellulose in lignocellulosic biomass in a paper with 426 citations. They added cellulase and BSA to Avicel cellulose and solids containing 56% cellulose and 28% lignin from dilute sulfuric acid pretreatment of corn stover. They observed that little BSA was adsorbed on Avicel cellulose, while pretreated corn stover solids adsorbed considerable amounts of this protein. On the other hand, cellulase was highly adsorbed on both substrates. Adding a 1% concentration of BSA to dilute acid pretreated corn stover prior to enzyme addition at 15 FPU/g cellulose enhanced filter paper (FPU) activity in solution by about a factor of 2 and beta-glucosidase activity in solution by about a factor of 14. Overall, BSA treatment reduced the adsorption of cellulase and particularly β-glucosidase on lignin. BSA treatment of pretreated corn stover solids prior to enzymatic hydrolysis increased 72 h glucose yields from about 82% to about 92% at a cellulase loading of 15 FPU/g cellulose or achieved about the same yield at a loading of 7.5 FPU/g cellulose. They also observed similar improvements for enzymatic hydrolysis of ammonia fiber explosion (AFEX)-pretreated corn stover and Douglas fir treated by SO2 steam explosion and for simultaneous saccharification and fermentation (SSF) of BSA pretreated corn stover. In addition, BSA treatment prior to hydrolysis reduced the need for β-glucosidase supplementation of SSF. They highlighted with nonspecific competitive, irreversible adsorption of BSA on lignin. Teymouri et al. (2005) optimized the ammonia fiber explosion (AFEX) pretreatment parameters for enzymatic hydrolysis of corn stover to obtain maximum sugar yields in a paper with 422 citations. They varied AFEX pretreatment conditions (temperature, moisture content, ammonia loadings, and treatment time) to find an optimum. Optimal pretreatment conditions for corn stover were temperature (90°C), ammonia loading (1.0 kg of ammonia:kg of dry corn stover), moisture content of corn stover (60% (dry weight basis (dwb)), and residence time (holding at target temperature,

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5 min). They obtained approximately 100% of the theoretical glucose yield and 80% of theoretical xylose yield during enzymatic hydrolysis of the optimal treated corn stover using 60 FPU of cellulase enzyme/g of glucan. Further, the ethanol yield of optimally AFEX-treated corn stover was increased up to 2.3 times over that of an untreated sample.

22.3.3  The Hydrolysis of the Wood There are seven HCPs for the research front of the hydrolysis of the wood (Table 22.3). Larsson et al. (1999a) studied the impact of the severity of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by Saccharomyces cerevisiae in a paper with 866 citations. When the pretreatment severity (CS) of the acid hydrolysis conditions increased, the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose, and 3.0 and 3.4 for glucose above which it decreased. The decrease in the yield of monosaccharides coincided with the maximum concentrations of inhibitors such as furfural and 5-hydroxymethylfurfural (5-HMF). With the further increase in CS, the concentrations of furfural and 5-HMF decreased, while the formation of formic acid and levulinic acid increased. The yield of ethanol decreased at approximately CS 3; however, the volumetric productivity decreased at lower CS. Further, ethanol yield and volumetric productivity decreased with increasing concentrations of acetic acid, formic acid, and levulinic acid. Furfural and 5-HMF decreased the volumetric productivity but did not influence the final yield of ethanol. The decrease in volumetric productivity was more pronounced when 5-HMF was added to the fermentation, and this compound was depleted at a lower rate than furfural. The inhibition observed in hydrolysates produced in higher CS could not be fully explained by the effect of the inhibitors. Lee et al. (2009) extracted lignin from wood using ILs for enhanced enzymatic cellulose hydrolysis in a paper with 795 citations. They used the 1-ethyl-3-methylimidazolium acetate ([Emim] [CH3COO]). They observed that the cellulose in this pretreated wood flour became far less crystalline without undergoing solubilization. When 40% of the lignin was removed, the cellulose crystallinity index dropped below 45, resulting in more than 90% of the cellulose in wood flour to be hydrolyzed by Trichoderma viride cellulase. This catalyst was easily reused, thereby resulting in a highly concentrated solution of chemically unmodified lignin. Eriksson et al. (2002) investigated the mechanism of surfactant effect in enzymatic hydrolysis of steam-pretreated spruce in a paper with 741 citations. They observed that nonionic surfactants were the most effective. Studies of adsorption of the dominating cellulase of Trichoderma reesei, Cel7A (CBHI), during hydrolysis showed that the anionic and nonionic surfactants reduced enzyme adsorption to the spruce substrate. The approximate reduction of enzyme adsorption was from 90% adsorbed enzyme to 80% with surfactant addition. Surfactants had only a weak effect on cellulase temperature stability. The improved conversion of lignocellulose with surfactant was due to the reduction of the unproductive enzyme adsorption to the lignin part of the substrate, in turn due to hydrophobic interaction of surfactant with lignin on the spruce surface, which released unspecifically bound enzyme. Studer et al. (2011) study the effect of the lignin content in poplar on the sugar release through enzymatic hydrolysis as well as combined liquid hot water (LHW) pretreatment and enzymatic hydrolysis in a paper with 457 citations. They observed that the total amount of glucan and xylan released varied widely among samples, with total sugar yields of up to 92% of the theoretical maximum. There was a strong negative correlation between sugar release and lignin content only for pretreated samples with a ratio of syringyl and guaiacyl units (S/G ratio) less than 2.0. For higher S/G ratios, sugar release was generally higher, and the negative influence of lignin was less pronounced. When examined separately, only glucose release was correlated with lignin content and S/G ratio in this manner, whereas xylose release depended on the S/G ratio alone. For enzymatic hydrolysis without pretreatment, sugar release increased significantly with decreasing lignin content below 20%, irrespective of the S/G ratio. Furthermore, certain samples featuring average lignin content

82

TABLE 22.3 The Hydrolysis of the Wood No.

Papers

Biomass

Prt.

Larsson et al. (1999a)

Spruce

Acids

2

Lee et al. (2009) Eriksson et al. (2002) Studer et al. (2011) Zhu et al. (2009) Grethlein (1985) Larsson et al. (1999b)

Wood cellulose, lignin Spruce

Ionic liquids, enzymes

3 4 5 6 7

Poplar lignin Pine, spruce Hardwood, softwood Spruce

Surfactants, steam, enzymes Liquid hot water, enzymes Sulfite, enzymes, acids, disk milling Acids, enzymes Acids, alkali, sulfite, enzymes

Parameters Sugar yield, hydrolysate fermentability, pretreatment severity Cellulose hydrolysis, lignin extraction, cellulose crytallinity Lignocellulose hydrolysis, surfactant mechanisms Sugar release, lignin content, S/G ratio Cellulose hydrolysis, pretreatment effect Wood hydrolysis, pore volume effect Hydrolysate detoxification efficiency, methods

*, Female; cits., number of citations received for each paper; prt, biomass pretreatments.

Keywords

Lead Author

Affil.

Cits

Softwood, inhibitors, hydrolysis

Hahn-Hagerdal, Barbel* 7005389381

Lund Univ. Sweden

866

Wood cellulose, hydrolysis

Doherty, Thomas V. 57213283673 Tjerneld, Folke 7006446969 Wyman, Charles E.

Rensselaer Polytech. Inst. USA Lund Univ. Sweden

795

Lignocellulose, hydrolysis Populus lignin, sugar release Pine, spruce, saccharification Cellulosic, hydrolysis Spruce, lignocellulose, hydrolysate, detoxification

Zhu, Junyong 7405692678 Grethlein, Hans E. 7004179983 Jonsson, Leif J. 7102349315

741

Univ. Calif. Riverside USA USDA Forest Serv. USA MBI Int. USA

457

431

Umea Univ. Sweden

424

436

Bioethanol Fuel Production Processes. II

1

83

Biomass Hydrolysis: Review

and S/G ratios exhibited exceptional sugar release. They asserted that factors beyond lignin and S/G ratio influence recalcitrance to sugar release. Zhu et al. (2009) investigated the sulfite pretreatment (SPORL) for the enzymatic hydrolysis of spruce and red pine in a paper with 436 citations. The process consisted of sulfite pretreatment of wood chips under acidic conditions followed by mechanical size reduction using disk milling. After the SPORL pretreatment of spruce chips with 8%–10% bisulfite and 1.8%–3.7% sulfuric acid on oven dry (od) wood at 180°C for 30 min, they obtained more than 90% cellulose conversion of substrate with enzyme loading of about 14.6 FPU cellulase plus 22.5 cellobiase units (CBU) β-glucosidase per gram of o.d. substrate after 48 h hydrolysis. Glucose yield from enzymatic hydrolysis of the substrate per 100 g of untreated o.d. spruce wood (glucan content 43%) was about 37 g (excluding the dissolved glucose during pretreatment). Hemicellulose removal was as critical as lignin sulfonation for cellulose conversion in the SPORL process. Pretreatment altered the wood chips, which reduced the electric energy consumption for size reduction to about 19 Wh/kg o.d. untreated wood, or about 19 g glucose/Wh electricity. Furthermore, the SPORL produced low amounts of fermentation inhibitors, HMF, and furfural of about 5 and 1 mg/g of untreated o.d. wood, respectively. In addition, they obtained similar results when the SPORL was applied to red pine. Grethlein (1985) investigated the effect of pore size distribution on the rate of enzymatic hydrolysis of mild acid-pretreated hardwood and softwood in a paper with 431 citations. He observed that regardless of the substrate, the initial rate of hydrolysis using cellulase from Trichoderma reesei was linearly correlated with the pore volume of the substrate accessible to a nominal diameter of 51 Å representative of the size of the cellulase. In contrast, crystallinity index had no relationship to the rate of hydrolysis. Larsson et al. (1999b) compared different methods for the detoxification of dilute acid lignocellulose hydrolysates of spruce in a paper with 424 citations. These detoxification methods included: treatment with alkali (NaOH or calcium hydroxide (CaOH)), treatment with sulfite (0.1% [w/v] or 1% [w/v] at pH 5.5 or 10), evaporation of 10% or 90% of the initial volume, anion exchange (at pH 5.5 or 10), enzymatic detoxification with the phenoloxidase laccase, and detoxification with the Trichoderma reesei. They observed that anion exchange at pH 5.5 or 10, treatment with laccase, treatment with CaOH, and treatment with T. reesei were the most efficient detoxification methods. Evaporation of 10% of the initial volume and treatment with 0.1% sulfite were the least efficient detoxification methods. Treatment with laccase was the only detoxification method that specifically removed only one group of the inhibitors, namely, phenolic compounds. Anion exchange at pH 10 was the most efficient method for removing all three major groups of inhibitory compounds; however, it also resulted in loss of fermentable sugars.

22.3.4  The Hydrolysis of the Grass There are two HCPs for the research front of the hydrolysis of the grass (Table 22.4).

TABLE 22.4 The Hydrolysis of the Grass No. 1

2

Papers

Biomass

Prt.

Parameters

Keywords

Lead Author

Affil.

Chen and Alfalfa Enzymes, Enzyme engineering, Lignin, sugar Chen, Fang Univ. N. Dixon lignin acid, sugar yield yield 57188570847 Texas (2007) alkali USA Li et al. Switchgrass Acids, Hydrolysis, solid acids switchgrass, Singh, Seema* Joint (2010) cellulose ionic and ionic liquid saccharification 35264950300 Bioenergy liquids catalysts, sugar yields Inst. USA

*, Female; cits., number of citations received for each paper; prt, biomass pretreatments.

Cits 997

832

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Bioethanol Fuel Production Processes. II

Chen and Dixon (2007) modified lignin to improve sugar yields in a paper with 997 citations. In stems of transgenic alfalfa lines independently downregulated in each of six lignin biosynthetic enzymes, they observed that recalcitrance to both acid pretreatment and enzymatic digestion was directly proportional to the lignin content. Some transgenics yielded nearly twice as much sugar from cell walls as wild-type plants. Li et al. (2010) compared dilute acid and IL pretreatment of switchgrass in a paper with 832 citations. When subject to IL pretreatment, they observed that switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. This pretreatment enabled a significant enhancement in the rate of enzymatic hydrolysis of the cellulose component of switchgrass, with a rate increase of 16.7-fold, and a glucan yield of 96.0%. They asserted that this pretreatment offered unique advantages compared to the dilute acid pretreatment process for switchgrass although it was more expensive.

22.4 DISCUSSION 22.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass. The research in the fields of biomass hydrolysis has thus intensified in recent years. They hydrolysis of the agricultural residues, wood, biomass constituents, and grass has been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been a number of review papers for this field, there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents a review of the most-cited 25 articles in this field. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 300 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 422 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in biomass hydrolysis is given in Table 22.5. As Table 22.5 shows, there are three primary research fronts for this field: biomass constituents, agricultural residues, and wood with 64%, 40%, and 28% of the reviewed papers, respectively. Next, the other fronts are grass and hydrolysates with 8% of the reviewed papers each. There are no papers for the fronts of food waste, plants, and biomass in general. The most-prolific biomass constituents are cellulose and lignin with 40% and 24% of the reviewed papers, while corn stover is the most-prolific agricultural residue with 28% of the reviewed papers. The research fronts of biomass constituents, agricultural residues, and wood are over-represented with 18%, 10%, and 12% surplus, respectively. Further, on the individual basis, lignin, corn stover,

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Biomass Hydrolysis: Review

TABLE 22.5 The Most-Prolific Research Fronts for the Hydrolysis of the Biomass No.

Research Fronts

N Paper (%) Review

N Paper (%) Sample

Surplus

1

Biomass constituents Cellulose

64.0

53.6

10.4

40.0

33.6

6.4

Lignin

24.0

8.5

15.5

Other constituents

12.0

2.6

9.4

Hemicellulose

12.0

3.4

8.6

Lignocellulose 2

3 4 5 6 7 8

8.0

5.5

2.5

Agricultural residues Corn stover

40.0

32.3

7.7

28.0

12.8

15.2

Wheat straw

4.0

6.4

−2.4

Other residues

4.0

4.7

−0.7

Rice straw

4.0

4.3

−0.3

Sugarcane bagasse

0.0

4.3

−4.3

Wood Grass Hydrolysates Food waste Plants Biomass in general

28.0 8.0 8.0 0.0 0.0 0.0

15.7 3.4 14.9 1.7 0.4 9.8

12.3 4.6 −6.9 −1.7 −0.4 −9.8

N paper (%) review, the number of papers in the sample of 25 most cited papers; N paper (%) sample, the number of papers in the population sample of 235 papers.

hemicellulose, cellulose are over-represented in the reviewed papers with 6%–16% surplus each. Similarly, the fronts of biomass in general and hydrolysates are under-represented in the reviewed papers with 10% and 7% deficit, respectively.

22.4.2  The Hydrolysis of the Biomass Constituents There are six HCPs for the research front of the hydrolysis of the biomass constituents (Table 22.1). Suganuma et al. (2008) studied the hydrolysis of cellulose by solid acid catalysts and observed that amorphous carbon bearing SO3H, COOH, and OH performed as an efficient catalyst for the cellulose hydrolysis. Further, Onda et al. (2008) investigated the selective hydrolysis of cellulose over solid acid catalysts at temperatures higher than 90°C and observed that a sulfonated activatedcarbon catalyst showed a remarkably high yield of glucose. Sasaki et al. (2000) studied the dissolution and hydrolysis of cellulose in subcritical and supercritical water and at 400°C mainly obtained hydrolysis products, while in 320°C–350°C water, aqueous decomposition products of glucose were the main products. Further, Sasaki et al. (1998) proposed a new method to hydrolyze cellulose rapidly in subcritical and supercritical water to recover glucose, fructose, and oligomers and observed that hydrolysis product yields (around 75%) in supercritical water were much higher than those in subcritical water. Dadi et al. (2006) studied the cellulose hydrolysis kinetics using an IL pretreatment and observed that the initial enzymatic hydrolysis rates were approximately 50-fold higher for regenerated cellulose as compared to untreated cellulose (Avicel PH-101). Further, Kristensen et al. (2009) investigated the determinants of the sugar yield in the enzymatic hydrolysis of lignocellulosic substrates

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with high concentration and observed that the decreasing biomass conversion at increasing biomass concentrations was a generic or intrinsic effect. These HCPs present a sample of the research for the hydrolysis of the biomass constituents, primarily cellulose. It is notable that both chemical and enzymatic pretreatments play a crucial role in the cellulose hydrolysis to improve the sugar yield.

22.4.3  The Hydrolysis of the Agricultural Residues There are 10 HCPs for the research front of the hydrolysis of the agricultural residues (Table 22.2). Silverstein et al. (2007) compared four chemical pretreatment methods for the hydrolysis of cotton stalks in a paper and observed that a modified severity parameter (log M0) explained most of the variation in xylan or lignin reduction through simple linear regressions. Further, Saha et al. (2005) studied the dilute acid pretreatment, enzymatic hydrolysis, and fermentation of wheat straw and observed that the maximum yield of monomeric sugars from wheat straw by dilute H2SO4 pretreatment and enzymatic saccharification using cellulase, β-glucosidase, xylanase, and esterase was 565 10 mg/g. Lloyd and Wyman (2005) studied the combined sugar (xylose and glucose) yields for dilute sulfuric acid pretreatment of corn stover followed by enzymatic hydrolysis of the remaining solids and observed that up to 15% of the total potential sugar in the substrate could be released as glucose during pretreatment and between 15% and 90+% of the xylose remaining in the solid residue could be recovered in subsequent enzymatic hydrolysis. Further, Yang and Wyman (2004) studied the effect of xylan and lignin removal by batch and flow-through pretreatment on the enzymatic digestibility of corn stover cellulose and observed that increasing flow with just water enhanced the xylan dissolution rate, more than doubled total lignin removal, and increased cellulose digestibility. Luterbacher et al. (2014) investigated the sugar production from corn stover, hardwood, and softwood using GVL in a solvent mixture and observed that GVL promoted thermocatalytic hydrolysis through complete solubilization of the biomass. Further, Hsu et al. (2010) investigated the effect of the structural properties of the solid residues on the enzymatic hydrolysis of the dilute-acidpretreated rice straw and obtained a maximal sugar yield of 83% when the rice straw was pretreated with sulfuric acid followed by enzymatic hydrolysis. Ohgren et al. (2007) investigated the effect of hemicellulose and lignin removal on enzymatic hydrolysis of steam pretreated corn stover and obtained a near-theoretical glucose yield from acidcatalyzed and steam-pretreated corn stover when xylanases were used to supplement cellulases during hydrolysis. Further, Wyman et al. (2005) compared the sugar recovery from corn stover using ammonia explosion, aqueous ammonia recycle, controlled pH, dilute acid, flow-through, and lime pretreatments with the same cellulose and obtained high yields of glucose from cellulose by cellulase enzymes for each pretreatment. Yang and Wyman (2006) introduced BSA pretreatment to enhance enzymatic hydrolysis of cellulose in lignocellulosic biomass and observed that little BSA was adsorbed on Avicel cellulose, while pretreated corn stover solids adsorbed considerable amounts of this protein. Further, Teymouri et al. (2005) optimized the AFEX pretreatment parameters for enzymatic hydrolysis of corn stover and obtained approximately 100% of the theoretical glucose yield and 80% of theoretical xylose yield during enzymatic hydrolysis of the optimal treated corn stover. These HCPs present a sample of the research for the hydrolysis of the agricultural residues. It is notable that primarily both chemical and enzymatic pretreatments play a crucial role in the hydrolysis of these residues to improve the sugar yield.

22.4.4  The Hydrolysis of the Wood There are seven HCPs for the research front of the hydrolysis of the wood (Table 22.3).

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Larsson et al. (1999a) studied the impact of the severity of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by Saccharomyces cerevisiae and observed that when the pretreatment severity (CS) of the acid hydrolysis conditions increased, the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose, and 3.0 and 3.4 for glucose above which it decreased. Further Lee et al. (2009) extracted lignin from wood using ILs for enhanced enzymatic cellulose hydrolysis and observed that the cellulose in this pretreated wood flour became far less crystalline without undergoing solubilization. Eriksson et al. (2002) investigated the mechanism of surfactant effect in enzymatic hydrolysis of steam-pretreated spruce and observed that nonionic surfactants were the most effective. Further, Studer et al. (2011) studied the effect of the lignin content in poplar on the sugar release through enzymatic hydrolysis as well as combined LHW pretreatment and enzymatic hydrolysis and observed that the total amount of glucan and xylan released varied widely among samples, with total sugar yields of up to 92% of the theoretical maximum. Zhu et al. (2009) investigated the sulfite pretreatment (SPORL) for the enzymatic hydrolysis of spruce and red pine and obtained more than 90% cellulose conversion of substrate. Further, Grethlein (1985) investigated the effect of pore size distribution on the rate of enzymatic hydrolysis of mild acid-pretreated hardwood and softwood and observed that regardless of the substrate, the initial rate of hydrolysis using cellulase was linearly correlated with the pore volume of the substrate. Finally, Larsson et al. (1999b) compared different methods for the detoxification of dilute acid lignocellulose hydrolysates of spruce and observed that anion exchange at pH 5.5 or 10, treatments with laccase, CaOH, and T. reesei were the most efficient detoxification methods. These HCPs present a sample of the research for the hydrolysis of the wood. It is notable that both chemical and enzymatic pretreatments play a crucial role in the wood hydrolysis to improve the sugar yield.

22.4.5  The Hydrolysis of the Grass There are two HCPs for the research front of the hydrolysis of the grass (Table 22.4). Chen and Dixon (2007) modified lignin to improve sugar yields and observed that recalcitrance to both acid pretreatment and enzymatic digestion was directly proportional to lignin content. Further, Li et al. (2010) compared dilute acid and IL pretreatment of switchgrass and when subject to IL pretreatment observed that switchgrass exhibited reduced cellulose crystallinity, increased surface area, and decreased lignin content compared to dilute acid pretreatment. These HCPs present a sample of the research for the hydrolysis of the grass. It is notable that both chemical and enzymatic pretreatments play a crucial role in the grass hydrolysis to improve the sugar yield.

22.5  CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 422 citations each is given under four primary headings: the hydrolysis of the biomass constituents, agricultural residues, wood, and grass as there is no HCP for algae, food waste, and industrial waste. Further, the most-prolific biomass constituents are cellulose and lignin, while corn stover is the most-prolific agricultural residue. The usual characteristics of these HCPs are that pretreatments are often used in combination of other pretreatments for the hydrolysis of the biomass. In this way, the biomass hydrolysis is effective in disrupting the biomass constituents such as cellulose, lignin, and hemicellulose of these feedstocks resulting in improved sugar and bioethanol yield. The key findings on these research fronts should be read in the light of the increasing public concerns about climate change, GHG emissions, and global warming as these concerns have been

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certainly behind the boom in the research on the bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. These studies emphasize the importance of proper incentive structures for the efficient development and application of biomass hydrolysis to enhance sugar and bioethanol yield of the biomass after the hydrolysis of the biomass and the following fermentation of the resulting hydrolysates in the light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the USA, Europe, Canada, China, and Japan had developed strong incentive structures for the effective development and application of hydrothermal pretreatments for bioethanol and sugar production. With the recent supply shocks, for example, due to the COVID-19 pandemics and Russian invasion of Ukraine, it is expected the public incentives for the research and development for the bioethanol fuels as a green alternative to crude oil-based gasoline and diesel fuels would increase in the coming years. In this context, the stakeholders involved in the biomass hydrolysis would have a significant first-mover advantage. It is recommended that such review studies are performed for the primary research fronts of biomass hydrolysis as well as the biomass constituents, wood, grass, and agricultural residues.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the biomass hydrolysis has been gratefully acknowledged.

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23 Scientometric Study Wood Hydrolysis Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

23.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009) and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining energy security (Kruyt et al., 2009; Winzer, 2012) in supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), COVID-19 pandemic (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2012) in the aftermath of Russian invasion of Ukraine (Reeves, 2014) and COVID-19 pandemic. However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to bioethanol production through hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of biomass and hydrolysates, respectively. Wood has been one of the most-studied biomasses for bioethanol production (Galbe and Zacchi, 2002; Gregg et al., 1998). In this context, research in the field of wood hydrolysis (Alvarez et al., Gregg and Saddler, 1996) has thus intensified in recent years. Enzymatic (Alvarez et al., 2016; Gregg and Saddler, 1996), chemical (Cara et al., 2008; Larsson et al., 1999), hydrothermal (Cara et al., 2006), and mechanical hydrolysis (Inoue et al., 2008; Palm and Zacchi, 2003) of wood have been widely researched to increase sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). A scientometric analysis has been used in this context to inform the primary stakeholders about the current state of research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As there have been no scientometric studies on wood hydrolysis, this chapter presents a scientometric study of research in wood hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts and the highly cited 25 papers.

23.2  MATERIALS AND METHODS A search for this study was carried out using the Scopus database (Burnham, 2006) in May 2022. DOI: 10.1201/9781003226499-31

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As a first step for the search of the relevant literature, keywords were selected using the first most-cited 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This extended keyword list was provided in the appendix for future replication studies. As a second step, two sets of data were used for this study. First, a population sample of around 1,832 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 183 most-cited papers, corresponding to 10% of the population papers, was used to examine the scientometric characteristics of these citation classics. Scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, key scientometric findings for both datasets were discussed to highlight the research landscape for wood hydrolysis. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

23.3 RESULTS 23.3.1  The Most Prolific Documents in Wood Hydrolysis Information on the types of documents for both datasets is given in Table 23.1. Articles and conference papers dominate both the sample (97%) and population (98%) papers as they are underrepresented in the sample papers by 1%. Further, review papers and short surveys have a surplus as they are over-represented in the sample papers by 1% as they constitute 2% and 1% of the sample and population papers, respectively. It is further noted that 98% of population papers were published in journals while 1% of each of them was published in book series and books. On the contrary, all sample papers were published in journals.

23.3.2  The Most Prolific Authors in Wood Hydrolysis Information about the most-prolific 23 authors with at least 2.2% of sample papers each is given in Table 23.2.

TABLE 23.1 Documents in Wood Hydrolysis Documents Article Conference paper Review Short Survey Note Letter Book chapter Book Editorial Sample size

Sample Dataset (%)

Population Dataset (%)

Surplus (%)

90.2 7.1 1.1 1.1 0.5 0.0 0.0 0.0 0.0 183

94.2 3.9 0.8 0.2 0.4 0.3 0.2 0.0 0.0 1,832

−4.0 3.2 0.3 0.9 0.1 −0.3 −0.2 0.0 0.0

Population dataset, The number of papers (%) in the set of the 1,832 population papers; Sample dataset, The number of papers (%) in the set of 183 highly cited papers.

No.

Author Name

Author Code

Sample Papers (%)

Population Papers (%)

Surplus

Institution

Country

HI

N

Res. Front

 1  2  3  4

Saddler, Jack N. Zacchi, Guido Galbe, Mats Zhu, Junyong

7005297559 7006727748 7003788758 7405692678

13.7 8.2 4.9 4.9

3.3 2.0 1.5 1.5

10.4 6.2 3.4 3.4

Univ. British Columbia Lund Univ. Lund Univ. USDA Forest Serv.

Canada Sweden Sweden USA

96 67 50

403 204 131

EH, HH EH, HH EH, HH. EH, CH

 5  6  7  8

Gregg, David J. Chandra, Richard P. Parajo, Juan C. Gleisner, Roland

4.4 3.8 3.3 3.3

0.5 1.3 1.3 0.8

3.9 2.5 2.0 2.5

Univ. British Columbia Trinity Western Univ. Univ. Vigo USDA Forest Serv.

Canada Canada Spain USA

21 31 61 27

27 75 300 69

EH, CH EH, HH. HH EH, CH, MH

 9

Sun, Runcang

7005324246 7401681548 7005594129 6603256087 55928749700 55661525600

2.7

1.5

1.2

Dalian Polytech. Univ.

China

108

1045

10 11

Pan, Xuejun Liden, Gunnar

57203296000 7004458708

2.7 2.7

0.8 0.8

1.9 1.9

USA Sweden

44 48

116 143

12 13 14 15 16

Garrote, Gil Viikari, Liisa* Yong, Qiang Huang, Caoxing Jahan, M. Sarwar

2.7 2.7 2.2 2.2 2.2

0.4 0.4 1.4 1.2 1.0

2.3 2.3 0.8 1.0 1.2

Spain Finland China China Bangladesh

45 55 35 33 27

10 194 256 132 139

HH EH EH, CH EH, CH CH

17 18 19 20 21 22 23

Sixta, Herbert Castro, Eulogio Ruiz, Encarnacion* Zhang, Xiao Cara, Cristobal Mansfield, Shawn D. Ladisch, Michael R.

6603849654 7006720604 7003718716 56495726800 35488891600 35509750400 6701635986 7102441948 25646493300 47062428700 22949914200 7006421766 7005670397

Univ. Wisconsin Madison Chalmers Univ. Technol. Univ. Vigo Univ. Helsinki Nanjing Forestry Univ. Nanjing Forestry Univ. BCSIR

EH., CH, HH, MH EH, CH EH HH

2.2 2.2 2.2 2.2 2.2 2.2 2.2

0.9 0.8 0.6 0.6 0.5 0.5 0.3

1.3 1.4 1.6 1.6 1.7 1.7 1.9

Aalto Univ. Univ. Jaen Univ. Jaen Washington State Univ. Univ. Jaen Univ. British Columbia Purdue Univ.

Finland Spain Spain USA Spain Canada USA

51 43 33 30 32 68 59

303 183 75 92 53 274 290

HH EH, CH, HH EH, CH, HH EH, MH EH, HH EH, HH EH, HH

93

*, Female; Author code, the unique code given by Scopus to authors; CH, Chemical hydrolysis; EH, Enzymatic hydrolysis; HH, Hydrothermal hydrolysis; HI, H-index; MH, Mechanical hydrolysis; N, number of papers published by each author; Population papers, the number of papers authored in the population dataset; Sample papers, the number of papers authored in the sample dataset.

Wood Hydrolysis: Scientometric Study

TABLE 23.2 Most Prolific Authors in Wood Hydrolysis

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The most prolific authors are Jack N. Saddler with 13.7% of sample papers, followed by Guido Zacchi with 8.2% of sample papers. Other prolific researchers are Mats Galbe, Junyong Zhu, David J. Gregg, Richard P. Chandra, Juan C. Parajo, and Roland Gleisner with 3.3%–4.9% of sample papers each. The most influential author is Jack N. Saddler with a 10.4% surplus, followed by Guido Zacchi with a 6.2% surplus. Other influential authors are David J. Gregg, Mats Galbe, Junyong Zhu, Richard P. Chandra, and Roland Gleisner with 2.5%–3.9% surplus each. The most prolific institution for the sample dataset is the University of British Columbia with three authors each while Lund University, Nanjing Forestry University, University of Jaen, University of Vigo, and USDA Forest Service are the other prolific institutions with two authors each. In total, 14 institutions house these top authors. On the other hand, the most prolific country for the sample dataset is the United States and Spain with five authors each while Canada, China, and Finland house four, three, and two authors, respectively. In total, seven countries house these top authors. The most-prolific research front is the enzymatic hydrolysis of wood with 19 authors, while the other prolific research fronts are the hydrothermal and chemical hydrolysis of wood with 14 and 10 authors, respectively. The other minor front is the mechanical hydrolysis of wood with three authors. On the other hand, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only two of these top researchers are female with a representation rate of 9%. Additionally, there are other authors with a relatively low citation impact and with 0.6%–1.4% of population papers each: Hasan Jameel, Pedram Fatehi, Arthur J. Ragauskas, Junhua Zhang, Chenhuan Lai, Qingxi Hou, Leif J. Jonsson, Yingjuan Fu, Yongcan Jin, Menghua Qin, Zhaojiang Wang, Yong Xu, Hou-Min Chang, Takashi Endo, Yunqiao Pu, Charles E. Wyman, Guihua Yang, Jose L. Alonso, Juanita Freer, Seung-Hwan Lee, Zhiqiang Li, Xianzhi Meng, Gerald A. Tuskan, and Feng Xu.

23.3.3  The Most Prolific Research Output by Years in Wood Hydrolysis Information about papers published between 1970 and 2022 is given in Figure 23.1. This figure clearly shows that the bulk of research papers in the population dataset was published primarily in the 2010s with 54% of population dataset. Publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 13%, 12%, 8%, 5%, and 1% respectively. Additionally, 3% of population papers were published between 1914 and 1969. Similarly, the bulk of research papers in the sample dataset was published in the 2000s and 2010s with 38% and 41% of sample dataset, respectively. Publication rates for the 1990s, 1980s, and 1970s were 13%, 6%, and 0% of sample papers, respectively. Additionally, 1% of sample papers was published in the pre-1970s and 2020s each. The most prolific publication year for the population dataset is 2020 with 6.8% of dataset while 73% of population papers was published between 2009 and 2022. Similarly, 79% of sample papers was published between 1999 and 2015 while the most prolific publication years were 2009 and 2010 with 9.8% of sample papers each. The other prolific years were 2008 and 2011 with 6.6% and 7.1% of sample papers, respectively.

23.3.4  The Most Prolific Institutions in Wood Hydrolysis Information about the most prolific 15 institutions publishing papers on wood hydrolysis with at least 2.7% of sample papers each is given in Table 23.3. The most prolific institution is the University of British Columbia with 13.1% of sample papers, followed by the Lund University and USDA Forest Service with 9.3% and 8.2% of sample papers, respectively. The other prolific institutions are the National Renewable Energy Laboratory (NREL), South China University of Technology, University of Wisconsin–Madison, and University of New Brunswick with 3.8%–5.5% of sample papers each.

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Number of papers (%)

10

Population papers Sample papers

8

6

4

2

0

FIGURE 23.1  The research output by years regarding wood hydrolysis. This Figure shows the number of papers in percentages for the period from 1970 to 2022 for both sample and population papers. The bulk of sample papers was published between 1996 and 2016 peaking in 2009 and 2010 while the bulk of population papers was published between 2008 and 2022, peaking in 2020. There was a rising trend for the research output for population papers between 2008 and 2014 and thereafter it lost its momentum and became flat.

TABLE 23.3 The Most Prolific Institutions in Wood Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Institutions Univ. British Columbia Lund Univ. USDA Forest Serv. Natl. Renew. Ener. Lab. S. China Univ. Technol. Univ. Wisconsin-Madison Univ. New Brunswick Auburn Univ. Beijing Forestry Univ. Tianjin Univ. Sci. Technol. Univ. Vigo VTT Tech. Res. Ctr. Aalto Univ. Nanjing Forestry Univ. Univ. Jaen

Country

Sample Papers (%)

Canada Sweden USA USA China USA USA USA China China Spain Finland Finland China Spain

13.1 9.3 8.2 5.5 4.9 4.9 3.8 3.3 3.3 3.3 3.3 3.3 2.7 2.7 2.7

Population Papers (%) 4.3 3.1 3.7 1.7 4.5 1.5 2.5 0.8 3.2 2.1 1.6 1.0 1.8 4.6 1.2

Surplus (%) 8.8 6.2 4.5 3.8 0.4 3.4 1.3 2.5 0.1 1.2 1.7 2.3 0.9 -1.9 1.5

The top country for these most prolific institutions is the United States with five institutions, followed by China with four institutions. Further, Finland and Spain house two institutions each. In total, only six countries house these top institutions. On the other hand, the institution with the most citation impact is the University of British Columbia with an 8.8% surplus, followed by the Lund University and USDA Forest Service with a 6.2% and 4.5% surplus, respectively. The other influential institutions are the NREL, University of

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Wisconsin–Madison, Auburn University, and VTT Technical Research Center with a 2.3%–3.8% surplus each. Similarly, the institution with the least impact is the Nanjing Forestry University with a 1.9% deficit. Additionally, there are other institutions with a relatively low citation impact and with 0.8%– 2.3% of the population papers each: Qilu University of Technology, NC State University, Oak Ridge National Laboratory, Kyoto University, Royal Institute of Technology, Chinese Academy of Sciences, Northwest A&F University, Chinese Academy of Forestry, University of Tennessee, Knoxville, National Institute of Advanced Industrial Science and Technology, Lakehead University, SUNY College of Environmental Science and Forestry, Chalmers University of Technology, SCION, Georgia Institute of Technology, Umea University, Forestry and Forest Products Research Institute, University of Maine, BCSIR Laboratories, Russian Academy of Sciences, University of Concepcion, Seoul National University, Inventia Inc., and South China Agricultural University.

23.3.5  The Most Prolific Funding Bodies in Wood Hydrolysis Information about the most prolific 15 funding bodies funding at least 1.6% of sample papers each is given in Table 23.4. Only 43% and 52% of sample and population papers were funded, respectively. The most prolific funding body is the Government of Canada with 7.7% of sample papers, closely followed by the Natural Sciences and Engineering Research Council of Canada and National Natural Science Foundation of China with 7.1% and 6% of sample papers, respectively. The other prolific bodies are Natural Resources Canada, European Commission, U.S. Department of Energy, National Science Foundation and Seventh Framework Program with 2.7%–4.4% of sample papers each. On the other hand, the most prolific countries for these top funding bodies are Canada and the United States with four funding bodies each, followed by China with three bodies. The other prolific countries are Sweden and the European Union (EU) with two bodies each. In total, only four countries and the EU house these top funding bodies.

TABLE 23.4 The Most Prolific Funding Bodies in Wood Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Funding Bodies

Country

Sample Paper No. (%)

Govnt. Canada Natrl. Sci. Eng. Res. Counc. Canada Natl. Natr. Sci. Found. China Natrl. Resourc. Canada US Dept. Ener. Eur. Commis. Natl. Sci. Found. Seventh Framew. Prog. Natrl. Sci. Found. Jiangsu Prov. Swedish Energy Agcy. USDA China Schol. Counc. Canada Res. Chairs Swedish Natl. Brd. Indust. Tech. Devnt. US Forest Service

Canada Canada China Canada USA EU USA EU China Sweden USA China Canada Sweden USA

7.7 7.1 6.0 4.4 3.8 3.8 2.7 2.7 2.2 2.2 1.6 1.6 1.6 1.6 1.6

Population Paper No. (%) 3.4 3.8 11.7 0.7 2.9 2.1 1.7 0.7 1.5 1.3 2.2 1.5 0.8 0.2 0.5

Surplus (%) 4.3 3.3 −5.7 3.7 0.9 1.7 1.0 2.0 0.7 0.9 −0.6 0.1 0.8 1.4 1.1

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The funding body with the most citation impact is the Government of Canada with a 4.3% surplus, followed by Natural Resources Canada and Natural Sciences and Engineering Research Council of Canada with 3.7% and 3.3% surplus, respectively. The other influential bodies are the Seventh Framework Program and European Commission with 2% and 1.7% surplus, respectively. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with a 6% deficit. The other funding bodies with a relatively low citation impact and with 0.5%–1.9% of population papers each are Ministry of Education, Culture, Sports, Science and Technology, Priority Academic Program Development of Jiangsu Higher Education Institutions, Fundamental Research Funds for the Central Universities, National Key Research and Development Program of China, Japan Society for the Promotion of Science, National Research Foundation of Korea, Ministry of Education of the People’s Republic of China, Office of Science, National Institute of Food and Agriculture, Biological and Environmental Research, Nanjing Forestry University, Ministry of Economics and Competition, National Council of Scientific and Technological Development, Ministry of Finance, Ministry of science, Technology, and Innovation, Ministry of Education, Science and Technology, Northwest Advanced Renewables Alliance, Swedish Research Council, Academy of Finland, Research Support Foundation of the State of Sao Paulo, Kmepse Foundation, Laboratory Directed Research and Development, National Basic Research Program of China (973 Program), National Nuclear Security Administration, Natural Science Foundation of Guangdong Province, and South China University of Technology.

23.3.6  The Most Prolific Source Titles in Cellulose Hydrolysis Information about the most prolific 13 source titles publishing at least 2.2% of sample papers each in wood hydrolysis is given in Table 23.5. The most prolific source title is Bioresource Technology with 27% of sample papers, followed by Biotechnology and Bioengineering, Enzyme and Microbial Technology, and Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology with 11%, 7%, and 6% of sample papers, respectively. The other prolific titles are Biotechnology for Biofuels,

TABLE 23.5 The Most Prolific Source Titles in Wood Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13

Source Titles

Sample Papers (%)

Population Papers (%)

Surplus (%)

Bioresource Technology Biotechnology and Bioengineering Enzyme and Microbial Technology Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Biotechnology for Biofuels Biotechnology Progress Industrial and Engineering Chemistry Research Biomass and Bioenergy Process Biochemistry Applied Microbiology and Biotechnology Applied Biochemistry and Biotechnology Journal of Chemical Technology and Biotechnology Green Chemistry

26.8 10.9 6.6 6.0

15.0 2.2 1.4 1.7

11.8 8.7 5.2 4.3

4.4 3.8 3.3 2.7 2.7 2.7 2.2 2.2

3.1 1.3 1.6 1.8 1.0 0.5 2.7 1.1

1.3 2.5 1.7 0.9 1.7 2.2 −0.5 1.1

2.2

0.7

1.5

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Biotechnology Progress, and Industrial and Engineering Chemistry Research with 3.3%–4.4% of sample papers each. On the other hand, the source title with the most citation impact is Bioresource Technology with a 12% surplus, followed by Biotechnology and Bioengineering with a 9% surplus. The other influential titles are Enzyme and Microbial Technology, Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology, and Biotechnology Progress with 2.5%–5.7% surpluses each. Similarly, the source title with the least impact is Applied Biochemistry and Biotechnology with a 1% deficit. The other source titles with a relatively low citation impact with 0.5%–4.3% of the population paper each are Bioresources, Industrial Crops and Products, Holzforschung, ACS Sustainable Chemistry and Engineering, Cellulose, Carbohydrate Polymers, Wood Science and Technology, Journal of Wood Chemistry and Technology, Journal of Wood Science, Biomass Conversion and Biorefinery, Cellulose Chemistry and Technology, Bioenergy Research, Renewable Energy, Biotechnology Letters, Fuel, Agricultural and Biological Chemistry, RSC Advances, International Journal of Biological Macromolecules, Journal of the Society of Chemical Industry Japan, Separation and Purification Technology, Carbohydrate Research, Industrial and Engineering Chemistry, Journal of Biobased Materials and Bioenergy, and PLOS One.

23.3.7  The Most Prolific Countries in Wood Hydrolysis Information about the most prolific 12 countries publishing at least 1.6% of sample papers each in wood hydrolysis is given in Table 23.6. The most prolific country is the United States with 33% of sample papers, followed by Canada, China, and Sweden with 19%, 17%, and 13% of sample papers, respectively. The other prolific countries are Spain, Japan, and Finland with 6%–7% of sample papers each. Further, five European countries listed in Table 23.6 produce 30% and 21% of sample and population papers, respectively, with a 9% surplus. On the other hand, the country with the most citation impact is the United States with a 13% surplus, followed by Canada and Sweden with 8% and 5% surplus, respectively. Similarly, the country with the least citation impact is China with a 7% deficit, followed by Japan and Brazil with 3% and 1% deficit, respectively.

TABLE 23.6 The Most Prolific Countries in Wood Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12

Countries USA Canada China Sweden Spain Japan Finland India Germany Austria Bangladesh Brazil

Sample Papers (%) 33.3 19.1 16.9 13.1 7.1 6.0 5.5 2.7 2.2 2.2 2.2 1.6

Population Papers (%) 20.0 11.6 25.2 7.8 5.5 9.2 4.1 2.8 2.6 1.2 1.1 2.3

Surplus (%) 13.3 7.5 −8.3 5.3 1.6 −3.2 1.4 −0.1 −0.4 1.0 1.1 −0.7

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TABLE 23.7 The Most Prolific Scopus Subject Categories in Wood Hydrolysis No. 1 2 3 4 5 6 7 8 9

Scopus Subject Categories Chemical Engineering Biochemistry. Genetics and Molecular Biology Environmental Science Immunology and Microbiology Energy Chemistry Engineering Materials Science Agricultural and Biological Sciences

Sample Papers (%)

Population Papers (%)

72.7 48.1 42.6 41.5 39.9 14.2 7.7 7.1 4.9

49.2 27.9 33.7 18.8 33.2 20.7 9.5 18.2 15.4

Surplus (%) 23.5 20.2 8.9 22.7 6.7 −6.5 −1.8 −11.1 −10.5

Additionally, there are other countries with a relatively low citation impact and with 0.5%–4.6% of sample papers each: South Korea, France, Russia, the United Kingdom, New Zealand, Portugal, Norway, Chile, Poland, Denmark, Indonesia, Belgium, Italy, Switzerland, Malaysia, Australia, Netherlands, Thailand, Egypt, Mexico, Slovakia, South Africa, and Taiwan.

23.3.8  The Most Prolific Scopus Subject Categories in Wood Hydrolysis Information about the most prolific nine Scopus subject categories indexing at least 4.9% of sample papers each is given in Table 23.7. The most prolific Scopus subject category in wood hydrolysis is Chemical Engineering with 73% of sample papers, closely followed by Biochemistry. Genetics and Molecular Biology, Environmental Science, Immunology and Microbiology, and Energy with 40%–48% of sample papers each. It is noted that the Social Sciences including Economics and Business account for only 1.2% of population studies. On the other hand, the Scopus subject category with the most citation impact is Chemical Engineering with a 24% surplus, closely followed by Immunology and Microbiology and Biochemistry, Genetics and Molecular Biology with 23% and 20% surplus, respectively. The other influential categories are Environmental Science and Energy with 10% and 7% surplus, respectively. Similarly, the Scopus subject categories with the least citation impact are Materials Science and Agricultural and Biological Sciences with an 11% deficit each, followed by Chemistry and Engineering with 7% and 2% deficits, respectively.

23.3.9  The Most Prolific Scopus Keywords in Wood Hydrolysis Information about the keywords used with at least 5.5% or 3.8% of sample or population papers, respectively, is given in Table 23.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most prolific keyword set provided by the Scopus database. These keywords are grouped into five headings: biomass, hydrolysis, pretreatments, other processes, and products of hydrolysis. There most prolific keywords related to biomass and biomass constituents are lignin, cellulose, wood, hemicellulose, biomass, softwood, and lignocellulose with 21%–62% of sample papers each. The prolific keywords related to wood hydrolysis are hydrolysis, enzymatic hydrolysis, and saccharification with 21%–73% of sample papers each while those related to the biomass pretreatments

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TABLE 23.8 The Most Prolific Keywords in Wood Hydrolysis No. 1

2

3

Keywords

Sample Papers (%)

Population Papers (%)

Surplus (%)

Lignin

61.7

42.5

19.2

Cellulose

53.6

37.9

15.7

Wood

44.8

30.5

14.3

Hemicellulose

31.7

16.0

15.7

Biomass

30.6

22.1

8.5

Softwood

22.4

10.4

12.0

Lignocellulose

21.3

10.0

11.3

Xylan

16.9

9.4

7.5

Eucalyptus

16.9

8.9

8.0

Spruce (Picea)

16.9

6.0

10.9

Carbohydrates

15.8

12.1

3.7

Biomass

Hardwood

7.7

6.9

0.8

Poplar (Populus)

7.1

11.6

−4.5

Douglas fir

7.1

Bamboo

6.6

8.1

−1.5

Wood chip

6.6

2.9

3.7

Fir (Abies)

6.6

Tree

6.0

Lignocellulosic biomass

4.9

6.5

−1.6

Kraft pulp

4.9

4.2

0.7

Hydrolysis

73.2

48.8

24.4

Enzymatic hydrolysis

42.6

29.1

13.5

Saccharification

20.8

19.0

1.8

Autohydrolysis

7.7

4.6

3.1

Enzymatic digestibility

7.1

6.2

0.9

Enzymatic saccharification

4.9

9.6

−4.7

Acid hydrolysis

4.4

3.9

0.5

Cellulases

32.2

11.7

20.5

Pretreatment

29.5

24.8

4.7

Enzyme activity

25.1

17.1

8.0

Sulfuric acids

24.0

9.6

14.4

Steam

24.0

8.5

15.5

Alcohol

23.5

8.2

15.3

Enzymes

19.1

13.0

6.1

Water vapor

14.8

4.2

10.6

Temperature

14.2

6.6

7.6

Fungi

12.6

6.6

6.0

Acids

12.6

5.1

7.5

Water

9.3

6.4

7.1

6.6 6.0

Hydrolysis

Pretreatments

2.9 (Continued )

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TABLE 23.8 (Continued ) The Most Prolific Keywords in Wood Hydrolysis No.

Sample Papers (%)

Population Papers (%)

Surplus (%)

Delignification

Keywords

8.7

9.3

−0.6

Enzymolysis

8.7

9.2

−0.5

Enzymatic activity

8.7

5.9

2.8

Yeast

8.7

5.8

2.9

pH

8.2

8.2

Trichoderma reesei

6.6

6.6

Hypocrea jecorina

6.0

6.0

Sodium hydroxide 4

5

3.8

−3.8

Other processes Fermentation

32.8

17.7

15.1

Biotechnology

20.2

7.6

12.6

Saccharomyces cerevisiae

14.2

4.7

9.5

Adsorption

10.4

4.5

5.9

Degradation

8.2

5.6

2.6

Dissolution

8.2

5.1

3.1

Biotransformation

7.1

2.7

4.4

Bioconversion

6.0

2.8

3.2

Extraction

4.9

4.4

0.5

Detoxification

3.8

5.1

−1.3

Sugar

46.4

24.2

22.2

Ethanol

35.5

17.8

17.7

Glucose

33.9

24.2

9.7

Xylose

18.6

10.3

8.3

Acetic acid

12.0

8.1

3.9

Furfural

10.4

6.2

4.2

Aldehydes

8.2

4.6

3.6

Glucan

8.2

3.2

5.0

Biofuel

6.0

9.9

−3.9

Polysaccharides

6.0

3.8

2.2

Bioethanol

5.5

6.9

−1.4

Ethanol production

5.5

3.8

1.7

Mannose

5.5

2.3

3.2

4.6

−4.6

Hydrolysis products

Fermentable sugars

are cellulases, pretreatments, enzyme activity, sulfuric acids, and steam with 24%–32% of sample papers each. The prolific keywords related to other processes are fermentation, biotechnology, Saccharomyces cerevisiae, and adsorption with 10%–33% of sample papers each. Further, those related to hydrolysis products are sugar, ethanol, glucose, and xylose with 19%–46% of sample papers each. It is noted that only 5.5% of indexed papers employs the bioethanol keyword. Further, the most influential keywords are glucose, Saccharomyces cerevisiae, biomass, xylose, pH, eucalyptus, enzyme activity, temperature, xylan, acids, and Douglas fir with 7%–10% surplus each.

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TABLE 23.9 The Most Prolific Research Fronts in Wood Hydrolysis No. 1 2

Research Fronts Enzymatic hydrolysis Chemical hydrolysis Acid hydrolysis

4

65.0 38.3 20.2

Solvent hydrolysis

6.0

Ionic liquid hydrolysis

3.8

Alkaline hydrolysis

2.7

Sulfite hydrolysis

2.2

H2O2 hydrolysis

1.6

Water hydrolysis

0.5

Surfactant hydrolysis

0.5

Wet oxidation hydrolysis 3

N Paper (%) Sample

Hydrothermal hydrolysis Steam explosion hydrolysis

0.5 33.9 20.2

Autohydrolysis

7.7

Liquid hot water hydrolysis

3.3

Hydrothermal hydrolysis in general

1.6

Hot compressed water hydrolysis

1.1

Mechanical hydrolysis Milling hydrolysis

2.2 1.6

Microwave hydrolysis

0.5

Ultrasound hydrolysis

0.0

N paper (%) sample, The number of papers in the population sample of 183 papers.

23.3.10  The Most Prolific Research Fronts in Wood Hydrolysis Information about the research fronts for sample papers in wood hydrolysis is given in Table 23.9. As this table shows, there are three primary research fronts for this field – the enzymatic, chemical, and hydrothermal hydrolysis of wood – with 65%, 38%, and 34% of sample papers, respectively. The other minor research front is the mechanical hydrolysis of wood with 2% of sample papers. The most prolific chemical hydrolysis is acid hydrolysis with 20.2% of sample papers. The other prolific chemical hydrolyses are solvent, ionic liquid (IL), alkaline, and sulfite hydrolysis with 2.2%–6% of sample papers each. Similarly, the most prolific hydrothermal hydrolysis is steam explosion hydrolysis with 20.2% of sample papers. The other hydrothermal hydrolyses are autohydrolysis, liquid hot water, and hydrothermal hydrolysis in general with 1.6%–7.7% of sample papers each. Finally, milling hydrolysis is the most prolific mechanical hydrolysis with 1.6% of sample papers. Table 23.10 provides data on the wood biomass used in the studies for wood hydrolysis. There are three primary research fronts – hardwood, softwood, and wood in general – with 54%, 36%, and 15% of sample papers, respectively. On the other hand, pine and spruce are the most prolific softwood used in these studies with 10% and 8% of sample papers, respectively. Similarly, the most prolific hardwoods are poplar, eucalyptus, and bamboo with 12%, 10%, and 8% of sample papers, respectively.

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TABLE 23.10 The Most Prolific Research Fronts of the Wood Biomass Used for Wood Hydrolysis No. 1 2

3

Research Fronts

N Paper (%) Sample

Wood in general Softwood Softwood in general

17.5 36.1 16.9

Pine

9.8

Spruce

7.7

Douglas fir

1.6

Hardwood Poplar

54.1 11.5

Eucalyptus

10.4

Hardwood in general

8.7

Bamboo

8.2

Aspen

3.3

Other hardwood

3.3

Birch

2.7

Olive tree

2.2

Willow

2.2

Beech

1.6

N paper (%) sample, The number of papers in the population sample of 183 papers.

23.4 DISCUSSION 23.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of bioethanol prior to bioethanol production through hydrolysis and fermentation of the biomass and hydrolysates, respectively. Research in the field of wood hydrolysis has thus intensified in recent years. Enzymatic, chemical, hydrothermal, and mechanical hydrolyses of wood have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, COVID-19 shocks, or war-related shocks as in the case of the Russian invasion of Ukraine. Scientometric analysis has been widely used in this context to inform the primary stakeholders about the current state of research in a selected research field. As there have been no scientometric studies on wood hydrolysis, this chapter presents a scientometric study of research in wood hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts.

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As a first step for the search of the relevant literature, keywords were selected using the first most-cited 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replication studies. Further, a selected list of keywords was presented in Table 23.8. As a second step, two sets of data were used for this study. First, a population sample of over 1,832 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 183 most-cited papers, corresponding to 10% of the population dataset, was used to examine the scientometric characteristics of these citation classics. Scientometric characteristics of both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for wood hydrolysis. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

23.4.2  The Most Prolific Documents in Wood Hydrolysis Articles (together with conference papers) dominate both sample (97%) and population (98%) papers (Table 23.1). Further, review papers and articles have a surplus (1%) and deficit (1%), respectively. The representation of reviews and short surveys in sample papers is not extraordinarily high (2%). Scopus differs from the Web of Science database in differentiating and showing articles (90%) and conference papers (7%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared to those published only in conference proceedings. Similarly, Scopus differs from the Web of Science database in introducing short surveys (1%). Hence, the total number of articles and review papers in the sample dataset are 97% and 2%, respectively. It is observed during the search process that there has been inconsistency in the classification of documents in Scopus as well as in other databases such as the Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, many of the papers classified as reviews by Scopus are not actually reviews but ordinary articles. In this context, it would be helpful to provide a classification note for the published papers in books and journals in the first instance as it is done in some journals following good practice. It would also be helpful to use the document types listed in Table 23.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from experimental chapters as they are done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as it is done in the Web of Science database.

23.4.3  The Most Prolific Authors in Wood Hydrolysis There have been the most-prolific 23 authors with at least 2.2% of sample papers each as given in Table 23.2. These authors have shaped the development of research in this field. The most prolific authors are Jack N. Saddler and to a lesser extent Guido Zacchi, Mats Galbe, Junyong Zhu, David J. Gregg, Richard P. Chandra, Juan C. Parajo, and Roland Gleisner. It is noted that these top researchers are mostly from the United States and to a lesser extent Europe and Canada. It is important to note the inconsistencies in indexing author names in Scopus and other databases. It is especially an issue for names with more than two components such as ‘Blake Sam de

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105

Hyun Zhu’. The probable outcomes are ‘Zhu, B.S.D.H.’. ‘de Hyun Zhu, B.S.’ or ‘Hyun Zhu, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative while the last choice is an undesired outcome as two last words are taken as the last name. It is a good practice to combine the words of the last name with a hyphen if there are two words for the last name: ‘Hyun-Zhu, B.S.D.’. It is noted that inconsistent indexing of author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in shortening Chinese names, for example, ‘Yingyong Zhu’ is often shortened as ‘Zhu, Y.’, ‘Zhu, Y.-Y.’, and ‘Zhu, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhu Y.’ where the last word is taken as the last name and the first word is taken as a single forename. In most academic databases such as PUBMED and EBSCO, this first version is used predominantly. However, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Zhu, Y.Y.’. Therefore, there have been difficulties to locate papers for Chinese authors. In such cases, the use of unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for authors especially for authors with common names such as ‘Zhu, X.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of scientometric studies as well as allocating credit to authors as there are same author entries for different authors with the same name, e.g. ‘Zhu, X.’ in the databases. In this context, the coding of authors in the Scopus database is a welcome innovation compared to other databases such as the Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system, especially for common names. For example, some papers for a certain author may be allocated to another researcher with a different author code. It is possible that Scopus uses a number of software programs to differentiate author names and the program may not be false-proof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes it difficult to differentiate authors with common names and makes scientometric studies further difficult in the author domain. Therefore, author names should be given in all books and journals in the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Zhu, H.J.’, ‘Zhu’, ‘Zhu, H’, or ‘Zhu, J’ instead of ‘Zhu, Hyun Jae’. There are also inconsistencies in naming authors with more than two components by authors themselves in journal papers and book chapters, for example, ‘Zhu, A.P.C.’ might be given as ‘Zhu, A.’, ‘Zhu, A.P.’, ‘Zhu, C.’ or ‘Zhu, A.C.’ in journals and books. This also makes the scientometric studies difficult in author domain. Hence, contributing authors should use their names consistently in their publications. The other critical issue regarding author names is inconsistencies in the spelling of author names in national spellings (e.g., Çölgeçüğ, Söğüt) rather than in English spellings (e.g., Colgecug, Sogut) in the Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where author names are given only in English spellings. It is observed that national spellings of author names do not help in conducting scientometric studies as well as in allocating credits to authors as sometimes there are different author entries for English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are the University of British Columbia and to a lesser extent Lund University, Nanjing Forestry University, University of Jaen, University of Vigo, and USDA Forest Service. Further, the most prolific countries for the sample dataset are the United States and Spain and to a lesser extent Canada, China, and Finland. These findings confirm the dominance of the United States and Europe and to a lesser extent Canada and China in this field.

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The most-prolific research fronts are the enzymatic hydrolysis of wood and to a lesser extent the hydrothermal, chemical, and mechanical hydrolyses of wood. It is also noted that there is a significant gender deficit for the sample dataset as surprisingly only two of these top researchers are female with a 9% representation rate. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with a strong gender deficit. In this context, it is worth noting the level of representation of researchers from minority groups in science on the basis of race, sexuality, age, and disability besides gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

23.4.4  The Most Prolific Research Output by Years in Wood Hydrolysis The research output observed between 1970 and 2022 is illustrated in Figure 23.1. This figure clearly shows that the bulk of research papers in the population dataset was published primarily in the 2010s. Similarly, the bulk of research papers in the sample dataset was published in the last two decades. These findings suggest that the most-prolific sample and population papers were primarily published in the last two decades. These are thought-provoking findings. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in research in this field in the last two decades. Based on these findings, the size of population papers is likely to more than double in the current decade, provided that public concerns about climate change, greenhouse gas emissions, and global warming are translated efficiently into research funding in this field. Furthermore, there have been additional incentives for research on wood hydrolysis due to the current supply shocks due to the COVID-19 pandemic and the Russian invasion of Ukraine as there have been public pressures for the replacement of crude oil-based gasoline and diesel fuels with bioethanol fuels and biodiesel fuels.

23.4.5  The Most Prolific Institutions in Wood Hydrolysis The most prolific 15 institutions publishing papers on wood hydrolysis with at least 2.7% of sample papers each given in Table 23.3 have shaped the development of research in this field. The most prolific institutions are the Lund University and USDA Forest Service and to a lesser extent NREL, South China University of Technology, University of Wisconsin–Madison, and University of New Brunswick. Further, the top countries for these most prolific institutions are the United States and China and to a lesser extent Finland and Spain. In total, only six countries house these top institutions. On the other hand, the institutions with the most citation impact are the University of British Columbia and to a lesser extent the Lund University, USDA Forest Service, NREL, University of Wisconsin–Madison, Auburn University, and VTT Technical Research Center. These findings confirm the dominance of the United States and to a lesser extent Canadian and European institutions in this research field.

23.4.6  The Most Prolific Funding Bodies in Wood Hydrolysis The most prolific 15 funding bodies funding at least 1.6% of sample papers each are given in Table 23.4. It is noted that only 43% and 52% of sample and population papers were funded, respectively. The most prolific funding bodies are the Government of Canada, Natural Sciences and Engineering Research Council of Canada, and National Natural Science Foundation of China and

Wood Hydrolysis: Scientometric Study

107

to a lesser extent Natural Resources Canada, European Commission U.S. Department of Energy, National Science Foundation, and Seventh Framework Program. Further, the most prolific countries for these top funding bodies are the United States and to a lesser extent China, Sweden, and EU. In total, four countries and EU house these top funding bodies. The funding bodies with the most citation impact are the Government of Canada, Natural Resources Canada and Natural Sciences and Engineering Research Council of Canada, Seventh Framework Program, and European Commission. Further, the funding body with the least impact is the National Natural Science Foundation of China. These findings on the funding of research in this field suggest that the level of funding, mostly in the last two decades, is modestly intensive and it has been largely instrumental in enhancing research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding especially for the sample papers, there is ample room to enhance funding in this field. With the current supply shocks, it is expected that funding for wood hydrolysis would increase substantially in the coming decades, especially for crude oil- and foreign exchange-deficient countries. It is also remarkable that China and to a lesser extent Brazil, Canada, Europe, Japan, and the United States dominate the research funding in this field.

23.4.7  The Most Prolific Source Titles in Wood Hydrolysis The most prolific 13 source titles publishing at least 2.2% of sample papers each in wood hydrolysis have shaped the development of research in this field (Table 23.5). The most prolific source titles are Bioresource Technology and to a lesser extent Biotechnology and Bioengineering, Enzyme and Microbial Technology, and Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology, Biotechnology for Biofuels, Biotechnology Progress, and Industrial and Engineering Chemistry Research. Further, the source titles with the most citation impact are Bioresource Technology and to a lesser extent Biotechnology and Bioengineering, Enzyme and Microbial Technology, Applied Biochemistry, Biotechnology Part A Enzyme Engineering and Biotechnology, and Biotechnology Progress. Similarly, the source title with the least impact is Applied Biochemistry and Biotechnology. It is noted that these top source titles are primarily related to bioresources, biotechnology, microbiology, and chemical engineering. This finding suggests that the journals in this field have significantly shaped the development of research in this field as they focus on wood hydrolysis.

23.4.8  The Most Prolific Countries in Wood Hydrolysis The most prolific 12 countries publishing at least 1.6% of sample papers each have significantly shaped the development of research in this field (Table 23.6). The most prolific countries are the United States and to a lesser extent Canada, China, Sweden, Spain, Japan, and Finland. On the other hand, countries with the most citation impact are the United States and to a lesser extent Canada and Sweden. Further, five European countries listed in Table 23.6 produce 30% and 21% of sample and population papers, respectively, with a 9% surplus. Close examination of these findings suggests that the United States, Europe, China, Canada, and Japan are the major producers of research in this field. It is a fact that the United States has been a major player in science (Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The United States has further developed a strong research infrastructure to support its corn- and grass-based bioethanol industry (Vadas et al., 2008). However, China has been a rising mega star in scientific research in competition with the United States and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009).

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Next, Europe has been a persistent player in scientific research in competition with both the United States and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the United States and Brazil (Gnansounou, 2010). Additionally, Brazil has also been a persistent player in scientific research at a moderate level (Glanzel et al., 2006). Brazil has also developed a strong research infrastructure to support its biomass-based bioethanol industry (Macedo et al., 2008).

23.4.9  The Most Prolific Scopus Subject Categories in Wood Hydrolysis The most prolific nine Scopus subject categories indexing at least 4.9% of sample papers each, given in Table 23.7, have shaped the development of research in this field. The most prolific Scopus subject categories in wood hydrolysis are Chemical Engineering and to a lesser extent Biochemistry, Genetics and Molecular Biology, Environmental Science, Immunology and Microbiology, and Energy. These findings are thought provoking suggesting that the primary subject categories are related to chemical engineering, molecular biology, microbiology, energy, and environmental sciences. The other key finding is that social sciences are not well represented in both sample and population papers, as in most fields in bioethanol fuels. These findings are not surprising as the key research fronts in this field relate to the development and applications of wood hydrolysis.

23.4.10  The Most Prolific Scopus Keywords in Wood Hydrolysis A limited number of keywords have shaped the development of research in this field as shown in Table 23.8 and the appendix. These keywords are grouped into five headings: biomass, hydrolysis, pretreatments, other processes, and products of hydrolysis. The prolific keywords related to biomass and biomass constituents are lignin, cellulose, wood, hemicellulose, biomass, softwood, and lignocellulose while those related to the pretreatment are cellulases, pretreatments, enzyme activity, sulfuric acids, and steam. The prolific keywords related to wood hydrolysis are hydrolysis, enzymatic hydrolysis, and saccharification while those related to other processes are fermentation, biotechnology, Saccharomyces cerevisiae, and adsorption. Further, those related to hydrolysis products are sugar, ethanol, glucose, and xylose and it is noted that only 5.4% of indexed papers employs the bioethanol keyword. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of samples for each keyword highlights the intensity of research in the relevant research areas. Relevant keywords are presented in Table 23.8 as well as in the Appendix.

23.4.11  The Most Prolific Research Fronts in Wood Hydrolysis As Table 23.9 shows, there are three primary research fronts for this field: the enzymatic, chemical, and hydrothermal hydrolysis of wood. Another minor research front is the mechanical hydrolysis of wood. The most prolific chemical hydrolysis is acid hydrolysis while the other prolific chemical hydrolyses are solvent, IL, alkaline, and sulfite hydrolysis. Similarly, the most prolific hydrothermal hydrolysis is steam explosion hydrolysis while the other hydrothermal hydrolyses are autohydrolysis, liquid hot water, and hydrothermal hydrolysis in general. Finally, milling is the most prolific mechanical hydrolysis. These findings are thought provoking in seeking ways to increase bioethanol yield through wood hydrolysis at the global scale. It is clear that all these research fronts have public importance and merit substantial funding and other incentives.

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Similarly, Table 23.10 provides data on the wood biomass used in the studies for wood hydrolysis. There are three primary research fronts: hardwood, softwood, and wood in general. On the other hand, pine and spruce are the most prolific softwood used in these studies. Similarly, the most prolific hardwoods are poplar, eucalyptus, and bamboo. These findings suggest that although there are a large number of wood species, the research has focused on a relatively small number of wood species for the wood hydrolysis studies. In the end, these most-cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of these wood hydrolysis processes (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

23.5  CONCLUSION AND FUTURE RESEARCH Research on wood hydrolysis has been mapped through a scientometric study of both sample (183 papers) and population (1,832 papers) datasets. The critical issue in this study has been to obtain a representative sample of research as in any other scientometric studies. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of wider population studies. This keyword set was provided in the Appendix and the relevant keywords are presented in Table 23.8. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of research in this field. For this purpose, the Scopus database has been selected. Journal coverage of this database has been notably wider than that of the Web of Science and other multi-subject databases. The key scientometric properties of research in this field have been determined and discussed in this chapter. It is evident that a limited number of documents, authors, institutions, publication periods, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of research in this field. There is ample scope to increase the efficiency of scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. Research in this field has boomed in the last two decades possibly promoted by public concerns about global warming, greenhouse gas emissions, and climate change. However, it is noted that the research output lost its momentum after 2013 and became flat. Further, the institutions from the United States and China and to a lesser extent Finland and Spain have mostly shaped the research in this field. The relatively modest funding rate of 52% for the population papers suggests that funding in this field significantly enhanced research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance research in this field further as only 43% of sample papers declared any funding. It is expected that the current supply shocks such as the COVID-19 shocks and the shocks due to the Russian invasion of Ukraine would increase the funding rate in this research field as bioethanol fuels are a green alternative to crude oil-based gasoline and diesel fuels. The United States and Europe and to a lesser extent China and Japan have been the major producers of research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, grass, and other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives.

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The primary Scopus subject categories have been Biochemistry, Genetics and Molecular Biology, Environmental Science, Immunology and Microbiology, and Energy as the focus of research has been on wood hydrolysis to increase sugar and bioethanol yield. Further, social sciences are not well represented in both sample and population papers as in most fields in bioethanol fuels. These findings are not surprising. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only the bioethanol keyword would not be much helpful. The Scopus keywords are grouped into five headings: biomass, hydrolysis, pretreatments, other processes, and products of hydrolysis. These groups of keywords highlight the potential primary research fronts for these fields. There are three primary research fronts for this field: the enzymatic, chemical, and hydrothermal hydrolysis of wood. The other minor research front is the mechanical hydrolysis of wood. Similarly, there are three primary research fronts for the biomass used in these studies: hardwood, softwood, and wood in general. On the other hand, pine and spruce are the most prolific softwood used in these studies. Further, the most prolific hardwoods are poplar, eucalyptus, and bamboo. These findings are thought provoking. The focus of these most-cited 183 papers as well as 1,832 population papers is the development and utilization of wood hydrolysis to increase sugar and bioethanol yield. These studies highlight strong structure-processing-property relationships for wood hydrolysis for bioethanol fuels and their derivatives. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of research in this field as in other scientific fields, especially in the aftermath of significant global supply shocks such as the Russian invasion of Ukraine and COVID-19 shocks. It is recommended that further scientometric studies are carried out for the primary types of wood hydrolysis. It is further recommended that reviews of the most-cited papers are carried out for each research front to complement these scientometric studies. Next, scientometric studies of hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS Contribution of highly cited researchers in the field of wood hydrolysis has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR WOOD HYDROLYSIS ((TITLE (hydrolysis OR saccharif* OR prehydroly* OR posthydroly* OR *hydrolysis OR digestibili* OR digestible OR accessibility OR “sugar recovery” OR “fermentable sugars” OR “reducing sugars” OR “sugar yield*” OR “sugar production” OR “sugar release” OR “sugar extraction” OR *oligosaccharides OR recalcitrance OR hydrolysate* OR hydrolyzate* OR prehydrolysate* OR *prehydrolyzate* OR inhibitor* OR “degradation products” OR “degradation compounds” OR xylose OR pentose* OR hexose* OR glucose OR detoxif* OR “xylose recovery” OR “enzymatic degradation”) OR SRCTITLE (hydrolysis)) AND (TITLE (wood OR softwood* OR hardwood* OR woody OR woods OR “olive tree*” OR eucalyptus OR pine OR pinus OR araucaria OR poplar* OR aspen* OR populus OR cedar OR cedrus OR kraft* OR spruce OR picea OR beech OR fagus OR oak OR quercus OR willow* OR salix OR cypress OR cupressus OR prosopis OR birch OR betula OR alder OR alnus OR bamboo* OR phyllostachys OR “douglas fir” OR pseudotsuga OR chestnut OR castanea OR timber OR maple* OR acer OR yew OR *taxus OR “western hemlock” OR tsuga OR larch OR larix OR fir OR abies OR elm OR ulmus OR hickory OR carya OR mahogany OR swietenia OR “palm tree” OR meranti))) AND NOT (SUBJAREA (medi OR phar OR dent OR vete OR eart OR nurs OR neur OR heal OR psyc) OR TITLE (aquatic OR {non-wood} OR char OR moisture OR nanopart* OR volatile* OR hydrochar* OR biochar* OR soil* OR chrysomela OR ester* OR nanocrystal* OR “nano-fibril*” OR nanofibril* OR nanocompposit* OR nanowhisker* OR “glucosidase inhibitor*” OR photosynth* OR lettuce OR

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viscose OR gasoline OR herbivor* OR cadmium OR leaf OR disc OR ageing OR *butanol OR chicks OR fenton OR frog* OR *protein OR protein* OR antioxida* OR gut OR root* OR tissue* OR semisolid OR pyroly* OR light OR xylitol OR succinic OR liver OR “thermal degradation” OR corrosion OR dye OR spathaspora OR cinnamon* OR *alkanoates OR polyols OR pheromone* OR effluent* OR trehalase OR *sensor OR seed* OR protease OR lactic OR antigen* OR embryo* OR browser* OR nanotube* OR germ* OR taxane* OR xanthine OR mast OR *taxel OR sycamore OR carotenoid* OR *glucomannan OR butanediol OR *lactone OR joints) OR SRCTITLE (plant* OR food* OR ecol* OR animal* OR oecol* OR phyto* OR organic OR talanta OR medic* OR chromat* OR forest* OR botan* OR hazard*)) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “sh”)) AND (LIMIT-TO (LANGUAGE, “English”))

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24 Review

Wood Hydrolysis Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

24.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomassbased bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2019, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to bioethanol production through hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. Wood has been one of the most-studied biomasses for bioethanol production (Galbe and Zacchi, 2002; Gregg et al., 1998). In this context, research in the field of wood hydrolysis (Alvarez et al., 2016; Del Rio et al., 2022) has thus intensified in recent years. Enzymatic (Alvarez et al., 2016; Gregg and Saddler, 1996), chemical (Cara et al., 2008; Larsson et al., 1999a), hydrothermal (Cara et al., 2006; Del Rio et al., 2022), and mechanical hydrolyses (Inoue et al., 2008; Palm and Zacchi, 2003) of wood have been widely researched to increase sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been a limited number of review papers on wood hydrolysis (Alvarez et al., 2016; Del Rio et al., 2022; Gregg and Saddler, 1996; Kapu and Trajano, 2014; Zhu et al., 2010), there has been no updated review of the most-cited 25 articles in this field. Thus, this chapter presents a review of the most-cited 25 articles in the field of wood hydrolysis. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

24.2  MATERIALS AND METHODS Search for this study was carried out using the Scopus database (Burnham, 2006) in May 2022. As a first step for search of the relevant literature, keywords were selected using the most-cited first 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the Appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 195 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape. DOI: 10.1201/9781003226499-32

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24.3 RESULTS Brief information about the 25 most-cited papers with at least 195 citations each on wood hydrolysis is given below. The primary research fronts are enzymatic hydrolysis of wood in combination with hydrothermal, chemical, and mechanical pretreatments and the other issues regarding wood hydrolysis with 16 and nine highly cited papers (HCPs), respectively.

24.3.1 Enzymatic Hydrolysis of Wood Combined with Other Pretreatments There are 16 HCPs for the research front of enzymatic hydrolysis of wood in combination with hydrothermal, chemical, and mechanical pretreatments (Table 24.1).

TABLE 24.1 Enzymatic Hydrolysis of Wood Combined with Other Pretreatments No.

Papers

 1 Studer et al. (2011)  2 Zhu et al. (2009b)

 3 Kumar et al. (2012)  4

 5

 6

 7

 8

 9

10

Biomass

Prt.

Poplar

LHW, enzymes

Spruce, pine

Sulfite, H2SO4, milling, cellulase, β-glucosidase Enzymes

Douglas fir

Parameters Sugar yield, lignin content, S/G ratio, LHW effect Sulfite and enzymatic pretreatments, sugar yield

Keywords Populus, sugar release

Lead Author

Affil.

Wyman, Charles E. 7004396809 Spruce, pine, Zhu, Junyong saccharification 7405692678

Cellulose accessibility, Softwood, enzyme loading, accessibility hydrolysis efficiency, lignin effect Zhao et al. Spruce NaOH, urea, Enzymatic hydrolysis Spruce, (2008) enzyme efficiency, alkaline hydrolysis pretreatment, sugar yield, temperature effect Olive tree, Cara et al. Olive tree H2SO4, enzymes Sugar yield, acid concentration, fermentable (2008) temperature sugars, saccharification Wyman Poplar H2SO4, Pretreatment type, sugar Poplar, wood, et al. ammonia, yields, ethanol yields sugar recovery (2009) Ca(OH)2, SO2, enzymes Ko et al. Hardwood LHW, enzymes Pretreatment severity, Hardwood, (2015) hydrolysis inhibition by hydrolysis lignin Cara et al. Olive tree Steam explosion, Pretreatments, Olive tree, (2006) alkaline H2O2, delignification, sugar hydrolysis β-glucosidase yield Grous et al. Poplar Steam explosion, Pretreatment effect, sugar Poplar, (1986) T. Reesei, A. yield, pore size hydrolysis niger Cantarella Poplar Steam explosion, Fermentation inhibitors, Poplar, wood, et al. Celluclast and cellulase inhibition, hydrolysis, (2004) Novozym ethanol yield inhibitors cellulase

Univ. Calif. Riverside USA USDA Forest Serv. USA Saddler, Jack N. Univ. 7005297559 British Columbia Canada Deng, Yulin Georgia 55261848900 Inst. Technol. USA Castro, Univ. Jaen Eulogio Spain 7102441948

Cits 458

436

298

264

261

Wyman, Charles E. 7004396809

Univ. Calif. 250 Riverside USA

Ladisch, Michael R. 7005670397 Castro, Eulogio 7102441948 Converse, Alvin O. 7003591261 Cantarella, Maria* 7003630895

Purdue Univ. USA Univ. Jaen Spain

240

228

Dartmouth 225 Coll. USA Univ. l’Aquilla Italy

224

(Continued )

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TABLE 24.1 (Continued ) Enzymatic Hydrolysis of Wood Combined with Other Pretreatments No.

Papers

Biomass

14 Tengborg et al. (2001) 15 Zhu et al. (2009a)

Douglas fir

Prt.

Parameters

Keywords

Lead Author

Steam explosion, Lignin effect Softwood, NaOH, protein, remediation, hydrolysis enzymes delignification, protein addition 12 Rahikainen Softwood Acids, Celluclast Hydrolysis inhibition by Softwood, et al. lignin, enzyme binding hydrolysis (2011) 13 Lu et al. Douglas Steam, hot Sugar yield, enzyme Softwood, (2002) fir alkaline H2O2, adsorption, pretreatment hydrolysis SO2, cellulase type 11 Pan et al. (2005)

Spruce

Wood

16 Gupta et al. Prosopis (2009)

Affil.

Gilkes, Neil R. Univ. 35493889000 British Columbia Canada Kruus, Aalto Univ. Kristiina* Finland 55887400600 Mansfield, Univ. Shawn D. British 7006421766 Columbia Canada Steam, enzyme Hydrolysis inhibition by Softwood, Zacchi, Guido Lund hydrolysate hydrolysis 7006727748 University Sweden Milling, sulfite, Milling energy Wood, Zhu, Junyong USDA enzyme consumption, sugar saccharification 7405692678 Forest yield, substrate-specific Serv. surface USA Hydrolysis, Kuhad, Central H2SO4, Na2SO3, Hydrolysis, NaClO2, delignification, woody, Ramesh C. Univ. cellulase, fermentation inhibitors, Prosopis 55663451900 Haryana β-glucosidase fermentation India

Cits 216

212

207

204

201

196

*, Female; Cits., The number of citations received for each paper; Prt, Biomass pretreatments.

24.3.1.1  Enzymatic Hydrolysis of Wood with Hydrothermal Pretreatments 24.3.1.1.1  Enzymatic Hydrolysis of Wood with Steam Explosion Pretreatment Kumar et al. (2012) investigated the effect of cellulose accessibility and enzyme loading on the efficiency of enzymatic hydrolysis of steam-pretreated Douglas fir in a paper with 298 citations. They observed that the lignin component significantly influenced the swelling and accessibility of cellulose as at low enzyme loadings of 5 FPU/g cellulose, only 16% of cellulose present in the steam-pretreated softwood was hydrolyzed while almost complete hydrolysis was achieved with the delignified substrate. When lignin was added back in the same proportions to the highly accessible and swollen, delignified steam-pretreated softwood and to Avicel cellulose, hydrolysis yields decreased by 9% and 46%, respectively. However, when higher enzyme loadings were employed, greater availability of the enzyme could overcome the limitations imposed by both lignin’s restrictions on cellulose accessibility and direct binding of enzymes, resulting in a near-complete hydrolysis of cellulose. They concluded that the lignin present in steam-pretreated softwood bound enzymes and limited cellulose accessibility. Cara et al. (2006) carried out enhanced enzymatic hydrolysis of olive trees by steam explosion (SE) and alkaline hydrogen peroxide (H2O2) delignification in a paper with 228 citations. They performed SE pretreatment at 190°C, 210°C, 230°C, and 240°C for 5 min. They further delignified the water-insoluble fiber with an alkaline hydrogen peroxide (H2O2) pretreatment. They finally performed enzymatic hydrolysis using a commercial cellulolytic complex supplemented with β-glucosidase at 10% (w/v) pretreated material concentration. They observed that delignification enhanced enzymatic hydrolysis yields of steam-pretreated olive tree wood. Up to 80% of lignin in the original wood was solubilized, leaving a cellulose-rich residue that led to a concentrated glucose

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solution of 51.3 g/L after 72 h of enzymatic hydrolysis in the best case. They obtained the maximum overall process yield, taking into account both sugars present in the liquid from steam pretreatment and glucose from the steamed, delignified, and hydrolyzed solid at the lowest steam pretreatment temperature assayed. As a result, they recovered 28.8 g of sugars of 54.7 g available (52.6%) from 100 g of raw material. Grous et al. (1986) explored the effect of batch SE pretreatment on the rate of subsequent enzymatic hydrolysis of hybrid poplar in a paper with 225 citations. They observed that this pretreatment was effective as the glucose yield obtained after 24 h of enzymatic hydrolysis using enzymes from Trichoderma reesei and Aspergillus niger was in excess of 90% of the potential, whereas the corresponding yield from non-pretreated substrate was only 15%. They attributed this improvement in sugar yield primarily to the increase in pore surface area accessible to enzyme molecules as there was a considerable increase in pore volume. Pretreated wood that was subsequently ovendried hydrolyzed poorly and showed a reduction in available pore volume after drying. Further, xylans were readily hydrolyzed to xylose during pretreatment, and owing to decomposition, the amount of xylose in solution after steam pretreatment decreased as the severity of reaction conditions increased while the converse was true for glucose. Cantarella et al. (2004) investigated the effect of fermentation inhibitors of formic, acetic, and levulinic acids and furfural, 5-hydroxymethylfurfural (5-HMF), syringaldehyde, 4-hydroxybenzaldehyde, and vanillin released during SE pretreatment of poplar on subsequent enzymatic hydrolysis and simultaneous saccharification and fermentation (SSF) in a paper with 224 citations. They used a blend of Celluclast and Novozym cellulase complexes. SE temperature and time conditions were 214°C and 6 min, respectively, resulting in a log Ro of 4.13. Enzyme activities for endoglucanase, exoglucanase, and β-glucosidase were 5.76, 0.55, and 5.98 U/mg, respectively. They observed that acetic acid (2 g/L), furfural, 5-HMF, syringaldehyde, 4-hydroxybenzaldehyde, and vanillin (0.5 g/L) did not significantly affect the enzyme activity, whereas formic acid (11.5 g/L) inactivated the enzymes and levulinic acid (29.0 g/L) partially affected cellulase. They did not detect synergism and cumulative concentration effects of these compounds. The presence of acetic acid, vanillin, and 5-HMF (0.5 g/L) in SSF of 100 gDw/L biomass gave rise to ethanol yields of 84.0%, 73.5%, and 91.0% respectively, with respective lag phases of 42, 39, and 58 h. Rahikainen et al. (2011) explored the interaction of cellulases with softwood lignin with commercial T. reesei cellulases (Celluclast) and lignin-rich residues isolated from steam-pretreated softwood by enzymatic and acid hydrolysis in a paper with 212 citations. They observed that both lignin preparations inhibited hydrolysis of microcrystalline Avicel cellulose and adsorbed the major cellulases present in the commercial cellulase mixture. They then observed severe inactivation of ligninbound enzymes at 45°C; however, at 4°C, the enzymes retained well their activity. Furthermore, very strong interactions formed between the residue and enzymes at 45°C because the enzymes were not released from the residue in electrophoresis. Finally, heat-induced denaturation might take place on the surface of softwood lignin at hydrolysis temperature. Lu et al. (2002) determined the sugar yield and enzyme adsorption profile obtained during hydrolysis of SO2-catalyzed steam-exploded Douglas fir and post-pretreated steam-exploded Douglas fir substrates in a paper with 207 citations. After hot alkaline hydrogen peroxide posttreatment, they observed that the rates and yield of hydrolysis attained from the post-pretreated Douglas fir were significantly higher, even at lower enzyme loadings, than those obtained with the corresponding steam-exploded Douglas fir. The enzymatic adsorption profiles observed during hydrolysis of the two substrates were significantly different. Finally, the enzyme remained relatively active for three rounds of recycling. Tengborg et al. (2001) investigated reduced inhibition of enzymatic hydrolysis of steampretreated softwood in a paper with 204 citations. They noted that it was preferable to include the sugar-rich prehydrolysate, obtained after the pretreatment step, in enzymatic hydrolysis of the solid fraction. They observed that the prehydrolysate inhibited cellulose conversion in the

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enzymatic hydrolysis step. When the prehydrolysate was included in enzymatic hydrolysis, cellulose conversion was reduced by up to 36%. However, this inhibition could be overcome by fermentation of the prehydrolysate prior to enzymatic hydrolysis. 24.3.1.1.2  Enzymatic Hydrolysis of Wood with Liquid Hot Water Pretreatment Studer et al. (2011) tested 47 extreme phenotypes from poplar (Populus trichocarpa) trees for total sugar release through enzymatic hydrolysis alone as well as through combined liquid hot water (LHW) pretreatment and enzymatic hydrolysis in a paper with 458 citations. They observed that the total amount of glucan and xylan released varied widely among samples, with total sugar yields of up to 92% of the theoretical maximum. There was a strong negative correlation between sugar release and lignin content for pretreated samples with a syringyl/guaiacyl (S/G) ratio of less than two. For higher S/G ratios, sugar release was generally higher, and the negative influence of lignin was less pronounced. Only glucose release was correlated with lignin content and S/G ratio in this manner, whereas xylose release depended on the S/G ratio alone. For enzymatic hydrolysis without pretreatment, sugar release increased significantly with decreasing lignin content below 20%, irrespective of the S/G ratio. Furthermore, certain samples featuring average lignin content and S/G ratios exhibited exceptional sugar release. They asserted that factors beyond lignin and S/G ratio influenced recalcitrance to sugar release. Ko et al. (2015) explored the effect of LHW pretreatment severity on the properties of hardwood lignin and enzymatic hydrolysis of cellulose in a paper with 240 citations. They observed that hardwood pretreated with LHW at severities ranging from log Ro = 8.25 to 12.51 resulted in 80%–90% recovery of the initial lignin in the residual solids. The ratio of acid-insoluble lignin to acid-soluble lignin increased, and they observed the formation of spherical lignin droplets on the cell wall surface. When lignins were isolated from hardwoods pretreated at increasing severities and characterized based on glass transition temperature (Tg), the Tg of isolated lignins increased from 171°C to 180°C as the severity increased from log Ro = 10.44 to 12.51. The increase in Tg suggested that the condensation reactions of lignin molecules occurred during pretreatment and altered the lignin structure. Lignins derived from more severely pretreated hardwoods had higher Tg values and showed a more pronounced inhibition of enzymatic hydrolysis. 24.3.2.2  Enzymatic Hydrolysis of Wood with Chemical Pretreatments Wyman et al. (2009) used the leading pretreatment technologies based on ammonia fiber expansion (AFEX), aqueous ammonia recycle, dilute H2SO4, lime (Ca(OH)2), neutral pH, and sulfur dioxide (SO2) for poplar and hydrolyzed the remaining solids from each technology to sugars using the same enzymes in a paper with 250 citations. Overall, they observed that poplar was more recalcitrant to conversion to sugars compared to corn stover and that sugar yields from the combined operations of pretreatment and enzymatic hydrolysis varied more among pretreatments. However, the application of more severe pretreatment conditions gave good yields from SO2 and Ca(OH)2, and a recombinant yeast strain fermented the mixed stream of glucose and xylose sugars released by enzymatic hydrolysis of water-washed solids from all pretreatments to ethanol with similarly high yields. 24.3.2.2.1  Enzymatic Hydrolysis of Wood with Sulfite Pretreatment Zhu et al. (2009b) developed a process using sulfite pretreatment to overcome the recalcitrance of lignocellulose (SPORL) for robust and efficient bioconversion of softwoods in a paper with 436 citations. This process consisted of sulfite treatment of wood chips under acidic conditions followed by mechanical size reduction using disk milling. After the SPORL pretreatment of spruce chips with 8%–10% bisulfite and 1.8%–3.7% H2SO4 on oven-dry (od) wood at 180°C for 30 min, they obtained more than 90% cellulose conversion of the substrate with an enzyme loading of about 14.6 FPU cellulase plus 22.5 CBU β-glucosidase per gram of od substrate after 48 h of hydrolysis. Glucose yield from enzymatic hydrolysis of the substrate per 100 g of untreated od spruce wood (glucan content

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43%) was about 37 g (excluding the dissolved glucose during pretreatment). Hemicellulose removal was critical as lignin sulfonation for cellulose conversion in the SPORL process. This pretreatment altered the wood chips, which reduced electric energy consumption for size reduction to about 19 Wh/kg od of untreated wood or about 19 g glucose/Wh electricity. Furthermore, SPORL produced low amounts of fermentation inhibitors, HMF and furfural, of about 5 and 1 mg/g of untreated od wood, respectively. Zhu et al. (2009a) characterized a specific surface to evaluate the efficiencies of milling and sulfite pretreatment of wood for enzymatic hydrolysis in a paper with 201 citations. They correlated the substrate-specific surface and mechanical milling energy consumption to enzymatic hydrolysis of glucose yield. They observed that thermomechanical disk milling (DM-I), exposing cellulose, was more effective than a high-pressure thermomechanical disk milling (DM-II), exposing lignin, in subsequent enzymatic hydrolysis. However, DM-I was more energy intensive than DM-II. Both DMs that produced fibers were more efficient in enzymatic hydrolysis than hammer milling that produced fiber bundles. Chemical pretreatment not only increased cellulose conversion but also reduced mechanical milling energy consumption. Sulfite pretreatment was the most efficient pretreatment in terms of glucose yield and milling energy consumption. 24.3.2.2.2  Enzymatic Hydrolysis of Wood with Alkaline Pretreatment Zhao et al. (2008) optimized alkaline pretreatment of spruce at a low temperature in both the presence and absence of urea to enhance enzymatic hydrolysis of spruce in a paper with 264 citations. They observed that the enzymatic hydrolysis rate and efficiency could be significantly improved by this pretreatment. At a low temperature, NaOH alone or NaOH–urea mixture solution could slightly remove lignin, hemicelluloses, and cellulose in the spruce, disrupt the connections between hemicelluloses, cellulose, and lignin, and alter the structure of the treated biomass to make cellulose more accessible to hydrolysis enzymes. Moreover, wood fiber bundles could be broken down into small and loose lignocellulosic particles by chemical pretreatment. Therefore, the efficiency of enzymatic hydrolysis of untreated mechanical fibers could also be remarkably enhanced by NaOH or NaOH/ urea solution pretreatment. For spruce, up to 70% of glucose yield could be obtained for the cold temperature pretreatment (−15°C) using 7% NaOH/12% urea solution, but only 20% and 24% of glucose yields were obtained at temperatures of 23°C and 60°C, respectively. The best condition for chemical pretreatment was 3% NaOH/12% urea and −15°C with over 60% of glucose conversion. Pan et al. (2005) developed strategies to enhance enzymatic hydrolysis of pretreated Douglas fir by reducing the effect of this residual lignin on enzymatic hydrolysis of cellulose with high residual lignin content in a paper with 216 citations. Pretreatment of Douglas fir by SE produced a substrate containing 43% of lignin. They developed two strategies for reducing the effect of this residual lignin on enzymatic hydrolysis of cellulose: mild alkaline extraction and protein addition. Extraction with cold 1% NaOH reduced the lignin content by only 7%, but cellulose-to-glucose conversion was increased by about 30%. Before alkaline extraction, the addition of exogenous protein resulted in a significant improvement in cellulose hydrolysis, but this protein effect was substantially diminished after alkaline pretreatment. Lignin reduced cellulose hydrolysis by forming a physical barrier that prevents enzyme access and by non-productively binding cellulolytic enzymes. Cold alkali selectively removed a fraction of lignin from steam-exploded Douglas fir with a high affinity for protein. The relative importance of the two mechanisms by which residual lignin affects hydrolysis was different according to the pre- and post-treatment methods used. 24.3.2.2.3  Enzymatic Hydrolysis of Wood with Acid Pretreatment Cara et al. (2008) obtained sugars from olive tree biomass by dilute H2SO4 pretreatment and further enzymatic hydrolysis of the pretreated solid residues in a paper with 261 citations. They performed pretreatment at 0.2%, 0.6%, 1.0%, and 1.4% (w/w) H2SO4 concentrations in the range of 170°C–210°C. As a maximum, they recovered 83% of hemicellulosic sugars in the raw material in the prehydrolysate obtained at 170°C and 1% H2SO4 concentration, but the enzyme accessibility

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121

of the corresponding pretreated solid was not very high. In turn, the maximum enzymatic hydrolysis yield (76.5%) was attained from a pretreated solid at 210°C and 1.4% acid concentration in which cellulose solubilization was detected. Moreover, sugar recovery in the prehydrolysate was the poorest one. They obtained the maximum sugar yield of 36.3 g sugar/100 g raw material when pretreating the biomass at 180°C and 1% acid concentration, representing 75% of all sugars in the raw material. Gupta et al. (2009) hydrolyzed and fermented Prosopis juliflora for ethanol production by Saccharomyces cerevisiae and Pichia stipitis-NCIM 3498 in a paper with 196 citations. They observed that dilute H2SO4 (3.0%, v/v) pretreatment resulted in hydrolysis of hemicelluloses to pentose sugars along with fermentation inhibitors such as furfural, HMF, phenolics, and acetic acid. The acid-pretreated substrate was delignified to the extent of 93.2% by the combined action of sodium sulfite (Na2SO3) (5.0%, w/v) and sodium chlorite (NaClO2) (3.0%, w/v). The remaining cellulosic residue was enzymatically hydrolyzed in 0.05 M citrate phosphate buffer (pH 5.0) using 3.0 U of filter paper cellulase (FPase) and 9.0 U of β-glucosidase per mL of citrate phosphate buffer. They obtained the maximum enzymatic hydrolysis of cellulosic material (82.8%) after 28 h incubation at 50°C. Fermentation of both acid and enzymatic hydrolysates, containing 18.24 and 37.47 g/L sugars, with P. stipitis and S. cerevisiae produced 7.13 and 18.52 g/L of ethanol with a corresponding yield of 0.39 and 0.49 g/g, respectively.

24.3.2 Other Issues Regarding Wood Hydrolysis There are nine HCPs for other issues regarding wood hydrolysis (Table 24.2). These are hydrolysate detoxification, sole enzymatic, sole acidic, sole ionic liquid, and sole hydrothermal hydrolysis of wood with one to three HCPs each. 24.3.2.1  Hydrolysate Detoxification Larsson et al. (1999b) detoxified dilute acid hydrolysates of spruce to improve both cell growth and ethanol production by S. cerevisiae in a paper with 424 citations. The applied detoxification methods included pretreatment with alkali [sodium hydroxide (NaOH) or calcium hydroxide (Ca(OH)2)]; pretreatment with sulfite (0.1% [w/v] or 1% [w/v] at pH 5.5 or 10); evaporation of 10% or 90% of the initial volume; anion exchange (at pH 5.5 or 10); enzymatic detoxification with phenoloxidase laccase; and detoxification with T. reesei. They observed that anion exchange at pH 5.5 or 10 and pretreatment with laccase, Ca(OH)2, and T. reesei were the most efficient detoxification methods. Evaporation of 10% of the initial volume and pretreatment with 0.1% sulfite were the least efficient detoxification methods. Pretreatment with laccase was the only detoxification method that specifically removed only phenolic compounds. Anion exchange at pH 10 was the most efficient method for removing aliphatic acids, furan derivatives, and phenolic compounds although it resulted in loss of fermentable sugars. Jonsson et al. (1998) detoxified wood hydrolysates with laccase and peroxidase from Trametes versicolor to increase the fermentability of willow hydrolysate pretreated with steam and SO2 in a paper with 341 citations. They employed laccase and lignin peroxidase purified from T. versicolor. They then employed S. cerevisiae for ethanolic fermentation of hydrolysates. They observed a more rapid consumption of glucose and increased ethanol productivity for samples treated with laccase. Treatment of the hydrolysate with lignin peroxidase also resulted in improved fermentability. The mechanism of laccase detoxification involved the removal of monoaromatic phenolic compounds present in the hydrolysate hinting that phenolic compounds were important inhibitors of the fermentation process. 24.3.2.2  Sole Enzymatic Hydrolysis of Wood Mooney et al. (1998) used four Douglas fir pulps – a refiner mechanical pulp (RMP), sulfonated RMP, delignified RMP, and a kraft pulp – to determine whether the lignin content and initial pore

122

TABLE 24.2 Other Issues Regarding Hydrolysis of Wood No.

Papers

Biomass

Prt.

1

Larsson et al. (1999a)

Spruce

H2SO4

2

Lee et al. (2009)

Wood

[EMIM][CH3COO], T. viride

3

Larsson et al. (1999b)

Spruce

4

Garrote et al. Eucalyptus (1999) Mooney et al. Douglas-fir (1998)

NaOH, Ca(OH)2, sulfite, evaporation, anion exchange, phenoloxidase laccase, T. reesei Hydrothermal

5

7

Jonsson et al. (1998) Xiao et al. (2004)

8

Taherzadeh et al. (1997)

9

Zhang et al.(2007)

Willow Softwood

Alder, aspen, birch, willow, pine, spruce Bamboo

Laccase, T. versicolor lignin peroxidase Cellulases, β-glucosidase

Keywords

Hydrolysis severity, sugar yield, hydrolysate fermentability, fermentation inhibitors Lignin extraction, ionic liquid, and enzymatic pretreatment

Softwood, hydrolysis, inhibitors

Hydrolysate detoxification and fermentability, sugar yield, fermentation inhibitors

Spruce, hydrolysates, detoxification

Hydrothermal hydrolysis kinetics and modelling Lignin content, initial pore volume, cellulose adsorption, hydrolysis efficiency Hydrolysate detoxification and fermentability, fermentation inhibitors Enzyme activity inhibition by sugars, hydrolysate sugar content

Affil.

Cits

Hahn-Hagerdal, Barbel* 7005389381 Doherty, Thomas V. 57213283673 Jonsson, Leif J. 7102349315

Lund Univ. Sweden

867

Rensselaer Polytech. Inst. USA Umea Univ. Sweden

797

Wood, autohydrolysis, xylooligosaccharide Softwoods, hydrolysis

Parajo, Juan C. 7005594129 Saddler, Jack N. 7005297559

Univ. Vigo Spain

376 363

Wood, hydrolysates, detoxification Softwood, hydrolysis

Jonsson, Leif J. 7102349315 Saddler, Jack N. 7005297559

Wood, hydrolysates

Taherzadeh, Mohammad J. 6701407496 Zhang, Xiaoyu 56176446300

Univ. British Columbia Canada Umea Univ. Sweden Univ. British Columbia Canada Univ. Boras, Sweden

Wood, hydrolysis

H2SO4

Acid hydrolysis and fermentation, wood species, fermentation inhibitors

E. taxodii, T. versicolor

Fungi type, sugar yield, cellulase Bamboo, hydrolysis adsorption capacity, pretreatment time, lignin content

*, Female; Cits., The number of citations received for each paper; Prt, Biomass pretreatments.

Lead Author

Huazhong Univ. Sci. Technol. China

424

341 319

253

195

Bioethanol Fuel Production Processes. II

6

Cellulase

Parameters

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volume affected cellulase adsorption and substrate hydrolysis in a paper with 363 citations. When compared on the basis of lignin content, they observed from the cellulase treatment of the sulfonated RMP that the proportion of lignin did not affect enzyme adsorption when the fibers were sufficiently swollen. However, the initial adsorption of cellulase did not always translate to fast and complete hydrolysis. Although modification of lignin resulted in a dramatically increased fiber saturation point, the median pore width was not increased accordingly. In contrast, the delignified RMP had a higher median pore width and was hydrolyzed more completely, suggesting that steric hindrance from the residual lignin might be the rate-limiting characteristic in this situation. Hydrolysis of the kraft pulp indicated that the larger average particle size of this substrate might have been an inhibiting factor since it was hydrolyzed more slowly than the delignified RMP despite having a higher median pore width and lower lignin content. Xiao et al. (2004) determined the inhibition effects of glucose and other sugar monomers during cellulase and β-glucosidase hydrolysis of two types of Avicel cellulose and acetic acid-pretreated softwood in a paper with 319 citations. They observed that the increased glucose content in the hydrolysate resulted in a dramatic increase in the degrees of inhibition on both β-glucosidase and cellulase activities. Supplementation of mannose, xylose, and galactose during cellobiose hydrolysis did not show any inhibitory effects on β-glucosidase activity. However, these sugars had significant inhibitory effects on cellulase activity during cellulose hydrolysis. They recommended high substrate consistency hydrolysis with supplementation of hemicellulose to minimize end product inhibition effects while producing hydrolysate with high glucose concentration. Zhang et al. (2007) screened 34 isolates of white rot fungi for biological pretreatment of bamboo culms (Phyllostachys pubescens) with Echinodontium taxodii 2538 and T. versicolor G20 in a paper with 195 citations. They observed that the sugar yield of bamboo culms pretreated with these two fungi through enzymatic hydrolysis increased with increasing pretreatment time. The sugar yield of bamboo culms pretreated with T. versicolor G20 and E. taxodii 2538 increased 5.15-fold and 8.76fold, respectively, after 120-day pretreatment. E. taxodii 2538 preferentially degraded the lignin of bamboo culms. The pretreated bamboo culms showed a significant increase in the initial adsorption capacity to cellulase (4.20-fold and 6.66-fold for T. versicolor G20 and E. taxodii 2538, respectively, after 120 days) and a decrease in lignin content (12.00% and 29.14% for T. versicolor G20 and E. taxodii 2538, respectively, after 120 days) with increasing pretreatment time. The initial adsorption capacity and lignin content of bamboo culms were correlated to fermentable sugar yield. 24.3.2.3  Sole Acid Hydrolysis of Wood Larsson et al. (1999a) investigated the effect of severity (CS) of dilute sulfuric acid (H2SO4) hydrolysis of spruce on sugar (mannose and glucose) yield and on the fermentability of the hydrolysate by S. cerevisiae in a paper with 867 citations. They observed that when the CS of hydrolysis conditions increased, the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose and 3.0 and 3.4 for glucose above which it decreased. The decrease in the yield of sugars coincided with the maximum concentrations of fermentation inhibitors of furfural and 5-HMF. With a further increase in CS, the concentrations of these inhibitors decreased while the formation of formic acid and levulinic acid increased. The yield of ethanol decreased at approximately CS 3; however, the volumetric productivity decreased at lower CS. Ethanol yield and volumetric productivity decreased with increasing concentrations of acetic acid, formic acid, and levulinic acid. Furfural and 5-HMF decreased the volumetric productivity but did not influence the final yield of ethanol. The decrease in volumetric productivity was more pronounced when 5-HMF was added to fermentation, and this compound was depleted at a lower rate than furfural. They asserted that the inhibition observed in hydrolysates produced in higher CS could not be fully explained by the effect of the by products furfural, 5-HMF, acetic acid, formic acid, and levulinic acid. Taherzadeh et al. (1997) fermented dilute acid hydrolysates from alder, aspen, birch, willow, pine, and spruce in a paper with 253 citations. They prepared these hydrolysates by the hydrolysis

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process using H2SO4 (5 g/L) at temperatures between 188°C and 234°C and with a holding time of 7 min. They then fermented them anaerobically by S. cerevisiae (10 g of d.w./L) at a temperature of 30°C and an initial pH of 5.5. They observed that the fermentabilities were quite different for different wood species, and only hydrolysates of spruce produced at 188°C and 198°C, hydrolysates of pine produced at 188°C, and hydrolysates of willow produced at 198°C could be completely fermented within 24 h. From the sum of concentrations of the known inhibitors, furfural and 5-HMF, a good prediction of the maximum ethanol production rate could be obtained, regardless of the origin of the hydrolysate. Furthermore, in hydrolysates that fermented well, furfural and HMF were taken up and converted by the yeast, concomitant with the uptake of glucose. 24.3.2.4  Sole Ionic Liquid Hydrolysis of Wood Lee et al. (2009) used 1-ethyl-3-methylimidazolium acetate ([EMIM][CH3COO]) as a pretreatment solvent to extract lignin from wood flour in a paper with 797 citations. They observed that cellulose in the pretreated wood flour became far less crystalline without undergoing solubilization. When 40% of lignin was removed, the cellulose crystallinity index dropped below 45, resulting in more than 90% of the cellulose in wood flour being hydrolyzed by T. viride cellulase. This catalyst was easily reused, thereby resulting in a highly concentrated solution of chemically unmodified lignin. 24.3.2.5  Sole Hydrothermal Hydrolysis of Wood Garrote et al. (1999) subjected Eucalyptus globulus to hydrothermal pretreatments under mild operational conditions of 145°C–190°C, liquor-to-solid ratio 6–10 g/g, and reaction times up to 7.5 h in a paper with 376 citations. They observed that negligible effects were caused by hydrothermal pretreatments on both cellulose and lignin. They developed kinetic models to describe hydrolysis of hemicelluloses and found that xylan degradation, xylooligosaccharide and xylose generation, and xylose dehydration to furfural were accurately described by models based on pseudo-homogeneous, first-order kinetics with Arrhenius-type temperature dependence.

24.4 DISCUSSION 24.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of bioethanol prior to bioethanol production through hydrolysis and fermentation of the biomass and hydrolysates, respectively. Wood has been one of the most-studied biomass for bioethanol production. In this context, research in the field of wood hydrolysis has thus intensified in recent years. Enzymatic, chemical, hydrothermal, and mechanical hydrolyses of wood have been widely researched to increase sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance research in this field. Although there have been a number of review papers for this field, there has been no review of the most-cited 25 articles in this field. Thus, this chapter presents a review of the most-cited 25 articles in this field. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field. As a first step for the search of the relevant literature, keywords were selected using the most cited first 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the Appendix of Konur (2023) for future replication studies.

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As a second step, a sample dataset was used for this study. The first 25 articles with at least 195 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape. Information about the thematic research fronts for the reviewed and sample papers in wood hydrolysis is given in Table 24.3. As this table shows, there are three primary research fronts for this field: enzymatic, chemical, and hydrothermal hydrolysis of wood with 88%, 56%, and 36% of reviewed papers, respectively. Next, the other minor front is the mechanical hydrolysis of wood with 8% of reviewed papers. On an individual basis, acid, alkaline, and sulfite hydrolysis are the key chemical hydrolyses with 28%, 16%, and 12% of reviewed papers, respectively, while SE and milling are the most prolific hydrothermal and mechanical hydrolyses with 24% and 8% of reviewed papers, respectively. Further, enzymatic, chemical, alkaline, and sulfite hydrolyses are over-represented in reviewed papers with 23%, 17%, 13%, and 10% surplus, respectively. Similarly, autohydrolysis and solvent hydrolysis are under-represented by 7% and 6% deficit, respectively. On the other hand, Table 24.4 provides data on wood biomass used in studies for wood hydrolysis. There are two primary research fronts – hardwood and softwood – with 64% and 56% of reviewed papers, respectively. The other front is the wood in general with 8% of reviewed papers.

TABLE 24.3 Thematic Research Fronts for Wood Hydrolysis No. 1 2

N Paper (%) Review

N Paper (%) Sample

Surplus

Enzymatic hydrolysis Chemical hydrolysis Acid hydrolysis

Research Fronts

88.0 56.0 28.0

65.0 38.8 20.2

23.0 17.2 7.8

Alkaline hydrolysis

16.0

2.7

13.3

Sulfite hydrolysis

12.0

2.2

9.8

H2O2 hydrolysis

8.0

1.6

6.4

Ionic liquid hydrolysis

4.0

3.8

0.2

Ammonia hydrolysis

4.0

0.5

3.5

Solvent hydrolysis

0.0

6.0

-6.0

Water hydrolysis

0.0

0.5

-0.5

Surfactant hydrolysis

0.0

0.5

-0.5

Wet oxidation hydrolysis 3

4

0.0

0.5

-0.5

Hydrothermal hydrolysis Steam explosion hydrolysis

36.0 24.0

33.9 20.2

2.1 3.8

Liquid hot water hydrolysis

8.0

3.3

4.7

Hydrothermal hydrolysis in general

4.0

1.6

2.4

Autohydrolysis

0.0

7.7

-7.7

Hot compressed water hydrolysis

0.0

1.1

-1.1

Mechanical hydrolysis Milling hydrolysis

8.0 8.0

2.2 1.6

5.8 6.4

Microwave hydrolysis

0.0

0.5

-0.5

Ultrasound hydrolysis

0.0

0.0

0.0

N paper (%) review, The number of papers in the sample of 25 most cited papers; N paper (%) sample, The number of papers in the population sample of 183 papers.

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TABLE 24.4 The Most Prolific Research Fronts for Wood Biomass Used for Wood Hydrolysis No. 1 2

Research Fronts

N Paper (%) Sample

Surplus (%)

8.0 56.0 24.0

17.5 36.1 7.7

Douglas fir

16.0

1.6

14.4

8.0

9.8

−1.8

Pine

−9.5 19.9 16.3

8.0

16.9

−8.9

Hardwood Poplar

64.0 16.0

54.1 11.5

9.9 4.5

Olive tree

8.0

2.2

5.8

Other hardwood

8.0

3.3

4.7

Willow

8.0

2.2

5.8

Aspen

4.0

3.3

0.7

Bamboo

4.0

8.2

−4.2

Beech

4.0

1.6

2.4

Birch

4.0

2.7

1.3

Eucalyptus

4.0

10.4

−6.4

Hardwood in general

4.0

8.7

−4.7

Softwood in general 3

N Paper (%) Review

Wood in general Softwood Spruce

N paper (%) review, The number of papers in the sample of 25 most cited papers; N paper (%) sample, The number of papers in the population sample of 183 papers.

On the other hand, spruce and Douglas fir are the most prolific softwoods used in these studies with 24% and 16% of reviewed papers, respectively. Similarly, the most prolific hardwood is poplar with 16% of reviewed papers. Further, softwood, spruce, and Douglas fir are largely over-represented in reviewed papers with 20%, 16%, and 14% surplus, respectively. The other over-represented woods are hardwood, olive tree, and willow with a 6%–10% surplus each. Similarly, wood in general, softwood in general, eucalyptus, and hardwood, in general, are under-represented by 10%, 9%, 6%, and 5% deficit, respectively.

24.4.2 Enzymatic Hydrolysis of Wood Combined with Other Pretreatments There are 16 HCPs for the research front of enzymatic hydrolysis of wood combined with other pretreatments (Table 24.1). 24.4.2.1 Enzymatic Hydrolysis of Wood Combined with Hydrothermal Pretreatments Studer et al. (2011) tested 47 extreme phenotypes from poplar trees for total sugar release through enzymatic hydrolysis alone as well as through combined LHW pretreatment and enzymatic hydrolysis and observed that there was a strong negative correlation between sugar release and lignin content for pretreated samples with an S/G ratio less than two. Further, Ko et al. (2015) explored the effect of LHW pretreatment severity on the properties of hardwood lignin and enzymatic hydrolysis of cellulose and observed that hardwood pretreated with LHW at severities ranging from log Ro = 8.25 to 12.51 resulted in 80%–90% recovery of the initial lignin in the residual solids. Kumar et al. (2012) investigated the effect of cellulose accessibility and enzyme loading on the efficiency of enzymatic hydrolysis of steam-pretreated Douglas fir and observed that the lignin

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component significantly influenced the swelling and accessibility of cellulose at low enzyme loadings of 5 FPU/g of cellulose. Further, Cara et al. (2006) carried out the enhanced enzymatic hydrolysis of olive trees by SE and alkaline H2O2 delignification and observed that delignification enhanced enzymatic hydrolysis yields of steam-pretreated olive tree wood. Grous et al. (1986) explored the effect of batch SE pretreatment on the rate of subsequent enzymatic hydrolysis of hybrid poplar and observed that this pretreatment was effective as the glucose yield obtained after 24 h of enzymatic hydrolysis was in excess of 90% of the potential. Further, Cantarella et al. (2004) investigated the effect of fermentation inhibitors during SE pretreatment of poplar on subsequent enzymatic hydrolysis and SSF and observed that acetic acid, furfural, 5-HMF, syringaldehyde, 4-hydroxybenzaldehyde, and vanillin did not significantly affect the enzymatic activity, whereas formic acid inactivated the enzymes and levulinic acid partially affected cellulase. Pan et al. (2005) developed strategies to enhance enzymatic hydrolysis of pretreated Douglas fir by reducing the effect of this residual lignin on enzymatic hydrolysis of cellulose with a high residual lignin content and developed two strategies for reducing the effect of this residual lignin on enzymatic hydrolysis of cellulose: mild alkali extraction and protein addition. Further, Rahikainen et al. (2011) explored the interaction of cellulases with softwood lignin with Celluclast and lignin-rich residues and observed that both lignin preparations inhibited the hydrolysis of microcrystalline cellulose. Lu et al. (2002) determined sugar yield and enzyme adsorption profile obtained during hydrolysis of SO2-catalyzed steam-exploded Douglas fir and post-pretreated steam-exploded Douglas fir substrates and observed that the rates and yield of hydrolysis attained from the post-pretreated Douglas fir were significantly higher, even at lower enzyme loadings, than those obtained with the corresponding steam-exploded Douglas fir. 24.4.2.2  Enzymatic Hydrolysis of Wood Combined with Chemical Pretreatments Zhu et al. (2009b) developed a process using sulfite pretreatment (SPORL) for robust and efficient bioconversion of softwoods and obtained more than 90% cellulose conversion of substrate with enzyme loading of cellulase plus β-glucosidase. Further, Zhu et al. (2009a) used sulfite pretreatment (SPORL) for enzymatic hydrolysis of spruce and red pine and obtained more than 90% cellulose conversion of the substrate with an enzyme loading of cellulase plus β-glucosidase. Further, Zhao et al. (2008) optimized alkaline pretreatment of spruce at a low temperature in both the presence and absence of urea to enhance enzymatic hydrolysis of spruce and observed that the enzymatic hydrolysis rate and efficiency could be significantly improved by this pretreatment. Cara et al. (2008) obtained sugars from olive tree biomass by dilute H2SO4 pretreatment and further enzymatic hydrolysis of the pretreated solid residues and, they recovered 83% of hemicellulosic sugars in the raw material in the prehydrolysate obtained at 170°C and 1% H2SO4 concentration. Further, Wyman et al. (2009) used the leading pretreatment technologies based on AFEX, aqueous ammonia recycle, dilute H2SO4, lime (Ca(OH)2), neutral pH, and sulfur dioxide (SO2) for poplar and hydrolyzed the remaining solids from each technology to sugars and observed that poplar was more recalcitrant to conversion to sugars and that sugar yields from the combined operations of pretreatment and enzymatic hydrolysis varied more among pretreatments. Tengborg et al. (2001) investigated the reduced inhibition of enzymatic hydrolysis of steam-pretreated softwood and observed that the prehydrolysate inhibited cellulose conversion in the enzymatic hydrolysis step. Further, Gupta et al. (2009) hydrolyzed and fermented P. juliflora for ethanol production by S. cerevisiae and P. stipitis-NCIM 3498 and observed that dilute H2SO4 (3.0%, v/v) pretreatment resulted in hydrolysis of hemicelluloses to pentose sugars along with fermentation inhibitors such as furfural, HMF, phenolics, and acetic acid. These HCPs present a sample of research primarily for enzymatic hydrolysis of wood in a combination of chemical, hydrothermal, and mechanical pretreatments. It is notable that enzymatic pretreatments aided by these pretreatments play a crucial role in wood hydrolysis to improve sugar yield.

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24.4.3 Other Issues Regarding Wood Hydrolysis There are nine HCPs for other issues regarding wood hydrolysis (Table 24.2). These are hydrolysate detoxification, sole enzymatic, sole acid, sole ionic liquid, and sole hydrothermal hydrolysis of wood with one to three HCPs each. 24.3.2.1  Hydrolysate Detoxification Larsson et al. (1999b) detoxified dilute acid hydrolysates of spruce to improve both cell growth and ethanol production by S. cerevisiae and observed that anion exchange at pH 5.5 or 10 and pretreatment with laccase, Ca(OH)2, and T. reesei were the most efficient detoxification methods. Further, Jonsson et al. (1998) detoxified wood hydrolysates with laccase and peroxidase to increase the fermentability of willow hydrolysate pretreated with steam and SO2 and observed a more rapid consumption of glucose and increased ethanol productivity for samples treated with laccase. 24.3.2.2  Sole Enzymatic Hydrolysis Mooney et al. (1998) used four Douglas fir pulps – a RMP, sulfonated RMP, delignified RMP, and a kraft pulp – and observed that the proportion of lignin did not affect enzyme adsorption when the fibers were sufficiently swollen. Further, Xiao et al. (2004) determined the inhibition effects of glucose and other sugar monomers during cellulase and β-glucosidase hydrolysis of two types of Avicel cellulose and acetic acid-pretreated softwood and observed that the increased glucose content in the hydrolysate resulted in a dramatic increase in the degrees of inhibition on both β-Glucosidase and cellulase activities. Finally, Zhang et al. (2007) screened 34 isolates of white rot fungi for the biological pretreatment of bamboo culms (P. pubescence) and observed that the sugar yield of bamboo culms pretreated with these two fungi through enzymatic hydrolysis increased with increasing pretreatment time. 24.3.2.3  Sole Acid Hydrolysis of Wood Larsson et al. (1999a) investigated the effect of the severity (CS) of dilute H2SO4 hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate and observed that when the CS of hydrolysis conditions increased, the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose and 3.0 and 3.4 for glucose above which it decreased. Further, Taherzadeh et al. (1997) fermented dilute acid hydrolysates from alder, aspen, birch, willow, pine, and spruce and observed that fermentability was quite different for different wood species, and only hydrolysates of spruce, pine, and willow could be completely fermented within 24 h. 24.3.2.4  Sole Ionic Liquid Hydrolysis of Wood Lee et al. (2009) used [EMIM][CH3COO] to extract lignin from wood flour and observed that cellulose in the pretreated wood flour became far less crystalline without undergoing solubilization. 24.3.2.5  Sole Hydrothermal Hydrolysis of Wood Garrote et al. (1999) subjected E. globulus to hydrothermal pretreatments under mild operational conditions and observed that negligible effects were caused by hydrothermal treatments on both cellulose and lignin. These HCPs present a sample of research for hydrolysis of wood, other than enzymatic hydrolysis. It is notable that these pretreatments play a crucial role in wood hydrolysis to improve sugar yield.

24.5  CONCLUSION AND FUTURE RESEARCH Brief information about key research fronts covered by the 25 most-cited papers with at least 195 citations each is given under two primary headings: enzymatic hydrolysis and other issues regarding wood hydrolysis.

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The usual characteristics of these HCPs are that pretreatments are often used in combination with other pretreatments for hydrolysis of biomass. In this way, wood hydrolysis is effective in disrupting the cellulose microstructure resulting in improved sugar and bioethanol yield. Key findings on these research fronts should be read in light of increasing public concerns about climate change, greenhouse gas emissions, and global warming as these concerns have been certainly behind the boom in research on bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. These studies emphasize the importance of proper incentive structures for the efficient development and application of wood hydrolysis to enhance sugar and bioethanol yield of the biomass after hydrolysis of the biomass and the following fermentation of resultant hydrolysates in light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the United States, Canada, China, Japan, and Europe had developed strong incentive structures for the effective development and application of hydrothermal pretreatments for bioethanol and sugar production. With the recent supply shocks, for example, due to the COVID-19 pandemic and Russian invasion of Ukraine, it is expected that public incentives for research and development of bioethanol fuels as a green alternative to crude oil-based gasoline and diesel fuels would increase in the coming years. In this context, the stakeholders involved in wood hydrolysis would have a significant firstmover advantage. It is recommended that such review studies are performed for the primary research fronts of wood hydrolysis as well as biomass constituents, softwood and hardwood.

ACKNOWLEDGMENTS The contribution of highly cited researchers in the field of wood hydrolysis has been gratefully acknowledged.

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25 Scientometric Study Straw Hydrolysis Ozcan Konur (Formerly) Ankara Yildirim Beyazit University

25.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), COVID-19 pandemic (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2001) in the aftermath of Russian invasion of Ukraine (Reeves, 2014) and COVID-19 pandemic. However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol fuel (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to its production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and hydrolysates, respectively. Wheat straw (Alemdar and Sain, 2008a,b) and rice straw (Daifullah et al., 2007; Gadde et al., 2009) have been among the most studied biomass for the bioethanol production (Binod et al., 2010; Hans et al., 2019). In this context, the research in the field of straw hydrolysis (Kristensen et al., 2007; Satlewal et al., 2018; Tan et al., 2021) has thus intensified in recent years. The enzymatic hydrolysis (Chen et al., 2008; Tabka et al., 2006; Taniguchi et al., 2005) combined with chemical (Hsu et al., 2010; Kootstra et al., 2009; Kumar et al., 2016), hydrothermal (Bjerre et al., 1996; Garcia-Aparicio et al., 2006; Tabka et al., 2006), and mechanical pretreatments (Da Silva et al., 2010; Hideno et al., 2009; Ma et al., 2009) of straw have been widely researched to increase the sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures (North) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As there have been no scientometric studies on the straw hydrolysis as of May 2022, this book chapter presents a scientometric study of the research in straw hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts.

DOI: 10.1201/9781003226499-33

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25.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This extended keyword list was provided in the appendix for future replication studies. As a second step, two sets of data were used for this study. First, a population sample of around 1,075 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 108 most cited papers, corresponding to 10% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the straw hydrolysis. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

25.3 RESULTS 25.3.1  The Most Prolific Documents in the Straw Hydrolysis The information on the types of documents for both datasets is given in Table 25.1. The articles and conference papers dominate both the sample (99%) and population (99%) papers. Further, surprisingly, there are no papers indexed by Scopus as a review. It is further notable that 98% of the population papers were published in journals while 2% of them were published in book series. On the contrary, all of the sample papers were published in the journals.

25.3.2  The Most Prolific Authors in the Straw Hydrolysis The information about the 20 most prolific authors with at least 2.8% of sample papers each is given in Table 25.2. The most prolific author is Mercedes Ballesteros with 5.6% of the sample papers, followed by Hongzhang Chen and Keikhosro Karimi with 4.6% of the sample papers each. The other prolific TABLE 25.1 Documents in the Straw Hydrolysis Documents Article Conference paper Review Short Survey Note Letter Book chapter Book Editorial Sample size

Sample Dataset (%) 95.4 3.7 0.0 0.0 0.0 0.9 0.0 0.0 0.0 108

Population Dataset (%) 96.6 2.5 0.6 0.0 0.0 0.3 0.2 0.0 0.0 1,075

Surplus (%) −1.2 1.2 −0.6 0.0 0.0 0.6 −0.2 0.0 0.0

Population dataset, The number of papers (%) in the set of the 1,075 population papers; Sample dataset, The number of papers (%) in the set of 108 highly cited papers.

No.

Population Papers (%)

Surplus

Country

HI

N

5.6 4.6 4.6 3.7

1.5 1.6 0.7 1.2

4.1 3.0 3.9 2.5

CIEMAT Chinese Acad. Sci. Isfahan Univ. CIEMAT

Spain China Iran Spain

49 45 54 37

134 230 212 72

6602732963 8919301300 8919301100 7404346720 6701407496

3.7 3.7 3.7 3.7 3.7

0.8 0.7 0.7 0.7 0.6

2.9 3.0 3.0 3.0 3.1

CIEMAT Wuhan Inst. Technol. Wuhan Inst. Technol. Huazhong Agr. Univ. Univ. Boras

Spain China China China Sweden

37 19 27 50 64

70 449 201 292 405

EH, CH, HH EH, CH, HH CH EH, CH, HH, MH EH, CH, HH EH, CH, MH EH, CH, MH EH, CH, MH CH

7102150211 7006656876 32267539500 7202946302 7402417332 7007102939 57194220606 57200761078 35731148500 55733159900 7203077540

3.7 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

0.6 0.8 0.8 0.8 0.8 0.7 0.7 0.6 0.6 0.6 0.5

3.1 2.0 2.0 2.0 2.0 2.1 2.1 2.2 2.2 2.2 2.3

Tech. Univ. Denmark USDA Agr. Res. Serv. Chinese Acad. Sci. USDA Agr. Res. Serv. Chinese Acad. Sci. Washington State Univ. CIEMAT Univ. Valladolid AIST AIST Chalmers Univ. Technol.

Denmark USA China USA China USA Spain Spain Japan Japan Sweden

38 50 23 50 45 70 34 25 39 29 59

62 186 49 159 216 286 53 73 113 84 244

HH EH, CH EH, CH EH, CH EH, CH HH EH, HH, MH EH, CH EH, HH, MH EH, HH, MH EH, HH

Author Name

Author Code

1 2 3 4

Ballesteros, Mercedes* Chen, Hongzhang Karimi, Keikhosro Negro, Michael J.

7006110611 7501614171 10046195700 6701512649

5 6 7 8 9

Ballesteros, Ignacio Zhu, Shengdong Wu, Yuanxin Yu, Ziniu Taherzadeh, Mohammad J. Thomsen, Anne B.* Cotta, Michael A. Qi, Benkun* Saha, Badal C. Wan, Yinhua Ahring, Birgitte K.* Oliva, Jose M. Bolado, Silvia* Endo, Takashi Inoue, Hiroyuki Olsson, Lisbeth*

10 11 12 13 14 15 16 17 18 19 20

Sample Papers (%)

Institution

Res. Front

Straw Hydrolysis: Scientometric Study

TABLE 25.2 Most Prolific Authors in the Straw Hydrolysis

*, Female; Author code, the unique code given by Scopus to the authors; CH, Chemical hydrolysis; EH, Enzymatic hydrolysis; HH, Hydrothermal hydrolysis; HI, H-index; MH, Mechanical hydrolysis; N, number of papers published by each author; Population papers, the number of papers authored in the population dataset; Sample papers, the number of papers authored in the sample dataset.

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researchers are Michael J. Negro, Ignacio Ballesteros, Shengdong Zhu, Yuanxin Wu, Ziniu Yu, Mohammad J. Taherzadeh, and Anne B. Thomsen with 3.7% of the sample papers each. The most influential author is Mercedes Ballesteros with 4.1% surplus, followed by Keikhosro Karimi with 3.9% surplus. The other influential authors are Mohammad J. Taherzadeh, Anne B. Thomsen, Hongzhang Chen, Shengdong Zhu, Yuanxin Wu, and Ziniu Yu with 3.0%–3.1% surplus each. The most prolific institution for the sample dataset is the Center for Energy, Environmental and Technological Research (CIEMAT) and Chinese Academy of Sciences with four and three authors, respectively, while the National Institute of Advanced Industrial Science and Technology (AIST), USDA Agricultural Research Service, and Wuhan Institute of Technology are the other prolific institutions with two authors each. In total, 13 institutions house these top authors. The most prolific country for these top authors is China with six authors, followed by Spain and the USA with four and three authors, respectively. Additionally, Japan and Sweden have two authors each. In total, six countries house these top authors. The most prolific research front is the enzymatic hydrolysis of straw with 16 authors, while the other prolific research fronts are the chemical, hydrothermal, and mechanical hydrolysis of straw with 14, 10, and 7 authors, respectively. On the other hand, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only six of these top researchers are female with a representation rate of 30%. Additionally, there are other authors with the relatively low citation impact and with 0.7%–1.1% of the population papers each: Yongcan Jin, Caoxing Huang, Chenhuan Lai, Ines C. Roberto, Xinxing Wu, Qiang Yong, Ken Tokuyasu, Ruchi Agrawal, Claus Felby, Mats Galbe, Ravi P. Gupta, Lata Nain, Wei Tang, Anju Arora, Rosana Goldbeck, Alberto Gonzales, Akihiro Kondo, Jose A. Ramirez, Alok Satlewal, Riki Shiroma, Elia Tomas-Pejo, Manuel Vazquez, and Keith W. Waldron.

25.3.3  The Most Prolific Research Output by Years in Straw Hydrolysis Information about papers published between 1970 and 2022 is given in Figure 25.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily 16 14

Number of papers (%)

12

Population papers Sample papers

10 8 6 4 2 0

FIGURE 25.1  The research output by years regarding the straw hydrolysis. This Figure shows the number of papers in percentages for the period between 1970 and 2022 for both sample and population papers. The bulk of the sample papers were published between 2005 and 2015 peaking in 2009 while the bulk of the population papers were published between 2008 and 2022, peaking in 2018. There was a rising trend for the research output for the population papers between 2008 and 2014, and thereafter, it lost its momentum becoming flat.

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in the 2010s with 64% of the population dataset. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 12%, 10%, 4%, 5%, and 1%, respectively. Additionally, 1% of the population papers were published between 1920 and 1969. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 49 and 41% of the sample dataset, respectively. The publication rates for the 1990s, 1980s, and 1970s were 8%, 2%, and 0% of the sample papers, respectively. Additionally, 1% of the sample papers were published in the pre-1970s and 2020s each. The most prolific publication year for the population dataset is 2018 with 7.8% of the dataset while 82% of the population papers were published between 2009 and 2022. Similarly, 84% of the sample papers were published between 2003 and 2015 while the most prolific publication years are 2009 with 14.8% of the sample papers. The other prolific years are 2011, 2006, and 2008 with 11.1%, 10.2%, and 8.3% of the sample papers, respectively.

25.3.4  The Most Prolific Institutions in the Straw Hydrolysis Information about the most prolific 15 institutions publishing papers on the straw hydrolysis with at least 2.8% of the sample papers each is given in Table 25.3. The most prolific institution is the Technical University of Denmark with 11.1% of the sample papers, followed by the Chinese Academy of Sciences and CIEMAT with 9.3% and 5.6% of the sample papers, respectively. The other prolific institutions are the Isfahan University of Technology, Huazhong Agricultural University, Wuhan Institute of Technology, and University of Boras with 3.7%–4.6% of the sample papers each. The top country for these most prolific institutions is China with five institutions, followed by Spain with two institutions. In total, only ten countries house these top institutions. On the other hand, the institution with the most citation impact is the Technical University of Denmark with 8.8% surplus, followed by the Chinese Academy of Sciences with 4.6% surplus. The other influential institutions are the Isfahan University of Technology, CIEMAT, and University of Boras with 3.1%–3.8% surplus each. Similarly, the institution with the least impact is the Tsinghua University with 1.6% surplus.

TABLE 25.3 The Most Prolific Institutions in Straw Hydrolysis No.

Institutions

Country

 1  2  3  4  5

Tech. Univ. Denmark Chinese Acad. Sci. CIEMAT Isfahan Univ. Technol. Huazhong Agr. Univ.

Denmark China Spain Iran China

 6  7  8  9 10 11 12 13 14 15

Wuhan Inst. Technol. Univ. Boras Tsinghua Univ. USDA Agr. Res. Serv. Natl. Inst. Adv. Ind. Sci. Technol. Harbin Inst. Technol. Univ. Valladolid Inst. Nucl. Ener. Res. Ener. Res. Ctr. Helsinki Univ.

China Sweden China USA Japan China Spain Taiwan Netherlands Finland

Population Papers (%)

Surplus (%)

11.1 9.3 5.6 4.6 3.7

2.3 4.7 1.9 0.8 1.1

8.8 4.6 3.7 3.8 2.6

3.7 3.7 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8

0.9 0.6 1.2 1.1 0.8 0.7 0.7 0.6 0.4 0.3

2.8 3.1 1.6 1.7 2.0 2.1 2.1 2.2 2.4 2.5

Sample Papers (%)

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Additionally, there are other institutions with the relatively low citation impact and with 0.7%–3.3% of the population papers each: Nanjing Forestry University, South China University of Technology, University of Sao Paulo, Lund University, State University of Campinas, Copenhagen University, Korea University, USDA Agricultural Research Service, National Technical University of Athens, Sao Paulo State University, National Agriculture and Food Research Organization, Indian Agricultural Research Institute, Nanjing Agricultural University, Shanghai Jiao Tong University, Nanjing Tech University, Guru Nanak Dev University, CSIC, King Mongkut’s University of Technology, University of Jaen, Beijing University of Chemical Technology, Food Research Institute, Indian Oil Corporation Inc., Henan Agricultural University, Indian Institute of Technology Guwahati, Tamaulipas Autonomous University, Northwest A&F University, and National Energy and Geology Laboratory.

25.3.5  The Most Prolific Funding Bodies in the Straw Hydrolysis Information about the ten most prolific funding bodies funding at least 2.8% of the sample papers each is given in Table 25.4. Only 47% and 56% of the sample and population papers were funded, respectively. The most prolific funding body is the National Natural Science Foundation of China with 6.5% of the sample papers, closely followed by the Ministry of Science and Technology of China with 5.6% of the sample papers. The other prolific bodies are the National Basic Research Program of China (973 Program) and Seventh Framework Program with 3.7% of the sample papers each. On the other hand, the most prolific country for these top funding bodies is China with four funding bodies, followed by the EU and Spain with two bodies each. In total, only four countries and the EU house these top funding bodies. The funding body with the most citation impact is the National Basic Research Program of China (973 Program) with 2.5% surplus, followed by the Junta of Castile and Leon, Ministry of Science and Innovation, and Seventh Framework Program with 2.1%–2.3% surplus each. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 7.6% deficit. The other funding bodies with the relatively low citation impact and with 0.8%–2.6% of the population papers each are Research Support Foundation of the State of Sao Paulo, National Key Research and Development Program of China, National Council for Scientific and Technological Development, Higher Education Personnel Improvement Coordination, Priority Academic Program Development of Jiangsu Higher Education Institutions, Biotechnology and Biological Sciences Research Council, China Scholarship Council, Swedish Energy Agency, Ministry of Education, Culture, Sports, Science and TABLE 25.4 The Most Prolific Funding Bodies in straw Hydrolysis No.  1  2  3  4  5  6  7  8  9 10

Funding Bodies Natl. Natr. Sci. Found. China Minist. Sci. Technol. China Seventh Framew. Prog. Natl. Basis Res. Prog. China Minist. Sci. Innov. Junta of Castile and Leon Eur. Commis. Chinese Acad. Sci. Minist. Educ. Sci. Technol. Minist. Sci. Technol.

Country

Sample Paper No. (%)

Population Paper No. (%)

China China EU China Spain Spain EU China Japan India

6.5 5.6 3.7 3.7 2.8 2.8 2.8 2.8 2.8 2.8

14.1 3.8 1.6 1.2 0.6 0.5 2.5 2.3 1.6 1.3

Surplus (%) −7.6 1.8 2.1 2.5 2.2 2.3 0.3 0.5 1.2 1.5

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Technology, Ministry of Science and Innovation, National Research Foundation of Korea, Department of Science and Technology, Ministry of Science and Technology, Fundamental Research Funds for the Central Universities, Ministry of Education of China, National Science Foundation, Indian Council of Agricultural Research, Ministry of Agriculture, Forestry and Fisheries, UK Research and Innovation, Japan Society for the Promotion of Science, Ministry of Finance of Japan, Nanjing Forestry University, and Special Fund for Agro-scientific Research in the Public Interest.

25.3.6  The Most Prolific Source Titles in the Straw Hydrolysis Information about the most prolific 17 source titles publishing at least 1.9% of the sample papers each in straw hydrolysis is given in Table 25.5. The most prolific source title is Bioresource Technology with 38% of the sample papers. The other prolific journals are Biomass and Bioenergy, Biotechnology and Bioengineering, Applied Biochemistry and Biotechnology, Enzyme and Microbial Technology, Process Biochemistry with 4.6%–7.4% of the sample papers each. On the other hand, the source title with the most citation impact is the Bioresource Technology with 19% surplus. The other influential titles are Biomass and Bioenergy, Biotechnology and Bioengineering, Enzyme and Microbial Technology, and Biosystems Engineering with 3.0%–3.8% surplus each. Similarly, the source title with the least impact is Industrial Crops and Products with 1.4% deficit. The other source titles with the relatively low citation impact with 0.7%–2.1% of the population paper each are Renewable Energy, Bioresources, Biomass Conversion and Biorefinery, Journal of Chemical Technology and Biotechnology, Journal of Agricultural Science, RSC Advances, Advanced Materials Research, Bioprocess and Biosystems Engineering, Waste and Biomass Valorization, Bioenergy Research, Preparative Biochemistry and Biotechnology, Bioresource Technology Reports, Industrial and Engineering Chemistry Research, Journal of Agricultural and Food Chemistry, Journal of Biobased Materials and Bioenergy, Journal of Bioscience and Bioengineering, ACS Sustainable Chemistry and Engineering, and Journal of Cleaner Production.

TABLE 25.5 The Most Prolific Source Titles in Straw Hydrolysis No.  1  2  3  4  5  7  6  8  9 10 11 12 13 14 15 16 17

Source Titles Bioresource Technology Biomass and Bioenergy Biotechnology and Bioengineering Applied Biochemistry and Biotechnology Enzyme and Microbial Technology Process Biochemistry Biosystems Engineering Applied Microbiology and Biotechnology Biotechnology for Biofuels Carbohydrate Polymers Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Biochemical Engineering Journal Biotechnology Progress European Journal of Applied Microbiology and Biotechnology Fuel Industrial Crops and Products Journal of Biotechnology

Sample Papers (%)

Population Papers (%)

Surplus (%)

38.0 7.4 5.6 4.6 4.6 4.6 3.7 2.8 2.8 2.8 1.9

19.3 3.6 2.0 3.7 1.1 2.7 0.7 1.6 3.2 1.2 0.5

18.7 3.8 3.6 0.9 3.5 1.9 3.0 1.2 −0.4 1.6 1.4

1.9 1.9 1.9

0.7 0.6 0.4

1.2 1.3 1.5

1.9 1.9 1.9

0.5 3.3 0.7

1.4 −1.4 1.2

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TABLE 25.6 The Most Prolific Countries in the Straw Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Countries China USA Spain Denmark India Sweden Iran Japan South Korea France Netherlands Brazil Taiwan Germany Finland

Sample Papers (%) 25.9 14.8 13.0 12.0 5.6 5.6 5.6 4.6 4.6 3.7 3.7 2.8 2.8 2.8 2.8

Population Papers (%) 29.8 9.2 6.2 4.1 12.9 3.0 2.2 5.6 4.4 2.4 1.2 4.9 1.9 1.5 1.2

Surplus (%) −3.9 5.6 6.8 7.9 −7.3 2.6 3.4 −1.0 0.2 1.3 2.5 −2.1 0.9 1.3 1.6

25.3.7  The Most Prolific Countries in the Straw Hydrolysis Information about the most prolific 15 countries publishing at least 2.8% of sample papers each in straw hydrolysis is given in Table 25.6. The most prolific country is China with 26% of the sample papers, followed by the USA, Spain, and Denmark with 15%, 13%, and 12% of the sample papers, respectively. The other prolific countries are India, Sweden, Iran, Japan, and South Korea with 5%–6% of the sample papers each. Further, six European countries listed in Table 25.6 produce 40% and 17% of the sample and population papers, respectively. On the other hand, the country with the most citation impact is Denmark with 7.9% surplus, followed by Spain and the USA with 6.8% and 5.6% surplus, respectively. The other influential countries are Iran, Sweden, and Netherlands with 2.5% to 3.6% of the sample papers each. Similarly, the country with the least citation impact is India with 7.3% deficit, followed by China, Brazil, and Japan with 4%, 2%, and 1% deficit, respectively. Additionally, there are other countries with relatively low citation impact and with 0.5%–4.5% of the sample papers each: the UK, Thailand, Canada, Portugal, Mexico, Greece, Poland, Turkey, Egypt, Vietnam, Australia, Austria, Italy, Pakistan, Indonesia, Israel, Malaysia, Slovakia, South Africa, Norway, and Russia.

25.3.8  The Most Prolific Scopus Subject Categories in the Straw Hydrolysis Information about the most prolific eight Scopus subject categories indexing at least 7.4% of the sample papers each is given in Table 25.7. The most prolific Scopus subject category in the straw hydrolysis is Chemical Engineering with 69% of sample papers, closely followed by Environmental Science and Energy with 54% and 52% of the sample papers, respectively. The other prolific category is Biochemistry. Genetics and Molecular Biology, Immunology and Microbiology, and Agricultural and Biological Sciences accounted for

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TABLE 25.7 The Most Prolific Scopus Subject Categories in the Straw Hydrolysis No. 1 2 3 4 5 6 7 8

Scopus Subject Categories Chemical Engineering Environmental Science Energy Biochemistry. Genetics and Molecular Biology Immunology and Microbiology Agricultural and Biological Sciences Chemistry Engineering

Sample Papers (%)

Population Papers (%)

Surplus (%)

68.5 53.7 51.9 39.8

50.5 39.9 42.3 33.5

18.0 13.8 9.6 6.3

32.4 15.7 8.3 7.4

22.2 18.6 13.8 8.9

10.2 −2.9 −5.5 −1.5

40%, 32%, and 16% of the sample papers, respectively. It is notable that the Social Sciences including Economics and Business account for only 1.7% of the population studies. On the other hand, the Scopus subject category with the most citation impact is the Chemical Engineering with 18% surplus, closely followed by Environmental Science, Immunology and Microbiology, and Energy with 14%, 10%, and 10% surplus, respectively. Similarly, the Scopus subject categories with the least citation impact are Chemistry, Agricultural and Biological Sciences, and Engineering with 6%, 3%, and 2% deficit, respectively.

25.3.9  The Most Prolific Scopus Keywords in the Straw Hydrolysis Information about the keywords used with at least 9.3% or 5.4% of the sample or population papers, respectively, is given in Table 25.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most prolific keyword set provided by Scopus database. These keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. The most prolific keyword related to the biomass and biomass constituents is wheat with 82% of the sample papers, followed by straw, cellulose, lignin, and rice with 69%, 53%, 48%, and 48% of the sample papers, respectively. The other prolific keywords are rice straw, biomass, lignocellulose, wheat straw, and hemicellulose with 26%–41% of the sample papers each. The most prolific keyword related to the straw hydrolysis is hydrolysis with 82% of the sample papers, followed by enzymatic hydrolysis and saccharification with 52% and 29% of the sample papers, respectively. The most prolific keyword related to the biomass pretreatments is pretreatment with 50% of the sample papers, followed by enzyme activity, cellulases, and enzymes with 43%, 38%, and 25% of the sample papers, respectively. The most prolific keyword related to the other processes is fermentation with 43% of the sample papers, followed by biotechnology and Saccharomyces cerevisiae with 27% and 17% of the sample papers, respectively. Further, the most prolific keyword related to the hydrolysis products is sugar with 69% of the sample papers, followed by glucose, ethanol, and xylose with 43%, 42%, and 23% of the sample papers, respectively. It is notable that only 11.1% of the indexed papers employ the bioethanol keyword. Further, the most influential keywords are sugar, wheat, hydrolysis, rice, pretreatment, straw, Saccharomyces cerevisiae, enzyme activity, biotechnology, and glucose with 16%–40% surplus each.

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TABLE 25.8 The Most Prolific Keywords in Straw Hydrolysis No. 1

2

Keywords

4

Population Papers (%)

Surplus (%)

Wheat

82.4

50.7

31.7

Straw

68.5

51.1

17.4

Cellulose

52.8

41.4

11.4

Lignin

48.1

36.9

11.2

Rice

47.2

28.1

19.1

Rice straw

40.7

39.5

1.2

Biomass

36.1

25.6

10.5

Lignocellulose

28.7

15.9

12.8

Wheat straw

26.9

38.8

−11.9

Hemicellulose

25.9

15.1

10.8

Carbohydrates

18.5

15.5

3.0

Lignocellulosic biomass

13.0

11.3

1.7

Xylan

10.2

5.4

4.8

Hydrolysis Hydrolysis

82.4

53.7

28.7

Enzymatic hydrolysis

51.9

38.7

13.2

Saccharification

28.7

32.5

−3.8

Enzymatic saccharification 3

Sample Papers (%)

Biomass

9.3

10.0

−0.7

Pretreatments Pretreatment

50.0

31.2

18.8

Enzyme activity

42.6

26.3

16.3

Cellulases

38.0

26.0

12.0

Enzymes

35.2

24.2

11.0

Alcohol

24.1

11.2

12.9

Temperature

23.1

10.7

12.4

Steam

21.3

8.5

12.8

Enzymolysis

19.4

8.0

11.4

Fungi

16.7

11.4

5.3

Yeast

14.8

10.9

3.9

Sulfuric acids

13.0

7.2

5.8

Enzyme kinetics

12.0

0.0

12.0

Delignification

11.1

8.2

2.9

Water

11.1

4.8

6.3

pH

10.2

7.6

2.6

Glucosidase

10.2

4.6

5.6

Sodium hydroxide

9.3

6.1

3.2

Xylosidase

9.3

3.9

5.4

Other processes Fermentation

42.6

30.1

12.5

Biotechnology

26.9

11.2

15.7

Saccharomyces cerevisiae

16.7

0.0

16.7 (Continued )

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TABLE 25.8 (Continued ) The Most Prolific Keywords in Straw Hydrolysis No.

5

Population Papers (%)

Surplus (%)

Degradation

Keywords

Sample Papers (%) 13.0

6.2

6.8

SSF

10.2

6.0

4.2

Hydrolysis products Sugar

69.4

29.1

40.3

Glucose

42.6

27.0

15.6

Ethanol

41.7

28.4

13.3

Xylose

23.1

12.4

10.7

Biofuel

17.6

15.4

2.2

Furfural

13.9

5.3

8.6

Alcohol production

12.0

5.9

6.1

Bioethanol

11.1

14.9

−3.8

Acetic acid

11.1

5.4

5.7

Ethanol production

10.2

6.8

3.4

Fermentable sugars

10.2

5.8

4.4

Polysaccharides

8.3

10.9

−2.6

Bioethanol production

6.5

7.2

−0.7

25.3.10  The Most Prolific Research Fronts in Straw Hydrolysis Information about the thematic research fronts for the sample papers in straw hydrolysis is given in Table 25.9. As this table shows, there are five primary research fronts for this field: the enzymatic, chemical, hydrothermal, mechanical hydrolysis of straw and hydrolysis of straw in general with 73%, 53%, 19%, 12%, and 19% of the sample papers, respectively. The most prolific chemical hydrolysis is alkaline and acid hydrolysis with 17% and 14% of the sample papers, respectively. The other prolific chemical hydrolysis is solvent, ammonia, ionic liquid, and hydrogen peroxide (H2O2) hydrolysis with 3%–7% of the sample papers each. Similarly, the most prolific hydrothermal hydrolysis is steam explosion hydrolysis with 10% of the sample papers. The other hydrothermal hydrolysis is wet oxidation hydrolysis, hydrothermal hydrolysis in general, and liquid hot water hydrolysis with 2%–3% of the sample papers each. Finally, microwave and milling hydrolysis are the most prolific mechanical hydrolysis with 7% and 5% of the sample papers, respectively. Table 25.10 provides data on the straw biomass used in the studies for the straw hydrolysis. There are two primary research fronts: wheat straw and rice straw with 59% and 36% of the sample papers, respectively. The other prolific research fronts are barley, sorghum, soybean, and sugarcane straw with 2%–6% of the sample papers each. The other straw accounts for 14.9% of the sample papers. The most prolific straws are barley and sorghum straw.

25.4 DISCUSSION 25.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, fuel cells, and biochemical production in a biorefinery context.

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TABLE 25.9 The Most Prolific Thematic Research Fronts the Straw Hydrolysis No. 1 2

Research Fronts

N Paper (%) Sample

Enzymatic hydrolysis Chemical hydrolysis Alkaline hydrolysis

73.1 52.8 16.7

Acid hydrolysis

13.9

Solvent hydrolysis

7.4

Ammonia hydrolysis

4.6

Ionic liquid hydrolysis

4.6

H2O2 hydrolysis

2.8

CO2 hydrolysis

0.9

Ozone hydrolysis

0.9

Surfactant hydrolysis 3

0.9

Hydrothermal hydrolysis Steam explosion hydrolysis

18.5 10.2

Wet oxidation hydrolysis

2.8

Hydrothermal hydrolysis in general

1.9

Liquid hot water hydrolysis

1.9

Autohydrolysis

0.9

Hot compressed water hydrolysis 4

0.9

Mechanical hydrolysis Microwave hydrolysis

12.0 6.5

Milling hydrolysis 5

4.6

Ultrasound hydrolysis

0.9

Hydrolysis in general

18.5

N paper (%) sample, The number of papers in the population sample of 108 papers.

TABLE 25.10 The Most Prolific Research Fronts the Straw Biomass Used for the Straw Hydrolysis No.

Research Fronts

1 2 3

Wheat straw Rice straw Other straw Barley straw

N Paper (%) Sample 54.6 33.3 14.9 5.6

Sorghum straw

2.8

Soybean straw

1.9

Sugarcane straw

1.9

Corn straw

0.9

Rapeseed straw

0.9

Rye straw

0.9

N paper (%) sample, The number of papers in the population sample of 108 papers.

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However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass. Wheat straw and rice straw have been among the most studied biomass for the bioethanol production. In this context, research in the field of straw hydrolysis has thus intensified in recent years. In recent years, enzymatic hydrolysis combined with chemical, hydrothermal, and mechanical pretreatments of straw has been widely researched to increase the sugar and bioethanol yield. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, COVID-19 shocks starting in December 2019, or warrelated shocks as in the case of Russian invasion of Ukraine starting in February 2022. The scientometric analysis has been widely used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As there have been no scientometric studies on the straw hydrolysis, this book chapter presents a scientometric study of the research in the straw hydrolysis as of May 2022. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replication studies. Further, a selected list of the keywords was presented in Table 25.8. As a second step, two sets of data were used for this study. First, a population sample of over 1,075 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 108 most cited papers, corresponding to 10% of the population dataset was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the straw hydrolysis. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

25.4.2  The Most Prolific Documents in the Straw Hydrolysis Articles (together with conference papers) dominate both the sample (99%) and population (99%) papers (Table 25.1). Further, surprisingly, there are no papers indexed by Scopus as a review. This is extremely interesting finding as straw has been one of the most studied biomass for the bioethanol fuel production. Scopus differs from the Web of Science database in differentiating and showing articles (95%) and conference papers (4%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared with those published only in the conference proceedings. Similarly, Scopus differs from Web of Science database in introducing short surveys (0%). Hence, the total number of articles and review papers in the sample dataset are 99% and 0%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus as well as in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, many of the papers classed as reviews in the population dataset by Scopus are not actually reviews, but ordinary articles. Similarly, there are two review papers for the sample data with 2% of the sample papers.

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In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance as it is done in some journals following good practice. It would also be helpful to use the document types listed in Table 25.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from the experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as well as it is done in the Web of Science database.

25.4.3  The Most Prolific Authors in the Straw Hydrolysis There have been 20 most prolific authors with at least 2.8% of the sample papers each as given in Table 25.2. These authors have shaped the development of the research in this field. The most prolific authors are Mercedes Ballesteros and to a lesser extent Keikhosro Karimi, Mohammad J. Taherzadeh, Anne B. Thomsen, Hongzhang Chen, Shengdong Zhu, Yuanxin Wu, and Ziniu Yu. It is notable that these top researchers are mostly from China and Europe. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for the names with more than two components such as ‘Blake Sam de Hyun Karimi’. The probable outcomes are ‘Karimi, B.S.D.H.’, ‘de Hyun Karimi, B.S.’, or ‘Hyun Karimi, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative while the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name by a hyphen if there are two words for the last name: ‘Hyun-Karimi, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening Chinese names. For example, ‘Yingyong Yu’ is often shortened as ‘’Yu, Y.’, ‘Yu, Y.-Y.’, and ‘Yu, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Yu Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PUBMED and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Yu, Y.Y.’. Therefore, there have been difficulties to locate papers for the Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors especially for the authors with common names such as ‘Yu, X.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies as well as allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Yu, X.’ in the databases. In this context, the coding of authors in Scopus database is a welcome innovation compared with the other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system especially for common names. For example, some of the papers for a certain author maybe allocated to another researcher with a different author code. It is possible that Scopus uses a number of software programs to differentiate the author names and the program may not be false-proof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Yu, H.J.’, ‘Yu’, ‘Yu, H.’, or ‘Yu, J.’ instead of ‘Yu, Hyun Jae’.

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There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Yu, A.P.C.’ might be given as ‘Yu, A.’, ‘Yu, A.P.’, ‘Yu, C.’, or ‘Yu, A.C.’ in the journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., ‘Çağatay, Gökçe’) rather than in the English spellings (e.g., ‘Cagatay, Gokce’) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in the English spellings. It is observed that national spellings of the author names do not help in conducting scientometric studies as well in allocating credits to the authors as sometimes there are the different author entries for the English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are the Chinese Academy of Sciences and to a lesser extent AIST, CIEMAT, USDA Agricultural Research Service, and Wuhan Institute of Technology. Further, the most prolific countries for the sample dataset are China and to a lesser extent Spain, the USA, Japan, and Sweden. These findings confirm the dominance of China and to a lesser extent the USA, Europe, and Japan in this field. The most prolific research fronts are the enzymatic hydrolysis of straw and to a lesser extent the hydrothermal, chemical, and mechanical hydrolysis of straw. This finding hints that enzymatic pretreatments are used in combination with other pretreatments to hydrolyze straw to obtain sugars and ethanol. It is also notable that there is significant gender deficit for the sample dataset as surprisingly only six of these top researchers are female with 30% representation rate. This finding is the most thought-provoking with strong public policy implications. Hence. Institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with strong gender deficit. In this context, it is worth noting the level of representation of researchers from minority groups in science on the basis of race, sexuality, age, and disability besides the gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

25.4.4  The Most Prolific Research Output by Years in the Straw Hydrolysis The research output observed between 1970 and 2022 is illustrated in Figure 25.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. However, it is notable that the research output for the population papers become flat after 2014, losing its momentum raising questions about the reasons for its stagnation after 2014. Especially, there has been no sharp rise in the research output in 2020 and 2021 after the supply shocks caused by the COVID 19 pandemic and the Ukrainian war. These findings suggest that the most prolific sample (90%) and population papers (86%) were primarily published in the last two decades. These are the thought-provoking findings. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field in the last two decades. Based on these findings, the size of the population papers likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming are translated efficiently to the research funding in this field. Furthermore, there is a crucial need for additional incentives for the research on the straw hydrolysis due to the current supply shocks due to the COVID-19 pandemic and Russian invasion of Ukraine as there have been public pressures for the replacement of crude oil-based gasoline and diesel fuels by bioethanol fuels and biodiesel fuels.

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25.4.5  The Most Prolific Institutions in the Straw Hydrolysis The 15 most prolific institutions publishing papers on the straw hydrolysis with at least 2.8% of the sample papers each given in Table 25.3 have shaped the development of the research in this field. The most prolific institutions are the Technical University of Denmark, the Chinese Academy of Sciences and to a lesser extent CIEMAT, Isfahan University of Technology, Huazhong Agricultural University, Wuhan Institute of Technology, and University of Boras. Further, the top countries for these most prolific institutions are China and, to a lesser extent, Spain. In total, only ten countries house these top institutions. On the other hand, the institutions with the most citation impact are the Technical University of Denmark and to a lesser extent the Chinese Academy of Sciences, Isfahan University of Technology, CIEMAT, and University of Boras. These findings confirm the dominance of the Chinese and European institutions in this research field.

25.4.6  The Most Prolific Funding Bodies in the Straw Hydrolysis The ten most prolific funding bodies funding at least 2.8% of the sample papers each are given in Table 25.4. It is notable that only 47% and 56% of the sample and population papers were funded, respectively. The most prolific funding bodies are the National Natural Science Foundation of China, the Ministry of Science and Technology of China and to a lesser extent the National Basic Research Program of China (973 Program) and Seventh Framework Program. Further, the most prolific countries for these top funding bodies are China and, to a lesser extent, Spain and the European Union (EU). In total, four countries and the EU house these top funding bodies. The funding bodies with the most citation impact are the National Basic Research Program of China (973 Program) and to a lesser extent the Junta of Castile and Leon, Ministry of Science and Innovation, and Seventh Framework Program. Further, the funding body with the least impact is the National Natural Science Foundation of China. These findings on the funding of the research in this field suggest that the level of the funding, mostly in the last two decades, is relatively modest, and it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in the light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding especially for the sample papers, there is ample room to enhance funding in this field. With the current supply shocks, it is expected that funding for the straw hydrolysis would increase substantially in the coming decades, especially for the crude oil- and foreign exchange-deficient countries. It is also remarkable that the China and to a lesser extent the Europe dominate the research funding in this field.

25.4.7  The Most Prolific Source Titles in Straw Hydrolysis The 17 most prolific source titles publishing at least 21.9% of the sample papers each in straw hydrolysis have shaped the development of the research in this field (Table 25.5). The most prolific source titles are Bioresource Technology and to a lesser extent Biomass and Bioenergy, Biotechnology and Bioengineering, Applied Biochemistry and Biotechnology, Enzyme and Microbial Technology, Process Biochemistry. Further, the source titles with the most citation impact are the Bioresource Technology, and to a lesser extent Biomass and Bioenergy, Biotechnology and Bioengineering, Enzyme and Microbial Technology, and Biosystems Engineering. Similarly, the source title with the least impact is Industrial Crops and Products. It is notable that these top source titles are primarily related to the bioresources, biomass, biotechnology, microbiology, and chemical engineering. This finding suggests that Bioresource Technology and the other prolific journals in this field have significantly shaped the development of the research in this field as they focus on the straw hydrolysis.

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25.4.8  The Most Prolific Countries in the Straw Hydrolysis The 15 most prolific countries publishing at least 2.8% of the sample papers each have significantly shaped the development of the research in this field (Table 25.6). The most prolific countries are China and to a lesser extent USA, Spain, Denmark, India, Sweden, Iran, Japan, and South Korea. On the other hand, the countries with the most citation impact are Denmark and to a lesser extent Spain, the USA, Iran, Sweden, and Netherlands. Further, six European countries listed in table 25.6 produce 40% and 17% of the sample and population papers, respectively, with 23% surplus. It is also notable that India is the second largest producer of the population papers with 13% after China. The close examination of these findings suggests that the USA, Europe, China, India, Iran, and Japan are the major producers of the research in this field. It is a fact that the USA has been a major player in science Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The USA has further developed a strong research infrastructure to support its corn and grass-based bioethanol industry (Vadas et al., 2008). However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009). Next, Europe has been a persistent player in the scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010). Additionally, Brazil has also been a persistent player in scientific research at a moderate level (Glanzel et al., 2006). Brazil has also developed a strong research infrastructure to support its biomass-based bioethanol industry (Macedo et al., 2008).

25.4.9  The Most Prolific Scopus Subject Categories in Straw Hydrolysis The eight most prolific Scopus subject categories indexing at least 7.4% of the sample papers each, given in Table 25.7, have shaped the development of the research in this field. The most prolific Scopus subject categories in the straw hydrolysis are Chemical Engineering and to a lesser extent Environmental Science, Energy, Biochemistry. Genetics and Molecular Biology, Immunology and Microbiology, and Agricultural and Biological Sciences. These findings are thought-provoking suggesting that the primary subject categories are related to chemical engineering, molecular biology, microbiology, energy, and environmental sciences. The other key finding is that social sciences are not well represented in both the sample and population papers, as in the most fields in bioethanol fuels. These findings are not surprising as the key research fronts in this field relate to the straw hydrolysis.

25.4.10  The Most Prolific Scopus Keywords in Straw Hydrolysis A limited number of keywords have shaped the development of the research in this field as shown in Table 25.8 and the appendix. These keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. The prolific keywords related to the biomass and biomass constituents are wheat and to a lesser extent, straw, cellulose, lignin, rice, rice straw, biomass, lignocellulose, wheat straw, and hemicellulose while those related to the pretreatment are pretreatment and to a lesser extent enzyme activity, cellulases, and enzymes. The prolific keywords related to the straw hydrolysis are hydrolysis and to a lesser extent enzymatic hydrolysis and saccharification, while those related to the other processes are fermentation and to a lesser extent biotechnology and Saccharomyces cerevisiae. Further, those related to the

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hydrolysis products are sugar and, to a lesser extent, glucose, ethanol, and xylose and it is notable that only 11.1% of the indexed papers employs bioethanol keyword. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas. The relevant keywords are presented in Table 25.8 as well as in the appendix.

25.4.11  The Most Prolific Research Fronts in Straw Hydrolysis As Table 25.9 shows, there are five primary thematic research fronts for this field: the enzymatic, chemical, hydrothermal, mechanical hydrolysis of straw, and hydrolysis of straw in general. The most prolific chemical hydrolysis are alkaline and acid hydrolysis and, to a lesser extent, solvent, ammonia, ionic liquid, and hydrogen peroxide (H2O2) hydrolysis. Similarly, the most prolific hydrothermal hydrolysis is steam explosion hydrolysis and, to a lesser extent, wet oxidation hydrolysis, hydrothermal hydrolysis in general, and liquid hot water hydrolysis. Finally, microwave and milling are the most prolific mechanical hydrolysis. These findings are thought-provoking in seeking ways to increase bioethanol yield through the straw hydrolysis at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives as enzymatic pretreatments are often used in combination with other pretreatments to increase both sugar and ethanol yield. Similarly, Table 25.10 provides data on the straw used in the studies for the straw hydrolysis. There are two primary research fronts: wheat straw and rice straw. The other straws are from barley, sorghum, soybean, sugarcane, corn, rapeseed, and rye, accounting 15% of the sample papers. These findings suggest that although there are a large number of straw, the research has focused on a relatively small number of straws for the straw hydrolysis studies: wheat and rice straw. In the end, these most cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of these straw hydrolysis processes (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

25.5  CONCLUSION AND FUTURE RESEARCH The research on the straw hydrolysis has been mapped through a scientometric study of both sample (108 papers) and population (1,073 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the Appendix, and the relevant keywords are presented in Table 25.8. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose. Scopus database has been selected. The journal coverage of this database has been notably wider than that of Web of Science and other multi-subject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication periods, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all the academic databases. In this respect, it seems that authors, journals, and academic

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databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. However, the stagnation of the research output for the population papers after 2014 is highly striking raising questions about its possible sources. Further, the institutions from the China and Europe have mostly shaped the research in this field. The relatively modest funding of 56% for the population papers suggests that funding in this field significantly enhanced the research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further as only 47% of the sample papers declared any funding. It is expected that the current supply shocks such as the COVID-19 shocks and the shocks due to Russian invasion of Ukraine would increase the funding rate in this research field as bioethanol fuels are a green alternative to crude oil-based gasoline and diesel fuels. Europe, China, and to a lesser extent the USA, India, Iran, Japan, and South Korea have been the major producers and users of bioethanol fuels from different types of biomass, such as corn, sugarcane, and grass, as well as other types of biomass. It is evident that these countries have welldeveloped research infrastructure in bioethanol fuels and their derivatives. The primary Scopus subject categories have been Chemical Engineering and to a lesser extent Environmental Science, Energy, Biochemistry. Genetics and Molecular Biology, Immunology and Microbiology, and Agricultural and Biological Sciences as the focus of the research has been on the development and utilization of the straw hydrolysis to increase the sugar and bioethanol yield at large. Further, social sciences are not well represented in both the sample and population papers as in the most fields in bioethanol fuels. These findings are not surprising. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. These groups of keywords highlight the potential primary research fronts for these fields. There are five thematic primary research fronts for this field: the enzymatic, chemical, hydrothermal, mechanical hydrolysis of straw and hydrolysis of straw in general. Similarly, there are two primary research fronts for the straw used in these studies: wheat and rice straw. These findings are thought-provoking. The focus of these 108 most cited papers as well as 1,075 population papers is the development and utilization of straw hydrolysis to increase the sugar and bioethanol yield. These studies highlight strong structure-processing-property relationships for pretreatments and hydrolysis of bioethanol fuels and their derivatives. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields especially in the aftermath of the significant global supply shocks such as Russian invasion of Ukraine and the COVID-19 shocks. It is recommended that further scientometric studies are carried out for the primary thematic research fronts of the straw hydrolysis. It is further recommended that reviews of the most cited papers are carried out for each research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the straw hydrolysis has been gratefully acknowledged.

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APPENDIX: THE KEYWORD SET FOR STRAW HYDROLYSIS (TITLE (hydrolysis OR saccharif* OR prehydroly* OR posthydroly* OR *hydrolysis OR digestibili* OR digestible OR accessibility OR “sugar recover*” OR “fermentable sugars” OR “reducing sugars” OR “sugar yield*” OR “sugar production” OR “sugar release” OR “sugar extraction” OR “sugar generat*” OR *oligosaccharides OR recalcitrance OR hydrolysate* OR hydrolyzate* OR prehydrolysate* OR *prehydrolyzate* OR inhibitor* OR “degradation products” OR “degradation compounds” OR xylose OR pentose* OR hexose* OR glucose OR detoxif* OR “xylose recovery” OR “enzymatic degradation”) AND TITLE (straw OR straws OR *straw)) AND NOT (SUBJAREA (medi OR phar OR dent OR vete OR eart OR nurs OR neur OR heal OR psyc) OR TITLE (“oil product*” OR *butanol OR diet OR {into furfural} OR ruminal OR root* OR lactic OR succinic OR *butyrate OR *hydrogen OR *diesel OR “algal growth” OR polymer* OR “acid product*” OR soil* OR “growth inhibitor*” OR nutrient* OR nutrit* OR denitrif* OR pyroly* OR biogas OR “algal inhib*” OR “fuel cell*” OR “bacterial cellulose” OR nanofiber* OR ruminant* OR xylitol OR cattle OR “anaerobic digestion” OR prebiot* OR antioxid* OR rumen OR bulls OR butanediol OR *toxin) OR SRCTITLE (animal* OR ecol* OR hydrobiol* OR plant* OR dairy OR aqua*)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”))

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26 Review

Straw Hydrolysis Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

26.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2019, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), fuel cells (Antolini, 2007, 2009), and biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass. Wheat straw (Alemdar and Sain, 2008a,b) and rice straw (Daifullah et al., 2007; Gadde et al., 2009) have been among the most studied biomass for the bioethanol production (Binod et al., 2010; Hans et al., 2019). In this context, the research in the field of straw hydrolysis (Kristensen et al., 2007; Satlewal et  al., 2018) has thus intensified in recent years. The enzymatic hydrolysis (Chen et al., 2008; Tabka et al., 2006) combined with chemical (Hsu et al., 2010; Kootstra et al., 2009), hydrothermal (Bjerre et al., 1996; Tabka et al., 2006), and mechanical pretreatments (Da Silva et al., 2010; Ma et al., 2009) of straw have been widely researched to increase the sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there has been a limited number of review papers on straw hydrolysis (Kristensen et al., 2007; Satlewal et al., 2018; Talebnia et al., 2010), there has been no updated review of the 25 most cited articles in this field. Thus, this book chapter presents a review of the 25 most cited articles in the field of the straw hydrolysis. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

26.2  MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 178 citations each were selected for the review study. Key findings from each paper were taken from the 156

DOI: 10.1201/9781003226499-34

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abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

26.3 RESULTS The brief information about 25 most cited papers with at least 178 citations each on the straw hydrolysis is given below. The primary research front is the enzymatic hydrolysis of straw combined with other pretreatments with 21 highly cited papers (HCPs). The other research front is the sole acid and enzymatic hydrolysis of straw with four HCPs.

26.3.1 The Enzymatic Hydrolysis of Straw Combined with Other Pretreatments There are 21 HCPs for the research front of the enzymatic hydrolysis of straw in combination with other pretreatments. The key research front is the enzymatic hydrolysis of straw combined with chemical pretreatments with 16 HCPs. There are also five and four HCPs for enzymatic hydrolysis of straw combined with mechanical and hydrothermal pretreatments, respectively. 26.3.1.1 The Enzymatic Hydrolysis of Straw Combined with Chemical Pretreatments There are 16 HCPs for the research front of the enzymatic hydrolysis of straw in combination with chemical pretreatments (Table 26.1). The key research fronts are alkaline, ionic liquid, acid, and solvent pretreatments with six, three, three, and two HCPs, respectively. Additionally, there is one HCP each for ammonia and ozone pretreatments. The papers related to two research fronts are noted down in only one of these research fronts. However, they are listed in the tables related to both research fronts. 26.3.1.2 The Enzymatic Hydrolysis of Straw Combined with Alkaline Pretreatment Zhu et al. (2005) studied microwave and alkaline pretreatment of rice straw and its enzymatic hydrolysis in a paper with 232 citations. They observed that that higher microwave power with shorter pretreatment time and the lower microwave power with longer pretreatment time had almost the same effect on the weight loss and composition at the same energy consumption. The rice straw had a weight loss of 44.6% and composition of cellulose 69.2%, lignin 4.9% and hemicellulose 10.2% after 30-min combined microwave and alkaline pretreatment at 700 W, while it only had a weight loss of 41.5% and composition of cellulose 65.4%, lignin 6.0% and hemicellulose 14.3% after 70-min alkaline-alone pretreatment. Hence, combined microwave and alkaline pretreatment could remove more lignin and hemicellulose from rice straw with shorter pretreatment time compared with the alkali-alone one. As a result, rice straw pretreated by combined microwave and alkaline pretreatment had a higher hydrolysis rate and glucose content in the hydrolysate in comparison with the one by alkali alone. Saha and Cotta (2006) explored the alkaline H2O2 pretreatment and enzymatic hydrolysis of wheat straw for the conversion of its cellulose and hemicellulose to fermentable sugars in a paper with 212 citations. They observed that the maximum yield of sugars from wheat straw (8.6%, w/v) by this pretreatment (2.15% H2O2, v/v; pH 11.5; 35°C; 24 h) and enzymatic hydrolysis (45°C, pH 5.0, 120 h) by cellulase, β-glucosidase, and xylanase using 0.16 mL of each enzyme preparation per g of straw was 672 mg/g (96.7% yield). During the pretreatment, no measurable quantities of furfural and hydroxymethylfurfural (HMF) were produced. The concentration of ethanol (per L) from alkaline H2O2 pretreated enzyme saccharified wheat straw (66.0 g) hydrolysate by recombinant Escherichia coli strain FBR5 at pH 6.5 and 37°C in 48 h was 18.9 g with a yield of 0.46 g per g of available sugars (0.29 g/g straw). Finally, the ethanol concentration (per L) was 15.1 g with a yield of 0.23 g/g of straw in the case of simultaneous saccharification and fermentation (SSF) by the E. coli strain at pH 6.0 and 37°C in 48 h.

158

TABLE 26.1 The Enzymatic Hydrolysis of Straw Combined with Chemical Pretreatments No. 1 2 3 4 5

7 8

Biomass

Prt.

Wheat H2SO4, cellulase, β-glucosidase, straw xylanase, esterase Rice straw H2SO4, enzymes Wheat Wet oxidation, alkali, straw exo-β-xylosidase Wheat [EMIM]DEP, enzymes straw Rice straw Lactic acid and choline chloride Wheat [EMIM]Ac, enzymes straw Rice straw Aqueous ammonia soaking, cellulase, xylanase Rice straw Alkali, microwave, enzymes

Parameters

Keywords

Lead Author

Pretreatment, hydrolysis, ethanol yield, hydrolysate detoxification, SSF, SHF Pretreatment, hydrolysis, sugar yield, pore volume Pretreatment, hydrolysis, sugar yield, fermentation inhibitors Pretreatment, hydrolysis, sugar yield, ethanol yield Pretreatment, hydrolysis, sugar yield

Straw, saccharification Straw, hydrolysis

Pretreatment, hydrolysis, sugar yield

Straw, hydrolysis

Pretreatment, hydrolysis, sugar yield, SSF Pretreatment, hydrolysis, sugar yield, pretreatment type

Straw, saccharification Straw, hydrolysis

Saha, Badal C. 72029463020 Guo, Gia-Luen 7402768046 Bjerre, Anne B.* 6701773173 Xian, Mo 35338324700 Kumar, Adepu K. 24481614900 Mazza, Giuseppe 7102002968 Kim, Kyoung-Heon 34770896300 Zhu, Shengdong 8919301300

Straw, hydrolysis Straw, hydrolysis Straw, hydrolysis

Affil.

Cits

USDA Agr. Res. Serv. USA

646

Inst. Nucl. Ener. Res. Taiwan

479

Danish Technol. Inst. Denmark Chinese Acad. Sci. China

347

Indian Inst. Technol. India

288

Summerland Res. Devnt. Ctr. Canada Korea Univ. S. Korea

250

Wuhan Inst. Technol. China

232

296

245

(Continued)

Bioethanol Fuel Production Processes. II

6

Papers Saha et al. (2005) Hsu et al. (2010) Bjerre et al. (1996)** Li et al. (2009) Kumar et al. (2016) Fu et al. (2010) Ko et al. (2009) Zhu et al. (2005)

No.

Papers

9

Tabka et al. (2006)*

10

Nguyen et al. (2010)

11

Wildschut et al. (2013) Saha and Cotta (2006) Garcia-Cubero et al. (2009) McIntosh and Vancov (2010) Chen et al. (2008) McIntosh and Vancov (2011)

12 13 14

15 16

Biomass

Prt.

Parameters

Keywords

Lead Author

Affil.

Cits

H2SO4, steam, cellulase xylanase, feruloyl esterase, Tween 20 Rice straw Ammonia, [EMIM]Ac, enzymes

Pretreatment, hydrolysis, sugar yield, enzyme types

Straw, saccharification

Tabka, Mohamed G.12344629700

Aix Marseille Univ. France

229

Pretreatment, hydrolysis, sugar yield, IL recycling

Sim, Sang Jun 55665584200

Korea Univ. S. Korea

214

Wheat straw Wheat straw Wheat and rye straw Sorghum straw

Pretreatment, hydrolysis, sugar yield, acid catalyst Pretreatment, hydrolysis, sugar yield, ethanol yield, SSF Pretreatment, hydrolysis, sugar yield, pretreatment conditions Pretreatment, hydrolysis, sugar yield, optimization

Straw, fermentable sugars Straw, digestible

Huijgen, Wouter J. J. 10142093700 Saha, Badal C. 72029463020 Bolado, Silvia* 57200761078 Vancov, Tony 6508255700

Ener. Res. Ctr. Netherlands

212

USDA Agr. Res. Serv. USA

212

Univ. Valladolid Spain

213

Elizabeth Macarthur Agr. Inst. Australia

208

Zhejiang Univ. China

178

Elizabeth Macarthur Agr. Inst. Australia

178

Wheat straw

Corn straw Wheat straw

Solvent, H2SO4, enzymes Alkaline H2O2, cellulase, β-glucosidase, xylanase Ozone, NaOH, enzymes NaOH, β-glucosidase, xylanase, cellulase NaOH, Tween 80, T. reesei cellulose, cellobiase NaOH, cellulase, β-glucosidase, xylanase

Pretreatment, hydrolysis, sugar yield, pretreatment types Pretreatment, hydrolysis, sugar yield

Straw, saccharification Straw, digestibility Straw, saccharification

Straw, hydrolysis, Xia, Liming reducing sugars 7201955949 Straw Vancov, Tony 6508255700

Straw Hydrolysis: Review

TABLE 26.1 (Continued) The Enzymatic Hydrolysis of Straw Combined with Chemical Pretreatments

*, Female; **, papers listed without a brief note which was given in a related research front; Cits., Number of citations received for each paper; Prt, Biomass pretreatments.

159

160

Bioethanol Fuel Production Processes. II

McIntosh and Vancov (2010) explored the enzymatic hydrolysis of sorghum straw using dilute sodium hydroxide (NaOH) pretreatment in a paper with 208 citations. They observed that both solids and lignin content were inversely proportional to the severity of this pretreatment. Higher temperatures and alkali strength were crucial for maximizing sugar recoveries from enzymatic hydrolysis. Total sugar release peaked when sorghum straw was pretreated in 2% NaOH at 121°C for 60 min; representing a 5.6-fold higher yield compared with samples pretreated at 60°C in the absence of alkali. Similarly, 4.3-fold increases in total sugars from samples treated with 2% NaOH at 60°C for 90 min, confirmed the importance of alkaline pretreatment. Addition of β-glucosidase and xylanase to hydrolysis mixtures enhanced reaction rates and final sugar yields, while reducing cellulase dosage 4-fold. Finally, hydrolysis efficiency of pretreated solids approached 90% and 95% (w/w) with as little as 2.5 and 5.0 FPU cellulase/g, respectively. Chen et al. (2008) studied the enzymatic hydrolysis of corn straw for the production of sugars in a paper with 178 citations. After corn straw was pretreated with 2% NaOH at 80°C for 1 h to delignify, they hydrolyzed the cellulosic residues by cellulase from Trichoderma reesei ZU-02 and observed that the hydrolysis yield at 48 h was 65.9%. A certain amount of cellobiose was accumulated in the hydrolysate due to low cellobiase activity in T. reesei cellulase. Supplementing cellobiase from Aspergillus niger ZU-07 greatly reduced the inhibitory effect caused by cellobiose, and the hydrolysis yield at 48 h was improved to 81.2% with cellobiase activity enhanced to 10 CBU/g substrate. The addition of 5 g/L Tween 80 improved the enzymatic hydrolysis by increasing the hydrolysis yield of 7.5%. After 72 h of hydrolysis, the sugar concentration reached 89.5 g/L with a hydrolysis yield of 83.3%. Finally, the hydrolysate from fed-batch hydrolysis contained 56.7 g/L glucose, 23.6 g/L xylose, and 5.7 g/L arabinose. McIntosh and Vancov (2011) used of dilute NaOH pretreatment followed by enzyme hydrolysis of wheat straw to produce sugars in a paper with 178 citations. They observed that recoverable solids and lignin contents were inversely proportional to the severity of the pretreatment process. Elevating temperature and alkaline strengths maximized hemicellulose and lignin solubilization and enhanced enzymatic hydrolysis. Pretreating wheat straw with 2% NaOH for 30 min at 121°C improved enzyme hydrolysis 6.3-fold compared with control samples. Similarly, a 4.9-fold increase in total sugar yields from samples treated with 2% NaOH at 60°C for 90 min, confirmed the importance of alkaline pretreatment. Further, a combination of cellulase, β-glucosidase, and xylanase maximized monomeric sugar release, particularly for substrates with higher xylan contents. These combined enzyme activities increased total sugar release 1.65-fold and effectively reduced cellulase enzyme loadings 3-fold. Prehydrolysate liquors contained 4-fold more total phenolics compared with enzyme hydrolysis mixtures as harsher pretreatment conditions provided hydrolysates with reduced phenolic content and greater fermentation potential. 26.3.1.3 The Enzymatic Hydrolysis of Straw Combined with Ionic Liquid Pretreatment Li et al. (2009) used 1-ethyl-3-methylimidazolium diethyl phosphate ([Emim]DEP) to accelerate enzymatic hydrolysis of wheat straw in a paper with 296 citations. They observed that the yield of sugars at 130°C for 30 min reached 54.8% after being enzymatically hydrolyzed for 12 h. They next observed that Saccharomyces cerevisiae could ferment glucose efficiently, and the ethanol production was 0.43 g/g glucose within 26 h. Fu et al. (2010) extracted lignin from wheat straw by 1-ethyl-3-methylimidazolium acetate ([Emim]Ac) and enzymatic hydrolysis of the cellulosic residues in a paper with 250 citations. They obtained the optimal result of 52.7% of acid insoluble lignin in wheat straw at 150°C after 90 min, yielding more than 95% cellulose digestibility of the residue. Further, little cellulose was extracted, and the extracted lignin was recovered by acid precipitation. Nguyen et al. (2010) studied the pretreatment of rice straw with ammonia and [Emim]Ac for its enzymatic hydrolysis with 214 citations. They observed that the combined use of ammonia and this IL pretreatment exhibited a synergy effect for rice straw with 82% of the cellulose recovery and 97% of the enzymatic glucose conversion. This cooperative effect showed over 90% of the glucose conversion even with a reduced enzyme usage and incubation time. Finally,

Straw Hydrolysis: Review

161

this IL was successfully recycled more than 20 times. The 20th-recycled IL showed 74% of the cellulose recovery and 78% of the glucose conversion to rice straw. 26.3.1.4 The Enzymatic Hydrolysis of Straw Combined with Acid Pretreatment Saha et al. (2005) evaluated dilute sulfuric acid (H2SO4) pretreatment and enzymatic hydrolysis for conversion of wheat straw cellulose and hemicellulose to sugars in a paper with 646 citations. They observed that the maximum yield of sugars from wheat straw (7.83%, w/v, DS) by dilute H2SO4 (0.75%, v/v) pretreatment and enzymatic hydrolysis (45°C, pH 5.0, 72 h) using cellulase, β-glucosidase, xylanase, and esterase was 565 mg/g. Under this condition, no measurable quantities of furfural and HMF were produced. The yield of ethanol (per liter) from acid pretreated enzyme saccharified wheat straw (78.3 g) hydrolysate by recombinant E. coli strain FBR5 was 19 g with a yield of 0.24 g/g DS. Further, detoxification of the acid and enzyme treated wheat straw hydrolysate by overliming reduced the fermentation time from 118 to 39 h in the case of separate hydrolysis and fermentation (SHF) (35°C, pH 6.5), and increased the ethanol yield from 13 to 17 g/L and decreased the fermentation time from 136 to 112 h in the case of SSF (35°C, pH 6.0). Hsu et al. (2010) optimized the dilute H2SO4 pretreatment of rice straw and explored the effect of the structural properties of the solid residues on the enzymatic hydrolysis in a paper with 479 citations. They obtained a maximal sugar yield of 83% when the rice straw was pretreated with 1% (w/w) H2SO4 with a reaction time of 1–5 min at 160°C or 180°C, followed by enzymatic hydrolysis. The complete release of sugars increased the pore volume of the pretreated solid residues and resulted in an efficiency of 70% for the enzymatic hydrolysis. The extra pore volume was generated by the release of acid-soluble lignin and this resulted in the enzymatic hydrolysis being enhanced by nearly 10%. However, the increase in the crystallinity index of the pretreated rice straw was limited. 26.3.1.5 The Enzymatic Hydrolysis of Straw Combined with Solvent Pretreatment Kumar et al. (2016) pretreated rice straw using natural deep eutectic solvents (NADES), and separated high-quality lignin and holocellulose in a single step in a paper with 288 citations. They observed that the extracted lignin was of high purity (more than 90%), and nearly 60% (w/w) of total lignin was separated from the lignocellulosic biomass. Addition of 5.0% (v/v) water during pretreatment significantly enhanced the total lignin extraction, and nearly 22% more lignin was released from the residual biomass into the NADES extract. The crystallinity index ratio was marginally decreased from 46.4% to 44.3%, indicating subtle structural alterations in the crystalline and amorphous regions of the cellulosic fractions. Lactic acid/choline chloride at molar ratio of 5:1 extracted maximum lignin of 68 mg/g from the rice straw biomass, and subsequent enzymatic hydrolysis of the residual holocellulose enriched biomass showed maximum reducing sugars of 333 1 mg/g with a hydrolysis efficiency of 36% in 24 h at 10% solids loading. Wildschut et al. (2013) studied wheat straw fractionation by ethanol organosolv as pretreatment for enzymatic cellulose hydrolysis. They observed that optimization of the process towards enzymatic digestibility resulted in a maximum glucose yield of 86% without the use of a catalyst (lignin yield 84%, organosolv at 210°C, 50% w/w aqueous ethanol). Using 30 mM H2SO4 as catalyst resulted in similar glucose and lignin yields at a lower temperature (190°C, 60% w/w aqueous ethanol). Further, lowering the pretreatment temperature by using an acid catalyst substantially improved the yield of the hemicellulose derivatives xylose and furfural. 26.3.1.6 The Enzymatic Hydrolysis of Straw Combined with Ammonia Pretreatment Ko et al. (2009) pretreated rice straw using aqueous ammonia solution at moderate temperatures to enable production of the maximum amount of sugars from enzymatic hydrolysis in a paper with 245 citations. They observed that the optimal reaction conditions, which resulted in an enzymatic digestibility of 71.1%, were 69°C, 10 h and an ammonia concentration of 21% (w/w). They then evaluated

162

Bioethanol Fuel Production Processes. II

the effects of different commercial cellulases and the additional effect of xylanase. Additionally, they carried out a SSF with rice straw hydrolysates. 26.3.1.7 The Enzymatic Hydrolysis of Straw Combined with Ozone Pretreatment Garcia-Cubero et al. (2009) pretreated wheat and rye straw with ozone for their enzymatic hydrolysis in a paper with 213 citations. They observed that the acid insoluble lignin content of the biomass was reduced in all experiments involving hemicellulose degradation while near negligible losses of cellulose were observed. They obtained the enzymatic hydrolysis yields of up to 88.6% and 57% compared to 29% and 16% in non-ozonated biomass respectively. Moisture content and type of biomass showed the most significant effects on ozonolysis. Additionally, ozonolysis experiments in basic medium with NaOH evidenced a reduction in solubilization and/or degradation of lignin and reliable cellulose and hemicellulose degradation. 26.3.1.8 The Enzymatic Hydrolysis of Straw Combined with Mechanical Pretreatments There are five HCPs for the research front of the enzymatic hydrolysis of straw in combination with mechanical pretreatments. (Table 26.2). The key research front is milling pretreatment with three HCPs, followed by the microwave pretreatments with two HCPs. 26.3.1.8.1 The Enzymatic Hydrolysis of Straw Combined with Milling Pretreatments Da Silva et al. (2010) compared the effectiveness of ball milling (BM) and wet disk milling (WDM) of sugarcane bagasse and straw for enzymatic hydrolysis and ethanol fermentation in a paper with 266 citations. They observed that glucose and xylose hydrolysis yields at optimum conditions for BM-pretreated bagasse and straw were 78.7% and 72.1% and 77.6% and 56.8%, respectively. Maximum glucose and xylose yields for bagasse and straw using WDM were 49.3% and 36.7% and 68.0% and 44.9%, respectively. Thus, BM improved the enzymatic hydrolysis by decreasing the crystallinity, while the defibrillation effect observed for WDM samples favored enzymatic conversion. Further, ethanol yields from total sugars using a C6-fermenting strain reached 89.8% and 91.8% for bagasse and straw hydrolysates, respectively, and 82% and 78% when using a C6/C5 fermenting strain. Hideno et al. (2009) explored the WDM pretreatment for enzymatic hydrolysis of rice straw using Acremonium cellulase in a paper with 185 citations. They observed that glucose and xylose yields by WDM, BM, and hot compressed water (HCW) pretreatments were 78.5% and 41.5%, 89.4%, and 54.3%, and 70.3% and 88.6%, respectively. WDM and HCW pretreatments increased sugar yields without decreasing their crystallinity. Further, the feature size of the wet disk-milled rice straw was similar to that of HCW-pretreated rice straw. Finally, the energy consumption of wet disk milling was lower than that of other pretreatments. Silva et al. (2012) studied the effects of sieve-based grinding, jet milling, and by BM on the enzymatic hydrolysis of wheat straw using T. reesei enzymes in a paper with 180 citations. They produced a large range of wheat straw powders from coarse to fine particles using sieve-based grindings and then ultra-fine particles by jet milling and by BM. They observed that the wheat straw degradability was enhanced by the decrease of particle size until a limit: nearly 100 μm, up to 36% total carbohydrate and 40% glucose hydrolysis yields. BM samples overcame this limit up to 46% total carbohydrate and 72% glucose yields as a consequence of cellulose crystallinity reduction (from 22% to 13%). Thus, BM was an effective pretreatment with similar glucose yield and superior carbohydrate yield compared to steam explosion pretreatment. 26.3.1.8.2 The Enzymatic Hydrolysis of Straw Combined with Microwave Pretreatments Ma et al. (2009) optimized the enzymatic hydrolysis of rice straw by microwave pretreatment in a paper with 226 citations. They observed that microwave intensity (MI), irradiation time

Straw Hydrolysis: Review

TABLE 26.2 The Enzymatic Hydrolysis of Straw Combined with Mechanical Pretreatments No. 1

2 3 4

Papers

Biomass

Prt.

Da Silva et al. (2010) Zhu et al. (2005)** Ma et al. (2009) Hideno et al. (2008)

Sugarcane straw

Ball and wet disk milling, enzymes

Pretreatment, hydrolysis, sugar yield, ethanol yield

Straw, hydrolysis

Bon, Elba P. S.*7007036976

Fed. Univ. Rio de JaneiroBrazil

266

Rice straw Rice straw Rice straw

Alkali, microwave, enzymes

Pretreatment, hydrolysis, sugar yield, pretreatment type Pretreatment, hydrolysis, sugar yield, microwave optimization Pretreatment, hydrolysis, sugar yield, pretreatment type

Straw, hydrolysis Straw, saccharification Straw, hydrolysis

Zhu, Shengdong 8919301300 Wu, Yue-Jin 55531536300 Inoue, Hirochika 55733159900

Wuhan Inst. Technol. China Chinese Acad. Sci.China

232

AISTJapan

185

Microwave, enzymes Wet disk milling, ball milling, HCW, Acremonium cellulase

Parameters

Keywords

Lead Author

Affil.

Cits

226

163

164

Bioethanol Fuel Production Processes. II

(IT), and substrate concentration (SC) were main factors governing the enzymatic hydrolysis of rice straw. The maximal efficiencies of cellulose, hemicellulose, and total hydrolysis were respectively increased by 30.6%, 43.3%, and 30.3% under the optimal conditions of MI 680 W, IT 24 min and SC 75 g/L. They determined that microwave pretreatment could disrupt the silicified waxy surface, break down the lignin-hemicellulose complex and partially remove silicon and lignin. 26.3.1.9 The Enzymatic Hydrolysis of Straw Combined with Hydrothermal Pretreatments There are four HCPs for the research front of the enzymatic hydrolysis of straw in combination with hydrothermal pretreatments. (Table 26.3). The research fronts are steam explosion, liquid hot water (LHW), HCW, and wet oxidation pretreatments with one HCP each. 26.3.1.9.1 The Enzymatic Hydrolysis of Straw Combined with Steam Explosion Pretreatment Tabka et al. (2006) pretreated wheat straw with diluted H2SO4 followed by steam explosion in a paper with 229 citations. They explored the enzymatic pretreatments by hydrolases (cellulases and xylanases from T. reesei, recombinant feruloyl esterase (FAE) from A. niger and oxidoreductases (laccases from Pycnoporus cinnabarinus). They observed a synergistic effect between cellulases, FAE and xylanase under a critical enzymatic concentration (10 U/g of cellulases, 3 U/g of xylanase and 10 U/g of FAE). The yield of enzymatic hydrolysis was enhanced by increasing the temperature from 37°C to 50°C and addition of Tween 20. 26.3.1.9.2 The Enzymatic Hydrolysis of Straw Combined with Liquid Hot Water Pretreatment Perez et al. (2008) optimized LHW pretreatment conditions to enhance sugar recovery from wheat straw for ethanol production in a paper with 210 citations. They observed that optimal conditions were 188°C and 40 min, leading to hemicellulose-derived sugars (HDS) recovery yield of 43.6% of HDS content in raw material and enzymatic hydrolysis yield of 79.8% of theoretical. They obtained 71.2% of HDS recovery at 184°C and 24 min, whereas, conditions of 214°C and 2.7 min led to a maximum hydrolysis yield of 90.6% of theoretical. So, they recommended that a two-step pretreatment would be the most adequate process configuration to get a maximum recovery of fermentable sugars. 26.3.1.9.3 The Enzymatic Hydrolysis of Straw Combined with Wet Oxidation Pretreatment Bjerre et al. (1996) carried out the wet oxidation process of wheat straw to break down cellulose to glucose enzymatically and to dissolve hemicellulose without producing any fermentation inhibitors in a paper with 347 citations. They observed that wet oxidation combined with base addition readily oxidized lignin from wheat straw facilitating the polysaccharides for enzymatic hydrolysis. The optimal conditions (20 g/L straw, 170°C, 5 to 10 min) gave about 85% w/w yield of converting cellulose to glucose. Further, the process water, containing dissolved hemicellulose and carboxylic acids, was a direct nutrient source for A. niger producing exo-β-xylosidase. Finally, they did not observe any fermentation inhibitors.

26.3.2 The Sole Acid and Enzymatic Hydrolysis of Straw There are three and one HCPs for the sole acid and enzymatic hydrolysis of straw, respectively (Table 26.4).

Straw Hydrolysis: Review

TABLE 26.3 The Enzymatic Hydrolysis of Straw Combined with Hydrothermal Pretreatments No. 1 2 3 4

Papers Bjerre et al. (1996) Tabka et al. (2006) Perez et al. (2008) Hideno et al. (2009)**

Biomass Wheat straw Wheat straw Wheat straw Rice straw

Prt. Wet oxidation, alkali, exo-β-xylosidase H2SO4, steam, cellulase xylanase, feruloyl esterase, Tween 20 LHW, enzymes Wet disk milling, ball milling, HCW, Acremonium cellulase

Parameters Pretreatment, hydrolysis, sugar yield, fermentation inhibitors Pretreatment, hydrolysis, sugar yield, enzyme types Pretreatment, hydrolysis, sugar yield Pretreatment, hydrolysis, sugar yield, pretreatment type

Keywords Straw, hydrolysis Straw, saccharification Straw, sugar recovery Straw, hydrolysis

Lead Author Bjerre, Anne B.* 6701773173 Tabka, Mohamed G. 12344629700 Manzanares, Paloma* 55779406300 Inoue, Hirochika 55733159900

Affil.

Cits

Danish Technol. Inst. Denmark Aix Marseille Univ. France CIEMAT Spain

347

210

AIST Japan

185

229

*, Female; **, papers listed without a brief note which was given in a related research front; Cits., Number of citations received for each paper; Prt, Biomass pretreatments.

165

166

TABLE 26.4 The Sole Acid and Enzymatic Hydrolysis of Straw No. 1

2

3

Taniguchi et al. (2005) Karimi et al. (2006) Kootstra et al. (2009) Roberto et al. (2003)

Biomass

Prt.

Parameters

Keywords

Lead Author

Affil.

Cits

Rice straw

P. ostreatus

Pretreatment, hydrolysis, sugar yield

Straw, hydrolysis

Taniguchi, Masayuki 57191455447

Niigata Univ. Japan

318

Rice straw

H2SO4

Pretreatment, hydrolysis, sugar yield, fermentation inhibitors

Straw, hydrolysis

Univ. Boras Sweden

281

Wheat straw

Fumaric acid, maleic acid, H2SO4

Pretreatment, hydrolysis, sugar yield, acid types

Straw, hydrolysis

Taherzadeh, Mohammad J. 6701407496 Kootstra, A. Maarten J. 25321144400

Wageningen Univ. Res. Netherlands

271

Rice straw

H2SO4

Pretreatment, hydrolysis, sugar yield

Straw, hydrolysis

Roberto, Ines C.* 7003893391

Univ. Sao Paulo Brazil

198

*, Female; Cits., Number of citations received for each paper; Prt, Biomass pretreatments.

Bioethanol Fuel Production Processes. II

4

Papers

Straw Hydrolysis: Review

167

26.3.2.1 The Sole Acid Hydrolysis of Straw Karimi et al. (2006) hydrolyzed rice straw by dilute H2SO4 at high temperature and pressure in one and two stages in a paper with 281 citations. They showed the ability of first stage hydrolysis to depolymerize xylan to xylose with a maximum yield of 80.8% at hydrolysis pressure of 15 bar, 10 min retention time and 0.5% acid concentration. They achieved the best results of the hydrolysis, when 0.5% acid was added prior to each stage in two-stage hydrolysis. The optimal conditions were the hydrolysis pressure and the retention time of 30 bar and 3 min in the second stage hydrolysis, where a total of 78.9% of xylan and 46.6% of glucan were converted to xylose and glucose, respectively in the two stages. Further, formation of fermentation inhibitors of furfural and HMF were functions of the hydrolysis pressure, acid concentration, and retention time, whereas the concentration of acetic acid was almost constant at pressure of higher than 10 bar and a total retention time of 10 min. Kootstra et al. (2009) compared the efficiencies of fumaric and maleic acids, and H2SO4 in wheat straw pretreatment in a paper with 271 citations. They observed that at 150°C and 20%–30% (w/w) dry wheat straw, the pretreatment with dilute fumaric or maleic acid could be a serious alternative to dilute H2SO4 pretreatment. Roberto et al. (2003) determined the effects of H2SO4 concentration and reaction time on the production of sugars and on the fermentation inhibitors in a paper with 198 citations. They carried out the hydrolysis of rice straw at 121 C in a 350–1 batch hydrolysis reactor. They optimized the hydrolysis conditions to attain high xylose selectivity. They observed the optimum H2SO4 concentration of 1% and reaction time of 27 min. Under these conditions, they obtained 77% of xylose yield and 5.0 g/g of selectivity. 26.3.2.2 The Sole Enzymatic Hydrolysis of Straw Taniguchi et al. (2005) evaluated the effects of enzymatic pretreatment of rice straw using Pleurotus ostreatus for its susceptibility to enzymatic hydrolysis in a paper with 318 citations. They observed that P. ostreatus selectively degraded the lignin fraction of rice straw rather than the holocellulose component. After the pretreatment, the residual amounts of cellulose and hemicellulose were 83% and 52% of those in untreated rice straw, respectively. By enzymatic hydrolysis with a commercial cellulase preparation for 48 h, 52% holocellulose and 44% cellulose in the pretreated rice straw were solubilized. The net sugar yields based on the amounts of holocellulose and cellulose of untreated rice straw were 33% for total soluble sugar from holocellulose and 32% for glucose from cellulose. They determined that the increase in susceptibility of rice straw to enzymatic hydrolysis by pretreatment with P. ostreatus was caused by partial degradation of the lignin seal. Further, when the content of Klason lignin was less than 15% of the total weight of the pretreated straw, they observed enhanced degrees of enzymatic solubilization of holocellulose and cellulose fractions as the content of Klason lignin decreased.

26.4 DISCUSSION 26.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass and hydrolysates, respectively. Wheat straw and rice straw have been among the most studied biomass for the bioethanol production. In this context, the research in the field of straw hydrolysis has thus intensified in recent years. The enzymatic hydrolysis combined with chemical, hydrothermal, and mechanical pretreatments of straw have been widely researched to increase the sugar and bioethanol yield in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been a number of review papers for this field, there has been no review of the 25 most cited articles in this field. Thus, this book chapter presents

168

Bioethanol Fuel Production Processes. II

a review of the 25 most cited articles in this field. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 178 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape. Information about the thematic research fronts for the reviewed and sample papers in straw hydrolysis is given in Table 26.5. As this table shows, there are two primary research fronts for this field: enzymatic and chemical hydrolysis of straw with 88% and 76% of the reviewed papers, respectively. Next, the other minor fronts are the mechanical and hydrothermal hydrolysis of straw with 20% and 16% of the reviewed papers, respectively. On the individual basis, acid and alkaline hydrolysis are the key chemical hydrolysis with 28% of the reviewed papers each while ionic liquid, solvent, ammonia, and surfactant hydrolysis are the other prolific chemical hydrolysis with 8%–12% of the reviewed papers each.

TABLE 26.5 The Thematic Research Fronts for the Straw Hydrolysis No. 1 2

Research Fronts

N Paper (%) Review

N Paper (%) Sample

Surplus

88.0 76.0 28.0

73.1 52.8 16.7

14.9 23.2 11.3

Acid hydrolysis

28.0

13.9

14.1

Ionic liquid hydrolysis

12.0

4.6

7.4

8.0

7.4

0.6

Enzymatic hydrolysis/pretreatments Chemical hydrolysis/pretreatments Alkaline hydrolysis

Solvent hydrolysis

3

4

5

Ammonia hydrolysis

8.0

4.6

3.4

Surfactant hydrolysis

8.0

0.9

7.1

H2O2 hydrolysis

4.0

2.8

1.2

Ozone hydrolysis

4.0

0.9

3.1

CO2 hydrolysis

0.0

0.9

−0.9

16.0 4.0

18.5 10.2

−2.5 −6.2

Hydrothermal hydrolysis/pretreatments Steam explosion hydrolysis Wet oxidation hydrolysis

4.0

2.8

1.2

Liquid hot water hydrolysis

4.0

1.9

2.1

Hot compressed water hydrolysis

4.0

0.9

3.1

Hydrothermal hydrolysis in general

0.0

1.9

−1.9

Autohydrolysis

0.0

0.9

−0.9

20.0 12.0

12.0 4.6

8.0 7.4

Mechanical hydrolysis/pretreatments Milling hydrolysis Microwave hydrolysis

8.0

6.5

1.5

Ultrasound hydrolysis

0.0

0.9

−0.9

Hydrolysis in general

0.0

18.5

−18.5

N paper (%) review, The number of papers in the sample of 25 most cited papers; N paper (%) sample, The number of papers in the population sample of 108 papers.

169

Straw Hydrolysis: Review

Similarly, milling and microwave hydrolysis are the prolific mechanical pretreatments with 12% and 8% of the reviewed papers, respectively as there is no HCP for the ultrasonic pretreatments. Finally, there is one HCP each for steam explosion, wet oxidation, LHW, and HCW pretreatments with 4% of the reviewed papers each. Further, chemical, enzymatic, and mechanical hydrolysis are over-represented in the reviewed papers with 23%, 14%, and 8% surplus, respectively, while the hydrothermal hydrolysis is underrepresented by 3%. Additionally, the hydrolysis in general is under-represented by 19%. Similarly, on the individual basis, acid, alkaline, ionic liquid, surfactant, ammonia, and ozone pretreatments are over-represented in the reviewed papers for the chemical hydrolysis by 3%–14% each. Similarly, for the mechanical hydrolysis, milling and microwave pretreatments are over-represented in the reviewed papers by 7% and 2%, respectively. Finally, for the hydrothermal hydrolysis, HCW, LHW, and wet oxidation are over-represented in the reviewed papers by 3%, 2%, and 1%, respectively, while steam explosion pretreatment is under-represented by 7% in the reviewed papers. On the other hand, Table 26.6 provides data on the straw biomass used in the studies for the straw hydrolysis. There are two primary research fronts: wheat and rice straw with 48% and 40% of the reviewed papers, respectively. The other fronts are corn, rye, and sorghum straw with 4% of the reviewed papers each. Further, rice and corn straw are largely over-represented in the reviewed papers with 7% and 3% surplus, respectively. Similarly, wheat and barley straw are under-represented by 7% and 6 5% deficit, respectively.

26.4.2 The Enzymatic Hydrolysis of Straw Combined with Other Pretreatments There are 21 HCPs for the research front of the enzymatic hydrolysis of straw in combination with other pretreatments. The key research front is the enzymatic hydrolysis of straw combined with chemical pretreatments with 16 HCPs. There are also five and four HCPs for enzymatic hydrolysis of straw combined with mechanical and hydrothermal pretreatments, respectively. 26.4.2.1 The Enzymatic Hydrolysis of Straw Combined with Chemical Pretreatments There are 16 HCPs for the research front of the enzymatic hydrolysis of straw in combination with chemical pretreatments (Table 26.1). The key research fronts are alkaline, ionic liquid, acid and TABLE 26.6 The Most Prolific Research Fronts the Straw Biomass Used for the Straw Hydrolysis No.

Research Fronts

1 2 3

Wheat straw Rice straw Other straw Corn straw

N Paper (%) review

N Paper (%) Sample

Surplus (%)

48.0 40.0 16.0 4.0

54.6 33.3 14.9 0.9

−6.6 6.7 1.1 3.1

Rye straw

4.0

0.9

3.1

Sorghum straw

4.0

2.8

1.2

Sugarcane

4.0

1.9

2.1

Barley straw

0.0

5.6

−5.6

Rapeseed straw

0.0

0.9

−0.9

Soybean straw

0.0

1.9

−1.9

N paper (%) review, The number of papers in the sample of 25 most cited papers; N paper (%) sample, The number of papers in the population sample of 108 papers.

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solvent pretreatments with six, three, three, and two HCPs, respectively. Additionally, there is one HCP each for ammonia and ozone pretreatments. 26.4.2.1.1 The Enzymatic Hydrolysis of Straw Combined with Alkaline Pretreatment Zhu et al. (2005) studied microwave and alkaline pretreatment of rice straw and its enzymatic hydrolysis and observed that that the combined microwave and alkaline pretreatment could remove more lignin and hemicellulose from rice straw with shorter pretreatment time compared with the alkali-alone one. Further, Saha and Cotta (2006) explored the alkaline H2O2 pretreatment and enzymatic hydrolysis of wheat straw for the conversion of its cellulose and hemicellulose to sugars and observed that the maximum yield of sugars from wheat straw by this pretreatment and enzymatic hydrolysis by cellulase, β-glucosidase, and xylanase was 672 mg/g (96.7% yield). McIntosh and Vancov (2010) explored the enzymatic hydrolysis of sorghum straw using dilute NaOH pretreatment and observed that both solids and lignin content were inversely proportional to the severity of the pretreatment. Further, Chen et al. (2008) studied the enzymatic hydrolysis of corn straw for the production of sugars and observed that the hydrolysis yield at 48 h was 65.9%. Finally, McIntosh and Vancov (2011) used of dilute NaOH pretreatment followed by enzymatic hydrolysis of wheat straw to produce sugars and observed that recoverable solids and lignin contents were inversely proportional to the severity of the pretreatment process. 26.4.2.1.2 The Enzymatic Hydrolysis of Straw Combined with Ionic Liquid Pretreatment Li et al. (2009) used [Emim]DEP to accelerate enzymatic hydrolysis of wheat straw and observed that the yield of sugars at 130°C for 30 min reached 54.8% after being enzymatically hydrolyzed for 12 h. Further, Fu et al. (2010) extracted lignin from wheat straw by [EMIM]Ac and enzymatic hydrolysis of the cellulosic residues and obtained the optimal result yielding more than 95% cellulose digestibility of the residue. Finally, Nguyen et al. (2010) studied the pretreatment of rice straw with ammonia and [Emim]Ac for its enzymatic hydrolysis and observed that the combined use of ammonia and IL pretreatments exhibited a synergy effect for rice straw with 82% of the cellulose recovery and 97% of the enzymatic glucose conversion. 26.4.2.1.3 The Enzymatic Hydrolysis of Straw Combined with Acid Pretreatment Saha et al. (2005) evaluated dilute H2SO4 pretreatment and enzymatic hydrolysis for conversion of wheat straw cellulose and hemicellulose to sugars and observed that the maximum yield of sugars from wheat straw by dilute H2SO4 pretreatment and enzymatic hydrolysis using cellulase, β-glucosidase, xylanase, and esterase was 565 mg/g. Further, Hsu et al. (2010) optimized the dilute H2SO4 pretreatment of rice straw and explored the effect of the structural properties of the solid residues on the enzymatic hydrolysis and obtained a maximal sugar yield of 83% when the rice straw was pretreated with 1% (w/w) H2SO4 with a reaction time of 1–5 min at 160°C or 180°C, followed by enzymatic hydrolysis. 26.4.2.1.4 The Enzymatic Hydrolysis of Straw Combined with Solvent Pretreatment Kumar et al. (2016) pretreated rice straw using NADESs and separated high-quality lignin and holocellulose in a single step and observed that the extracted lignin was of high purity, and nearly 60% (w/w) of total lignin was separated from the lignocellulosic biomass. Further, Wildschut et al. (2013) studied wheat straw fractionation by ethanol organosolv as pretreatment for enzymatic cellulose hydrolysis and observed that the optimization of the process towards enzymatic digestibility resulted in a maximum glucose yield of 86% without the use of a catalyst.

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26.4.2.1.5 The Enzymatic Hydrolysis of Straw Combined with Ammonia Pretreatment Ko et al. (2009) pretreated rice straw using aqueous ammonia solution at moderate temperatures to enable production of the maximum amount of sugars from enzymatic hydrolysis and observed that the optimal reaction conditions, which resulted in an enzymatic digestibility of 71.1%, were 69°C, 10 h and an ammonia concentration of 21% (w/w). 26.4.2.1.6 The Enzymatic Hydrolysis of Straw Combined with Ozone Pretreatment Garcia-Cubero et al. (2009) pretreated wheat and rye straw with ozone for their enzymatic hydrolysis in a paper and observed that the acid insoluble lignin content of the biomass was reduced in all experiments involving hemicellulose degradation with near negligible losses of cellulose. These HCPs present a representative sample of the research on the enzymatic hydrolysis of straw combined with chemical pretreatments. They hint that chemical pretreatments together with enzymatic pretreatments enable the conversion of straw to sugars for the ethanol production with a higher sugar and ethanol yield. 26.4.2.2 The Enzymatic Hydrolysis of Straw Combined with Mechanical Pretreatments There are five HCPs for the research front of the enzymatic hydrolysis of straw in combination with mechanical pretreatments (Table 26.2). The key research front is milling pretreatment with three HCPs, followed by the microwave pretreatment with two HCPs. 26.4.2.2.1 The Enzymatic Hydrolysis of Straw Combined with Milling Pretreatment Da Silva et al. (2010) compared the effectiveness of BM and WDM of sugarcane bagasse and straw for enzymatic hydrolysis and ethanol fermentation and observed that glucose and xylose hydrolysis yields at optimum conditions for BM-pretreated bagasse and straw were 78.7%, and 72.1% and 77.6% and 56.8%, respectively. Further, Hideno et al. (2009) explored the WDM pretreatment for enzymatic hydrolysis of rice straw using cellulase and observed that glucose and xylose yields by WDM, BM, and HCW pretreatments were 78.5% and 41.5%, 89.4% and 54.3%, and 70.3% and 88.6%, respectively. Finally, Silva et al. (2012) studied the effects of sieve-based grinding, jet milling, and by BM on the enzymatic hydrolysis of wheat straw using T. reesei enzymes and observed that the wheat straw degradability was enhanced by the decrease of particle size until a limit: nearly 100 μm, up to 36% total carbohydrate and 40% glucose hydrolysis yields. 26.4.2.2.2 The Enzymatic Hydrolysis of Straw Combined with Microwave Pretreatments Ma et al. (2009) optimized the enzymatic hydrolysis of rice straw by microwave pretreatment and observed that microwave intensity, irradiation time, and substrate concentration were main factors governing the enzymatic hydrolysis of rice straw. These HCPs present a representative sample of the research on the enzymatic hydrolysis of straw combined with mechanical pretreatments. They hint that these mechanical pretreatments together with enzymatic pretreatments enable the conversion of straw to sugars for the ethanol production with a higher sugar and ethanol yield. 26.4.2.3 The Enzymatic Hydrolysis of Straw Combined with Hydrothermal Pretreatments There are four HCPs for the research front of the enzymatic hydrolysis of straw in combination with hydrothermal pretreatments (Table 26.3). The research fronts are steam explosion, LHW, HCW, and wet oxidation pretreatments with one HCP each.

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26.4.2.3.1 The Enzymatic Hydrolysis of Straw Combined with Steam Explosion Pretreatment Tabka et al. (2006) pretreated wheat straw with diluted H2SO4 followed by steam explosion and observed a synergistic effect between cellulases, FAE, and xylanase under a critical enzymatic concentration. 26.4.2.3.2 The Enzymatic Hydrolysis of Straw Combined with Liquid Hot Water Pretreatment Perez et al. (2008) optimized LHW pretreatment conditions to enhance sugar recovery from wheat straw for ethanol production and observed that optimal conditions were 188°C and 40 min, leading to hemicellulose-derived sugars (HDS) recovery yield of 43.6% of HDS content in raw material and enzymatic hydrolysis yield of 79.8% of theoretical. 26.4.2.3.3 The Enzymatic Hydrolysis of Straw Combined with Wet Oxidation Pretreatment Bjerre et al. (1996) carried out the wet oxidation process of wheat straw to break down cellulose to glucose enzymatically and to dissolve hemicellulose without producing any fermentation inhibitors and observed that wet oxidation combined with base addition readily oxidized lignin from wheat straw facilitating the polysaccharides for enzymatic hydrolysis. These HCPs present a representative sample of the research on the enzymatic hydrolysis of straw combined with hydrothermal pretreatments. They hint that these hydrothermal pretreatments together with enzymatic pretreatments enable the conversion of straw to sugars for the ethanol production with a higher sugar and ethanol yield.

26.4.3 The Sole Acid and Enzymatic Hydrolysis of Straw There are three and one HCPs for the sole acid and enzymatic hydrolysis of straw, respectively (Table 26.4). 26.4.3.1 The Sole Acid Hydrolysis of Straw Karimi et al. (2006) hydrolyzed rice straw by dilute H2SO4 at high temperature and pressure in one and two stages and showed the ability of first stage hydrolysis to depolymerize xylan to xylose with a maximum yield of 80.8% at hydrolysis pressure of 15 bar, 10 min retention time and 0.5% acid concentration. Further, Kootstra et al. (2009) compared the efficiencies of fumaric, maleic, and sulfuric acids in wheat straw pretreatment and observed that the pretreatment with dilute fumaric or maleic acid could be a serious alternative to dilute sulfuric acid pretreatment. Finally, Roberto et al. (2003) determined the effects of H2SO4 concentration and reaction time on the production of sugars and on the fermentation inhibitors and observed the optimum H2SO4 concentration of 1% and reaction time of 27 min. 26.4.3.2 The Sole Enzymatic Hydrolysis of Straw Taniguchi et al. (2005) evaluated the effects of biological pretreatment of rice straw using P. ostreatus for its susceptibility to enzymatic hydrolysis and observed that P. ostreatus selectively degraded the lignin fraction of rice straw rather than the holocellulose component. These HCPs present a representative sample of the research on the sole acid and enzymatic hydrolysis of straw. They hint that these pretreatments enable the conversion of straw to sugars for the ethanol production with a higher sugar and ethanol yield.

26.5 CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most cited papers with at least 178 citations each is given under two primary headings: the enzymatic hydrolysis of straw combined with other pretreatments and the sole acid and enzymatic hydrolysis of straw. The usual characteristics of these HCPs are that chemical, mechanical, and hydrothermal pretreatments are often used in combination of enzymatic pretreatments for the hydrolysis of the straw.

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In this way, the straw hydrolysis is effective in disrupting the biomass microstructure resulting in improved sugar and bioethanol yield. The key findings on these research fronts should be read in the light of the increasing public concerns about climate change, GHG emissions, and global warming as these concerns have been certainly behind the boom in the research on the bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. These studies emphasize the importance of proper incentive structures for the efficient development and application of straw hydrolysis to enhance sugar and bioethanol yield of the biomass after the hydrolysis of the biomass and the following fermentation of the resulting hydrolysates in the light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the Japan, the USA and to a lesser extent Australia, Brazil, Europe, and S. Korea had developed strong incentive structures for the effective development and application of enzymatic hydrolysis of straw for efficient bioethanol and sugar production. With the recent supply shocks, for example, due to the COVID-19 pandemic and Russian invasion of Ukraine, it is expected the public incentives for the research and development for the bioethanol fuels as a green alternative to crude oil-based gasoline and diesel fuels would increase in the coming years. In this context, the stakeholders involved in the straw hydrolysis would have a significant first-mover advantage in benefiting from these incentives. It is recommended that such review studies are performed for the primary thematic research fronts of straw hydrolysis as well as the straw constituents.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the straw hydrolysis has been gratefully acknowledged.

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27 Scientometric Study

Cellulose Hydrolysis Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

27.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining the energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), COVID-19 pandemics (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2001) in the aftermath of Russian invasion of Ukraine (Reeves, 2014) and COVID-19 pandemics. However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass, and hydrolysates, respectively. The research in the field of cellulose hydrolysis (Arantes and Saddler, 2010; Chang et al., 1981; Kostylev and Wilson, 2012) has thus intensified in recent years. The enzymatic hydrolysis (Enari and Niku-Paavola, 1987; Eriksson, 1978; Gregg and Saddler, 1996), acid hydrolysis (Huang and Fu, 2013; Li and Zhao, 2007; Onda et al., 2008), ionic liquid hydrolysis (Cheng et al., 2011; Dadi et al., 2006; Lee et al., 2009), and other hydrolysis (Bjerre et al., 1996; Kim and Hong, 2001; Liu and Wyman, 2005) of cellulose have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As there have been no scientometric studies on the cellulose hydrolysis, this book chapter presents a scientometric study of the research in cellulose hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts.

27.2 MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. 176

DOI: 10.1201/9781003226499-35

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As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This extended keyword list was provided in the appendix for future replication studies. As a second step, two sets of data were used for this study. First, a population sample of around 2,093 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 209 most-cited papers, corresponding to 10% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the cellulose hydrolysis. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

27.3 RESULTS 27.3.1 The Most-Prolific Documents in the Cellulose Hydrolysis The information on the types of documents for both datasets is given in Table 27.1. The articles and conference papers dominate both the sample (94%) and population (95%) papers as they are underrepresented in the sample papers by 1%. Further, review papers and short surveys have a surplus as they are over-represented in the sample papers by 4% as they constitute 6% and 2% of the sample and population papers, respectively. It is further notable that 98% of the population papers were published in journals while 1% each of them was published in book series and books. On the contrary, 99.5% of the sample papers were published in the journals.

27.3.2 The Most-Prolific Authors in the Cellulose Hydrolysis The information about the most-prolific 23 authors with at least 1.4% of sample papers each is given in Table 27.2. TABLE 27.1 Documents in the Cellulose Hydrolysis Documents Article Review Conference paper Letter Book chapter Book Note Short Survey Editorial Sample size

Sample Dataset (%) 90.0 5.7 3.8 0.5 0.0 0.0 0.0 0.0 0.0 209

Population Dataset (%) 92.0 2.2 3.1 0.9 1.0 0.4 0.4 0.1 0.0 2,093

Surplus (%) −2.0 3.5 0.7 −0.4 −1.0 −0.4 −0.4 −0.1 0.0

Population dataset; the number of papers (%) in the set of the 2,093 population papers; sample dataset; the number of papers (%) in the set of 209 highly cited papers.

178

TABLE 27.2 Most-Prolific Authors in the Cellulose Hydrolysis No.

Author Name Wyman, Charles E Zhang, Yi H. P. Saddler, Jack N. Lynd, Lee R. Himmel, Michael E. Ragauskas, Arthur J. Arantes, Valdeir Fan, Liangtseng Yang, Bin Fukuoka, Atsushi Kobayashi, Hirokazu Tsao, George T. Mandels, Mary H.* Pan, Xuejun Ladisch, Michael R. Torget, Robert W. Wilson, David B. Johnson, David K. Schall, Constance A.* Valjamae, Priit Zhu, Zhiguang Hara, Kenji Zhang, Tao

Author Code 7004396809 34876090400 7005297559 35586183800 7007125552 7006265204 56344492500 55743058500 7404473046 57204339585 55598722800 7005733848 6701602607 57203296000 7005670397 16074144000 57202955232 24550868900 6603671396 6602485147 25029372500 55382811100 56158944400

Sample Papers (%)

Population Papers (%)

Surplus

Institution

Country

HI

N

4.3 4.3 3.8 3.3 2.4 1.9 1.9 1.9 1.9 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4

1.0 0.6 1.4 0.4 0.6 0.5 0.4 0.3 0.2 0.7 0.6 0.5 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2

3.3 3.7 2.4 2.9 1.8 1.4 1.5 1.6 1.7 0.7 0.8 0.9 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.2 1.2

Univ. Calif. Riverside Chinese Acad. Sci. Univ. British Columbia Dartmouth Coll. NREL Univ. Tennessee Knoxville Univ. Sao Paulo Kansas State Univ. Washington State Univ. Hokkaido Univ. Hokkaido Univ. Purdue Univ. US Army Natick Res. Univ. Wisconsin Madison Purdue Univ. NREL Cornell Univ. NREL Univ. Toledo EniferBio Inc. Chinese Acad. Sci. Tokyo Univ. Technol. Chinese Acad. Sci.

USA China Canada USA USA USA Brazil USA USA Japan Japan USA USA USA USA USA USA USA USA Finland China Japan China

80 56 96 74 73 90 26 80 39 61 34 47 24 44 59 20 59 38 18 22 24 35 99

286 176 403 286 421 743 56 670 93 278 82 265 46 116 290 26 233 99 59 38 65 74 678

Res. Front Enzym. Enzym. Enzym. Enzym. Enzym., acid Enzym. Enzym. Enzym. Enzyme. Acid Acid CO 2 Enzym. Enzym. Enzym. Acid Enzym. Enzym. IL Enzym. Acid Acid IL

*, female; acid: acid hydrolysis; author code, the unique code given by Scopus to the authors; Enzym., enzymatic hydrolysis; HI, H-index; IL, ionic liquid hydrolysis; N, number of papers published by each author; population papers, the number of papers authored in the population dataset; sample papers, the number of papers authored in the sample dataset.

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 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

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The most-prolific authors are Charles E. Wyman and Yi H. P. Zhang with 4.3% of the sample papers each working primarily on the cellulose enzymatic hydrolysis. Next, Jack N. Saddler and Lee R. Lynd publish 3.8% and 3.3% of the sample papers, respectively, while Michael E. Himmel, Arthur J. Ragauskas, Valdier Arantes, Liangtseng Fan, and Bin Yang are the other prolific authors with 1.9%–2.4% of the sample papers each. The most influential author is Yi H. P. Zhang with 3.7% surplus, followed by Charles E. Wyman and Lee R. Lynd with 3.3% and 2.9% surplus, respectively. The other influential authors are Jack N. Saddler and Michael E. Himmel with 2.4% and 1.8% surplus, respectively. The most-prolific institutions for the sample dataset are the Chinese Academy of Sciences and National Renewable Energy Laboratory (NREL) with three authors each while Purdue University houses two authors. On the other hand, the most-prolific country for the sample dataset is the USA with 14 authors while China and Japan house three and two authors, respectively. The most-prolific research front is the enzymatic hydrolysis of cellulose with 14 authors, while the other prolific research front is the acid hydrolysis of cellulose with six authors. The other fronts are ionic liquid and CO2 hydrolysis of cellulose with two and one authors, respectively. On the other hand, there is significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only two of these top researchers are female with a representation rate of 9%. Additionally, there are other authors with the relatively low citation impact and with 0.4%– 0.8% of the population papers each: Arkady P. Sinistsyn, Runcang Sun, Alexander V. Gusakov, Folke Tjerneld, Feng Xu, Tina Jeoh, Oxana P. Taran, Shuguang Shen, Hongwei Wu, Yun Yu, Bruce E. Dale, Zhen Fang, Nikolay V. Gromov, Haining Na, Yoshitaka Ogiwara, Valentin N. Parmon, Liangcai Peng, Antje C. Spiess, and Jin Zhu.

27.3.3 The Most-Prolific Research Output by Years in Cellulose Hydrolysis Information about papers published between 1970 and 2022 is given in Figure 27.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s with 47% of the population dataset. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 10%, 11%, 10%, 10%, and 5% respectively. Additionally, 5% of the population papers were published between 1885 and 1969. 14

Number of papers (%)

12

Population papers Sample papers

10 8 6 4 2 0

FIGURE 27.1  The research output by years regarding the cellulose hydrolysis.

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Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 33 and 40% of the sample dataset, respectively. The publication rates for the 1990s, 1980s, and 1970s were 14%, 10%, and 5% of the sample papers, respectively. The most-prolific publication year for the population dataset was 2014 with 5.4% of the dataset while 40% of the population papers were published between 2010 and 2021. Similarly, 58% of the sample papers were published between 2006 and 2014 while the most-prolific publication years were 2009 and 2011 with 8% and 9% of the sample papers, respectively.

27.3.4 The Most-Prolific Institutions in the Cellulose Hydrolysis Information about the most-prolific 17 institutions publishing papers on the cellulose hydrolysis with at least 1.9% of the sample papers each is given in Table 27.3. The most-prolific institutions are the Chinese Academy of Sciences, NREL, and Dartmouth College with 6.2% of the sample papers each. The other prolific institutions are the University of British Columbia, Georgia Institute of Technology, and Virginia Polytechnic Institute and State University with 2.9%–5.7% of the sample papers, respectively. The top country for these most-prolific institutions is the USA with 12 institutions while Finland houses two institutions. In total, only five countries house these top institutions. On the other hand, the institution with the most citation impact is the Dartmouth College with 5.2% surplus, followed by the NREL and University of British Columbia with 4.2% and 3.6% surplus, respectively. The other prolific institutions are Chinese Academy of Sciences, Georgia Institute of Technology, and Virginia Polytechnic Institute and State University with 2.1%–2.5% surplus each. Additionally, there are other institutions with the relatively low citation impact and with 0.6%– 2.4% of the population papers each: South China University of Technology, University of Sao Paulo, Beijing Forestry University, Kyoto University, CNRS, Russian Academy of Sciences, United States Department of Agriculture (USDA), Lund University, Purdue University, Hokkaido University, Aalto University, Lomonosov Moscow State University, Tianjin University, University of California, Davis, Indian Institute of Technology Delhi, Huazhong Agricultural University, NC State University, TABLE 27.3 The Most-Prolific Institutions in Cellulose Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17

Institutions Chinese Acad. Sci. Natl. Renew. Ener. Lab. Dartmouth Coll. Univ. British Columbia Georgia Inst. Technol. Virginia Polytech. Inst. State Univ. Purdue Univ. Univ. Calif. Riverside Univ. Calif. Berkeley Tohoku Univ. Univ. Wisconsin-Madison VTT Tech. Res. Ctr. Auburn Univ. Oak Ridge Natl. Lab. Kansas State Univ. Cornell Univ. Univ. Tartu

Country

Sample Papers (%)

Population Papers (%)

Surplus (%)

China USA USA Canada USA USA USA USA USA Japan USA Finland USA USA USA USA Finland

6.2 6.2 6.2 5.7 3.3 2.9 2.4 2.4 2.4 2.4 1.9 1.9 1.9 1.9 1.9 1.9 1.9

3.7 2.0 1.0 2.1 0.8 0.8 1.1 0.7 0.6 0.5 0.9 0.8 0.6 0.5 0.5 0.4 0.3

2.5 4.2 5.2 3.6 2.5 2.1 1.3 1.7 1.8 1.9 1.0 1.1 1.3 1.4 1.4 1.5 1.6

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Nanjing Forestry University, Qingdao Institute of Bioenergy and Bioprocess Technology, The Ohio State University, Taiyuan University of Technology, RWTH Aachen University, Beijing University of Chemical Technology, Copenhagen University, and Shaanxi University of Science and Technology.

27.3.5 The Most-Prolific Funding Bodies in the Cellulose Hydrolysis Information about the most-prolific 15 funding bodies funding at least 1.4% of the sample papers each is given in Table 27.4. Only 28% and 39% of the sample and population papers were funded, respectively. The most-prolific funding body is the National Natural Science Foundation of China with 4.3% of the sample papers, closely followed by the US Department of Energy and Ministry of Science and Technology of China with 3.3% and 2.9% of the sample papers, respectively. On the other hand, the most-prolific country for these top funding bodies is China with five funding bodies, while Brazil, Canada, the EU, Japan, and the USA house two funding bodies each. In total, five countries and the EU house these top funding bodies. The funding body with the most citation impact is the Seventh Framework Program of the EU with 1.7% surplus, while the US Department of Energy and Natural Resources Canada are the other influential funding bodies with 1.1%–1.2% surplus each. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 7% deficit. It is notable that this funding body funds 10.9% of the population papers. The other funding bodies with the relatively low citation impact and with 0.5%–2.1% of the population papers each are Japan Society for the Promotion of Science, Research Support Foundation of the State of Sao Paulo, Higher Education Personnel Improvement, Office of Science, Natural Sciences and Engineering Research Council of Canada, China Scholarship Council, Japan Science and Technology Agency, National High-tech Research and Development Program, Government of Canada, China Postdoctoral Science Foundation, National Basic Research Program of China (973 Program), Department of Biotechnology, Ministry of Science and Technology, India, Department of Science and Technology, Ministry of Science and Technology, India, Laboratory Directed Research and Development, National Nuclear Security Administration, New Energy and Industrial Technology Development Organization, and Russian Foundation for Basic Research. TABLE 27.4 The Most-Prolific Funding Bodies in Cellulose Hydrolysis No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Funding Bodies Natl. Natr. Sci. Found. China US Dept. Ener. Minist. Sci. Technol., China Minist. Educ. Cult. Sport. Sci. Technol. Minist. Educ., China Eur. Commis. Natrl. Sci. Eng. Res. Counc. Canada Seventh Framew. Prog. Natl. Sci. Found. Natl. Counc. Sci. Technol. Devnt. Fundam. Res. Fund. Centr. Univ. Minist. Finance Minist. Sci. Technol. Innov. Natl. Key Res. Devnt. Prog., China Natrl. Resourc. Canada

Country China USA China Japan China EU Canada EU USA Brazil China Japan Brazil China Canada

Sample Paper No. (%) 4.3 3.3 2.9 1.9 1.9 1.9 1.9 1.9 1.4 1.4 1.4 1.4 1.4 1.4 1.4

Population Paper No. (%)

Surplus (%)

10.9 2.1 1.9 2.4 1.9 1.1 0.9 0.2 1.9 1.8 1.3 0.8 0.7 0.7 0.3

−6.6 1.2 1.0 −0.5 0.0 0.8 1.0 1.7 −0.5 −0.4 0.1 0.6 0.7 0.7 1.1

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27.3.6 The Most-Prolific Source Titles in the Cellulose Hydrolysis Information about the most-prolific 17 source titles publishing at least 1.4% of the sample papers each in cellulose hydrolysis is given in Table 27.5. The most-prolific source title is Biotechnology and Bioengineering with over 22% of the sample papers, followed by Bioresource Technology with 11% of the sample papers. Green Chemistry, Applied and Environmental Microbiology, Biotechnology for Biofuels, and Industrial and Engineering Chemistry Research are the other prolific journals with 3%–5% of the sample papers each. On the other hand, the source title with the most citation impact is the Biotechnology and Bioengineering with 16% surplus while Bioresource Technology, Applied and Environmental Microbiology, and Green Chemistry are the other influential journals with 3%–4% surplus each. Similarly, the source title with the least impact is Cellulose with 2% deficit, followed by Carbohydrate Polymers with 1% deficit. The other source titles with the relatively low citation impact with 0.5%–1.6% of the population paper each are Biotechnology Letters, Textile Research Journal, ACS Sustainable Chemistry and Engineering, RSC Advances, Bioresources, Industrial Crops and Products, Journal of Applied Polymer Science, Applied Microbiology and Biotechnology, Biomass and Bioenergy, Cellulose Chemistry and Technology, International Journal of Biological Macromolecules, Biomass Conversion and Biorefinery, Process Biochemistry, Fuel, Journal of Chemical Technology and Biotechnology, Chemical Engineering Journal, Journal of Dairy Science, Advanced Materials Research, Biotechnology Bioengineering Symposium, and Energy and Fuels.

27.3.7 The Most-Prolific Countries in the Cellulose Hydrolysis Information about the most-prolific 16 countries publishing at least 1.4% of sample papers each in cellulose hydrolysis is given in Table 27.6. TABLE 27.5 The Most-Prolific Source Titles in Cellulose Hydrolysis No.  1  2  3  4  5  7  6  8  9 10 11 12 13 14 15 16 17

Source Titles Biotechnology and Bioengineering Bioresource Technology Green Chemistry Applied and Environmental Microbiology Biotechnology for Biofuels Industrial and Engineering Chemistry Research Carbohydrate Polymers ChemSusChem Applied Biochemistry and Biotechnology Proceedings of the National Academy of Sciences of the United States of America Cellulose Biomacromolecules Biotechnology Progress Enzyme and Microbial Technology Journal of the American Chemical Society Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Journal of Physical Chemistry B

Sample Papers (%)

Population Papers (%)

Surplus (%)

21.5 10.5 4.8 4.3 3.3 3.3 2.9 2.9 2.4 2.4

5.7 6.8 1.8 0.9 1.9 1.3 3.7 0.8 2.2 0.4

15.8 3.7 3.0 3.4 1.4 2.0 −0.8 2.1 0.2 2.0

1.9 1.9 1.9 1.4 1.4 1.4

3.7 0.7 0.6 1.4 1.0 0.9

−1.8 1.2 1.3 0.0 0.4 0.5

1.4

0.3

1.1

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TABLE 27.6 The Most-Prolific Countries in the Cellulose Hydrolysis No.

Countries

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

USA China Japan Canada Sweden S. Korea Germany Finland Denmark UK Spain Estonia India Brazil Netherlands Taiwan

Sample Papers (%) 48.8 14.8 11.0 7.2 4.3 2.4 2.4 2.4 2.4 1.9 1.9 1.9 1.4 1.4 1.4 1.4

Population Papers (%) 22.5 21.9 9.5 4.7 2.3 2.9 2.7 2.0 1.2 3.2 2.7 0.3 4.7 3.3 0.9 0.8

Surplus (%) 26.3 −7.1 1.5 2.5 2.0 −0.5 −0.3 0.4 1.2 −1.3 −0.8 1.6 −3.3 −1.9 0.5 0.6

The most-prolific country is the USA with 49% of the sample papers while China, Japan, Canada, and Sweden are the other prolific countries with 15%, 11%, 7%, and 4% of the sample papers, respectively. Additionally, eight European countries listed in Table 27.6 produce 18% and 15% of the sample and population papers, respectively. On the other hand, the country with the most citation impact is the USA with 26% surplus while Canada, Sweden, Estonia, Japan, and Denmark are the other influential countries with 1.2%–2.5% surplus each. Similarly, the country with the least citation impact is China with 7% deficit while India, Brazil, the UK, Spain, S. Korea, and Germany have 0.3%–3.3% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.5%– 2.7% of the sample papers each: France, Russia, Australia, Malaysia, Austria, Italy, Thailand, Belgium, Egypt, Iran, Israel, Romania, Poland, Portugal, Hungary, Indonesia, Saudi Arabia, and Singapore.

27.3.8 The Most-Prolific Scopus Subject Categories in the Cellulose Hydrolysis Information about the most-prolific ten Scopus subject categories indexing at least 2.9% of the sample papers each is given in Table 27.7. The most-prolific Scopus subject category in the cellulose hydrolysis is Chemical Engineering with 61% of sample papers, closely followed by Biochemistry, and Genetics and Molecular Biology and Immunology and Microbiology with 53% and 41% of the sample papers, respectively. The other prolific subject categories are Environmental Science, Energy, Chemistry, and Materials Science with 15%–32% of the sample papers each. It is notable that the Social Sciences including Economics and Business account for only 1.1% of the population studies. On the other hand, the Scopus subject category with the most citation impact is the Immunology and Microbiology with 19% surplus, closely followed by Biochemistry, and Genetics and Molecular Biology and Chemical Engineering with 18% and 15% surplus, respectively. Similarly, the Scopus subject category with the least citation impact is Chemistry with 10% deficit, closely followed by Materials Science with 9% deficit.

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TABLE 27.7 The Most-Prolific Scopus Subject Categories in the Cellulose Hydrolysis No.  1  2  3  4  5  6  7  8  9 10

Scopus Subject Categories Chemical Engineering Biochemistry. Genetics and Molecular Biology Immunology and Microbiology Environmental Science Energy Chemistry Materials Science Engineering Agricultural and Biological Sciences Multidisciplinary

Sample Papers (%) 60.8 53.1 40.7 31.6 22.0 18.7 14.8 8.1 5.3 2.9

Population Papers (%) 45.6 34.7 21.6 21.5 18.9 29.1 23.6 8.3 9.8 1.7

Surplus (%) 15.2 18.4 19.1 10.1 3.1 −10.4 −8.8 −0.2 −4.5 1.2

27.3.9 The Most-Prolific Scopus Keywords in the Cellulose Hydrolysis Information about the keywords used with at least 5.3% or 3.2% of the sample or population papers, respectively, is given in Table 27.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most-prolific keyword set provided by Scopus database. These keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. There are 12 keywords selected related to the biomass and biomass constituents: cellulose, lignin biomass, microcrystalline cellulose, lignocellulose, cellobiose, and carbohydrates with 10%–81% of the sample papers each. The prolific keywords related to the cellulose hydrolysis are hydrolysis, enzymatic hydrolysis, cellulose hydrolysis, and saccharification with 13%–72% of the sample papers each while those related to the biomass pretreatments are cellullases, enzymes, ionic liquids, enzyme activity, glucosidase, temperature, Trichoderma reesei, pretreatment, and enzymolysis with 10%–47% of the sample papers each. The prolific keywords related to the other processes are adsorption and fermentation with 9%–10% of the sample papers each. Further, those related to the hydrolysis products are glucose, sugar, ethanol, and biofuel with 11%–32% of the sample papers each. It is notable that only 2.9% of the indexed papers employ bioethanol keyword. Further, the most influential keywords are glucosidase, enzyme activity, temperature, enzymatic hydrolysis, glucose, ethanol, enzyme binding, lignocellulose, and Hypocrea jecorina with 5%–10% surplus each.

27.3.10 The Most-Prolific Research Fronts in Cellulose Hydrolysis Information about the research fronts for the sample papers in cellulose hydrolysis with regard to the biomass used in these pretreatments is given in Table 27.9. As Table 27.9 shows, there are two primary research fronts for this field: the enzymatic and chemical hydrolysis of cellulose with 55% and 49% of the population papers, respectively. The other minor research fronts are the hydrothermal and mechanical hydrolysis of cellulose with 7% and 4% of the population papers, respectively. Additionally, there is a research front for the hydrolysis of cellulose in general with 9% of the population papers. The research fronts for the chemical hydrolysis of cellulose are acid, ionic liquid, water, solvent, alkaline, ammonia, CO2, and surfactant hydrolysis of cellulose with 1%–23% of the population

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TABLE 27.8 The Most-Prolific Keywords in Cellulose Hydrolysis No. 1

Keywords Cellulose

3

Population Papers (%)

Surplus (%)

80.9

77.1

3.8

Lignin

27.3

13.9

13.4

Biomass

27.3

13.5

13.8

Microcrystalline cellulose

15.3

11.8

3.5

Lignocellulose

10.5

5.3

5.2

Cellobiose

10.0

5.5

4.5

Carbohydrates

10.0

5.4

4.6

7.2

2.3

4.9

Zea mays (corn, maize)

2

Sample Papers (%)

Biomass

Hemicellulose

6.2

3.6

2.6

Cellulose derivatives

5.7

3.1

2.6

Wood

5.3

3.3

2.0

Lignocellulosic biomass

4.8

8.9

−4.1 23.0

Hydrolysis Hydrolysis

72.2

49.2

Enzymatic hydrolysis

27.8

21.8

6.0

Cellulose hydrolysis

16.3

13.2

3.1

Saccharification

13.4

11.9

1.5

Pretreatments Cellulases

40.7

27.7

13.0

Enzymes

28.2

24.9

3.3

Ionic liquids

28.2

11.6

16.6

Enzyme activity

22.5

13.7

8.8

Glucosidase

18.2

8.7

9.5

Temperature

13.4

6.7

6.7

Trichoderma reesei

11.0

9.3

1.7

Pretreatment

10.5

5.7

4.8

Enzymolysis

10.0

6.0

4.0

Fungi

9.6

6.0

3.6

Hypocrea jecorina

9.1

3.9

5.2

Pretreatment

8.6

5.4

3.2

Water

8.6

4.4

4.2

Alcohol

8.1

3.9

4.2

Sulfuric acids

7.7

3.2

4.5

Enzymatic activity

7.2

3.3

3.9

Cellulose 1,4 beta cellobiosidase

6.7

3.1

3.6

Acids

6.2

2.3

3.9

Enzyme inhibition

5.3

3.2

2.1

Glucan synthase

5.3

2.7

2.6

Phosphoric acid

5.3

1.8

3.5

Enzyme binding

5.3

0.0

5.3

pH

2.9

3.9

−1.0 (Continued)

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TABLE 27.8 (Continued) The Most-Prolific Keywords in Cellulose Hydrolysis No. 4

5

Keywords

Sample Papers (%)

Other processes Adsorption

Population Papers (%)

Surplus (%)

10.0

6.5

3.5

Fermentation

8.6

6.8

1.8

Saccharomyces cerevisiae

2.9

1.9

1.0

Hydrolysis products Glucose

32.1

26.4

5.7

Sugar

22.5

8.4

14.1

Ethanol

12.4

7.1

5.3

Biofuel

11.0

7.1

3.9

2.9

3.5

−0.6

Bioethanol

TABLE 27.9 The Most-Prolific Research Fronts in the Cellulose Hydrolysis No. 1 2

3

4

5

Research Fronts

N Paper (%) Sample

Enzymatic hydrolysis Chemical hydrolysis Acid hydrolysis

54.5 48.8 23.0

Ionic liquid hydrolysis

12.4

Water hydrolysis

2.4

Solvent hydrolysis

4.3

Alkaline hydrolysis

2.4

Ammonia hydrolysis

1.4

CO2 hydrolysis

1.4

Surfactant hydrolysis

1.4

Hydrothermal hydrolysis Steam explosion hydrolysis

7.2 3.3

Hot compressed water hydrolysis

1.9

Liquid hot water hydrolysis

1.9

Mechanical hydrolysis Microwave hydrolysis

3.8 1.9

Milling hydrolysis

1.0

Ultrasound hydrolysis

1.0

Hydrolysis in general

9.1

N paper (%) sample, the number of papers in the population sample of 209 papers.

papers each. Similarly, the research fronts for the hydrothermal hydrolysis of cellulose are steam hydrolysis, hot compressed water hydrolysis, and liquid hot water hydrolysis of cellulose with 1.9%– 3.3% of the population papers each. Finally, the research fronts for the mechanical hydrolysis of cellulose are microwave, milling, and ultrasound hydrolysis of cellulose with 1%–1.9% of the population papers each.

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27.4 DISCUSSION 27.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass and hydrolysates, respectively. The research in the fields of cellulose hydrolysis has thus intensified in recent years. The enzymatic, acid, ionic liquid, and other hydrolysis of cellulose have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, COVID-19 shocks, or war-related shocks as in the case of Russian invasion of Ukraine. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As there have been no scientometric studies on the cellulose hydrolysis, this book chapter presents a scientometric study of the research in the cellulose hydrolysis. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 300 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replication studies. Further, a selected list of the keywords is presented in Table 27.8. As a second step, two sets of data were used for this study. First, a population sample of over 2,093 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 209 most-cited papers, corresponding to 10% of the population dataset, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the cellulose hydrolysis. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

27.4.2 The Most-Prolific Documents in the Cellulose Hydrolysis Articles (together with conference papers) dominate both the sample (94%) and population (95%) papers (Table 27.1). Further, review papers and articles have a surplus (4%) and deficit (2%). respectively. The representation of the reviews and short surveys in the sample papers is not extraordinarily high (6%). Scopus differs from the Web of Science database in differentiating and showing articles (90%) and conference papers (4%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared to those published only in the conference proceedings. Similarly, Scopus differs from Web of Science database in introducing short surveys (0%). Hence, a total number of articles and review papers in the sample dataset are 94% and 6%, respectively.

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It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus as well as in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of the reviews in the sample dataset was manually found as 8% compared to 6% as indexed by Scopus, reducing the number of articles and conference papers to 92% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 27.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from the experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as well as it is done in the Web of Science database.

27.4.3 The Most-Prolific Authors in the Cellulose Hydrolysis There have been most-prolific 23 authors with at least 1.4% of the sample papers each as given in Table 27.2. These authors have shaped the development of the research in this field. The most-prolific authors are Charles E. Wyman and Yi H. P. Zhang and to a lesser extent Jack N. Saddler, Lee R. Lynd, Michael E. Himmel, Arthur J. Ragauskas, Valdier Arantes, Liangtseng Fan, and Bin Yang. It is notable that these top researchers are mostly from the USA. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for the names with more than two components such as ‘Blake Sam de Hyun Zhang’. The probable outcomes are ‘Zhang, B.S.D.H.’, ‘de Hyun Zhang, B.S.’, or ‘Hyun Zhang, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative while the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name by a hyphen: ‘Hyun-Zhang, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening Chinese names. For example, ‘Yuoyong Zhang’ is often shortened as ‘Zhang, Y.’, ‘Zhang, Y.-Y.’, and ‘Zhang, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhang Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PUBMED and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate the Chinese names efficiently: ‘Zhang, Y.Y.’. Therefore, there have been difficulties to locate papers for the Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors, especially for the authors with common names such as ‘Zhang, X.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies as well as allocating credit to the authors as there are the same author entries for different authors with the same name, for example, ‘Zhang, X.’ in the databases. In this context, the coding of authors in Scopus database is a welcome innovation compared to the other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system especially for common names. For example, some of the papers for a certain author may be allocated to another researcher with a different author code. It is possible that Scopus

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uses a number of software programs to differentiate the author names and the program may not be false-proof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Zhang, H.J.’, ‘Zhang’, ‘Zhang, H.’, or ‘Zhang, J.’ instead of ‘Zhang, Hyun Jae’. There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Zhang, A.P.C.’ might be given as ‘Zhang, A.’, ‘Zhang, A.P.’, ‘Zhang, C.’, or ‘Zhang, A.C.’ in the journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Çümpaçölgeçen, Söğüt) rather than in the English spellings (e.g., Cumpacolgecen, Sogut) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in the English spellings. It is observed that national spellings of the author names do not help in conducting scientometric studies as well in allocating credits to the authors as sometimes there are the different author entries for the English and National spellings in the Scopus database. The most-prolific institutions for the sample dataset are the Chinese Academy of Sciences and NREL and to a lesser extent Purdue University. Further, the most-prolific countries for the sample dataset are the USA and to a lesser extent China and Japan. These findings confirm the dominance of the USA and, to a lesser extent, of China and Japan in this field. The most-prolific research fronts are the enzymatic and acid hydrolysis of cellulose. The other prolific fronts are the ionic liquid and CO2 hydrolysis of cellulose. It is also notable that there is significant gender deficit for the sample dataset as surprisingly only two of these top researchers are female with 9% representation rate. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policy-makers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from the minority groups in science on the basis of race, and sexuality, age, and disability besides the gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

27.4.4 The Most-Prolific Research Output by Years in the Cellulose Hydrolysis The research output observed between 1970 and 2022 is illustrated in Figure 27.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. However, it is notable that the research output became flat after 2014 losing its momentum raising questions about its possible causes. These findings suggest that the most-prolific sample and population papers were primarily published in the last two decades. These are the thought-provoking findings. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field in the last two decades. Based on these findings, the size of the population papers likely to more than double in the current decade provided that the public concerns about climate change, greenhouse gas emissions, and global warming are translated efficiently to the research funding in this field. Furthermore,

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there is a need for the additional incentives for the research on the cellulose hydrolysis due to the current supply shocks due to the COVID-19 pandemics, and Russian invasion of Ukraine as there have been public pressures for the replacement of crude oil-based gasoline and diesel fuels by bioethanol fuels and biodiesel fuels. However, there was no sharp rise in the research output in 2020 and 2021.

27.4.5 The Most-Prolific Institutions in the Cellulose Hydrolysis The most-prolific 17 institutions publishing papers on the cellulose hydrolysis with at least 1.7% of the sample papers each given in Table 27.3 have shaped the development of the research in this field. The most-prolific institutions are the Chinese Academy of Sciences, NREL, and Dartmouth College, and to a lesser extent, the University of British Columbia, Georgia Institute of Technology, and Virginia Polytechnic Institute and State University. Further, the top countries for these mostprolific institutions are the USA and to a lesser extent Finland. In total, only five countries house these top institutions. On the other hand, the institutions with the most citation impact are Dartmouth College and to a lesser extent NREL, University of British Columbia, Chinese Academy of Sciences, Georgia Institute of Technology, and Virginia Polytechnic Institute and State University. These findings confirm the dominance of the US and to a lesser extent Canadian and Chinese institutions in this research field.

27.4.6 The Most-Prolific Funding Bodies in the Cellulose Hydrolysis The most-prolific 15 funding bodies funding at least 1.4% of the sample papers each are given in Table 27.4. It is notable that only 28% and 39% of the sample and population papers were funded, respectively. The most-prolific funding bodies are National Natural Science Foundation of China and to a lesser extent the US Department of Energy and Ministry of Science and Technology of China. Further, the most-prolific countries for these top funding bodies are China and to a lesser extent Brazil, Canada, the EU, Japan, and the USA. In total, five countries and the EU house these top funding bodies. The funding bodies with the most citation impact are the Seventh Framework Program of the EU and to a lesser extent the US Department of Energy and Natural Resources Canada. Further, the funding body with the least impact is the National Natural Science Foundation of China. It is notable that this funding body single-handedly funds 10.9% of the population papers. These findings on the funding of the research in this field suggest that the level of the funding, mostly in the last two decades, is not intensive, and nevertheless, it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding especially for the sample papers, there is ample room to enhance funding in this field. With the current supply shocks, it is expected that funding for the cellulose hydrolysis would increase substantially in the coming decades. It is also remarkable that China and to a lesser extent Brazil, Canada, the EU, Japan, and the USA dominate the research funding in this field.

27.4.7 The Most-Prolific Source Titles in Cellulose Hydrolysis The most-prolific 17 source titles publishing at least 1.4% of the sample papers each in cellulose hydrolysis have shaped the development of the research in this field (Table 27.5). The most-prolific source titles are Biotechnology and Bioengineering and to a lesser extent Bioresource Technology, Green Chemistry, Applied and Environmental Microbiology, Biotechnology for Biofuels, and Industrial and Engineering Chemistry Research.

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Further, the source titles with the most citation impact are the Biotechnology and Bioengineering and to a lesser extent Bioresource Technology, Applied and Environmental Microbiology, and Green Chemistry. It is notable that these top source titles are primarily related to the bioresources, biotechnology, microbiology, and chemical engineering. This finding suggests that the journals in this field have significantly shaped the development of the research in this field as they focus on the cellulose hydrolysis.

27.4.8 The Most-Prolific Countries in the Cellulose Hydrolysis The most-prolific 16 countries publishing at least 1.4% of the sample papers each have significantly shaped the development of the research in this field (Table 27.6). The most-prolific countries are the USA and to a lesser extent Europe, China, Japan, and Canada. On the other hand, the countries with the most citation impact are the USA and to a lesser extent Canada, Sweden, Estonia, Japan, and Denmark. Additionally, eight European countries listed in Table 27.6 produce 18% and 15% of the sample and population papers, respectively. The close examination of these findings suggests that the USA, Europe, China, Canada, and Japan are the major producers of the research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The USA has further developed a strong research infrastructure to support its corn- and grass-based bioethanol industry (Vadas et al., 2008). However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009). Next, Europe has been a persistent player in the scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010). Additionally, Brazil has also been a persistent player in scientific research at a moderate level (Glanzel et al., 2006). Brazil has also developed a strong research infrastructure to support its biomass-based bioethanol industry (Macedo et al., 2008).

27.4.9 The Most-Prolific Scopus Subject Categories in Cellulose Hydrolysis The most-prolific ten Scopus subject categories indexing at least 2.9% of the sample papers each, given in Table 27.7, have shaped the development of the research in this field. The most-prolific Scopus subject categories in the cellulose hydrolysis are Chemical Engineering and to a lesser extent Biochemistry, and Genetics and Molecular Biology, Immunology and Microbiology, Environmental Science, Energy, Chemistry, and Materials Science. These findings are thought-provoking suggesting that the primary subject categories are related to chemical engineering, molecular biology, microbiology, energy, materials science, and environmental sciences. The other key finding is that Social Sciences are not well represented in both the sample and population papers, as in the most fields in bioethanol fuels. These findings are not surprising as the key research fronts in this field relate to the development and applications of cellulose hydrolysis.

27.4.10 The Most-Prolific Scopus Keywords in Cellulose Hydrolysis A limited number of keywords have shaped the development of the research in this field as shown in Table 27.8 and Appendix. These keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis.

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The prolific keywords related to the biomass and biomass constituents are cellulose, lignin biomass, microcrystalline cellulose, lignocellulose, cellobiose, and carbohydrates while those related to the pretreatment are cellullases, enzymes, ionic liquids, enzyme activity, glucosidase, temperature, Trichoderma reesei, pretreatment, and enzymolysis. The prolific keywords related to the cellulose hydrolysis are hydrolysis, enzymatic hydrolysis, cellulose hydrolysis, and saccharification while those related to the other processes are adsorption and fermentation. Further, those related to the hydrolysis products are glucose, sugar, ethanol, and biofuel, and it is notable that only 3% of the indexed papers employ bioethanol keyword. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas. The relevant keywords are presented in Table 8 as well as in Appendix.

27.4.11 The Most-Prolific Research Fronts in Cellulose Hydrolysis As Table 27.9 shows, there are two primary research fronts for this field: enzymatic and chemical hydrolysis of cellulose while the other research fronts are hydrothermal and mechanical hydrolysis of cellulose. Additionally, there is a research front for the hydrolysis of cellulose in general. The research fronts for the chemical hydrolysis of cellulose are acid, ionic liquid, water, solvent, alkaline, ammonia, CO2, and surfactant hydrolysis of cellulose. Similarly, the research fronts for the hydrothermal hydrolysis of cellulose are steam explosion, hot compressed water, and liquid hot water hydrolysis of cellulose. Finally, the research fronts for the mechanical hydrolysis of cellulose are microwave, milling, and ultrasound hydrolysis of cellulose. These findings are thought-provoking in seeking ways to increase bioethanol yield through the cellulose hydrolysis at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. In the end, these most-cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of these cellulose hydrolysis processes (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

27.5 CONCLUSION AND FUTURE RESEARCH The research on the cellulose hydrolysis has been mapped through a scientometric study of both sample (209 papers) and population (2,093 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in Appendix, and the relevant keywords are presented in Table 27.8. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose, Scopus database has been selected. The journal coverage of this database has been notably wider than that of Web of Science and other multi-subject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication periods, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all the academic databases. In this respect, it seems that authors, journals, and academic

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databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Further, the institutions from the USA and Finland have mostly shaped the research in this field. The relatively modest funding of 39% for the population papers suggests that funding in this field nevertheless enhanced the research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further as only 28% of the sample papers declared any funding. It is expected that the current supply shocks such as the COVID-19 shocks and the shocks due to Russian invasion of Ukraine would increase the funding rate in this research field as bioethanol fuels are a green alternative to crude oil-based gasoline and diesel fuels. The USA and to a lesser extent Europe, China, Japan, and Canada have been the major producers of the research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass as well as other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives. The primary Scopus subject categories have been Chemical Engineering and to a lesser extent Biochemistry. Genetics and Molecular Biology, Immunology and Microbiology, Environmental Science, Energy, Chemistry, and Materials Science as the focus of the research have been on the development and utilization of the cellulose hydrolysis to increase the sugar and bioethanol yield at large. Further, Social Sciences are not well represented in both the sample and population papers as in the most fields in bioethanol fuels. These findings are not surprising. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under the five headings: biomass, hydrolysis, pretreatments, other processes, and products of the hydrolysis. These groups of keywords highlight the potential primary research fronts for these fields. There are two primary research fronts for this field: the enzymatic and chemical hydrolysis of cellulose. The other research fronts are the hydrothermal and mechanical hydrolysis of cellulose. These findings are thought-provoking. The focus of these most-cited 209 papers as well as 2,093 population papers is the development and utilization of cellulose hydrolysis to increase the sugar and bioethanol yield. These studies highlight strong structure–processing–property relationships for pretreatments for bioethanol fuels and their derivatives. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields especially in the aftermath of the significant global supply shocks such as Russian invasion of Ukraine and the COVID-19 shocks. It is recommended that further scientometric studies are carried out for the primary types of the cellulose hydrolysis. It is further recommended that reviews of the most-cited papers are carried out for each research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the cellulose hydrolysis has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR CELLULOSE HYDROLYSIS ((TITLE (hydrolysis OR saccharif* OR prehydroly* OR posthydroly* OR *hydrolysis OR digestibili* OR digestible OR accessibility OR ‘sugar recovery’ OR ‘fermentable sugars’ OR ‘reducing

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sugars’ OR ‘sugar yield*’ OR ‘sugar production’ OR ‘sugar release’ OR ‘sugar extraction’ OR *oligosaccharides OR recalcitrance OR hydrolysate* OR hydrolyzate* OR prehydrolysate* OR *prehydrolyzate* OR inhibitor* OR ‘degradation products’ OR ‘degradation compounds’ OR xylose OR pentose* OR hexose* OR glucose OR detoxif* OR ‘xylose recovery’ OR ‘enzymatic degradation’) AND TITLE (cellulose* OR avicel)) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR neur OR dent OR heal OR psyc OR eart) OR TITLE (nanocrystal* OR nanopartic* OR ‘nano crystal*’ OR nanowhisker* OR nanocomposit* OR nanofibril* OR thaxtomin* OR nanofiber* OR morlin OR filler* OR cationic OR ‘hydrothermal carbon*’ OR hydrochar* OR derivative* OR levulinic OR silane* OR carboxymethyl OR ‘anaerobic digestion’ OR diet* OR pyroly* OR *hydrogen OR monocot OR gasification OR ‘thermal degradation’ OR conformation* OR *acetobacter OR {bacterial cellulose} OR pig* OR amylase OR gels OR lactose OR acetate OR protein* OR {cellulose synthesis}) OR SRCTITLE (animal*))) AND (LIMIT-TO (SRCTYPE, ‘j’) OR LIMIT-TO (SRCTYPE, ‘k’) OR LIMIT-TO (SRCTYPE, ‘b’)) AND (LIMIT-TO (DOCTYPE, ‘ar’) OR LIMIT-TO (DOCTYPE, ‘cp’) OR LIMIT-TO (DOCTYPE, ‘re’) OR LIMIT-TO (DOCTYPE, ‘ch’) OR LIMIT-TO (DOCTYPE, ‘le’) OR LIMIT-TO (DOCTYPE, ‘bk’) OR LIMIT-TO (DOCTYPE, ‘no’) OR LIMIT-TO (DOCTYPE, ‘sh’)) AND (LIMIT-TO (LANGUAGE, ‘English’))

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Konur, O. 2011. The scientometric evaluation of the research on the algae and bio-energy. Applied Energy 88:3532–3540. Konur, O. 2012a. Prof. Dr. Ayhan Demirbasʼ scientometric biography. Energy Education Science and Technology Part A: Energy Science and Research 28:727–738. Konur, O. 2012b. The evaluation of the biogas research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:1277–1292. Konur, O. 2012c. The evaluation of the global energy and fuels research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 30:613–628. Konur, O. 2012d. The evaluation of the research on the biodiesel: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1003–1014. Konur, O. 2012e. The evaluation of the research on the bioethanol: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1051–1064. Konur, O. 2012f. The evaluation of the research on the biofuels: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:903–916. Konur, O. 2012g. The evaluation of the research on the biohydrogen: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:323–338. Konur, O. 2012h. The evaluation of the research on the microbial fuel cells: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:309–322. Konur, O. 2012i. The scientometric evaluation of the research on the production of bioenergy from biomass. Biomass and Bioenergy 47:504–515. Konur, O. 2015. Current state of research on algal bioethanol. In Marine Bioenergy: Trends and Developments, Ed. S. K. Kim and C. G. Lee, pp. 217–244. Boca Raton, FL: CRC Press. Konur, O., Ed. 2018a. Bioenergy and Biofuels. Boca Raton, FL: CRC Press. Konur, O. 2018b. Bioenergy and biofuels science and technology: Scientometric overview and citation classics. In Bioenergy and Biofuels, Ed. O. Konur, pp. 3–63. Boca Raton: CRC Press. Konur, O. 2019. Cyanobacterial bioenergy and biofuels science and technology: A scientometric overview. In Cyanobacteria: From Basic Science to Applications, Ed. A. K. Mishra, D. N. Tiwari and A. N. Rai, pp. 419–442. Amsterdam: Elsevier. Konur, O. 2020a. The scientometric analysis of the research on the bioethanol production from green macroalgae. In Handbook of Algal Science, Technology and Medicine, Ed. O. Konur, pp. 385–401. London: Academic Press. Konur, O., Ed. 2020b. Handbook of Algal Science, Technology and Medicine. London: Academic Press. Konur, O., Ed. 2021a. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021b. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 1. Biodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021c. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 2. Biodiesel Fuels based on the Edible and Nonedible Feedstocks, Wastes, and Algae: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021d. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 3. Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O. and F. L. Matthews. 1989. Effect of the properties of the constituents on the fatigue performance of composites: A review. Composites 20:317–328. Kostylev, M. and D. Wilson. 2012. Synergistic interactions in cellulose hydrolysis. Biofuels 3:61–70. Kruyt, B., D. P. van Vuuren, H. J. de Vries and H. Groenenberg. 2009. Indicators for energy security. Energy Policy 37:2166–2181. Le Billon, P. 2001. Angolaʼs political economy of war: The role of oil and diamonds, 1975-2000. African Affairs 100:55–80. Lee, S. H., T. V. Doherty, R. J. Linhardt and J. S. Dordick. 2009. Ionic liquid-mediated selective extraction of lignin from wood leading to enhanced enzymatic cellulose hydrolysis. Biotechnology and Bioengineering 102:1368–1376. Leydesdorff, L. 2000. Is the European Union becoming a single publication system? Scientometrics 47:265–280. Leydesdorff, L. and C. Wagner. 2009. Is the United States losing ground in science? A global perspective on the world science system. Scientometrics 78:23–36.

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Leydesdorff, L., C. S. Wagner and L. Bornmann. 2014. The European Union, China, and the United States in the top-1% and top-10% layers of most-frequently cited publications: Competition and collaborations. Journal of Informetrics 8:606–617. Leydesdorff, L. and P. Zhou. 2005. Are the contributions of China and Korea upsetting the world system of science? Scientometrics 63:617–630. Li, C. and Z. K. Zhao. 2007. Efficient acid-catalyzed hydrolysis of cellulose in ionic liquid. Advanced Synthesis and Catalysis 349:1847–1850. Li, H., S. M. Liu, X. H. Yu, S. L. Tang and C. K. Tang. 2020. Coronavirus disease 2019 (COVID-19): Current status and future perspectives. International Journal of Antimicrobial Agents 55:105951. Li, S. Z. and C. Chan-Halbrendt. 2009. Ethanol production in (the) Peopleʼs Republic of China: Potential and technologies. Applied Energy 86:S162–S169. Lin, Y. and S. Tanaka. 2006. Ethanol fermentation from biomass resources: Current state and prospects. Applied Microbiology and Biotechnology 69:627–642. Liu, C. and C. E. Wyman. 2005. Partial flow of compressed-hot water through corn stover to enhance hemicellulose sugar recovery and enzymatic digestibility of cellulose. Bioresource Technology 96:1978–1985. Ma, X., L. Sun and C. Song. 2002. A new approach to deep desulfurization of gasoline, diesel fuel and jet fuel by selective adsorption for ultra-clean fuels and for fuel cell applications. Catalysis Today 77:107–116. Macedo, I. C., J. E. A. Seabra and J. E. A. R. Silva. 2008. Greenhouse gases emissions in the production and use of ethanol from sugarcane in Brazil: The 2005/2006 averages and a prediction for 2020. Biomass and Bioenergy 32:582–595. Morschbacker, A. 2009. Bio-ethanol based ethylene. Polymer Reviews 49:79–84. Najafi, G., B. Ghobadian and T. Tavakoli, et al. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86:630–639. Newman, P. W. G. and J. R. Kenworthy. 1989. Gasoline consumption and cities: A comparison of U.S. cities with a global survey. Journal of the American Planning Association 55:24–37. North, D. C. 1991. Institutions. Journal of Economic Perspectives 5:97–112. Olsson, L. and B. Hahn-Hagerdal. 1996. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme and Microbial Technology 18:312–331. Onda, A., T. Ochi and K. Yanagisawa. 2008. Selective hydrolysis of cellulose into glucose over solid acid catalysts. Green Chemistry 10:1033–1037. Reeves, S. 2014. To Russia with love: How moral arguments for a humanitarian intervention in Syria opened the door for an invasion of the Ukraine. Michigan State University International Law Review 23:199. Sanchez, O. J. and C. A. Cardona. 2008. Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource Technology 99:5270–5295. Sun, Y. and J. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: A review. Bioresource Technology 83:1–11. Taherzadeh, M. J. and K. Karimi. 2007. Enzyme-based hydrolysis processes for ethanol from lignocellulosic materials: A review. BioResources 2:707–738. Taherzadeh, M. J. and K. Karimi. 2008. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: A review. International Journal of Molecular Sciences 9:1621–1651. Vadas, P. A., K. H. Barnett and D. J. Undersander 2008. Economics and energy of ethanol production from alfalfa, corn, and switchgrass in the Upper Midwest, USA. Bioenergy Research 1:44–55. Winzer, C. 2012. Conceptualizing energy security. Energy Policy 46:36–48.

28 Review

Cellulose Hydrolysis Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

28.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2019, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of cellulose hydrolysis (Arantes and Saddler, 2010; Chang et al., 1981; Kostylev and Wilson, 2012) has thus intensified in recent years. The enzymatic hydrolysis (Enari and Niku-Paavola, 1987; Eriksson, 1978; Gregg and Saddler, 1996), acid hydrolysis (Huang and Fu, 2013; Li and Zhao, 2007; Onda et al., 2008; Pang et al., 2010; Selig et al., 2007; Suganuma et al., 2008), ionic liquid hydrolysis (Cheng et al., 2011; Dadi et al., 2006; Lee et al., 2009; Zhao et al., 2009), and other hydrolysis (Bjerre et al., 1996; Kim and Hong, 2001; Liu and Wyman, 2005; Sasaki et al., 1998, 2000) of cellulose have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been a number of review papers on the cellulose hydrolysis (Arantes and Saddler, 2010; Cheng et al., 2011; Enari and Niku-Paavola, 1987; Eriksson, 1978; Gregg and Saddler, 1996; Huang and Fu, 2013; Kostylev and Wilson, 2012; Mansfield et al., 1999), there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents a review of the most-cited 25 articles in the field of the cellulose hydrolysis. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field.

28.2 MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in Appendix of Konur (2023) for future replication studies. 198

DOI: 10.1201/9781003226499-36

Cellulose Hydrolysis: Review

199

As a second step, a sample dataset was used for this study. The first 25 articles with at least 269 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

28.3 RESULTS The brief information about 25 most-cited papers with at least 269 citations each on the cellulose hydrolysis is given below. The primary research fronts are the enzymatic and chemical hydrolysis of cellulose with 10 and 15 highly cited papers (HCPs), respectively. The primary research fronts for the chemical hydrolysis of cellulose are the acid, ionic liquid, and other chemical hydrolysis of cellulose with six, five, and four HCPs, respectively.

28.3.1 The Enzymatic Hydrolysis of Cellulose There are 10 HCPs for the research front of the enzymatic hydrolysis of cellulose (Table 28.1). Yang and Wyman (2006) studied the cellulase and bovine serum albumin (BSA) pretreatments to enhance enzymatic hydrolysis of cellulose in corn stover and Avicel cellulose in a paper with 427 citations. They added cellulase and BSA to Avicel cellulose and solids containing 56% cellulose and 28% lignin from dilute H2SO4 pretreatment of corn stover. They observed that little BSA was adsorbed on Avicel cellulose, while pretreated corn stover solids adsorbed considerable BSA amounts. On the other hand, cellulase was highly adsorbed on both substrates. Adding a 1% concentration of BSA to dilute acid pretreated corn stover prior to enzyme addition at 15 FPU/g cellulose enhanced filter paper activity in solution by about a factor of 2 and β-glucosidase activity in solution by about a factor of 14. Overall, BSA pretreatment reduced adsorption of cellulase and particularly β-glucosidase on lignin. Further, BSA pretreatment of pretreated corn stover solids prior to enzymatic hydrolysis increased 72 h glucose yields from about 82 to about 92% at a cellulase loading of 15 FPU/g cellulose or achieved about the same yield at a loading of 7.5 FPU/g cellulose. Hall et al. (2010) investigated the cellulose crystallinity as a primary predictor of the enzymatic cellulose hydrolysis rate in a paper with 418 citations. Although the crystallinity of pure cellulosic Avicel cellulose played a major role in determining the rate of hydrolysis by cellulases from Trichoderma reesei, they showed that it stayed constant during enzymatic conversion and the initial rate of the cellulase-catalyzed hydrolysis of cellulose was linearly proportional to the crystallinity index of Avicel cellulose. Further, despite correlation with the adsorption capacity of cellulases onto cellulose, at a given enzyme loading, the initial enzymatic rate continued to increase with a decreasing crystallinity index, even though the bound enzyme concentration stayed constant. They supported the determinant role of crystallinity rather than adsorption as a primary predictor of the enzymatic cellulose hydrolysis rate. Finally, in the conversion of cellulose, the (021) face of the cellulose crystal was preferentially attacked by Cel7A from T. reesei. Jeoh et al. (2007) investigated the relationship between cellulase digestibility of pretreated biomass and cellulose accessibility in a paper with 417 citations. They measured the key factors governing cellulase digestibility in pretreated corn stover samples and pure cellulosic substrates by directly probing cellulase binding and activity using a purified cellobiohydrolase (Cel7A) from T. reesei. They observed that cellulose conversion improved when T. reesei Cel7A bound in higher concentrations, indicating that the enzyme had greater access to the substrate. Factors such as the pretreatment severity, drying after pretreatment, and cellulose crystallinity directly impacted cellulase accessibility. They asserted that the best pretreatment schemes for rendering biomass more digestible to cellobiohydrolase enzymes were those that improved access to the cellulose in biomass cell walls, as well as those able to reduce the crystallinity of cell wall cellulose.

200

TABLE 28.1 The Enzymatic Hydrolysis of Cellulose No.  1

Papers

Biomass

Prt.

Parameters

Keywords

Corn stover and Avicel cellulose

H2SO4, cellulase, BSA

Cellulose hydrolysis, acid, and enzymatic pretreatments

Cellulose, hydrolysis

Avicel cellulose

T. reesei cellulases

Corn stover and Avicel cellulose

T. reesei cellobiohydrolases

Cellulose crystallinity, cellulose enzymatic hydrolysis rate Cellulose accessibility, cellulase digestibility determinants

 4

Fan et al. (1980)

Cellulose

Enzymes

Cellulose enzymatic hydrolysis, cellulose crystallinity, and surface area

Cellulose, hydrolysis Cellulose, accessibility, digestibility Cellulose, hydrolysis

 5

Qing et al. (2010)

Avicel cellulose

 6

Mandels et al. (1974) Steinberg et al. (1977) Yoshida et al. (2008) Kumar et al. (2012) Rahikainen et al. (2013)

Waste cellulose

Cellulase, β-glucosidase, Spezyme CP Milling, T. Viride, alkali

Cellulose enzymatic hydrolysis inhibitors, xylooligomer concentration, enzymatic pretreatments Cellulose enzymatic hydrolysis, hydrolysis determinants, pretreatments

Cellulose, hydrolysis, inhibitors Cellulose, hydrolysis

A. niger, A. phoenicis β-glucosidase, Trichoderma cellulase Milling, cellulase, β-glucosidase, xylanase, NaClO2 Steam, enzymes

Cellulose enzymatic hydrolysis, β-glucosidase production

Cellulose, hydrolysis

Cellulose crystallinity, cellulose enzymatic hydrolysis, delignification

Cellulose, hydrolysis

Cellulose accessibility, enzyme loading, cellulose enzymatic hydrolysis, delignification Cellulose enzymatic hydrolysis, lignin’s inhibitory effect, enzyme binding, and inhibition

Cellulose, accessibility

 2  3

 7

 8

 9

10

Cellulose

Miscanthus cellulose and hemicellulose Douglas-fir cellulose, Avicel cellulose Spruce and wheat straw cellulose

Steam explosion, T. reesei

*, Female; Cits., Number of citations received for each paper; Prt, Biomass pretreatments.

Cellulose, inhibitory

Lead Author Wyman, Charles E. 7004396809 Hall, Melanie* 55900277400 Johnson, David K. 24550868900 Fan, Liangtseng 55743058500 Wyman, Charles E. 7004396809 Mandels, Mary* 6701602607 Reese, Elwyn T. 7005940046 Yoshida, Makoto 57080400900 Saddler, Jack N. 7005297559 Rahikainen, Jenni L.* 54080403900

Affil.

Cits

Dartmouth Coll. USA

427

Univ. Graz Austria NREL USA

418

Kansas State Univ. USA

411

Dartmouth Coll. USA

403

U.S. Army Natick Lab., USA US Army Natick Res., USA Tokyo Univ. Agr. Technol. Japan Univ. British Columbia, Canada VTT Tech. res. Ctr. Finland

400

417

348

299

298

292

Bioethanol Fuel Production Processes. II

Yang and Wyman (2006) Hall et al. (2010) Jeoh et al. (2007)

Cellulose Hydrolysis: Review

201

Fan et al. (1980) investigated the relative effects of the crystallinity and surface area of cellulose fibers upon the enzymatic hydrolysis of cellulose and the change of these structural parameters of cellulose during the course of hydrolysis in a paper with 417 citations. They observed that the hydrolysis rate was mainly dependent upon the fine structural order of cellulose which could best be represented by the crystallinity rather than the simple surface area. Further, surface area was not a major limiting factor that slowed down hydrolysis in the late stages of hydrolysis. Qing et al. (2010) investigated the xylooligomers as strong inhibitors of cellulose hydrolysis by enzymes in a paper with 403 citations. They added xylan and various xylooligomers to Avicel hydrolysis at low enzyme loadings with a greater effect than adding equal amounts of xylose or when added separately. Furthermore, they observed that xylooligomers were inhibitorier than xylan or xylose in terms of a decreased initial hydrolysis rate and a lower final glucose yield even for a low concentration of 1.67 mg/mL. For example, at a higher concentration of 12.5 mg/mL, xylooligomers lowered initial hydrolysis rates of Avicel cellulose by 82% and the final hydrolysis yield by 38%. Mixed degree of depolymerization (DP) xylooligomers showed strong inhibition on cellulase enzymes but not on β-glucosidase enzymes. Then, a large portion of the xylooligomers was hydrolyzed by Spezyme CP enzyme preparations, indicating competitive inhibition by mixed xylooligomers, and xylooligomers were more powerful inhibitors than glucose and cellobiose. Mandels et al. (1974) investigated the enzymatic hydrolysis of waste-based cellulose by T. viride in a paper with 400 citations. They hydrolyzed a variety of pure and complex cellulosic materials by culture filtrates and observed that hydrolysis of 5% slurries after 48 h ranged from 2% to 92%. Further, the rate and extent of hydrolysis was controlled by degree of crystallinity, particle size, and presence of impurities. The best pretreatment was ball milling which gave good size reduction, maximum bulk density, and maximum susceptibility. Hammer milling, fluid energy milling, colloid milling, or alkali pretreatments were less satisfactory. Dissolving cellulose in cuprammonium, or carbon disulfide (viscose), and then re-precipitating gave a susceptible, but low bulk density product. Steinberg et al. (1977) investigated the microbial production of β-glucosidase on enzymatic hydrolysis of cellulose in a paper with 348 citations. They developed a method for production of Aspergillus niger and A. phoenicis as producers of β-glucosidase in liquid culture. When Trichoderma cellulase preparations were supplemented with β-glucosidase from Aspergillus during practical hydrolysis of cellulose, they observed that glucose was the predominant product and the rate of hydrolysis was significantly increased. They asserted that the stimulatory effect of β-glucosidase was due to the removal of inhibitory levels of cellobiose. Yoshida et al. (2008) investigated the effects of cellulose crystallinity, hemicellulose, and lignin on the enzymatic hydrolysis of Miscanthus sinensis to sugars in a paper with 299 citations. They ground an air-dried biomass by ball milling and separated the powder into four fractions. They hydrolyzed each fraction with commercially available cellulase and β-glucosidase. They observed that the yield of sugars increased as the crystallinity of the substrate decreased. The addition of xylanase increased the yield of both pentoses and glucose. Delignification by the sodium chlorite (NaClO2) method improved the initial rate of hydrolysis by cellulolytic enzymes significantly, resulting in a higher yield of sugars as compared to that for untreated samples. When delignified biomass was hydrolyzed with cellulase, β-glucosidase, and xylanase, hemicellulose was hydrolyzed completely into monosaccharides, and the conversion rate of glucan to glucose was 90.6%. Kumar et al. (2012) investigated the effect of cellulose accessibility and enzyme loading on the efficiency of enzymatic hydrolysis of steam-pretreated Douglas fir in a paper with 298 citations. They observed that the lignin component significantly influenced the accessibility of cellulose, as at low enzyme loadings (5 FPU/g cellulose), only 16% of the cellulose present in the steam-pretreated softwood was hydrolyzed while almost complete hydrolysis was achieved with the delignified substrate. When lignin was added back in the same proportions to the highly accessible and delignified steam-pretreated softwood and to a cellulose control such as Avicel cellulose, the hydrolysis yields decreased by 9% and 46%, respectively. However, when higher enzyme loadings were employed, the greater availability of the enzyme could overcome the limitations imposed by both the lignin’s

202

Bioethanol Fuel Production Processes. II

restrictions on cellulose accessibility and direct binding of the enzymes, resulting in a near-complete hydrolysis of the cellulose. They concluded that lignin present in steam-pretreated softwood bound enzymes and limited cellulose accessibility. Rahikainen et al. (2013) studied the effect of lignin as an inhibitory biopolymer for the enzymatic hydrolysis of spruce and wheat straw focusing on the role of lignin in non-productive enzyme adsorption in a paper with 292 citations. As botanical origin and biomass pretreatment resulted in differences in lignin structure, they explored the effect of these differences on enzyme binding and inhibition. They isolated lignin from steam explosion-pretreated and non-pretreated spruce and wheat straw, and monitored binding of T. reesei Cel7A (CBHI) to the lignin films. They observed that steam pretreatment altered the lignin structure, leading to increased enzyme adsorption. Thus, they asserted that the positive effect of steam pretreatment, opening the cell wall matrix to make polysaccharides more accessible, might be compromised by the structural changes of lignin that increased non-productive enzyme adsorption.

28.3.2 The Chemical Hydrolysis of Cellulose There are 15 HCPs for the research front of the chemical hydrolysis of cellulose (Table 28.2). The primary research fronts are the acid, ionic liquid, and other chemical hydrolysis of cellulose with six, five, and four HCPs, respectively. 28.3.2.1 The Acid Hydrolysis of Cellulose Suganuma et al. (2008) studied the hydrolysis of cellulose by solid acid catalysts in a paper with 868 citations. They observed that amorphous carbon bearing SO3H, COOH, and OH performed as an efficient catalyst for the cellulose hydrolysis. They observed that the apparent activation energy for the hydrolysis of cellulose into glucose using the carbon catalyst was 110 kJ/mol, smaller than that for sulfuric acid (H2SO4) under optimal conditions (170 kJ/mol). Further, this carbon catalyst could be readily separated from the saccharide solution after reaction for reuse in the reaction without loss of activity. They asserted that the superb catalytic performance of the carbon catalyst was due the ability of the material to adsorb β-1,4 glucan, which did not adsorb to other solid acids. Yang and Wyman (2004) investigated the effect of xylan and lignin removal by batch and flowthrough pretreatment on the corn stover cellulose enzymatic hydrolysis in a paper with 558 citations. They compared xylan and lignin removal and enzymatic hydrolysis of cellulose for corn stover pretreated with water only and also with 0.1 wt% sulfuric acid (H2SO4) in batch and flowthrough reactors over a range of flow rates between 160°C and 220°C. They observed that increasing flow with just water enhanced the xylan dissolution rate, more than doubled total lignin removal, and increased cellulose digestibility. Furthermore, adding dilute H2SO4 increased the rate of xylan removal for both batch and flowthrough systems. However, adding acid also increased the lignin removal rate with flow, but less lignin was left in solution when acid was added in batch. Although the enzymatic hydrolysis of pretreated cellulose was related to xylan removal, they observed that the cellulose digestibility was much better for flowthrough compared with batch systems, for the same degree of xylan removal. Cellulose digestibility for flowthrough reactors was related to lignin removal as well. Onda et al. (2008) investigated the selective hydrolysis of cellulose over solid acid catalysts at temperatures higher than 90°C in a paper with 511 citations. Among the solid acid catalysts tested, such as the H-form zeolite catalysts and the sulfated and sulfonated catalysts, they observed that a sulfonated activated-carbon catalyst showed a remarkably high yield of glucose, which was due to the high hydrothermal stability and the excellent catalytic property attributed to the strong acid sites of SO3H functional groups and the hydrophobic planes. Selig et al. (2007) investigated the deposition of lignin droplets produced during dilute acid pretreatment on the rate of the enzymatic hydrolysis of cellulose in a paper with 380 citations. They observed that these droplets were produced from corn stover during pretreatment under neutral and

No. 1

2

3

4

Papers

Biomass

Prt.

Parameters

Keywords

Suganuma et al. (2008) Lee et al. (2009)

Cellulose

Solid acids

Cellulose hydrolysis, solid acid catalysts

Cellulose, hydrolysis

Wood cellulose, lignin

[Emim][CH3COO], T. viride

Cellulose, hydrolysis

Sasaki et al. (2000) Sasaki et al. (1998) Yang and Wyman (2004) Onda et al. (2008) Dadi et al. (2006)

Cellulose, cellobiose

Sub- and supercritical water

Cellulose enzymatic hydrolysis, lignin extraction, ionic liquid, and enzymatic pretreatment Cellulose hydrolysis and dissolution, temperature effect

Cellulose

Sub- and supercritical water

Corn stover cellulose, xylan, lignin Cellulose

Water, H2SO4

Solid acids

Cellulose

[Bmim]Cl, enzymes

Cellulose enzymatic hydrolysis, pretreatment effect

Cellulose hydrolysis Cellulose, saccharification

Lead Author

Cellulose, hydrolysis

Tokyo Inst. Technol. Japan Rensselaer Polytech. Inst. USA Tohoku Univ. Japan

Cellulose hydrolysis and dissolution, temperature effect

Cellulose, hydrolysis

Arai, Kunio 7403965625

Tohoku Univ. Japan

577

Cellulose hydrolysis, batch and flowthrough reactors, xylan, and lignin removal Cellulose hydrolysis, solid acids

Cellulose, digestibility

Dartmouth Coll. USA

358

Kochi Univ. Japan Univ. Toledo USA

511

8

Zhao et al. (2009)

Avicel cellulose

T. reesei cellulose, [BMIM]Cl

Cellulose enzymatic hydrolysis, ionic liquid pretreatment

Cellulose, hydrolysis

9

Selig et al. (2007)

Corn stover cellulose

Acids, enzymes

Cellulose enzymatic hydrolysis, lignin droplet deposition

Cellulose, hydrolysis

Selig, Michael J. 13613665100

6 7

Cits

Hara, Michikazu 7403345875 Doherty, Thomas V. 57213283673 Arai, Kunio 7403965625

Wyman, Charles E. 7004396809 Onda, Ayumu 56689677300 Schall, Constance A.* 6603671396 Zhao, Hua 7404778309

5

Affil.

868

797

Cellulose Hydrolysis: Review

TABLE 28.2 The Chemical Hydrolysis of Cellulose

600

493

Univ. N. 396 Colorado, USA, Cornell Univ. 380 USA (Continued)

203

204

TABLE 28.2 (Continued) The Chemical Hydrolysis of Cellulose No. 10

11

12

13

15

Biomass

Bjerre et al. (1996) Li and Zhao (2007) Cheng et al. (2011) Pang et al. (2010) Rollin et al. (2011) Shimizu et al. (2009)

Wheat straw cellulose

Wet oxidation, alkali, enzymes

Cellulose

[C4MIM]Cl

Avicel, switchgrass, pine, eucalyptus cellulose Cellulose

[C2MIM][OAc]

Switchgrass cellulose

Cellulase, organic solvent, cellulose solvent, ammonia, BSA Heteropolyacids

Cellobiose, cellulose

Prt.

Sulfonated CMK-3

Parameters Cellulose enzymatic hydrolysis, wet oxidation optimization, fermentation inhibitors Cellulose hydrolysis, IL pretreatment Cellulose crystallinity, IL pretreatment, cellulose hydrolysis kinetics Cellulose hydrolysis, solid acid, and thermal pretreatment Cellulose enzymatic hydrolysis, pretreatments, CACs, delignification, cellulase loadings Cellobiose and cellulose hydrolysis, heteropolyacid-based catalyst activity, and selectivity

*, Female; Cits., Number of citations received for each paper; Prt, Biomass pretreatments.

Keywords Cellulose, hydrolysis Cellulose, hydrolysis Cellulose, hydrolysis Cellulose, hydrolysis Cellulose, accessibility Cellulose, hydrolysis

Lead Author

Affil.

Cits

Bjerre, Anne B.* 6701773173 Zhao, Zongbao K. 35197609300 Singh, Seema* 35264950300

Danish Technol. Inst. Denmark Chinese Acad. Sci. China

347

Joint Bioenerg. Inst. USA

332

Zhang, Tao 56158944400 Zhang, Yi Heng P. 34876090400 Shimizu, Ken-ichi 23487066800

Chinese Acad. Sci. China Chinese Acad. Sci. China

296

Hokkaido Univ., Japan

269

339

285

Bioethanol Fuel Production Processes. II

14

Papers

Cellulose Hydrolysis: Review

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acidic pH at and above 130°C, and that they could deposit back onto the surface of residual biomass. The deposition of droplets produced under certain pretreatment conditions (acidic pH; more than 150°C) and captured onto pure cellulose had a negative effect (5%–20%) on the enzymatic hydrolysis of this substrate. Further, the droplet density (per unit area) was greater and droplet size more variable under conditions where the greatest impact on enzymatic cellulose conversion was observed. Pang et al. (2010) investigated the hydrolysis of cellulose into glucose over carbons sulfonated at elevated temperatures in a paper with 296 citations. They observed that the hydrolysis of cellulose over sulfonated carbons was promoted greatly by elevating the sulfonation temperature. With 250°C-sulfonated CMK-3 as a catalyst, the cellulose was selectively hydrolyzed into glucose with the glucose yield as high as 74.5%, which was the highest level reported at that time on solid acid catalysts. Shimizu et al. (2009) investigated the effects of Bronsted and Lewis acidities on activity and selectivity of heteropolyacid-based catalysts for hydrolysis of cellobiose and cellulose in a paper with 269 citations. They observed that heteropolyacids (H3PW12O40, H4SiW12O40) and salts of metal cations (Mn+) and PW12O403−(M3/nPW12O40) acted as effective homogeneous catalysts for selective hydrolysis of cellobiose and cellulose to glucose and total reducing sugars (TRSs), respectively, in an aqueous phase. For Bronsted acid catalysts, including mineral acid and heteropolyacids, the activity for both reactions increased with a decrease in the deprotonation enthalpies, indicating that stronger Bronsted acidity was more favorable. For M3/nPW12O40 of 11 kinds of metal ions (Ag+, Ca2+, Co2+, Y3+, Sn4+, Sc3+, Ru3+, Fe3+, Hf4+, Ga3+ and Al3+), the rate of cellulose hydrolysis increased with Lewis acidity of the cation, while the TRS selectivity was highest for cations with moderate Lewis acidity, such as Sn4+ and Ru3+. For the hydrolysis of cellobiose, cellulose, and lignocellulose, H3PW12O40 and Sn0.75PW12O40 showed higher TRS yield than H2SO4. 28.3.2.2 The Ionic Liquid Hydrolysis of Cellulose Lee et al. (2009) investigated the enhanced cellulose enzymatic hydrolysis through the extraction of lignin from wood by IL pretreatment in a paper with 797 citations. They used the 1-ethyl-3-methylimidazolium acetate ([Emim][CH3COO]) as a pretreatment solvent to extract lignin from wood flour. They observed that the cellulose in the pretreated wood flour became far-less crystalline without undergoing solubilization. Further, when 40% of the lignin was removed, the cellulose crystallinity index dropped below 45, resulting in more than 90% of the cellulose in wood flour to be hydrolyzed by T. viride cellulase. The ionic liquid was easily reused. Dadi et al. (2006) studied the cellulose hydrolysis kinetics using an IL pretreatment in a paper with 493 citations. They used 1-n-butyl-3-methylimidazolium chloride ([Bmim]Cl). They observed that the initial enzymatic hydrolysis rates were approximately 50-fold higher for regenerated cellulose as compared to untreated cellulose, Avicel PH-101, as measured by a soluble reducing sugar assay. Zhao et al. (2009) investigated the effect of the newly developed ILs on the cellulose enzymatic hydrolysis in a paper with 396 citations. They noted that the current ILs were limited to two chloride-based ILs such as 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) and 1-allyl-3-methylimidazolium chloride ([Amim]Cl) with adverse n-environmental effects. Thus, they studied a number of chloride- and acetate-based alternative ILs and observed that all regenerated celluloses were less crystalline (58%–75% lower) and more accessible to cellulase (more than two times) than untreated substrates. As a result, regenerated Avicel cellulose, filter paper, and cotton were hydrolyzed 2–10 times faster than the respective untreated celluloses. Further, a complete hydrolysis of Avicel cellulose could be achieved in 6 h given the T. reesei cellulase/substrate ratio (w/w) of 3:20 at 50°C. In addition, they observed that cellulase was more thermally stable (up to 60°C) in the presence of regenerated cellulose. Furthermore, the presence of various ILs during the hydrolysis induced different degrees of cellulase inactivation. Therefore, they recommended a thorough removal of IL residues after cellulose regeneration.

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Li and Zhao (2007) investigated the hydrolysis of cellulose in ILs in a paper with 339 citations. They developed a method for cellulose hydrolysis catalyzed by mineral acids in the 1-butyl-3-methylimidazolium chloride ([C4mim]Cl) that facilitated the hydrolysis of cellulose with dramatically accelerated reaction rates at 100°C under atmospheric pressure and without any other pretreatment. Cheng et al. (2011) investigated the effect of IL pretreatment on the cellulose crystalline structure in different feedstocks, including microcrystalline cellulose (Avicel), switchgrass, pine, and eucalyptus, as well as on cellulose hydrolysis kinetics of the resultant biomass in a paper with 332 citations. They pretreated these feedstocks using 1-ethyl-3-methyl imidazolium acetate ([C2mim][OAc]) at 120 and 160°C for 1, 3, 6, and 12 h. They observed that the IL pretreatment resulted in a loss of native cellulose crystalline structure, cellulose I. However, the transformation processes were distinctly different for Avicel cellulose and for the biomass samples. For Avicel cellulose, a transformation to cellulose II occurred for all processing conditions. For the biomass samples, IL pretreatment for most conditions resulted in an expanded cellulose I lattice. For switchgrass, first evidence of cellulose II only occurred after 12 h of pretreatment at 120°C. For eucalyptus, first evidence of cellulose II required more intense pretreatment (3 h at 160°C). For pine, there was no clear evidence of cellulose II content for the most intense pretreatment conditions of this study (12 h at 160°C). Notably, the rate of enzymatic hydrolysis of Avicel cellulose was slightly lower for pretreatment at 160°C compared with pretreatment at 120°C. For the biomass samples, the hydrolysis rate was much greater for pretreatment at 160°C compared with pretreatment at 120°C. They explained the result for Avicel cellulose by more complete conversion to cellulose II upon precipitation after pretreatment at 160°C. By comparison, for the biomass samples, there was another factor, likely lignin–carbohydrate complexes, also impacting the rate of cellulose hydrolysis in addition to cellulose crystallinity. 28.3.2.3 The Other Chemical Hydrolysis of Cellulose Sasaki et al. (2000) studied the dissolution and hydrolysis of cellulose in subcritical and supercritical water in a paper with 600 citations. At 400°C, they mainly obtained hydrolysis products, while in 320°C–350°C water, aqueous decomposition products of glucose were the main products. Further, below 350°C, the cellulose decomposition rate was lower than the glucose and cellobiose decomposition rates, while above 350°C, the cellulose hydrolysis rate drastically increased and became higher than the glucose and cellobiose decomposition rates. Further, below 280°C, cellulose particles became gradually smaller with increasing reaction time, but at high temperatures (300°C–320°C), cellulose particles disappeared with increasing transparency and much more rapidly than expected from the lower temperature results. Cellulose hydrolysis at high temperature took place with dissolution in water due to the cleavage of intra- and inter-molecular hydrogen linkages in the cellulose crystal. Thus, a homogeneous atmosphere was formed in supercritical water, and this resulted in the drastic increase of the cellulose decomposition rate above 350°C. Sasaki et al. (1998) proposed a new method to hydrolyze cellulose rapidly in subcritical and supercritical water to recover glucose, fructose, and oligomers in a paper with 577 citations. They observed that hydrolysis product yields (around 75%) in supercritical water were much higher than those in subcritical water. At a low-temperature region, the glucose or oligomer conversion rate was much faster than the hydrolysis rate of cellulose. Thus, even if the hydrolysis products, such as glucose or oligomers, were formed, their further decomposition rapidly took place and thus high yields of hydrolysis products could not be obtained. However, around the critical point, the hydrolysis rate jumped to more than an order of magnitude higher level and became faster than the glucose or oligomer decomposition rate. This was the reason why they obtained a high yield of hydrolysis products in supercritical water. Bjerre et al. (1996) investigated the effect of the combined wet oxidation and alkaline pretreatments of wheat straw on the enzymatic hydrolysis of cellulose in a paper with 347 citations. By using a specially constructed autoclave system, they observed that the best wet oxidation conditions were 20 g/L straw, 170°C, 5–10 min giving about 85% w/w yield of converting cellulose to glucose. The process waters, containing dissolved hemicellulose and carboxylic acids, were a direct nutrient

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source for A. niger producing exo-β-xylosidase. The fermentation inhibitors such as furfural and hydroxymethylfurfural were not observed following the wet oxidation pretreatment. Rollin et al. (2011) compared the pretreatment of switchgrass by cellulose solvent- and organic solvent-based lignocellulose fractionation (COSLIF) and soaking in aqueous ammonia (SAA) in a paper with 285 citations. Following these pretreatments, they conducted enzymatic hydrolysis at two cellulase loadings of 15 filter paper units (FPU)/g glucan and 3 FPU/g glucan with and without BSA blocking of lignin absorption sites. They observed that the lignin remaining after SAA had a significant negative effect on cellulase performance, despite the high level of delignification achieved with this pretreatment. However, there was no negative effect due to lignin for COSLIFpretreated substrate. Further, COSLIF fully disrupted the cell wall structure, resulting in a 16-fold increase in cellulose accessibility to cellulase (CAC), while SAA caused a 1.4-fold CAC increase. Thus, they concluded that increasing CAC was more important than delignification of the biomass.

28.4 DISCUSSION 28.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass and hydrolysates, respectively. The research in the field of cellulose hydrolysis has thus intensified in recent years. The enzymatic, acid, ionic liquid, and other chemical hydrolysis of cellulose have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been a number of review papers for this field, there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents a review of the most-cited 25 articles in this field. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in Appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 269 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in cellulose hydrolysis is given in Table 28.3. As Table 28.3 shows, there are two primary research fronts for this field: chemical and enzymatic hydrolysis of cellulose with 40% and 64% of the reviewed papers, respectively. Next, the other front is the cellulose hydrolysis in general with 9% of the sample papers. Further, there are no HCPs for the mechanical and hydrothermal hydrolysis of cellulose, while enzymatic hydrolysis, hydrothermal hydrolysis, and mechanical hydrolysis of cellulose are under-represented by 15%, 7%, and 4% deficit, respectively. On the individual basis, ionic liquid, water, ammonia, alkaline, and acid hydrolysis of cellulose are over-represented in the reviewed papers with 1%–8% surplus each. Similarly, the fronts of steam, liquid hot water, hot compressed water, microwave, CO2, surfactant, milling, and ultrasound hydrolysis of cellulose are under-represented in the reviewed papers with 1%–3% deficit each.

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TABLE 28.3 The Research Fronts for the Cellulose Hydrolysis No. 1 2

3

4

5

N Paper (%) Review

N Paper (%) Sample

Surplus

Enzymatic hydrolysis Chemical hydrolysis Acid hydrolysis

Research Fronts

40.0 64.0 24.0

54.5 48.8 23.0

−14.5 15.2 1.0

Ionic liquid hydrolysis

20.0

12.4

7.6

Water hydrolysis

8.0

2.4

5.6

Solvent hydrolysis

4.0

4.3

−0.3

Alkaline hydrolysis

4.0

2.4

1.6

Ammonia hydrolysis

4.0

1.4

2.6

CO2 hydrolysis

0.0

1.4

−1.4

Surfactant hydrolysis

0.0

1.4

−1.4

Hydrothermal hydrolysis Steam hydrolysis

0.0 0.0

7.2 3.3

−7.2 −3.3

Hot compressed water hydrolysis

0.0

1.9

−1.9

Liquid hot water hydrolysis

0.0

1.9

−1.9

Mechanical hydrolysis Microwave hydrolysis

0.0 0.0

3.8 1.9

−3.8 −1.9

Milling hydrolysis

0.0

1.0

−1.0

Ultrasound hydrolysis

0.0

1.0

−1.0

Hydrolysis in general

0.0

9.1

−9.1

N paper (%) review, the number of papers in the sample of 25 most-cited papers; N paper (%) sample, the number of papers in the population sample of 209 papers.

28.4.2 The Enzymatic Hydrolysis of Cellulose There are 10 HCPs for the research front of the enzymatic hydrolysis of cellulose (Table 28.1). Yang and Wyman (2006) studied the cellulase and BSA pretreatments to enhance enzymatic hydrolysis of cellulose in corn stover and Avicel cellulose and observed that BSA pretreatment reduced adsorption of cellulase and particularly β-glucosidase on lignin. Further, Hall et al. (2010) investigated the cellulose crystallinity as a primary predictor of the enzymatic cellulose hydrolysis rate and supported the determinant role of crystallinity rather than adsorption. Jeoh et al. (2007) investigated the relationship between cellulase digestibility of pretreated biomass and cellulose accessibility, and observed that cellulose conversion improved when T. reesei Cel7A bound in higher concentrations, indicating that the enzyme had greater access to the substrate. Further, Fan et al. (1980) investigated in depth the relative effects of the crystallinity and surface area of cellulose fibers upon the enzymatic hydrolysis of cellulose and observed that the hydrolysis rate was mainly dependent upon the fine structural order of cellulose. Qing et al. (2010) investigated the xylooligomers as strong inhibitors of cellulose hydrolysis by enzymes and observed that xylooligomers were inhibitorier than xylan or xylose in terms of a decreased initial hydrolysis rate and a lower final glucose yield. Further, Mandels et al. (1974) investigated the enzymatic hydrolysis of waste-based cellulose by T. viride and observed that hydrolysis of 5% slurries after 48 hr ranged from 2% to 92%. Steinberg et al. (1977) investigated the microbial production of β-glucosidase on enzymatic hydrolysis of cellulose and observed that glucose was the predominant product and the rate of hydrolysis was significantly increased. Further, Yoshida et al. (2008) investigated the effects of cellulose crystallinity, hemicellulose, and lignin on the enzymatic hydrolysis of miscanthus to sugars and observed that the yield of sugars increased as the crystallinity of the substrate decreased.

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Kumar et al. (2012) investigated the effect of cellulose accessibility and enzyme loading on the efficiency of enzymatic hydrolysis of steam-pretreated Douglas fir and observed that the lignin component significantly influenced the accessibility of cellulose. Further, Rahikainen et al. (2103) studied the effect of lignin as an inhibitory biopolymer for the enzymatic hydrolysis of spruce and wheat straw focusing on the role of lignin in non-productive enzyme adsorption and observed that steam pretreatment altered the lignin structure, leading to increased enzyme adsorption. These HCPs present a sample of the research primarily for the enzymatic hydrolysis of the cellulose, as a primary biomass constituent. It is notable that enzymatic pretreatments aided by chemical and other pretreatments play a crucial role in the cellulose hydrolysis to improve the sugar yield.

28.4.3 The Chemical Hydrolysis of Cellulose There are 15 HCPs for the research front of the chemical hydrolysis of cellulose (Table 28.2). The primary research fronts are the acid, ionic liquid, and other chemical hydrolysis of cellulose with six, five, and four HCPs, respectively. 28.4.3.1 The Acid Hydrolysis of Cellulose Suganuma et al. (2008) studied the hydrolysis of cellulose by solid acid catalysts and observed that amorphous carbon bearing SO3H, COOH, and OH performed as an efficient catalyst for the cellulose hydrolysis. Further, Yang and Wyman (2004) investigated the effect of xylan and lignin removal by batch and flowthrough pretreatment on the corn stover cellulose enzymatic hydrolysis and observed that adding acid increased the lignin removal rate with flow, but less lignin was left in solution when acid was added in batch. Onda et al. (2008) investigated the selective hydrolysis of cellulose over solid acid catalysts and observed that a sulfonated activated-carbon catalyst showed a remarkably high yield of glucose. Further, Selig et al. (2007) investigated the deposition of lignin droplets produced during dilute acid pretreatment on the rate of the enzymatic hydrolysis of cellulose and observed that these droplets were produced from corn stover and they could deposit back onto the surface of residual biomass. Pang et al. (2010) investigated the hydrolysis of cellulose into glucose over carbons sulfonated at elevated temperatures and observed that the hydrolysis of cellulose over sulfonated carbons was promoted greatly by elevating the sulfonation temperature. Further, Shimizu et al. (2009) investigated the effects of Bronsted and Lewis acidities on activity and selectivity of heteropolyacid-based catalysts for hydrolysis of cellobiose and cellulose and observed that heteropolyacids and salts of metal cations acted as effective homogeneous catalysts for selective hydrolysis of cellobiose and cellulose to glucose and TRSs, respectively. These HCPs present a sample of the research primarily for the acid hydrolysis of cellulose, as a primary chemical pretreatment. It is notable that acid pretreatments aided by enzymatic and other pretreatments play a crucial role in the cellulose hydrolysis to improve the sugar yield. 28.4.3.2 The Ionic Liquid Hydrolysis of Cellulose Lee et al. (2009) investigated the enhanced cellulose enzymatic hydrolysis through the extraction of lignin from wood by IL pretreatment and observed that the cellulose in the pretreated wood flour became far-less crystalline without undergoing solubilization. Further, Dadi et al. (2006) studied the cellulose hydrolysis kinetics using an IL pretreatment and observed that the initial enzymatic hydrolysis rates were approximately 50-fold higher for regenerated cellulose as compared to untreated Avicel cellulose. Zhao et al. (2009) investigated the effect of the ILs on the cellulose enzymatic hydrolysis and observed that all regenerated celluloses were significantly less crystalline and more accessible to cellulase than untreated substrates. Further, Li and Zhao (2007) investigated the hydrolysis of cellulose in ILs and facilitated the hydrolysis of cellulose with dramatically accelerated reaction rates. Finally, Cheng et al. (2011) investigated the effect of ionic liquid pretreatment on the cellulose

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crystalline structure in different feedstocks as well as on cellulose hydrolysis kinetics of the resultant biomass and observed that the IL pretreatment resulted in a loss of native cellulose crystalline structure, cellulose I. These HCPs present a sample of the research primarily for the IL hydrolysis of cellulose, as a primary chemical pretreatment. It is notable that IL pretreatments aided by enzymatic and other pretreatments play a crucial role in the cellulose hydrolysis to improve the sugar yield. 28.4.3.3 The Other Chemical Hydrolysis of Cellulose Sasaki et al. (2000) studied the dissolution and hydrolysis of cellulose in subcritical and supercritical water, and at 400°C, they mainly obtained hydrolysis products while in 320°C–350°C water, aqueous decomposition products of glucose were the main products. Further, Sasaki et al. (1998) proposed a new method to hydrolyze cellulose rapidly in subcritical and supercritical water to recover glucose, fructose, and oligomers and observed that hydrolysis product yields in supercritical water were much higher than those in subcritical water. Bjerre et al. (1996) investigated the effect of the combined wet oxidation and alkaline pretreatments of wheat straw on the enzymatic hydrolysis of cellulose and determined the best wet oxidation conditions giving about 85% w/w yield of converting cellulose to glucose. Further, Rollin et al. (2011) compared the pretreatment of switchgrass by COSLIF and SAA and observed that the lignin remaining after SAA had a significant negative effect on cellulase performance. These HCPs present a sample of the research primarily for the other chemical hydrolysis of cellulose such as supercritical water, alkaline, wet oxidation, solvent, and ammonia hydrolysis of cellulose. It is notable that these other chemical pretreatments aided by enzymatic and other pretreatments play a crucial role in the cellulose hydrolysis to improve the sugar yield.

28.5 CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 269 citations each is given under two primary headings: enzymatic and chemical hydrolysis of cellulose while there are no HCPs for mechanical and hydrothermal hydrolysis of cellulose. The primary research fronts for the chemical hydrolysis of cellulose are the acid, ionic liquid, and other chemical hydrolysis of cellulose. The usual characteristics of these HCPs are that enzymatic pretreatments are often used in combination with other pretreatments for the hydrolysis of the cellulose. In this way, the cellulose hydrolysis is effective in disrupting the cellulose microstructure resulting in improved sugar and bioethanol yield. The key findings on these research fronts should be read in light of the increasing public concerns about climate change, GHG emissions, and global warming as these concerns have been certainly behind the boom in the research on the bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. These studies emphasize the importance of proper incentive structures for the efficient development and application of cellulose hydrolysis to enhance sugar and bioethanol yield of the biomass after the hydrolysis of the biomass and the following fermentation of the resulting hydrolysates in light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the USA, Canada, China, Japan, and Europe had developed strong incentive structures for the effective development and application of hydrothermal pretreatments for bioethanol and sugar production. With the recent supply shocks, for example, due to the COVID-19 pandemics and Russian invasion of Ukraine, it is expected the public incentives for the research and development for the bioethanol fuels as a green alternative to crude oil-based gasoline and diesel fuels would increase in the coming years. In this context, the stakeholders involved in the cellulose hydrolysis would have a significant first-mover advantage.

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It is recommended that such review studies are performed for the primary research fronts of cellulose hydrolysis as well as the biomass constituents, wood, grass, and agricultural residues.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the cellulose hydrolysis has been gratefully acknowledged.

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Onda, A., T. Ochi and K. Yanagisawa. 2008. Selective hydrolysis of cellulose into glucose over solid acid catalysts. Green Chemistry 10:1033–1037. Pang, J., A. Wang, M. Zheng and T. Zhang. 2010. Hydrolysis of cellulose into glucose over carbons sulfonated at elevated temperatures. Chemical Communications 46:6935–6937. Qing, Q., B. Yang and C. E. Wyman. 2010. Xylooligomers are strong inhibitors of cellulose hydrolysis by enzymes. Bioresource Technology 101:9624–9630. Rahikainen, J. L., R. Martin-Sampedro and H. Heikkinen, et al. 2013. Inhibitory effect of lignin during cellulose bioconversion: The effect of lignin chemistry on non-productive enzyme adsorption. Bioresource Technology 133:270–278. Rollin, J. A., Z. Zhu, N. Sathitsuksanoh and Y. H. P. Zhang. 2011. Increasing cellulose accessibility is more important than removing lignin: A comparison of cellulose solvent-based lignocellulose fractionation and soaking in aqueous ammonia. Biotechnology and Bioengineering 108:22–30. Sanchez, O. J. and C. A. Cardona. 2008. Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource Technology 99:5270–5295. Sasaki, M., B. Kabyemela and R. Malaluan, et al. 1998. Cellulose hydrolysis in subcritical and supercritical water. Journal of Supercritical Fluids 13:261–268. Sasaki, M., Z. Fang, Y. Fukushima, T. Adschiri and K. Arai. 2000. Dissolution and hydrolysis of cellulose in subcritical and supercritical water. Industrial and Engineering Chemistry Research 39:2883–2890. Selig, M. J., S. Viamajala and S. R. Decker, et al. 2007. Deposition of lignin droplets produced during dilute acid pretreatment of maize stems retards enzymatic hydrolysis of cellulose. Biotechnology Progress 23:1333–1339. Shimizu, K. I., H. Furukawa, N. Kobayashi, Y. Itaya and A. Satsuma. 2009. Effects of Bronsted and Lewis acidities on activity and selectivity of heteropolyacid-based catalysts for hydrolysis of cellobiose and cellulose. Green Chemistry 11:1627–1632. Steinberg, D., P. Vijayakumar and E. T. Reese. 1977. β Glucosidase: Microbial production and effect on enzymatic hydrolysis of cellulose. Canadian Journal of Microbiology 23:139–147. Suganuma, S., K. Nakajima and M. Kitano, et al. 2008. Hydrolysis of cellulose by amorphous carbon bearing SO3H, COOH, and OH groups. Journal of the American Chemical Society 130:12787–12793. Sun, Y. and J. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: A review. Bioresource Technology 83:1–11. Taherzadeh, M. J. and K. Karimi. 2007. Enzyme-based hydrolysis processes for ethanol from lignocellulosic materials: A review. Bioresources 2:707–738. Taherzadeh, M. J. and K. Karimi. 2008. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: A review. International Journal of Molecular Sciences 9:1621–1651. Yang, B. and C. E. Wyman. 2004. Effect of xylan and lignin removal by batch and flowthrough pretreatment on the enzymatic digestibility of corn stover cellulose. Biotechnology and Bioengineering 86:88–98. Yang, B. and C. E. Wyman. 2006. BSA treatment to enhance enzymatic hydrolysis of cellulose in lignin containing substrates. Biotechnology and Bioengineering 94:611–617. Yoshida, M., Y. Liu and S. Uchida, et al. 2008. Effects of cellulose crystallinity, hemicellulose, and lignin on the enzymatic hydrolysis of Miscanthus sinensis to monosaccharides. Bioscience, Biotechnology and Biochemistry 72:805–810. Zhao, H., C. L. Jones and G. A. Baker, et al. 2009. Regenerating cellulose from ionic liquids for an accelerated enzymatic hydrolysis. Journal of Biotechnology 139:47–54.

Part 5 Hydrolysate Fermentation for Bioethanol Production

29

Hydrolysate and Substrate Fermentation Scientometric Study Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

29.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), coronavirus disease 2019 (COVID-19) pandemic (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2012) in the aftermath of the Russian invasion of Ukraine (Reeves, 2014). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before the bioethanol production through hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and hydrolysates, respectively. Research in the fields of hydrolysate and substrate fermentation has thus intensified in recent years as hydrolysate and substrate fermentation processes (Lin and Tanaka, 2006; Lynd et al., 2005), fermentation inhibitors (Palmqvist and Hahn-Hagerdal, 2000a,b), hydrolysate detoxification (Jonsson et al., 2013; Palmqvist and Hahn-Hagerdal, 2000b), and microorganism and substrate metabolic engineering (Dien et al., 2003; Jeffries, 2006) have been widely researched to increase bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of research in a selected research field (Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As the recently published scientometric studies focus on a certain research front (Calvo et al., 2022; Devos and Colla, 2022), this book chapter presents a scientometric study of research in the hydrolysate and substrate fermentation as a whole. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts.

DOI: 10.1201/9781003226499-38

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29.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 300 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix for future replication studies. As a second step, two sets of data were used for this study. First, a population sample of around 18,246 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 182 most-cited papers, corresponding to 1% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the hydrolysate and substrate fermentation. Additionally, many brief conclusions were drawn and many relevant recommendations were made to enhance the future research landscape.

29.3 RESULTS 29.3.1  The Most Prolific Documents in the Hydrolysate and Substrate Fermentation The information on the types of documents for both datasets is given in Table 29.1. The articles and conference papers, published in journals, dominate both the sample (78%) and population (96%) papers as they are underrepresented in the sample papers by 18%. Furthermore, review papers and short surveys have a surplus as they are overrepresented in the sample papers by 20% as they constitute 22% and 2% of the sample and population papers, respectively. It is further notable that 97% of the population papers were published in journals, while 2% and 1% of them were published in book series and books, respectively. On the contrary, 99% of the sample papers were published in journals.

TABLE 29.1 Documents in the Hydrolysate and Substrate Fermentation Documents Article Review Short survey Conference paper Book chapter Letter Note Book Editorial Sample size

Sample Dataset (%) Population Dataset (%) Surplus (%) 75.3 15.4 6.6 2.2 0.0 0.0 0.0 0.0 0.0 182

92.8 2.2 0.2 2.6 1.3 0.5 0.3 0.1 0.0 18,246

−17.5 13.2 6.4 −0.4 −1.3 −0.5 −0.3 −0.1 0.0

Population dataset, the number of papers (%) in the set of 18,246 population papers; sample dataset, the number of papers (%) in the set of 182 highly cited papers.

Hydrolysate and Substrate Fermentation: Scientometric Study

219

29.3.2  The Most Prolific Authors in the Hydrolysate and Substrate Fermentation The information about the 21 most prolific authors with at least 1.6% of sample papers each is given in Table 29.2. The most prolific author is Barbel Hahn-Hagerdal of Lund University of Sweden with 5.5% of the sample papers working primarily on the metabolic engineering of bacteria used to ferment the hydrolysates. Next, Johannes P. van Dijken publishes 4.4% of the sample papers, while Lonnie O. Ingram, Thomas W. Jeffries, Lee R. Lynd, Guido Zacchi, and Jack T. Pronk publish 3.3% of the sample papers each. The most influential author is Barbel Hahn-Hagerdal with 5.1% surplus, followed by Johannes P. van Dijken with 4.3% surplus. The other influential authors are Lonnie O. Ingram, Thomas W. Jeffries, Lee R. Lynd, Guido Zacchi, and Jack T. Pronk with around 3% surplus each. The most prolific institution for the sample dataset is Lund University with four authors, while Delft University of Technology and Chalmers University of Technology house three and two authors, respectively. In total, 14 institutions house these top authors. However, the most prolific country for the sample dataset is the USA with eight authors, closely followed by Sweden with seven authors. The other prolific country is the Netherlands with four authors. In total, five countries house these top authors. There are two primary research fronts for these top authors: metabolic engineering of the bacteria used to ferment hydrolysates and fermentation processes used to ferment the hydrolysates with 15 and 10 authors, respectively. The simultaneous saccharification and fermentation (SSF) and fermentation inhibitors form some of the second research fronts. However, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample ­dataset as surprisingly only four of these top researchers are female with a representation rate of 16%. Additionally, there are other authors with a relatively low citation impact and with 0.2%–0.3% of the population papers each: Ashok Pandey, Tao Shao, Tomohisa Hasunuma, Fengwu Bai, Carlos A. Rosa, Athanasios A. Koutinas, Carlos R. Soccol, Hugh H. Lawford, Bruce S. Dien, Jin-Ho Seo, Tianwei Tan, Sung-Koo Kim, Keiresho Karimi, Jose A. Teixeira, Poonam Nigam, Chiaki Ogino, Vijay Singh, Zhihao Dong, Maria E. Kanellaki, Mercedes Ballesteros, Rodney H. Bothast, Jie Bao, Horst W. Doelle, Marie W. Gorwa-Grauslund, Soo Rin Kim, Pattana Laopaiboon, David A. Mitchell, Mark R. Wilkins, Venkatesh Balan, Lakkana Laopaiboon, Bernard A. Prior, Boris U. Stambuk, and Johan M. Thevelein.

29.3.3 The Most Prolific Research Output by Years in Hydrolysate and Substrate Fermentation Information about papers published between 1970 and 2022 is given in Figure 29.1. This figure clearly shows that the bulk of the research papers in the population dataset was published primarily in the 2010s with 45% of the population datasets. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 11%, 16%, 11%, 9%, and 2%, respectively. Additionally, 2% of the population papers were published between 1841 and 1969. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 68 and 21% of the sample datasets, respectively. The publication rates for the 1990s, 1980s, and 1970s were 17%, 10%, and 1% of the sample papers, respectively. The most prolific publication year for the population dataset was 2021 with 5.1% of the datasets, while 57% of the population papers were published between 2010 and 2020. Similarly, 68% of the sample papers were published between 1999 and 2011, while the most prolific publication year was 2007 with 8.2% of the sample papers. The other prolific year was 2009 with 7.1% of the sample papers.

220

TABLE 29.2 Most Prolific Authors in the Hydrolysate and Substrate Fermentation No.

Author Code

Sample Papers (%)

Population Papers (%)

Surplus

Institution

Country

HI

N

Res. Front

Hahn-Hagerdal. Barbel* Van Dijken, Johannes P. Ingram, Lonnie O. Jeffries, Thomas W. Lynd. Lee R. Zacchi. Guido Pronk, Jack T. Galbe. Mats Gonzalez, Ramon Kondo, Akihiko Taherzadeh, Muhammad J. Liden, Gunnar Boles, Eckhard Palmqvist, Eva A.* Scheffers, W. Alexander Jin, Youg Su Olsson, Lisbeth* Cotta, Michael A. Kung, Limin Wyman. Charles E Niklasson, Claes

7005389381 7102979857 7102962097 7005806269 35586183800 7006727748 7005313057 7003788758 57192167471 57203868143 6701407496 7004458708 7005230946 6603821896 7004619362 57204009076 7203077540 7006656876 7005115861 7004396809 7003757959

5.5 4.4 3.3 3.3 3.3 3.3 3.3 2.7 2.7 2.2 2.2 2.2 2.2 2.2 2.2 1.6 1.6 1.6 1.6 1.6 1.6

0.4 0.1 0.5 0.3 0.2 0.2 0.2 0.2 0.1 0.5 0.4 0.2 0.1 0.1 0.1 0.4 0.3 0.2 0.2 0.1 0.1

5.1 4.3 2.8 3.0 3.1 3.1 3.1 2.5 2.6 1.7 1.8 2.0 2.1 2.1 2.1 1.2 1.3 1.4 1.4 1.5 1.5

Lund Univ. Delft Univ. Technol. Univ. Florida Xylome Corp. Dartmouth Coll. Lund Univ. Delft Univ. Technol. Lund Univ. Univ. S. Florida Tampa Kobe Univ. Boras Univ. Lund Univ. Goethe Univ. Frankfurt Follicum Inc. Delft Univ. Technol. Univ. Ill. Urb. Champ. Chalmers Univ. Technol. USDA Agr. Serv. Univ. Delaware Univ. Calif. Riverside Chalmers Univ. Technol.

Sweden Netherlands USA USA USA Sweden Netherlands Sweden USA Japan Sweden Sweden Germany Sweden Netherlands USA Sweden USA USA USA Sweden

75 68 72 58 74 67 74 50 35 89 64 48 47 19 37 46 59 50 41 80 30

258 190 281 156 286 204 293 131 108 792 405 143 134 31 93 212 244 186 102 286 83

Metabol. Eng. Metabol. Eng. Metabol. Eng. Metabol. Eng. Metabol. Eng. SSF Metabol. Eng. Ferm. Metabol. Eng. Metabol. Eng. Ferm. Ferm. Metabol. Eng. Ferm. Metabol. Eng. Metabol. Eng. Ferm. Ferm. Ferm. Ferm. Ferm.

*, Female; Author code, the unique code given by Scopus to the authors; Ferm., fermentation processes; HI, H-index; Metabol eng, metabolic engineering; N, number of papers published by each author; Population papers, the number of papers authored in the population dataset; Sample papers, the number of papers authored in the sample dataset.

Bioethanol Fuel Production Processes. II

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21

Author Name

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Hydrolysate and Substrate Fermentation: Scientometric Study 9 8

Number of papers (%)

7

Population papers Sample papers

6 5 4 3 2 1 0

FIGURE 29.1  Research output by years regarding the hydrolysate and substrate fermentation.

29.3.4 The Most Prolific Institutions in the Hydrolysate and Substrate Fermentation Information about the 16 most prolific institutions publishing papers on the hydrolysate and substrate fermentation with at least 2.2% of the sample papers each is given in Table 29.3. The most prolific institution is Lund University with 8.8% of the sample papers, followed by Delft University of Technology with 5.5% of the sample papers. The other prolific institutions are the Technical University of Denmark, Dartmouth College, and Rice University with 3%–4% of the sample papers each. TABLE 29.3 Most Prolific Institutions in Hydrolysate and Substrate Fermentation No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

Institutions Lund Univ. Delft Univ. Technol. Dartmouth Coll. Tech. Univ. Denmark Rice Univ. Bird Eng. Inc. Natl. Renew. Ener. Lab. Univ. Florida Univ. Ill. Urb. Champ. Chalmers Univ. Technol. Kobe Univ. Michigan State Univ. Univ. Sao Paulo Univ. Wisconsin-Madison USDA Agr. Res. Serv. USDA Forest Serv.

Country

Sample Papers (%)

Population Papers (%)

Surplus (%)

Sweden Netherlands USA Denmark USA Netherlands USA USA USA Sweden Japan USA Brazil USA USA USA

8.8 5.5 3.8 3.8 3.3 2.7 2.7 2.7 2.7 2.2 2.2 2.2 2.2 2.2 2.2 2.2

1.2 0.5 0.3 0.7 0.1 0.1 0.5 0.7 0.9 0.6 0.6 0.5 1.4 0.7 2.2 0.4

7.6 5.0 3.5 3.1 3.2 2.6 2.2 2.0 1.8 1.6 1.6 1.7 0.8 1.5 0.0 1.8

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The top country for these most prolific institutions is the USA with nine institutions, while the Netherlands and Sweden house two institutions each. In total, only six countries house these top institutions. However, the institution with the most citation impact is Lund University with 7.6% surplus, followed by Delft University of Technology with 5% surplus. The other prolific institutions are Technical University of Denmark, Dartmouth College, and Rice University with around 3% surplus each. Additionally, there are other institutions with a relatively low citation impact and with 0.5 to 1.4% of the population papers each: Chinese Academy of Sciences, University of Sao Paulo, US Department of Agriculture (USDA), Jiangnan University, China Agricultural University, Wageningen University & Research, Beijing University of Chemical Technology, French National Scientific Research Center (CNRS), Paulista State University, Seoul National University, Ministry of Agriculture of China, Tianjin University, Kyoto University, Korea University, Nanjing Agricultural University, Iowa State University, and Tianjin University of Science & Technology.

29.3.5 The Most Prolific Funding Bodies in the Hydrolysate and Substrate Fermentation Information about the ten most prolific funding bodies funding at least 1.6% of the sample papers each is given in Table 29.4. Only 29% and 38% of the sample and population papers were funded. The most prolific funding body is the US Department of Energy with 4.9% of the sample papers. The National Institute of General Medical Sciences, the New Energy and Industrial Technology Development Organization, the Swedish National Board for Industrial and Technical Development, and the USDA are the other prolific funding bodies. However, the most prolific country for these top funding bodies is the USA with four funding bodies, followed by Japan and Sweden with three funding bodies each. In total, only three countries house these top funding bodies. The funding body with the most citation impact is the US Department of Energy with 3.8% surplus. National Institute of General Medical Sciences, New Energy and Industrial Technology Development Organization, and Swedish National Board for Industrial and Technical Development are the other influential funding bodies with around 2% surplus each. Similarly, the funding bodies with the least citation impact are the Japan Society for the Promotion of Science and Ministry of Education, Culture, Sports, Science and Technology of Japan with less than 1% surplus each.

TABLE 29.4 Most Prolific Funding Bodies in Hydrolysate and Substrate Fermentation No.  1  2  3  4  5  6  7  8  9 10

Funding Bodies US Dept. Ener. Natl. Inst. Gen. Med. Sci. New Ener. Ind. Technol. Devnt. Prog. Swedish Natl. Board Ind. Tech. Devnt. US Dept. Agric. Japan Soc. Prom. Sci. Knut and Alice Wallenberg Found. Minist. Educ. Cult. Sport. Sci. Technol. Swedish Res. Counc. US Dept. Health Human Serv.

Country

Sample Paper No. (%)

Population Paper No. (%)

Surplus (%)

USA USA Japan Sweden USA Japan Sweden Japan

4.9 2.2 2.2 2.2 2.2 1.6 1.6 1.6

1.1 0.3 0.5 0.1 0.7 1.2 0.1 1.3

3.8 1.9 1.7 2.1 1.5 0.4 1.5 0.3

Sweden USA

1.6 1.6

0.1 0.4

1.5 1.2

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The other funding bodies with a relatively low citation impact and with 0.4%–6.2% of the population papers each are National Natural Science Foundation of China, National Council of Scientific and Technological Development, Ministry of Science and Technology of China, Coordination of Superior Level Appearance, National Key Research and Development Program of China, European Commission, Research Support Foundation of the State of São Paulo, Ministry of Science, Technology and Innovation, National Research Foundation of Korea, Fundamental Research Funds for the Central Universities, Ministry of Education of China, European Regional Development Fund, National Science Foundation, National Council of Science and Technology, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National High-Tech Research and Development Program, Ministry of Finance of Japan, China Postdoctoral Science Foundation, Chinese Academy of Sciences, National Basic Research Program of China (973 Program), Department of Biotechnology, Ministry of Science and Technology, India, Government of Canada, Ministry of Economy and Competitiveness, Priority Academic Program Development of Jiangsu Higher Education Institutions, and Research Support Foundation of the State of Minas Gerais. It is notable that the National Natural Science Foundation of China is the largest funder of the population papers.

29.3.6 The Most Prolific Source Titles in the Hydrolysate and Substrate Fermentation Information about the 15 most prolific source titles publishing at least 1.7% of the sample papers each in hydrolysate and substrate fermentation is given in Table 29.5. The most prolific source title is Applied and Environmental Microbiology with 12.1% of the sample papers, followed by Bioresource Technology with 8.8% of the sample papers. The other prolific titles are Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, Current Opinion in Biotechnology, Enzyme and Microbial Technology, and Biotechnology Progress with 3%–8% of the sample papers each. TABLE 29.5 Most Prolific Source Titles in Hydrolysate and Substrate Fermentation No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

Source Titles Applied and Environmental Microbiology Bioresource Technology Applied Microbiology and Biotechnology Biotechnology and Bioengineering Current Opinion in Biotechnology Enzyme and Microbial Technology Biotechnology Progress Journal of Biotechnology FEMS Yeast Research Metabolic Engineering Proceedings of the National Academy of Sciences of the United States of America Process Biochemistry Biomass and Bioenergy Journal of Dairy Science Biotechnology Advances Advances in Biochemical Engineering Biotechnology

Sample Papers (%)

Population Papers (%) Surplus (%)

12.1 8.8 7.7 7.7 4.4 3.3 3.3 2.7 2.7 2.7 2.7

2.0 4.8 2.9 2.9 0.1 1.4 0.8 0.8 0.6 0.4 0.1

10.1 4.0 4.8 4.8 4.3 1.9 2.5 1.9 2.1 2.3 2.6

2.2 1.6 1.6 1.6 1.6

1.4 1.0 0.9 0.2 0.1

0.8 0.6 0.7 1.4 1.5

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However, the source title with the most citation impact is Applied and Environmental Microbiology with 10.1% surplus. The other influential titles are Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, Current Opinion in Biotechnology, and Bioresource Technology with 4%–5% surplus each. Similarly, the source titles with the least impact are Biomass and Bioenergy, Journal of Dairy Science, and Process Biochemistry with less than 1% surplus each. The other source titles with a relatively low citation impact with 0.5%–2.8% of the population papers each are Biotechnology Letters, Applied Biochemistry and Biotechnology, Biotechnology for Biofuels, Journal of Industrial Microbiology and Biotechnology, Journal of Bioscience and Bioengineering, World Journal of Microbiology and Biotechnology, Journal of Bacteriology, Bioprocess and Biosystems Engineering, Biochemical Engineering Journal, International Journal of Food Microbiology, Journal of Applied Microbiology, Agricultural and Biological Chemistry, Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology, Journal of Agricultural and Food Chemistry, Animal Feed Science and Technology, Journal of Chemical Technology and Biotechnology, Fermentation, Industrial Crops and Products, Journal of Microbiology and Biotechnology, Renewable Energy, Bioresources, Food Microbiology, PLOS One, and Food Chemistry.

29.3.7 The Most Prolific Countries in the Hydrolysate and Substrate Fermentation Information about the 14 most prolific countries publishing at least 2.2% of sample papers each in hydrolysate and substrate fermentation is given in Table 29.6. The most prolific country is the USA with 36% of the sample papers, followed by Sweden with 12% of the sample papers. Japan, Canada, the Netherlands, Denmark, China, and Germany are the other prolific countries with 5%–8% of the sample papers each. It is notable that China is the second largest producer of the population paper just after the USA. It is further notable that six European countries listed in Table 29.6 produce 35% and 15% of the sample and population papers with 20% surplus. However, the country with the most citation impact is the USA with 19.4% surplus, followed by Sweden with 9.5% surplus. The other influential countries are the Netherlands, Denmark, and Canada with 2%–4% surplus each. Similarly, the country with the least citation impact is China with 10.8% deficit, while India, Brazil, S. Korea, and France have 1%–4% deficit each. TABLE 29.6 Most Prolific Countries in the Hydrolysate and Substrate Fermentation No.

Countries

Sample Papers (%)

 1  2  3  4  5  6  7  8  9 10 11 12 13 14

USA Sweden Japan Canada Netherlands Denmark China Germany India Spain S. Korea Brazil France S. Africa

35.7 12.1 8.2 6.0 6.0 5.5 4.9 4.9 4.4 4.4 3.8 3.3 2.2 2.2

Population Papers (%) 16.3 2.6 7.3 3.7 1.7 1.3 15.7 2.8 7.9 3.4 4.9 6.3 2.9 1.1

Surplus (%) 19.4 9.5 0.9 2.3 4.3 4.2 −10.8 2.1 −3.5 1.0 −1.1 −3.0 −0.7 1.1

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Additionally, there are other countries with relatively low citation impact and with 0.5%–3.7% of the sample papers each: the UK, Thailand, Italy, Mexico, Malaysia, Australia, Taiwan, Portugal, Nigeria, Indonesia, Iran, Belgium, Turkey, Poland, Egypt, Greece, Finland, Russia, Argentina, Pakistan, Switzerland, New Zealand, Colombia, and Cuba.

29.3.8 The Most Prolific Scopus Subject Categories in the Hydrolysate and Substrate Fermentation Information about the most prolific nine Scopus subject categories indexing at least 3.8% of the sample papers each is given in Table 29.7. The most prolific Scopus subject category in the hydrolysate and substrate fermentation is Biochemistry and Genetics and Molecular Biology with 72% of sample papers, closely followed by Immunology and Microbiology with 62% of the sample papers. The other prolific subject categories are Chemical Engineering, Environmental Science, Agricultural and Biological Sciences, and Energy with 15%–44% of the sample papers each. It is notable that Social Sciences including Economics and Business account only for 1.3% of the population studies. However, the Scopus subject category with the most citation impact is Biochemistry and Genetics and Molecular Biology with 21.4% surplus, closely followed by Immunology and Microbiology with 20.6% surplus. The other influential categories are Chemical Engineering and Environmental Science with 11% and 9% surplus, respectively. Similarly, the Scopus subject categories with the least citation impact are Agricultural and Biological Sciences and Chemistry with 8% and 7% deficit, respectively.

29.3.9  The Most Prolific Keywords in the Hydrolysate and Substrate Fermentation Information about the Scopus keywords used with at least 6.6% or 3.4% of the sample or population papers, respectively, is given in Table 29.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most prolific keyword set provided by the Scopus database. These keywords are grouped under eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. There are eight keywords selected related to biomass and biomass constituents where biomass, cellulose, lignocellulose, and lignin are the most prolific keywords with 18%–29% of the sample papers each. The most prolific keyword related to the hydrolysate and substrate fermentation is fermentation with 79% of the sample papers, while Saccharomyces cerevisiae, yeast, bacteria, fungi, and Escherichia coli are the most prolific keywords related to bacteria with 17%–55% of the sample papers each. Sugar, xylose, and glucose are the most prolific keywords related to the hydrolysates with 26 to 31% of the sample papers each, while genetic engineering, metabolic engineering, and gene expression are the most prolific keywords related to microbial engineering with 11%–20% of the sample papers each. TABLE 29.7 Most Prolific Scopus Subject Categories in the Hydrolysate and Substrate Fermentation No. 1 2 3 4 5 6 7 8 9

Scopus Subject Categories

Sample Papers (%)

Biochemistry. Genetics and Molecular Biology Immunology and Microbiology Chemical Engineering Environmental Science Agricultural and Biological Sciences Energy Engineering Multidisciplinary Chemistry

72.0 61.5 44.0 27.5 20.9 15.4 8.8 4.4 3.8

Population Papers (%) 50.6 40.9 33.4 18.7 29.0 14.2 9.7 2.5 10.3

Surplus (%) 21.4 20.6 10.6 8.8 −8.1 1.2 −0.9 1.9 −6.5

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TABLE 29.8 Most Prolific Keywords in Hydrolysate and Substrate Fermentation No. 1

2

Keywords

Sample Papers (%)

4

5

Surplus (%)

Biomass

29.1

10.7

18.4

Cellulose

24.7

8.8

15.9

Lignocellulose

20.9

4.5

16.4

Lignin

18.1

5.2

12.9

Glycerol

8.8

3.2

5.6

Hemicellulose

8.8

2.1

6.7

Zea mays (corn, maize)

7.1

7.1

0.0

Lignocellulosic biomass

6.6

2.5

4.1

Fermentation Fermentation

78.6

59.8

18.8

Metabolism

25.3

19.8

5.5

Carbohydrate metabolism

8.8

2.4

6.4

Bioreactors

8.2

10.7

−2.5

Detoxification

7.7

1.8

Fermentation technique

7.1

Ethanol fermentation

4.9

4.1

0.8

Simultaneous saccharification and fermentation

4.4

3.4

1.0

6.5

−6.5

Solid-state fermentation 3

Population Papers (%)

Biomass and biomass constituents

5.9 7.1

Bacteria Saccharomyces cerevisiae

54.9

22.2

32.7

Yeast

44.0

22.0

22.0

Bacteria

31.9

15.8

16.1

Fungi

21.4

9.8

11.6

Escherichia coli

17.0

6.4

10.6

Pichia stipitis

14.3

3.6

10.7

Fungal strain

12.1

4.2

7.9

Zymomonas mobilis

8.8

2.6

6.2

Clostridium

6.6

3.2

3.4

Fungus growth

6.0

3.4

2.6

Lactobacillus

5.5

6.6

−1.1

Bacterial strain

4.4

4.0

0.4

Bacterial growth

4.4

3.6

0.8

Hydrolysates Sugar

30.8

9.4

21.4

Xylose

28.6

8.0

20.6

Glucose

25.8

16.3

9.5

Pentose

9.3

9.3

Microbial engineering Genetic engineering

19.8

2.6

17.2

Metabolic engineering

13.7

3.0

10.7

Gene expression

11.0

4.2

6.8 (Continued )

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TABLE 29.8 (Continued) Most Prolific Keywords in Hydrolysate and Substrate Fermentation No.

6

7

8

Keywords

Sample Papers (%)

Population Papers (%)

Surplus (%)

Genetics

7.1

7.3

−0.2

Industrial microbiology

7.1

2.5

4.6

Bioengineering

6.6

Microbiology

6.0

7.0

−1.0

Biomass pretreatment Enzyme activity

16.5

9.6

6.9

Enzymes

14.3

7.5

6.8

Saccharification

14.3

6.7

7.6

Acetic acid

11.5

5.4

6.1

Cellulases

6.6

11.5

3.7

7.8

pH

9.3

8.4

0.9

Temperature

6.0

5.2

0.8

Lactic acid

6.0

4.5

1.5

Other processes Hydrolysis

25.8

9.3

16.5

Anaerobiosis

10.4

10.4

Biotransformation

6.6

2.1

4.5

Enzymatic hydrolysis

5.5

3.6

1.9

Fermentation products Ethanol

64.8

33.6

31.2

Alcohol

55.5

23.6

31.9

Biofuel

24.2

9.3

14.9

Alcohol production

16.5

7.6

8.9

Bioethanol

12.6

10.1

2.5

Ethanol production

12.6

6.7

5.9

Biofuel production

7.7

1.6

6.1

3.6

−3.6

Bio-ethanol production

Enzyme activity, enzymes, and saccharification are the most prolific keywords related to the biomass pretreatments with 14%–17% of the sample papers each, while hydrolysis and anaerobiosis are the most prolific keywords related to the other processes with 10% and 26% of the sample papers, respectively. Finally, the most prolific keywords related to the fermentation products are ethanol, alcohol, biofuel, and alcohol production with 17%–65% of the sample papers each. Furthermore, the most influential keywords are Saccharomyces cerevisiae, alcohol, ethanol, yeast, sugar, xylose, fermentation, biomass, genetic engineering, hydrolysis, lignocellulose, bacteria, and cellulose with 16%–33% surplus each.

29.3.10 The Most Prolific Research Fronts in Hydrolysate and Substrate Fermentation Information about the research fronts for the sample papers in hydrolysate and substrate fermentation with regard to the biomass and hydrolysates used in these pretreatments is given in Table 29.9.

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TABLE 29.9 Most Prolific Research Fronts for the Biomass and Hydrolysates Used for the Hydrolysate and Substrate Fermentation No.  1

 2

Research Fronts

N Paper (%) Sample

Hydrolysates Xylose

45.1 20.3

Glucose

10.4

Hydrolysates in general

4.4

Pentose

4.4

Hexose

2.7

Lactose

1.1

Arabinose

1.1

Cellobiose

0.5

Agricultural residues Corn stover

12.6 3.3

Corn silage

2.7

Wheat straw

2.2

Sugarcane bagasse

1.6

Other agr. residues

1.6

Rice straw

1.1

 3

Biomass constituents Cellulose

5.5 4.4

Lignin

1.1

 4  5  6  7  8  9 10

Algae Glycerol Biosyngas Wood Grass Sugar feedstocks Other biomass Starch feedstocks

4.4 3.8 3.3 3.3 2.2 2.2 2.7 1.1

Food waste

0.5

Industrial waste

0.5

Water hyacinth

0.5

N paper (%) sample, the number of papers in the population sample of 182 papers.

As Table 29.9 shows, there are two primary research fronts for this field: hydrolysates and agricultural residues with 45% and 13% of the sample papers, respectively. The other prolific research fronts are biomass constituents, algae, glycerol, biosyngas, wood, grass, sugar feedstocks, and other biomass with 3%–6% of the sample papers each. However, the most prolific hydrolysate is xylose with 20% of the sample papers, followed by glucose with 10% of the sample papers. Furthermore, the most prolific agricultural residues are corn stover and corn silage with 3% of the sample papers each, while the most prolific biomass constituent is cellulose with 4% of the sample papers. Information about the thematic research fronts for the sample papers in hydrolysate and substrate fermentation with regard to the fermentation processes is given in Table 29.10. As Table 29.10

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shows, there are two primary research fronts for this field: microorganism and substrate metabolic engineering and fermentation inhibitors with 34% and 13% of the sample papers, respectively. The other prolific research fronts are SSF, hydrolysate detoxification, and fermentation microorganisms with 6%–8% of the sample papers each.

29.4 DISCUSSION 29.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol before the bioethanol production through the hydrolysis and fermentation. The research in the fields of hydrolysate and substrate fermentation has thus intensified in recent years as hydrolysate and substrate fermentation processes, fermentation inhibitors, hydrolysate detoxification, and microorganism and substrate metabolic engineering have been widely researched to increase bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, war-related chocks as in the case of Russian invasion of Ukraine, or COVID-19 shocks. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of research in a selected research field. As the recent scientometric studies focus on a certain research front (Calvo et al., 2022; Devos and Colla, 2022), this book chapter presents a scientometric study of research in the hydrolysate and substrate fermentation as a whole. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this keyword list was provided in the appendix for future replication studies. Furthermore, a selected list of the keywords is presented in Table 29.8. As a second step, two sets of data were used for this study. First, a population sample of over 18,246 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 182 most-cited papers, corresponding to 1% of the population datasets, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the hydrolysate and substrate fermentation. Additionally, many brief conclusions were drawn and many relevant recommendations were made to enhance the future research landscape.

29.4.2 The Most Prolific Documents in the Hydrolysate and Substrate Fermentation Articles (together with conference papers) dominate both the sample (78%) and population (96%) papers (Table 29.1). Furthermore, review papers and articles have a surplus (23%) and deficit (20%), respectively. The representation of the reviews and short surveys in the sample papers is extraordinarily high (22%).

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TABLE 29.10 Most Prolific Thematic Research Fronts Regarding Fermentation Processes for the Hydrolysate and Substrate Fermentation No. 1 2 3 4 5 6 7 8 9

Research Fronts

N Paper (%) Sample

Microorganism and substrate metabolic engineering Fermentation inhibitors SSF Hydrolysate detoxification Fermentation microorganisms Fermentation in general Other fermentation issues Consolidated bioprocessing Microorganism ethanol tolerance

34.1 13.2 8.2 7.1 5.5 4.4 4.4 3.8 3.3

N paper (%) sample, the number of papers in the population sample of 182 papers.

Scopus differs from the Web of Science database in differentiating and showing articles (75%) and conference papers (2%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared with those published only in the conference proceedings. Similarly, Scopus differs from the Web of Science database in introducing short surveys (2%). Hence, the total number of articles and review papers in the sample dataset is 78% and 22%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus and in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of reviews in the sample dataset was manually found as nearly 30% compared with 22% as indexed by Scopus, reducing the number of articles and conference papers to 70% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 29.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers and it is done in the Web of Science database.

29.4.3 The Most Prolific Authors in the Hydrolysate and Substrate Fermentation There have been 21 most prolific authors with at least 1.6% of the sample papers each as given in Table 29.2. These authors have shaped the development of research in this field. The most prolific authors are Barbel Hahn-Hagerdal and Johannes P. van Dijken, and to a lesser extent Lonnie O. Ingram, Thomas W. Jeffries, Lee R. Lynd, Guido Zacchi, and Jack T. Pronk. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for names with more than two components such as ‘Blake Sam de Hyun Hagerdal’. The probable outcomes are ‘Hagerdal, B.S.D.H.’, ‘de Hyun Hagerdal, B.S.’, or ‘Hyun Hagerdal, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PubMed and EBSCO databases, this version is used predominantly. The second choice is a strong alternative,

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while the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name with a hyphen: ‘Hyun-Hagerdal, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers and allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening of Chinese names. For example, ‘YangYing Zhuang’ is often shortened as ‘Zhuang, Y.’, ‘Zhuang, Y.-Y.’, and ‘Zhuang, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhuang, Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PubMed and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Zhuang, Y.Y.’. Therefore, there have been difficulties in locating papers for Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors, especially for the authors with common names such as ‘Zhuang, X’ or ‘Huang, X’ or ‘Zhang, X’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies and allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Zhang, X’ in the databases. In this context, the coding of authors in the Scopus database is a welcome innovation compared with other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system, especially for common names. For example, some of the papers for a certain author may be allocated to another researcher with a different author code. It is possible that Scopus uses many software programs to differentiate the author names and the program may not be falseproof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes it difficult to differentiate authors with common names and makes scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Hagerdal, H.J.’ or just ‘Hagerdal’ instead of ‘Hagerdal, Hyun Jae’. There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Hagerdal, A.P.C.’ might be given as ‘Hagerdal, A’ or ‘Hagerdal, A.C.’ or ‘Hagerdal, A.P.’ or ‘Hagerdal, C’ in the journals and books. This also makes scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Seçilmiş, Özöğlü) rather than in the English spellings (e.g., Secilmis, Ozoglu) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in English spellings. It is observed that national spellings of the author names do not help much in conducting scientometric studies and in allocating credits to the authors as sometimes there are different author entries for the English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are Lund University and to a lesser extent Delft University of Technology and Chalmers University of Technology. The most prolific countries for the sample dataset are the USA and to a lesser extent Sweden and the Netherlands. These findings confirm the dominance of the USA and to a lesser extent of Europe in this field. The most prolific research fronts are the metabolic engineering of the bacteria and substrates used to ferment the hydrolysates and fermentation processes used to ferment the hydrolysates. The SSF and fermentation inhibitors are the constituents of the second front.

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It is also notable that there is a significant gender deficit for the sample dataset as surprisingly only four of these top researchers are female with a representation rate of 16%. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field and other scientific fields with strong gender deficit. In this context, it is worth noting the level of representation of the researchers from minority groups in science based on race, sexuality, age, and disability, besides gender (Blankenship, 1993, Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

29.4.4 The Most Prolific Research Output by Years in the Hydrolysate and Substrate Fermentation The research output observed between 1970 and 2022 is illustrated in Figure 29.1. This figure clearly shows that the bulk of the research papers in the population dataset was published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. There was a rising trend for the population papers between 2005 and 2014, and thereafter, it lost its momentum becoming flat. However, there was a second rising trend in the research output between 2019 and 2021. These findings suggest that the most prolific sample and population papers were primarily published in the last two decades. These are thought-provoking findings as there has been no significant research in this field in the pre-2000s, but there has been a significant research boom in the last two decades. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in research in this field in the last two decades. Furthermore, the supply shocks experienced due to the COVID-19 pandemic might also be behind the research boom in this field since 2019. Based on these findings, the size of the population papers is likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming, as well as the supply shocks, are translated efficiently to the research funding in this field.

29.4.5 The Most Prolific Institutions in the Hydrolysate and Substrate Fermentation The 16 most prolific institutions publishing papers on the hydrolysate and substrate fermentation with at least 2.2% of the sample papers each given in Table 29.3 have shaped the development of research in this field. The most prolific institutions are Lund University and to a lesser extent Delft University of Technology, Technical University of Denmark, Dartmouth College, and Rice University. Similarly, the top countries for these most prolific institutions are the USA and to a lesser extent the Netherlands and Sweden. In total, only six countries house these top institutions. However, the institutions with the most and least impact are Lund University and to a lesser extent Delft University of Technology. These findings confirm the dominance of the US and European institutions with a notable absence of China in this research field. These findings clearly hint that the USA and Europe dominate research in this field.

29.4.6 The Most Prolific Funding Bodies in the Hydrolysate and Substrate Fermentation The ten most prolific funding bodies funding at least 1.6% of the sample papers each are given in Table 29.4. It is notable that only 29% and 38% of the sample and population papers were funded. The most prolific funding bodies are the US Department of Energy and to a lesser extent the National Institute of General Medical Sciences, New Energy and Industrial Technology Development

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Organization, Swedish National Board for Industrial and Technical Development, and USDA. The most prolific countries for these top funding bodies are the USA and to a lesser extent Japan and Sweden. In total, only three countries house these top funding bodies. The funding bodies with the most citation impact are the US Department of Energy and to a lesser extent National Institute of General Medical Sciences, New Energy and Industrial Technology Development Organization, and Swedish National Board for Industrial and Technical Development, while the ones with the least impact are Japan Society for the Promotion of Science and to a lesser extent Ministry of Education, Culture, Sports, Science and Technology of Japan. These findings on the funding of research in this field suggest that the level of funding, mostly in the last two decades, is not intensive but nevertheless it has been largely instrumental in enhancing research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding as a whole, especially for the sample papers, there is ample room to enhance funding in this field.

29.4.7 The Most Prolific Source Titles in Hydrolysate and Substrate Fermentation The 15 most prolific source titles publishing at least 1.7% of the sample papers each in hydrolysate and substrate fermentation have shaped the development of research in this field (Table 29.5). The most prolific source titles are Applied and Environmental Microbiology and to a lesser extent Bioresource Technology, Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, Current Opinion in Biotechnology, Enzyme and Microbial Technology, and Biotechnology Progress. The source titles with the most citation impact are the Applied and Environmental Microbiology and to a lesser extent Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, Current Opinion in Biotechnology, and Bioresource Technology. Similarly, the source titles with the least impact are Biomass and Bioenergy and to a lesser extent Process Biochemistry. It is notable that these top source titles are primarily related to microbiology and to a lesser extent to bioresources, bioengineering, enzymes, and biotechnology. This finding suggests that the journals in this field have significantly shaped the development of research in this field as they focus primarily on the metabolic engineering of the microorganisms and substrates to produce ethanol with a high yield.

29.4.8 The Most Prolific Countries in the Hydrolysate and Substrate Fermentation The 14 most prolific countries publishing at least 2.2% of the sample papers each have significantly shaped the development of research in this field (Table 29.6). The most prolific countries are the USA and to a lesser extent Sweden, Japan, Canada, the Netherlands, Denmark, China, and Germany. It is notable that China is the second largest producer of the population paper just after the USA. It is further notable that six European countries listed in Table 29.6 produce 35% and 15% of the sample and population papers with 20% surplus. However, the countries with the most citation impact are the USA and to a lesser extent Sweden, the Netherlands, Denmark, and Canada. Similarly, the countries with the least impact are China and to a lesser extent India, Brazil, S. Korea, and France. Additionally, it is notable that these countries are underrepresented in the sample papers, while the USA, Sweden, Canada, the Netherlands, Denmark, China, and Germany are overrepresented. A close examination of these findings suggests that the USA, Europe, Japan, Canada, China, and India are the major producers of research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The USA has further developed a strong research infrastructure to support its corn and grass-based bioethanol industry (Vadas et al., 2008).

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However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009). Next, Europe has been a persistent player in scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010). Additionally, Brazil has also been a persistent player in scientific research at a moderate level (Glanzel et al., 2006). Brazil has also developed a strong research infrastructure to support its biomass-based bioethanol industry (Macedo et al., 2008).

29.4.9 The Most Prolific Scopus Subject Categories in Hydrolysate and Substrate Fermentation The nine most prolific Scopus subject categories indexing at least 3.8% of the sample papers each, respectively, given in Table 29.7 have shaped the development of research in this field. The most prolific Scopus subject categories in the hydrolysate and substrate fermentation are Biochemistry. Genetics and Molecular Biology, followed by Immunology and Microbiology and to a lesser extent Chemical Engineering, Environmental Science, Agricultural and Biological Sciences, and Energy. The Scopus subject categories with the most citation impact are Biochemistry and Genetics and Molecular Biology, followed by Immunology and Microbiology and to a lesser extent Chemical Engineering and Environmental Science. Similarly, the Scopus subject categories with the least citation impact are Agricultural and Biological Sciences and Chemistry. These findings are thought-provoking suggesting that the primary subject categories are related to genetics and microbiology as the core of research in this field concerns the metabolic engineering of the microorganisms and substrates to increase the ethanol yield. The other key finding is that social sciences are not well represented in both the sample and population papers as in most fields in bioethanol fuels.

29.4.10 The Most Prolific Keywords in Hydrolysate and Substrate Fermentation A limited number of keywords have shaped the development of research in this field as shown in Table 29.8 and the Appendix. These keywords are grouped under eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. The most influential keywords are Saccharomyces cerevisiae, alcohol, ethanol, yeast, sugar, xylose, fermentation, biomass, genetic engineering, hydrolysis, lignocellulose, bacteria, and cellulose. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of research in the relevant research areas. The relevant keywords are presented in Table 29.8 and in the Appendix.

29.4.11 The Most Prolific Research Fronts in Hydrolysate and Substrate Fermentation As Table 29.9 shows that there are two primary research fronts for this field: hydrolysates and agricultural residues, the other research fronts are biomass constituents, algae, glycerol, biosyngas, wood, grass, sugar feedstocks, and other biomass. However, the most prolific hydrolysate is xylose and glucose. Furthermore, the most prolific agricultural residues are corn stover and corn silage, while the most prolific biomass constituent is cellulose.

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Information about the research fronts for the sample papers in hydrolysate and substrate fermentation with regard to the fermentation processes is given in Table 29.10. As Table 29.10 shows, there are two primary research fronts for this field: microorganism metabolic engineering and fermentation inhibitors. The other prolific research fronts are SSF, hydrolysate detoxification, and fermentation microorganisms. These findings are thought-provoking in seeking ways to increase bioethanol yield through the hydrolysate and substrate fermentation at the global scale. It is clear that all of these research fronts with regard to both fermentation processes and biomass types have public importance and merit substantial funding and other incentives. However, it is notable that metabolic engineering of the substrates and microorganisms has become a core of the fermentation research to increase the ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. In the end, these most-cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of these fermentation processes (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

29.5  CONCLUSION AND FUTURE RESEARCH The research on the hydrolysate and substrate fermentation has been mapped through a scientometric study of both sample (182 papers) and population (18,246 papers) datasets. The critical issue in this study has been to obtain a representative sample of research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after many runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the Appendix, and the relevant keywords are presented in Table 29.8. However, it should be noted that it has been very difficult to compile a representative keyword set since this research field has been connected closely with many other fields. Therefore, it has been necessary to compile a keyword list to exclude papers concerned with the other research fields. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of research in this field. For this purpose, the Scopus database has been selected. The journal coverage of this database has been notably wider than that of the Web of Science and other multi-subject databases. The key scientometric properties of research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits based on age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Furthermore, the recent COVID-19 pandemic and the Russian invasion of Ukraine have resulted in global supply shocks shifting the focus of the stakeholders from crude oil- and natural gas-based fuels to biomassbased fuels such as bioethanol fuels. It is expected that there would be further incentives for the key stakeholders to carry out research for the fermentation of the hydrolysates and substrates to increase the ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. This might be truer for crude oil- and foreign exchange-deficient countries to maintain energy security in the face of the global supply shocks. The relatively modest funding rate of 38% for the population papers suggests that funding in this field significantly enhanced research in this field primarily in the last two decades, possibly more

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than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance research in this field further as only 29% of the sample papers declared any funding. The most prolific journals have been related to microbiology and to a lesser extent bioresources, bioengineering, enzymes, and biotechnology as the focus of the sample papers has been on the metabolic engineering of the substrates and microorganisms to improve the ethanol yield. The institutions from the USA and Europe have mostly shaped research in this field. Furthermore, the USA, Europe, Japan, Canada, China, and India have been the major producers of research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass and other types of biomass. It is evident that these countries have welldeveloped research infrastructure in bioethanol fuels and their derivatives. The primary Scopus subject categories are related to genetics and microbiology as the core of research in this field concerns the metabolic engineering of the microorganisms and substrates to increase the ethanol yield. The other key finding is that social sciences are not well represented in both the sample and population papers as in most fields in bioethanol fuels. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only the bioethanol keyword would not be much helpful. The Scopus keywords are grouped under eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. There are two primary research fronts regarding the biomass used for this field: hydrolysates and agricultural residues. The other research fronts are biomass constituents, algae, glycerol, biosyngas, wood, grass, sugar feedstocks, and other biomass. Similarly, there are two primary research fronts for this field regarding the fermentation processes: metabolic engineering of microorganisms and substrates and fermentation inhibitors, while the other prolific research fronts are SSF, hydrolysate detoxification, and fermentation microorganisms. These findings are thought-provoking. The focus of these 182 most-cited papers and 18,246 population papers is the fermentation processes and mechanisms to increase the bioethanol yield through the optimization of the fermentation processes and the metabolic engineering of the substrates and microorganisms. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of research in this field as in other scientific fields, especially in the aftermath of the significant global supply shocks such as COVID-19 pandemic and the Russian invasion of Ukraine. It is recommended that further scientometric studies are carried out for the primary types of the fermentation processes and the biomass used. It is further recommended that reviews of the mostcited papers are carried out for each research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the hydrolysate and substrate fermentation has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR HYDROLYSATE AND SUBSTRATE FERMENTATION (((TITLE ((ethanol* OR bioethanol) AND (ferment* OR coferment* OR *saccharomyces OR pichia OR candida OR yeast* OR escherichia OR zymomonas OR clostrid* OR bacter* OR dekkera OR “S. cerevisiae” OR hansenula OR kluyveromyces OR pachysolen OR alkalibaculum OR schwanniomyces OR rhizopus OR mucor OR corynebacterium OR microbe* OR microbial OR geobacillus OR klebsiella OR aspergillus OR caldicellulosiruptor OR “E. coli” OR thermoanaerobacterium OR

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scheffersomyces OR actinobacillus OR bifidobacterium OR bacillus OR bacilli OR bacterium OR thermoanaerobium OR fusarium OR “metabolic eng*” OR genetic* OR engineer* OR gene OR genom* OR recombinant OR regulon OR proteom* OR cloning OR transgen* OR xylose OR hydrolysate* OR glucose OR hydrolyzate* OR glucose OR pentose OR kloeckera OR oenococcus OR rhizomucor OR zymobacter OR rhynchosporium OR trametes OR acetobacter OR pleurotus OR fung*))) OR (TITLE ({separate hydrolysis and fermentation} OR {Simultaneous saccharification and fermentation} OR shf OR ssf OR sscf OR {consolidated bioprocessing} OR {Continuous ethanol production})) OR (TITLE ((xylose*) AND (util* OR consumption OR engineer* OR metabol*) AND (*saccharomyces OR pichia OR candida OR yeast* OR escherichia OR zymomonas OR clostrid* OR bacter* OR dekkera OR “S. cerevisiae” OR hansenula OR kluyveromyces OR pachysolen OR alkalibaculum OR schwanniomyces OR rhizopus OR mucor OR corynebacterium OR microbe* OR microbial OR ammonium OR geobacillus OR klebsiella OR aspergillus OR caldicellulosiruptor OR “E. coli” OR thermoanaerobacterium OR scheffersomyces OR actinobacillus))) OR (TITLE (ferment* OR coferment* OR ssf OR shf OR sscf) AND TITLE (*mannan OR waste OR wastes OR *grass OR biomass* OR lignocellul* OR bagasse* OR *cellulose OR *cellulosic OR avicel OR *wood OR woody OR *woods OR *algae OR *algal OR *alga OR chlorococum OR “olive tree*” OR chlamydomonas OR straw OR straws OR stover* OR eucalyptus OR miscanthus OR pine OR chlorella OR lignin OR cellulolytic OR xylan OR porphyridium OR nannochloropsis OR poplar* OR cedar OR pulp* OR “corn cob*” OR spruce OR beech OR oak OR pinus OR residue* OR willow* OR cypress OR sawdust OR prosopis OR “rice hull*” OR “*bean hull*” OR husk* OR birch OR cynara OR “*cane tops” OR “cotton stalk*” OR “corn stalk*” OR alfalfa OR “maize stem*” OR cornstalk* OR hyacinth OR salix OR corncob* OR “fruit bunch*” OR populus OR sage OR “*weed stem*” OR bamboo* OR birch OR “*flower stalk*” OR “barley hull*” OR “wheat bran” OR pseudostem* OR kernel OR fronds OR bluestem* OR reed OR arundo OR cellobiose* OR sawmill OR ulva OR seaweed* OR rhizome OR agave OR “coffee grounds” OR molass* OR triticale OR arborera OR saccharum OR potato* OR bioresource* OR eucheuma OR laminaria OR sargassum OR bode OR taro OR szarvasi OR eichhornia OR sago OR oxalate OR “dimethyl ether” OR artichoke OR *beet OR banana OR cardoon OR kenaf OR scrap* OR fir OR chestnut OR cistus OR garbage OR “distillers’ grains” OR “grape marc” OR medicago OR “shea meal” OR brassica OR buddleja OR “flax shives” OR gelidium OR saccharina OR ethane OR glycerol OR “olive stones” OR lantana OR “corn meal” OR “spent grain*” OR “almond shells” OR sugar OR sugars OR galactose OR *weed OR starch OR pistia OR lettuce OR hydrolysate* OR hydrolyzate* OR glucose OR maltose OR arabinose OR xylulose OR cellobiose OR xylose OR mannose OR fructose OR carbohydrate* OR monosaccharide* OR lactose OR sorghum OR prehydrolysate* OR synechocystis OR prehydrolyzate* OR cyanobacter* OR pentose OR hexose OR wheat OR corn OR maize OR zea OR *syngas OR “synthesis gas” OR scenedesmus OR whey OR pomace* OR “Carbon monoxide” OR mahula OR gracilaria OR kappaphycus OR alginate* OR spirulina OR chaetomorpha OR agarose OR gracilariopsis OR madhuca OR sucrose OR xylitol OR cassava OR *cane OR “paper sludge” OR co2 OR “carbon dioxide” OR solka OR “producer gas*” OR newspaper OR sugarcane OR “sugar cane” OR “sugar beet” OR silage OR saccharomyces OR pichia OR candida OR escherichia OR zymomonas OR clostrid* OR dekkera OR “S. cerevisiae” OR hansenula OR kluyveromyces OR pachysolen OR alkalibaculum OR schwanniomyces OR rhizopus OR mucor OR corynebacterium OR geobacillus OR klebsiella OR caldicellulosiruptor OR “E. coli” OR thermoanaerobacterium OR scheffersomyces OR actinobacillus OR bifidobacterium OR bacillus OR bacilli OR thermoanaerobium OR fusarium OR kloeckera OR oenococcus OR rhizomucor OR zymobacter OR rhynchosporium OR trametes OR acetobacter))) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR dent OR neur OR heal OR psyc) OR TITLE (“fermentable sugar*” OR *diesel OR *hydrogen OR {Microbial cellulose} OR lactic OR “Glycerol production” OR intest* OR “ethanolic extract” OR *methane OR methanolysis OR biopolymer* OR pozol OR torulaspora OR cadaverine OR *succinate OR lactis OR “value added” OR acetate OR phas OR lipid* OR sludge OR h2 OR biogas OR ch4 OR nutrit* OR propaned* OR gram OR “Technical photosynthesis” OR trypanos* OR

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resveratrol OR “fatty acid*” OR vibrio* OR “anaerobic digestion” OR bread* OR *hythane OR tanks OR dye* OR butanol OR gut OR *sorption OR “acetic ferment*” OR sensing OR cancer OR peptide* OR “microbial well” OR “microbial oil*” OR food* OR boiling OR flux OR “heavy metal” OR “yeast growth” OR antioxid* OR ceca OR carboxylate* OR fecal OR *milk OR flavon* OR enterohemor* OR pesticide OR gum OR {ns-2} OR hydrocolloid OR quinon* OR “bacterial cellulose” OR pectinase* OR ruminal OR oxidat* OR chrom* OR pectinase OR lysine OR spore* OR pear* OR wine* OR wastewater* OR insulin OR pinocembrin OR seed* OR rumen OR flour OR insect* OR finger* OR sensory OR colon OR rat OR phar* OR citric OR ester* OR sausage OR ethanolamine OR protease* OR *dough OR succinic OR carotenoid* OR lipase* OR tissue* OR tannin* OR mice OR “grape musts” OR “carbon metabolism” OR diffusion OR “Xylanase production” OR “amylase production” OR “dark fermentation” OR succinate OR heterologous OR tubers OR glucoamylase OR inducible OR brewing OR {non-fermenting} OR faecal OR thiol OR vinegar OR butyric OR {ethanol-type}))) OR (TITLE ((butanol OR butanediol OR *hydrogen OR glycerol OR *diesel OR *gas OR *methane OR ch4 OR h2 OR “co-products” OR biorefineries) AND (ethanol* OR bioethanol) AND (ferment* OR coferment* OR shf OR ssf OR sscf OR clostrid*))) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “sh”) OR LIMIT-TO (DOCTYPE, “ed”) OR LIMIT-TO (DOCTYPE, “bk”)) AND (LIMIT-TO (LANGUAGE, “English”))

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Hydrolysate and Substrate Fermentation Review Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

30.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2019, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before the bioethanol production through hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and hydrolysates, respectively. Research in the fields of hydrolysate and substrate fermentation has thus intensified in recent years as hydrolysate and substrate fermentation processes (Lin and Tanaka, 2006; Lynd et al., 2005), fermentation inhibitors (Palmqvist and Hahn-Hagerdal, 2000a,b), hydrolysate detoxification (Jonsson et al., 2013; Palmqvist and Hahn-Hagerdal, 2000b), and microorganism and substrate metabolic engineering (Dien et al., 2003; Jeffries, 2006) have been widely researched to increase bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been several review papers in this field (Dien et al., 2003; Jeffries, 2006; Jonsson et al., 2013; Lin and Tanaka, 2006; Lynd et al., 2005; Palmqvist and Hahn-Hagerdal, 2000a,b), there has been no review of the 25 most-cited papers in this field. Thus, this book chapter presents a review of the 25 most-cited articles in the field of hydrolysate and substrate fermentation. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

30.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 300 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. 242

DOI: 10.1201/9781003226499-39

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As a second step, a sample dataset was used for this study. The first 25 articles with at least 293 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, several brief conclusions were drawn and many relevant recommendations were made to enhance future research landscape.

30.3 RESULTS The brief information about the 25 most-cited papers with at least 293 citations each on hydrolysate and substrate fermentation is given below. The primary research fronts are microorganism and substrate metabolic engineering and hydrolysate and substrate fermentation with 12 and 6 highly cited papers (HCPs), respectively. Next, there are four and three HCPs for fermentation inhibitors and hydrolysate detoxification, respectively.

30.3.1  The Hydrolysate and Substrate Fermentation There are six HCPs for hydrolysate and substrate fermentation (Table 30.1). Wingren et al. (2003) carried out the technoeconomic evaluation of producing ethanol from softwood by comparing simultaneous saccharification and fermentation (SSF) and separate hydrolysis and fermentation (SHF) in a paper with 540 citations. They found that the ethanol production costs for the SSF and SHF base cases were 0.57 and 0.63 USD/L, respectively. The main reason for SSF being lower was the lower capital cost and higher overall ethanol yield. A major drawback of the SSF process was the problem with the recirculation of yeast following the SSF step. Major economic improvements in both SSF and SHF could be achieved by increasing the income from the solid fuel coproduct. This was done by lowering the energy consumption in the process by running the enzymatic hydrolysis or the SSF step at a higher substrate concentration and by recycling the process streams. Running SSF with the use of 8% rather than 5% nonsoluble solid material would result in a 19% decrease in production cost. If after distillation 60% of the stillage stream was recycled back to the SSF step, the production cost would be reduced by 14%. The cumulative effect of these various improvements resulted in a production cost of 0.42 USD/L for the SSF process. Harun et al. (2010) explored the suitability of Chlorococum sp. as a substrate for bioethanol production via Saccharomyces bayanus (S. bayanus) under different fermentation conditions in a paper with 395 citations. They obtained a maximum ethanol concentration of 3.83 g/L from 10 g/L of lipid-extracted microalgal residues. This productivity level (∼38% w/w) endorsed this microalga as a promising substrate for bioethanol production. Ballesteros et al. (2004) produced ethanol from steam-pretreated poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, and Brassica carinata (B. carinata) residue by the SSF process with Kluyveromyces marxianus CECT 10875 in a paper with 385 citations. They performed SSF experiments in laboratory conditions at 42°C, 10% (w/v) substrate concentration, and 15 FPU/g substrate of commercial cellulase. They obtained SSF yields in the range of 50%–72% of the maximum theoretical SSF yield, based on the glucose available in pretreated materials, in 72–82 h. Furthermore, they obtained maximum ethanol contents from 16 to 19 g/L in fermentation media, depending on the material tested. Dharmadi et al. (2006) investigated the anaerobic fermentation of glycerol by Escherichia coli (E. coli) in a paper with 333 citations. They observed that E. coli could ferment glycerol in a pHdependent manner. Glycerol fermentation was severely impaired by blocking the activity of enzyme formate hydrogen lyase (FHL). They showed that, unlike CO2, hydrogen had a negative impact on cell growth and glycerol fermentation. In addition, supplementation of the medium with CO2 partially restored the ability of an FHL-deficient strain to ferment glycerol. High pH resulted in low CO2 generation and availability as most CO2 was converted to bicarbonate and consequently very inefficient fermentation of glycerol. Most of the fermented glycerol was recovered in the reduced compounds of ethanol and succinate, which reflected the highly reduced state of glycerol and confirmed the fermentative nature of this process.

244

TABLE 30.1 Hydrolysate and Substrate Fermentation No.

Papers

Biomass/Hydrolysate

Prt.

Bacteria

Wingren et al. (2003)

Softwood

Enzymes

Na

2

Harun et al. (2010)

Chlorococum sp.

Na

S. bayanus

3

Ballesteros et al. (2004)

Cellulase

4

Dharmadi et al. (2006)

Poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, and B. carinata residue Glycerol

5

Cysewski and Wilke (1977

6

Lau and Dale (2009)

Parameters

Keywords

Lead Author

Affil.

Cits

Production costs, SSF, Softwood, SHF, SSF SHF, cost reduction, technoeconomics Microalgal fermentation, Microalgal, biomass, fermentation, ethanol yield bioethanol

Zacchi, Guido 7006727748

Lund Univ. Sweden

540

Harun, Razif 35315707300

395

K. marxianus

SSF, ethanol yield

Na

E. coli

Glycerol fermentation determinants

Na

S. cerevisiae

Corn stover hydrolysates

Ammonia, enzymes

S. cerevisiae

Fermentation system development, ethanol yield Ethanol yield and determinants

Univ. S. Florida USA Cyanotech Corp. USA Michigan State Univ. USA

333

Glucose

Manzanares, Paloma*55779406300 Gonzalez, Ramon 57192167471 Cysewski, Gerald R. 6602949760 Dale, Bruce E 7201511969

Univ. Putra Malaysia Malaysia CSIC Spain

*, female; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

Ethanol, lignocellulosic, simultaneous saccharification and fermentation, SSF, Kluyveromyces Fermentation, glycerol, Escherichia

Ethanol, fermentations

Cellulosic, ethanol, corn stover, Saccharomyces

385

294

293

Bioethanol Fuel Production Processes. II

1

Hydrolysate and Substrate Fermentation: Review

245

Cysewski and Wilke (1977) developed cell recycle and vacuum fermentation systems for continuous ethanol production in a paper with 294 citations. They employed cell recycle in both atmospheric pressure and vacuum fermentations to achieve high cell densities and rapid ethanol fermentation rates. They used S. cerevisiae ATCC No. 4126 at a fermentation temperature of 35°C. Employing a 10% glucose feed, they obtained a cell density of 50 g dry wt/L in atmospheric-cell recycle fermentations, which produced a fermentor ethanol productivity of 29.0 g/L-h. The vacuum fermentor eliminated ethanol inhibition by boiling away ethanol from the fermenting beer as it was formed. This permitted the rapid and complete fermentation of concentrated sugar solutions. At a total pressure of 50 mmHg and using a 33.4% glucose feed, they obtained ethanol productivities of 82 and 40 g/L-h with the vacuum system with and without cell recycle, respectively. Fermentor ethanol productivities were thus increased as much as twelvefold over conventional continuous fermentations. To maintain a viable yeast culture in the vacuum fermentor, a bleed of fermented broth was continuously withdrawn to remove nonvolatile compounds. It was also necessary to sparge the vacuum fermentor with pure oxygen to satisfy the trace oxygen requirement of the fermenting yeast. Lau and Dale (2009) produced ethanol using S. cerevisiae 424A (LNH-ST) from ammonia fiber expansion (AFEX)-treated corn stover in the SHF process in a paper with 293 citations. They obtained 191.5 g ethanol/kg untreated stover, at an ethanol concentration of 40.0 g/L (5.1 vol/vol%) without washing of pretreated biomass, detoxification, or nutrient supplementation. Enzymatic hydrolysis at high solids loading was the primary bottleneck affecting overall ethanol yield and titer. Degradation compounds in AFEX-pretreated biomass increased metabolic yield and specific ethanol production while decreasing cell biomass generation. Nutrients inherently present in stover and those resulting from biomass processing were sufficient to support microbial growth during fermentation.

30.3.2  The Fermentation Inhibitors There are four HCPs for fermentation inhibitors (Table 30.2). Larsson et al. (1999a) studied the effect of the severity of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by S. cerevisiae in a paper with 867 citations. They observed that when the pretreatment severity (CS) of the hydrolysis conditions increased, the yield of fermentable sugars increased to a maximum between CS 2.0 and 2.7 for mannose and 3.0 and 3.4 for glucose above which it decreased. The decrease in the yield of monosaccharides coincided with the maximum concentrations of furfural and 5-hydroxymethylfurfural (5-HMF). With the further increase in CS, the concentrations of furfural and 5-HMF decreased, while the formation of formic acid and levulinic acid increased. The yield of ethanol decreased at approximately CS 3; however, the volumetric productivity decreased at lower CS. Ethanol yield and volumetric productivity decreased with increasing concentrations of acetic acid, formic acid, and levulinic acid. Furfural and 5-HMF decreased the volumetric productivity but did not influence the final yield of ethanol. The decrease in volumetric productivity was more pronounced when 5-HMF was added to the fermentation, and this compound was depleted at a lower rate than furfural. They asserted that the inhibition observed in hydrolysates produced in higher CS could not be fully explained by the effect of the by-products. Delgenes et al. (1996) investigated the inhibitory effects of lignocellulose degradation products on ethanol fermentation of glucose and xylose by S. cerevisiae, Zymomonas mobilis (Z. mobilis), Pichia stipitis (P. stipitis), and Candida shehatae (C. shehatae) in a paper with 417 citations. They observed that vanillin was a strong inhibitor of both growth and ethanol production by xylosefermenting yeasts and S. cerevisiae when it was added to the culture media at a concentration of 1 g/L. Fermentative activities of Z. mobilis were greatly sensitive to the presence of hydroxybenzaldehyde (0.5 g/L). Furthermore, some of the inhibitors, particularly vanillin and furaldehyde, could be assimilated by the tested microbial strains, which resulted in the partial recovery in both growth and ethanol production processes on prolonged incubation.

246

TABLE 30.2 Fermentation Inhibitors No. 1

2

4

Biomass/ Hydrolysate

Larsson et al. Spruce, mannose, (1999a) glucose hydrolysates Delgenes Glucose, xylose et al. (1996) Zaldivar Hydrolysates et al. (1999) Palmqvist Na et al. (1999)

Prt.

Bacteria

H2SO4

S. cerevisiae

Na

S. cerevisiae, Z. mobilis, P. stipitis, C. shehatae E. coli

Na Na

S. cerevisiae, C. shehatae

Parameters Fermentation inhibitors, pretreatment severity, hydrolysate fermentability Fermentation inhibitors, fermenting bacteria, hydrolysate fermentation Fermentation inhibitors, fermentation Fermentation inhibitors, fermentation, ethanol yield

Keywords Softwood, fermentation, inhibitors

Lignocellulose, ethanol fermentations, glucose, xylose, Saccharomyces, Zymomonas, Pichia, Candida Fermentation, ethanologenic Escherichia Ethanol, yeasts

*, female; Cits., number of citations received for each paper; Na: nonavailable; Prt: biomass pretreatments.

Lead Author Hahn-Hagerdal, Barbel* 7005389381 Delgenes, Jean P. 7005849678 Ingram, Lonnie O. 7102962097 Hahn-Hagerdal, Barbel* 7005389381

Affil.

Cits

Lund Univ. Sweden

867

Univ. Montpellier France Univ. Florida USA Lund Univ. Sweden

417

356 351

Bioethanol Fuel Production Processes. II

3

Papers

Hydrolysate and Substrate Fermentation: Review

247

Zaldivar et al. (1999) investigated the toxicity of representative aldehydes (furfural, 5-HMF, 4-hydroxybenzaldehyde, syringaldehyde, and vanillin) as inhibitors of growth and ethanol production by ethanologenic derivatives of E. coli B (strains KO11 and LY01) in a paper with 356 citations. They observed that aromatic aldehydes were at least twice as toxic as furfural or HMF on a weight basis. The toxicities of all aldehydes (and ethanol) except furfural were additive when tested in binary combinations. In all cases, combinations with furfural were unexpectedly toxic. Although the potency of these aldehydes was directly related to hydrophobicity indicating a hydrophobic site of action, none caused sufficient membrane damage to allow the leakage of intracellular magnesium even when present at sixfold the concentrations required for growth inhibition. Of the aldehydes tested, only furfural strongly inhibited ethanol production in vitro. Palmqvist et al. (1999) investigated the effect of acetic acid, furfural, and p-hydroxybenzoic acid on ethanol yield (YEtOH) of S. cerevisiae, bakers’ yeast, S. cerevisiae ATCC 96581, and C. shehatae NJ 23 using a 23-full factorial design with three centerpoints in a paper with 351 citations. They observed that acetic acid inhibited the fermentation by C. shehatae NJ 23 markedly more than by bakers’ yeast, whereas there was no significant difference in tolerance toward the compounds between the S. cerevisiae strains. Furfural (2 g/L) and the lignin-derived compound p-hydroxybenzoic acid (2 g/L) did not affect any of the yeasts at the cell mass concentration used. Furfural concentrations up to 2 g/L stimulated Yx in the absence of acetic acid, whereas higher concentrations decreased Yx. Furfural and acetic acid interacted negatively on Yx and YEtOH. Acetic acid concentrations up to 9 g/L stimulated ethanol production rate (Q EtOH), whereas furfural (0–3 g/L) decreased QEtOH. Acetic acid in concentrations up to 10 g/L stimulated YEtOH in the absence of furfural, and furfural (0–2 g/L) slightly increased YEtOH in the absence of acetic acid, whereas higher concentrations caused inhibition. Acetic acid and furfural interacted negatively on YEtOH.

30.3.3  The Hydrolysate Detoxification There are three HCPs for the hydrolysate detoxification (Table 30.3). Saha et al. (2005) investigated the dilute acid pretreatment, enzymatic saccharification, and fermentation of wheat straw cellulose and hemicellulose to ethanol in a paper with 646 citations. They observed that the maximum yield of monomeric sugars from wheat straw (7.83%, w/v, DS) by dilute H2SO4 (0.75%, v/v) pretreatment and enzymatic saccharification (45°C, pH 5.0, 72 h) using cellulase, β-glucosidase, xylanase, and esterase was 565 mg/g. Under this condition, no measurable quantities of furfural and hydroxymethyl furfural were produced. The yield of ethanol (per liter) from acid-pretreated enzyme saccharified wheat straw (78.3 g) hydrolysate by recombinant E. coli strain FBR5 was 19 g with a yield of 0.24 g/g dry substance content (DS). Detoxification of the acid and enzyme-treated wheat straw hydrolysate by overliming reduced the fermentation time from 118 to 39 h in the case of SHF (35°C, pH 6.5), increased the ethanol yield from 13 to 17 g/L, and decreased the fermentation time from 136 to 112 h in the case of SSF (35°C, pH 6.0). Larsson et al. (1999b) compared different methods for the detoxification of hydrolysates of dilute acid-pretreated spruce to improve both cell growth and ethanol production by S. cerevisiae in a paper with 424 citations. These detoxification methods included treatment with alkali (NaOH or calcium hydroxide), treatment with sulfite (0.1% [w/v] or 1% [w/v] at pH 5.5 or 10), evaporation of 10% or 90% of the initial volume, anion exchange (at pH 5.5 or 10), enzymatic detoxification with the phenoloxidase laccase, and detoxification with Trichoderma reesei (T. reesei). They observed that an ion exchange at pH 5.5 or 10, treatment with laccase, treatment with calcium hydroxide, and treatment with T. reesei were the most efficient detoxification methods, while the evaporation of 10% of the initial volume and treatment with 0.1% sulfite were the least efficient detoxification methods. Treatment with laccase was the only detoxification method that specifically removed only phenolic compounds. Anion exchange at pH 10 was the most efficient method for removing all three major groups of inhibitory compounds: aliphatic acids, furan derivatives, and phenolic compounds. However, it also resulted in the loss of fermentable sugars.

248

TABLE 30.3 Hydrolysate Detoxification No. 1

2

Biomass/Hydrolysate

Wheat straw cellulose, hemicellulose hydrolysates Larsson et al. Spruce hydrolysates (1999b) Chandel et al. Sugarcane bagasse (2007) hydrolysates

Prt.

Bacteria

Parameters

H2SO4, cellulase, E. coli Sugar and ethanol yield, β-glucosidase, xylanase, hydrolysate detoxification, and esterase SSF Acids S. cerevisiae Detoxification methods, inhibitory compounds HCl C. shehatae Hydrolysate detoxification, fermentation inhibitors, ethanol yield

Cits., number of citations received for each paper; Na: nonavailable; Prt, biomass pretreatments.

Keywords

Lead Author

Wheat straw, fermentation, ethanol

Saha, Badal C. 7202946302

Detoxification, hydrolyzates, lignocellulose, spruce Detoxification, sugarcane, bagasse, hydrolysate, ethanol, Candida

Jonsson, Leif J. 7102349315 Kuhad, Ramesh C. 55663451900

Affil. USDA Agr. Res. Serv. USA Lund Univ. Sweden Central Univ. Haryana India

Cits 646

424 320

Bioethanol Fuel Production Processes. II

3

Papers Saha et al. (2005)

Hydrolysate and Substrate Fermentation: Review

249

Chandel et al. (2007) detoxified sugarcane bagasse hydrolysate to increase ethanol yield by C. shehatae National Collection of Industrial Microorganisms (NCIM) 3501 in a paper with 320 citations. They observed that the hydrolysis with 2.5% (v/v) HCl yielded 30.29 g/L total reducing sugars along with various fermentation inhibitors such as furans, phenolics, and acetic acid. They then treated the acid hydrolysate with anion exchange resin with about maximum reduction in furans (63.4%) and total phenolics (75.8%). Treatment of hydrolysate with activated charcoal caused 38.7% and 57.5% reduction in furans and total phenolics, respectively. Finally, laccase reduced total phenolics (77.5%) without affecting furans and acetic acid content in the hydrolysate. Fermentation of these hydrolysates with C. shehatae showed maximum ethanol yield (0.48 g/g) from anion exchange-treated hydrolysate, followed by activated charcoal (0.42 g/g), laccase (0.37 g/g), overliming (0.30 g/g), and neutralized hydrolysate.

30.3.4  The Microorganism and Substrate Metabolic Engineering There are 12 HCPs for the microorganism and substrate metabolic engineering (Table 30.4). Alper et al. (2006) showed the application of global transcription machinery engineering (gTME) to S. cerevisiae for improved glucose/ethanol tolerance in a paper with 633 citations. Mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype resulted from the combined effect of three separate mutations in the SPT15 gene. They asserted that gTME could provide a route to complex phenotypes that were not readily accessible by traditional methods. Zhang et al. (1995) engineered Z. mobilis metabolically to broaden its range of fermentable substrates to include xylose. For this purpose, they constructed two operons encoding xylose assimilation and pentose phosphate pathway enzymes and transformed them into Z. mobilis to generate a strain that grew on xylose and efficiently fermented it to ethanol. Thus, they performed anaerobic fermentation of xylose to ethanol through a combination of the pentose phosphate and Entner– Doudoroff pathways. Furthermore, this strain efficiently fermented both glucose and xylose. Fu et al. (2011) showed that genetic modification of switchgrass could produce phenotypically normal plants that had reduced thermal–chemical (≤180°C), enzymatic, and microbial recalcitrance in a paper with 512 citations. Downregulation of the switchgrass caffeic acid O-methyltransferase gene decreased lignin content modestly, reduced the syringyl:guaiacyl lignin monomer ratio, improved forage quality, and increased the ethanol yield by up to 38% using conventional biomass fermentation processes. The downregulated lines required less severe pretreatment and 300%–400% lower cellulase dosages for equivalent product yields using SSF with yeast. Furthermore, fermentation of diluted acid-pretreated transgenic switchgrass using Clostridium thermocellum with no added enzymes showed better product yields than those obtained with unmodified switchgrass. Ho et al. (1998) genetically engineered S. spp. for the cofermentation of glucose and xylose in a paper with 467 citations. They developed recombinant plasmids that could transform S. spp. into xylose-fermenting yeasts. These plasmids, designated pLNH31, pLNH32, pLNH33, and pLNH34, were 2 μm-based high-copy-number yeast-E. coli shuttle plasmids. In addition to the geneticin resistance and ampicillin resistance genes that served as dominant selectable markers, these plasmids also contained three xylose-metabolizing genes, a xylose reductase (XR) gene, a xylitol dehydrogenase (XDH) gene (both from P. stipitis), and a xylulokinase (XK) gene (from S. cerevisiae). These xylose-metabolizing genes were also fused to signals controlling gene expression from S. cerevisiae glycolytic genes. The transformation of S. sp. strain 1400 with each of these plasmids resulted in the conversion of strain 1400 from a non-xylose-metabolizing yeast to a xylose-metabolizing yeast that could effectively ferment xylose to ethanol and also effectively utilized xylose for aerobic growth. Kotter and Ciriacy (1993) studied the xylose fermentation by S. cerevisiae transformants expressing two key enzymes in xylose metabolism, XR, and XDH and in P. stipites in a paper with 422 citations. In the absence of respiration, they observed that S. cerevisiae cells converted half of the xylose to xylitol and ethanol, whereas P. stipitis cells displayed rather a homofermentative conversion of xylose to ethanol. Xylitol production by S. cerevisiae was a result of the dual cofactor dependence

No.

Papers

Biomass/ Hydrolysate

Prt.

Bacteria

250

TABLE 30.4 Microorganism and Substrate Metabolic Engineering Keywords

Lead Author

1

Alper et al. (2006)

Glucose

Na

S. cerevisiae

Microbial engineering, glucose fermentation

Parameters

Engineering, yeast, ethanol, Saccharomyces

Zhang et al. (1995)

Xylose

Na

Z. mobilis

Microbial engineering, xylose fermentation

3

Fu et al. (2011)

Switchgrass

Ho et al. (1998)

Glucose, xylose

Dixon, Richard A. 7402020530 Ho, Nancy W. Y.* 7102776244

Univ. N. Texas USA Purdue Univ.

512

4

Acids, C. thermocellum Microbial engineering, switchgrass cellulases fermentation, ethanol yield Na S. spp. Microbial engineering, glucose, and xylose fermentation

5

Kotter and Ciriacy (1993)

Xylose

Metabolic, engineering, pentose, ethanologenic, Zymomonas Genetic, lignin, ethanol, switchgrass Saccharomyces, cofermentation, glucose, xylose Xylose, fermentation, Saccharomyces

Massachusetts Inst. Technol. USA Verdezyne Inc. USA

633

2

Stephanopoulos, Gregory 24527470500 Picataggio, Stephen 6603484986

422

6

Goethe Univ. Frankfurt Germany Genetic, Escherichia, Ingram, Lonnie O. Univ. Florida ethanol, Zymomonas, genes 7102962097 USA Lignocellulose, xylose, Jeffries, Thomas W. Xylome Corp. fermenting, yeast, Pichia 7005806269 USA

S. cerevisiae, P. stipites

Microbial engineering, xylose fermentation

Ohta et al. (1991) Xylose, glucose

Na

7

Jeffries et al. (2007)

Na

E. coli, Z. mobilis P. stipites, S. cerevisiae

8

Ingram et al. Glucose Na (1987) Deng and Synechococcus sp. Na Coleman (1999) Eliasson et al. Xylose Na (2000)

E. coli, Z. mobilis Z. mobilis

Microbial engineering, glucose, and xylose fermentation Microbial engineering, xylose metabolism and fermentation, genome organization Microbial engineering, glucose Genetic, engineering, fermentation ethanol, Escherichia Microbial engineering, Ethanol, genetic, Synechococcus fermentation engineering, cyanobacteria Microbial engineering, ethanol yield, Xylose, fermentation, and determinants Saccharomyces

11

Kuyper et al. (2005)

Xylose

Na

S. cerevisiae, P. sp.

Microbial engineering, xylose fermentation

12

Alexandre et al. (2001)

Na

Na

S. cerevisiae

Microbial engineering, ethanol stress Gene, ethanol, Saccharomyces

9 10

Xylose

S. cerevisiae

*, female; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments..

Xylose, Saccharomyces, fermentation

Kotter, Peter 6603848645

Ingram, Lonnie O. 7102962097 Coleman, John R. 7402803364 Hahn-Hagerdal, Barbel* 7005389381 Pronk, Jack T. 7005313057 Blondin, Bruno 35610971500

Univ. Florida USA Univ. Toronto Canada Lund Univ. Sweden

Cits

549

467

400 382

378 339 327

Delft Univ. 325 Technol. Netherlands Univ. Montpellier 295 France

Bioethanol Fuel Production Processes. II

Na

Affil.

Hydrolysate and Substrate Fermentation: Review

251

of the XR and the generation of nicotinamide adenine dinucleotide phosphate (NADPH) by the pentose phosphate pathway. Further limitations of xylose utilization in S. cerevisiae cells were caused by an insufficient capacity of the non-oxidative pentose phosphate pathway, as indicated by the accumulation of sedoheptulose-7-phosphate and the absence of fructose-1,6-bisphosphate and pyruvate accumulation. By contrast, uptake at high substrate concentrations probably did not limit xylose conversion in S. cerevisiae XYL1/XYL2 transformants. Ohta et al. (1991) integrated Z. mobilis genes for pyruvate decarboxylase (PDC) and alcohol dehydrogenase II (adhB) into the E. coli chromosome within or near the pyruvate formate-lyase gene (PFL). They observed that the integration improved the stability of the Z. mobilis genes in E. coli, but further selection was required to increase expression. They selected spontaneous mutants for resistance to high level of chloramphenicol that also expressed high levels of the Z. mobilis genes. They then selected analogous mutants for increased expression of adhB on aldehyde indicator plates. These mutants were functionally equivalent to the previous plasmid-based strains for fermentation of xylose and glucose to ethanol. They obtained ethanol concentrations of 54.4 and 41.6 g/L from 10% glucose and 8% xylose, respectively. The efficiency of conversion exceeded theoretical limits (0.51 g of ethanol/g of sugar) based on added sugars because of the additional production of ethanol from the catabolism of complex nutrients. They finally introduced further mutations to inactivate succinate production and block homologous recombination. Jeffries et al. (2007) characterized the mechanism and regulation of xylose metabolism in P. stipitis and used genes from P. stipitis to engineer xylose metabolism in S. cerevisiae in a paper with 382 citations. They sequenced and assembled the complete genome of P. stipitis. They observed unusual aspects of genome organization, numerous genes for bioconversion, a preliminary insight into the regulation of central metabolic pathways, and several examples of co-localized genes with related functions. Furthermore, the genome sequence provided insight into how P. stipitis regulated its redox balance while very efficiently fermenting xylose under microaerobic conditions. Ingram et al. (1987) inserted the genes encoding essential enzymes of the fermentative pathway for ethanol production in Z. mobilis into E. coli under the control of a common promoter in a paper with 378 citations. They observed that adhB II and PDC from Z. mobilis were expressed at high levels in E. coli, resulting in increased cell growth and the production of ethanol as the principal fermentation product from glucose. They asserted that it was possible to change the fermentation products of an organism, such as E. coli, by the addition of genes encoding appropriate enzymes, which formed an alternative system for the regeneration of Nicotinamide adenine dinucleotide+ (NAD+). Deng and Coleman (1999) introduced new genes into Synechococcus sp. to create a novel pathway for fixed carbon utilization, which resulted in the synthesis of ethanol in a paper with 339 citations. They cloned the coding sequences of PDC and adhB II from Z. mobilis into the shuttle vector pCB4 and then used them to transform S. sp. strain PCC 7942. Under control of the promoter from the rbcLS operon encoding the cyanobacterial ribulose-l,5-bisphosphate carboxylase/ oxygenase (RuBisCo), they observed that the PDC and adh genes were expressed at high levels. The transformed S. sp. synthesized ethanol, which diffused from the cells into the culture medium. Eliasson et al. (2000) constructed a stable xylose-utilizing recombinant strain TMB 3001 for anaerobic xylose fermentation in a paper with 327 citations. They integrated the XYL1 and XYL2 genes from P. stipitis, encoding xylose reductase (XR) and xylitol dehydrogenase (XDH), respectively, and the endogenous XKS1 gene, encoding XK, under control of the PGK1 promoter into the chromosomal HIS3 locus of S. cerevisiae CEN.PK 113–7A. The strain expressed XR, XDH, and XK activities of 0.4–0.5, 2.7–3.4, and 1.5–1.7 U/mg, respectively, and was stable for more than 40 generations in continuous fermentations. They showed anaerobic ethanol formation from xylose by recombinant S. cerevisiae for the first time. However, the strain grew on xylose only in the presence of oxygen. They obtained ethanol yields of 0.45–0.50 mmol of C/mmol of C (0.35–0.38 g/g) and productivities of 9.7–13.2 mmol of C/h g (dry weight) of cells−1 (0.24–0.30 g/h g [dry weight] of cells−1) from xylose–glucose mixtures in anaerobic chemostat cultures, with a dilution rate of 0.06 h−1. They estimated the anaerobic ethanol yield on xylose at 0.27 mol of C/(mol of C of xylose)

252

Bioethanol Fuel Production Processes. II

(0.21 g/g), assuming a constant ethanol yield on glucose. The xylose uptake rate increased with increasing xylose concentration in the feed, from 3.3 mmol of C/h g (dry weight) of cells−1 when the xylose-to-glucose ratio in the feed was 1:3 to 6.8 mmol of C/h g (dry weight) of cells−1 when the feed ratio was 3:1. With a feed content of 15 g of xylose/L and 5 g of glucose/L, the xylose flux was 2.2 times lower than the glucose flux, indicating that transport limits the xylose flux. Kuyper et al. (2005) engineered a xylose-isomerase-expressing S. cerevisiae strain for rapid anaerobic xylose fermentation in a paper with 325 citations. They observed that S. cerevisiae strains that expressed the xylose isomerase gene from Piromyces sp. E2 could grow anaerobically on xylose with a μmax of 0.03 h−1. They overexpressed structural genes for all enzymes involved in the conversion of xylulose to glycolytic intermediates, in a xylose-isomerase-expressing S. cerevisiae strain. The overexpressed enzymes were XK, ribulose 5-phosphate isomerase, ribulose 5-phosphate epimerase, transketolase, and transaldolase. In addition, the GRE3 gene encoding aldose reductase was deleted to further minimize xylitol production. Unexpectedly, the resulting strain grew anaerobically on xylose in synthetic media with a μmax as high as 0.09 h−1 without any nondefined mutagenesis or selection. During growth of xylose, xylulose formation was absent and xylitol production was negligible. The specific xylose consumption rate in anaerobic xylose cultures was 1.1 g xylose/ (g biomass)/h. Mixtures of glucose and xylose were sequentially but completely consumed by anaerobic batch cultures, with glucose as the preferred substrate. Alexandre et al. (2001) investigated the global gene expression during short-term ethanol stress in S. cerevisiae in a paper with 295 citations. They used DNA microarrays to investigate the expression profile of yeast genes in response to ethanol in a paper with 295 citations. They observed that up to 3.1% of the genes encoded in the yeast genome were upregulated by at least a factor of 3 after 30-min ethanol stress (7% v/v). Concomitantly, 3.2% of the genes were downregulated by a factor of 3. Of the genes upregulated in response to ethanol, 49.4% belonged to the environmental stress response and 14.2% belong to the stress gene family. They showed that in addition to the previously identified ethanol-induced genes, a very large number of genes involved in ionic homeostasis, heat protection, trehalose synthesis, and antioxidant defense also responded to ethanol stress. A large number of the upregulated genes were involved in energy metabolism. Thus, the management of the energy pool (especially ATP) constituted an ethanol stress response and involved different mechanisms.

30.4 DISCUSSION 30.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol before the bioethanol production through the hydrolysis and fermentation of the biomass. Research in the fields of hydrolysate and substrate fermentation has thus intensified in recent years as hydrolysate and substrate fermentation processes, fermentation inhibitors, hydrolysate detoxification, and microorganism and substrate metabolic engineering have been widely researched to increase the sugar and bioethanol yield in this context in recent years. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance research in this field. Although there have been several review papers for this field, there has been no review of the 25 most-cited articles in this field. Thus, this book chapter presents a review of the 25 most-cited articles in this field. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

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As a first step for the search of the relevant literature, the keywords were selected using the first 300 most-cited population papers. The selected keyword list was optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 293 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance future research landscape. The primary research fronts are microorganism and substrate metabolic engineering and hydrolysate inhibitors with 48 and 24% of the 25 HCPs, respectively (Table 30.5). Next, hydrolysate detoxification and fermentation in general are the other prolific research fronts with 12% of the 25 HCPs each. It is notable that microorganism and substrate metabolic engineering, fermentation inhibitors, and fermentation in general are overrepresented in the 25 HCPs with 14%, 11%, and 8% surplus, respectively. Table 30.6 provides information on the biomass and hydrolysates used in these 25 HCPs. There are three primary types of the biomass: hydrolysates, agricultural residues, and wood with 68%, 24%, and 16% of the 25 HCPs, respectively. Furthermore, the other front is biomass in general with 4% of the reviewed papers. The other minor research fronts are biomass constituents, algae, glycerol, and grass with 4% of the 25 HCPs each. Xylose, glucose, and hydrolysates in general are the primary hydrolysates with 32%, 28%, and 24% of the 25 HCPs, respectively. Furthermore, wheat straw is the primary agricultural residue with 8% of the 25 HCPs. It is notable that the hydrolysates, agricultural residues, and wood are overrepresented with 23%, 11%, and 13% surplus, respectively. Furthermore, on an individual basis, hydrolysates in general, glucose, xylose, and other agricultural residues are overrepresented with 20%, 18%, 12%, and 6% surplus, respectively.

30.4.2  The Hydrolysate and Substrate Fermentation There are six HCPs for hydrolysate and substrate fermentation in general (Table 30.1). Wingren et al. (2003) carried out the technoeconomic evaluation of producing ethanol from softwood by comparing SSF and SHF processes and found that the ethanol production costs for the SSF and SHF scenarios were 0.57 and 0.63 USD/L, respectively. TABLE 30.5 Most Prolific Research Fronts for Hydrolysate and Substrate Fermentation No. 1 2 3 4 5 6 7 8 9

Research Fronts

N Paper (%) Review

N Paper (%) Sample

Surplus

Microorganism and substrate metabolic engineering Fermentation inhibitors Hydrolysate detoxification Fermentation in general SSF Fermentation microorganisms Other fermentation issues Microorganism ethanol tolerance Consolidated bioprocessing

48.0 24.0 12.0 12.0 8.0 4.0 4.0 4.0 0.0

34.1 13.2 7.1 4.4 8.2 5.5 4.4 3.3 3.8

13.9 10.8 4.9 7.6 –0.2 –1.5 –0.4 0.7 −3.8

N paper (%) review, the number of papers in the sample of 25 most-cited papers; N paper (%) sample, the number of papers in the population sample of 182 papers.

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TABLE 30.6 Most Prolific Research Fronts for the Biomass Used for Hydrolysate and Substrate Fermentation No. 1

2

Research Fronts

N Paper (%) Review

N Paper (%) Sample

Hydrolysates Xylose

68.0 32.0

45.1 20.3

22.9 11.7

Glucose

28.0

10.4

17.6

Hydrolysates in general

24.0

4.4

19.6

Pentose

0.0

4.4

–4.4

Hexose

0.0

2.7

–2.7

Lactose

0.0

1.1

–1.1

Arabinose

0.0

1.1

–1.1

Cellobiose

0.0

0.5

–0.5

24.0 8.0

12.6 2.2

11.4 5.8

Other agr. residues

8.0

1.6

6.4

Corn stover

4.0

3.3

0.7

Sugarcane bagasse

4.0

1.6

2.4

Corn silage

0.0

2.7

–2.7

Agricultural residues Wheat straw

Rice straw 3 4

5 6 7 8 9 10

Surplus (%)

0.0

1.1

–1.1

16.0 4.0 4.0

3.3 5.5 4.4

12.7 –1.5 –0.4

Lignin

0.0

1.1

–1.1

Algae Glycerol Grass Biosyngas Sugar feedstocks Other biomass Starch feedstocks

4.0 4.0 4.0 0.0 0.0 0.0 0.0

4.4 3.8 2.2 3.3 2.2 2.7 1.1

–0.4 0.2 1.8 –3.3 –2.2 –2.7 –1.1

Food waste

0.0

0.5

–0.5

Industrial waste

0.0

0.5

–0.5

Water hyacinth

0.0

0.5

–0.5

Wood Biomass constituents Cellulose

N paper (%) sample, the number of papers in the population sample of 182 papers; N paper (%) review, the number of papers in the sample of 25 most-cited papers.

Harun et al. (2010) explored the suitability of Chlorococum sp. as a substrate for bioethanol production via S. bayanus under different fermentation conditions and obtained a maximum ethanol concentration of 3.83 g/L from 10 g/L of lipid-extracted microalgae debris. However, Dharmadi et al. (2006) investigated the anaerobic fermentation of glycerol by E. coli and observed that E. coli could ferment glycerol in a pH-dependent manner. Ballesteros et al. (2004) produced ethanol from steam-pretreated poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, and B. carinata residue by an SSF with K. marxianus CECT 10875 and obtained SSF yields in the range of 50%–72% of the maximum theoretical SSF yield. Furthermore, Cysewski and Wilke (1977) developed cell recycle and vacuum fermentation systems for continuous ethanol production and employing a 10% glucose feed and obtained a fermentor ethanol productivity of 29.0 g/L-h. However, Lau and Dale (2009) produced ethanol using S. cerevisiae from

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AFEX-treated corn stover in SHF and obtained 191.5 g EtOH/kg untreated stover, at an ethanol concentration of 40.0 g/L (5.1 vol/vol%). These HCPs show a sample of research on hydrolysate and substrate fermentation in general using several microorganisms. The agricultural residues, glycerol, wood, algae, and glucose are used in these studies as a substrate. The study by Wingren et al. (2003) highlights the awareness of the research community on the technoeconomic aspects of fermentation processes.

30.4.3  The Fermentation Inhibitors There are four HCPs for fermentation inhibitors (Table 30.2). Larsson et al. (1999a) studied the effect of the severity of dilute sulfuric acid hydrolysis of spruce on sugar yield and the fermentability of the hydrolysate by S. cerevisiae and observed that the decrease in the yield of monosaccharides coincided with the maximum concentrations of furfural and 5-HMF, the key fermentation inhibitors. However, Delgenes et al. (1996) investigated the inhibitory effects of lignocellulose degradation products on ethanol fermentation of glucose and xylose by S. cerevisiae, Z. mobilis, P. stipitis, and C. shehatae and observed that vanillin was a strong inhibitor of both growth and ethanol production by xylose-fermenting yeasts and S. cerevisiae. Zaldivar et al. (1999) investigated the toxicity of representative aldehydes as inhibitors of growth and ethanol production by ethanologenic derivatives of E. coli B and observed that aromatic aldehydes were at least twice as toxic as furfural or HMF on a weight basis. However, Palmqvist et al. (1999) investigated the effect of acetic acid, furfural, and p-hydroxybenzoic acid on the Y EtOH of S. cerevisiae, bakers’ yeast, S. cerevisiae ATCC 96581, and C. shehatae NJ 23 and observed that acetic acid inhibited the fermentation by C. shehatae NJ 23 markedly more than by bakers’ yeast, whereas there was no significant difference in tolerance toward the compounds between the S. cerevisiae strains. These HCPs show a sample of research on the fermentation inhibitors produced during the biomass hydrolysis using several microorganisms. The studied inhibitors included furfural, 5-HMF, 4-hydroxybenzaldehyde, syringaldehyde, vanillin, acetic acid, p-hydroxybenzoic acid, hydroxybenzaldehyde, and furaldehyde. These studies highlight the importance of these inhibitors in fermentation processes with a strong impact on ethanol yield.

30.4.4  The Hydrolysate Detoxification There are three HCPs for the hydrolysate detoxification (Table 30.3). Saha et al. (2005) investigated the dilute acid pretreatment, enzymatic saccharification, and fermentation of wheat straw cellulose and hemicellulose to ethanol and observed that the detoxification of the acid and enzyme-treated wheat straw hydrolysate by overliming reduced the fermentation time and increased the ethanol yield. However, Larsson et al. (1999b) compared different methods for the detoxification of hydrolysates of dilute acid-pretreated spruce to improve both cell growth and ethanol production by S. cerevisiae and observed that an ion exchange at pH 5.5 or 10, treatment with laccase, treatment with calcium hydroxide, and treatment with T. reesei were the most efficient detoxification methods, while the evaporation of 10% of the initial volume and treatment with 0.1% sulfite were the least efficient detoxification methods. Furthermore, Chandel et al. (2007) detoxified sugarcane bagasse hydrolysate to increase ethanol yield by C. shehatae and treated the acid hydrolysate with anion exchange resin with about maximum reduction in furans (63.4%) and total phenolics (75.8%). These HCPs show a sample of research on hydrolysate detoxification to mitigate the adverse effects of the fermentation inhibitors on ethanol fermentation and ethanol yield. The studied detoxification methods included overliming, treatment with alkali, treatment with sulfite, evaporation of some of the initial volume, anion exchange, and enzymatic detoxification with the phenoloxidase

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laccase, detoxification with T reesei, anion exchange resin, and activated charcoal. These studies highlight the importance of hydrolysate detoxification in fermentation processes with a strong impact on ethanol yield.

30.4.5  The Microorganism and Substrate Metabolic Engineering There are 12 HCPs for the microorganism and substrate metabolic engineering (Table 30.4). Alper et al. (2006) showed the application of the gTME to S. cerevisiae for improved glucose/ ethanol tolerance and observed that the mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. However, Zhang et al. (1995) engineered Z. mobilis metabolically to broaden its range of fermentable substrates to include xylose. Fu et al. (2011) showed that genetic modification of switchgrass could produce phenotypically normal plants that had reduced thermal–chemical, enzymatic, and microbial recalcitrance. However, Ho et al. (1998) genetically engineered S. spp. for the cofermentation of glucose and xylose. However, Kotter and Ciriacy (1993) studied the xylose fermentation by S. cerevisiae transformants expressing two key enzymes in xylose metabolism, xylose reductase, and xylitol dehydrogenase and in P. stipites. Ohta et al. (1991) integrated Z. mobilis genes for PDC and adhB II into the E. coli chromosome within or near the pyruvate formate-lyase gene. However, Jeffries et al. (2007) characterized the mechanism and regulation of xylose metabolism in P. stipitis and used genes from P. stipitis to engineer xylose metabolism in S. cerevisiae. Ingram et al. (1987) inserted the genes encoding essential enzymes of the fermentative pathway for ethanol production in Z. mobilis into E. coli under the control of a common promoter. However, Deng and Coleman (1999) introduced new genes into Synechococcus sp. to create a novel pathway for fixed carbon utilization, which resulted in the synthesis of ethanol. Eliasson et al. (2000) constructed a stable xylose-utilizing recombinant strain TMB 3001 for anaerobic xylose fermentation. However, Kuyper et al. (2005) engineered a xylose-isomerase-expressing S. cerevisiae strain for rapid anaerobic xylose fermentation of xylose. Furthermore, Alexandre et al. (2001) investigated the global gene expression during short-term ethanol stress in S. cerevisiae. These HCPs show a sample of research on the metabolic engineering of the substrates (switchgrass and algae) and hydrolysates (glucose, xylose) to improve fermentation processes for improved ethanol yield. These studies highlight the importance of metabolic engineering in fermentation processes with a strong impact on ethanol yield.

30.5  CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 293 citations each is given under two primary headings: the microorganism and substrate metabolic engineering and hydrolysate and substrate fermentation. Next, the other two minor research fronts are fermentation inhibitors and hydrolysate detoxification. The usual characteristics of these HCPs are that fermentation processes build heavily on the biomass pretreatments and hydrolysis and metabolic engineering of the substrates and microorganisms is a crucial research front to improve fermentation processes for improved ethanol yield. The acceptance of the adverse effects of the fermentation inhibitors, produced during the biomass hydrolysis phase, and mitigated by the various detoxification methods are also significant milestones in improving the ethanol yield. The key findings on these research fronts should be read in light of the increasing public concerns about climate change, greenhouse gas (GHG) emissions, and global warming as these concerns have been certainly behind the boom in research on bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. The recent supply shocks caused

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by the coronavirus disease 2019 (COVID-19) pandemics and the Russian invasion of Ukraine also highlight the importance of the efficient production and utilization of bioethanol fuels as an alternative to crude oil-based gasoline and diesel fuels. As Table 30.5 shows, the primary research fronts are microorganism and substrate metabolic engineering and fermentation inhibitors and to a lesser extent hydrolysate detoxification and fermentation in general. It is notable that microorganism metabolic engineering, fermentation inhibitors, and fermentation in general are overrepresented. Similarly, as Table 30.6 shows there are three primary types of the biomass used in fermentation processes: hydrolysates, agricultural residues, and wood. The other minor fronts are biomass in general, biomass constituents, algae, glycerol, and grass. Furthermore, xylose, glucose, and hydrolysates in general are the primary hydrolysates. It is notable that hydrolysates, agricultural residues, and wood are overrepresented, while on an individual basis, hydrolysates in general, glucose, xylose, and other agricultural residues are overrepresented. These studies emphasize the importance of proper incentive structures for the efficient development and application of fermentation of the substrates and hydrolysates to enhance bioethanol yield of the substrates and hydrolysates in the light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the USA, China, Canada, and Europe had developed strong incentive structures for the effective development and application of fermentation processes for bioethanol production. In light of the supply shocks caused primarily by the COVID-19 pandemic and the Russian invasion of Ukraine, it is expected that the incentive structures such as public funding would be enhanced to increase the share of bioethanol fuels in the global fuel portfolio and a strong alternative to crude oil-based gasoline and diesel fuels. It is recommended that such review studies are performed for the primary research fronts of fermentation processes and substrates and hydrolysates.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of fermentation processes has been gratefully acknowledged.

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31

The Alternative Fermentation Processes for the Bioethanol Production Scientometric Study Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

31.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), coronavirus disease 2019 (COVID-19) pandemic (Fauci et al., 2020; Li et al., 2020), or wars (Hamilton, 1983; Jones, 2012) in the aftermath of the Russian invasion of Ukraine (Reeves, 2014). However, it is necessary to pretreat the biomass (Taherzadeh and Karimi, 2008; Yang and Wyman, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before the bioethanol production through the hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. Research in the field of alternative fermentation processes for bioethanol production for the separate hydrolysis and fermentation (SHF) to improve the ethanol yield has intensified in this context in recent years (Alfani et al., 2000; Hinman et al., 1992). The key research front has been the simultaneous saccharification and fermentation (SSF) of the biomass (Ballesteros et al., 2004; Olofsson et al., 2008). The other related research fields have been the consolidated bioprocessing (CBP) of the biomass (Lynd et al., 2005, Olson et al., 2012), continuous ethanol production (CEP) of the biomass (Brethauer and Wyman, 2010), simultaneous saccharification and cofermentation (SSCF) of the biomass (Gubicza et al., 2016), and continuous ethanol fermentation (CEF) (Georgieva and Ahring, 2007). Furthermore, these innovative research fields have focused on wood (Ballesteros et al., 2004), rice straw (Ko et al., 2009), wheat straw (Alfani et al., 2000), corn stover (Ohgren et al., 2006), and other biomass such as algae and grass (Ballesteros et al., 2004). However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). 260

DOI: 10.1201/9781003226499-40

Alternative Fermentation Processes: Scientometric Study

261

As the recently published scientometric studies focus on the fermentation processes in general (Calvo et al., 2022; Devos and Colla, 2022), this book chapter presents a scientometric study of research in alternative fermentation processes for bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts.

31.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in June 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix for future replicative studies. As a second step, two sets of data were used for this study. First, a population sample of 1,067 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 107 most-cited papers, corresponding to 10% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for alternative fermentation processes for bioethanol production. Additionally, several brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape.

31.3 RESULTS 31.3.1 The Most Prolific Documents in the Alternative Fermentation Processes for Bioethanol Production The information on the types of documents for both datasets is given in Table 31.1. The articles and conference papers, published in journals, dominate both the sample (89%) and population (95%) papers as they are underrepresented in the sample papers by 6%. Furthermore, review papers have a surplus as they are overrepresented in the sample papers by 8% as they constitute 11% and 3% of the sample and population papers, respectively. It is further notable that 97% of the population papers were published in journals, while 2% and 1% of them were published in book series and books, respectively. On the contrary, 99% of the sample papers were published in journals.

31.3.2 The Most Prolific Authors in the Alternative Fermentation Processes for Bioethanol Production The information about the 22 most prolific authors with at least 2.8% of sample papers each is given in Table 31.2. The most prolific author is Guido Zacchi of Lund University of Sweden with 15% of the sample papers, followed by Mats Galbe with 11.2% of the sample papers. The other prolific authors are Gunnar Liden, Charles E. Wyman, Mercedes Ballesteros, and Karel Grohmann with 4.8%–6.5% of the sample papers each.

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TABLE 31.1 Documents in Alternative Fermentation Processes for the Bioethanol Production Documents Article Review Conference paper Book chapter Letter Editorial Note Book Short survey Sample size

Sample Dataset (%) 84.1 11.2 4.7 0.0 0.0 0.0 0.0 0.0 0.0 107

Population Dataset (%) 90.0 3.1 4.8 1.4 0.6 0.1 0.1 0.0 0.0 1,067

Surplus (%) −5.9 8.1 −0.1 −1.4 −0.6 −0.1 −0.1 0.0 0.0

Population dataset, the number of papers (%) in the set of 1,067 population papers; Sample dataset, the number of papers (%) in the set of 107 highly cited papers.

The most influential author is Guido Zacchi with 12% surplus, followed by Mats Galbe with 9% surplus. The other influential authors are Gunnar Liden, Karel Grohmann, Mercedes Ballesteros, Harinder S. Oberoi, Charles E. Wyman, and Mark R. Wilkins with 3%–5.2% surplus each. The most prolific institutions for the sample dataset are Lund University and Center for Energy, Environmental and Technological Research (CIEMAT) with four authors each, while Kobe University and the University of l’Aquila house two authors each. In total, 15 institutions house these top authors. However, the most prolific country for the sample dataset is the USA with eight authors, followed by Spain, Sweden, and Japan with four, three, and two authors, respectively. In total, ten countries house these top authors. There is only one primary research front for these top authors: SSF with 20 authors. The other research front is the CBP with three authors. However, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only three of these top researchers are female with a representation rate of 14%. Additionally, there are other authors with a relatively low citation impact and with 0.6%–1.1% of the population papers each: Jie Bao, Lisbeth Olsson, Fengwu Bai, Juanita Freer, Elia Tomas-Pejo, Jian Zhang, Jaime Baeza, Venkatesh Balan, Michael A. Cotta, Masaki Kobayashi, Anshu S. Mathur, Yasunori Nagasaka, Kim Olofsson, Shaunita A. Rose, Diane D. Spindler, Bruce E. Dale, Ravi P. Gupta, Michael E. Himmel, Yoshio Itoh, Mingjie Jin, Kenji Kida, Seung-Wook Kim, Lakkana Laopaiboon, Pattana Laopaiboon, Hugh E. Lawford, Bing-Zhi Li, Poonam Nigam, Ashok Pandey, Arthur J. Ragauskas, Xiongjun Zhao, Mohammad J. Taherzadeh, Willem H. van Zyl, Thangavelu Viruthagiri, Ying-Jin Yuan, and Jia-Qing Zhu.

31.3.3 The Most Prolific Research Output by Years in Alternative Fermentation Processes for Bioethanol Production Information about papers published between 1970 and 2022 is given in Figure 31.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s with 57% of the population datasets. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 11%, 14%, 11%, 7%, and 0%, respectively. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 48 and 39% of the sample datasets, respectively. The publication rates for the 1990s, 1980s, and 1970s were 10%, 3%, and 0% of the sample papers, respectively.

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TABLE 31.2 The Most Prolific Authors in Alternative Fermentation Processes for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 13 12 13 14 15 16 17 18 19 20 21 22

Author Name

Author Code

Zacchi, Guido 7006727748 Galbe, Mats 7003788758 Liden, Gunnar 7004458708 Wyman, 7004396809 Charles E. Ballesteros, 7006110611 Mercedes* Grohmann, 7004503589 Karel Kondo, 57203868143 Akihiko Lynd, Lee R. 35586183800 Oliva, Jose M. Ballesteros, Ignacio Hasunuma, Tomohisa Wilkins, Mark R. Oberoi, Harinder S. Ingram, Lonnie O. Kim, Tae H. Banat, Ibrahim M. Negro, Maria J. Alkasrawi, Malek Reczey, Kati* Krishna, Sajja H. Alfani, Francesco Bansal, Sunil Cantarella, Maria*

Sample Population Papers (%) Papers (%) Surplus

Institution

Country

HI

N

Res. Front

Sweden Sweden Sweden USA

67 50 69 80

204 131 249 286

SSF SSF SSF SSF

Spain

49

134 SSF

15.0 11.2 6.5 4.7

3.0 2.2 1.3 1.7

12.0 9.0 5.2 3.0

4.7

1.4

3.3

Lund Univ. Lund Univ. Lund Univ. Univ. Calif. Riverside CIEMAT

4.7

1.1

3.6

USDA ARS

USA

39

99 SSF

3.7

1.1

2.6

Kobe Univ.

Japan

78

796 CBP

3.7

1.1

2.6

USA

74

57194220606 6602732963

3.7 3.7

0.9 0.8

2.8 2.9

Dartmouth Coll. CIEMAT CIEMAT

Spain Spain

34 37

286 CBP, SSF 53 SSF 70 SSF

23988816000

3.7

0.8

2.9

Kobe Univ.

Japan

40

187 CBP

56492323200

3.7

0.7

3.0

USA

27

95 SSF

6603479987

3.7

0.5

3.2

Univ. Nebraska Lincoln ICAR

India

26

65 SSF

7102962097

2.8

0.7

2.1

Univ. Florida

USA

72

281 SSF

57210847338 7005566530

2.8 2.8

0.7 0.6

2.1 2.2

Hanyang Univ. Univ. Ulster

S. Korea UK

23 72

88 SSF 287 SSF

6701512649

2.8

0.6

2.2

CIEMAT

Spain

37

72 SSF

9333556600

2.8

0.5

2.3

20

54 SSF

7004072336

2.8

0.5

2.3

32

80 SSF

55665122300

2.8

0.4

2.4

Univ. Wisconsin USA Parks. Budapest Univ. Hungary Technol. Econ. Emory Univ. USA

21

27 SSF

7003739898

2.8

0.3

2.5

Univ. l’Aquila

USA

19

68 SSF

56714908400

2.8

0.3

2.5

Univ. Bonn

Germany 110

993 SSF

7003630895

2.8

0.3

2.5

Univ. l’Aquila

Italy

21

98 SSF

*, Female researchers; Author code, the unique code given by Scopus to the authors; Population papers, the number of papers authored in the population dataset; Sample papers, the number of papers authored in the sample dataset.

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14

Number of papers (%)

12 10

Population papers Sample papers

8 6 4 2 0

FIGURE 31.1  Research output by years regarding alternative fermentation processes for bioethanol production.

The most prolific publication year for the population dataset was 2014 with 7.1% of the datasets, followed by 2013 with 6.9% of the papers. Furthermore, 74% of the population papers were published between 2008 and 2022. Similarly, 80% of the sample papers were published between 2003 and 2016, while the most prolific publication year was 2010 with 13.1% of the sample papers. The other prolific years were 2008 and 2009 with 9.3% and 7.5% of the sample papers, respectively.

31.3.4 The Most Prolific Institutions in the Alternative Fermentation Processes For Bioethanol Production Information about the 15 most prolific institutions publishing papers on alternative fermentation processes for bioethanol production with at least 2.8% of the sample papers each is given in Table 31.3. The most prolific institution is Lund University with 18.7% of the sample papers, followed by the National Renewable Energy Laboratory (NREL) and CIEMAT with 7.5% and 4.7% of the sample papers, respectively. The other prolific institutions are the USDA Agricultural Research Service, Oklahoma State University, Dartmouth College, Technical University of Denmark, Kobe University, and Budapest University of Technology and Economics with 3.7% of the sample papers each. The top country for these most prolific institutions is the USA with five institutions, while India houses two institutions each. In total, ten countries house these top institutions. However, the institution with the most citation impact is Lund University with 14% surplus, followed by the NREL with 4.9% surplus. The other prolific institutions are CIEMAT, Budapest University of Technology and Economics, and Oklahoma State University with 2.9%–3.3% surplus each. Additionally, there are other institutions with a relatively low citation impact and with 0.7%–1.9% of the population papers each: Chinese Academy of Sciences, Chalmers University of Technology, Dalian University of Technology, Tianjin University, Iowa State University, East China University of Science and Technology, Oak Ridge National Laboratory, University of Illinois Urbana Champaign, Korea University, Khon Kaen University, Korea Advanced Institute of Science and Technology, University of Concepcion, National Technical University of Athens, University of Sao Paulo,

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TABLE 31.3 The Most Prolific Institutions in Alternative Fermentation Processes for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Institutions Lund Univ. NREL CIEMAT Dartmouth Coll. Tech. Univ. Denmark Kobe Univ. USDA Agr. Res. Serv. Oklahoma State Univ. Budapest Univ. Technol. Econ. Stellenbosch Univ. Ulster Univ. Univ. Florida Centr. Food Technol. Res. Inst. ICAR Univ. l’Aquila

Country

Sample Papers (%)

Population Papers (%)

Sweden USA Spain USA Denmark Japan USA USA Hungary S. Africa UK USA India India Italy

18.7 7.5 4.7 3.7 3.7 3.7 3.7 3.7 3.7 2.8 2.8 2.8 2.8 2.8 2.8

4.7 2.6 1.4 1.4 1.3 1.1 1.0 0.8 0.7 1.4 1.2 0.8 0.5 0.5 0.3

Surplus (%) 14.0 4.9 3.3 2.3 2.4 2.6 2.7 2.9 3.0 1.4 1.6 2.0 2.3 2.3 2.5

University of Toronto, Kasetsart University, Shandong University, Sichuan University, Texas A&M University, Nanjing Tech University, Annamalai University, National Taiwan University of Science and Technology, University of California, Riverside, State University of Campinas, University of Antioquia, and University of Life Sciences in Poznan.

31.3.5 The Most Prolific Funding Bodies in the Alternative Fermentation Processes for Bioethanol Production Information about the 14 most prolific funding bodies funding at least 1.9% and 0.8% of the sample and population papers, respectively, is given in Table 31.4. Only 43% of the sample and population papers each were funded. The most prolific funding body is the US Department of Energy with 5.6% of the sample papers, followed by the European Commission with 4.7% of the sample papers. The other prolific funding bodies are the New Energy and Industrial Technology Development Organization and Ministry of Education, Culture, Sports, Science and Technology of Japan with 3.7% of sample papers each. However, the most prolific countries for these top funding bodies are the USA and Japan with four funding bodies each, followed by China with two funding bodies. In total, only six countries and the EU house these top funding bodies. The funding bodies with the most citation impact are the New Energy and Industrial Technology Development Organization and the US Department of Energy with 3.1% of the sample papers each, followed by the European Commission with 3% of the sample papers. The other prolific funding bodies are the Ministry of Education, Culture, Sports, Science and Technology and Ministry of Economy, Trade and Industry of Japan with 2.4% and 1.5% surplus, respectively. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 4% deficit. It is notable that the National Natural Science Foundation of China is the largest funder of the population papers. The other funding bodies with a relatively low citation impact and with 0.6%–2.5% of the population papers each are the Brazilian National Council for Scientific and Technological Development, National Key Research and Development Program of China, Ministry of Education of the People’s

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TABLE 31.4 The Most Prolific Funding Bodies in Alternative Fermentation Processes for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14

Funding Bodies US Dept. Ener. Eur. Comm. Minist. Educ. Cult. Sport. Sci. Technol. New Ener. Ind. Technol. Devnt. Prog. Minist. Sci. Technol. China Minist. Econ. Trade Ind. Natl. Natr. Sci. Found. Swedish Energ. Agcy. Japan Soc. Prom. Sci. Natl. Sci. Found. Minist. Sci. Technol. India Lab. Dir. Res. Devnt. Minist. Ed. Sci. Technol. USDA

Country

Sample Paper No. (%)

Population Paper No. (%)

Surplus (%)

USA EU Japan Japan China Japan China Sweden Japan USA India USA S. Korea USA

5.6 4.7 3.7 3.7 2.8 2.8 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9

2.5 1.7 2.2 0.6 2.8 0.4 6.1 1.8 1.4 1.2 0.9 0.8 0.8 0.8

3.1 3.0 1.5 3.1 0.0 2.4 -4.2 0.1 0.5 0.7 1.0 1.1 1.1 1.1

Republic of China, CAPES, Fundamental Research Funds for the Central Universities, Office of Science, Ministry of Science, Technology, and Innovation, Chinese Academy of Sciences, National Basic Research Program of China (973 Program), National Research Foundation of Korea, Sao Paulo Research Foundation, Natural Sciences and Engineering Research Council of Canada, Thailand Research Fund, China Postdoctoral Science Foundation, Ministry of Finance, National High-tech Research and Development Program, National Research Council of Thailand, National Research Foundation, Biological and Environmental Research, Biotechnology and Biological Sciences Research Council, European Regional Development Fund, Oak Ridge National Laboratory, and Priority Academic Program Development of Jiangsu Higher Education Institutions.

31.3.6 The Most Prolific Source Titles in the Alternative Fermentation Processes for Bioethanol Production Information about the 16 most prolific source titles publishing at least 1.9% of the sample papers each in alternative fermentation processes for bioethanol production is given in Table 31.5. The most prolific source title is Bioresource Technology with 19.6% of the sample papers, followed by Enzyme and Microbial Technology, Biotechnology and Bioengineering, and Process Biochemistry with 9.3%, 8.4%, and 6.5% of the sample papers, respectively. The other prolific titles are Applied Biochemistry and Biotechnology, Journal of Biotechnology, Biomass and Bioenergy, Biotechnology Progress, and Current Opinion in Biotechnology with 3.7%–5.6% of the sample papers each. However, the source title with the most citation impact is Bioresource Technology with 7.1% surplus, followed by Enzyme and Microbial Technology with 5% surplus. The other influential titles are Process Biochemistry, Journal of Biotechnology, Current Opinion in Biotechnology, and Biotechnology and Bioengineering with 3.2%–4% surplus each. Similarly, the source title with the least impact is the Biotechnology for Biofuels with 1.1% deficit. The other source titles with a relatively low citation impact with 0.6%–2.8% of the population papers each are Biotechnology Letters, Applied Microbiology and Biotechnology, Renewable Energy,

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TABLE 31.5 The Most Prolific Source Titles in Alternative Fermentation Processes for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

Source Titles Bioresource Technology Enzyme and Microbial Technology Biotechnology and Bioengineering Process Biochemistry Applied Biochemistry and Biotechnology Journal of Biotechnology Biomass and Bioenergy Biotechnology Progress Current Opinion in Biotechnology Biotechnology for Biofuels Fuel Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Journal of Industrial Microbiology and Biotechnology Applied and Environmental Microbiology Biotechnology Advances Journal of Agricultural and Food Chemistry

Sample Papers (%)

Population Papers (%)

Surplus (%)

19.6 9.3 8.4 6.5 5.6 5.6 4.7 3.7 3.7 2.8 2.8 2.8

10.3 2.2 5.2 2.5 3.7 1.6 2.3 1.0 0.4 3.9 1.7 1.5

9.3 7.1 3.2 4.0 1.9 4.0 2.4 2.7 3.3 −1.1 1.1 1.3

1.9

0.8

1.1

1.9 1.9 1.9

0.5 0.5 0.4

1.4 1.4 1.5

Bioresources, Industrial Crops and Products, Bioprocess and Biosystems Engineering, Bioenergy Research, Journal of Chemical Technology and Biotechnology, SAE Technical Papers, Journal of Bioscience and Bioengineering, Journal of Microbiology and Biotechnology, Waste and Biomass Valorization, Biochemical Engineering Journal, Bioprocess Engineering, Chemical Engineering Journal, Biofuels, Energy, Energy Sources Part A Recovery Utilization and Environmental Effects, Journal of Fermentation and Bioengineering, Korean Journal of Chemical Engineering, and World Journal of Microbiology and Biotechnology.

31.3.7 The Most Prolific Countries in the Alternative Fermentation Processes for Bioethanol Production Information about the 16 most prolific countries publishing at least 1.9% of sample papers each in alternative fermentation processes for bioethanol production is given in Table 31.6. The most prolific country is the USA with 33% of the sample papers, followed by Sweden, China, and India with 20%, 10%, and 10% of the sample papers, respectively. Japan, Spain, S. Korea, Denmark, and Hungary are the other prolific countries with 3.7%–8.4% of the sample papers each. Furthermore, China and India are the largest producers of population papers after the USA. Additionally, seven European countries produce 40% and 17% of the sample and population papers, respectively, with 23% surplus. However, the country with the most citation impact is the USA with 13.1% surplus, closely followed by Sweden with 12.6% surplus. The other influential countries are Hungary, Spain, Denmark, and Italy with 1.5%–3% surplus each. Similarly, the country with the least citation impact is China with 6% deficit, while Brazil, India, Japan, Canada, and S. Korea have 0.4%–2.8% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.6%–3.5% of the sample papers each: Thailand, Poland, Taiwan, Australia, Germany, Greece, Indonesia, Malaysia, Colombia, Pakistan, Chile, France, Mexico, Switzerland, Nigeria, Serbia, Turkey, Vietnam, Argentina, and Egypt.

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TABLE 31.6 The Most Prolific Countries in Alternative Fermentation Processes for the Bioethanol Production No.

Countries

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

USA Sweden China India Japan Spain S. Korea Denmark Hungary Brazil Canada UK S. Africa Italy Portugal Iran

Sample Papers (%) 32.7 19.6 10.3 10.3 8.4 5.6 4.7 3.7 3.7 2.8 2.8 2.8 2.8 2.8 1.9 1.9

Population Papers (%) 19.6 7.0 16.3 12.5 9.1 3.2 5.1 1.5 0.7 5.6 3.5 2.5 2.2 1.3 1.2 0.7

Surplus (%) 13.1 12.6 −6.0 −2.2 −0.7 2.4 −0.4 2.2 3.0 −2.8 −0.7 0.3 0.6 1.5 0.7 1.2

31.3.8 The Most Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production Information about the eight most prolific Scopus subject categories indexing at least 4.7% of the sample papers each is given in Table 31.7. The most prolific Scopus subject category in alternative fermentation processes for bioethanol production is Chemical Engineering with 73% of sample papers, closely followed by Biochemistry. Genetics and Molecular Biology and Immunology and Microbiology with 61% and 52% of the sample papers, respectively. The other prolific subject categories are Energy and Environmental Science with 34% of the sample papers each. It is notable that Social Sciences including Economics and Business account only for 1.3% of the population studies. However, the Scopus subject category with the most citation impact is Chemical Engineering with 21% surplus, followed by Biochemistry. Genetics and Molecular Biology and Immunology and Microbiology with 13 and 14% surplus, respectively. Similarly, the Scopus subject category with the least citation impact is Engineering with 7% deficit, followed by Chemistry and Agricultural and Biological Sciences with 2% deficit each.

31.3.9 The Most Prolific Keywords in the Alternative Fermentation Processes for Bioethanol Production Information about the Scopus keywords used with at least 7.5% or 2.8% of the sample or population papers, respectively, is given in Table 31.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most prolific keyword set provided by the Scopus database. These keywords are grouped under seven headings: biomass, fermentation, bacteria, hydrolysates, pretreatments, other processes, and products of the fermentation. The most prolific keyword related to the biomass and biomass constituents is cellulose with 45% of the sample papers, followed by biomass, Zea mays, lignin, and lignocellulose with 20%–33% of

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TABLE 31.7 The Most Prolific Scopus Subject Categories in Alternative Fermentation Processes for the Bioethanol Production No. 1 2 3 4 5 6 7 8

Scopus Subject Categories Chemical Engineering Biochemistry. Genetics and Molecular Biology Immunology and Microbiology Energy Environmental Science Agricultural and Biological Sciences Chemistry Engineering

Sample Papers (%) 72.9 60.7 52.3 33.6 33.6 10.3 9.3 4.7

Population Papers (%) 52.2 47.6 38.1 31.0 30.5 12.0 11.4 12.0

Surplus (%) 20.7 13.1 14.2 2.6 3.1 −1.7 −2.1 −7.3

TABLE 31.8 The Most Prolific Keywords in Alternative Fermentation Processes for the Bioethanol Production No. 1

2

3

Keywords

Sample Papers (%)

Population Papers (%)

Surplus (%)

Biomass and biomass constituents Cellulose

44.9

26.7

18.2

Biomass

32.7

18.3

14.4

Zea

29.9

13.1

16.8

Lignin

23.4

13.5

9.9

Lignocellulose

19.6

11.2

8.4

Triticum

17.8

6.2

11.6

Lignocellulosic biomass

12.1

6.6

5.5

Hemicellulose

11.2

4.9

6.3

Straw

9.3

5.3

4.0

Wood

7.5

2.8

4.7

Bagasse

4.7

4.8

−0.1

Fermentation Fermentation

89.7

67.6

22.1

Simultaneous saccharification and fermentation

51.4

41.7

9.7

Bioreactors

31.8

18.7

13.1

SSF

22.4

10.1

12.3

Ethanol concentrations

16.8

11.7

5.1

Consolidated bioprocessing

11.2

8.3

2.9

Ethanol yield

9.3

5.9

3.4

Ethanol fermentation

8.4

5.4

3.0

Simultaneous saccharification and cofermentation

6.5

5.4

1.1

Consolidated bio-processing

3.7

6.7

−3.0

Bacteria S. cerevisiae

62.6

33.8

28.8

Yeast

54.2

37.6

16.6

Bacteria

20.6

9.7

10.9 (Continued)

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TABLE 31.8 (Continued) The Most Prolific Keywords in Alternative Fermentation Processes for the Bioethanol Production No.

4

5

6

7

Keywords

Sample Papers (%)

Population Papers (%)

Surplus (%)

K. marxianus

15.9

9.9

6.0

Fungi

12.1

9.7

2.4

Fungal strain

8.4

4.2

4.2

Hydrolysates Glucose

29.9

19.5

10.4

Sugar

14.0

12.5

1.5

Xylose

11.2

6.1

5.1

Biomass pretreatment Cellulase

37.4

17.0

20.4

Enzyme activity

27.1

20.7

6.4

Enzymes

27.1

17.7

9.4

Temperature

15.0

8.5

6.5

Pre-treatment

14.0

5.7

8.3

Pretreatment

13.1

5.3

7.8

Water vapor

11.2

2.8

8.4

pH

9.3

6.2

3.1

Steam

7.5

3.1

4.4

Other processes Saccharification

67.3

46.1

21.2

Hydrolysis

46.7

25.8

20.9

Enzymatic hydrolysis

15.0

11.2

3.8

Fermentation products Ethanol

83.2

60.9

22.3

Alcohol

55.1

34.2

20.9

Biofuel

29.9

21.1

8.8

Ethanol production

24.3

17.2

7.1

Alcohol production

22.4

15.4

7.0

Bioethanol

19.6

24.9

−5.3

Biofuel production

10.3

4.4

5.9

Cellulosic ethanol

8.4

8.3

0.1

Bio-ethanol production

7.5

9.4

−1.9

the sample papers each. Furthermore, the most prolific keyword related to the bacteria is S. cerevisiae with 63% of the sample papers, followed by yeasts with 54% of the sample papers. The most prolific keyword related to fermentation is fermentation with 90% of the sample papers, followed by simultaneous saccharification and fermentation, bioreactors, and SSF with 51%, 32%, and 24% of the sample papers, respectively. Furthermore, the most prolific keyword related to hydrolysates is glucose with 30% of the sample papers, followed by sugar and xylose with 14 and 11% of the sample papers, respectively. The most prolific keyword related to biomass pretreatments is cellulases with 37% of the sample papers, followed by enzyme activity and enzymes with 27% of the sample papers each.

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Furthermore, the most prolific keyword related to the other processes is saccharification with 67% of the sample papers, followed by hydrolysis with 47% of the sample papers. Finally, the most prolific keyword related to fermentation products is ethanol with 83% of the sample papers, followed by alcohol with 59% of the sample papers. It is notable that bioethanol accounts only for 20% of the sample papers. However, the most influential keywords are lignin, simultaneous saccharification and fermentation, enzymes, biofuels, water vapor, lignocellulose, pre-treatment, pretreatment, ethanol production, and alcohol production with 7%–10% surplus each. Similarly, the least influential keywords are bioethanol, consolidated bio-processing, and bio-ethanol production with 2%–5% deficit each.

31.3.10 The Most Prolific Research Fronts in Alternative Fermentation Processes for Bioethanol Production Information about the research fronts for the sample papers in alternative fermentation processes for bioethanol production with regard to the biomass used in these pretreatments is given in Table 31.9. As this table shows, there are four primary research fronts with regard to the feedstocks for this field: agricultural residues, wood, biomass in general, and wastes with 30%, 16%, 15%, and 11% of the sample papers, respectively. The other research fronts are biomass constituents, grass, hydrolysates, algae, and other biomass with 7%, 5%, 4%, 2%, and 7% of the sample papers, respectively. On individual basis, the most prolific feedstocks are corn stover and lignocellulosic biomass with 10.3% of the sample papers each, followed by wheat straw and food waste with 8% and 7% of the sample papers, respectively. The other prolific feedstocks are cellulosic biomass, industrial wastes, and lignocellulose with 5% of the sample papers each. Information about the thematic research fronts for the sample papers in alternative fermentation processes for bioethanol production is given in Table 31.10. As this table shows, the most prolific research front is the SSF with 73% of the sample papers. The other prolific research fronts are the CBP and SSCF with 14% and 8% of the sample papers, respectively. Both CEP and CEF are underrepresented in the sample papers with 4% and 2% of the sample papers.

31.4 DISCUSSION 31.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol before the bioethanol production through hydrolysis and fermentation. Research in the field of alternative fermentation processes for bioethanol production for the SHF process to improve the ethanol yield has intensified in this context in recent years. The key research front has been the simultaneous SSF of the biomass. The other related research fields have been the CBP of the biomass, CEP of the biomass, SSCF of the biomass, and CEF. Furthermore, these innovative research fields have focused on wood, rice straw, wheat straw, corn stover, and other biomass such as algae and grass. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil price shocks, war-related shocks as in the case of the Russian invasion of Ukraine, or COVID-19 shocks. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of research in a selected research field. As the recent scientometric studies focus on

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TABLE 31.9 The Most Prolific Research Fronts for Alternative Fermentation Processes for the Bioethanol Production No. 1

Research Fronts Agricultural residues Corn stover

8.4

Rice straw

3.7

Corncobs

1.9

Sorghum bagasse

0.9

Sugarcane bagasse

2.8

Barley straw

0.9

Wood Biomass in general Lignocellulosic biomass Cellulosic biomass

4

5

6 7

8 9

29.9 10.3

Wheat straw

Sugarcane leaves 2 3

N Paper (%) Sample

Wastes Food waste

0.9 15.9 15.0 10.3 4.7 11.2 6.5

Industrial wastes

4.7

Biomass constituents Lignocellulose

6.6 4.7

Cellulose

1.9

Grass Hydrolysates Xylose

4.7 3.7 1.9

Glucose

0.9

Inulins

0.9

Algae Other biomass Sugarcane

1.9 6.5 0.9

Cactus

0.9

corn

1.9

potato

1.9

A. leptopus leaves

0.9

N paper (%) sample, the number of papers in the population sample of 107 papers.

the fermentation processes in general, this book chapter presents a scientometric study of research in alternative fermentation processes for the bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replicative studies. Furthermore, a selected list of the keywords is presented in Table 31.8.

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TABLE 31.10 The Most Prolific Thematic Research Fronts for Alternative Fermentation Processes for the Bioethanol Production No. 1 2 3 4 5

Research Fronts

N Paper (%) Sample

SSF Consolidated bioprocessing (CBP) SSCF Continuous ethanol production (CEP) Continuous ethanol fermentation (CEF)

72.9 14.0 8.4 3.7 1.9

N paper (%) sample, the number of papers in the population sample of 107 papers.

As a second step, two sets of data were used for this study. First, a population sample of 1,067 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 107 most-cited papers, corresponding to 10% of the population datasets, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for alternative fermentation processes for bioethanol production. Additionally, a number of brief conclusions were drawn and a number of relevant recommendations were made to enhance the future research landscape.

31.4.2 The Most Prolific Documents in the Alternative Fermentation Processes for Bioethanol Production Articles (together with conference papers) dominate both the sample (89%) and population (95%) papers (Table 31.1). Furthermore, review papers and articles have a surplus (8%) and deficit (6%), respectively. The representation of the reviews in the sample papers is relatively high (11%). Scopus differs from the Web of Science database in differentiating and showing articles (84%) and conference papers (5%) published in journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared with those published only in the conference proceedings. Hence, the total number of articles and review papers in the sample dataset is 89% and 11%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus and in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of reviews in the sample dataset was manually found as nearly 14% compared with 11% as indexed by Scopus, reducing the number of articles and conference papers to 86% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 31.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from experimental chapters as is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers and it is done in the Web of Science database.

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31.4.3 The Most Prolific Authors in the Alternative Fermentation Processes for Bioethanol Production There have been 22 most prolific authors with at least 2.8% of the sample papers each as given in Table 31.2. These authors have shaped the development of research in this field. The most prolific authors are Guido Zacchi, Mats Galbe, and to a lesser extent Gunnar Liden, Karel Grohmann, Mercedes Ballesteros, Harinder S. Oberoi, Charles E. Wyman, and Mark R. Wilkins. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for names with more than two components such as ‘Blake Sam de Hyun Liden’. The probable outcomes are ‘Liden, B.S.D.H.’, ‘de Hyun Liden, B.S.’, or ‘Hyun Liden, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PubMed and EBSCO, this version is used predominantly. The second choice is a strong alternative, while the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name by a hyphen: ‘Hyun-Liden, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers and allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening of Chinese names. For example, ‘YangYing Zhu’ is often shortened as ‘Zhu, Y.’, ‘Zhu, Y.-Y.’, and ‘Zhu, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhu, Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PubMed and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Zhu, Y.Y.’. Therefore, there have been difficulties in locating papers for Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors, especially for the authors with common names such as ‘Zhu, X’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies and allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Zhu, X’ in the databases. In this context, the coding of authors in the Scopus database is a welcome innovation compared with other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system, especially for common names. For example, some of the papers for a certain author may be allocated to another researcher with a different author code. It is possible that Scopus uses many software programs to differentiate the author names and the program may not be falseproof (Kim, 2018). In this context, it does not help that author names are not given in full in some journals and books. This makes it difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Liden, H.J.’ ‘Liden’, ‘Liden, H.’, or ‘Liden, J.’ instead of ‘Liden, Hyun Jae’. There are also inconsistencies in the naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Liden, APC’ might be given as ‘Liden, A.’ or ‘Liden, A.C.’ or ‘Liden, A.P.’ or ‘Liden, C.’ in journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Subaşı, Gökçe) rather than in English spellings (e.g., Subasi, Gokce) in the Scopus database. Scopus differs from the Web of Science database and many

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other databases in this respect where the author names are given only in English spellings. It is observed that national spellings of the author names do not help much in conducting scientometric studies and in allocating credits to the authors as sometimes there are different author entries for the English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are Lund University, CIEMAT, and to a lesser extent Kobe University and the University of l’Aquila. The most prolific countries for the sample dataset are the USA and to a lesser extent Spain, Sweden, and Japan. These findings confirm the dominance of the USA, Europe, and to a lesser extent of Japan in this field. The most prolific research front is the SSF, while CBP is the other research front. It is also notable that there is a significant gender deficit for the sample dataset surprisingly with a representation rate of 14%. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field and other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from minority groups in science based on race, sexuality, age, and disability, besides gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

31.4.4 The Most Prolific Research Output by Years in the Alternative Fermentation Processes for Bioethanol Production The research output observed between 1970 and 2022 is illustrated in Figure 31.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. However, there were rising and falling trends for the research output between 2005 and 2015 and thereafter for the population papers. These findings suggest that the most prolific sample and population papers were primarily published in the last two decades. These are the thought-provoking findings as there has been a significant research boom in the last two decades. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in research in this field in the last two decades. However, the supply shocks experienced due to the COVID-19 pandemic have not resulted in a sharp rise in the research output as expected. Based on these findings, the size of the population papers is likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming, as well as the supply shocks, are translated efficiently to the research funding in this field.

31.4.5 The Most Prolific Institutions in the Alternative Fermentation Processes for Bioethanol Production The 15 most prolific institutions publishing papers on alternative fermentation processes for bioethanol production with at least 2.8% of the sample papers each given in Table 31.3 have shaped the development of research in this field. The most prolific institutions are Lund University and to a lesser extent University of Florida, Kobe University, and University of Illinois Urbana Champaign. Similarly, the top countries for these most prolific institutions are the USA and to a lesser extent India. In total, only five countries house these top institutions. However, the institutions with the most citation impact are Lund University and to a lesser extent USDA Agricultural Research Service, Oklahoma State University, Dartmouth College, Technical University of Denmark, Kobe University, and Budapest University of Technology and Economics. These findings confirm the dominance of the US, European, and to a lesser extent of Japanese

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institutions with a notable absence of China in this research field. These findings clearly hint that the USA, Europe, and to a lesser extent Japan dominate research in this field.

31.4.6 The Most Prolific Funding Bodies in the Alternative Fermentation Processes for Bioethanol Production The nine most prolific funding bodies funding at least 14 funding bodies funding at least 1.9% and 0.8% of the sample and population papers, respectively, are given in Table 31.4. It is notable that only 43% of the sample and population papers each were funded. The most prolific funding bodies are the US Department of Energy, European Commission, and to a lesser extent New Energy and Industrial Technology Development Organization and Ministry of Education, Culture, Sports, Science and Technology of Japan. The most prolific countries for these top funding bodies are the USA and to a lesser extent China. In total, only six countries house these top funding bodies. The funding bodies with the most citation impact are the New Energy and Industrial Technology Development Organization and the US Department of Energy and to a lesser extent the European Commission Ministry of Education, Culture, Sports, Science and Technology and Ministry of Economy, Trade and Industry of Japan, while the one with the least impact is the National Natural Science Foundation of China. These findings on the funding of research in this field suggest that the level of funding, mostly in the last two decades, is modest, and nevertheless, it has been largely instrumental in enhancing research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding there is ample room to enhance funding in this field. This was also important in light of the falling research output trend after 2015.

31.4.7 The Most Prolific Source Titles in the Alternative Fermentation Processes for Bioethanol Production The 16 most prolific source titles publishing at least 1.9% of the sample papers each in alternative fermentation processes for bioethanol production have shaped the development of research in this field (Table 31.5). The most prolific source titles are Bioresource Technology and to a lesser extent Enzyme and Microbial Technology, Biotechnology and Bioengineering, Process Biochemistry, Applied Biochemistry and Biotechnology, Journal of Biotechnology, Biomass and Bioenergy, Biotechnology Progress, and Current Opinion in Biotechnology. The source titles with the most citation impact are Bioresource Technology, Enzyme and Microbial Technology, and to a lesser extent Process Biochemistry, Journal of Biotechnology, Current Opinion in Biotechnology, and Biotechnology and Bioengineering. Similarly, the source title with the least impact is the Biotechnology for Biofuels. It is notable that these top source titles are primarily related to bioresources, microbiology, bioengineering, and biotechnology. This finding suggests that Bioresource Technology and the other prolific journals in these fields have significantly shaped the development of research in this field as they focus primarily on alternative fermentation processes to produce ethanol with a high yield.

31.4.8 The Most Prolific Countries in the Alternative Fermentation Processes For Bioethanol Production The 16 most prolific countries publishing at least 1.9% of the sample papers each have significantly shaped the development of research in this field (Table 31.6).

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The most prolific countries are the USA, Sweden, and to a lesser extent China, India, Japan, Spain, S. Korea, Denmark, and Hungary. Furthermore, China and India are the largest producers of population papers after the USA. Additionally, seven European countries produce 40% and 17% of the sample and population papers, respectively, with 23% surplus. However, the countries with the most citation impact are the USA, Sweden, and to a lesser extent Hungary, Spain, Denmark, and Italy. Similarly, the countries with the least impact are China and to a lesser extent Brazil, India, Japan, Canada, and S. Korea. The close examination of these findings suggests that the USA, Europe, the Far East (China, Japan, and S. Korea), and India are the major producers of research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009). The USA has further developed a strong research infrastructure to support its corn and grass-based bioethanol industry (Gillon, 2010). However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Fang et al., 2010). Next, Europe has been a persistent player in scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010).

31.4.9 The Most Prolific Scopus Subject Categories in the Alternative Fermentation Processes for Bioethanol Production The eight most prolific Scopus subject categories indexing at least 4.7% of the sample papers each, respectively, given in Table 31.7, have shaped the development of research in this field. The most prolific Scopus subject categories in alternative fermentation processes for bioethanol production are Chemical Engineering, Biochemistry, Genetics and Molecular Biology, Immunology and Microbiology, and to a lesser extent Energy and Environmental Science. The Scopus subject categories with the most citation impact are Chemical Engineering, followed by Biochemistry, Genetics and Molecular Biology, and Immunology and Microbiology. Similarly, the Scopus subject categories with the least citation impact are Engineering and to a lesser extent Chemistry and Agricultural and Biological Sciences. These findings are thought-provoking, suggesting that the primary subject categories are related to chemical engineering, genetics, and microbiology as the core of research in this field concerns with alternative fermentation processes to increase the ethanol yield. The other finding is that social sciences are not well represented in both the sample and population papers as in most fields in bioethanol fuels.

31.4.10 The Most Prolific Keywords in the Alternative Fermentation Processes for Bioethanol Production A limited number of keywords have shaped the development of research in this field as shown in Table 31.8 and the Appendix. These keywords are grouped under seven headings: biomass, fermentation, bacteria, hydrolysates, pretreatments, other processes, and products of the fermentation. The most prolific keywords related to the biomass and biomass constituents are cellulose, biomass, Zea mays, lignin, and lignocellulose, while the most prolific keywords related to fermentation are fermentation, simultaneous saccharification and fermentation, bioreactors, and SSF. The most prolific keywords related to the bacteria are S. cerevisiae and to a lesser yeasts. Furthermore, the most prolific keywords related to hydrolysates are glucose, sugar, and xylose. The most prolific keyword related to biomass pretreatments are cellulases, enzyme activity, and enzymes. Furthermore, the most prolific keywords related to the other processes are saccharification

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and hydrolysis. Finally, the most prolific keywords related to fermentation products are ethanol and alcohol. It is notable that bioethanol as a keyword accounts only for 20% of the sample papers. However, the most influential keywords are lignin, simultaneous saccharification and fermentation, enzymes, biofuels, water vapor, lignocellulose, pre-treatment, pretreatment, ethanol production, and alcohol production. Similarly, the least influential keywords are bioethanol, consolidated bio-processing, and bio-ethanol production. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of research in the relevant research areas. The relevant keywords are presented in Table 31.8.

31.4.11 The Most Prolific Research Fronts in the Alternative Fermentation Processes For Bioethanol Production As Table 31.9 shows there are four primary research fronts with regard to the feedstocks for this field: agricultural residues, wood, biomass in general, wastes, and to a lesser extent biomass constituents, grass, hydrolysates, algae, and other biomass. On individual basis, the most prolific feedstocks are corn stover, lignocellulosic biomass, and to a lesser extent wheat straw, food waste, cellulosic biomass, industrial wastes, and lignocellulose. Information about the thematic research fronts for the sample papers in alternative fermentation processes for bioethanol production is given in Table 31.10. As this table shows, the most prolific research front is the SSF. The other prolific research fronts are the CBP and SSCF. Both CEP and CEF are underrepresented in the sample papers. These findings are thought-provoking in seeking ways to increase bioethanol yield through alternative fermentation processes for bioethanol production at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Furthermore, it is notable that alternative fermentation processes have become a core of the fermentation research to increase the ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. In the end, these most-cited papers in this field hint that the efficiency of alternative fermentation processes could be optimized using the structure, processing, and property relationships of these innovative fermentation processes (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

31.5  CONCLUSION AND FUTURE RESEARCH Research on alternative fermentation processes for bioethanol production has been mapped through a scientometric study of both sample (107 papers) and population (1,067 papers) datasets. The critical issue in this study has been to obtain a representative sample of research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after several runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the appendix, and the relevant keywords are presented in Table 31.8. However, it should be noted that it has been very difficult to compile a representative keyword set since this research field has been connected closely with many other fields. Therefore, it has been necessary to compile a keyword list to exclude papers concerned with the other research fields. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of research in this field. For this purpose, the Scopus database has been selected. The journal coverage of this database has been notably wider than that of the Web of Science and other multi-subject databases.

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The key scientometric properties of research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits based on age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. Research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Furthermore, the recent COVID-19 pandemic and the Russian invasion of Ukraine have resulted in global supply shocks shifting the focus of the stakeholders from the crude oil-based fuels to biomass-based fuels such as bioethanol fuels. It is expected that there would be further incentives for the key stakeholders to carry out research for alternative fermentation processes to increase the ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. This might be truer for the crude oil- and foreign exchange-deficient countries to maintain the energy security in the face of global supply shocks. However, it is notable that these supply shocks have not resulted yet in a sharp rise in the research output for the population papers in 2020 and 2021. The relatively modest funding rate of 43% for the population papers suggests that funding in this field significantly enhanced research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance research in this field further. The institutions from the USA and to a lesser extent Europe and Japan have mostly shaped research in this field. Furthermore, the USA, Europe, and to a lesser extent India and the Far East (China, Japan, and S. Korea) have been the major producers of research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass and other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under seven headings: biomass, fermentation, bacteria, hydrolysates, pretreatments, other processes, and products of the fermentation. There are three primary research fronts for this field: agricultural residues, wood, biomass in general, and wastes, while on individual basis, corn stover and lignocellulosic biomass to a lesser extent are the most prolific feedstocks. Furthermore, the most prolific thematic research front is the SSF with the other prolific research fronts of the CBP and SSCF. These findings are thought-provoking in seeking ways to increase bioethanol yield through alternative fermentation processes at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Furthermore, it is notable that alternative fermentation processes have become a core of the fermentation research to increase the ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of research in this field as in other scientific fields, especially in the aftermath of the significant global supply shocks such as COVID-19 shocks and the Russian invasion of Ukraine. It is recommended that further scientometric studies are carried out for the primary research fronts. It is further recommended that reviews of the most-cited papers are carried out for each primary research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

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ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of alternative fermentation processes for bioethanol production has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR ALTERNATIVE FERMENTATION PROCESSES FOR BIOETHANOL PRODUCTION (TITLE ((“simultaneous saccharification” OR “simultaneous hydrolysis” OR “continuous hydrolysis” OR “continuous ethanol” OR “continuous saccharification”) AND (*fermentation)) OR TITLE (ssf OR sscf OR “consolidated bioprocessing” OR “continuous ethanol product*”)) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR dent OR neur OR heal OR psyc OR eart) OR TITLE (*butanol OR “lactic acid” OR “solid state” OR succinic OR wetland* OR flag* OR *hydrogen OR hdh OR value* OR protease* OR *diesel OR wine OR aerosol OR *rna OR fruit* OR “organic acids” OR “ammonium carboxylates” OR “SSF produced cellulase” OR smf OR transgenic OR succinate OR depolymerization) OR SRCTITLE (“solid state” OR hydrogen)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “ed”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “sh”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”))

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Konur, O. 2006b. Teaching disabled students in higher education. Teaching in Higher Education 11:351–363. Konur, O. 2007a. A judicial outcome analysis of the Disability Discrimination Act: A windfall for the employers? Disability & Society 22:187–204. Konur, O. 2007b. Computer-assisted teaching and assessment of disabled students in higher education: The interface between academic standards and disability rights. Journal of Computer Assisted Learning 23:207–219. Konur, O. 2011. The scientometric evaluation of the research on the algae and bio-energy. Applied Energy 88:3532–3540. Konur, O. 2012a. Prof. Dr. Ayhan Demirbasʼ scientometric biography. Energy Education Science and Technology Part A: Energy Science and Research 28:727–738. Konur, O. 2012b. The evaluation of the biogas research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:1277–1292. Konur, O. 2012c. The evaluation of the global energy and fuels research: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 30:613–628. Konur, O. 2012d. The evaluation of the research on the biodiesel: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1003–1014. Konur, O. 2012e. The evaluation of the research on the bioethanol: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1051–1064. Konur, O. 2012f. The evaluation of the research on the biofuels: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:903–916. Konur, O. 2012g. The evaluation of the research on the biohydrogen: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:323–338. Konur, O. 2012h. The evaluation of the research on the microbial fuel cells: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 29:309–322. Konur, O. 2012i. The scientometric evaluation of the research on the production of bioenergy from biomass. Biomass and Bioenergy 47:504–515. Konur, O. 2015. Current state of research on algal bioethanol. In Marine Bioenergy: Trends and Developments, Ed. S. K. Kim and C. G. Lee, pp. 217–244. Boca Raton, FL: CRC Press. Konur, O., Ed. 2018a. Bioenergy and Biofuels. Boca Raton, FL: CRC Press. Konur, O. 2018b. Bioenergy and biofuels science and technology: Scientometric overview and citation classics. In Bioenergy and Biofuels, Ed. O. Konur, pp. 3–63. Boca Raton: CRC Press. Konur, O. 2019. Cyanobacterial bioenergy and biofuels science and technology: A scientometric overview. In Cyanobacteria: From Basic Science to Applications, Ed. A. K. Mishra, D. N. Tiwari and A. N. Rai, pp. 419–442. Amsterdam: Elsevier. Konur, O. 2020a. The scientometric analysis of the research on the bioethanol production from green macroalgae. In Handbook of Algal Science, Technology and Medicine, Ed. O. Konur, pp. 385–401. London: Academic Press. Konur, O., Ed. 2020b. Handbook of Algal Science, Technology and Medicine. London: Academic Press. Konur, O., Ed. 2021a. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021b. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 1. Biodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021c. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 2. Biodiesel Fuels based on the Edible and Nonedible Feedstocks, Wastes, and Algae: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O., Ed. 2021d. Handbook of Biodiesel and Petrodiesel Fuels: Science, Technology, Health, and Environment. Volume 3. Petrodiesel Fuels: Science, Technology, Health, and Environment. Boca Raton, FL: CRC Press. Konur, O. and F. L. Matthews. 1989. Effect of the properties of the constituents on the fatigue performance of composites: A review. Composites 20:317–328. Kruyt, B., D. P. van Vuuren, H. J. de Vries and H. Groenenberg. 2009. Indicators for energy security. Energy Policy 37:2166–2181. Leydesdorff, L. 2000. Is the European Union becoming a single publication system? Scientometrics 47:265–280. Leydesdorff, L. and C. Wagner. 2009. Is the United States losing ground in science? A global perspective on the world science system. Scientometrics 78:23–36. Leydesdorff, L. and P. Zhou. 2005. Are the contributions of China and Korea upsetting the world system of science? Scientometrics 63:617–630.

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Li, H., S. M. Liu, X. H. Yu, S. L. Tang and C. K. Tang. 2020. Coronavirus disease 2019 (COVID-19): Current status and future perspectives. International Journal of Antimicrobial Agents 55:105951. Lin, Y. and S. Tanaka. 2006. Ethanol fermentation from biomass resources: Current state and prospects. Applied Microbiology and Biotechnology 69:627–642. Lynd, L. R., W. H. van Zyl, J. E. McBride and M. Laser. 2005. Consolidated bioprocessing of cellulosic biomass: An update. Current Opinion in Biotechnology 16:577–583. Ma, X., L. Sun and C. Song. 2002. A new approach to deep desulfurization of gasoline, diesel fuel and jet fuel by selective adsorption for ultra-clean fuels and for fuel cell applications. Catalysis Today 77:107–116. Morschbacker, A. 2009. Bio-ethanol based ethylene. Polymer Reviews 49:79–84. Najafi, G., B. Ghobadian and T. Tavakoli, et al. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86:630–639. Newman, P. W. G. and J. R. Kenworthy. 1989. Gasoline consumption and cities: A comparison of U.S. cities with a global survey. Journal of the American Planning Association 55:24–37. North, D. C. 1991. Institutions. Journal of Economic Perspectives 5:97–112. Ohgren, K., A. Rudolf, M. Galbe and G. Zacchi. 2006. Fuel ethanol production from steam-pretreated corn stover using SSF at higher dry matter content. Biomass and Bioenergy 30:863–869. Olofsson, K., M. Bertilsson and G. Liden. 2008. A short review on SSF - An interesting process option for ethanol production from lignocellulosic feedstocks. Biotechnology for Biofuels 1:7. Olson, D. G., J. E. McBride, A. J. Shaw and L. R. Lynd. 2012. Recent progress in consolidated bioprocessing. Current Opinion in Biotechnology 23:396–405. Olsson, L. and B. Hahn-Hagerdal. 1996. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme and Microbial Technology 18:312–331. Reeves, S. 2014. To Russia with love: How moral arguments for a humanitarian intervention in Syria opened the door for an invasion of the Ukraine. Michigan State University International Law Review 23:199. Sanchez, O. J. and C. A. Cardona. 2008. Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource Technology 99:5270–5295. Sun, Y. and J. Cheng. 2002. Hydrolysis of lignocellulosic materials for ethanol production: A review. Bioresource Technology 83:1–11. Taherzadeh, M. J. and K. Karimi. 2007. Enzyme-based hydrolysis processes for ethanol from lignocellulosic materials: A review. Bioresources 2:707–738. Taherzadeh, M. J. and K. Karimi. 2008. Pretreatment of lignocellulosic wastes to improve ethanol and biogas production: A review. International Journal of Molecular Sciences 9:1621–1651. Winzer, C. 2012. Conceptualizing energy security. Energy Policy 46:36–48. Yang, B. and C. E. Wyman. 2008. Pretreatment: The key to unlocking low-cost cellulosic ethanol. Biofuels, Bioproducts and Biorefining 2:26–40.

32

The Alternative Fermentation Processes for the Bioethanol Production Review Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

32.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2019, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of alternative fermentation processes for bioethanol production for separate hydrolysis and fermentation (SHF) to improve ethanol yield has intensified in this context in recent years (Alfani et al., 2000; Hinman et al., 1992; Ohgren et al., 2007; Stenberg et al., 2000; Tomas-Pejo et al., 2008; Wingren et al., 2003). The key research front has been the simultaneous saccharification and fermentation (SSF) of the biomass (Ballesteros et al., 2004; Olofsson et al., 2008; Wingren et al., 2003). The other related research fields have been the consolidated bioprocessing (CBP) of biomass (Lynd et al., 2005, Olson et al., 2012), continuous ethanol production (CEP) of biomass (Bai et al., 2004; Brethauer and Wyman, 2010), simultaneous saccharification and cofermentation (SSCF) of biomass (Gubicza et al., 2016; Jin et al., 2010; Ohgren et al., 2006a), and continuous ethanol fermentation (CEF) (Georgieva and Ahring, 2007; Nagashima et al., 1984). Furthermore, these innovative research fields have focused on wood (Ballesteros et al., 2004; Cantarella et al., 2004; Hinman et al., 1992; Itoh et al., 2003; Wingren et al., 2003), rice straw (Karimi et al., 2006; Ko et al., 2009), wheat straw (Alfani et al., 2000; Linde et al., 2008; TomasPejo et al., 2008), corn stover (Ohgren et al., 2006a,b, 2007; Varga et al., 2004; Zhang et al., 2010a), and other biomass such as algae and grass (Ballesteros et al., 2004; Jang et al., 2012; Kadar et al., 2004; Krishna et al., 2001; Li et al., 2009; Wen et al., 2010; Zhang et al., 2010b). However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been several review papers on alternative fermentation research fields (Brethauer and Wyman, 2010; Lynd et al., 2005; Olofsson et al., 2008; Olson et al., 2012), there has been no review of the 25 most-cited papers in this field. 284

DOI: 10.1201/9781003226499-41

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285

Thus, this book chapter presents a review of the 25 most-cited articles in the field of alternative fermentation processes for bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

32.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in June 2022. As a first step for the search of the relevant literature, the keywords were selected using the 200 most-cited first population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 141 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape.

32.3 RESULTS The brief information about 25 most-cited papers with at least 141 citations each on alternative fermentation processes with a focus on alternative fermentation processes for bioethanol production is given below. The primary research fronts are the fermentation processes for wheat and rice straw and corn stover, wood, and other biomass with 11, 8, and 8 highly cited papers (HCPs), respectively.

32.3.1  The Alternative Fermentation Processes for the Straw and Corn Stover There are 11 HCPs for alternative fermentation processes for the straw and corn stover with six and five HCPs, respectively (Table 32.1). 32.3.1.1  Rice and Wheat Straw Ballesteros et al. (2004) carried out the SSF process for ethanol production from poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, and Brassica carinata (B. carinata) residues using the Kluyveromyces marxianus (K. marxianus) CECT 10875 in a paper with 385 citations. They pretreated biomass in a steam explosion pilot plant and performed SSF experiments at 42°C, 10% (w/v) substrate concentration, and 15 filter paper unit (FPU)/g substrate of commercial cellulase. They obtained the SSF yields in the range of 50%–72% of the maximum theoretical SSF yield, based on the glucose available in pretreated materials, in 72–82 h. Furthermore, they obtained maximum ethanol contents from 16 to 19 g/L in fermentation media, depending on the material tested. Karimi et al. (2006) carried out the SSF process using dilute acid-pretreated rice straw with Mucor indicus (M. indicus), Rhizopus oryzae (R. oryzae), and Saccharomyces cerevisiae (S. cerevisiae) in a paper with 258 citations. They performed the SSF process aerobically and anaerobically at 38°C, 50 g/L dry matter (DM) solid substrate concentration, and 15 or 30 FPU/g DM of a commercial cellulase. They observed that all the strains produced ethanol from the pretreated rice straw with an overall yield of 40%–74% of the maximum theoretical SSF yield, based on the glucan available in the solid substrate. Furthermore, R. oryzae had the best ethanol yield at 74% followed by M. indicus with an overall yield of 68% with 15 FPU/g DM of cellulase. Glycerol was the main byproduct of the SSF by M. indicus and S. cerevisiae with yields of 117 and 90 mg/g of equivalent glucose in the pretreated straw, respectively, while R. oryzae produced lactic acid as the major byproduct with a yield of 60 mg/g glucose equivalent in pretreated rice straw under anaerobic conditions. Ko et al. (2009) produced ethanol from rice straw using optimized aqueous ammonia soaking pretreatment and SSF process in a paper with 244 citations. They optimized the effects of the pretreatment temperature, pretreatment time, the concentration of ammonia and the solid-to-liquid

No.

Papers

Biomass/Hydrolysate

 1

Ballesteros et al. (2004)

 2

Ohgren et al. (2007)

Poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, B. carinata residue Corns stover

 3

Karimi et al. (2006)

 4

Prt.

Bacteria K. marxianus

Steam, enzymes

S. cerevisiae

Rice straw

Acids, cellulases

Ko et al. (2009)

Rice straw

Ammonia, cellulases

M. indicus, R. oryzae, S. cerevisiae Yeast

 5

Ohgren et al. (2006a)

Corn stover

Steam

S. cerevisiae

 6

Zhang et al. (2010a)

Corn stover

Steam, enzymes

Yeast

 7

Varga et al. (2004)

Corn stover

Wet oxidation, cellulases

S. cerevisiae

 8

Tomas-Pejo et al. (2008)

Wheat straw

Steam

S. cerevisiae

 9

Ohgren et al. (2006b) Alfani et al. (2000)

Corn stover

Steam

S. cerevisiae

Wheat straw

Steam, enzymes

Yeast

Wheat straw

Acids, steam

Yeast

10

11

Linde et al. (2008)

Parameters SSF, biomass type, ethanol yield, pretreatments

Keywords

Simultaneous saccharification, fermentation SSF, SHF, ethanol yield, Simultaneous fermentation inhibitors saccharification, fermentation SSF, pretreatments, ethanol Simultaneous yield, bacteria types, saccharification, byproducts fermentation SSF, pretreatment optimization, Simultaneous ethanol yield saccharification, fermentation SSCF, glucose and xylose Simultaneous fermentation, ethanol yield saccharification, co-fermentation SSF, solids loading, stirring Simultaneous system saccharification, ethanol fermentation SSF, solid and enzyme Simultaneous loadings, pretreatment saccharification, fermentation SSF, SHF, bacteria engineering, SSF fermentation inhibitors, ethanol yield SSF, solid loading, ethanol SSF yield SSF, SHF, ethanol yield, SSF pretreatment, solid and enzyme loading SSF, yeast and enzyme SSF loadings, pretreatment,

*, female; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

Lead Author Manzanares, Paloma* 55779406300 Zacchi, Guido 7006727748 Taherzadeh, Mohammad J. 6701407496 Kim, Kyoung Heon 34770896300 Zacchi, Guido 7006727748

Affil. CIEMAT Spain

385

Lund Univ. Sweden

270

Univ. Boras Sweden

258

Korea Univ. USA

244

Lund Univ. Sweden

221

Bao, Jie 57189034954

E. China Univ. Sci. Technol. China Thomsen, Anne Tech. Univ. B.* 7102150211 Denmark Denmark Oliva, Jose M. CIEMAT 57194220606 Spain Zacchi, Guido 7006727748 Alfani, Francesco 7003739898 Zacchi, Guido 7006727748

Cits

Lund Univ. Sweden Univ. L’Aquila Italy Lund Univ. Sweden

210

203

196

194 169

164

Bioethanol Fuel Production Processes. II

Cellulases, steam

286

TABLE 32.1 Alternative Fermentation Processes for the Straw and Corn Stover

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ratio on the degree of lignin removal, and the enzymatic digestibility using response surface methodology. They found that the optimal reaction conditions, which resulted in an enzymatic digestibility of 71.1%, were 69°C, 10 h, and an ammonia concentration of 21% (w/w). Additionally, they performed the SSF process to assess the ethanol production yield and productivity. Tomas-Pejo et al. (2008) produced ethanol from steam-exploded (SE) wheat straw using SSF and SHF processes with xylose and glucose-fermenting S. cerevisiae strains, F12, and Red Star in a paper with 196 citations. The F12 strain was engineered to allow xylose consumption, while Red Star was a robust hexose-fermenting strain used for industrial ethanol fermentations. They obtained the highest ethanol concentration, 23.7 g/L, using the whole slurry (10%, w/v) and the recombinant strain (F12) in an SSF process on consumed sugars of 0.43 g/g and a volumetric ethanol productivity of 0.7 g/Lh for the first 3 h. Ethanol concentrations obtained in SSF processes were in all cases higher than those from SHF at the same conditions. Furthermore, using the whole slurry, the final ethanol concentration was improved in all tests due to the increase in potential fermentable sugars in the fermentation broth. Fermentation inhibitors present in the pretreated wheat straw caused a significantly negative effect on the fermentation rate. However, furfural and HMF were completely metabolized by the yeast during SSF by metabolic redox reactions, while no xylitol was produced. Alfani et al. (2000) produced ethanol from steam-pretreated wheat straw using SSF and SHF processes in a paper with 169 citations. The temperature and time of steam explosion pretreatment were 200°C or 217°C and at 3 or 10 min, respectively. Biomass loading in the bioreactor ranged from 25 to 100 g/L (dry weight), while the enzyme-to-biomass mass ratio was 0.06. They obtained ethanol yields close to 81% of the theoretical in the two-step SHF process at hydrolysis and fermentation temperatures of 45°C and 37°C, respectively. The optimum temperature for the single-step SSF process was 37°C, and they obtained ethanol yields close to 68% of the theoretical. The SSF process required a much shorter overall process time (30 h) than the SHF process (96 h) and resulted in a large increase in ethanol productivity: 0.837 g/L/h for SSF compared with 0.313 g/L/h for SHF. Linde et al. (2008) produced ethanol from steam-pretreated wheat straw using the SSF process at low yeast and enzyme loadings in a paper with 164 citations. They kept the concentration of H2SO4 in the impregnation liquid before pretreatment low, 0.2%, and performed SSF at low enzyme loadings, 3–14 FPU/g water-insoluble solid (WIS), and a low yeast concentration, 2 g/L. They obtained the highest recovery of glucose (102%) and xylose (96%) after pretreatment at 190°C for 10 min, while they obtained a high overall ethanol yield, 67% of the theoretical based on glucose in the raw material. 32.3.1.2  Corn Stover Ohgren et al. (2007) compared SSF and SHF processes at 8% WIS, using steam-pretreated corn stover in a paper with 270 citations. The enzymatic loading was 10 FPU/g WIS, and the yeast concentration in SSF was 1 g/L (dry weight) of a S. cerevisiae strain. When the whole slurry from the pretreatment stage was used, diluted to 8% WIS with water and pH adjusted, SSF gave a 13% higher overall ethanol yield than SHF: 72.4 versus 59.1% of the theoretical. The fermentation inhibitors in the liquid fraction of the pretreated slurry affected SSF and SHF in different ways. The overall ethanol yield (based on the untreated raw material) decreased when SSF was run in the absence of inhibitors compared with SSF with inhibitors present. On the contrary, the presence of inhibitors decreased the overall ethanol yield in the case of SHF. However, the SHF yield achieved in the absence of inhibitors was still lower than the SSF yield achieved with inhibitors present. Ohgren et al. (2006a) used a recombinant strain of S. cerevisiae, TMB3400, in the SSF process of whole steam-pretreated slurry of corn stover at high WIS in a paper with 221 citations. They observed that TMB3400 cofermented glucose and xylose with relatively high ethanol yields giving a high final ethanol concentration. The ethanol productivity increased with increasing concentration of pretreatment hydrolysate in the yeast production medium when SSF was performed in a fed-batch mode.

288

Bioethanol Fuel Production Processes. II

Zhang et al. (2010a) produced ethanol from steam-pretreated corn stover in a SSF process under different solids loadings and different enzyme dosages in a paper with 210 citations. They observed that the helical impeller stirring had better performances in the saccharification yield, ethanol yield, and energy cost than those of the Rushton impeller stirring. They recommended that a balance for achieving the optimal energy cost between the increased mixing energy cost and the reduced distillation energy cost at the high solids loading should be made. They tested the potentials of the new bioreactor under various SSF conditions for obtaining optimal ethanol yield. Varga et al. (2004) produced ethanol from the pretreated corn stover in a non-isothermal SSF process as a function of the solid and enzyme loadings in a paper with 203 citations. They pretreated stover by alkaline and acidic wet oxidation (WO) (195°C, 15 min, 12 bar oxygen). They added small amounts of cellulases at 50°C at first and then added more cellulases in combination with dried S. cerevisiae at 30°C. They observed that the phenols (0.4–0.5 g/L) and carboxylic acids (4.6–5.9 g/L) were present in the hemicellulose-rich hydrolysate at subinhibitory levels. Based on the cellulose available in the WO corn stover, they obtained 83% of the theoretical ethanol yield under optimized SSF conditions. They achieved this with a substrate concentration of 12% DM acidic WO corn stover at 30 FPU/g DM (43.5 FPU/g cellulose) enzyme loading. Even with 20 and 15 FPU/g DM (corresponding to 29 and 22 FPU/g cellulose) enzyme loading, they obtained ethanol yields of 76% and 73%, respectively. After 120 h of SSF, they obtained the highest ethanol concentration of 52 g/L (6 vol%). They showed that the cellulose in pretreated corn stover could be efficiently fermented to ethanol with up to 15% DM concentration as a further increase in substrate concentration reduced the ethanol yield significantly. Ohgren et al. (2006b) produced ethanol from steam-pretreated corn stover using the SSF process at high DM content in a paper with 194 citations. They performed SSF on steam-pretreated corn stover at 5%, 7.5%, and 10% WIS with 2 g/L hexose-fermenting S. cerevisiae. They observed that SSF at 10% WIS resulted in an ethanol yield of 74% based on the glucose content in the raw material and an ethanol concentration of 25 g/L. Furthermore, neither a higher yeast concentration (5 g/L) nor yeast cultivated on the liquid after the pretreatment resulted, under these conditions, in a higher overall ethanol yield.

32.3.2  The Alternative Fermentation Processes for the Wood There are eight HCPs for alternative fermentation processes for the wood (Table 32.2). Wingren et al. (2003) carried out a technoeconomic analysis of the enzymatic processes involved in the production of ethanol from softwood comparing SSF and SHF processes in a paper with 540 citations. They found that the ethanol production costs for the SSF and SHF processes were 0.57 and 0.63 U.S. dollar (USD)/L, respectively. Further, the capital cost was lower and the overall ethanol yield was higher for the SSF process. However, the recirculation of yeast following the SSF step was problematic. They recommended that the production cost for both processes could be lowered by increasing the income from the solid fuel coproduct. The energy consumption in the process could also be lowered by running the enzymatic hydrolysis or the SSF step at a higher substrate concentration and by recycling the process streams. For example, running SSF with the use of 8% rather than 5% non-soluble solid material would result in a 19% decrease in production cost. Further, if after distillation, 60% of the stillage stream was recycled back to the SSF step, the production cost would be reduced by 14%. The cumulative effect of these various improvements was a production cost of 0.42 USD/L for the SSF process. Cantarella et al. (2004) studied the effect of fermentation inhibitors released during steam explosion treatment of poplar on subsequent enzymatic hydrolysis and SSF in a paper with 224 citations. They hydrolyzed SE poplar with a blend of Celluclast and Novozym cellulases in the presence of the fermentation inhibitors produced during the preceding SE pretreatment process. The SE temperature and time were 214°C and 6 min, respectively, resulting in a log R0 of 4.13. In enzymatic hydrolysis tests at 45°C, biomass loading in the bioreactor was 100 gDW/L (dry weight) and the enzyme-to-biomass ratio was 0.06 g/gDW. The enzyme activities for endoglucanase, exoglucanase, and β-glucosidase were 5.76, 0.55, and 5.98 U/mg, respectively. They observed that acetic acid

No.

Papers

Biomass/Hydrolysate

Prt.

Bacteria

Parameters

1

Wingren et  al. (2003)

Softwood

Enzymes

Na

2

Ballesteros et al. (2004) Cantarella et al. (2004)

Poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, B. carinata residue Poplar

Cellulases, steam

K. marxianus

Steam, Celluclast, Novozym cellulase

Yeast

4

Itoh et al. (2003)

Beech

S. cerevisiae

5

Hinman et al. (1992) Alkasrawi et al. (2003) Rudolf et al. (2005) Stenberg et al. (2000)

Wood

Solvent, C. subvermispora, D. squalens, P. ostreatus, C. versicolor Na

Na

Softwood

Tween 20

Yeast

Spruce

Steam

Yeast

SSF, batch type, solid loadings

Softwood

Steam, SO2

S. cerevisiae

SSF, solid and enzyme loadings, ethanol yield, SHF

3

6

7

8

SSF, SHF, technoeconomic analysis, production costs SSF, biomass type, ethanol yield, pretreatments SSF, pretreatment, fermentation inhibitors, ethanol yield SSF, pretreatments, enzyme types, ethanol yield SSF, technoeconomics, plant capacity, wood cost, ethanol cost SSF, Tween 20, ethanol yield, cost

Lead Author

Affil.

Cits

SSF

Keywords

Zacchi, Guido 7006727748

Lund Univ. Sweden

540

Simultaneous saccharification, fermentation SSF

Manzanares, Paloma* 55779406300 Cantarella, Maria* 7003630895

CIEMAT Spain

385

Univ. L’Aquila Italy

224

Simultaneous saccharification, fermentation SSF

Watanabe, Takashi 57125173500 Hinman, Norman D. 6701762811 Zacchi, Guido 7006727748

Kyoto Univ. Japan NREL USA

175

Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation

Rudolf, Andreas 6603896561 Zacchi, Guido 7006727748

Lund Univ. Sweden Lund Univ. Sweden Lund Univ. Sweden

Alternative Fermentation Processes: Review

TABLE 32.2 Alternative Fermentation Processes for the Wood

163

161

145

144

*, female; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

289

290

Bioethanol Fuel Production Processes. II

(2 g/L), furfural, 5-hydroxymethylfurfural (5-HMF), syringaldehyde, 4-hydroxybenzaldehyde, and vanillin (0.5 g/L) did not significantly affect the enzyme activity, whereas formic acid (11.5 g/L) inactivated the enzymes and levulinic acid (29.0 g/L) partially affected the cellulase. Furthermore, the untreated SE biomass during the enzymatic attack gave rise to a non-fermentable hydrolysate, which became fermentable when rinsed SE biomass was used. The presence of acetic acid, vanillin, and 5-HMF (0.5 g/L) in SSF of 100 gDw/L biomass gave rise to ethanol yields of 84.0%, 73.5%, and 91.0%, respectively, with respective lag phases of 42, 39, and 58 h. Itoh et al. (2003) produced ethanol from beech using the SSF process in a paper with 173 citations. They used bioorganosolv pretreatments by ethanolysis and white rot fungi, Ceriporiopsis subvermispora (C. subvermispora), Dichomitus squalens, Pleurotus ostreatus, and Coriolus versicolor. They produced ethanol using S. cerevisiae AM12 and a commercial cellulase preparation, Meicelase, from Trichoderma viride. Among the four strains, they observed that C. subvermispora gave the highest yield on SSF. The yield of ethanol obtained after pretreatment with C. subvermispora for 8 weeks was 0.294 g/g of ethanolysis pulp (74% of theoretical) and 0.176 g/g of beech (62% of theoretical). The yield was 1.6 times higher than that obtained without the fungal treatments. The biological pretreatments saved 15% of the electricity needed for the ethanolysis. Hinman et al. (1992) carried out a technoeconomic analysis of the ethanol production from wood using the SSF process in a paper with 163 citations. They based the reference case design on a plant capacity of 1,920 dry t/d and a wood cost of $42/dry t. In this case, they found that the production cost of the ethanol product was about $1.22/gal. The combined effects of optimizing SSF enzyme loading, increasing plant capacity to 10,000 dry t/d, and reducing the wood cost to $34/dry t were to reduce the production cost to about $0.95/gal. Alkasrawi et al. (2003) explored the effect of Tween 20 surfactant as an additive in SSF of softwood in a paper with 161 citations. They observed that Tween 20 addition at 2.5 g/L had several positive effects on SSF. First, the ethanol yield increased by 8%. Second, the amount of enzyme loading was reduced by 50%, while maintaining a constant yield. Third, the enzyme activity increased in the liquid fraction at the end of SSF. Finally, the time required to attain maximum ethanol concentration was reduced. They noted that Tween 20 as an additive in SSF could significantly lower the operational cost of the SSF process. Rudolf et al. (2005) produced ethanol from steam-pretreated spruce in a batch and fed-batch type SSF process in a paper with 145 citations. They increased the fibrous content of steam-pretreated spruce to 10% by adapting the yeast to the inhibitory substrate and using a fed-batch process. They started the fed-batch experiments with a batch fermentation containing 6% DM. They then added fibrous slurry from the pretreatment four times during the first 24 h giving a final DM content corresponding to 10%. They produced the yeast aerobically on the hemicellulose hydrolysate obtained from the pretreatment. They then carried out the SSF batch and fed-batch experiments with a cell mass concentration of 2, 3, and 5 g/L. With adapted yeast, they completely converted the available hexoses within 72 h and the final ethanol concentrations reached 40–44 g/L. Furthermore, there were no major differences in performance between batch and fed batch, but the ethanol productivity during the first 24 h was higher in the fed-batch SSF experiments, particularly during the experiments with a cell mass concentration of 2 and 3 g/L. Stenberg et al. (2000) produced ethanol from SO2-impregnated and steam-pretreated softwood in a SSF process as a function of solid (2%–10% w/w) and enzyme (5–32 FPU/g cellulose) loadings in a paper with 145 citations. They used commercial enzymes in combination with S. cerevisiae. They observed that SSF was sensitive to contamination because lactic acid was produced. The ethanol yield increased with increasing cellulase loading. Furthermore, they obtained the highest ethanol yield, 68% of the theoretical based on the glucose and mannose present in the original wood, at 5% substrate concentration. This yield corresponded to 82% of the theoretical based on the cellulose and soluble glucose and mannose present at the start of SSF. Furthermore, a higher substrate concentration caused inefficient fermentation, whereas a lower substrate concentration, 2%, resulted in

Alternative Fermentation Processes: Review

291

increased formation of lactic acid, which lowered the yield. Compared with SHF, they concluded that SSF gave a higher yield and doubled productivity.

32.3.3  The Alternative Fermentation Processes for the Other Biomass There are eight HCPs for alternative fermentation processes for the other biomass: Sorghum sp. bagasse, B. carinata residue, industrial wastes, cellulose, corncobs, sugarcane leaves, Antigonon leptopus (A. leptopus) leaves, Saccharina japonica (S. japonica), Bermuda grass, reed, rapeseed, and citrus peels (Table 32.3). Kadar et al. (2004) produced ethanol from industrial waste (Solka-Floc, OCC waste cardboard, and paper sludge) using the SSF process in a paper with 221 citations. They compared two yeast strains, a commercially available baker’s yeast and K. marxianus, in two types of SSF experiments, i.e., isothermal SSF and SSF with temperature profiling (NSSF). They observed that old corrugated containers or cardboard (OCC) waste and paper sludge could be used as substrates for ethanol ­production in SSF. There was no significant difference observed between S. cerevisiae and K.  ­marxianus for the SSF. The ethanol yields were in the range of 0.31–0.34 g/g for both strains used. SSF resulted in higher ethanol yields compared with non-isothermal SSF. Wen et al. (2010) combined cellulase production, cellulose hydrolysis, and sugar fermentation into a single step, CBP, in a paper with 193 citations. They engineered S. cerevisiae strains to display a series of uni-, bi-, and trifunctional mini-cellulosomes. These mini-cellulosomes consisted of a mini-scaffolding containing a cellulose-binding domain and three cohesin modules, which were tethered to the cell surface through the yeast a-agglutinin adhesion receptor, and up to three types of cellulases, an endoglucanase, cellobiohydrolase, and a β-glucosidase, each bearing a C-terminal dockerin. Cell surface assembly of the mini-cellulosomes was dependent on the expression of the mini-scaffolding, indicating that the formation of the complex was dictated by the high-affinity interactions between cohesins and dockerins. Compared to the unifunctional and bifunctional minicellulosomes, the quaternary trifunctional complexes showed enhanced enzyme–enzyme synergy and enzyme proximity synergy. They observed that surface display of the trifunctional mini-cellulosomes gave yeast cells the ability to simultaneously break down and ferment phosphoric acidswollen cellulose to ethanol with a titer of 1.8 g/L. Zhang et al. (2010b) produced ethanol from high DM corncob using fed-batch SSF after combined pretreatment in a paper with 169 citations. They pretreated biomass with acid and alkali. They obtained an ethanol concentration as high as 69.2 g/L with 19% DM using batch SSF, resulting in an 81.2% overall ethanol yield. For the fed-batch process using a high solid concentration, they pretreated biomass with dilute sulfuric acid–NaOH and then added at different amounts during the first 24 h, to yield a final DM content of 25% (w/v). They observed that SSF conditions with cellulase loading of 22.8 FPU/g glucan, S. cerevisiae loading of 5 g/L, and substrate supplementation every 4 h yielded the highest ethanol concentration of 84.7 g/L after 96 h. This corresponded to a 79% overall ethanol yield. Krishna et al. (2001) produced ethanol from sugarcane leaves and A. leptopus leaves using Trichoderma reesei (T. reesei) cellulase and yeast cells in a SSF process in a paper with 158 citations. They compared the ability of Kluyveromyces fragilis (K. fragilis) NCIM 3358 with S. cerevisiae NRRL-Y-132. They observed that K. fragilis performed better in the SSF process and resulted in high yields of ethanol (2.5%–3.5% w/v) compared with S. cerevisiae (2.0%–2.5% w/v). Furthermore, they obtained increased ethanol yields when the cellulase was supplemented with β-glucosidase. The conversions with K. fragilis were completed in a short time. Finally, the substrates were in the following order in terms of fast conversions: Solka-Floc>A. leptopus>sugarcane. Jang et al. (2012) produced ethanol from the acid-pretreated S. japonica in a SSF process in a paper with 153 citations. They dried biomass by hot air, ground with a hammer mill, and filtered with a 200-mesh sieve before pretreatment. They then hydrolyzed it by thermal acid hydrolysis with H2SO4 and Termamyl 120 L, while the optimal saccharification conditions were 10% (w/v) seaweed

292

TABLE 32.3 Alternative Fermentation Processes for the Other Biomass No.

Papers

Biomass/Hydrolysate

Prt.

Bacteria

Parameters

Poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, B. carinata residue Industrial wastes

Cellulases, steam

K. marxianus

SSF, biomass type, ethanol yield, pretreatments

Na

SSF, biomass type, bacteria type, ethanol yield

Cellulose

Cellulosomes, cellulases, acids

S. cerevisiae, K. marxianus S. cerevisiae

Zhang et al. (2010b) Krishna et al. (2001) Jang et al. (2012)

Corncobs

Acids, alkalis, cellulases

S. cerevisiae

Sugarcane and A. leptopus leaves

Cellulases, β-glucosidase

K. fragilis, S. cerevisiae

SSF, pretreatments, solid, enzyme, yeast loadings, ethanol yield SSF, yeast types, enzyme types, ethanol yield

S. japonica

Acids, Termamyl 120 L.

P. angophorae

SSF, pretreatment, sugar and acid yields

7

Li et al. (2009)

Bermuda grass, reed, rapeseed

Acids, acetone

Yeast

SSF, solid loadings, ethanol yield, batch type

8

Wilkins et al. (2007)

Citrus peels

Steam, pectinase, cellulase, β-glucosidase

S. cerevisiae

SSF, ethanol yield, d-limonene concentration, enzyme loading, pH

2

3

4

5

6

CBP, bacteria engineering, ethanol yield

Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

Keywords Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation, SSF0 Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation Simultaneous saccharification, fermentation

Lead Author Manzanares, Paloma* 55779406300 Reczey, Kati* 7004072336

Affil.

Cits

CIEMAT Spain

385

221

Zhao, Huimin 7404778848

Budapest Univ. Technol. Econ. Hungary Univ. Ill. U. C. USA

Su, Rongxin 35249849000

Tianjin Univ. China

161

Krishna, Sajja H. 55665122300 Kim, Sung-Koo 57195386876 Chang, Ho N. 24431318500

Emory Univ. USA

158

Pukyong Natl. Univ. S. Korea

153

KAIST S. Korea

150

Wilkins, MR 56492323200

Univ. Nebraska Lincoln USA

141

193

Bioethanol Fuel Production Processes. II

Ballesteros et al. (2004) Kadar et al. 2004 Wen et al. (2010)

1

Alternative Fermentation Processes: Review

293

slurry, 40 mM H2SO4, and 1 g dcw/L isolated Bacillus sp. JS-1. They found that the reducing sugar concentration was 45.6 g/L, while the total yield of the hydrolysis was 69.1%. They finally obtained the highest ethanol concentration, 7.7 g/L (9.8 ml/L), with a theoretical yield of 33.3%, by SSF with 0.39 g dcw/L Bacillus sp. JS-1 and 0.45 g dcw/L of the yeast, Pichia angophorae KCTC 17574. Li et al. (2009) produced ethanol from Bermuda grass, reed, and rapeseed pretreated with phosphoric acid–acetone in a SSF process with a batch and fed-batch mode in a paper with 150 citations. They obtained 16 g/L of ethanol after 96 h of fermentation when the batch SSF experiments were conducted in a 3% low-effective cellulose. When batch SSF experiments were conducted with a higher cellulose content (10% effective cellulose for reed and Bermuda grass and 5% for rapeseed), they obtained higher ethanol concentrations and yields (of more than 93%). The fed-batch SSF strategy was adopted to increase the ethanol concentration further. Furthermore, when a higher WIS (up to 36%) was applied, the ethanol concentration reached 56 g/L of an inhibitory concentration of the yeast strain used in this study at 38°C. Wilkins et al. (2007) produced ethanol from steam-pretreated citrus peels in a SSF process by S. cerevisiae at 37°C as a function of d-limonene concentration, enzyme loading, and pH in a paper with 141 citations. They observed that ethanol concentrations after 24 h were reduced in fermentations with initial d-limonene concentrations greater than or equal to 0.33% (v/v) and final (24 h) d-limonene concentrations greater than or equal to 0.14% (v/v). Furthermore, ethanol production was reduced when enzyme loadings were (IU or FPU/g peel dry solids) less than 25, pectinase; 0.02, cellulase; and 13, β-glucosidase. Finally, ethanol production was greatest when the initial pH of the peel waste was adjusted to 6.0.

32.4 DISCUSSION 32.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat biomass to enhance the yield of the bioethanol before bioethanol production through the hydrolysis and fermentation of biomass and hydrolysates, respectively. The research in the field of alternative fermentation processes for bioethanol production for the SHF to improve ethanol yield has intensified in this context in recent years. The key research front has been the SSF of biomass. The other related research fields have been the CBP of biomass, continuous ethanol production (CEP) of biomass, SSCF of biomass, and continuous ethanol fermentation (CEF). Furthermore, this innovative research field has a focus on wood, rice straw, wheat straw, corn stover, and other biomass such as grass or algae. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been several review papers for this field, there has been no review of the 25 most-cited articles in this field. Thus, this book chapter presents a review of the 25 most-cited articles on alternative fermentation processes for bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the 200 most-cited first population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replicative studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 141 citations each were selected for the review study. Key findings from each paper were taken from the

294

Bioethanol Fuel Production Processes. II

abstracts of these papers and were discussed. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in alternative fermentation processes for bioethanol production with regard to biomass used in these processes is given in Table 32.4. As this table shows, there are three primary research fronts for this field: agricultural residues, wood, and other biomass with 48%, 32%, and 24% of the sample papers, respectively. Furthermore, corn stover and wheat straw are prolific agricultural residues on an individual basis. Furthermore, alternative fermentation processes of agricultural residues and wood are the most influential research fronts with 18% and 16% surplus, respectively, followed by wheat and rice straw with 8% and 4% surplus, respectively. Similarly, other feedstocks and lignocellulosic biomass are the least influential research fronts with 26% and 10% deficit, respectively. Furthermore, cellulosic biomass and lignocellulose are the fronts with 5% deficit each.

TABLE 32.4 The Most Prolific Research Fronts for Alternative Fermentation Processes for Bioethanol Production No. 1

2 3

Research Fronts

N Paper Review (%)

N Paper (%) Sample

Surplus (%)

Agricultural residues Corn stover

48.0 20.0

29.9 10.3

18.1 9.7

Wheat straw

16.0

8.4

7.6

Rice straw

8.0

3.7

4.3

Corncobs

4.0

1.9

2.1

Sorghum bagasse

4.0

0.9

3.1

Sugarcane bagasse

0.0

2.8

−2.8

Barley straw

0.0

0.9

−0.9

Sugarcane leaves

0.0

0.9

−0.9

Wood Other feedstocks Food waste

32.0 24.0 4.0

15.9 49.5 6.5

16.1 −25.5 −2.5

Grass

4.0

4.7

−0.7

Industrial wastes

4.0

4.7

−0.7

Algae

4.0

1.9

2.1

Cellulose

4.0

1.9

2.1

A. leptopus leaves

4.0

0.9

3.1

Lignocellulosic biomass

0.0

10.3

−10.3

Cellulosic biomass

0.0

4.7

−4.7

Lignocellulose

0.0

4.7

−4.7

corn

0.0

1.9

−1.9

potato

0.0

1.9

−1.9

Xylose

0.0

1.9

−1.9

Cactus

0.0

0.9

−0.9

Glucose

0.0

0.9

−0.9

Inulins

0.0

0.9

−0.9

Sugarcane

0.0

0.9

−0.9

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 107 papers.

295

Alternative Fermentation Processes: Review

TABLE 32.5 The Most Prolific Thematic Research Fronts for Alternative Fermentation Processes for Bioethanol Production No. 1 2 3 4 5

Research Fronts

N Paper Review (%)

N Paper (%) Sample

Surplus (%)

SSF Consolidated bioprocessing (CBP) SSCF Continuous ethanol fermentation (CEF) Continuous ethanol production (CEP)

92.0 4.0 4.0 0.0 0.0

72.9 14.0 8.4 1.9 3.7

19.1 −10.0 −4.4 −1.9 −3.7

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 107 papers.

Information about the thematic research fronts for the sample papers in alternative fermentation processes for bioethanol production is given in Table 32.5. As this table shows, there is only one primary research front for this field: the SSF with 92% of the reviewed papers and the other fronts as the CBP and SSCF with 4% of the reviewed papers each. Furthermore, the SSF is the most influential front with 19% surplus as they are overrepresented in the HCPs. Similarly, the CBP is the least influential research front with 10% deficit as they are underrepresented in the HCPs as a significant part of the population papers are reviews. Furthermore, SSCF, CEP, and CEH are also underrepresented in the HCPs by 4%, 4%, and 2%, respectively.

32.4.2  The Alternative Fermentation Processes for the Straw and Corn Stover There are 11 HCPs for alternative fermentation processes for the straw and corn stover with six and five HCPs, respectively (Table 32.1). 32.4.2.1  Rice and Wheat Straw Ballesteros et al. (2004) carried out the SSF process for ethanol production from poplar, eucalyptus, Sorghum sp. bagasse, wheat straw, and B. carinata residues using the K. marxianus and obtained the SSF yields in the range of 50%–72% of the maximum theoretical SSF yield. Furthermore, Karimi et al. (2006) carried out the SSF process using dilute acid-pretreated rice straw with M. indicus, R. oryzae, and S. cerevisiae and observed that all the strains produced ethanol from the pretreated rice straw with an overall ethanol yield of 40%–74% of the maximum theoretical SSF yield. Ko et al. (2009) produced ethanol from rice straw using optimized aqueous ammonia soaking pretreatment and SSF process and determined the optimal reaction conditions for the maximum ethanol yield and productivity. Furthermore, Tomas-Pejo et al. (2008) produced ethanol from SE wheat straw using SSF and SHF processes and obtained the highest ethanol concentration in an SSF process and volumetric ethanol productivity. Alfani et al. (2000) produced ethanol from steam-pretreated wheat straw using SSF and SHF processes and obtained ethanol yields close to 68% of the theoretical. Furthermore, Linde et al. (2008) produced ethanol from steam-pretreated wheat straw using the SSF process at low yeast and enzyme loadings and obtained a high overall ethanol yield, 67% of the theoretical. 32.4.2.2  Corn Stover Ohgren et al. (2007) compared SSF and SHF processes at 8% WIS using steam-pretreated corn stover and observed that the SSF process gave a 13% higher overall ethanol yield than SHF. Further, Ohgren et al. (2006a) used a recombinant strain of S. cerevisiae, TMB3400, in the SSF process of

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whole steam-pretreated slurry of corn stover at high WIS and observed that TMB3400 cofermented glucose and xylose with relatively high ethanol yields giving a high final ethanol concentration. Zhang et al. (2010a) produced ethanol from steam-pretreated corn stover in a SSF process under different solid loadings and different enzyme dosages and observed that the helical impeller stirring had better performances in the saccharification yield, ethanol yield, and energy cost than those of the Rushton impeller stirring. Further, Varga et al. (2004) produced ethanol from the pretreated corn stover in a non-isothermal SSF process as a function of the solid and enzyme loadings and obtained 83% of the theoretical ethanol yield under optimized SSF conditions. Ohgren et al. (2006b) produced ethanol from steam-pretreated corn stover using the SSF process at high DM content and observed that SSF at 10% WIS resulted in an ethanol yield of 74% and an ethanol concentration of 25 g/L. These HCPs show a sample of the research on alternative fermentation processes of rice and wheat straw and corn stover for bioethanol production. These studies hint that the SSF processes result in higher ethanol yield and productivity with respect to the SHF process. Furthermore, biomass type, pretreatments, fermentation inhibitors, production costs, ethanol yield, fermentation type, bacteria types, solid and enzyme loadings, and bacteria engineering are among the key research variables studied in these HCPs for the rice and wheat straw and corn stover.

32.4.3  The Alternative Fermentation Processes for the Wood There are eight HCPs for alternative fermentation processes for bioethanol production (Table 32.2). Wingren et al. (2003) carried out a technoeconomic analysis of the enzymatic processes involved in the production of ethanol from softwood comparing SSF and SHF processes and found that the ethanol production costs for the SSF and SHF processes were 0.57 and 0.63 USD/L, respectively. Furthermore, Cantarella et al. (2004) studied the effect of fermentation inhibitors released during steam explosion pretreatment of poplar on subsequent enzymatic hydrolysis and SSF and observed that the untreated SE biomass during the enzymatic attack gave rise to a non-fermentable hydrolysate, which became fermentable when rinsed SE biomass was used. Itoh et al. (2003) produced ethanol from beech using the SSF process and observed that among the four strains, C. subvermispora gave the highest yield on SSF. Furthermore, Hinman et al. (1992) carried out a technoeconomic analysis of the ethanol production from wood using the SSF process and found that the estimate of the production cost of the ethanol product was about $1.22/gal. Alkasrawi et al. (2003) explored the effect of Tween 20 surfactant as an additive in SSF of softwood and observed that Tween 20 addition at 2.5 g/L had several positive effects on SSF: The ethanol yield increased by 8%. Furthermore, Rudolf et al. (2005) produced ethanol from steampretreated spruce in a batch and fed-batch type SSF process and with adapted yeast, while they completely converted the available hexoses within 72 h and the final ethanol concentrations reached 40–44 g/L. Stenberg et al. (2000) produced ethanol from SO2-impregnated and steam-pretreated softwood in a SSF process as a function of solid and enzyme loadings and observed that SSF was sensitive to contamination because lactic acid was produced. These HCPs show a sample of the research on alternative fermentation processes of wood for bioethanol production. These studies hint that the SSF processes result in higher ethanol yield and productivity with respect to the SHF process for this biomass as well. Further, technoeconomic analysis, production costs, biomass type, pretreatments, fermentation inhibitors, ethanol yield, fermentation type, enzyme types, plant capacity, batch type, and solid and enzyme loadings are among the key research variables studied in these HCPs for the wood.

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32.4.4  The Alternative Fermentation Processes for the Other Biomass There are eight HCPs for alternative fermentation processes for the other biomass: Sorghum sp. bagasse, B. carinata residue, industrial wastes, cellulose, corncobs, sugarcane leaves, A. leptopus leaves, S. japonica, Bermuda grass, reed, rapeseed, and citrus peels (Table 32.3). Kadar et al. (2004) produced ethanol from industrial waste (Solka-Floc, OCC waste cardboard, and paper sludge) using the SSF process and observed that OCC waste and paper sludge could be used as substrates for ethanol production in SSF. Furthermore, Wen et al. (2010) combined cellulase production, cellulose hydrolysis, and sugar fermentation into a single step, CBP, and observed that surface display of the trifunctional mini-cellulosomes gave yeast cells the ability to simultaneously break down and ferment phosphoric acid-swollen cellulose to ethanol with a titer of 1.8 g/L. Zhang et al. (2010b) produced ethanol from high DM corncob using fed-batch SSF after combined pretreatment and determined the optimal SSF conditions with cellulase loading, S. cerevisiae loading, and substrate supplementation, yielding the highest ethanol concentration. Furthermore, Krishna et al. (2001) produced ethanol from sugarcane leaves and A. leptopus leaves using T. reesei cellulase and yeast cells in a SSF process and observed that K. fragilis performed better in the SSF process and resulted in high yields of ethanol compared with S. cerevisiae. Jang et al. (2012) produced ethanol from the acid-pretreated S. japonica in a SSF process and determined optimal reducing sugar concentration and hydrolysis yield. Furthermore, Li et al. (2009) produced ethanol from Bermuda grass, reed, and rapeseed pretreated with phosphoric acid–acetone and obtained 16 g/L of ethanol when the batch SSF experiments were conducted in a 3% loweffective cellulose. Wilkins et al. (2007) produced ethanol from steam-pretreated citrus peels in a SSF process by S. cerevisiae at 37°C as a function of d-limonene concentration, enzyme loading, and pH and observed that d-limonene affected ethanol concentrations. These HCPs show a sample of the research on alternative fermentation processes of other biomass such as grass, algae, and food and industrial wastes for bioethanol production. These studies hint that the SSF processes result in higher ethanol yield and productivity with respect to the SHF process for these types of biomass as well. Further, biomass type, ethanol yield pretreatments, bacteria type, fermentation type, bacteria engineering, solid, enzyme, and yeast loadings, yeast types, enzyme types, sugar and acid yields, batch type, d-limonene concentration, enzyme loading, and pH are among the key research variables studied in these HCPs for this biomass.

32.5  CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 141 citations each is given under two primary headings: alternative fermentation processes for the rice and wheat straw, wood, and other biomass such as algae and grass. The usual characteristics of these HCPs are that alternative fermentation processes such as SSF, CBP, and SSCF, as a viable alternative to the SHF processes, result in higher ethanol yield and productivity and this research field has crucial importance in the fermentation research to improve the ethanol yield. The key findings on these research fronts should be read in light of the increasing public concerns about climate change, greenhouse gas (GHG) emissions, and global warming as these concerns have been certainly behind the boom in the research on bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. The recent supply shocks caused by the coronavirus disease 2019 (COVID-19) pandemics and the Russian invasion of Ukraine also highlight the importance of the production and utilization of the bioethanol fuels as an alternative to crude oil-based gasoline and diesel fuels.

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As Table 32.4 shows, the primary research fronts with regard to feedstocks are agricultural residues, wood, and other biomass. On an individual basis, corn stover and wheat straw are the most prolific research fronts. Further, agricultural residues and wood are the most influential feedstocks, followed by wheat and rice straw. Similarly, other feedstocks and lignocellulosic biomass are the least influential feedstocks, while cellulosic biomass and lignocellulose are the other feedstocks with deficits. Similarly, Table 32.5 shows that the SSF is the primary fermentation process for agricultural residues, wood, and other biomass. The other research fronts are the CBP and SSCF. Furthermore, the SSF is the most influential front as they are overrepresented in the HCPs. Similarly, the CBP is the least influential research front as they are underrepresented in the HCPs. Further, SSCF, CEP, and CEH are also underrepresented in the HCPs. These studies emphasize the importance of proper incentive structures for the efficient development and application of fermentation of the substrates and hydrolysates to enhance bioethanol yield of the substrates and hydrolysates in light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as Europe and to a lesser extent USA, China, and S. Korea have developed strong incentive structures for the effective development and application of alternative fermentation processes for bioethanol production. In light of the supply shocks caused primarily by the COVID-19 pandemic and the Russian invasion of Ukraine, it is expected that the incentive structures such as public funding would be enhanced to increase the share of bioethanol fuels in the global fuel portfolio as a strong alternative to crude oil-based gasoline and diesel fuels. In this context, it is expected that the most prolific researchers, institutions countries, funding bodies, and journals would have a first-mover advantage to benefit from such potential incentives. It is recommended that such review studies are performed for the primary research fronts of alternative fermentation processes for wood, agricultural residues, and other biomass.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of alternative fermentative processes has been gratefully acknowledged.

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Metabolic Engineering for the Bioethanol Production Scientometric Study Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

33.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining the energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), COVID-19 pandemics (Fauci et al., 2020; Li et al., 2020), or wars (Jones, 2012; Le Billon, 2001) in the aftermath of the Russian invasion of Ukraine (Reeves, 2014). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of the metabolic engineering for the bioethanol production to improve the ethanol yield has intensified in this context in recent years (Dien et al., 2003; Jeffries and Jin, 2004). The key research fronts have been the metabolic engineering of Saccharomyces cerevisiae (Hahn-Hagerdal et al., 2001, 2007), Escherichia coli (Dharmadi et al., 2006; Ingram et al., 1987), Zymomonas mobilis (Deanda et al., 1996; Seo et al., 2005), Pichia (Cereghino et al., 2002; Hong et  al., 2002), Clostridium (Argyros et al., 2011; Mermelstein et al., 1992), other microorganisms (Ho  et al., 1998; Inui et al., 2005), and the substrates such as corn (Torney et al., 2007), grass (Fu et al., 2011), and cyanobacteria (Deng and Coleman, 1999; Dexter and Fu, 2009). However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a). As the recently published scientometric studies focus on the fermentation process in general (Calvo et al., 2022; Devos and Colla, 2022), this book chapter presents a scientometric study of the research in the metabolic engineering for the bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. 302

DOI: 10.1201/9781003226499-42

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33.2 MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix for future replicative studies. As a second step, two sets of data were used for this study. First, a population sample of 3,352 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 168 most-cited papers, corresponding to 5% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the metabolic engineering for the bioethanol production. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

33.3 RESULTS 33.3.1 The Most-Prolific Documents in the Metabolic Engineering for Bioethanol Production The information on the types of documents for both datasets is given in Table 33.1. The articles and conference papers, published in journals, dominate both the sample (90%) and population (94%) papers as they are under-represented in the sample papers by 4%. Further, review papers and short surveys have a surplus as they are over-represented in the sample papers by 7% as they constitute 10% and 3% of the sample and population papers, respectively. It is further notable that 97% of the population papers were published in journals, while 2 and 1% of them were published in book series and books, respectively. On the contrary, 99% of the sample papers were published in the journals.

33.3.2 The Most-Prolific Authors in the Metabolic Engineering for Bioethanol Production The information about the most-prolific 21 authors with at least 2.4% of sample papers each is given in Table 33.2. The most-prolific author is Barbel Hahn-Hagerdal of Lund University of Sweden with 10.7% of the sample papers, followed by Lonnie O. Ingram with 9.5% of the sample papers. The other prolific authors are Youg Su Jin, Marie F. Gorwa-Grauslund, Keelnatham T. Shanmugam, Thomas W. Jeffries, and Sean W. York with 4.8%–6.0% of the sample papers each. The most influential author is Barbel Hahn-Hagerdal with 9.3% surplus, followed by Lonnie O. Ingram with 7.9% surplus. The other influential authors are Keelnatham T. Shanmugam, Marie F. Gorwa-Grauslund, Youg Su Jin, Sean W. York, and Thomas W. Jeffries, with 4.2%–4.8% surplus each. The most-prolific institution for the sample dataset is the University of Florida with four authors, while the Chalmers University of Technology, Delft University of Technology, Kobe University, and Lund University house two authors each. In total, 15 institutions house these top authors. On the other hand, the most-prolific country for the sample dataset is the USA with 11 authors, followed by Sweden with five authors. The other prolific countries are Japan and Netherlands with two authors each. In total, five countries house these top authors.

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TABLE 33.1 Documents in the Metabolic Engineering for the Bioethanol Production Documents Article Review Short Survey Conference paper Book chapter Note Editorial Letter Book Sample size

Sample Dataset (%) 88.1 7.1 3.0 1.8 0.0 0.0 0.0 0.0 0.0 168

Population Dataset (%) 92.0 2.9 0.5 1.8 1.2 1.0 0.2 0.2 0.1 3,352

Surplus (%) −3.9 4.2 2.5 0.0 −1.2 −1.0 −0.2 −0.2 −0.1

Population dataset, the number of papers (%) in the set of the 3,352 population papers; sample dataset, the number of papers (%) in the set of 168 highly cited papers.

There are two primary research fronts for these top authors: Metabolic engineering of the S. cerevisiae and E. coli used to ferment the hydrolysates with 15 and 7 authors, respectively. There is also one paper for Chlostridium. On the other hand, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only six of these top researchers are female with a representation rate of 29%. Additionally, there are other authors with the relatively low citation impact and with 0.3%– 0.7% of the population papers each: Hugh H. Lawford, Tomohisa Hasunuma, Joyce D. Rousseau, Akinori Matsushika, Pratap R. Patnaik, Jin-Ho Seo, Daniel G. Olson, Johan M. Thevelein, Michael A. Cotta, Bruce S. Dien, Yang Gu, Weihong Jiang, Adam M. Guss, Yong-Cheol Park, Andriy A. Sibirny, Ying-Jin Yuan, Feng-Wu Bai, Steven D. Brown, Yue-Qin Tang, Shengde Zhou, Suk-Jin Ha, Tsutomu Kodaki, Siqing Liu, Dewey D. Y. Ryu, Badal C. Saha, Ryosuke Yamada, Sinqing Zhao, Xiaoming Bao, Sung Ong Han, Alfredo Martinez, Liangcai Peng, Shigeki Sawayama, Hiroshi Shimizu, Sheng Yang, and Shang-Tian Yang.

33.3.3 The Most-Prolific Research Output by Years in Metabolic Engineering for Bioethanol Production Information about papers published between 1970 and 2022 is given in Figure 33.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s with 48% of the population dataset. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 14%, 20%, 13%, 4%, and 2% respectively. Additionally, 1% of the population papers were published between 1949 and 1969. Further, there was a rising trend for the research out for the population papers between 2006 and 2012, but after that, it became flat losing its momentum. Further, there was no sharp rise in the research out in 2020 and 2021 due to the supply shocks. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 61 and 20% of the sample dataset, respectively. The publication rates for the 1990s, 1980s, and 1970s were 15%, 4%, and 0% of the sample papers, respectively. The most-prolific publication year for the population dataset was 2019 with 5.9% of the dataset, followed by 2021 with 5.8% of the papers. Further, 66% of the population papers were published between 2007 and 2022. Similarly, 86% of the sample papers were published between 1998 and 2014, while the most-prolific publication year was 2009 with 10.1% of the sample papers. The other prolific years were 2007, 2001, 2003, and 2004 with 6%–6.9% of the sample papers each.

No.

Author Name

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21

Hahn-Hagerdal. Barbel* Ingram, Lonnie O. Jin, Youg Su Gorwa-Grauslund, Marie F.* Shanmugam, Keelnatham T. Jeffries, Thomas W. York, Sean W. Kondo, Akihiko Yomano, Lorraine P.* Pronk, Jack T. Jonsson, Leif J. Van Dijken, Johannes P. Kim, Soo Rin* Ho, Nancy W. Y.* Fukuda, Hideki Gonzalez, Ramon Lynd, Lee R. Olsson, Lisbeth* Nielsen, Jens Clark, David P. Papoutsakis, Eleftherios T.

Author Code 7005389381 7102962097 57204009076 6603563787 7006092922 7005806269 7005247145 57203868143 6602324950 7005313057 7102349315 7102979857 36659584200 7102776244 55425022800 57192167471 35586183800 7203077540 55572933700 7404789961 7005949204

Sample Papers (%) 10.7 9.5 6.0 5.4 5.4 4.8 4.8 4.2 4.2 3.6 3.6 3.6 3.0 3.0 3.0 3.0 2.4 2.4 2.4 2.4 2.4

Population Papers (%) 1.4 1.6 1.5 0.7 0.6 0.6 0.5 1.4 0.5 0.5 0.3 0.2 0.7 0.4 0.3 0.3 0.6 0.6 0.5 0.3 0.3

Surplus (%) 9.3 7.9 4.5 4.7 4.8 4.2 4.3 2.8 3.7 3.1 3.3 3.4 2.3 2.6 2.7 2.7 1.8 1.8 1.9 2.1 2.1

Institution Lund Univ. Univ. Florida Univ. Ill. U. C. Lund Univ. Univ. Florida Xylome Corp. Univ. Florida Kobe Univ. Univ. Florida Delft Univ. Technol. Umea Univ. Delft Univ. Technol. Kyungpook Natl. Univ. Purdue Univ. Kobe Univ. Univ. S. Florida Tampa Dartmouth Coll. Chalmers Univ. Technol. Chalmers Univ. Technol. S. Ill. Univ. Carbondale Univ. Delaware

Country

HI

N

Res. Front

Sweden USA USA Sweden USA USA USA Japan USA Netherlands Sweden Netherlands S. Korea USA Japan USA USA Sweden Sweden USA USA

75 72 46 38 55 58 24 89 30 74 39 68 24 33 56 35 74 59 112 29 73

258 281 212 102 150 156 30 792 43 293 147 190 74 74 222 108 286 244 901 67 340

S E S S E S E S E S S S S S S E S S S E C

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TABLE 33.2 The Most-Prolific Authors in the Metabolic Engineering for the Bioethanol Production

Author code, the unique code given by Scopus to the authors; C., Clostridium; E, Escherichia; population papers, the number of papers authored in the population dataset; S, Saccharomyces; sample papers, the number of papers authored in the sample dataset.

305

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12

Number of papers (%)

10

Population papers Sample papers

8

6

4

2

0

FIGURE 33.1  T  he research output by years regarding the metabolic engineering for the bioethanol production.

33.3.4 The Most-Prolific Institutions in the Metabolic Engineering for Bioethanol Production Information about the most-prolific 17 institutions publishing papers on the metabolic engineering for the bioethanol production with at least 2.4% of the sample papers each is given in Table 33.3. The most-prolific institution is the Lund University with 14.3% of the sample papers, followed by the University of Florida with 8.9% of the sample papers. The other prolific institutions are the Kobe University and University of Illınois Urbana Champaign with 4.2% of the sample papers each. The top country for these most-prolific institutions is the USA with 11 institutions, while Japan and Netherlands house two institutions each. In total, only five countries house these top institutions. On the other hand, the institution with the most citation impact is the Lund University with 12.4% surplus, followed by the University of Florida with 7.2% surplus. The other prolific institutions are Bird Engineering Inc., Massachusetts Institute of Technology, and Delft University of Technology with 2.9%–3.4% surplus each. Additionally, there are other institutions with the relatively low citation impact and with 0.6%– 2.0% of the population papers each: Chinese Academy of Sciences, Tianjin University, Seoul National University, Oak Ridge National Laboratory, Korea University, Tianjin University of Science & Technology, Jiangnan University, East China University of Science and Technology, National Institute of Advanced Industrial Science and Technology, University of Toronto, Shanghai Jiao Tong University, University of Sao Paulo, Osaka University, Lawrence Berkeley National Laboratory, Tsinghua University, Institute of Microbial Technology India, University of California Davis, Catholic University Leuven, National Renewable Energy Laboratory, Korea Research Institute of Bioscience and Biotechnology, Stellenbosch University, Dalian University of Technology, CNRS, Kyowa Kirin Co., Chinese Academy of Agricultural Sciences, University of Georgia, Michigan State University, Korea Advanced Institute of Science and Technology, University of Minho, Shandong University, and Huazhong Agricultural University.

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TABLE 33.3 The Most-Prolific Institutions in Metabolic Engineering for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17

Institutions Lund Univ. Univ. Florida Univ. Ill. Urb. Champ. Kobe Univ. Tech. Univ. Denmark USDA Agr. Res. Serv. Univ. Wisconsin-Madison Delft Univ. Technol. Massachusetts Inst. Technol. Bird. Eng. Inc. Kyoto Univ. Purdue Univ. Rice Univ. USDA Forest Serv. Univ. Calif. Berk. Dartmouth Coll. Northwestern Univ.

Country Sweden USA USA Japan Denmark USA USA Netherlands USA Netherlands Japan USA USA USA USA USA USA

Sample Papers (%)

Population Papers (%)

Surplus (%)

14.3 8.9 4.2 4.2 3.6 3.6 3.6 3.6 3.6 3.6 3.0 3.0 3.0 3.0 2.4 2.4 2.4

1.9 1.7 1.9 1.6 1.7 1.3 1.1 0.7 0.4 0.2 1.0 0.8 0.5 0.5 0.7 0.7 0.3

12.4 7.2 2.3 2.6 1.9 2.3 2.5 2.9 3.2 3.4 2.0 2.2 2.5 2.5 1.7 1.7 2.1

33.3.5 The Most-Prolific Funding Bodies in the Metabolic Engineering for Bioethanol Production Information about the most-prolific nine funding bodies funding at least 1.8% of the sample papers each is given in Table 33.4. Only 31% and 48% of the sample and population papers were funded. The most-prolific funding body is the National Institute of General Medical Sciences with 4.8% of the sample papers. New Energy and Industrial Technology Development Organization, the US Department of Agriculture, US Department of Energy, Ministry of Education, Culture, Sports, Science and Technology, and Seventh Framework Program are the other prolific funding bodies with 2.4%–3.6% of the sample papers each. On the other hand, the most-prolific country for these top funding bodies is the USA with three funding bodies, followed by Japan and the EU with two funding bodies each. In total, only four countries and the EU house these top funding bodies. The funding body with the most citation impact is the National Institute of General Medical Sciences with 3.3% surplus. The US Department of Agriculture, New Energy and Industrial Technology Development Organization, and Seventh Framework Program are the other influential funding bodies with around 1.6%–2.2% surplus each. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 10% deficit. This funding body is the largest funder of population papers with over 11% funding rate. The other funding bodies with the relatively low citation impact and with 0.6%–2.9% of the population papers each are the Ministry of Science and Technology of China, National Key Research and Development Program of China, Japan Society for the Promotion of Science, National Research Foundation of Korea, National Science Foundation, Ministry of Education of China, National Council for Scientific and Technological Development, Fundamental Research Funds for the Central Universities, National High-Tech Research and Development Program, Higher Education Personnel Improvement Coordination, Chinese Academy of Sciences, Research Support Foundation of the State of Sao Paulo, Office of Science, Energy Biosciences Institute, Ministry

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TABLE 33.4 The Most-Prolific Funding Bodies in Metabolic Engineering for the Bioethanol Production No. 1 2 3 4 5 6 7 8 9

Funding Bodies Natl. Inst. Gen. Med. Sci. New Ener. Ind. Technol. Devnt. Prog. US Dept. Ener. US Dept. Agric. Minist. Educ. Cult. Sport. Sci. Technol. Seventh Framew. Prog. Natl. Natr. Sci. Found. China Eur. Comm. Biotechnol. Biol. Sci. Res. Counc.

Country USA Japan USA USA Japan EU China EU UK

Sample Paper No. (%) 4.8 3.6 3.0 3.0 2.4 2.4 1.8 1.8 1.8

Population Paper No. (%)

Surplus (%)

1.5 1.5 2.3 0.8 2.7 0.8 11.4 1.3 0.7

3.3 2.1 0.7 2.2 −0.3 1.6 −9.6 0.5 1.1

of Science, ICT and Future Planning, National Basic Research Program of China (973 Program), Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, Natural Sciences and Engineering Research Council of Canada, UK Research and Innovation, Biological and Environmental Research, and Ministry of Finance of Japan.

33.3.6 The Most-Prolific Source Titles in the Metabolic Engineering for Bioethanol Production Information about the most-prolific 18 source titles publishing at least 1.8% of the sample papers each in metabolic engineering for the bioethanol production is given in Table 33.5. The most-prolific source title is Applied and Environmental Microbiology with 19.6% of the sample papers, followed by Applied Microbiology and Biotechnology with 7.7% of the sample papers. The other prolific titles are Biotechnology and Bioengineering, Metabolic Engineering, Proceedings of the National Academy of Sciences of the United States of America, FEMS Yeast Research, and Journal of Bacteriology with 4.2%–6.5% of the sample papers each. On the other hand, the source title with the most citation impact is the Applied and Environmental Microbiology with 15.8% surplus, followed by Proceedings of the National Academy of Sciences of the United States of America with 5% surplus. The other influential titles are Metabolic Engineering, FEMS Yeast Research, Biotechnology and Bioengineering, and Applied Microbiology and Biotechnology with 2.8%–3.9% surplus each. Similarly, the source title with the least impact is the Biotechnology for Biofuels with 0.4% deficit. The other source titles with the relatively low citation impact with 0.5%–3.0% of the population paper each are Biotechnology Letters, Bioresource Technology, Journal of Industrial Microbiology and Biotechnology, Journal of Biotechnology, Genome Announcements, Plos One, Journal of Bioscience and Bioengineering, Applied Biochemistry and Biotechnology, Process Biochemistry, Agricultural and Biological Chemistry, Bioscience Biotechnology and Biochemistry, BMC Genomics, Biochemical Engineering Journal, Scientific Reports, World Journal of Microbiology and Biotechnology, Bioprocess and Biosystems Engineering, Food Chemistry, Journal of Microbiology and Biotechnology, Microbiology Resource Announcements, Current Genetics, LWT, Biotechnology and Bioprocess Engineering, Journal of Agricultural and Food Chemistry, Protein Expression and Purification, AMB Express, MGG Molecular General Genetics, Journal of Applied Microbiology, Journal of Biological Chemistry, and Food Research International.

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TABLE 33.5 The Most-Prolific Source Titles in Metabolic Engineering for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18

Source Titles Applied and Environmental Microbiology Applied Microbiology and Biotechnology Biotechnology and Bioengineering Metabolic Engineering Proceedings of the National Academy of Sciences of the United States of America FEMS Yeast Research Journal of Bacteriology Microbial Cell Factories Biotechnology Progress Enzyme and Microbial Technology Current Opinion in Biotechnology Biotechnology for Biofuels Yeast Applied Biochemistry and Biotechnology Part A Enzyme Engineering and Biotechnology Advances in Biochemical Engineering Biotechnology Biotechnology Advances Nature Biotechnology Energy And Environmental Science

Sample Papers (%)

Population Papers (%)

Surplus (%)

19.6 7.7 6.5 6.5 5.4

3.8 4.9 3.3 2.6 0.4

15.8 2.8 3.2 3.9 5.0

4.8 4.2 3.0 3.0 2.4 2.4 1.8 1.8 1.8

1.4 2.1 1.3 1.2 1.3 0.2 2.2 1.0 0.8

3.4 2.1 1.7 1.8 1.1 2.2 −0.4 0.8 1.0

1.8

0.2

1.6

1.8 1.8 1.8

0.2 0.1 0.1

1.6 1.7 1.7

33.3.7 The Most-Prolific Countries in the Metabolic Engineering for Bioethanol Production Information about the most-prolific 15 countries publishing at least 1.8% of sample papers each in metabolic engineering for the bioethanol production is given in Table 33.6. The most-prolific country is the USA with 48% of the sample papers, followed by Sweden and Japan with 16 and 11% of the sample papers, respectively. Germany, China, the UK, Denmark, S. Korea, and Netherlands are the other prolific countries with 3.6%–7.7% of the sample papers each. Further, eight European countries produce 44% and 19% of the sample and population papers, respectively, with 25% surplus. On the other hand, the country with the most citation impact is the USA with 24.1% surplus, followed by Sweden with 13% surplus. The other influential countries are Germany, Denmark, Netherlands, and the UK with 1.7%–2.8% surplus each. Similarly, the country with the least citation impact is China with 14.4% deficit, while S. Korea, Japan, and Canada have 0.4%–3.9% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.5%–5.1% of the sample papers each: India, Brazil, Spain, Taiwan, Australia, Italy, Portugal, Thailand, Belgium, Russia, Egypt, Turkey, Poland, Austria, Malaysia, Indonesia, Iran, Singapore, Pakistan, Ukraine, Argentina, and Greece.

33.3.8 The Most-Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production Information about the most-prolific nine Scopus subject categories indexing at least 3.6% of the sample papers each is given in Table 33.7.

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TABLE 33.6 The Most-Prolific Countries in the Metabolic Engineering for the Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

Countries

Sample Papers (%)

USA Sweden Japan Germany China UK Denmark S. Korea Netherlands Canada France Mexico S. Africa Finland Switzerland

48.2 16.1 10.7 7.7 6.5 4.8 4.8 3.6 3.6 3.0 3.0 1.8 1.8 1.8 1.8

Population Papers (%) 24.1 3.1 11.5 4.9 20.9 3.1 2.2 7.5 1.8 3.4 2.5 1.3 1.0 0.8 0.6

Surplus (%) 24.1 13.0 −0.8 2.8 −14.4 1.7 2.6 −3.9 1.8 −0.4 0.5 0.5 0.8 1.0 1.2

TABLE 33.7 The Most-Prolific Scopus Subject Categories in the Metabolic Engineering for the Bioethanol Production No. 1 2 3 4 5 6 7 8 9

Scopus Subject Categories Biochemistry. Genetics and Molecular Biology Immunology and Microbiology Chemical Engineering Environmental Science Agricultural and Biological Sciences Multidisciplinary Energy Engineering Chemistry

Sample Papers (%) 82.1 73.8 36.9 24.4 20.8 7.7 4.8 4.8 3.6

Population Papers (%)

Surplus (%)

74.5 53.9 34.9 14.6 16.6 3.6 8.9 5.7 7.3

7.6 19.9 2.0 9.8 4.2 4.1 −4.1 −0.9 −3.7

The most-prolific Scopus subject category in the metabolic engineering for the bioethanol production is Biochemistry; Genetics and Molecular Biology with 82% of sample papers, closely followed by Immunology and Microbiology with 74% of the sample papers. The other prolific subject categories are Chemical Engineering, Environmental Science, and Agricultural and Biological Sciences with 21%–37% of the sample papers each. It is notable that Social Sciences including Economics and Business account only for 1.3% of the population studies. On the other hand, the Scopus subject category with the most citation impact is the Immunology and Microbiology with 19.9% surplus, followed by Environmental Science and Biochemistry and Genetics and Molecular Biology with 9.8% and 7.6% surplus, respectively. The other influential categories are Agricultural and Biological Sciences and Multidisciplinary with 4.2% and 4.1% surplus, respectively. Similarly, the Scopus subject category with the least citation impact is Energy with 4.1% deficit, followed by Chemistry with 3.7% deficit.

Metabolic Engineering: Scientometric Study

311

33.3.9 The Most-Prolific Keywords in the Metabolic Engineering for Bioethanol Production Information about the Scopus keywords used with at least 6.5% or 4.9% of the sample or population papers, respectively, is given in Table 33.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most-prolific keyword set provided by Scopus database. These keywords are grouped under the eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. The most-prolific keywords related to the biomass and biomass constituents are biomass, lignocellulose, and cellulose with 17%–25% of the sample papers each, while the most-prolific keyword related to fermentation is fermentation with 74% of the sample papers. The most-prolific keyword related to the bacteria is S. cerevisiae with 78% of the sample papers. The other prolific keywords are yeast, bacteria, E. coli, fungal strain, and P. stipitis with 20% to 32% of the sample papers each. The most-prolific keyword related to the hydrolysates is xylose with 45% of the sample papers, while the other prolific keywords are glucose and sugars with 33% and 16% of the sample papers, respectively. The most-prolific keyword related to the metabolic engineering is genetic engineering with 35% of the sample papers, followed by metabolism, gene expression, and metabolic engineering with 21% to 30% of the sample papers each. The other prolific keywords are plasmids, genetics, biotechnology, mutations, recombinant proteins, genes, and bioengineering with 12%–18% of the sample papers each. Enzyme activity is the most-prolific keyword related to the pretreatments with 21% of the sample papers, while the other prolific keywords are enzymes and acetic acid. Further, hydrolysis is the most-prolific keyword related to the other processes with 13.1% of the sample papers, followed by anaerobiosis with 12.5% of the sample papers. Finally, ethanol and alcohol are the most-prolific keywords related to the fermentation products with 70% of the sample papers each, while the other prolific keywords are biofuels, alcohol production, and xylitol with 17 to 19% of the sample papers each. It is notable that only 10% of the sample papers are indexed for bioethanol keyword. Further, the most influential keywords are S. cerevisiae, alcohol, ethanol, xylose, genetic engineering, P stipitis, fermentation, lignocellulose, biomass, xylitol, and glucose with 12%–36% surplus each.

33.3.10 The Most-Prolific Research Fronts in Metabolic Engineering for Bioethanol Production Information about the research fronts for the sample papers in metabolic engineering for the bioethanol production with regard to the biomass and hydrolysates used in these pretreatments is given in Table 33.9. As this table shows, there are three primary research fronts for this field: hydrolysates, biomass constituents, and other feedstocks with 58%, 11%, and 12% of the sample papers, respectively. On the individual basis, xylose is the most-prolific hydrolysate with 38% of the sample papers, while cellulose and lignocellulose are the prolific biomass constituents with 5% and 4% of the sample papers, respectively. Finally, algae, glycerol, and wood are the prolific feedstocks with 2%–4% of the sample papers each. Information about the thematic research fronts for the sample papers in metabolic engineering for the bioethanol production with regard to the microorganisms used in the fermentation processes is given in Table 33.10. As this table shows, there are four primary research fronts for this field: S. cerevisiae, Escherichia coli, microorganisms in general, and Clostridium with 49%, 16%, 10%, and 9% of the sample papers, respectively. The other prolific research fronts are P stipitis, other microorganisms, Z. mobilis, B. subtilis, and Klebsiella with 1%–5% of the sample papers each.

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TABLE 33.8 The Most-Prolific Keywords in Metabolic Engineering for the Bioethanol Production No. 1

Keywords

4

5

Surplus (%)

25.0

11.4

13.6

Lignocellulose

19.0

5.1

13.9

Cellulose

17.3

8.1

9.2

Lignin

11.9

4.3

7.6

9.5

5.8

3.7

74.4

59.5

14.9

Fermentation Fermentation Anaerobic fermentation

3

Population Papers (%)

Biomass

Glycerol 2

Sample Papers (%)

Biomass and biomass constituents

7.1

7.1

Bacteria S. cerevisiae

78.0

42.2

Yeast

31.5

22.0

9.5

Bacteria

29.2

18.6

10.6

E. coli

28.0

18.4

9.6

Fungal strain

20.2

9.8

10.4

P. stipitis

19.6

3.3

16.3

Z. mobilis

13.7

4.1

9.6

Fungus growth

10.7

5.2

5.5

Fungi

8.9

3.8

5.1

Clostridium

7.1

2.9

4.2

Bacterial strain

6.0

8.5

−2.5

Bacterial growth

5.4

6.4

−1.0

Hydrolysates Xylose

44.6

15.3

29.3

Glucose

32.7

20.4

12.3

Sugar

15.5

6.9

8.6

35.8

Pentose

7.7

7.7

Arabinose

7.1

7.1

Microbial engineering Genetic engineering

35.1

12.6

22.5

Metabolism

29.8

34.8

−5.0

Gene expression

22.6

19.5

3.1

Metabolic engineering

20.8

13.2

7.6

Plasmid

17.9

10.6

7.3

Genetics

16.7

26.0

−9.3

Biotechnology

16.7

7.0

9.7

Mutation

14.3

6.9

7.4

Recombinant proteins

11.9

12.7

−0.8

Genes

11.9

11.8

0.1

Bioengineering

11.9

4.1

7.8

Protein expression

10.7

9.0

1.7

Nucleotide sequence

10.7

7.6

3.1

Gene overexpression

10.7

4.7

6.0 (Continued)

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TABLE 33.8 (Continued) The Most-Prolific Keywords in Metabolic Engineering for the Bioethanol Production No.

6

7

Sample Papers (%)

Population Papers (%)

Alcohol dehydrogenase

Keywords

10.1

5.8

4.3

Fungal gene

10.1

5.0

5.1

Gene deletion

10.1

4.9

5.2

Aldehyde reductase

10.1

0.0

10.1

Xylulokinase

10.1

0.0

10.1

Oxidoreductase

9.5

3.4

6.1

Xylose isomerase

8.9

0.0

8.9

Molecular cloning

8.3

5.4

2.9

Molecular sequence data

8.3

5.3

3.0

Xylose reductase

7.1

2.7

4.4

Protein engineering

7.1

0.0

7.1

Industrial microbiology

6.5

4.1

2.4

Recombination, genetic

6.5

0.0

6.5

Microbiology

6.0

6.4

−0.4

Gene expression regulation

5.4

6.4

−1.0

Bacterial proteins

5.4

4.9

0.5

Bacterial gene

4.8

6.4

−1.6

Biomass pretreatment Enzyme activity

20.8

14.9

5.9

Enzymes

13.1

7.5

5.6

Acetic acid

12.5

6.4

6.1

Enzymology

6.3

−6.3

pH

5.6

−5.6

Other processes Hydrolysis

13.1

6.5

6.6

Anaerobiosis

12.5

2.7

9.8

7.1

2.5

4.6

Fermentation products Ethanol

70.2

40.7

29.5

Alcohol

69.6

34.2

35.4

Biofuel

19.0

11.6

7.4

Alcohol production

18.5

12.3

6.2

Xylitol

17.3

3.8

13.5

Ethanol production

11.9

7.7

4.2

Bioethanol

10.1

9.2

0.9

Biotransformation 8

Surplus (%)

33.4 DISCUSSION 33.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in the fuel cells, and in the biochemical production in a biorefinery context.

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TABLE 33.9 The Most-Prolific Research Fronts for the Biomass and Hydrolysates Used for the Metabolic Engineering for the Bioethanol Production No. 1

Research Fronts Hydrolysates Xylose

5.4

Hydrolysates in general

4.8

Pentose

4.2

Arabinose

3.0

Cellobiose

1.8

Hexose

0.6

Biomass constituents Cellulose

0.6 10.8 5.4

Lignocellulose

4.2

Hemicellulose

0.6

Lignin 3

57.7 37.5

Glucose

Lactose 2

N Paper (%) Sample

Other feedstocks Algae

0.6 12.0 3.6

Glycerol

2.4

Wood

1.8

Agricultural residues

1.2

Ethane

1.2

Other biomass

1.2

Grass

0.6

N paper (%) sample, the number of papers in the ­population sample of 168 papers.

TABLE 33.10 The Most-Prolific Thematic Research Fronts for the Metabolic Engineering for the Bioethanol Production with Regard to the Microorganisms No.

Research Fronts

1 2 3 4 5 6 7 8 9

S. cerevisiae E. coli Microorganisms in general Clostridium Other microorganisms P. stipitis Z. mobilis B. subtilis Klebsiella

N Paper (%) Sample 49.4 15.7 9.9 8.7 4.8 4.8 3.6 1.8 1.2

N paper (%) sample, the number of papers in the population sample of 168 papers.

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However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation. The research in the field of the metabolic engineering for the bioethanol production to improve the ethanol yield has intensified in this context in recent years. The key research fronts have been the metabolic engineering of S. cerevisiae, Z. mobilis, Pichia, Clostridium, other microorganisms, and the substrates such as corn and cyanobacteria. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil shocks, war-related shocks as in the case of Russian invasion of Ukraine or COVID-19 shocks. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As the recent scientometric studies focus on the fermentation processes in general, this book chapter presents a scientometric study of the research in the metabolic engineering for the bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first most-cited 200 papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replicative studies. Further, a selected list of the keywords was presented in Table 33.8. As a second step, two sets of data were used for this study. First, a population sample of 3,352 papers was used to examine the scientometric characteristics of the population data. Secondly, a sample of 168 most-cited papers, corresponding to 5% of the population dataset, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the metabolic engineering for the bioethanol production. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

33.4.2 The Most-Prolific Documents in the Metabolic Engineering for Bioethanol Production Articles (together with conference papers) dominate both the sample (90%) and population (94%) papers (Table 33.1). Further, review papers and articles have a surplus (7%) and deficit (4%). respectively. The representation of the reviews and short surveys in the sample papers is high (10%). Scopus differs from the Web of Science database in differentiating and showing articles (88%) and conference papers (2%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared to those published only in the conference proceedings. Similarly, Scopus differs from Web of Science database in introducing short surveys (3%). Hence, the total number of articles and review papers in the sample dataset are 90% and 10%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus as well as in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of the reviews in the sample dataset was

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manually found as nearly 15% compared to 10% as indexed by Scopus, reducing the number of articles and conference papers to 85% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 33.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from the experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers as well as it is done in the Web of Science database.

33.4.3 The Most-Prolific Authors in the Metabolic Engineering for Bioethanol Production There have been most-prolific 21 authors with at least 2.4% of the sample papers each as given in Table 33.2. These authors have shaped the development of the research in this field. The most-prolific authors are Barbel Hahn-Hagerdal, Lonnie O. Ingram, and to a lesser extent, Youg Su Jin, Marie F. Gorwa-Grauslund, Keelnatham T. Shanmugam, Thomas W. Jeffries, and Sean W. York. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for the names with more than two components such as ‘Blake Sam de Hyun Jin’. The probable outcomes are ‘Jin, B.S.D.H.’, ‘de Hyun Jin, B.S.’, or ‘Hyun Jin. B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is taken as the last name. In most of the academic databases such as PUBMED and EBSCO databases, this version is used predominantly. The second choice is a strong alternative, while the last choice is an undesired outcome as two last words are taken as the last name. It is a good practice to combine the words of the last name by a hyphen: ‘Hyun-Jin, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers as well as allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening Chinese names. For example, ‘YangYing Jin’ is often shortened as ‘Jin, Y.’, ‘Jin, Y.-Y.’, and ‘Jin, Y.Y.’, as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Jin, Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PUBMED and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently. Therefore, there have been difficulties in locating papers for the Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors especially for the authors with common names such as ‘Jin, X.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies as well as allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Jin, X.’ in the databases. In this context, the coding of authors in Scopus database is a welcome innovation compared to the other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system especially for common names. For example, some of the papers for a certain author maybe allocated to another researcher with a different author code. It is possible that Scopus uses a number of software programs to differentiate the author names and the program may not be false-proof (D’Angelo and van Eck, 2020). In this context, it does not help that author names are not given in full in some journals and books. This makes difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use

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their full names in their papers. Instead, they use initials for their forenames: ‘Jin, H.J.’, ‘Jin’, ‘Jin, H.’, or ‘Jin, J.’ instead of ‘Jin, Hyun Jae’. There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Jin, A.P.C.’ might be given as ‘Jin, A’ or ‘Jin, A.C.’ or ‘Jin, A.P.’ or ‘Jin, C.’ in the journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Şeçil, Özgökçe) rather than in the English spellings (e.g., Secil, Ozgokce) in Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in the English spellings. It is observed that national spellings of the author names do not help much in conducting scientometric studies as well in allocating credits to the authors as sometimes there are the different author entries for the English and National spellings in the Scopus database. The most-prolific institutions for the sample dataset are the University of Florida and, to a lesser extent, Chalmers University of Technology, Delft University of Technology, Kobe University, and Lund University. The most-prolific countries for the sample dataset are the USA and to a lesser extent Sweden, Japan, and Netherlands. These findings confirm the dominance of the USA and to a lesser extent of the Europe and Japan in this field. The most-prolific research fronts are the metabolic engineering of the bacteria and to a lesser extent of substrates used to ferment them. On the individual basis, metabolic engineering of the S. cerevisiae and E. coli used to ferment the hydrolysates and substrates is the prolific research front. It is also notable that there is a significant gender deficit for the sample dataset as surprisingly with a representation rate of 29%. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient measures to reduce the gender deficit in this field as well as other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from the minority groups in science on the basis of race, sexuality, age, and disability, besides the gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

33.4.4 The Most-Prolific Research Output by Years in the Metabolic Engineering for Bioethanol Production The research output observed between 1970 and 2022 is illustrated in Figure 33.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. Further, there was a rising trend for the research out for the population papers between 2006 and 2012, but after that, it became flat losing its momentum. Further, there was no sharp rise in the research out in 2020 and 2021 due to the supply shocks. These findings suggest that the most-prolific sample and population papers were primarily published in the last two decades. These are the thought-provoking findings as there has been a significant research boom in the last two decades. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field in the last two decades. Furthermore, the supply shocks experienced due to the COVID-19 pandemics have not resulted in the expected sharp rise in the research output for the population papers in 2020 and 2021. Based on these findings, the size of the population papers likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming, as well as the supply shocks, is translated efficiently to the research funding in this field.

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33.4.5 The Most-Prolific Institutions in the Metabolic Engineering for Bioethanol Production The most-prolific 18 institutions publishing papers on the metabolic engineering for the bioethanol production with at least 2.4% of the sample papers each given in Table 33.3 have shaped the development of the research in this field. The most-prolific institutions are the Lund University and, to a lesser extent, University of Florida, Kobe University, and University of Illınois Urbana Champaign. Similarly, the top countries for these most-prolific institutions are USA and, to a lesser extent, Japan and Netherlands. In total, only five countries house these top institutions. On the other hand, the institutions with the most citation impact are Lund University and, to a lesser extent, University of Florida, Bird Engineering Inc., Massachusetts Institute of Technology, and Delft University of Technology. These findings confirm the dominance of the US, European, and Japanese institutions with a notable absence of China in this research field. These findings clearly hint that the USA, Europe, and to a lesser extent, Japan dominate the research in this field.

33.4.6 The Most-Prolific Funding Bodies in the Metabolic Engineering for Bioethanol Production The most-prolific nine funding bodies funding at least 1.8% of the sample papers each is given in Table 33.4. It is notable that only 31% and 48% of the sample and population papers were funded. The most-prolific funding bodies are the National Institute of General Medical Sciences and, to a lesser extent, New Energy and Industrial Technology Development Organization, the US Department of Agriculture, US Department of Energy, Ministry of Education, Culture, Sports, Science and Technology, and Seventh Framework Program. The most-prolific countries for these top funding bodies are the USA and, to a lesser extent, Japan and the EU. In total, only four countries house these top funding bodies. The funding bodies with the most citation impact are the National Institute of General Medical Sciences and, to a lesser extent, the US Department of Agriculture, New Energy and Industrial Technology Development Organization, and Seventh Framework Program, while the one with the least impact is the National Natural Science Foundation of China. It is notable that this funding body is the largest funder of population papers with over 11% funding rate. These findings on the funding of the research in this field suggest that the level of the funding, mostly in the last two decades, is modest but it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in the light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding for the sample papers and stagnation of the research output after 2012, there is ample room to enhance funding in this field.

33.4.7 The Most-Prolific Source Titles in the Metabolic Engineering for Bioethanol Production The most-prolific 15 source titles publishing at least 1.7% of the sample papers each in the metabolic engineering for the bioethanol production have shaped the development of the research in this field (Table 33.5). The most-prolific source titles are Applied and Environmental Microbiology and, to a lesser extent, Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, Metabolic Engineering, Proceedings of the National Academy of Sciences of the United States of America, FEMS Yeast Research, and Journal of Bacteriology. The source titles with the most impact are the Applied and Environmental Microbiology and, to a lesser extent, the Proceedings of the National Academy of Sciences of the United States of

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America, Metabolic Engineering, FEMS Yeast Research, Biotechnology and Bioengineering, and Applied Microbiology and Biotechnology. Similarly, the source title with the least impact is the Biotechnology for Biofuels. It is notable that these top source titles are primarily related to the microbiology, metabolic engineering, genetics, bioengineering, microorganisms, and biotechnology. This finding suggests that Applied and Environmental Microbiology and the other prolific journals in these fields have significantly shaped the development of the research in this field as they focus primarily on the metabolic engineering of the microorganisms and substrates to produce ethanol with a high yield.

33.4.8 The Most-Prolific Countries in the Metabolic Engineering for Bioethanol Production The most-prolific 15 countries publishing at least 1.8% of the sample papers each have significantly shaped the development of the research in this field (Table 33.6). The most-prolific countries are the USA and, to a lesser extent, Sweden, Japan, Germany, China, the UK, Denmark, S. Korea, and Netherlands. Further, eight European countries produce 44% and 19% of the sample and population papers, respectively, with 25% surplus. On the other hand, the countries with the most citation impact are the USA and, to a lesser extent, Sweden, Germany, Denmark, Netherlands, and the UK. Similarly, the countries with the least impact are China and, to a lesser extent, S. Korea, Japan, and Canada. The close examination of these findings suggests that the USA, Europe, and the Far East (China, Japan, and S. Korea) are the major producers of the research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009; Leydesdorff et al., 2014). The USA has further developed a strong research infrastructure to support its corn- and grass-based bioethanol industry (Vadas et al., 2008). However, China has been a rising mega star in the scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Li and Chan-Halbrendt, 2009). Next, Europe has been a persistent player in the scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010).

33.4.9 The Most-Prolific Scopus Subject Categories in the Metabolic Engineering for Bioethanol Production The most-prolific nine Scopus subject categories indexing at least 3.6% of the sample papers each, respectively, given in Table 33.7 have shaped the development of the research in this field. The most-prolific Scopus subject categories in the metabolic engineering for the bioethanol production are Biochemistry, Genetics and Molecular Biology, Immunology and Microbiology, and to a lesser extent, Chemical Engineering, Environmental Science, and Agricultural and Biological Sciences. The Scopus subject categories with the most citation impact are the Immunology and Microbiology and, to a lesser extent, Environmental Science and Biochemistry, Genetics and Molecular Biology, Agricultural and Biological Sciences and Multidisciplinary. Similarly, the Scopus subject categories with the least citation impact are Energy and Chemistry. These findings are thought-provoking suggesting that the primary subject categories are related to genetics and microbiology as the core of the research in this field concerns with the metabolic engineering of the microorganisms and substrates to increase the ethanol yield. The other finding is that social sciences are not well represented in both the sample and population papers as in the most fields in bioethanol fuels.

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33.4.10 The Most-Prolific Keywords in the Metabolic Engineering for Bioethanol Production A limited number of keywords have shaped the development of the research in this field as shown in Table 33.8 and in Appendix. These keywords are grouped under eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. The most-prolific keywords related to the biomass and biomass constituents are biomass, lignocellulose, and cellulose, while the most-prolific keyword related to fermentation is fermentation. The most-prolific keywords related to the bacteria are S. cerevisiae and, to a lesser extent, yeast, bacteria, E. coli, fungal strain, and P. stipitis. Further, the most-prolific keywords related to the hydrolysates are xylose and, to a lesser extent, glucose and sugars. The most-prolific keyword related to the metabolic engineering is genetic engineering followed by metabolism, gene expression, and metabolic engineering, plasmids, genetics, biotechnology, mutations, recombinant proteins, genes, and bioengineering. Enzyme activity is the most-prolific keyword related to the pretreatments, followed by enzymes and acetic acid. Further, hydrolysis is the most-prolific keyword related to the other processes, followed by anaerobiosis. Finally, ethanol and alcohol are the most-prolific keywords related to the fermentation products, while the other prolific keywords are biofuels, alcohol production, and xylitol. It is notable that only 10% of the sample papers are indexed for bioethanol keyword. Further, the most influential keywords are S. cerevisiae, alcohol, ethanol, xylose, genetic engineering, P. stipitis, fermentation, lignocellulose, biomass, xylitol, and glucose. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas. The relevant keywords are presented in Table 33.8 as well as in Appendix.

33.4.11 The Most-Prolific Research Fronts in the Metabolic Engineering for Bioethanol Production As Table 33.9 shows, there are three primary research fronts for this field: hydrolysates, biomass constituents, and other feedstocks, while on the individual basis, xylose is the most-prolific hydrolysate. Further cellulose and lignocellulose are the prolific biomass constituents, while algae, glycerol, and wood are the prolific feedstocks. On the other hand, information about the thematic research fronts for the sample papers in metabolic engineering for the bioethanol production with regard to the microorganisms used in the fermentation processes given in Table 33.10 shows that there are four primary research fronts for this field: S. cerevisiae, E. coli, microorganisms in general, and Clostridium. The other prolific research fronts are P. stipitis, other microorganisms, Z. mobilis, B. subtilis, and Klebsiella. These findings are thought-provoking in seeking ways to increase bioethanol yield through the metabolic engineering for the bioethanol production at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. However, it is notable that metabolic engineering of the substrates and microorganisms has become a core of the fermentation research to increase the ethanol yield and to make it more competitive with the crude oil-based gasoline and petrodiesel fuels. In the end, these most-cited papers in this field hint that the efficiency of bioethanol fuels and their derivatives could be optimized using the structure, processing, and property relationships of microorganisms and substrates (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

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33.5 CONCLUSION AND FUTURE RESEARCH The research on the metabolic engineering of bacteria and substrates for the bioethanol production has been mapped through a scientometric study of both sample (168 papers) and population (3,352 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after a number of runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the Appendix, and the relevant keywords are presented in Table 33.8. However, it should be noted that it has been very difficult to compile a representative keyword set since this research field has been connected closely with many other fields. Therefore, it has been necessary to compile a keyword list to exclude papers concerned with the other research fields. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose, Scopus database has been selected. The journal coverage of this database has been notably wider than that of Web of Science and other multisubject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all the academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits on the basis of age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Furthermore, the recent COVID-19 pandemics and Russian invasion of Ukraine have resulted in a global supply shocks shifting the focus of the stakeholders from the crude oil-based fuels to biomass-based fuels such as bioethanol fuels. It is expected that there would be further incentives for the key stakeholders to carry out the research for the metabolic engineering of bacteria and substrates to increase the ethanol yield and to make it more competitive with the crude oil-based gasoline and petrodiesel fuels. This might be truer for the crude oil- and foreign exchange-deficient countries to maintain the energy security at the face of the global supply shocks. The relatively modest funding rate of 48% for the population papers suggests that funding in this field significantly enhanced the research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further with the relatively modest funding rate of 31% for the sample papers and with the stagnation of the research output for the population papers after 2012. The most-prolific source titles have been related to microbiology, metabolic engineering, bioengineering, genetics, microorganisms, and biotechnology as the focus of the sample papers has been on the metabolic engineering of the substrates and microorganisms to improve the ethanol yield. The institutions from the USA and, to a lesser extent, Europe and Japan have mostly shaped the research in this field. Further, the USA, Europe, and the Far East (China, Japan, and S. Korea) have been the major producers of the research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass, as well as other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives.

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The primary Scopus subject categories are related to genetics and microbiology as the core of the research in this field concerns with the metabolic engineering of the microorganisms and substrates to increase the ethanol yield. The other key finding is that social sciences are not well represented in both the sample and population papers as in most fields in bioethanol fuels. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under the eight headings: biomass, fermentation, bacteria, hydrolysates, microbial engineering, pretreatments, other processes, and products of the fermentation. There are three primary research fronts with regard to the feedstocks for this field: hydrolysates, biomass constituents, and other feedstocks, while on the individual basis, xylose is the most-prolific hydrolysate. Further, cellulose and lignocellulose are the prolific biomass constituents, while algae, glycerol, and wood are the prolific feedstocks. On the other hand, there are four primary thematic research fronts with regard to the bacteria for this field: S. cerevisiae, E. coli, microorganisms in general, and Clostridium. Further, the most-prolific substrates have been corn, grass, and cyanobacteria. These findings are thought-provoking in seeking ways to increase bioethanol yield through the metabolic engineering of bacteria and substrates for the bioethanol production at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Further, it is notable that metabolic engineering of the substrates and microorganisms has become a core of the fermentation research to increase the ethanol yield and to make it more competitive with the crude oil-based gasoline and diesel fuels. These findings are thought-provoking. The focus of these most-cited 168 papers as well as 3,356 population papers is the metabolic engineering to increase the bioethanol yield. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields especially in the aftermath of the significant global supply shocks such as COVID-19 pandemics and the Russian invasion of Ukraine. It is recommended that further scientometric studies are carried out for the primary research fronts. It is further recommended that reviews of the most-cited papers are carried out for each primary research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the metabolic engineering for the bioethanol production has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR METABOLIC ENGINEERING FOR BIOETHANOL PRODUCTION ((TITLE (ethanol* OR bioethanol OR ferment* OR coferment* OR “xylose utiliz*” OR “pentose utiliz*” OR “butanol ethanol” OR “xylose consumption” OR “xylose metabolism” OR “lactate ethanol” OR “xylose pathway” OR ssf OR “pentose pathway” OR “utilization of xylose” OR “arabinose utiliz*” OR “xylose utilis*” OR “pentose utilis*”) AND (TITLE (engineer* OR bioengineer* OR genes OR genom* OR genetic* OR recombinant OR proteom* OR cloning OR transgen* OR mutant* OR mutagen* OR transcriptom* OR “synthetic yeast” OR “minimal esch*” OR “modified saccharomy*” OR “enteric bacter*” OR overexpression OR expression OR “functional assembly” OR “metabolic rewiring” OR metabolom* OR metagenom* OR “directed evol*” OR “metabolic pathway” OR “*displaying enzyme*”) OR SRCTITLE (genom* OR “metabolic eng*” OR proteom* OR genetic*))) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR dent OR

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neur OR heal OR psyc OR eart) OR SRCTITLE (plant* OR botan* OR animal* OR “food microbiology” OR dairy OR feed OR brewing OR hydrogen OR medicine OR lipid* OR postharvest* OR wine OR enol* OR ecol* OR crop*) OR TITLE (“solid state” OR drosoph* OR sausage* OR ethanolamine OR sulfide* OR wine* OR tissue* OR food* OR sludge OR fermentum OR grape OR “carbon source” OR human OR cadaverine OR *succinate OR sensing OR resveratrol OR liver OR fibrosis OR hepat* OR *alanine OR propanediol OR “ethanol consumption” OR “propionic acid” OR grain OR receptor* OR opiate* OR methyl OR edible OR *lactic OR *hydrogen OR “amino acid” OR “process engineering” OR kimchi OR algorith* OR exposure OR hangover OR brewing OR keratinase OR methan* OR gabaa OR sake OR succinic OR flavonoid* OR butyrate OR butaned* OR co2 OR trichoderma OR bean OR intoxication OR neur* OR biogas OR mice OR h2 OR monascus OR hepar* OR tea OR *surfactants OR “ethanol aversion” OR ph OR “*propanoic acid” OR cytotox* OR “Xylitol product*” OR “acid production” OR chiral OR parasite* OR coffee OR homolactate OR “carbon metabolism” OR brain OR cytokine OR “fatty acid*”))) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “sh”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “bk”) OR LIMIT-TO (DOCTYPE, “ed”)) AND (LIMIT-TO (LANGUAGE, “English”))

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34

Metabolic Engineering for the Bioethanol Production Review Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

34.1 INTRODUCTION The crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Alvira et al., 2010; Hill et al., 2006; Konur, 2012, 2015, 2020; Sun and Cheng, 2002) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in the fuel cells (Antolini, 2007, 2009), and in the biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) prior to the bioethanol production through the hydrolysis (Alvira et al., 2010; Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and HahnHagerdal, 1996) of the biomass and their hydrolysates, respectively. The research in the field of the metabolic engineering for the bioethanol production to improve the ethanol yield has intensified in this context in recent years (Dien et al., 2003; Jeffries and Jin, 2004). The key research fronts have been the metabolic engineering of Saccharomyces cerevisiae (Hahn-Hagerdal, 2001, 2007), Escherichia coli (Dharmadi et al., 2006; Ingram et al., 1987; Ohta et al., 1991), Zymomonas mobilis (Deanda et al., 1996; Zhang et al., 1995), Pichia (Hong et al., 2002; Jeffries et al., 2007), Clostridium (Argyros et al., 2011; Mermelstein et al., 1992), other microorganisms (Ho et al., 1998; Inui et al., 2005; Shaw et al., 2008), and the substrates such as corn (Torney et al., 2007), grass (Fu et al., 2011), and cyanobacteria (Deng and Coleman, 1997; Dexter and Fu, 2009; Enquist-Newman et al., 2014; Gao et al., 2012). However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been a number of review papers on the metabolic engineering for the bioethanol production (Dien et al., 2003; Hahn-Hagerdal, 2001, 2007; Hong et al., 2002; Jeffries and Jin, 2004; Torney et al., 2007), there has been no review of the most cited 25 papers in this field. Thus, this book chapter presents a review of the most-cited 25 articles in the field of the metabolic engineering of the microorganisms and substrates for the bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field.

34.2 MATERIALS AND METHODS The search for this study was carried out using Scopus database (Burnham, 2006) in May 2022. DOI: 10.1201/9781003226499-43

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As a first step for the search of the relevant literature, the keywords were selected using the most-cited first 200 population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 225 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape.

34.3 RESULTS The brief information about 25 most-cited papers with at least 225 citations each on the metabolic engineering for the bioethanol production is given below. The primary research fronts are the microorganism and substrate metabolic engineering with 21 and 4 highly cited papers (HCPs), respectively. The key research fronts for the microorganism metabolic engineering are the metabolic engineering of Saccharomyces cerevisiae and other microorganisms with 11 and 10 HCPs, respectively.

34.3.1 The Metabolic Engineering of Saccharomyces cerevisiae There are 11 HCPs for the metabolic engineering of Saccharomyces cerevisiae (Table 34.1). Alper et al. (2006) applied the global transcription machinery engineering (gTME) to S. cerevisiae for improved glucose and ethanol tolerance in a paper with 633 citations. They observed that the mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. The desired phenotype resulted from the combined effect of three separate mutations in the SPT15 gene: serine substituted for phenylalanine (Phe177Ser) and, similarly, Tyr195His, and Lys218Arg. They asserted that gTME could provide a route to complex phenotypes that were not readily accessible by traditional methods. Eliasson et al. (2000) constructed a stable xylose-utilizing recombinant S. cerevisiae strain TMB 3001 for anaerobic xylose fermentation in a paper with 327 citations. They integrated the XYL1 and XYL2 genes from Pichia stipitis, encoding xylose reductase (XR) and xylitol dehydrogenase (XDH), respectively, and the endogenous XKS1 gene, encoding xylulokinase (XK), under control of the PGK1 promoter into the chromosomal HIS3 locus of S. cerevisiae CEN.PK 113–7A. The strain expressed XR, XDH, and XK activities of 0.4–0.5, 2.7–3.4, and 1.5–1.7 U/mg, respectively, and was stable for more than 40 generations in continuous fermentations. They showed anaerobic ethanol formation from xylose by recombinant S. cerevisiae. However, the strain grew on xylose only in the presence of oxygen. They obtained ethanol yields of 0.45 to 0.50 mmol of C/mmol of C (0.35–0.38 g/g) and productivities of 9.7–13.2 mmol of C/h g (dry weight) of cells−1 (0.24 to 0.30 g/h g [dry weight] of cells−1) from xylose-glucose mixtures in anaerobic chemostat cultures, with a dilution rate of 0.06 h−1. They estimated the anaerobic ethanol yield on xylose at 0.27 mol of C/(mol of C of xylose) (0.21 g/g), assuming a constant ethanol yield on glucose. The xylose uptake rate increased with increasing xylose concentration in the feed, from 3.3 mmol of C/h g (dry weight) of cells−1 when the xylose-to-glucose ratio in the feed was 1:3–6.8 mmol of C/h g (dry weight) of cells−1 when the feed ratio was 3:1. With a feed content of 15 g of xylose/L and 5 g of glucose/L, the xylose flux was 2.2 times lower than the glucose flux, indicating that transport limits the xylose flux. Kuyper et al. (2005a) engineered a xylose-isomerase-expressing S. cerevisiae strain for the rapid anaerobic xylose fermentation in a paper with 325 citations. They observed that S. cerevisiae strains

No. 1 2 3 4 5 6

7 8 9 10 11

Papers

Biomass/ Hydrolysate

Prt.

Bacteria

Parameters

Keywords

Lead Author

Affil.

Cits

Microbial engineering, glucose fermentation Microbial engineering, ethanol yield and determinants Microbial engineering, xylose fermentation Microbial engineering, ethanol stress Microbial engineering, xylose fermentation Microbial engineering, synergistic SSF, ethanol yield

Engineering, ethanol Fermentation, recombinant Engineering, fermentation Gene, ethanol, expression Expression, fermentation Engineered, fermentation, ethanol Engineering, fermenting Engineering, bioethanol Expression, fermentation Ethanol, recombinant Synthetic yeast platform, ethanol

Stephanopoulos, Gregory 24527470500 Hahn-Hagerdal, Barbel* 7005389381 Pronk, Jack T. 7005313057 Blondin, Bruno 35610971500 Pronk, Jack T. 7005313057 Kondo, Akihiko 57203868143

Massachusetts Inst. Technol. USA Lund Univ. Sweden Delft Univ. Technol. Netherlands Univ. Montpellier France Delft Univ. Technol. Netherlands Kobe Univ. Japan

633

Pronk, Jack T. 7005313057 Nielsen, Jens 55572933700 Jonsson, Leif J. 7102349315 Jonsson, Leif J. 7102349315 Yoshikuni, Yasuo 7102890080

Delft Univ. Technol. Netherlands Chalmers Univ. Technol. Sweden Umea Univ. Sweden Umea Univ. Sweden Joint Genome Inst. USA

277

Alper et al. (2006) Eliasson et al. (2000) Kuyper et al. (2005a) Alexandre et al. (2001) Kuyper et al. (2003) Fujita et al. (2004)

Glucose

Na

S. cerevisiae

Xylose

Na

S. cerevisiae

Xylose

Na

Na

Na

S. cerevisiae, P. sp. S. cerevisiae

Xylose

Na

S. cerevisiae

Cellulose

Acids

S. cerevisiae

Kuyper et al. (2005b) Bro et al. (2006)

Xylose, glucose Glucose

Na

S. cerevisiae

Na

S. cerevisiae

Larsson et al. (2001) Martin et al. (2002) Enquist-Newman et al. (2014)

Spruce hydrolysate Sugar bagasse hydrolysate Alginate

Acid

S. cerevisiae

Steam

S. cerevisiae

Na

S. cerevisiae, A. cruciatus

Microbial engineering, mixed sugar utilization Microbial engineering, glucose fermentation, ethanol yield Microbial engineering, hydrolysate fermentation Microbial engineering, detoxification, ethanol yield Microbial engineering, alginate fermentation, ethanol yield

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Metabolic Engineering: Review

TABLE 34.1 The Metabolic Engineering of S. cerevisiae

325 295 284 280

276 243 242 237

*, Female; cits., number of citations received for each paper; Na, nonavailable; prt, biomass pretreatments.

329

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that expressed the xylose isomerase gene from Piromyces sp. E2 could grow anaerobically on xylose with a μmax of 0.03 h−1. They overexpressed structural genes for all enzymes involved in the conversion of xylulose to glycolytic intermediates, in a xylose-isomerase-expressing S. cerevisiae strain. The overexpressed enzymes were xylulokinase, ribulose 5-phosphate isomerase, ribulose 5-phosphate epimerase, transketolase, and transaldolase. In addition, the GRE3 gene encoding aldose reductase was deleted to further minimize xylitol production. Unexpectedly, the resulting strain grew anaerobically on xylose in synthetic media with a μmax as high as 0.09 h−1. During growth on xylose, xylulose formation was absent and xylitol production was negligible. The specific xylose consumption rate in anaerobic xylose cultures was 1.1 g xylose/(g biomass)/h. Finally, mixtures of glucose and xylose were sequentially but completely consumed by anaerobic batch cultures, with glucose as the preferred substrate. Alexandre et al. (2001) investigated the global gene expression during short-term ethanol stress in S. cerevisiae in a paper with 295 citations. They observed that up to 3.1% of the genes encoded in the yeast genome were upregulated by at least a factor of three after 30 min ethanol stress (7% v/v). Further, 3.2% of the genes were downregulated by a factor of three. Of the genes upregulated in response to ethanol, 49.4% belonged to the environmental stress response and 14.2% belong to the stress gene family. They showed that in addition to the previously identified ethanol-induced genes, a very large number of genes involved in ionic homeostasis, heat protection, trehalose synthesis, and antioxidant defense also responded to ethanol stress. A large number of the upregulated genes were involved in energy metabolism. Thus, management of the energy pool constituted an ethanol stress response and involved different mechanisms. Fujita et al. (2004) performed simultaneous and synergistic hydrolysis and fermentation of amorphous cellulose to ethanol using a yeast strain codisplaying the three cellulolytic enzymes in a paper with 279 citations. They constructed a whole-cell biocatalyst through codisplay of three types of cellulolytic enzyme on the cell surface of S. cerevisiae. When a cell surface display system based on α-agglutinin was used, Trichoderma reesei endoglucanase II and cellobiohydrolase II and Aspergillus aculeatus β-glucosidase I were simultaneously codisplayed as individual fusion proteins with the C-terminal-half region of α-agglutinin. A yeast strain codisplaying endoglucanase II and cellobiohydrolase II showed significantly higher hydrolytic activity with amorphous cellulose (phosphoric acid-swollen cellulose) than the one displaying only endoglucanase II, and its main product was cellobiose while codisplay of β-glucosidase 1, endoglucanase II, and cellobiohydrolase II enabled the yeast strain to directly produce ethanol from the amorphous cellulose, with a yield of approximately 3 g/L from 10 g/L within 40 h. The yield (in grams of ethanol produced per gram of carbohydrate consumed) was 0.45 g/g, which corresponded to 88.5% of the theoretical yield. Kuyper et al. (2003) functionally expressed xylose isomerase for the efficient xylose fermentation by S. cerevisiae in a paper with 284 citations. They observed that xylose metabolism in the P. sp. E2 proceeded via a xylose isomerase rather than via the xylose reductase and xylitol dehydrogenase pathway found in xylose-metabolizing yeasts. They functionally expressed the XylA gene encoding the P. sp. xylose isomerase in S. cerevisiae. The engineered S. cerevisiae strain grew very slowly on xylose. It coconsumed xylose in aerobic and anaerobic glucose-limited chemostat cultures at rates of 0.33 and 0.73 mmol/(g biomass)/h, respectively. Bro et al. (2006) used a genome-scale reconstructed metabolic network of S. cerevisiae for metabolic engineering of the redox metabolism to obtain decreased glycerol and increased ethanol yields on glucose under anaerobic conditions in a paper with 276 citations. The best-scored strategies completely eliminated formation of glycerol and increased ethanol yield with 10%. They expressed a nonphosphorylating, nicotinamide adenine dinucleotide phosphate (NADP)+-dependent glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in S. cerevisiae. The engineered strain had a 40% lower glycerol yield on glucose while the ethanol yield increased with 3% without affecting the maximum specific growth rate. Similarly, expression of nonphosphorylating GAPDH in a strain harboring xylose reductase and xylitol dehydrogenase led to an improvement in ethanol yield by up to 25% on xylose/glucose mixtures.

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Kuyper et al. (2005b) engineered glucose and xylose utilization by a xylose-fermenting S. ­cerevisiae strain in a paper with 277 citations. They previously engineered a S. cerevisiae strain overexpressing the native genes in addition to the P. XylA xylose isomerase gene for the conversion of xylulose to glycolytic intermediaries. This engineered strain (RWB 217) exhibited unprecedentedly high specific growth rates and ethanol production rates under anaerobic conditions with xylose as the sole carbon source. However, when RWB 217 was grown on glucose-xylose mixtures, they observed a diphasic growth pattern with a relatively slow consumption of xylose in the second growth phase. After prolonged cultivation in an anaerobic, xylose-limited chemostat, they obtained a culture with improved xylose uptake kinetics. This culture also exhibited improved xylose consumption in glucose-xylose mixtures. They obtained a further improvement in mixed-sugar utilization by prolonged anaerobic cultivation in automated sequencing-batch reactors on glucose-xylose mixtures. A final single-strain isolate (RWB 218) rapidly consumed glucose-xylose mixtures anaerobically, in synthetic medium, with a specific rate of xylose consumption exceeding 0.9 g/g/h. When the kinetics of zero trans-influx of glucose and xylose of RWB 218 were compared to that of the initial strain, there was a twofold higher capacity (Vmax) as well as an improved Km for xylose in the selected strain. Larsson et al. (2001) expressed laccase from Trametes versicolor under control of the PGK1 promoter in S. cerevisiae to increase its resistance to phenolic fermentation inhibitors in lignocellulose hydrolysates to improve the ethanol yield in a paper with 243 citations. They observed that the laccase activity could be enhanced twofold by simultaneous overexpression of the homologous t-SNARE Sso2p. The factors affecting the level of active laccase obtained, besides the cultivation temperature, included pH and aeration. The laccase-producing transformant had the ability to convert coniferyl aldehyde at a faster rate than a control transformant not expressing laccase, which enabled faster growth and ethanol formation. The laccase-producing transformant was also able to ferment a dilute acid spruce hydrolysate at a faster rate than the control transformant. They observed a decrease in the content of low-molecular-mass aromatic compounds, accompanied by an increase in the content of high-molecular-mass compounds, during fermentation with the laccase-expressing strain, illustrating that laccase was active even at the very low levels of oxygen supplied. Martin et al. (2002) pretreated sugarcane bagasse by steam explosion at 205°C and 215°C and hydrolyzed with cellulolytic enzymes in a paper with 242 papers. They then subjected the hydrolysates to enzymatic detoxification by treatment with the phenoloxidase laccase and to chemical detoxification by overliming. Approximately 80% of the phenolic compounds were specifically removed by the laccase treatment. Overliming partially removed the phenolic compounds but also other fermentation inhibitors such as acetic acid, furfural, and 5-hydroxymethylfurfural (HMF). They next fermented the hydrolysates with the recombinant xylose-utilizing S. cerevisiae laboratory strain TMB 3001, a CEN.PK derivative with overexpressed xylulokinase activity and expressing the xylose reductase and xylitol dehydrogenase of P. stipitis, and the S. cerevisiae strain ATCC 96581. They observed that the fermentative performance of the lab strain in undetoxified hydrolysate was better than the performance of the industrial strain. They observed an almost twofold increase of the specific productivity of the strain TMB 3001 in the detoxified hydrolysates compared to the undetoxified hydrolysates. The ethanol yield in the fermentation of the hydrolysate detoxified by overliming was 0.18g/g dry bagasse, whereas it reached only 0.13g/g dry bagasse in the undetoxified hydrolysate. Finally, they observed partial xylose utilization with low xylitol formation. Enquist-Newman et al. (2014) engineered the alginate and mannitol catabolic pathways in S. cerevisiae to improve the ethanol yield in a paper with 237 citations. They discovered an alginate monomer, 4-deoxy-l-erythro-5-hexoseulose urinate, (DEHU) transporter from the Asteromyces cruciatus. They observed that the genomic integration and overexpression of the gene encoding this transporter, together with the necessary bacterial alginate and deregulated native mannitol catabolism genes, conferred the ability of an S. cerevisiae strain to efficiently metabolize DEHU and mannitol. When this platform was further adapted to grow on mannitol and DEHU under anaerobic conditions, they found that it was capable of ethanol fermentation from mannitol and

332

Bioethanol Fuel Production Processes. II

DEHU, achieving titers of 4.6% (v/v) (36.2 g/L) and yields up to 83% of the maximum theoretical yield from consumed sugars.

34.3.2 The Metabolic Engineering of Other Microorganisms There are 10 HCPs for the metabolic engineering of other microorganisms (Table 34.2). The key research fronts are the metabolic engineering of E. coli and Z. mobilis with three HCPs each. 34.3.2.1 The Metabolic Engineering of Zymomonas mobilis Zhang et al. (1995) engineered Zymomonas mobilis metabolically to broaden its range of fermentable substrates to include xylose. For this purpose, they constructed two operons encoding xylose assimilation and pentose phosphate pathway enzymes and transformed them into Z. mobilis in order to generate a strain that grew on xylose and efficiently fermented it to ethanol. Thus, they performed anaerobic fermentation of xylose to ethanol through a combination of the pentose phosphate and Entner-Doudoroff pathways. Furthermore, this strain efficiently fermented both glucose and xylose. Ohta et al. (1991) integrated Z. mobilis genes for pyruvate decarboxylase (pdc) and alcohol dehydrogenase II (adhB) into the Escherichia coli chromosome within or near the pyruvate formatelyase gene (pfl). They observed that the integration improved the stability of the Z. mobilis genes in E. coli. However, further selection was required to increase expression. They selected spontaneous mutants for resistance to high level of chloramphenicol that also expressed high levels of the Z. mobilis genes. They then selected analogous mutants for increased expression of alcohol dehydrogenase on aldehyde indicator plates. These mutants were functionally equivalent to the previous plasmid-based strains for the fermentation of xylose and glucose to ethanol. They obtained ethanol concentrations of 54.4 and 41.6 g/L from 10% glucose and 8% xylose, respectively. The efficiency of conversion exceeded theoretical limits (0.51 g of ethanol/g of sugar) on the basis of added sugars because of the additional production of ethanol from the catabolism of complex nutrients. They finally introduced further mutations to inactivate succinate production (frd) and to block homologous recombination (recA). Seo et al. (2005) report the complete genome sequence of Z. mobilis ZM4 (ATCC31821), in a paper with 225 citations The genome consisted of 2,056,416 base pairs forming a circular chromosome with 1,998 open reading frames (ORFs) and three ribosomal RNA transcription units. The genome lacked recognizable genes for 6-phosphofructokinase and for two enzymes in the tricarboxylic acid cycle, the 2-oxoglutarate dehydrogenase complex, and malate dehydrogenase, so glucose could be metabolized only by the Entner-Doudoroff pathway. They used the whole genome microarrays for genomic comparisons with the Z. mobilis type strain ZM1 (ATCC10988) revealing that 54 ORFs predicted to encode for transport and secretory proteins, transcriptional regulators, and oxidoreductase in the ZM4 strain were absent from ZM1. Most of these ORFs were also actively transcribed in association with ethanol production by ZM4. 34.3.2.2 The Metabolic Engineering of Escherichia coli Ingram et al. (1987) inserted the genes encoding essential enzymes of the fermentative pathway for ethanol production in Z. mobilis into Escherichia coli under the control of a common promoter in a paper with 378 citations. They observed that alcohol dehydrogenase II and pyruvate decarboxylase from Z. mobilis were expressed at high levels in E. coli, resulting in increased cell growth and the production of ethanol as the principal fermentation product from glucose. They asserted that it was possible to change the fermentation products of an organism, such as E. coli, by the addition of genes encoding appropriate enzymes which formed an alternative system for the regeneration of NAD+. Dharmadi et al. (2006) investigated the anaerobic fermentation of glycerol by E. coli in a paper with 333 citations. They observed that E. coli could ferment glycerol in a pH-dependent manner. Glycerol fermentation was severely impaired by blocking the activity of enzyme formate hydrogen

No. 1

Papers

2

Zhang et al. (1995) Ho et al. (1998)

3

Ohta et al. (1991)

4

Jeffries et al. (2007) Ingram et al. (1987) Dharmadi et al. (2006) Shaw et al. (2008) Inui et al. (2005)

5 6 7 8 9 10

Yazdani and Gonzalez (2008) Seo et al. (2005)

Biomass/ Hydrolysate

Prt.

Bacteria

Xylose

NA

Z. mobilis

Glucose, xylose Xylose, glucose Xylose

Na

S. spp.

NA

E. coli, Z. mobilis

Na

Glucose

NA

P. stipitis, S. cerevisiae E. coli, Z. mobilis

Glycerol

Na

E. coli

Xylose, glucose Glucose

Na

Glycerol

Na

T. saccharolyticum C. glutamicum, Z. mobilis E. coli

Glucose

Na

Z. mobilis

Na

Parameters

Keywords

Lead Author

Microbial engineering, xylose fermentation Microbial engineering, glucose and xylose fermentation Microbial engineering, glucose and xylose fermentation Microbial engineering, xylose metabolism and fermentation, genome organization Microbial engineering, glucose fermentation Microbial engineering, glycerol fermentation determinants Microbial engineering, mixed sugar utilization, ethanol yield Microbial engineering, ethanol yield

Engineering, ethanologenic Cofermentation, genetically, engineered Genetic, ethanol, genes

Microbial engineering, ethanol yield

Engineering, ethanol

Microbial engineering,

Genome, ethanologenic

Picataggio, Stephen 6603484986 Ho, Nancy W. Y.* 7102776244 Ingram, Lonnie O. 7102962097 Jeffries, Thomas W. 7005806269 Ingram, Lonnie O. 7102962097 Gonzalez, Ramon 57192167471 Lynd, Lee R. 35586183800 Yukawa, Hideaki 7102231148 Gonzalez, Ramon 57192167471 Seo, Jeong-Sun 16640202500

Genome, fermenting Genetic, engineering, ethanol Fermentation, engineering Engineering, ethanol Engineering, ethanol

Affil.

Cits

Verdezyne Inc. USA Purdue Univ.

549

Univ. Florida USA Xylome Corp. USA Univ. Florida USA Univ. S. Florida USA Dartmouth Coll. USA Green Earth Inst. Japan Univ. S. Florida USA Seoul Natl. Univ. S. Korea

400

467

Metabolic Engineering: Review

TABLE 34.2 The Metabolic Engineering of the Other Microorganisms

382 378 333 271 248 232 225

*, Female; cits., number of citations received for each paper; Na, nonavailable; prt, biomass pretreatments.

333

334

Bioethanol Fuel Production Processes. II

lyase (FHL). They showed that, unlike CO2, hydrogen had a negative impact on cell growth and glycerol fermentation. In addition, supplementation of the medium with CO2 partially restored the ability of an FHL-deficient strain to ferment glycerol. High pH resulted in low CO2 generation and availability as most CO2 was converted to bicarbonate, and consequently very inefficient fermentation of glycerol. Most of the fermented glycerol was recovered in the reduced compounds of ethanol and succinate which reflected the highly reduced state of glycerol and confirmed the fermentative nature of this process. Yazdani and Gonzalez (2008) engineered E. coli for the efficient conversion of glycerol to ethanol in a paper with 232 citations. They capitalized on the high degree of reduction of carbon in glycerol, thus enabling the production of not only ethanol but also coproducts hydrogen and formate. They constructed two strains for the coproduction of ethanol-hydrogen and ethanol-formate: SY03 and SY04, respectively. They obtained high ethanol yields in both strains by minimizing the synthesis of by-products succinate and acetate through mutations that inactivated fumarate reductase (ΔfrdA) and phosphate acetyltransferase (Δpta), respectively. Strain SY04, which produced ethanol-formate, also contained a mutation that inactivated formate-hydrogen lyase (ΔfdhF), thus preventing the conversion of formate to CO2 and H2. They achieved high rates of glycerol utilization and product synthesis by simultaneous overexpression of glycerol dehydrogenase (gldA) and dihydroxyacetone kinase (dhaKLM), which were the enzymes responsible for the conversion of glycerol to glycolytic intermediate dihydroxyacetone phosphate. The resulting strains, SY03 (pZSKLMgldA) and SY04 (pZSKLMgldA), produced ethanol-hydrogen and ethanol-formate from unrefined glycerol at yields exceeding 95% of the theoretical maximum and specific rates in the order of 15–30 mmol/gcell/h. 34.3.2.3 The Metabolic Engineering of Other Microorganisms There are four HCPs for the metabolic engineering of other microorganisms: Saccharomyces spp., Pichia stipidis, Thermoanaerobacterium saccharolyticum, and Corynebacterium glutamicum. Ho et al. (1998) genetically engineered Saccharomyces spp. for the cofermentation of glucose and xylose in a paper with 467 citations. They developed recombinant plasmids that could transform S. spp. into xylose-fermenting yeasts. These plasmids, designated pLNH31, -32, -33, and -34, were 2 μm-based high-copy-number yeast-E. coli shuttle plasmids. In addition to the geneticin resistance and ampicillin resistance genes that served as dominant selectable markers, these plasmids also contained three xylose-metabolizing genes, a xylose reductase gene, a xylitol dehydrogenase gene (both from P. stipitis), and a xylulokinase gene (from S. cerevisiae). These xylose-metabolizing genes were also fused to signals controlling gene expression from S. cerevisiae glycolytic genes. They found that the transformation of S. sp. strain 1400 with each of these plasmids resulted in the conversion of strain 1400 from a nonxylose-metabolizing yeast to a xylose-metabolizing yeast that could effectively ferment xylose to ethanol and also effectively utilized xylose for aerobic growth. Jeffries et al. (2007) characterized the mechanism and regulation of xylose metabolism in P. stipitis and used genes from P. stipitis to engineer xylose metabolism in S. cerevisiae in a paper with 382 citations. They sequenced and assembled the complete genome of P. stipitis. They observed unusual aspects of genome organization, numerous genes for bioconversion, a preliminary insight into regulation of central metabolic pathways and several examples of colocalized genes with related functions. Further, the genome sequence provided insight into how P. stipitis regulated its redox balance while very efficiently fermenting xylose under microaerobic conditions. Shaw et al. (2008) engineered Thermoanaerobacterium saccharolyticum, fermented xylan and biomass-derived sugars, to produce ethanol at high yield in a paper with 271 citations. They observed that the deletion of genes involved in organic acid formation (acetate kinase, phosphate acetyltransferase, and L-lactate dehydrogenase) resulted in a strain able to produce ethanol as the only detectable organic product and substantial changes in electron flow relative to the wild type. Ethanol formation in the engineered strain (ALK2) utilized pyruvate:ferredoxin oxidoreductase with electrons transferred from ferredoxin to NADP. The homoethanologenic phenotype was stable for more than 150 generations in continuous culture. Further, the growth rate of strain ALK2 was

Metabolic Engineering: Review

335

similar to the wild-type strain, with a reduction in cell yield proportional to the decreased adenosine triphosphate (ATP) availability resulting from acetate kinase inactivation. Glucose and xylose were coutilized, and utilization of mannose and arabinose commenced before glucose and xylose were exhausted. Using strain ALK2 in simultaneous saccharification and fermentation (SSF) experiments at 50°C allowed a 2.5-fold reduction in cellulase loading compared with using S cerevisiae at 37°C. The maximum ethanol titer produced by strain ALK2, 37 g/L, was the highest reported at that time for a thermophilic anaerobe. Inui et al. (2005) engineered the central metabolic pathway of Corynebacterium glutamicum to produce ethanol in a paper with 248 citations. They constructed a recombinant strain which expressed the Z. mobilis genes coding for pyruvate decarboxylase (pdc) and alcohol dehydrogenase (adhB). They observed that both genes placed under the control of the C. glutamicum IdhA promoter were expressed at high levels in C. glutamicum, resulting, under oxygen-deprivation conditions, in a significant yield of ethanol from glucose in a process characterized by the absence of cellular growth. Addition of pyruvate in trace amounts to the reaction mixture induced a twofold increase in the ethanol production rate. They observed a similar effect when acetaldehyde was added. Disruption of the lactate dehydrogenase (IdhA) gene led to a threefold higher ethanol yield than wild type, with no lactate production. Moreover, inactivation of the phosphoenolpyruvate carboxylase (ppc) and IdhA genes resulted in a significant amount of ethanol production and a dramatic decrease in succinate without any lactate production, when pyruvate was added. Since the reaction occurred in the absence of cell growth, the ethanol volumetric productivity increased in proportion to cell density of C. glutamicum in a process under oxygen-deprivation conditions. Finally, the intracellular NADH concentrations in C. glutamicum were correlated to oxygen-deprived metabolic flows.

34.3.3 The Metabolic Engineering of the Substrates There are four HCPs for the metabolic engineering of substrates for cyanobacteria and switchgrass with three and one HCPs, respectively (Table 34.3). Fu et al. (2011) showed that genetic modification of switchgrass could produce phenotypically normal plants that had reduced thermal-chemical (≤180°C), enzymatic, and microbial recalcitrance in a paper with 512 citations. Downregulation of the switchgrass caffeic acid O-methyltransferase gene decreased lignin content modestly, reduced the syringyl:guaiacyl lignin monomer ratio, improved forage quality, and increased the ethanol yield by up to 38% using conventional biomass fermentation processes. The downregulated lines required less severe pretreatment and 300%–400% lower cellulase dosages for equivalent product yields using SSF with yeast. Furthermore, fermentation of diluted acid-pretreated transgenic switchgrass using Clostridium thermocellum with no added enzymes showed better product yields than obtained with unmodified switchgrass. Deng and Coleman (1999) introduced new genes into Synechococcus sp. in order to create a novel pathway for fixed carbon utilization which resulted in the synthesis of ethanol in a paper with 339 citations. They cloned the coding sequences of pyruvate decarboxylase (pdc) and alcohol dehydrogenase II (adh) from Z. mobilis into the shuttle vector pCB4 and then used them to transform S. sp. strain PCC 7942. Under control of the promoter from the rbcLS operon encoding the cyanobacterial ribulose-l,5- bisphosphate carboxylase/oxygenase (RuBisCo), they observed that the pdc and adh genes were expressed at high levels. The transformed S. sp. synthesized ethanol, which diffused from the cells into the culture medium. Gao et al. (2012) produced ethanol from CO2 in genetically engineered S. sp. in a paper with 258 citations. They constructed a S. sp. PCC6803 mutant strain with significantly high ethanol-producing efficiency (5.50 g/L, 212 mg/L/day) by genetically introducing pyruvate decarboxylase (pdc) from Z. mobilis and overexpressing endogenous alcohol dehydrogenase (adh) through homologous recombination at two different sites of the chromosome, and disrupting the biosynthetic pathway of

336

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TABLE 34.3 The Metabolic Engineering of the Substrates No.

Papers

Biomass/ Hydrolysate Switchgrass

1

Fu et al. (2011)

2

Deng and S. sp Coleman (1999)

3

Gao et al. (2012)

S. sp.

4

Dexter and Fu (2009)

S. sp.

Prt.

Bacteria

Parameters

Acids, C. thermocellum Microbial cellulases engineering, switchgrass fermentation, ethanol yield Na Z. mobilis Microbial engineering, ethanol production Na Z. mobilis, E. Microbial coli engineering, consolidate bioprocessing, ethanol production Na Z. mobilis Microbial engineering, ethanol production

Keywords Genetic, ethanol

Lead Author

Affil.

Cits

Dixon, Richard A. 7402020530

Univ. N. Texas USA

512

Ethanol, Coleman, genetic, John R. engineering 7402803364 Engineered, ethanol, genetically

Univ. 339 Toronto Canada

Lu, Xuefeng Chinese 55619293343 Acad. Sci. China

Engineering, Fu, ethanol Pengcheng 7202037416

Hainan Univ. China

258

248

Cits., number of citations received for each paper; Na, nonavailable; prt, biomass pretreatments.

poly-β-hydroxybutyrate. In total, they cloned nine alcohol dehydrogenases from different cyanobacterial strains and expressed in E. coli to test ethanol-producing efficiency. Dexter and Fu (2009) constructed a S. sp. PCC 6803 strain that could photoautotrophically convert CO2 to bioethanol in a paper with 248 citations. They used a double homologous recombination system to integrate the pyruvate decarboxylase (pdc) and alcohol dehydrogenase II (adh) genes from obligately ethanol producing Z. mobilis into the S. sp. PCC 6803 chromosome under the control of the strong, light-driven psbAII promoter. They obtained an average yield of 5.2 mmol optical density (OD730)/unit/L/day.

34.4 DISCUSSION 34.4.1 Introduction The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in the fuel cells, and in the biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis and fermentation of the biomass. The research in the field of the metabolic engineering for the bioethanol production to improve the ethanol yield has intensified in this context in recent years. The key research fronts have been the metabolic engineering of S. cerevisiae, E. coli, Z. mobilis, Pichia, Clostridium, other microorganisms, and the substrates such as corn, grass, and cyanobacteria.

Metabolic Engineering: Review

337

However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been a number of review papers for this field, there has been no review of the most-cited 25 articles in this field. Thus, this book chapter presents a review of the most-cited 25 articles on the metabolic engineering of the microorganisms and substrates for the bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on the future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the mostcited first 200 population papers. The selected keyword list was optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replicative studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 225 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, a number of brief conclusions were drawn, and a number of relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in metabolic engineering for the bioethanol production with regard to the biomass and hydrolysates used in these pretreatments is given in Table 34.4. As this table shows, there are four primary research fronts for this field: The metabolic engineering of the hydrolysates, algae, other feedstocks, and glycerol with 56%, 16%, 16%, and 8% of the sample papers, respectively. On the individual basis, the metabolic engineering of the xylose and glucose is the most-prolific research fronts with 36% of the sample papers each, while wood, agricultural residues, grass, and biomass constituents are the prolific other feedstocks with 4% of the sample papers each. Further, the metabolic engineering of the glucose is the most influential research front with 31% surplus, followed by algae and glycerol with 12% and 6% surplus, respectively. Similarly, biomass constituents, lignocellulose, pentose, and arabinose are the least influential research fronts with 3 to 7% deficit each. Information about the thematic research fronts for the sample papers in metabolic engineering for the bioethanol production with regard to the microorganisms and substrates used is given in Table 34.5. As this table shows, there are four primary research fronts for this field: The metabolic engineering of S. cerevisiae, Z. mobilis, other microorganisms, and E. coli with 52%, 32%, 24%, and 16% of the sample papers, respectively. Further, metabolic engineering of Z. mobilis and other microorganisms is the most influential research fronts with 28% and 19% surplus, respectively. Similarly, microorganisms in general and Clostridium are the least influential research fronts with 10% and 9% deficits, as they are not represented in the reviewed paper sample.

34.4.2 The Metabolic Engineering of Saccharomyces cerevisiae There are 11 HCPs for the metabolic engineering of S. cerevisiae (Table 34.1). Alper et al. (2006) showed the application of the gTMEto S. cerevisiae for improved glucose and ethanol tolerance and observed that the mutagenesis of the transcription factor Spt15p and selection led to dominant mutations that conferred increased tolerance and more efficient glucose conversion to ethanol. Further, Eliasson et al. (2000) constructed a stable xylose-utilizing recombinant S. cerevisiae strain TMB 3001 for anaerobic xylose fermentation and showed anaerobic ethanol formation from xylose by recombinant S. cerevisiae. Kuyper et al. (2005a) engineered a xylose-isomerase-expressing S. cerevisiae strain for the rapid anaerobic xylose fermentation of xylose and observed that S. cerevisiae strains that expressed the xylose isomerase gene from P. sp. E2 could grow anaerobically on xylose. Further, Alexandre et al. (2001) investigated the global gene expression during short-term ethanol stress in S. cerevisiae and found that management of the energy pool constituted an ethanol stress response and involved different mechanisms.

338

Bioethanol Fuel Production Processes. II

TABLE 34.4 The Most Prolific Research Fronts for the Biomass and Hydrolysates Used for the Metabolic Engineering for the Bioethanol Production No. 1

2 3 4

Research Fronts

N Paper Review (%)

N Paper (%) Sample

Surplus (%)

Hydrolysates Xylose

56.0 36.0

57.7 37.5

−1.7 −1.5

Glucose

30.6

36.0

5.4

Hydrolysates in general

8.0

4.8

3.2

Pentose

0.0

4.2

−4.2

Arabinose

0.0

3.0

−3.0

Cellobiose

0.0

1.8

−1.8

Hexose

0.0

0.6

−0.6

Lactose

0.0

0.6

−0.6

16.0 8.0 16.0 4.0

3.6 2.4 14.4 1.8

12.4 5.6 1.6 2.2

Agricultural residues

4.0

1.2

2.8

Grass

4.0

0.6

3.4

Biomass constituents

4.0

10.8

−6.8

Cellulose

4.0

5.4

−1.4

Lignocellulose

0.0

4.2

−4.2

Hemicellulose

0.0

0.6

−0.6

Lignin

0.0

0.6

−0.6

Ethane

0.0

1.2

−1.2

Other biomass

0.0

1.2

−1.2

Algae Glycerol Other feedstocks Wood

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 168 papers.

Fujita et al. (2004) performed that simultaneous and synergistic hydrolysis and fermentation of amorphous cellulose to ethanol using a yeast strain codisplaying the three cellulolytic enzymes and constructed a whole-cell biocatalyst through codisplay of three types of cellulolytic enzyme on the cell surface of S. cerevisiae. Further, Kuyper et al. (2003) functionally expressed xylose isomerase for the efficient xylose fermentation by S. cerevisiae and observed that xylose metabolism in the P. sp. E2 proceeded via a xylose isomerase rather than via the xylose reductase and xylitol dehydrogenase pathway. Bro et al. (2006) used a genome-scale reconstructed metabolic network of S. cerevisiae for metabolic engineering of the redox metabolism to obtain decreased glycerol and increased ethanol yields on glucose under anaerobic conditions and found that engineered strain had a 40% lower glycerol yield on glucose while the ethanol yield increased with 3% without affecting the maximum specific growth rate. Further, Kuyper et al. (2005b) engineered glucose and xylose utilization by a xylosefermenting S. cerevisiae strain and found that this engineered strain (RWB 217) exhibited unprecedentedly high specific growth rates and ethanol production rates under anaerobic conditions with xylose as the sole carbon source. Larsson et al. (2001) expressed laccase from T. versicolor under control of the PGK1 promoter in S. cerevisiae to increase its resistance to phenolic fermentation inhibitors in lignocellulose hydrolysates to improve the ethanol yield and observed a decrease in the content of low-molecular-mass

339

Metabolic Engineering: Review

TABLE 34.5 The Most Prolific Thematic Research Fronts for the Metabolic Engineering for the Bioethanol Production No. 1 2 3 4 5 6 7 8 9

Research Fronts

N Paper Review (%)

Saccharomyces cerevisiae Zymomonas mobilis Other microorganisms Escherichia coli Pichia stipitis Microorganisms in general Clostridium Bacillus subtilis Klebsiella

52.0 32.0 24.0 16.0 4.0 0.0 0.0 0.0 0.0

N Paper (%) Sample Surplus (%) 49.4 3.6 4.8 15.7 4.8 9.9 8.7 1.8 1.2

2.6 28.4 19.2 0.3 –0.8 –9.9 –8.7 –1.8 –1.2

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 168 papers.

aromatic compounds, accompanied by an increase in the content of high-molecular-mass compounds. Further, Martin et al. (2002) fermented the hydrolysates with the recombinant xylose-­ utilizing S. cerevisiae laboratory strain TMB 3001 and observed that the fermentative performance of the lab strain in undetoxified hydrolysate was better than the performance of the industrial strain. Finally, Enquist-Newman et al. (2014) engineered the alginate and mannitol catabolic pathways in S. cerevisiae to improve ethanol yield and observed that this strain conferred the ability of an S. cerevisiae strain to efficiently metabolize DEHU and mannitol. These HCPs show a sample of the research on the metabolic engineering of S. cerevisiae for bioethanol production. These studies hint that the metabolic engineering has been helpful in improving the ethanol yield.

34.4.3 The Metabolic Engineering of Other Microorganisms There are 10 HCPs for the metabolic engineering of other microorganisms (Table 34.2). The key research fronts are the metabolic engineering of E. coli and Z. mobilis with three HCPs each. 34.4.3.1 The metabolic engineering of Z. mobilis Zhang et al. (1995) engineered Z. mobilis metabolically and found that this strain efficiently fermented both glucose and xylose. Further, Ohta et al. (1991) integrated Z. mobilis genes for pyruvate decarboxylase (pdc) and alcohol dehydrogenase II (adhB) into the E. coli chromosome within or near the pyruvate formate-lyase gene (pfl) and observed that the integration improved the stability of the Z. mobilis genes in E. coli. Finally, Seo et al. (2005) reported the complete genome sequence of Z. mobilis ZM4 (ATCC31821) and observed that most of these ORFs were also actively transcribed in association with ethanol production by ZM4. 34.4.3.2 The Metabolic Engineering of E. coli Ingram et al. (1987) inserted the genes encoding essential enzymes of the fermentative pathway for ethanol production in Z. mobilis into E. coli under the control of a common promoter and observed that alcohol dehydrogenase II and pyruvate decarboxylase from Z. mobilis were expressed at high levels in E. coli, resulting in increased cell growth and the production of ethanol as the principal fermentation product from glucose.

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Dharmadi et al. (2006) investigated the anaerobic fermentation of glycerol by E. coli and observed that E. coli could ferment glycerol in a pH-dependent manner. Further, Yazdani and Gonzalez (2008) engineered E. coli for the efficient conversion of glycerol to ethanol and obtained high ethanol yields in engineered strains. 34.4.3.4 The Metabolic Engineering of Other Microorganisms There are four HCPs for the metabolic engineering of other microorganisms: S. spp., P. stipidis, T. saccharolyticum, and C. glutamicum. Ho et al. (1998) genetically engineered S. spp. for the cofermentation of glucose and xylose and developed recombinant plasmids that could transform S. spp. into xylose-fermenting yeasts. Further, Jeffries et al. (2007) characterized the mechanism and regulation of xylose metabolism in P. stipitis and used genes from P. stipitis to engineer xylose metabolism in S. cerevisiae and observed unusual aspects of genome organization, numerous genes for bioconversion. Shaw et al. (2008) engineered T. saccharolyticum, fermenting xylan and biomass-derived sugars, to produce ethanol at high yield and observed that the deletion of genes involved in organic acid formation resulted in a strain able to produce ethanol as the only detectable organic product and substantial changes in electron flow relative to the wild type. Further, Inui et al. (2005) engineered the central metabolic pathway of C. glutamicum to produce ethanol and observed that the genes placed under the control of the C. glutamicum IdhA promoter were expressed at high levels in C. glutamicum, resulting, under oxygen-deprivation conditions, in a significant yield of ethanol from glucose in a process characterized by the absence of cellular growth. These HCPs show a sample of the research on the metabolic engineering of other microorganisms to improve the ethanol yield. These studies highlight the importance of metabolic engineering using a wide range of microorganisms with a strong impact on the ethanol yield.

34.4.4 The Metabolic Engineering of the Substrates There are four HCPs for the metabolic engineering of substrates for cyanobacteria and switchgrass with three and one HCPs, respectively (Table 34.3). Fu et al. (2011) showed that genetic modification of switchgrass could produce phenotypically normal plants that had reduced thermal-chemical, enzymatic, and microbial recalcitrance Further, Deng and Coleman (1999) introduced new genes into S. sp. in order to create a novel pathway for fixed carbon utilization which resulted in the synthesis of ethanol in a paper with 339 citations. Gao et al. (2012) produced ethanol from CO2 in genetically engineered S. sp. and cloned nine alcohol dehydrogenases from different cyanobacterial strains and expressed in E. coli to test ethanol-producing efficiency. Further, Dexter and Fu (2009) constructed a S. sp. PCC 6803 strain that could photoautotrophically convert CO2 to bioethanol. These HCPs show a sample of the research on the metabolic engineering of the substrates such as cyanobacteria and grass to improve the ethanol yield. These studies highlight the importance of metabolic engineering of the substrates with a strong impact on the ethanol yield.

34.5 CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 225 citations each is given under two primary headings: metabolic engineering of the microorganism and substrates. Further, the research fronts for the microorganism metabolic engineering are the metabolic engineering of S. cerevisiae and other microorganisms. The usual characteristics of these HCPs are that microorganisms and substrates are genetically engineered to improve the ethanol yield during the fermentation processes and this research field has a crucial importance to improve fermentation processes for the improved ethanol yield.

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The key findings on these research fronts should be read in the light of the increasing public concerns about climate change, GHG emissions, and global warming as these concerns have been certainly behind the boom in the research on the bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. The recent supply shocks caused by the COVID-19 pandemics and the Russian invasion of Ukraine also highlight the importance of the production and utilization of the bioethanol fuels as an alternative to the crude oil-based gasoline and petrodiesel fuels. As Table 34.4 shows, the primary feedstocks are the hydrolysates, algae, other feedstocks, and glycerol at the macroscale. On the individual basis, xylose and glucose are the most prolific feedstocks, while wood, agricultural residues, grass, and biomass constituents are the prolific other feedstocks. Similarly, as Table 34.5 shows, four primary thematic research fronts with regard to the microorganisms for this field: S. cerevisiae, Z. mobilis, other microorganisms, and E. coli. Further, the prolific substrates are cyanobacteria and grass. These studies emphasize the importance of proper incentive structures for the efficient development and application of fermentation of the substrates and hydrolysates to enhance bioethanol yield of the substrates and hydrolysates in the light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as the USA, Europe, and the Far East had developed strong incentive structures for the effective development and application of fermentation processes for bioethanol production. In the light of the supply shocks caused primarily by the COVID-19 pandemics and Russian invasion of Ukraine, it is expected that the incentive structures such as public funding would be enhanced to increase the share of bioethanol fuels in the global fuel portfolio as a strong alternative to crude oil-based gasoline and petrodiesel fuels. In this context, it is expected that the most prolific researchers, institutions countries, funding bodies, and journals would have a first-mover advantage. It is recommended that such review studies are performed for the primary research fronts of the metabolic engineering of the microorganisms and substrates.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the metabolic engineering of the microorganisms and substrates has been gratefully acknowledged.

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Konur, O. 2002b. Assessment of disabled students in higher education: Current public policy issues. Assessment and Evaluation in Higher Education 27:131–152. Konur, O. 2002c. Access to employment by disabled people in the UK: Is the Disability Discrimination Act working? International Journal of Discrimination and the Law 5:247–279. Konur, O. 2006a. Participation of children with dyslexia in compulsory education: Current public policy issues. Dyslexia 12:51–67. Konur, O. 2006b. Teaching disabled students in higher education. Teaching in Higher Education 11:351–363. Konur, O. 2007a. A judicial outcome analysis of the Disability Discrimination Act: A windfall for the employers? Disability & Society 22:187–204. Konur, O. 2007b. Computer-assisted teaching and assessment of disabled students in higher education: The interface between academic standards and disability rights. Journal of Computer Assisted Learning 23:207–219. Konur, O. 2012. The evaluation of the research on the bioethanol: A scientometric approach. Energy Education Science and Technology Part A: Energy Science and Research 28:1051–1064. Konur, O. 2015. Current state of research on algal bioethanol. In Marine Bioenergy: Trends and Developments, Ed. S. K. Kim and C. G. Lee, pp. 217–244. Boca Raton, FL: CRC Press. Konur, O. 2020. The scientometric analysis of the research on the bioethanol production from green macroalgae. In Handbook of Algal Science, Technology and Medicine, Ed. O. Konur, pp. 385–401. London: Academic Press. Konur, O. 2023. Metabolic engineering for the bioethanol production: Scientometric study. In Bioethanol Fuel Production Processes. II: Biomass Hydrolysis, Fermentation, and Bioethanol Fuel Separation. Handbook of Bioethanol Fuels Volume 2, Ed. O. Konur. Boca Raton, FL: CRC Press. Kuyper, M., H. R. Harhangi and A. K. Stave, et al. 2003. High-level functional expression of a fungal xylose isomerase: The key to efficient ethanolic fermentation of xylose by Saccharomyces cerevisiae? FEMS Yeast Research 4:69–78. Kuyper, M., M. M. P. Hartog and M. J. Toirkens, et al. 2005a. Metabolic engineering of a xylose-isomerase-expressing Saccharomyces cerevisiae strain for rapid anaerobic xylose fermentation. FEMS Yeast Research 5:399–409. Kuyper, M., M. J. Toirkens and J. A. Diderich, et al. 2005b. Evolutionary engineering of mixed-sugar utilization by a xylose-fermenting Saccharomyces cerevisiae strain. FEMS Yeast Research 5:925–934. Larsson, S., P. Cassland and L. J. Jonsson. 2001. Development of a Saccharomyces cerevisiae strain with enhanced resistance to phenolic fermentation inhibitors in lignocellulose hydrolysates by heterologous expression of laccase. Applied and Environmental Microbiology 67:1163–1170. Lin, Y. and S. Tanaka. 2006. Ethanol fermentation from biomass resources: Current state and prospects. Applied Microbiology and Biotechnology 69:627–642. Ma, X., L. Sun and C. Song. 2002. A new approach to deep desulfurization of gasoline, diesel fuel and jet fuel by selective adsorption for ultra-clean fuels and for fuel cell applications. Catalysis Today 77:107–116. Martin, C., M. Galbe, C. F. Wahlbom, B. Hahn-Hagerdal and L. J. Jonsson. 2002. Ethanol production from enzymatic hydrolysates of sugarcane bagasse using recombinant xylose-utilising Saccharomyces cerevisiae. Enzyme and Microbial Technology 31:274–282. Mermelstein, L. D., N. E. Welker, G. N. Bennett and E. T. Papoutsakis. 1992. Expression of cloned homologous fermentative genes in Clostridium acetobutylicum ATCC 824. Bio/Technology 10:190. Morschbacker, A. 2009. Bio-ethanol based ethylene. Polymer Reviews 49:79–84. Najafi, G., B. Ghobadian and T. Tavakoli, et al. 2009. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy 86:630–639. Newman, P. W. G. and J. R. Kenworthy. 1989. Gasoline consumption and cities: A comparison of U.S. cities with a global survey. Journal of the American Planning Association 55:24–37. North, D. C. 1991. Institutions. Journal of Economic Perspectives 5:97–112. Ohta, K., D. S. Beall, J. P. Mejia, K. T. Shanmugam and L. O. Ingram. 1991. Genetic improvement of Escherichia coli for ethanol production: Chromosomal integration of Zymomonas mobilis genes encoding pyruvate decarboxylase and alcohol dehydrogenase II. Applied and Environmental Microbiology 57:893–900. Olsson, L. and B. Hahn-Hagerdal. 1996. Fermentation of lignocellulosic hydrolysates for ethanol production. Enzyme and Microbial Technology 18:312–331. Sanchez, O. J. and C. A. Cardona. 2008. Trends in biotechnological production of fuel ethanol from different feedstocks. Bioresource Technology 99:5270–5295. Seo, J. S., H. Chong and H. S. Park, et al. 2005. The genome sequence of the ethanologenic bacterium Zymomonas mobilis ZM4. Nature Biotechnology 23:63–68.

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35

The Utilization of the Saccharomyces cerevisiae for the Bioethanol Production Scientometric Study Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

35.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Hill et al., 2006; Konur, 2012e, 2015, 2019, 2020a) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). Bioethanol fuels also play a critical role in maintaining energy security (Kruyt et al., 2009; Winzer, 2012) in the supply shocks (Kilian, 2008, 2009) related to oil price shocks (Hamilton, 2003, 2009), coronavirus disease 2019 (COVID-19) pandemic (Fauci et al., 2020; Li et al., 2020), or wars (Hamilton, 1983; Jones, 2012) in the aftermath of the Russian invasion of Ukraine (Reeves, 2014). However, it is necessary to pretreat the biomass (Taherzadeh and Karimi, 2008; Yang and Wyman, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before bioethanol production through hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. Research in the field of the utilization of Saccharomyces cerevisiae (S. cerevisiae) for bioethanol production to improve ethanol yield has intensified in this context as the most-studied yeast in the fermentation of feedstocks to produce bioethanol in recent years (Almeida et al., 2007; Matsushika et al., 2009; van Maris et al., 2006). The key research fronts have been the fermentation of the xylose (Eliasson et al., 2000; Kotter and Ciriacy, 1993; Kuyper et al., 2003), fermentation of the glucose (Bro et al., 2006; Delgenes et al., 1996; Najafpour et al., 2004), fermentation of the biomass in general (Karimi et al., 2006; Lau and Dale, 2009; Larsson et al., 2001), fermentation inhibitors (Delgenes et al., 1996; Gorsich et al., 2006; Larsson et al., 2001), ethanol stress (Alexandre et al., 2001; You et al., 2003), and fermentation of other hydrolysates such as arabinose and cellobiose (Becker and Boles, 2003; Ha et al., 2011). Furthermore, the metabolic engineering of S. cerevisiae (Eliasson et al., 2000; Kotter and Ciriacy, 1993; Kuyper et al., 2003) has been a major cross-subject research front. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field (Garfield, 1955; Konur, 2011, 2012a,b,c,d,e,f,g,h,i, 2015, 2018b, 2019, 2020a).

DOI: 10.1201/9781003226499-44

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Bioethanol Fuel Production Processes. II

As the recently published scientometric studies focus on the fermentation process in general (Calvo et al., 2022; Devos and Colla, 2022), this book chapter presents a scientometric study of the research in the utilization of S. cerevisiae for bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts.

35.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in June 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix for future replicative studies. As a second step, two sets of data were used for this study. First, a population sample of 2,424 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 242 most-cited papers, corresponding to 10% of the population papers, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these both sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the utilization of S. cerevisiae for bioethanol production. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape.

35.3 RESULTS 35.3.1  The Most Prolific Documents in the Utilization of the S. cerevisiae for Bioethanol Production The information on the types of documents for both datasets is given in Table 35.1. The articles and conference papers, published in journals, dominate both the sample (95%) and population (97%) papers as they are underrepresented in the sample papers by 2%. Furthermore, review papers have TABLE 35.1 Documents in the Utilization of S. cerevisiae for Bioethanol Production Documents Article Review Short survey Conference paper Letter Book chapter Note Book Editorial Sample size

Sample Dataset (%) 93.8 2.9 1.7 1.2 0.4 0.0 0.0 0.0 0.0 242

Population Dataset (%) 95.4 1.7 0.2 1.5 0.7 0.3 0.2 0.0 0.0 2,424

Surplus (%) −1.6 1.2 1.5 −0.3 −0.3 −0.3 −0.2 0.0 0.0

Population dataset, the number of papers (%) in the set of 2,424 population papers; Sample dataset, the number of papers (%) in the set of 242 highly cited papers.

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a surplus as they are overrepresented in the sample papers by 3% as they constitute 5% and 2% of the sample and population papers, respectively. It is further notable that 99% of the population papers were published in journals, while 1% and 0.2% of them were published in book series and books, respectively. On the contrary, 99% of the sample papers were published in the journals.

35.3.2  The Most Prolific Authors in the Utilization of S. cerevisiae for Bioethanol Production The information about the 20 most prolific authors with at least 2.1% of sample papers each is given in Table 35.2. The most prolific author is Barbel Hahn-Hagerdal of Lund University of Sweden with 15% of the sample papers, followed by Marie F. Gorwa-Grauslund and Jack T. Prong with 6% of the

TABLE 35.2 Most Prolific Authors in the Utilization of S. cerevisiae for Bioethanol Production No.  1

Author Name

Author Code

Sample Population Papers (%) Papers (%) Surplus

Institution

Country

Res. HI N Front

Hahn-Hagerdal, Barbel* Gorwa-Grauslund, Marie F.* Pronk, Jack T. Van Dijken, Johannes P. Liden, Gunnar Jin, Yong-Su Van Zyl, Willem H. Kondo, Akihiko Olsson, Lisbeth*

7005389381

14.5

2.6

11.9

Lund Univ.

Sweden

76 258 M

6603563787

5.8

1.5

4.3

Lund Univ.

Sweden

38 102 M, P

7005313057 7102979857

5.8 5.0

1.4 0.5

4.4 4.5

Delft Univ Technol. Netherlands 74 293 M Delft Univ Technol. Netherlands 68 190 M

7004458708 57204009076 7005925838 57203868143 7203077540

4.5 3.7 3.7 3.3 2.9

0.8 2.5 1.0 2.1 1.2

3.7 1.2 2.7 1.2 1.7

Sweden USA S. Africa Japan Sweden

48 46 44 78 59

Jonsson, Leif J. Van Maris, Antonius J. A. Penttila, Merja

7102349315 6506130520

2.9 2.5

0.5 1.0

2.4 1.5

Lund Univ. Univ. Ill. U. C. Stellenbosch Univ. Kobe Univ. Chalmers Univ. Technol. Lund Univ. Royal Inst. Technol.

Sweden Sweden

39 147 M 44 98 M

7005401826

2.5

0.7

1.8

Finland

77 286 M

6701407496

2.5

0.5

2.0

Sweden

64 406 P

14

Taherzadeh, Mohammad J Johansson, Bengt

VTT Tech. Res. Ctr. Univ. Boras

55984151300

2.5

0.5

2.0

Sweden

51 443 M

15 16

Seo, Jin-Ho Kim, Soo Rin*

7401784005 36659584200

2.1 2.1

1.4 1.1

0.7 1.0

S. Korea S. Korea

42 254 M 24 78 M

17 18

Kodaki, Tsutomu Boles, Eckhard

26643620800 7005230946

2.1 2.1

0.4 0.4

1.7 1.7

Japan Germany

32 97 M 47 134 M

19 20

Fukuda, Hideki Niklasson, Claes

55425022800 7003757959

2.1 2.1

0.3 0.3

1.8 1.8

Chalmers Univ. Technol. Seoul Natl. Univ. Kyungpook Natl. Univ. Kyoto Univ. Goethe Univ. Frankf. Kobe Univ. Chalmers Univ. Technol.

Japan Sweden

56 222 M 30 83 P

 2  3  4  5  6  7  8  9 10 11 12 13

143 212 187 796 244

P M M, P M P

Author code, the unique code given by Scopus to the authors; M, metabolic engineering of S. cerevisiae; P, ethanol production in general; Population papers, the number of papers authored in the population dataset; Sample papers, the number of papers authored in the sample dataset.

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sample papers each. The other prolific authors are Johannes P. van Dijken, Gunnar Liden, Yong-Su Jin, Willem H. van Zyl, and Akihiko Kondo with 3 to 5% of the sample papers each. Furthermore, the most influential author is Barbel Hahn-Hagerdal with 12% surplus, followed by Johannes P. van Dijken, Jack T. Pronk, and Marie F. Gorwa-Grauslund with 4.3%–4.5% surplus each. The other influential authors are Gunnar Liden, Willem H. van Zyl, and Leif J. Jonsson with 2.4%–3.7% surplus each. The most prolific institution for the sample dataset is Lund University with four authors, while Chalmers University of Technology has three authors. The other prolific institutions are Delft University of Technology and Kobe University with two authors each. In total, 13 institutions house these top authors. However, the most prolific country for the sample dataset is Sweden with nine authors, followed by Japan with four authors, while the Netherlands and S. Korea house two authors each. In total, eight countries house these top authors. There are two primary research fronts for these top authors: metabolic engineering of S. cerevisiae and the production of bioethanol in general with 14 and 6 authors, respectively. However, there is a significant gender deficit (Beaudry and Lariviere, 2016) for the sample dataset as surprisingly only four of these top researchers are female with a representation rate of 20%. Additionally, there are other authors with a relatively low citation impact and with 0.5%–1.2% of the population papers each: Tomohisa Hasunuma, Xiaoming Bao, Lucilia Domingues, YongCheol Park, Yue-Qin Tang, Carlos A. Rosa, Boris U. Stambuk, Feng-Wu Bai, Jin Hou, Johan M. Thevelein, Ying-Jin Yuan, Hiroshi Takagi, Yu Shen, Xin-Qing Zhao, Kenji Kida, Chiaki Ogino, Jamie H. D. Cate, Maria Kanellaki, Athanasios A. Koutinas, Eung Joong Oh, Jose A. Teixeira, Zi-Yuan Xia, Sung-Koo Kim, Won-Heong Lee, Yeon-Woo Ryu, Daisuke Watanabe, and Ryosuke Yamada.

35.3.3 The Most Prolific Research Output by Years in Utilization of the S. cerevisiae for Bioethanol Production Information about papers published between 1970 and 2022 is given in Figure 35.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s with 47% of the population datasets. The publication rates for the 2020s, 2000s, 1990s, 1980s, and 1970s were 11%, 21%, 12%, 7%, and 1%, respectively. Similarly, the bulk of the research papers in the sample dataset were published in the 2000s and 2010s with 56 and 21% of the sample datasets, respectively. The publication rates for the 1990s, 1980s, and 1970s were 14%, 6%, and 2% of the sample papers, respectively. The most prolific publication years for the population dataset were 2013 and 2014 with 6.1% of the datasets each. Furthermore, 69% of the population papers were published between 2006 and 2022. Similarly, 76% of the sample papers were published between 2000 and 2014, while the most prolific publication year was 2007 with 9.9% of the sample papers. The other prolific years were 2003 and 2009 with 7.4% and 7.7% of the sample papers, respectively. It is notable that there was a flat trend between 1982 and 2004 for the research output of population papers, while there was a rising trend between 2005 and 2012 and a falling trend between 2013 and 2022 for the population papers. Furthermore, there was no sharp rise in the research output for the population papers in 2020 and 2021 due to the supply shocks.

35.3.4 The Most Prolific Institutions in the Utilization of the S. cerevisiae for Bioethanol Production Information about the 17 most prolific institutions publishing papers on the utilization of S. cerevisiae for bioethanol production with at least 2.1% of the sample papers each is given in Table 35.3.

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Number of papers (%)

10

Population papers Sample papers

8

6

4

2

0

FIGURE 35.1  Research output by years regarding the utilization of S. cerevisiae for bioethanol production.

TABLE 35.3 The Most Prolific Institutions in Utilization of S. cerevisiae for Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17

Institutions Lund Univ. Delft Univ. Technol. Tech. Univ. Denmark Stellenbosch Univ. Bird Eng. Inc. Kobe Univ. Chalmers Univ. Technol. Univ. Ill. U. C. Univ. Calif. Berkeley VTT Tech. Res. Ctr. Lawrence Berkeley Natl. Lab. Seoul Natl. Univ. Kyoto Univ. Univ. Minho Shandong Univ. USDA Goethe Univ. Frankf.

Country

Sample Papers (%)

Sweden Netherlands Denmark S. Africa Netherlands Japan Sweden USA USA Finland USA S. Korea Japan Portugal China USA Germany

17.4 5.8 5.4 4.1 3.7 3.3 3.3 2.9 2.5 2.5 2.5 2.1 2.1 2.1 2.1 2.1 2.1

Population Papers (%) 4.5 2.3 1.6 1.6 0.4 2.1 1.8 2.9 1.1 1.1 0.8 1.9 1.5 1.4 1.3 0.8 0.4

Surplus (%) 12.9 3.5 3.8 2.5 3.3 1.2 1.5 0.0 1.4 1.4 1.7 0.2 0.6 0.7 0.8 1.3 1.7

The most prolific institution is Lund University with 17.4% of the sample papers, followed by the Delft University of Technology and Technical University of Denmark with 5.8% and 5.4% of the sample papers, respectively. The other prolific institutions are Stellenbosch University, Bird Engineering Inc., Kobe University, and Chalmers University of Technology 3.3%–4.1% of the sample papers each. The top country for these most prolific institutions is the USA with four institutions, while Japan, the Netherlands, and Sweden house two institutions each. In total, 11 countries house these top institutions.

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However, the institution with the most citation impact is Lund University with 13% surplus, followed by the Delft University of Technology, Technical University of Denmark, and Bird Engineering Inc. with 3.7%–5.4% surplus each. The other influential institutions are Stellenbosch University, Lawrence Berkeley National Laboratory, and Goethe University Frankfurt with 1.7%–2.5% surplus each. Additionally, there are other institutions with a relatively low citation impact and with 0.6%–1.7% of the population papers each: Tianjin University, Osaka University, Korea University, National Institute of Advanced Industrial Science and Technology, Chinese Academy of Sciences, University of Sao Paulo, State University of Campinas, Kyungpook National University, Sichuan University, USDA Agricultural Research Service, Federal University of Sao Carlos, KU Leuven, University of Saskatchewan, Federal University of Santa Catarina, Riken, Federal University of Minas Gerais, Dalian University of Technology, East China University of Science and Technology, Nara Institute of Science and Technology, Shanghai Jiao Tong University, National Research Institute for Agriculture, Food and Environment (INRAE), Beijing University of Chemical Technology, University of Patras, University of Wisconsin-Madison, Kookmin University, Jiangnan University, Chonnam National University, Khon Kaen University, Osmania University, and Tianjin University of Science & Technology.

35.3.5 The Most Prolific Funding Bodies in the Utilization of the S. cerevisiae for Bioethanol Production Information about the 13 most prolific funding bodies funding at least 1.2% of the sample papers each is given in Table 35.4. Only 23% and 42% of the sample and population papers were funded, respectively. The most prolific funding body is the New Energy and Industrial Technology Development Organization with 4.1% of the sample papers, followed by the European Commission and the Ministry of Education, Culture, Sports, Science and Technology with 3.7% and 2.9% of the sample papers, respectively. However, the most prolific country for these top funding bodies is Japan with four funding bodies, followed by China with three funding bodies. The other prolific countries are Sweden and the EU with two funding bodies each. In total, only five countries and the EU house these top funding bodies. The funding bodies with the most citation impact are the New Energy and Industrial Technology Development Organization and European Commission with 2.2% and 2.1% of the sample papers,

TABLE 35.4 The Most Prolific Funding Bodies in Utilization of S. cerevisiae for Bioethanol Production No.

Funding Bodies

 1  2  3  4  5  6  7  8  9 10 11 12 13

New Ener. Ind. Technol. Devnt. Prog. Eur. Comm. Minist. Educ. Cult. Sport. Sci. Technol. Natl. Natr. Sci. Found. Japan Soc. Prom. Sci. Minist. Sci. Technol. China Natl. Key Res. Devnt. China Swedish Energ. Agcy. US Dept. Energy Seventh Frame. Prog. Natl. Res. Found. Minist. Econ. Trade Ind. Swedish Found. Int. Coop. Res. HE

Country Sample Paper No. (%) Population Paper No. (%) Surplus (%) Japan EU Japan China Japan China China Sweden USA EU S. Africa Japan Sweden

4.1 3.7 2.9 1.7 1.7 1.7 1.7 1.7 1.2 1.2 1.2 1.2 1.2

1.9 1.6 2.8 5.9 2.3 2.1 1.8 1.0 1.0 0.9 0.7 0.5 0.2

2.2 2.1 0.1 −4.2 −0.6 −0.4 −0.1 0.7 0.2 0.3 0.5 0.7 1.0

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respectively. Similarly, the funding body with the least citation impact is the National Natural Science Foundation of China with 4% deficit. This funding body was the largest funder of the population paper with 5.9% funding rate. The other funding bodies with a relatively low citation impact and with 0.5%–3% of the population papers each are the Brazilian National Council for Scientific and Technological Development, Coordination for the Improvement of Higher Education Personnel (CAPES), National Research Foundation of Korea, Sao Paulo Research Foundation, Energy Biosciences Institute, Ministry of Science and Innovation, Ministry of Education, Chinese Academy of Sciences, National High-Tech Research and Development Program, Foundation for Science and Technology, National Council for Science and Technology, Fundamental Research Funds for the Central Universities, National Research Council of Thailand, European Regional Development Fund, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, Minas Gerais State Agency for Research and Development, National Institute of General Medical Sciences, Academy of Finland, Ministry of Science and Technology of Brazil, Thailand Research Fund, Council of Scientific and Industrial Research, India, Government of Canada, and National Basic Research Program of China (973 Program).

35.3.6 The Most Prolific Source Titles in the Utilization of the S. cerevisiae for Bioethanol Production Information about the 13 most prolific source titles publishing at least 1.7% of the sample papers each in utilization of S. cerevisiae for bioethanol production is given in Table 35.5. The most prolific source title is Applied and Environmental Microbiology with 19.4% of the sample papers, followed by Bioresource Technology, Applied Microbiology and Biotechnology, and Biotechnology and Bioengineering with 9.1%, 8.7%, and 7.9% of the sample papers, respectively. The other prolific titles are FEMS Yeast Research, Biotechnology for Biofuels, Enzyme and Microbial Technology, and Metabolic Engineering with 4.1%–5.4% of the sample papers each. However, the source title with the most citation impact is Applied and Environmental Microbiology with 14% surplus, followed by Bioresource Technology, Biotechnology and Bioengineering, and Applied Microbiology and Biotechnology with 4.2%, 3.5%, and 3% surplus, respectively. The other

TABLE 35.5 The Most Prolific Source Titles in Utilization of S. cerevisiae for Bioethanol Production No.  1  2  3  4  5  6  7  8  9 10 11 12 13

Source Titles Applied and Environmental Microbiology Bioresource Technology Applied Microbiology and Biotechnology Biotechnology and Bioengineering FEMS Yeast Research Biotechnology for Biofuels Enzyme and Microbial Technology Metabolic Engineering Journal of Biotechnology Yeast Microbiology Proceedings of the National Academy of Sciences of the United States of America RSC Advances

Sample Papers (%)

Population Papers (%)

Surplus (%)

19.4 9.1 8.7 7.9 5.4 5.0 4.5 4.1 2.9 2.5 1.7 1.7

5.2 4.9 5.7 4.4 2.8 2.9 2.3 1.4 1.8 1.6 0.4 0.2

14.2 4.2 3.0 3.5 2.6 2.1 2.2 2.7 1.1 0.9 1.3 1.5

1.7

0.2

1.5

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influential titles are Metabolic Engineering and FEMS Yeast Research with 2.7% and 2.6% surplus, respectively. Similarly, the source title with the least impact is the Yeast with 1% surplus. The other source titles with a relatively low citation impact with 0.7%–3.3% of the population papers each are Biotechnology Letters, Journal of Industrial Microbiology and Biotechnology, Applied Biochemistry and Biotechnology, Journal of Bioscience and Bioengineering, Process Biochemistry, Journal of Microbiology and Biotechnology, World Journal of Microbiology and Biotechnology, Biomass and Bioenergy, Biotechnology Progress, AMB Express, FEMS Microbiology Letters, Renewable Energy, Annals of Microbiology, Bioprocess and Biosystems Engineering, PLOS One, Antonie van Leeuwenhoek International Journal of General and Molecular Microbiology, Bioscience Biotechnology and Biochemistry, Journal of Applied Microbiology, Fermentation, and Letters in Applied Microbiology.

35.3.7 The Most Prolific Countries in the Utilization of the S. cerevisiae for Bioethanol Production Information about the 16 most prolific countries publishing at least 2.1% of sample papers each in utilization of S. cerevisiae for bioethanol production is given in Table 35.6. The most prolific country is the USA with 23% of the sample papers, closely followed by Sweden with 21% of the sample papers. Japan, the Netherlands, China, and Denmark are the other prolific countries with 7%–11% of the sample papers each. It is notable that China is the largest producer of population papers with 14.1% production rate. Furthermore, eight European countries listed in Table 35.6 produce 57% and 27% of the sample and population papers, respectively. However, the country with the most citation impact is Sweden with 13.7% surplus, closely followed by the USA with 9.6% surplus. The other influential countries are Denmark and the Netherlands with 4.4% and 4.2% surplus, respectively. Similarly, the country with the least citation impact is China with 7.1% deficit, while S. Korea, Brazil, India, and Canada have 0.9%–3.1% deficit each. Additionally, there are other countries with relatively low citation impact and with 0.6%–3.2% of the sample papers each: Spain, Thailand, Italy, Turkey, Mexico, Australia, Indonesia, Greece, Belgium, Iran, Malaysia, Poland, Egypt, Pakistan, Switzerland, Nigeria, Serbia, and Taiwan. TABLE 35.6 The Most Prolific Countries in the Utilization of S. cerevisiae for Bioethanol Production No.

Countries

 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16

USA Sweden Japan Netherlands China Denmark Portugal India France UK S. Africa Germany S. Korea Brazil Finland Canada

Sample Papers (%)

Population Papers (%)

Surplus (%)

23.1 20.7 10.7 7.4 7.0 7.0 5.4 5.0 5.0 5.0 4.5 4.1 3.7 3.3 2.5 2.1

13.5 7.0 10.1 3.2 14.1 2.6 3.3 6.6 3.4 3.2 2.6 2.7 6.8 6.1 1.4 3.0

9.6 13.7 0.6 4.2 −7.1 4.4 2.1 −1.6 1.6 1.8 1.9 1.4 −3.1 −2.8 1.1 −0.9

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TABLE 35.7 The Most Prolific Scopus Subject Categories in the Utilization of S. cerevisiae for Bioethanol Production No.

Scopus Subject Categories

1

Biochemistry, Genetics and Molecular Biology Immunology and Microbiology Chemical Engineering Environmental Science Agricultural and Biological Sciences Energy

2 3 4 5 6

Sample Papers (%)

Population Papers (%)

Surplus (%)

76.9

67.4

9.5

76.9 41.7 35.5 21.1

61.1 39.2 19.7 13.7

15.8 2.5 15.8 7.4

17.4

14.6

2.8

35.3.8 The Most Prolific Scopus Subject Categories in the Utilization of the S. cerevisiae for Bioethanol Production Information about the six most prolific Scopus subject categories indexing at least 17.4% of the sample papers each is given in Table 35.7. The most prolific Scopus subject categories in the utilization of S. cerevisiae for bioethanol production are Biochemistry, Genetics and Molecular Biology, and Immunology and Microbiology with 77% of sample papers each, followed by Chemical Engineering and Environmental Science with 42% and 36% of the sample papers, respectively. The other prolific subject categories are Agricultural and Biological Sciences and Energy with 21% and 17% of the sample papers, respectively. It is notable that Social Sciences including Economics and Business account only for 0.6% of the population studies. However, the Scopus subject categories with the most citation impact are Immunology and Microbiology and Environmental Science with 16% surplus each, followed by Biochemistry, Genetics and Molecular Biology, and Agricultural and Biological Sciences with 10% and 7% surplus, respectively.

35.3.9 The Most Prolific Keywords in the Utilization of the S. cerevisiae for Bioethanol Production Information about the Scopus keywords used with at least 7.9% or 3.8% of the sample or population papers, respectively, is given in Table 35.8. For this purpose, keywords related to the keyword set given in the appendix are selected from a list of the most prolific keyword set provided by the Scopus database. These keywords are grouped under eight headings: biomass, fermentation, bacteria, metabolic engineering, hydrolysates, pretreatments, other processes, and products of the fermentation. The most prolific keyword related to biomass and biomass constituents is biomass with 22% of the sample papers, followed by cellulose, lignin, and lignocellulose with 12%–18% of the sample papers each. Furthermore, the most prolific keyword related to the bacteria is S. cerevisiae with 100% of the sample papers, followed by yeasts with 66% of the sample papers. The most prolific keyword related to the metabolic engineering is genetic engineering with 17.4% of the sample papers, followed by gene expression with 16.5% of the sample papers. Furthermore, the most prolific keyword related to the fermentation is fermentation with 73% of the sample papers. The most prolific keyword related to hydrolysates is xylose with 42% of the sample papers, followed by sugar and glucose with 36% and 39% of the sample papers, respectively. Furthermore, the most prolific keyword related to the biomass pretreatments is enzyme activity with 25% of the sample papers, followed by enzymes with 13% of the sample papers.

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TABLE 35.8 The Most Prolific Keywords in Utilization of S. cerevisiae for Bioethanol Production No. 1

Keywords Biomass

5

6

21.5

13.5

8.0

18.2

8.9

9.3

13.2

5.5

7.7

Lignocellulose

12.0

5.8

6.2

7.9

7.4

0.5 6.4

Fermentation 72.7

66.3

Xylose fermentation

8.3

4.4

3.9

Ethanol fermentation

5.0

7.5

−2.5

100.0

100.0

0.0

Yeast

66.1

54.0

12.1

Fungi

49.6

36.6

13.0

P. stipitis

23.6

6.9

16.7

Fungus growth

17.8

11.3

6.5

Fungus culture

8.3

4.5

3.8

Bacteria S. cerevisiae

4

Surplus (%)

Cellulose

Fermentation

3

Population Papers (%)

Lignin Glycerol 2

Sample Papers (%)

Biomass and biomass constituents

Metabolic engineering Genetic engineering

17.4

7.8

9.6

Gene expression

16.5

13.1

3.4

Metabolic engineering

13.2

9.1

4.1

Genes

10.7

7.4

3.3

Xylose reductase

10.7

4.0

6.7

Gene overexpression

9.9

4.0

5.9

Aldehyde reductase

9.5

3.4

6.1

Oxidoreductase

9.1

3.7

5.4

Genetics

8.7

15.3

−6.6

Fungal gene

8.7

5.6

3.1

Xylulokinase

8.7

2.5

6.2

Recombinant proteins

7.9

4.1

3.8

Xylitol dehydrogenase

7.9

7.9

Hydrolysates Xylose

41.7

18.6

23.1

Glucose

39.3

28.5

10.8

Sugar

36.4

17.9

18.5

Enzyme activity

25.2

14.8

10.4

Enzymes

13.2

6.4

6.8

9.5

7.2

Biomass pretreatment

pH

2.3 (Continued)

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TABLE 35.8 (Continued) The Most Prolific Keywords in Utilization of S. cerevisiae for Bioethanol Production No. 7

8

Keywords

Sample Papers (%)

Population Papers (%)

Surplus (%)

Other processes Hydrolysis

16.1

9.5

6.6

Biotechnology

12.4

7.1

5.3

Saccharification

7.9

7.7

0.2

Fermentation products Ethanol

67.4

55.9

11.5

Alcohol

62.4

43.5

18.9

Xylitol

18.6

6.0

12.6

Alcohol production

18.2

16.0

2.2

Ethanol production

13.2

13.3

−0.1

Biofuel

9.9

11.4

−1.5

Bioethanol

9.5

17.1

−7.6

Ethanol yield

3.7

4.2

−0.5

3.8

−3.8

Cellulosic ethanol

The most prolific keyword related to the other processes is hydrolysis with 16% of the sample papers, followed by biotechnology with 12% of the sample papers. Finally, the most prolific keyword related to fermentation products is ethanol with 67% of the sample papers, followed by alcohol with 62% of the sample papers. It is notable that bioethanol accounts only for 10% of the sample papers. However, the most influential keywords are xylose, alcohol, sugar, Pichia stipitis (P. stipitis), fungi, xylitol, yeast, ethanol, glucose, enzyme activity, genetic engineering, cellulose, and biomass with 8%–23% surplus each. Similarly, the least influential keywords are bioethanol, genetics, cellulosic ethanol, ethanol fermentation, and biofuel with 2%–8% deficit each.

35.3.10 The Most Prolific Research Fronts in Utilization of the S. cerevisiae for Bioethanol Production Information about the research fronts for the sample papers in the utilization of S. cerevisiae for bioethanol production with regard to the biomass used for bioethanol production is given in Table 35.9. As this table shows, there is only one primary research front with regard to feedstocks for this field: the fermentation of hydrolysates using S. cerevisiae with 65% of the sample papers. The other research fronts are the fermentation of agricultural residues, wood, grass, and other feedstocks with 6%, 1%, 1%, and 12% of the sample papers, respectively. On individual basis, the most prolific feedstock is xylose with 40% of the sample papers each, followed by glucose and hydrolysates in general with 11% and 7% of the sample papers, respectively. The other prolific feedstocks are arabinose, lignocellulosic biomass, food waste, cellobiose, cellulose, and sugarcane bagasse with 1.7%–3.3% of the sample papers each.

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TABLE 35.9 The Most Prolific Research Fronts for the Utilization of S. cerevisiae for Bioethanol Production No. 1

2

3 4 5

Research Fronts

N Paper (%) Sample

Hydrolysates Xylose

64.5 39.7

Glucose

11.2

Hydrolysate in general

7.0

Arabinose

3.3

Cellobiose

1.7

Galactose

0.8

Pentose

0.8

Agricultural residues Sugarcane bagasse

5.7 1.7

Rice straw

1.2

Wheat straw

1.2

Corn stover

0.8

Corncobs

0.4

Sorghum bagasse

0.4

Wood Grass Other feedstocks Lignocellulosic biomass

1.2 0.8 12.3 2.5

Food waste

2.1

Cellulose

1.7

Sorghum

0.8

Starch

0.8

Sugarcane

0.8

Carob

0.4

corn

0.4

Industrial wastes

0.4

Inulins

0.4

Jerusalem artichoke

0.4

Mahula

0.4

Soybeans

0.4

Sugar beet

0.4

Xylan

0.4

N paper (%) sample, the number of papers in the population sample of 242 papers.

Information about the thematic research fronts for the sample papers in the utilization of S. cerevisiae for bioethanol production is given in Table 35.10. As this table shows, the most prolific research front is the fermentation of hydrolysates using S. cerevisiae with 65% of the sample papers, followed by the metabolic engineering of feedstocks using S. cerevisiae with 54% of the sample papers. The other prolific research fronts are biomass fermentation using S. cerevisiae and ethanol stress of S. cerevisiae with 22% and 15% of the sample papers, respectively.

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TABLE 35.10 The Most Prolific Thematic Research Fronts for the Utilization of S. cerevisiae for Bioethanol Production No. 1

Research Fronts Hydrolysate fermentation Xylose fermentation

64.5 39.7

Glucose fermentation

11.2

Hydrolysate fermentation in general

7.0

Arabinose fermentation

3.3

Cellobiose fermentation

1.7

Galactose fermentation

0.8

Pentose fermentation 2 3 4 5 6 7

N Paper (%) Sample

Metabolic engineering Biomass fermentation Ethanol stress Ethanol production in general Fermentation inhibitors Fermentation processes

0.8 53.7 22.3 14.5 7.4 6.2 2.9

N paper (%) sample, the number of papers in the population sample of 242 papers.

35.4 DISCUSSION 35.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol before bioethanol production through hydrolysis and fermentation. The research in the field of the utilization of S. cerevisiae for bioethanol production to improve ethanol yield has intensified as the most-studied yeast in the fermentation of feedstocks to produce bioethanol in this context in recent years. The key research fronts have been the fermentation of the xylose, glucose, biomass and other hydrolysates, fermentation inhibitors, and ethanol stress. Furthermore, the metabolic engineering of S. cerevisiae has been a major cross-subject research front. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. This is especially important to maintain energy security in the cases of supply shocks such as oil price shocks, war-related shocks as in the case of the Russian invasion of Ukraine, or COVID-19 shocks. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As the recent scientometric studies focus on the fermentation processes in general, this book chapter presents a scientometric study of the research in the utilization of S. cerevisiae for bioethanol production. It examines the scientometric characteristics of both the sample and population data presenting the scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited papers. The selected keyword list was then optimized to obtain a representative

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sample of papers for the searched research field. A copy of this extended keyword list was provided in the appendix for future replicative studies. Furthermore, a selected list of the keywords is presented in Table 35.8. As a second step, two sets of data were used for this study. First, a population sample of 2,424 papers was used to examine the scientometric characteristics of the population data. Second, a sample of 242 most-cited papers, corresponding to 10% of the population datasets, was used to examine the scientometric characteristics of these citation classics. The scientometric characteristics of these sample and population datasets were presented in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts. Lastly, the key scientometric findings for both datasets were discussed to highlight the research landscape for the utilization of S. cerevisiae for bioethanol production. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape.

35.4.2 The Most Prolific Documents in the Utilization of the S. cerevisiae for Bioethanol Production Articles (together with conference papers) dominate both the sample (95%) and population (97%) papers (Table 35.1). Furthermore, review papers and articles have a surplus (3%) and deficit (2%), respectively. The representation of the reviews in the sample papers is relatively modest (5%). Scopus differs from the Web of Science database in differentiating and showing articles (94%) and conference papers (1%) published in the journals separately. However, it should be noted that these conference papers are also published in journals as articles, compared with those published only in the conference proceedings. Hence, the total number of articles and review papers in the sample dataset is 95% and 5%, respectively. It is observed during the search process that there has been inconsistency in the classification of the documents in Scopus and in other databases such as Web of Science. This is especially relevant for the classification of papers as reviews or articles as the papers not involving a literature review may be erroneously classified as a review paper. There is also a case of review papers being classified as articles. For example, the total number of reviews in the sample dataset was manually found as nearly 7% compared with 5% as indexed by Scopus, reducing the number of articles and conference papers to 93% for the sample dataset. In this context, it would be helpful to provide a classification note for the published papers in the books and journals at the first instance. It would also be helpful to use the document types listed in Table 35.1 for this purpose. Book chapters may also be classified as articles or reviews as an additional classification to differentiate review chapters from experimental chapters as it is done by the Web of Science. It would be further helpful to additionally classify the conference papers as articles or review papers and it is done in the Web of Science database.

35.4.3 The Most Prolific Authors in the Utilization of the S. cerevisiae for Bioethanol Production There have been 20 most prolific authors with at least 2.1% of the sample papers each as given in Table 35.2. These authors have shaped the development of research in this field. The most prolific authors are Barbel Hahn-Hagerdal and to a lesser Marie F. Gorwa-Grauslund, Jack T. Prong, Johannes P. van Dijken, Gunnar Liden, Yong-Su Jin, Willem H. van Zyl, and Akihiko Kondo. It is important to note the inconsistencies in indexing of the author names in Scopus and other databases. It is especially an issue for names with more than two components such as ‘Blake Sam de Hyun Liden’. The probable outcomes are ‘Liden, B.S.D.H.’, ‘de Hyun Liden, B.S.’, or ‘Hyun Liden, B.S.D.’. The first choice is the gold standard of the publishing sector as the last word in the name is

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taken as the last name. In most of the academic databases such as PubMed and EBSCO databases, this version is used predominantly. The second choice is a strong alternative, while the last choice is an undesired outcome as two last words are taken as the last name. It is good practice to combine the words of the last name by a hyphen: ‘Hyun-Liden, B.S.D.’. It is notable that inconsistent indexing of the author names may cause substantial inefficiencies in the search process for the papers and allocating credit to the authors as there are different author entries for each outcome in the databases. There are also inconsistencies in the shortening of Chinese names. For example, ‘YangYing Zhu’ is often shortened as ‘Zhu, Y.’, ‘Zhu, Y.-Y.’, and ‘Zhu, Y.Y.’ as it is done in the Web of Science database as well. However, the gold standard in this case is ‘Zhu, Y’ where the last word is taken as the last name and the first word is taken as a single forename. In most of the academic databases such as PubMed and EBSCO, this first version is used predominantly. Nevertheless, it makes sense to use the third option to differentiate Chinese names efficiently: ‘Zhu, Y.Y.’. Therefore, there have been difficulties in locating papers for Chinese authors. In such cases, the use of the unique author codes provided for each author by the Scopus database has been helpful. There is also a difficulty in allowing credit for the authors, especially for the authors with common names such as ‘Zhu, X.’ in conducting scientometric studies. These difficulties strongly influence the efficiency of the scientometric studies and allocating credit to the authors as there are the same author entries for different authors with the same name, e.g., ‘Zhu, X.’ in the databases. In this context, the coding of authors in the Scopus database is a welcome innovation compared with other databases such as Web of Science. In this process, Scopus allocates a unique number to each author in the database (Aman, 2018). However, there might still be substantial inefficiencies in this coding system, especially for common names. For example, some of the papers for a certain author may be allocated to another researcher with a different author code. It is possible that Scopus uses many software programs to differentiate the author names and the program may not be falseproof (Kim, 2018). In this context, it does not help that author names are not given in full in some journals and books. This makes it difficult to differentiate authors with common names and makes the scientometric studies further difficult in the author domain. Therefore, the author names should be given in all books and journals at the first instance. There is also a cultural issue where some authors do not use their full names in their papers. Instead, they use initials for their forenames: ‘Liden, H.J.’ ‘Liden’, ‘Liden, H.’, or ‘Liden, J.’ instead of ‘Liden, Hyun Jae’. There are also inconsistencies in naming of the authors with more than two components by the authors themselves in journal papers and book chapters. For example, ‘Liden, A.P.C.’ might be given as ‘Liden, A’ or ‘Liden, A.C.’ or ‘Liden, A.P.’ or ‘Liden, C’ in the journals and books. This also makes the scientometric studies difficult in the author domain. Hence, contributing authors should use their name consistently in their publications. The other critical issue regarding the author names is the inconsistencies in the spelling of the author names in the national spellings (e.g., Subaşı, Gökçe) rather than in English spellings (e.g., Subasi, Gokce) in the Scopus database. Scopus differs from the Web of Science database and many other databases in this respect where the author names are given only in English spellings. It is observed that national spellings of the author names do not help much in conducting scientometric studies and in allocating credits to the authors as sometimes there are different author entries for the English and National spellings in the Scopus database. The most prolific institutions for the sample dataset are the Lund University and to a lesser extent Chalmers University of Technology, Delft University of Technology, and Kobe University. The most prolific countries for the sample dataset are Sweden and to a lesser extent Japan, the Netherlands, and S. Korea. These findings confirm the dominance of Europe and to a lesser extent of Japan and S. Korea in this field. The most prolific research fronts are the metabolic engineering of S. cerevisiae and the production of bioethanol in general. It is also notable that there is a significant gender deficit for the sample dataset as surprisingly with a representation rate of 20%. This finding is the most thought-provoking with strong public policy implications. Hence, institutions, funding bodies, and policymakers should take efficient

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measures to reduce the gender deficit in this field and other scientific fields with strong gender deficit. In this context, it is worth to note the level of representation of the researchers from minority groups in science based on race, sexuality, age, and disability, besides gender (Blankenship, 1993; Dirth and Branscombe, 2017; Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b).

35.4.4 The Most Prolific Research Output by Years in the Utilization of the S. cerevisiae for Bioethanol Production The research output observed between 1970 and 2022 is illustrated in Figure 35.1. This figure clearly shows that the bulk of the research papers in the population dataset were published primarily in the 2010s. Similarly, the bulk of the research papers in the sample dataset were published in the last two decades. It is notable that there was a flat trend between 1982 and 2004 for the research output of population papers, while there was a rising trend between 2005 and 2012 and a falling trend between 2013 and 2022 for the population papers. Furthermore, there was no sharp rise in the research output for the population papers in 2020 and 2021 due to the supply shocks. These findings suggest that the most prolific sample and population papers were primarily published in the last two decades. These are the thought-provoking findings as there has been a significant research boom in the last two decades. In this context, the increasing public concerns about climate change (Change, 2007), greenhouse gas emissions (Carlson et al., 2017), and global warming (Kerr, 2007) have been certainly behind the boom in the research in this field in the last two decades. Furthermore, the supply shocks experienced due to the COVID-19 pandemic have not surprisingly resulted in a sharp rise in the research output for the population papers. Based on these findings, the size of the population papers is likely to more than double in the current decade, provided that the public concerns about climate change, greenhouse gas emissions, and global warming, as well as the supply shocks, are translated efficiently to the research funding in this field.

35.4.5 The Most Prolific Institutions in the Utilization of the S. cerevisiae for Bioethanol Production The 17 most prolific institutions publishing papers on the utilization of S. cerevisiae for bioethanol production with at least 2.1% of the sample papers each given in Table 35.3 have shaped the development of the research in this field. The most prolific institutions are the Lund University and to a lesser extent Delft University of Technology, Technical University of Denmark, Stellenbosch University, Bird Engineering Inc., Kobe University, and Chalmers University of Technology. Similarly, the top countries for these most prolific institutions are the USA and to a lesser extent Japan, the Netherlands, and Sweden. In total, 11 countries house these top institutions. However, the institutions with the most citation impact are Lund University and to a lesser extent Delft University of Technology, Technical University of Denmark, Bird Engineering Inc., Stellenbosch University, Lawrence Berkeley National Laboratory, and Goethe University Frankfurt. These findings confirm the dominance of the US, European, and to a lesser extent of Japanese institutions with a notable absence of China in this research field.

35.4.6 The Most Prolific Funding Bodies in the Utilization of the S. cerevisiae for Bioethanol Production The 13 most prolific funding bodies funding at least 1.2% of the sample papers each are given in Table 35.4. It is notable that only 23% and 42% of the sample and population papers each were funded.

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The most prolific funding bodies are the New Energy and Industrial Technology Development Organization and to a lesser extent the European Commission and the Ministry of Education, Culture, Sports, Science and Technology. The most prolific countries for these top funding bodies are Japan and to a lesser extent China, Sweden, and the EU. In total, only five countries and the EU house these top funding bodies. These findings on the funding of the research in this field suggest that the level of funding, mostly in the last two decades, is relatively modest, but it has been largely instrumental in enhancing the research in this field (Ebadi and Schiffauerova, 2016) in light of North’s institutional framework (North, 1991). However, considering the relatively low levels of funding for the sample papers and the falling trend in the research output for the population papers after 2012, there is ample room to enhance funding in this field.

35.4.7 The Most Prolific Source Titles in the Utilization of the S. cerevisiae for Bioethanol Production The 13 most prolific source titles publishing at least 1.7% of the sample papers each in the utilization of S. cerevisiae for bioethanol production have shaped the development of the research in this field (Table 35.5). The most prolific source titles are Applied and Environmental Microbiology and to a lesser extent Bioresource Technology, Applied Microbiology and Biotechnology, Biotechnology and Bioengineering, FEMS Yeast Research, Biotechnology for Biofuels, Enzyme and Microbial Technology, and Metabolic Engineering. The source titles with the most impact are Applied and Environmental Microbiology and to a lesser extent Bioresource Technology, Biotechnology and Bioengineering, Applied Microbiology and Biotechnology, Metabolic Engineering, and FEMS Yeast Research. Similarly, the source title with the least impact is Yeast. It is notable that these top source titles are primarily related to bioresources, microbiology, bioengineering, enzymes, genetics, and biotechnology. This finding suggests that Applied and Environmental Microbiology and the other prolific journals in these fields have significantly shaped the development of the research in this field as they focus primarily on the utilization of S. cerevisiae to produce ethanol with a high yield.

35.4.8 The Most Prolific Countries in the Utilization of the S. cerevisiae for Bioethanol Production The 16 most prolific countries publishing at least 2.1% of the sample papers each have significantly shaped the development of the research in this field (Table 35.6). The most prolific countries are the USA, Sweden, and to a lesser extent Japan, the Netherlands, China, and Denmark. It is notable that China is the largest producer of population papers with 14.1% production rate. Furthermore, eight European countries listed in Table 35.6 produce 57% and 27% of the sample and population papers, respectively. On the, the countries with the most citation impact are Sweden and to a lesser extent the USA, Denmark, and the Netherlands. Similarly, the countries with the least impact are China and to a lesser extent S. Korea, Brazil, India, and Canada. A close examination of these findings suggests that the USA, Europe, and the Far East (China, Japan, and S. Korea) are the major producers of the research in this field. It is a fact that the USA has been a major player in science (Leydesdorff and Wagner, 2009). The USA has further developed a strong research infrastructure to support its corn and grass-based bioethanol industry (Gillon, 2010). However, China has been a rising mega star in scientific research in competition with the USA and Europe (Leydesdorff and Zhou, 2005). China is also a major player in this field as a major producer of bioethanol (Fang et al., 2010).

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Next, Europe has been a persistent player in scientific research in competition with both the USA and China (Leydesdorff, 2000). Europe has also been a persistent producer of bioethanol along with the USA and Brazil (Gnansounou, 2010).

35.4.9 The Most Prolific Scopus Subject Categories in the Utilization of the S. cerevisiae for Bioethanol Production The six most prolific Scopus subject categories indexing at least 17.4% of the sample papers each, respectively, given in Table 35.7 have shaped the development of the research in this field. The most prolific Scopus subject categories in the utilization of S. cerevisiae for bioethanol production are Biochemistry, Genetics and Molecular Biology, Immunology and Microbiology, and to a lesser extent Chemical Engineering, Environmental Science, Agricultural and Biological Sciences, and Energy. The Scopus subject categories with the most citation impact are Immunology, Microbiology and Environmental Science, and to a lesser extent Biochemistry, Genetics and Molecular Biology, and Agricultural and Biological Sciences. These findings are thought-provoking suggesting that the primary subject categories are related to chemical engineering, genetics, and microbiology as the core of the research in this field concerns with the utilization of S. cerevisiae to increase the ethanol yield. The other finding is that social sciences are not well represented in both the sample and population papers as in most fields in bioethanol fuels.

35.4.10 The Most Prolific Keywords in the Utilization of the S. cerevisiae for Bioethanol Production A limited number of keywords have shaped the development of the research in this field as shown in Table 35.8 and the Appendix. These keywords are grouped under eight headings: biomass, fermentation, bacteria, metabolic engineering, hydrolysates, pretreatments, other processes, and products of the fermentation. The most prolific keywords related to biomass and biomass constituents are biomass and to a lesser extent cellulose, lignin, and lignocellulose, while the most prolific keyword related to fermentation is fermentation. The most prolific keywords related to the bacteria are S. cerevisiae and to a lesser yeasts. Furthermore, the most prolific keywords related to hydrolysates are xylose, sugar, and glucose. The most prolific keywords related to the metabolic engineering are genetic engineering and gene expression. The most prolific keywords related to the biomass pretreatments are enzyme activity and enzymes. Furthermore, the most prolific keywords related to the other processes are hydrolysis and biotechnology. Finally, the most prolific keywords related to fermentation products are ethanol and alcohol. It is notable that bioethanol accounts only for 10% of the sample papers. However, the most influential keywords are xylose, alcohol, sugar, P. stipitis, fungi, xylitol, yeast, ethanol, glucose, enzyme activity, genetic engineering, cellulose, and biomass. Similarly, the least influential keywords are bioethanol, genetics, cellulosic ethanol, ethanol fermentation, and biofuel. These findings suggest that it is necessary to determine the keyword set carefully to locate the relevant research in each of these research fronts. Additionally, the size of the samples for each keyword highlights the intensity of the research in the relevant research areas. The relevant keywords are presented in Table 35.8.

35.4.11 The Most Prolific Research Fronts in the Utilization of the S. cerevisiae for Bioethanol Production As Table 35.9 shows, the only one primary research front for this field with regard to feedstocks is as follows: the fermentation of hydrolysates using S. cerevisiae. The other research fronts are the

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fermentation of agricultural residues, wood, grass, and other feedstocks. On individual basis, the most prolific feedstocks are xylose, glucose, and hydrolysates. The other prolific feedstocks are arabinose, lignocellulosic biomass, food waste, cellobiose, cellulose, and sugarcane bagasse. Information about the thematic research fronts for the sample papers in the utilization of S. cerevisiae for bioethanol production is given in Table 35.10. As this table shows, the most prolific research front is the fermentation of hydrolysates using S. cerevisiae, followed by the metabolic engineering of feedstocks using S. cerevisiae. The other prolific research fronts are biomass fermentation using S. cerevisiae and ethanol stress of S. cerevisiae. These findings are thought-provoking in seeking ways to increase bioethanol yield through the utilization of S. cerevisiae for bioethanol production at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Furthermore, it is notable that utilization of S. cerevisiae has become a core of the fermentation research to increase ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. In the end, these most-cited papers in this field hint that the efficiency of utilization of S. cerevisiae could be optimized using the structure, processing, and property relationships of these innovative fermentation processes and S. cerevisiae (Formela et al., 2016; Konur, 2018a, 2020b, 2021a,b,c,d; Konur and Matthews, 1989).

35.5  CONCLUSION AND FUTURE RESEARCH Research on the utilization of S. cerevisiae for bioethanol production has been mapped through a scientometric study of both sample (242 papers) and population (2,424 papers) datasets. The critical issue in this study has been to obtain a representative sample of the research as in any other scientometric study. Therefore, the keyword set has been carefully devised and optimized after several runs in the Scopus database. It is a representative sample of the wider population studies. This keyword set was provided in the appendix, and the relevant keywords are presented in Table 35.8. However, it should be noted that it has been very difficult to compile a representative keyword set since this research field has been connected closely with many other fields. Therefore, it has been necessary to compile a keyword list to exclude papers concerned with the other research fields. The other issue has been the selection of a multidisciplinary database to carry out the scientometric study of the research in this field. For this purpose, the Scopus database has been selected. The journal coverage of this database has been notably wider than that of the Web of Science and other multi-subject databases. The key scientometric properties of the research in this field have been determined and discussed in this book chapter. It is evident that a limited number of documents, authors, institutions, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts have shaped the development of the research in this field. There is ample scope to increase the efficiency of the scientometric studies in this field in the author and document domains by developing consistent policies and practices in both domains across all academic databases. In this respect, it seems that authors, journals, and academic databases have a lot to do. Furthermore, the significant gender deficit as in most scientific fields emerges as a public policy issue. The potential deficits based on age, race, disability, and sexuality need also to be explored in this field as in other scientific fields. The research in this field has boomed in the last two decades possibly promoted by the public concerns on global warming, greenhouse gas emissions, and climate change. Furthermore, the recent COVID-19 pandemic and the Russian invasion of Ukraine have resulted in global supply shocks shifting the focus of the stakeholders from crude oil-based fuels to biomass-based fuels such as bioethanol fuels. It is expected that there would be further incentives for the key stakeholders to carry out the research for the utilization of S. cerevisiae to increase ethanol yield and to make it

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more competitive with crude oil-based gasoline and petrodiesel fuels. This might be truer for crude oil- and foreign exchange-deficient countries to maintain energy security at the face of the global supply shocks. The relatively modest funding rate of 42% for the population papers suggests that funding in this field significantly enhanced the research in this field primarily in the last two decades, possibly more than doubling in the current decade. However, it is evident that there is ample room for more funding and other incentives to enhance the research in this field further. The institutions from the USA and to a lesser extent Europe and Japan have mostly shaped the research in this field. Furthermore, Europe and to a lesser extent the USA and the Far East (China, Japan, and S. Korea) have been the major producers of the research in this field as the major producers and users of bioethanol fuels from different types of biomass such as corn, sugarcane, and grass and other types of biomass. It is evident that these countries have well-developed research infrastructure in bioethanol fuels and their derivatives. It emerges that ethanol is more popular than bioethanol as a keyword with strong implications for the search strategy. In other words, the search strategy using only bioethanol keyword would not be much helpful. The Scopus keywords are grouped under eight headings: biomass, fermentation, bacteria, metabolic engineering, hydrolysates, pretreatments, other processes, and products of the fermentation. There is only one primary research front for this field with regard to feedstocks: the fermentation of hydrolysates using S. cerevisiae, while on the individual basis, xylose, glucose, and hydrolysates in general are the most prolific feedstocks. These findings are thought-provoking in seeking ways to increase bioethanol yield through the utilization of S. cerevisiae at the global scale. It is clear that all of these research fronts have public importance and merit substantial funding and other incentives. Furthermore, it is notable that utilization of S. cerevisiae has become a core of the fermentation research to increase ethanol yield and to make it more competitive with crude oil-based gasoline and diesel fuels. Thus, the scientometric analysis has a great potential to gain valuable insights into the evolution of the research in this field as in other scientific fields, especially in the aftermath of the significant global supply shocks such as COVID-19 pandemic and the Russian invasion of Ukraine. It is recommended that further scientometric studies are carried out for the primary research fronts. It is further recommended that reviews of the most-cited papers are carried out for each primary research front to complement these scientometric studies. Next, the scientometric studies of the hot papers in these primary fields are carried out.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the utilization of S. cerevisiae for bioethanol production has been gratefully acknowledged.

APPENDIX: THE KEYWORD SET FOR THE UTILIZATION OF S. CEREVISIAE FOR BIOETHANOL PRODUCTION (TITLE (“saccharomyces cerevisiae” OR “s. cerevisiae” OR “baker’s yeast*”) AND TITLE (ethanol* OR bioethanol OR ferment* OR coferment* OR ssf OR sscf OR arabinose OR cellobiose OR xylose OR “consolidated processing” OR hydrolysate* OR hydrolyzate*)) AND NOT (SUBJAREA (medi OR phar OR vete OR nurs OR dent OR neur OR heal OR psyc) OR TITLE (wine* OR *butanol OR ester* OR isoprenoid* OR grape OR resveratrol OR brew* OR food* OR succin* OR fitness OR “solid state” OR *sorption OR “fatty acid” OR olefin* OR keto OR nitrogen OR vivo OR enol* OR lactic) OR SRCTITLE (food OR animal* OR bevera* OR dairy OR aqua* OR ecol* OR “biological Chemistry” OR lwt* OR enol* OR nucleic OR wine OR poultry OR fish* OR brew* OR organic OR electr*)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”)

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OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “le”) OR LIMIT-TO (DOCTYPE, “ch”) OR LIMIT-TO (DOCTYPE, “sh”) OR LIMIT-TO (DOCTYPE, “no”) OR LIMIT-TO (DOCTYPE, “ed”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (SRCTYPE, “j”) OR LIMIT-TO (SRCTYPE, “k”) OR LIMIT-TO (SRCTYPE, “b”))

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36

The Utilization of the Saccharomyces cerevisiae for the Bioethanol Production Review Ozcan Konur

(Formerly) Ankara Yildirim Beyazit University

36.1 INTRODUCTION Crude oil-based gasoline fuels (Ma et al., 2002; Newman and Kenworthy, 1989) have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels (Hill et al., 2006, 2009). Hence, biomass-based bioethanol fuels (Hill et al., 2006; Konur, 2012, 2015, 2019, 2020) have increasingly been used in blending gasoline fuels (Hsieh et al., 2002; Najafi et al., 2009), in fuel cells (Antolini, 2007, 2009), and in biochemical production (Angelici et al., 2013; Morschbacker, 2009) in a biorefinery context (Fernando et al., 2006; Huang et al., 2008). However, it is necessary to pretreat the biomass (Alvira et al., 2010; Taherzadeh and Karimi, 2008) to enhance the yield of the bioethanol (Hahn-Hagerdal et al., 2006; Sanchez and Cardona, 2008) before bioethanol production through the hydrolysis (Sun and Cheng, 2002; Taherzadeh and Karimi, 2007) and fermentation (Lin and Tanaka, 2006; Olsson and Hahn-Hagerdal, 1996) of the biomass and hydrolysates, respectively. The research in the field of the utilization of Saccharomyces cerevisiae (S. cerevisiae) for bioethanol production to improve the ethanol yield has intensified in this context as the moststudied yeast in the fermentation of the feedstocks to produce bioethanol in recent years (Almeida et al., 2007; Matsushika et al., 2009; van Maris et al., 2006). The key research fronts have been the fermentation of xylose (Eliasson et al., 2000; Kotter and Ciriacy, 1993; Kuyper et al., 2003, 2005a,b), fermentation of the glucose (Bro et al., 2006; Delgenes et al., 1996; Najafpour et al., 2004), fermentation of the biomass in general (Karimi et al., 2006; Lau and Dale, 2009; Larsson et al., 2001), fermentation inhibitors (Delgenes et al., 1996; Gorsich et al., 2006; Larsson et al., 2001), ethanol stress (Alexandre et al., 2001; You et al., 2003), and fermentation of other hydrolysates such as arabinose and cellobiose (Becker and Boles, 2003; Ha et al., 2011). Furthermore, the metabolic engineering of S. cerevisiae (Eliasson et al., 2000; Kotter and Ciriacy, 1993; Kuyper et al., 2003, 2005a,b) has been a major cross-subject research front. However, it is essential to develop efficient incentive structures (North, 1991) for the primary stakeholders to enhance the research in this field (Konur, 2000, 2002a,b,c, 2006a,b, 2007a,b). Although there have been several review papers on the utilization of S. cerevisiae for bioethanol production (Almeida et al., 2007; Matsushika et al., 2009; van Maris et al., 2006), there has been no review of the 25 most-cited papers in this field. Thus, this book chapter presents a review of the 25 most-cited articles in the field of the utilization of S. cerevisiae for bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field.

DOI: 10.1201/9781003226499-45

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36.2  MATERIALS AND METHODS The search for this study was carried out using the Scopus database (Burnham, 2006) in June 2022. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This final keyword set was provided in the appendix of Konur (2023) for future replication studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 205 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape.

36.3 RESULTS The brief information about the 25 most-cited papers with at least 205 citations each on the utilization of S. cerevisiae for bioethanol production is given below. The primary research fronts are the utilization of S. cerevisiae for the xylose fermentation and fermentation of the other feedstocks with 13 and 12 highly cited papers (HCPs), respectively.

36.3.1  The Utilization of the S. cerevisiae for the Fermentation of Xylose There are 13 HCPs for the utilization of S. cerevisiae for the fermentation of xylose standalone and with other feedstocks with seven and six HCPs, respectively (Table 36.1). 36.3.1.1  Xylose Fermentation Kotter and Ciriacy (1993) utilized xylose in S. cerevisiae transformants expressing xylose reductase (XR) and xylitol dehydrogenase (XDH) and in Pichia stipitis (P. stipitis) to produce ethanol in a paper with 423 citations. In the absence of respiration, they observed that S. cerevisiae cells converted half of xylose to xylitol and ethanol, whereas P. stipitis cells displayed rather a homofermentative conversion of xylose to ethanol. Xylitol production by S. cerevisiae was a result of the dual cofactor dependence of the XR and the generation of nicotinamide adenine dinucleotide phosphate (NADPH) by the pentose phosphate pathway (PPP). Further limitations of xylose utilization in S. cerevisiae cells were very likely caused by an insufficient capacity of the non-oxidative PPP, as indicated by the accumulation of sedoheptulose-7-phosphate and the absence of fructose1,6-bisphosphate and pyruvate accumulation. By contrast, uptake at high substrate concentrations did not limit xylose conversion in S. cerevisiae XYL1/XYL2 transformants. Eliasson et al. (2000) fermented xylose by a recombinant S. cerevisiae strain, TMB 3001, to produce ethanol in a paper with 327 citations. They integrated the XYL1 and XYL2 genes from P. stipitis, encoding XR and XDH, respectively, and the endogenous XKS1 gene, encoding xylulokinase (XK), under the control of the PGK1 promoter into the chromosomal HIS3 locus of S. cerevisiae CEN.PK 113–7A. They observed that this strain expressed XR, XDH, and XK activities of 0.4–0.5, 2.7–3.4, and 1.5–1.7 U/mg, respectively, and was stable for more than 40 generations in continuous fermentations. This strain grew on xylose only in the presence of oxygen. They obtained ethanol yields of 0.45–0.50 mmol of C/mmol of C (0.35–0.38 g/g) and productivities of 9.7–13.2 mmol of C/h g (dry weight) of cells−1 (0.24–0.30 g/h g (dry weight) of cells−1) from xylose–glucose mixtures in anaerobic chemostat cultures, with a dilution rate of 0.06 h−1. They estimated the anaerobic ethanol yield on xylose at 0.27 mol of C/(mol of C of xylose) (0.21 g/g), assuming a constant ethanol yield on glucose. The xylose uptake rate increased with increasing xylose concentration in the feed, from 3.3 mmol of C/h g (dry weight) of cells−1 when the xylose-to-glucose ratio in the feed was 1:3–6.8 mmol of C/h g (dry weight) of cells−1 when the feed ratio was 3:1. Furthermore, with a feed content of 15 g of xylose/L and 5 g of glucose/L, the xylose flux was 2.2 times lower than the glucose flux, indicating that transport limited the xylose flux.

Biomass/ Hydrolysate

Res. Fronts

Prt./ Enzymes

Yeasts

Parameters

Keywords

Lead Author

Affil.

Cits

Kotter and Ciriacy (1993) Eliasson et al. (2000)

Xylose

Xylose, metabol.,

XR, XDH, PPP

S. cerevisiae, P. stipitis

Xylose, fermentation

Kotter, Peter 6603848645

Xylose, metabol.

XR, XDH, XK

S. cerevisiae

Xylose, fermentation

Hahn-Hagerdal, Barbel* 7005389381

Goethe Univ. Frankf. Germany Lund Univ. Sweden

423

Xylose

Kuyper et al. (2005a)

Xylose

Xylose, metabol.

S. cerevisiae

Xylose, fermentation

Pronk, Jack T. 7005313057

Delft Univ. Technol. Netherlands

325

Kuyper et al. (2003) Kuyper et al. (2005b) Martin et al. (2002)

Xylose

Xylose, metabol.

XK, RPI, RPE, TA, TK, AR, XI XR, XDH, XI

Xylose utilization, recombinant strain, transformants, yeast types Fermentation, recombinant strain, ethanol productivity and yield, xylose uptake rate, enzymes Fermentation, recombinant strain, enzymes, xylose uptake rate

3

4

XI

S. cerevisiae

Xylose, sugarcane bagasse

Xylose, biomass ferment., metabol., ferment. inhib.

Steam, cellulases, XK, XR, XDH

S. cerevisiae

Fermentation, biomass, xylose, detoxification, enzymes, recombinant strain, ethanol yield

Ethanol, xylose

Jonsson, Leif J. 7102349315

Delft Univ. Technol. Netherlands Delft Univ. Technol. Netherlands Umea Univ. Sweden

284

Xylose, glucose, metabol.

Xylose, ethanolic, fermentation Xylose, fermenting

Pronk, Jack T. 7005313057

Xylose, glucose

Fermentation, recombinant strain, enzymes, xylose uptake rate Fermentation, recombinant strain, xylose uptake rate

No.

Papers

1

2

5

6

S. cerevisiae

Pronk, Jack T. 7005313057

327

Utilization of the Saccharomyces cerevisiae: Review

TABLE 36.1 The Utilization of S. cerevisiae for the Fermentation of the Xylose

277

242

(Continued)

371

372

TABLE 36.1 (Continued) The Utilization of S. cerevisiae for the Fermentation of the Xylose No.

Papers

 7

Kotter et al. (1990) Ohgren et al. (2006) Demeke et al. (2013)

 8

 9

Biomass/ Hydrolysate

Res. Fronts

Xylose

Xylose, metabol.

Glucose, xylose, corn stover Xylose, glucose, reed, spruce, wheat straw hydrolysates Xylose

Glucose, xylose, biomass ferment., metabol., SSCF Ferment. inhib., xylose, glucose, biomass ferment., metabol.

Prt./ Enzymes

Yeasts

Parameters

Keywords

Lead Author

Affil.

Cits

XR, XDH, XR2, XDH2 Steam

S. cerevisiae

Recombinant strain, enzymes, xylose utilization

Xylose

Kotter, Peter 6603848645

224

S. cerevisiae

Glucose, xylose, co-fermentation

Zacchi, Guido 7006727748

XI, PPP

S. cerevisiae

SSCF, recombinant strain, ethanol yield and concentration, batch mode Fermentation inhibitors, recombinant strain, xylose uptake rate, SHF, ethanol titer, SSF

Goethe Univ. Frankf. Germany Lund Univ. Sweden

Xylose, fermenting

Thevelein, Johan M. 7005611313

KU Leuven Belgium

211

Xylose, metabol.

XI

S. cerevisiae

Xylose fermentation, recombinant strain, enzymes, ethanol productivity Xylose fermentation, recombinant strain, enzymes, xylose consumption rate, ethanol productivity, and concentration Xylose fermentation, recombinant protein, enzymes, ethanol yield Xylose co-fermentation, recombinant strain, ethanol yield

Xylose

Boles, Eckhard 7005230946

209

Xylose, fermentation

Gorwa-Grauslund, Marie F.*

Goethe Univ. Frankf. Germany Lund Univ. Sweden

Xylose, fermentation

Pronk, Jack T. 7005313057

207

Xylose, cellobiose, fermentation

Ha, Suk-Jin 7202501164

Delft Univ. Technol. Netherlands Kangwon Natl. Univ. S. Korea

Brat et al. (2009)

11

Karhumaa et al. (2007)

Xylose, spruce hydrolysates

Xylose, biomass ferment., metabol.

XR, XDH, XI, PPP, AR

S. cerevisiae

12

Kuyper et al. (2004) Ha et al. (2011)

Xylose

Xylose, metabol.

IS

S. cerevisiae

Xylose, cellobiose

Xylose, cellobiose, metabol.

Na

S. cerevisiae

13

*, female; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

208

205

Bioethanol Fuel Production Processes. II

10

221

Utilization of the Saccharomyces cerevisiae: Review

373

Kuyper et al. (2005a) engineered a xylose isomerase (XI)-expressing S. cerevisiae strain for rapid anaerobic xylose fermentation in a paper with 325 citations. They observed that S. cerevisiae strains expressing the XI gene, XylA gene, from Piromyces sp. E2 could grow anaerobically on xylose with a μmax of 0.03 h−1. They then overexpressed structural genes for all enzymes involved in the conversion of xylulose to glycolytic intermediates, in a XylA-expressing S. cerevisiae strain. The overexpressed enzymes were XK, ribulose 5-phosphate isomerase (RPI), ribulose 5-phosphate epimerase (RPE), transketolase (TK), and transaldolase (TA). In addition, they deleted the GRE3 gene encoding aldose reductase (AR) to further minimize xylitol production. They found that the resulting strain grew anaerobically on xylose in synthetic media with a μmax as high as 0.09 h−1 without any nondefined mutagenesis or selection. During the growth of xylose, xylulose formation was absent and xylitol production was negligible. Furthermore, the specific xylose uptake rate in anaerobic xylose cultures was 1.1 g xylose/(g biomass)/h, while mixtures of glucose and xylose were sequentially but completely consumed by anaerobic batch cultures, with glucose as the preferred substrate. Kuyper et al. (2003) studied the functional expression of Piromyces sp. E2 XI as a key to efficient ethanolic fermentation of xylose by S. cerevisiae in a paper with 284 citations. They observed that xylose metabolism in P. sp. E2 proceeded via a XI rather than via the XR/XDH pathway found in xylose-metabolizing yeasts. They expressed the XylA gene encoding the P. sp. XI functionally in S. cerevisiae. Heterologous isomerase activities in cell extracts, assayed at 30°C, were 0.3–1.1 μmol/ min/(mg protein), with a Km for xylose of 20 mM. The engineered S. cerevisiae strain grew very slowly on xylose. It co-consumed xylose in aerobic and anaerobic glucose-limited chemostat cultures at rates of 0.33 and 0.73 mmol/(g biomass)/h, respectively. Kotter et al. (1990) constructed a xylose-utilizing S. cerevisiae transformant by isolating P. stipitis XDH gene, XDH2, in a paper with 224 citations. They screened a P. stipitis complementary deoxyribonucleic acid (cDNA) library in λgt11 using antisera against P. stipitis XR and XDH, respectively. The resulting cDNA clones served as probes for screening a P. stipitis genomic library. They isolated the genomic XYL2 gene and determined the nucleotide sequence of the 1089 bp structural gene and of adjacent noncoding regions. The XDH2 open-reading frame is coded for a protein of 363 amino acids with a predicted molecular mass of 38.5 kDa. They actively expressed the XYL2 gene in S. cerevisiae transformants. They observed that S. cerevisiae cells transformed with a plasmid, pRD1, containing both the XR gene (XR1) and the XDH gene (XDH2), grew on xylose as a sole carbon source. In contrast to aerobic glucose metabolism, S. cerevisiae XR1-XDH2 transformants utilized xylose almost entirely oxidatively. Brat et al. (2009) screened nucleic acid databases for sequences encoding putative XIs and finally cloned and successfully expressed a highly active new kind of XI from Clostridium phytofermentans (C. phytofermentans) in S. cerevisiae in a paper with 209 citations. They observed that heterologous expression of this enzyme conferred on the yeast cells the ability to metabolize D-xylose and to use it as the sole carbon and energy source. The new enzyme had low sequence similarities to the XIs from P. sp. strain E2 and Thermus thermophilus, which were the only two XIs previously functionally expressed in S. cerevisiae. The activity and kinetic parameters of the new enzyme were comparable to those of the P. sp. XI. Importantly, the new enzyme was far less inhibited by xylitol, which accrued as a side product during xylose fermentation. Furthermore, the expression of the gene could be improved by adapting its codon usage to that of the highly expressed glycolytic genes of S. cerevisiae. Furthermore, the expression of the bacterial XI in an industrially employed yeast strain enabled it to grow on xylose and to ferment xylose to ethanol. Kuyper et al. (2004) engineered S. cerevisiae in a paper with 207 citations. They showed that the anaerobic conversion of xylose to ethanol, without substantial by-product formation, was possible in S. cerevisiae when a heterologous IS was functionally expressed. They observed that transformants expressing the XylA gene from P. sp. E2 grew in the synthetic medium in shake–flask cultures on xylose with a specific growth rate of 0.005 h−1. After prolonged cultivation on xylose, they obtained a mutant strain that grew aerobically and anaerobically on xylose, at specific growth rates

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of 0.18 and 0.03 h−1, respectively. The anaerobic ethanol yield was 0.42 g ethanol/g xylose, and also, by-product formation was comparable to that of glucose-grown anaerobic cultures. Only minimal genetic engineering was required to recruit a functional xylose metabolic pathway in S. cerevisiae. Furthermore, the activities and/or regulatory properties of native S. cerevisiae gene products could subsequently be optimized via evolutionary engineering. 36.3.1.2  Xylose Fermentation together with Other Feedstocks Kuyper et al. (2005b) engineered mixed xylose and glucose utilization by a xylose-fermenting S. cerevisiae strain in a paper with 277 citations. They previously reported about a S. cerevisiae strain that, in addition to the P. sp. XylA gene, overexpressed the native genes for the conversion of xylulose to glycolytic intermediates. This engineered strain (RWB 217) exhibited unprecedentedly high specific growth rates and ethanol production rates under anaerobic conditions with xylose as the sole carbon source. However, when RWB 217 was grown on glucose–xylose mixtures, they observed a diauxic growth pattern with a relatively slow consumption of xylose in the second growth phase. After prolonged cultivation in an anaerobic, xylose-limited chemostat, they obtained a culture with improved xylose uptake kinetics. This culture also exhibited improved xylose consumption in glucose–xylose mixtures. They obtained a further improvement in mixedsugar utilization by prolonged anaerobic cultivation in automated sequencing-batch reactors on glucose–xylose mixtures. They found that a final single strain (RWB 218) rapidly consumed glucose–xylose mixtures anaerobically, in the synthetic medium, with a specific rate of xylose consumption exceeding 0.9 g/g/h. When the kinetics of zero trans-influx of glucose and xylose of RWB 218 were compared to that of the initial strain, there were a twofold higher capacity (Vmax) and an improved Km for xylose in the selected strain. They concluded that the kinetics of xylose fermentation was no longer a bottleneck in the industrial production of bioethanol with yeast. Martin et al. (2002) produced ethanol from enzymatic hydrolysates of sugarcane bagasse using recombinant xylose-utilizing S. cerevisiae in a paper with 242 citations. They pretreated sugarcane bagasse by steam explosion at 205°C and 215°C and hydrolyzed with cellulolytic enzymes. They then detoxified the hydrolysates enzymatically with the phenoloxidase laccase and chemically by overliming. They removed approximately 80% of the phenolic compounds by the laccase treatment. Overliming partially removed the phenolic compounds, but also other fermentation inhibitors such as acetic acid, furfural, and 5-hydroxymethylfurfural (HMF). They fermented hydrolysates with the recombinant xylose-utilizing S. cerevisiae laboratory strain TMB 3001, a CEN.PK derivative with overexpressed XK activity and expressing the XR and XDH of P. stipitis and S. cerevisiae strain ATCC 96581. They found that the fermentative performance of the laboratory strain in nondetoxified hydrolysate was better than the performance of the industrial strain. They also observed an almost twofold increase in the specific productivity of the strain TMB 3001 in the detoxified hydrolysates compared with the non-detoxified hydrolysates. The ethanol yield in the fermentation of the hydrolysate detoxified by overliming was 0.18 g/g dry bagasse, whereas it reached only 0.13 g/g dry bagasse in the non-detoxified hydrolysate. Finally, they observed partial xylose utilization with low xylitol formation. Ohgren et al. (2006) carried out the simultaneous saccharification and co-fermentation (SSCF) of glucose and xylose in steam-pretreated corn stover at high fiber content with S. cerevisiae in a paper with 221 citations. They used a recombinant strain of S. cerevisiae, TMB3400, in simultaneous saccharification and fermentation (SSF) of whole pretreated slurry of corn stover at high water-insoluble solids (WIS). They observed that TMB3400 co-fermented glucose and xylose with relatively high ethanol yields giving high final ethanol concentration. Furthermore, the ethanol productivity increased with increasing concentration of pretreatment hydrolysate in the yeast production medium and when SSF was performed in a fed-batch mode. Demeke et al. (2013) developed D-xylose-fermenting and inhibitor-tolerant industrial S. cerevisiae strain, Ethanol Red, with high performance in reed, spruce, and wheat straw hydrolysates using metabolic and evolutionary engineering in a paper with 211 citations. They inserted an expression

Utilization of the Saccharomyces cerevisiae: Review

375

cassette containing 13 genes including C. phytofermentans XylA, encoding D-XI, and enzymes of the PPP in two copies in the genome of Ethanol Red. Subsequent ethyl methanesulfonate (EMS) mutagenesis, genome shuffling, and selection in D-xylose-enriched lignocellulose hydrolysate, followed by multiple rounds of evolutionary engineering in complex medium with D-xylose, gradually established efficient D-xylose fermentation. The best-performing strain, GS1.11–26, showed a maximum specific D-xylose consumption rate of 1.1 g/g DW/h in the synthetic medium, with complete attenuation of 35 g/L D-xylose in about 17 h. In SHF of lignocellulose hydrolysates of giant reed (Arundo donax), spruce, and a wheat straw mixture, the maximum specific D-xylose consumption rate was 0.36, 0.23, and 1.1 g/g DW inoculum/h, and the final ethanol titer was 4.2%, 3.9%, and 5.8% (v/v), respectively. In SSF of Arundo hydrolysate, GS1.11–26 produced 32% more ethanol than the parent strain Ethanol Red, due to efficient D-xylose utilization. The high D-xylose fermentation capacity was stable after extended growth in glucose. Cell extracts of strain GS1.11–26 displayed 17-fold higher XI activity compared with the parent strain, but overexpression of XI alone was not enough to establish D-xylose fermentation. The high D-xylose consumption rate was due to the synergistic interaction between the high XI activity and one or more mutations in the genome. Furthermore, the GS1.11–26 had a partial respiratory defect causing a reduced aerobic growth rate. They concluded that the GS1.11–26 strain used glucose and D-xylose with high consumption rates and partial co-fermentation in various lignocellulose hydrolysates with very high ethanol yield. Karhumaa et al. (2007) compared the P. stipitis XR-XDH and the P. sp. XI pathways for xylose fermentation in an isogenic strain background by recombinant S. cerevisiae in a paper with 208 citations. They carried out genetic modifications known to improve xylose fermentation (overexpressed XK, overexpressed non-oxidative PPP, and deletion of the AR gene GRE3). They studied the two isogenic strains and the industrial xylose-fermenting strain TMB 3400 regarding their xylose fermentation capacity in the defined mineral medium and in non-detoxified lignocellulosic hydrolysate. They observed that in the defined mineral medium, the xylose consumption rate, the specific ethanol productivity, and the final ethanol concentration were significantly higher in the XR- and XDH-carrying strain, whereas the highest ethanol yield was achieved with the strain carrying XI. While the laboratory strains only fermented a minor fraction of glucose in the nondetoxified lignocellulose hydrolysate, the industrial strain TMB 3400 fermented nearly all the sugar available. Xylitol was formed by the XR-XDH-carrying strains only in mineral medium, whereas in lignocellulose hydrolysate no xylitol formation was detected. In conclusion, despite byproduct formation, the XR-XDH xylose utilization pathway resulted in faster ethanol production than using the best presently reported XI pathway in the strain background investigated. Ha et al. (2011) engineered S. cerevisiae for simultaneous cellobiose and xylose fermentation in a paper with 205 citations. In these yeast strains, hydrolysis of cellobiose took place inside yeast cells through the action of an intracellular β-glucosidase following import by a high-affinity cellodextrin transporter. Intracellular hydrolysis of cellobiose minimized glucose repression of xylose fermentation allowing co-consumption of cellobiose and xylose. The resulting yeast strains co-fermented cellobiose and xylose simultaneously and exhibited improved ethanol yield when compared to fermentation with either cellobiose or xylose as sole carbon sources. They also observed improved yields and productivities from co-fermentation experiments performed with simulated cellulosic hydrolysates, suggesting this was a promising co-fermentation strategy for cellulosic biofuel production. The successful integration of cellobiose and xylose fermentation pathways in yeast was a critical step toward enabling economic ethanol production.

36.3.2 The Utilization of the S. cerevisiae for the Fermentation of the Other Feedstocks There are 18 HCPs for the utilization of S. cerevisiae for the fermentation of the other feedstocks (Table 36.2). However, the notes are made only for 12 HCPs in this section as the notes for six HCPs were made in the previous section. The key research fronts are glucose fermentation, biomass

376

TABLE 36.2 The Utilization of S. cerevisiae for the Fermentation of the Other Feedstocks No.

Papers

Biomass/ Hydrolysate

Res. Fronts

Prt./ Enzymes

Yeasts

Parameters

Keywords

Lead Author

Affil.

Cits

Fermentation inhibitors, yeast types, ethanol productivity

Ethanol, fermentation, glucose

Delgenes, Jean P. 7005849678

Univ. Montpellier France

417

Ethanol stress, gene expression, ethanol stress response SHF, fermentation, ethanol yield and titer, fermentation inhibitors In silico metabolic engineering, ethanol yield, enzymes

Ethanol

Blondin, Bruno 35610971500

295

Ethanol

Dale, Bruce E. 7201511969

Ethanol

Nielsen, Jens 55572933700

Univ. Montpellier France Michigan State Univ. USA Chalmers Univ. Technol. Denmark Delft Univ. Technol. Netherlands Univ. Boras Sweden

Delgenes et al. (1996)

Glucose

Glucose, ferment. inhib.

Na

2

Alexandre et al. (2001) Lau and Dale (2009) Bro et al. (2006)

Na

Ethanol stress, metabol.

Na

S. cerevisiae, Z. mobilis, P. stipitis, C. shehatae S. cerevisiae

Corn stover

Biomass ferment., ferment. inhib.

AFEX

S. cerevisiae

Glucose

Glucose, metabol.

GAPDH, GAPN, XR, XDH

S. cerevisiae

Xylose, glucose

Xylose, glucose, metabol.

XI

S. cerevisiae

Fermentation, recombinant strain, xylose uptake rate

Xylose, fermenting

Pronk, Jack T. 7005313057

Rice straw

Biomass ferment., SSF

Acids, cellulases

Glucose, metabol.

Na

SSF, fermentation, ethanol yield, by-products, strain types Fermentation types, ICR, batch, ethanol productivity, sugar uptake rate

Fermentation, ethanol

Glucose

S. cerevisiae, M. indicus, R. oryzae S. cerevisiae

Taherzadeh, Mohammad J. 6701407496 Najafpour, Ghasem 55485049700

3

4

5

6

7

Kuyper et al. (2005b) Karimi et al. (2006) Najafpour et al. (2004)

Fermentation, ethanol

Babol Noshirvani Univ. Technol. Iran

293

277

277

258

246

(Continued)

Bioethanol Fuel Production Processes. II

1

No. 8

9

Papers Larsson et al. (2001) Martin et al. (2002)

Biomass/ Hydrolysate Spruce

Xylose, sugarcane bagasse

Res. Fronts Biomass ferment., ferment. inhib., metabol. Xylose, biomass ferment., metabol, ferment. inhib.

Prt./ Enzymes

Yeasts

Parameters

Laccase, acids

S. cerevisiae

Fermentation inhibitors, recombinant strain

Fermentation

Keywords

Jonsson, Leif J. 7102349315

Umea Univ. Sweden

243

Steam, cellulases, XK, XR, XDH Na

S. cerevisiae

Ethanol, xylose

Jonsson, Leif J. 7102349315

Umea Univ. Sweden

242

S. cerevisiae

Fermentation, biomass, xylose, detoxification, enzymes, recombinant strain, ethanol yield Ethanol tolerance, UFA, oleic acids, recombinant strain Fermentation inhibitors, recombinant strain

Ethanol

Knipple, Douglas C. 6701774835 Gorsich, Steven W. 6507209026 Jonsson, Leif J. 7102349315

Cornell Univ. USA

241

Central Michigan Univ. USA Umea Univ. Sweden

230

Liden, Gunnar 7004458708

Lund Univ. Sweden

10

You et al (2003)

Na

Ethanol stress, metabol.

11

Gorsich et al. (2006) Larsson et al. (2000) Taherzadeh et al. (1997)

Na

Ferment. inhib., metabol.

PPP

S. cerevisiae

Na

Ferment. inhib.

Na

S. cerevisiae

Fermentation inhibitors, fermentation, cell growth

Ethanolic, fermentation

Glucose

Glucose

Na

S. cerevisiae

Glucose fermentation, pH, acetic acid concentration, ethanol yield

Glucose, ethanol

12

13

Pentose

Lead Author

Affil.

Cits

Utilization of the Saccharomyces cerevisiae: Review

TABLE 36.2 (Continued) The Utilization of S. cerevisiae for the Fermentation of the Other Feedstocks

227

222

(Continued)

377

378

TABLE 36.2 (Continued) The Utilization of S. cerevisiae for the Fermentation of the Other Feedstocks No.

Papers

Biomass/ Hydrolysate

Res. Fronts

Glucose, xylose, corn stover

Glucose, xylose, biomass ferment., metabol., SSCF

14

Ohgren et al. (2006)

15

Demeke et al. Xylose, glucose, (2013) reed, spruce, wheat straw hydrolysates Becker and Arabinose Boles (2003)

16

Prt./ Enzymes Steam

Yeasts S. cerevisiae

Ferment. inhib., xylose,XI, PPP glucose, biomass ferment., metabol.

S. cerevisiae

Arabinose, metabol.

RK

S. cerevisiae

Karhumaa Xylose, spruce et al. (2007) hydrolysates

Xylose, biomass ferment., metabol.

XR, XDH, XI, S. cerevisiae PPP, AR

18

Ha et al. (2011)

Xylose, cellobiose, metabol.

Na

Xylose, cellobiose

S. cerevisiae

Keywords

Lead Author

SSCF, recombinant strain, Glucose, xylose, Zacchi, Guido ethanol yield and co-fermentation 7006727748 concentration, batch mode Fermentation inhibitors, Xylose, fermenting Thevelein, Johan recombinant strain, xylose M. 7005611313 uptake rate, SHF, ethanol titer, SSF Arabinose fermentation, Arabinose, ethanol Boles, Eckhard recombinant strain, enzyme, 7005230946 ethanol yield and productivity Xylose fermentation, Xylose, fermentation Gorwa-Grauslund, recombinant strain, enzymes, Marie F.* xylose consumption rate, ethanol productivity and concentration Xylose co-fermentation, Xylose, cellobiose, Ha, Suk-Jin recombinant strain, ethanol fermentation 7202501164 yield

Affil.

Cits

Lund Univ. Sweden

221

KU Leuven Belgium

211

Goethe Univ. Frankf. Germany

209

Lund Univ. Sweden

208

Kangwon Natl. 205 Univ. S. Korea

*, female; **, the notes for these papers were noted down in the other section; Cits., number of citations received for each paper; Na, nonavailable; Prt, biomass pretreatments.

Bioethanol Fuel Production Processes. II

17

Parameters

Utilization of the Saccharomyces cerevisiae: Review

379

fermentation, and fermentation inhibitors with seven HCPs each. The other research fronts are ethanol stress, arabinose fermentation, and cellobiose fermentation with two, one, and one HCPs, respectively. 36.3.2.1  Fermentation Inhibitors Delgenes et al. (1996) studied the inhibitory effects of six fermentation inhibitors on ethanol fermentation of glucose by S. cerevisiae and of xylose by Zymomonas mobilis (Z. mobilis), P. stipitis, and Candida shehatae (C. shehatae) in batch cultures in a paper with 417 citations. They observed that vanillin was a strong inhibitor of both growth and ethanol production by xylose-fermenting yeasts and S. cerevisiae when it was added to the culture media at a concentration of 1 g/L. Furthermore, some of the inhibitors, particularly vanillin and furaldehyde, could be assimilated by these yeasts, which resulted in the partial recovery in both growth and ethanol production processes on prolonged incubation. Larsson et al. (2001) developed a S. cerevisiae strain with enhanced resistance to phenolic fermentation inhibitors in lignocellulose hydrolysates by heterologous expression of laccase in a paper with 243 citations. They expressed laccase from Trametes versicolor under the control of the PGK1 promoter in S. cerevisiae to increase its resistance to phenolic inhibitors in lignocellulose hydrolysates. They observed that the laccase activity could be enhanced twofold by simultaneous overexpression of the homologous t-SNARE Sso2p. The factors affecting the level of active laccase obtained, besides the cultivation temperature, included pH and aeration. They cultivated laccaseexpressing and Sso2p-overexpressing S. cerevisiae in the presence of coniferyl aldehyde to examine resistance to lignocellulose-derived phenolic fermentation inhibitors. They found that the laccaseproducing transformant had the ability to convert coniferyl aldehyde at a faster rate than a control transformant not expressing laccase, which enabled faster growth and ethanol formation. The laccase-producing transformant also fermented a dilute acid spruce hydrolysate at a faster rate than the control transformant. They observed a decrease in the content of low-molecular-mass aromatic compounds, accompanied by an increase in the content of high-molecular-mass compounds, during fermentation with the laccase-expressing strain, illustrating that laccase was active even at the very low levels of oxygen supplied. In conclusion, phenolic compounds were important as fermentation inhibitors and using laccase-expressing yeast strains for producing ethanol from lignocellulose was helpful. Gorsich et al. (2006) engineered S. cerevisiae to be more tolerant to fermentation inhibitors, furfural, and 5-HMF in ethanol bioconversion in a paper with 230 citations. They screened a S. cerevisiae gene disruption library for mutants with growth deficiencies in the presence of furfural to identify target genes involved in furfural tolerance. They hypothesized that overexpression of these genes would provide a growth benefit in the presence of furfural. They identified 62 mutants whose corresponding genes functioned in a wide spectrum of physiological pathways, suggesting that furfural tolerance was a complex process. They focused on zwf1, gnd1, rpe1, and tkl1, which represented genes encoding PPP enzymes. At various concentrations of furfural and HMF, they observed a clear association with higher sensitivity to these inhibitors in these mutants. PPP mutants were inefficient at reducing furfural to the less toxic furfuryl alcohol, as a result of an overall decreased abundance of reducing equivalents or of NADPH’s role in stress tolerance. Furthermore, overexpression of ZWF1 in S. cerevisiae allowed growth at furfural concentrations that were normally toxic. They concluded that there was a strong relationship between PPP genes and furfural tolerance and identified additional putative target genes involved in furfural tolerance. Larsson et al. (2000) studied the effect of lignocellulose-derived aromatic compounds on oxygen-limited growth and ethanolic fermentation by S. cerevisiae in a paper with 227 citations. They assayed the effect of hydroxy-methoxy-benzaldehydes, diphenols/quinones, and phenylpropane derivatives on S. cerevisiae cell growth and ethanol formation using defined medium and oxygen-limited conditions. They observed that the inhibition affected by the hydroxy-methoxybenzaldehydes was highly dependent on the positions of the substituents. They next observed a

380

Bioethanol Fuel Production Processes. II

major difference in inhibition by the oxidized and reduced form of a diphenol/quinone where the oxidized form was inhibitor. Transformations of aromatic compounds occurring during the fermentation included aldehyde reduction, quinone reduction, and double bond saturation. They detected aromatic alcohols as products of reductions of the corresponding aldehydes, namely hydroxy-methoxy-benzaldehydes and coniferyl aldehyde. They next detected high-molecularmass compounds and the corresponding diphenol as products of quinone reduction. Furthermore, together with coniferyl alcohol, they identified dihydroconiferyl alcohol as major transformation products of coniferyl aldehyde. 36.3.2.2  Glucose Fermentation Bro et al. (2006) studied the in silico aided metabolic engineering of S. cerevisiae for improved bioethanol production in a paper with 277 citations. They used a genome-scale reconstructed metabolic network of S. cerevisiae to develop several strategies for metabolic engineering of the redox metabolism that would lead to decreased glycerol and increased ethanol yields on glucose under anaerobic conditions. They predicted best-scored strategies to completely eliminate the formation of glycerol and increase ethanol yield by 10%. They expressed a non-phosphorylating, NADP+dependent glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in S. cerevisiae. They found that the resulting strain had a 40% lower glycerol yield on glucose, while the ethanol yield increased by 3% without affecting the maximum specific growth rate. Similarly, expression of non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN) in a strain harboring XR and XDH led to an improvement in ethanol yield by up to 25% on xylose/glucose mixtures. Najafpour et al. (2004) produced ethanol in an immobilized cell reactor (ICR) using S. cerevisiae to improve the performance of the fermentation process in a paper with 246 citations. They performed the immobilization of S. cerevisiae by the enriched cell-cultured media harvested at the exponential growth phase. They carried out the fixed cell-loaded ICR at the initial stage of operation and entrapped the cell by calcium alginate. The production of ethanol was steady after 24 h of operation. They observed that the concentration of ethanol was affected by the media flow rates and residence time distribution from 2 to 7 h. In addition, they carried out the batch fermentation with 50 g/L glucose concentration. In batch fermentation, sugar consumption and ethanol production were 99.6% and 12.5% v/v, respectively, after 27 h, while those in the ICR were 88.2% and 16.7% v/v, respectively, with 6-h retention time. They obtained nearly 5% ethanol production with high glucose concentration (150 g/L) at 6-h retention time and a yield of 38% with 150 g/L glucose. The yield was improved by approximately 27% on ICR, and a 24-h fermentation time was reduced to 7 h. Productivity of the ICR was 1.3, 2.3, and 2.8 g/L h for 25, 35, and 50 g/L of glucose concentration, respectively, while the productivity of ethanol in batch fermentation with 50 g/L glucose s 0.29 g/L h. Maximum production of ethanol in ICR when compared to batch reactor increased approximately 10-fold. Furthermore, they converted high sugar concentration (150 g/L) in the ICR column to ethanol. Taherzadeh et al. (1997) determined the permissible region of growth of S. cerevisiae on glucose under anaerobic conditions as a function of both pH and the concentration of added acetic acid to the medium in a paper with 222 citations. They observed that in the absence of acetic acid, growth was possible at a pH as low as 2.5, whereas a total acetic acid addition of 10 g/L increased the minimum allowable pH for growth to 4.5. The concentration of the non-dissociated form of acetic acid should not exceed 5 g/L in the medium for growth to occur. The addition of acetic acid had a profound effect on growth energetics, thereby leading to an increased ethanol yield on glucose. At a concentration of 3.3 g/L of non-dissociated acetic acid, the ethanol yield was 20% higher than without added acetic acid. Furthermore, the biomass and glycerol yields decreased by 45% and 33%, respectively. 36.3.2.3  Ethanol Stress and Tolerance Alexandre et al. (2001) studied the global gene expression during short-term ethanol stress in S. cerevisiae using DNA microarrays in a paper with 295 citations. They observed that up to 3.1% of the genes encoded in the yeast genome were upregulated by at least a factor of 3 after 30-min

Utilization of the Saccharomyces cerevisiae: Review

381

ethanol stress (7% v/v). At the same time, 3.2% of the genes were downregulated by a factor of 3. Of the genes upregulated in response to ethanol, 49.4% belonged to the environmental stress response and 14.2% belonged to the stress gene family. In addition to the previously identified ethanol-induced genes, a very large number of genes involved in ionic homeostasis, heat protection, trehalose synthesis, and antioxidant defense also responded to ethanol stress. Furthermore, a large number of the upregulated genes were involved in energy metabolism. Thus, they concluded that the management of the energy pool (especially ATP) constituted an ethanol stress response and involved different mechanisms. You et al. (2003) studied the ethanol tolerance in S. cerevisiae as a function of cellular oleic acid content in a paper with 241 citations. The unsaturated fatty acid (UFA) composition of S. cerevisiae consisted almost exclusively of the mono-UFAs of palmitoleic acid and oleic acid, with the former predominating. Both UFAs were formed in S. cerevisiae by the oxygen- and NADH-dependent desaturation of palmitic acid and stearic acid, respectively, catalyzed by a single integral membrane desaturase encoded by the OLE1 gene. They found that oleic acid was the most efficacious UFA in overcoming the toxic effects of ethanol in growing yeast cells. Furthermore, the only other UFA tested that conferred a nominal degree of ethanol tolerance was cis-vaccenic acid, whereas palmitoleic acid did not confer any ethanol tolerance. They also showed that the most ethanol-tolerant transformant, which expressed the insect desaturase TniNPVE, produced twice as much oleic acid as palmitoleic acid in the absence of ethanol, and underwent a fourfold increase in the ratio of oleic acid to palmitoleic acid in response to exposure to 5% ethanol. They concluded that ethanol tolerance in yeast resulted from the incorporation of oleic acid into lipid membranes, affecting a compensatory decrease in membrane fluidity that counteracted the fluidizing effects of ethanol. 36.3.2.4  Biomass Fermentation Lau and Dale (2009) produced ethanol from ammonia fiber expansion (AFEX)-treated corn stover (CS) using S. cerevisiae 424A(LNH-ST) in separate hydrolysis and fermentation (SHF) in a paper with 293 citations. They obtained 191.5 g EtOH/kg untreated CS, at an ethanol concentration of 40.0 g/L (5.1 vol/vol%) without washing of pretreated biomass, detoxification, or nutrient supplementation. Enzymatic hydrolysis at high solid loading was the primary bottleneck affecting overall ethanol yield and titer. The fermentation inhibitors in AFEX-pretreated biomass increased metabolic yield and specific ethanol production while decreasing cell biomass generation. Nutrients inherently present in CS and those resulting from biomass processing were sufficient to support microbial growth during fermentation. They concluded that this platform improved the economics of cellulosic ethanol production by reducing the costs associated with raw materials, process water, and capital equipment. Karimi et al. (2006) produced ethanol from dilute acid-pretreated rice straw using Mucor indicus (M. indicus), Rhizopus oryzae (R. oryzae), and S. cerevisiae in a SSF process in a paper with 258 citations. They carried out the SSF experiments aerobically and anaerobically at 38°C, 50 g/L dry matter (DM) solid substrate concentration, and 15 or 30 filter paper unit (FPU)/g DM of commercial cellulose for an average of 2–3 days. They observed that all the strains produced ethanol from the pretreated rice straw with an overall yield of 40%–74% of the maximum theoretical SSF yield, based on the glucan available in the solid substrate. Furthermore, R. oryzae had the best ethanol yield as 74% from rice straw followed by M. indicus with an overall yield of 68% with 15 FPU/g DM of cellulase. Glycerol was the main by-product of the SSF by M. indicus and S. cerevisiae with yields of 117 and 90 mg/g of equivalent glucose in the pretreated straw, respectively, while R. oryzae produced lactic acid as the major by-product with a yield of 60 mg/g glucose equivalent in pretreated rice straw under anaerobic conditions. 36.3.2.5  Arabinose Fermentation Becker and Boles (2003) engineered S. cerevisiae strain able to utilize the l-arabinose for growth and to ferment it to ethanol in a paper with 209 citations. After overexpression of a bacterial l-arabinose utilization pathway consisting of Bacillus subtilis AraA and Escherichia coli AraB and

382

Bioethanol Fuel Production Processes. II

AraD and simultaneous overexpression of the l-arabinose-transporting yeast galactose permease, they selected an l-arabinose-utilizing yeast strain by sequential transfer in l-arabinose media. The crucial prerequisite for efficient utilization of l-arabinose was a lowered activity of l-ribulokinase (RK). Moreover, high l-arabinose uptake rates and enhanced TA activities favored the utilization of l-arabinose. With a doubling time of about 7.9 h in a medium with l-arabinose as the sole carbon source, an ethanol production rate of 0.06–0.08 g of ethanol per g (dry weight) · h−1 under oxygenlimiting conditions, and high ethanol yields, this yeast strain was useful for efficient fermentation of hexoses and pentoses in cellulosic biomass hydrolysates.

36.4 DISCUSSION 36.4.1 Introduction Crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline and petrodiesel fuels, in fuel cells, and in biochemical production in a biorefinery context. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol before bioethanol production through the hydrolysis and fermentation of the biomass. The research in the field of the utilization of S. cerevisiae for bioethanol production to improve the ethanol yield has intensified in this context in recent years. The key research front has been the fermentation of the xylose, fermentation of the glucose, fermentation of the biomass, fermentation inhibitors, ethanol stress, and fermentation of other hydrolysates. Furthermore, the metabolic engineering of S. cerevisiae has been a cross-subject research front. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. Although there have been several review papers for this field, there has been no review of the 25 most-cited articles in this field. Thus, this book chapter presents a review of the 25 most-cited articles on the utilization of S. cerevisiae for bioethanol production. Then, it discusses the key findings of these highly influential papers and comments on future research priorities in this field. As a first step for the search of the relevant literature, the keywords were selected using the first 200 most-cited population papers. The selected keyword list was then optimized to obtain a representative sample of papers for the searched research field. This keyword list was provided in the appendix of Konur (2023) for future replicative studies. As a second step, a sample dataset was used for this study. The first 25 articles with at least 205 citations each were selected for the review study. Key findings from each paper were taken from the abstracts of these papers and were discussed. Additionally, many brief conclusions were drawn and several relevant recommendations were made to enhance the future research landscape. Information about the research fronts for the sample papers in the utilization of S. cerevisiae for bioethanol production with regard to the feedstocks used in these processes is given in Table 36.3. As this table shows, there are two primary research fronts for this field: fermentation of hydrolysates and agricultural residues with 72% and 24% of these HCPs, respectively. The other research fronts are the fermentation of wood and grass with 12% and 4% of these HCPs, respectively. Furthermore, on individual basis, the most prolific research fronts are the fermentation of xylose and glucose and 52% and 28% of these HCPs, respectively. Furthermore, the utilization of S. cerevisiae for the fermentation of agricultural residues, wood, and hydrolysates is the most influential research fronts with 18%, 11%, and 8% surplus, respectively. Furthermore, on individual basis, fermentation of the glucose, xylose, corn stover, and rice straw with S. cerevisiae is the most influential research fronts with 7%–17% surplus each. Similarly, fermentation of the other feedstocks is the least influential research front with 12% deficit. Furthermore, on individual basis, fermentation of the hydrolysates in general, lignocellulosic biomass, food waste, sugarcane bagasse, and cellulose are the fronts with 2%–7% deficit each.

383

Utilization of the Saccharomyces cerevisiae: Review

TABLE 36.3 The Most Prolific Research Fronts for the Utilization of S. cerevisiae for the Bioethanol Production No. 1

2

3 4 5

Research Fronts

N Paper Review (%)

N Paper (%) Sample

Surplus (%)

Hydrolysates Xylose

72.0 52.0

64.5 39.7

7.5 12.3

Glucose

28.0

11.2

16.8

Arabinose

4.0

3.3

0.7

Cellobiose

4.0

1.7

2.3

Hydrolysates in general

0.0

7.0

−7.0

Galactose

0.0

0.8

−0.8

Pentose

0.0

0.8

−0.8

24.0 8.0

5.7 1.2

18.3 6.8

Corn stover

8.0

0.8

7.2

Wheat straw

4.0

1.2

2.8

Sorghum bagasse

4.0

0.4

3.6

Agricultural residues Rice straw

Sugarcane bagasse

0.0

1.7

−1.7

Corncobs

0.0

0.4

−0.4

12.0 4.0 0.0 0.0

1.2 0.8 12.3 2.5

10.8 3.2 −12.3 −2.5

Wood Grass Other feedstocks Lignocellulosic biomass Food waste

0.0

2.1

−2.1

Cellulose

0.0

1.7

−1.7 −0.8

Sorghum

0.0

0.8

Starch

0.0

0.8

−0.8

Sugarcane

0.0

0.8

−0.8

Carob

0.0

0.4

−0.4

Corn

0.0

0.4

−0.4 −0.4

Industrial wastes

0.0

0.4

Inulins

0.0

0.4

−0.4

Jerusalem artichoke

0.0

0.4

−0.4

Mahula

0.0

0.4

−0.4

Soybeans

0.0

0.4

−0.4

Sugar beet

0.0

0.4

−0.4

Xylan

0.0

0.4

−0.4

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 242 papers.

Information about the thematic research fronts for the sample papers in the utilization of S. cerevisiae for bioethanol production is given in Table 36.4. As this table shows, there are two primary research fronts for this field: the metabolic engineering of S. cerevisiae and fermentation of xylose with 80% and 52% of these HCPs, respectively. The other prolific research fronts are glucose fermentation, biomass fermentation, and fermentation inhibitors with 28% of these HCPs each. Furthermore, the other minor research fronts are ethanol stress, fermentation of the other hydrolysates, and the fermentation processes such as SSF and simultaneous SSCF with 8% of these HCPs each.

384

Bioethanol Fuel Production Processes. II

TABLE 36.4 The Most Prolific Thematic Research Fronts for the Utilization of S. cerevisiae for the Bioethanol Production No. 1 2 3 4 5 6 7 8

Research Fronts Metabolic engineering Xylose fermentation Biomass fermentation Glucose fermentation Fermentation inhibitors Ethanol stress Other hydrolysate fermentation Fermentation processes

N Paper (%) Review 80.0 52.0 28.0 28.0 28.0 8.0 8.0 8.0

N Paper (%) Sample 53.7 39.7 22.3 11.2 6.2 14.5 6.6 2.9

Surplus (%) 26.3 12.3 5.7 16.8 21.8 −6.5 1.4 5.1

N Paper (%) review, the number of papers in the sample of 25 reviewed papers; N paper (%) sample, the number of papers in the population sample of 242 papers.

Furthermore, the metabolic engineering of S. cerevisiae is the most influential front with 26% surplus, followed by fermentation inhibitors, glucose fermentation, and xylose fermentation with 22%, 17%, and 12% surplus, respectively. Similarly, ethanol stress is the least influential research front with 7% deficit.

36.4.2  The Utilization of the S. cerevisiae for the Fermentation of Xylose There are 13 highly cited papers (HCPs) for the utilization of S. cerevisiae for the fermentation of xylose standalone and with other feedstocks with seven and six HCPs, respectively (Table 36.1). These HCPs show a sample of the research on the utilization of S. cerevisiae in the fermentation of xylose for bioethanol production. These studies hint that the metabolically engineered S. cerevisiae strains enable the most difficult xylose fermentation with improved ethanol yield and productivity. Furthermore, the primary research front, as expected, is the metabolic engineering of S. cerevisiae with 13 HCPs. The other prolific research fronts are the biomass fermentation, glucose fermentation, and fermentation inhibitors with seven HCPs each. The other minor research fronts are ethanol stress and the other hydrolysate fermentation with two HCPs each. Additionally, the key research parameters are the recombinant S. cerevisiae strains, enzymes, xylose uptake rate, and ethanol yield with 13, 9, 7, and 5 HCPs, respectively. The other prolific research fronts are ethanol productivity, ethanol concentration, and xylose utilization with three HCPs each. 36.4.2.1  Xylose Fermentation Kotter and Ciriacy (1993) utilized xylose in S. cerevisiae transformants expressing XR and XDH and in P. stipitis to produce ethanol and in the absence of respiration and observed that S. cerevisiae cells converted half of xylose to xylitol and ethanol. Furthermore, Eliasson et al. (2000) fermented xylose by a recombinant S. cerevisiae strain, TMB 3001, to produce ethanol and observed that this strain expressed XR, XDH, and XK activities and was stable for more than 40 generations in continuous fermentations. Kuyper et al. (2005a) engineered a XI-expressing S. cerevisiae strain for rapid anaerobic xylose fermentation and observed that S. cerevisiae strains expressing the XylA gene from P. sp. E2 could grow anaerobically on xylose. Kuyper et al. (2003) studied the functional expression of P. sp. E2 XI as a key to efficient ethanolic fermentation of xylose by S. cerevisiae and observed that xylose

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metabolism in P. sp. E2 proceeded via a XI rather than via the XR/XDH pathway found in xylosemetabolizing yeasts. Kotter et al. (1990) constructed a xylose-utilizing S. cerevisiae transformant by isolating P. stipitis XDH gene, XDH2, and observed that S. cerevisiae cells transformed with a plasmid, pRD1, containing both the XR gene (XR1) and the XDH gene (XDH2), grew on xylose as a sole carbon source. Furthermore, Brat et al. (2009) screened nucleic acid databases for sequences encoding putative XIs and finally cloned and successfully expressed a highly active new kind of XI from C. phytofermentans in S. cerevisiae in a paper with 209 citations. Finally, Kuyper et al. (2004) engineered S. cerevisiae and showed that the anaerobic conversion of xylose to ethanol, without substantial by-product formation, was possible in S. cerevisiae when a heterologous IS was functionally expressed. 36.4.2.2  Xylose Fermentation together with Other Feedstocks Kuyper et al. (2005b) engineered mixed xylose and glucose utilization by a xylose-fermenting S. cerevisiae strain and obtained a culture with improved xylose uptake kinetics. Furthermore, Martin et al. (2002) produced ethanol from enzymatic hydrolysates of sugarcane bagasse using recombinant xylose-utilizing S. cerevisiae and they removed approximately 80% of the phenolic compounds by the laccase treatment. Ohgren et al. (2006) carried out the SSCF of glucose and xylose in steam-pretreated corn stover at high fiber content with S. cerevisiae and observed that TMB3400 co-fermented glucose and xylose with relatively high ethanol yields giving high final ethanol concentration. Furthermore, Demeke et al. (2013) developed D-xylose-fermenting and inhibitor-tolerant industrial S. cerevisiae strain, Ethanol Red, with high performance in reed, spruce, and wheat straw hydrolysates using metabolic and evolutionary engineering and observed that the best-performing strain, GS1.11–26, showed a maximum specific D-xylose consumption rate. Karhumaa et al. (2007) compared the P. stipitis XR-XDH and the P. sp. XI pathways for xylose fermentation in an isogenic strain background by recombinant S. cerevisiae and observed that the xylose consumption rate, the specific ethanol productivity, and the final ethanol concentration were significantly higher in the XR- and XDH-carrying strain, whereas the highest ethanol yield was achieved with the strain carrying XI. Furthermore, Ha et al. (2011) engineered S. cerevisiae for simultaneous cellobiose and xylose fermentation and found that the resulting yeast strains co-fermented cellobiose and xylose simultaneously and exhibited improved ethanol yield when compared to fermentation with either cellobiose or xylose as sole carbon sources.

36.4.3 The Utilization of the S. cerevisiae for the Fermentation of the Other Feedstocks There are 18 HCPs for the utilization of S. cerevisiae for the fermentation of the other feedstocks (Table 36.2). However, the notes are made only for 12 HCPs in this section as the notes for six HCPs were made in the previous section. The key research fronts are glucose fermentation, biomass fermentation, and fermentation inhibitors with seven HCPs each. The other research fronts are ethanol stress, arabinose fermentation, and cellobiose fermentation with two, one, and one HCPs, respectively. These HCPs show a sample of the research on the utilization of S. cerevisiae for the fermentation of other feedstocks such as biomass and other hydrolysates for bioethanol production. These studies hint that the metabolically engineered S. cerevisiae strains enable the fermentation of xylose with improved ethanol yield and productivity. Furthermore, the primary research front, as expected, is the metabolic engineering of S. cerevisiae with 13 HCPs. The other research fronts are the biomass fermentation, glucose fermentation, and fermentation inhibitors with four, three, and two HCPs, respectively.

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Additionally, the key research parameters are the recombinant S. cerevisiae strains, ethanol yield, fermentation inhibitors, ethanol productivity, and enzymes with 13, 8, 7, 5, and 4 HCPs, respectively. The other prolific research fronts are ethanol stress, batch modes, and ethanol titer with two HCPS each. 36.4.3.1  Fermentation Inhibitors Delgenes et al. (1996) studied the inhibitory effects of six fermentation inhibitors on ethanol fermentation of glucose by S. cerevisiae and of xylose by Z. mobilis, P. stipitis, and C. shehatae and observed that vanillin was a strong inhibitor of both growth and ethanol production by xylosefermenting yeasts and S. cerevisiae when it was added to the culture media. Furthermore, Larsson et al. (2001) developed a S. cerevisiae strain with enhanced resistance to phenolic fermentation inhibitors in lignocellulose hydrolysates by heterologous expression of laccase and observed that the laccase activity could be enhanced twofold by simultaneous overexpression of the homologous t-SNARE Sso2p. Gorsich et al. (2006) engineered S. cerevisiae to be more tolerant to fermentation inhibitors, furfural and 5-HMF in ethanol bioconversion, and observed a clear association with higher sensitivity to these inhibitors in these mutants. Furthermore, Larsson et al. (2000) studied the effect of lignocellulose-derived aromatic compounds on oxygen-limited growth and ethanolic fermentation by S. cerevisiae and observed that the inhibition affected by the hydroxy-methoxy-benzaldehydes was highly dependent on the positions of the substituents. 36.4.3.2  Glucose Fermentation Bro et al. (2006) studied the in silico aided metabolic engineering of S. cerevisiae for improved bioethanol production and found that the resulting strain had a 40% lower glycerol yield on glucose while the ethanol yield increased by 3% without affecting the maximum specific growth rate. Furthermore, Najafpour et al. (2004) produced ethanol in an immobilized cell reactor using S. cerevisiae to improve the performance of the fermentation process and obtained nearly 5% ethanol production with high glucose concentration (150 g/L) at 6-h retention time and a yield of 38% with 150 g/L glucose. Finally, Taherzadeh et al. (1997) determined the permissible region of growth of S. cerevisiae on glucose under anaerobic conditions as a function of both pH and the concentration of added acetic acid to the medium and observed that in the absence of acetic acid, growth was possible at a pH as low as 2.5, whereas a total acetic acid addition of 10 g/L increased the minimum allowable pH for growth to 4.5. 36.4.3.3  Ethanol Stress and Tolerance Alexandre et al. (2001) studied the global gene expression during short-term ethanol stress in S. cerevisiae using DNA microarrays and observed that up to 3.1% of the genes encoded in the yeast genome were upregulated by at least a factor of three after 30 min ethanol stress (7% v/v). Furthermore, You et al. (2003) studied the ethanol tolerance in S. cerevisiae as a function of cellular oleic acid content and found that oleic acid was the most efficacious UFA in overcoming the toxic effects of ethanol in growing yeast cells. 36.4.3.4  Biomass Fermentation Lau and Dale (2009) produced ethanol from ammonia fiber expansion (AFEX)-treated corn stover (CS) using S. cerevisiae 424A(LNH-ST) in separate hydrolysis and fermentation (SHF) and obtained 191.5 g EtOH/kg untreated CS, at an ethanol concentration of 40.0 g/L (5.1 vol/vol%). Furthermore, Karimi et al. (2006) produced ethanol from dilute acid-pretreated rice straw using M. indicus, R. oryzae, and S. cerevisiae in a SSF process and observed that all the strains produced ethanol from the pretreated rice straw with an overall yield of 40%–74% of the maximum theoretical SSF yield.

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36.4.3.5  Arabinose Fermentation Becker and Boles (2003) engineered S. cerevisiae strain to utilize the l-arabinose for growth and to ferment it to ethanol and selected an l-arabinose-utilizing yeast strain by sequential transfer in l-arabinose media. The crucial prerequisite for efficient utilization of l-arabinose was a lowered activity of l-ribulokinase (RK).

36.5  CONCLUSION AND FUTURE RESEARCH The brief information about the key research fronts covered by the 25 most-cited papers with at least 205 citations each is given under two primary headings: The utilization of S. cerevisiae for the fermentation of xylose and other feedstocks such as glucose and biomass. The usual characteristics of these HCPs are that the utilization of S. cerevisiae results in higher ethanol yield and productivity and this research field has a crucial importance in the fermentation research to improve the ethanol yield since this yeast is the most-studied one in this field. The key findings on these research fronts should be read in light of the increasing public concerns about climate change, greenhouse gas (GHG) emissions, and global warming as these concerns have been certainly behind the boom in the research on bioethanol production as an alternative to crude oil-based gasoline and diesel fuels in the last decades. The recent supply shocks caused by the coronavirus disease 2019 (COVID-19) pandemics and the Russian invasion of Ukraine also highlight the importance of the production and utilization of the bioethanol fuels as an alternative to crude oil-based gasoline and petrodiesel fuels. Table 36.3 shows there are two primary research fronts for this field with regard to feedstocks: fermentation of hydrolysates and to lesser extent agricultural residues. The other minor research fronts are the fermentation of wood and grass. Furthermore, on individual basis, the most prolific research fronts are the fermentation of xylose and glucose. Similarly, as Table 36.4 shows, there are two primary thematic research fronts for this field: the metabolic engineering of S. cerevisiae and fermentation of the xylose. The other prolific research fronts are glucose fermentation, biomass fermentation, and fermentation inhibitors. Furthermore, the other minor research fronts are ethanol stress, fermentation of the other hydrolysates, and the fermentation processes such as SSF and simultaneous SSCF. These studies emphasize the importance of proper incentive structures for the efficient development and application of fermentation of the substrates and hydrolysates to enhance bioethanol yield of the substrates and hydrolysates in light of North’s institutional framework (North, 1991). In this context, the major producers and users of bioethanol fuels such as Europe and to a lesser extent USA have developed strong incentive structures for the effective development and application of utilization of S. cerevisiae for bioethanol production. In light of the supply shocks caused primarily by the COVID-19 pandemic and the Russian invasion of Ukraine, it is expected that the incentive structures such as public funding would be enhanced to increase the share of bioethanol fuels in the global fuel portfolio as a strong alternative to crude oil-based gasoline and petrodiesel fuels. In this context, it is expected that the most prolific researchers, institutions countries, funding bodies, and journals would have a first-mover advantage to benefit from such potential incentives. It is recommended that such review studies are performed for the primary research fronts of the utilization of S. cerevisiae for wood, agricultural residues, and other biomass.

ACKNOWLEDGMENTS The contribution of the highly cited researchers in the field of the utilization of S. cerevisiae for bioethanol production has been gratefully acknowledged.

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Part 6 Bioethanol Fuel Separation

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Distillation Separation of Bioethanol The Current Advances Huidong Chen, Di Cai, Jieyi Wen, Changsheng Su, and Jianghong Wang Beijing University of Chemical Technology

37.1 INTRODUCTION As a solution to the gradual depletion of fossil fuels and the increasing greenhouse gases emission in the atmosphere, liquid biofuels derived from biomass materials have garnered significant attention owing to the sustainable and environmental-friendly attributes (Azhar et al., 2017; Gupta and Verma, 2015). Among different types of liquid biofuel candidates, bioethanol is the most widely used one, which can be obtained by fermentation from the abundant biomass resources, such as lignocellulose and the starchy/sugar-based materials (Zhang et al., 2019, 2020). Advantages of using bioethanol in blending fuels are obvious owing to the higher octane number, broader flammability limits, and higher flame speeds than gasoline (Balat and Balat, 2009; John et al., 2011). It is evidenced that the gasoline/diesel-ethanol blend could significantly reduce the CO2, unburned hydrocarbon (UHC), and particulate matter (PM) emissions (Manzetti and Andersen, 2015; Shahir et al., 2015). The production of bioethanol is supported by policy in many countries and the increasing yearly global production in the past decades showing the broad prospects (Ahlgren and di Lucia, 2014; Cooper et al., 2020; Saravanan et al., 2018; Wu et al., 2021). As the oldest fermentation type to be commercialization, the production of bioethanol and its application in transportation sector has a long history. Typically, the whole bioethanol production process can be divided into two units: the upstream unit and the downstream unit. For the upstream unit, fermentable sugars derived from biomass are catabolized by microorganisms, producing ethanol, CO2, and other organic by-products as impurities (Liu et al., 2019; Naghshbandi et al., 2019; Wang et al., 2018a). Ethanol concentration in the end, fermentation broth, is highly influenced by the initial sugar’s titer in substrate and the effects of inhibitors (Su et al., 2020a; Wang et al., 2018b), which are always depending on the type of feedstock and the fermentation conditions (Hegely and Lang, 2020). Besides, the tolerance of microorganisms to ethanol should also be considered (Zhang et al., 2018a). Generally, the fermentation broth always contains 4–12 wt% of ethanol (Singh and Rangaiah, 2019). Other components are by-products and water, of which water is the predominant fraction in the broth (Sanchez et al., 2020a,b). The downstream unit of bioethanol production mainly contains a separation system for alcohol concentration and a following dehydration system for fuel-guide production (Karimi et al., 2021). Because of the low concentration of ethanol remaining in broth, the separation of bioethanol is energy-intensive. It is estimated that the separation cost occupied ~10% of overall production cost of bioethanol from lignocelluloses (Tao et al., 2014). In the past years, although numerous types of energy-saving separation techniques have been developed to separate bioethanol from broth in laboratory and pilot scales, till now, the most mature and well-proven technique in industry is still distillation (Outram et al., 2017). Compared with other bioethanol separation methods, distillation exhibited the advantages of high solvent recovery (Zentou et al., 2019). To overcome the inherent DOI: 10.1201/9781003226499-47

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weakness of high energy demand, improving the energy efficiency and economics of distillation has always been the focus of research. In recent years, energy recovery method including the heat integration, multiple effect distillation, thermal coupling, the adoption of heat pumps or organic Rankine cycle, and novel sequences has ever been considered and applied in bioethanol separation from broth, and the connection of other bioethanol separation and dehydration techniques has been conducted and analyzed (Lodi and Modesto, 2018; Zhao et al., 2018). In this chapter, the current advances of bioethanol distillation techniques are reviewed. In order to clearly explain the principle, difficulties, and opportunities of bioethanol distillation, the thermodynamic behavior and the phase equilibrium of the ethanol-water binary solution are emphasized, followed by summarizing the alternative distillation sequences and process intensification methods. Technical trends of the integration of distillation processes and the process evaluation in the aspect of technoeconomic feasibility and life cycle assessment for better decision-making of the suitable distillation processes for bioethanol separation are also included.

37.2  CHEMICAL COMPONENTS IN FEED AND THE BASIC PROPERTIES Except for ethanol, the main solvent product to be separated, other chemicals in the feeding streams for distillation are dependent on the microorganisms, the feedstocks, and the preseparation methods (such as the nondistillation techniques of pervaporation, vapor permeation, salting-out, gas stripping, and extraction) (Cai et al., 2016a,b; Chen et al., 2018; Wen et al., 2018; Zhang et al., 2021). Most of the impurities in the bioethanol fermentation broth are always alcohols and acids/ phenolic compounds that are derived from the side-metabolic pathways of the microorganisms, and the by-products from the upstream pretreatment and saccharification processes (Cai et al., 2013; 2017; Kwak and Jin, 2017). According to the differences in boiling point, the organic impurities can be divided into the higher boiling point (than ethanol) compounds and the lower boiling point compounds. The higher boiling point compounds include propanol, butanol, amyl alcohol, acetic acid, and butyric acid, etc., while lower boiling point compounds include methanol, acetone, etc. In addition, a large amount of CO2, the by-product from Embden–Meyerhof pathway (EMP), can be dissolved in the bioethanol fermentation broth, which should be considered in the following distillation processes. Boiling points of the components in bioethanol fermentation broth are summarized in Table 37.1. Among the above components, CO2 is a kind of supercritical compound, which kept supercritical state and cannot be liquefied at most separation conditions (e.g., at room temperature and atmospheric pressure) in distillation process (Zhao and Zhang, 2014). Therefore, when designing the distillation sequence, it is necessary to have a vapor overhead stream at the first column to discharge the dissolved CO2 in the feeding fermentation broth. As for alcohols existed in the fermentation broth, since the hydroxyl group in alcohols molecules is a polar group and hydrogen bond would be formed between ethanol and water, the corresponding aqueous solution often exhibited nonideal properties (Toth, 2019). The nonideality in such aqueous solution could greatly affects the thermodynamic behavior of vapor-liquid equilibrium of the components. The azeotrope of ethanol-water is a typical example of the thermodynamic behavior (Figure 37.1) (Katzen, 1999). Consequently, ethanol production with purity higher than 95 wt% cannot be effectively separated by the conventional distillation sequences. Therefore, a more complex alternative distillation sequences is required to separate the dehydrated ethanol production. Besides, in some researches, other polar substance can be introduced into the distillation process so that destroying the phase equilibrium of alcohols-water azeotropic system or forming a new azeotropic system. This is the thermodynamic basis of extractive distillation and azeotropic distillation. Process simulation can accurately calculate the key parameters in distillation process. Therefore, as for the designation of ethanol distillation sequences, process simulation by commercial software is the common method (Kraemer et al., 2011; Silva et al., 2018). Physical properties of each

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TABLE 37.1 Boiling Points of the Constitutions in Bioethanol Fermentation Broth (Batista et al., 2012) Component

Boiling Point (°C)

Water Ethanol Methanol Isopropanol Propanol Isobutanol N-butanol 2-Butanol Isoamyl alcohol 2-Methyl-1-butanol 1-Pentanol 1-Hexanol Methyl acetate Ethyl acetate Acetaldehyde Acetone Acetic acid Propionic acid CO2

100.0 78.4 64.7 82.4 97.1 108.0 118.0 99.0 132.0 127.5 138.0 158.0 56.9 77.1 20.2 56.5 118.1 141.0 −78.0

105

P=1 atm

100

T (ºC)

95 90 85

87.2 mol%

80 75

0.0

0.1

0.2

0.3

0.4

0.5

x.y Ethanol

0.6

0.7

0.8

0.9

1.0

FIGURE 37.1  Vapor liquid equilibrium of ethanol-water binary system at 1.013 bar. Figure shows the temperature (T) and liquid/vapor molar fraction of ethanol (x.y ethanol) for the ethanol-water system at 1 atm. The azeotropic composition of ethanol-water at 1.0 atm is 87.2% mol of ethanol.

component in the bioethanol fermentation broth and all kinds of unit models in the bioethanol distillation processes are all contained in the database of software. The accuracy of the simulation results determines the reliability of the distillation sequences. The key for a high accuracy is the selection of suitable thermodynamic model that could well

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describe the phase equilibrium behavior of the components. Basically, there are two classes of thermodynamic models for phase equilibrium: equation of state (EOS) and activity coefficient model. A large number of experimental results showed that NRTL and UNIQUAC model that belongs to the activity coefficient model are the more accurate models for the simulation of bioethanol distillation process (Palacios-Bereche et al., 2015; Zhang et al., 2018b; Zhao et al., 2017).

37.3  THE BASIC BIOETHANOL DISTILLATION PROCESSES The main task for bioethanol distillation process is the concentration of low-titer products (normally 4–12 wt%) into a higher concentration one (~95 wt%) from the water-enriched fermentation broth. The basic principle of distillation is the different relative volatility of the components. In distillation process, the vapor and liquid phases are counter-current contacted inside the equipment to realize mass and energy transfers. The high boiling point components are enriched in the liquid phase of the bottom of column, while the light components are transferred into the vapor phase. After multistage contact of the vapor and liquid phases inner the column, concentrations of the high boiling point components and the light components are gradually increased and finally reached maximized titer in the bottom of column and the top of column, respectively. Basically, according to the operation modes, the distillation process can be divided into two classes, the batch distillation and the continuous distillation. Batch distillation is always adopted in bioethanol separation after solid fermentation (Li et al., 2017; Yu et al., 2014). Vinasse after fermentation is putted at the bottom of distillation column, and ethanol can be obtained after condensation on the head of column. Ethanol production can be further purified by repeated the batch operation for several times. During the process, ethanol and other organic impurities that are dissolved in fermentation broth can be separated from the distillation column with a sequence from low boiling point components to high boiling point components. Compared with continuous distillation process, batch distillation is more flexible. Nevertheless, because of the repeated heating procedure and the time-consuming reconstruction of the vapor-liquid equilibrium, the separation efficiency of batch distillation is far behind the continuous process, and the process is more energy-intensive. Besides, because of the unstable output of the product, batch distillation also has difficulties in process control. Thus, in modern large-scale bioethanol industry, continuous distillation is always adopted rather than batch distillation. Continuous distillation is always connected by several equipment, in which process streams with stable flows and products are continuously outputted from the distillation system. After being fed into the continuous distillation process, the bioethanol fermentation broth passes through each distillation equipment one by one and gradually completes the separation according to the designed processing sequence. Bioethanol, organic impurities, and wastewater can be separated at different positions of the distillation process. The energy requirement of bioethanol separation and the total separation cost is highly depending on the designation of the continuous distillation sequences. Besides, ethanol concentration and components of the feeding broth also affect the distillation process performances. Therefore, it is necessary to discuss the components of the basic compounds in bioethanol fermentation broth and the thermodynamic properties of the components (e.g., the boiling point, the azeotropic systems, etc.) so as to ensure a suitable continuous distillation process with low cost and low energy requirement. Typically, the basic sequence of the distillation process for bioethanol separation contains at least two units. In the first distillation unit, CO2 is extracted from the vapor overhead outlet of the column, and the components with lower boiling points (such as methanol and acetone) are obtained from the liquid overhead outlet. Ethanol and the other heavy components with higher boiling points are recovered at the bottom of the column and are pumped into the second distillation unit. Then, a small amount of CO2 that are still dissolved in the bottom outlet of the first column is separated from the top of the second distillation column. At the same time, the azeotrope of ethanol and water can be recovered from the liquid overhead outlet in the second column.

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FIGURE 37.2  Schematic diagram for the basic distillation sequences of bioethanol. The basic sequence of the distillation process for bioethanol separation contains two units. In the first distillation unit (beer column), ethanol and the other heavy components with higher boiling points are recovered in the bottom of column and are pumped into the second distillation unit (rectifier column). The azeotrope of ethanol and water can be recovered from the liquid overhead outlet in this rectifier column. Wastewater dissolved with the majority of high boiling point impurities is recovered from the bottom of the second column.

Wastewater dissolved with the majority of high boiling point impurities is recovered from the bottom of the second column. Schematic diagram for the basic distillation sequences is shown in Figure 37.2.

37.4  ALTERNATIVE BIOETHANOL DISTILLATION PROCESSES Based on the basic bioethanol distillation process, ethanol can be successfully separated from the dilute fermentation broth that contains impurities. Nevertheless, this process can be further intensified aiming to improve the purity of product and decrease the energy requirement. Because of the limitation of thermodynamics and the properties of azeotrope (ethanol-water mixture) that are also pointed out in the aforementioned sections (Figure 37.1) (Katzen, 1999), it is difficult to obtain an ethanol product with purity higher than 95 wt% using the basic distillation sequence at atmospheric pressure. To decrease the energy consumption and get a high-purity product, there is a need to further improve the basic distillation sequence. During the past decades, variable pressure distillation processes and extractive distillation processes are advocated in previous researches. As for the decrease of energy requirement, energy integration and thermal coupling techniques, such as multieffect distillation HIDiC (internal thermal coupling distillation column), heat pump, and dividing-wall columns, are widely applied and the corresponding alternative bioethanol distillation processes are optimized.

37.4.1  Dehydration Directions 37.4.1.1  Variable Pressure Distillation According to the vapor liquid equilibrium of ethanol-water binary system (Figure 37.1), since the bioethanol concentration in the fermentation broth is always below 12 wt%, the feed components of the distillation process are always falling on the left side of the diagram. The minimum point

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in the TXY curve for the ethanol-water phase diagram is the azeotropic point of the mixture. The influences of the azeotropic point on the ethanol distillation process can be described as follows: If the ethanol concentration of the feed stream falls on the left of this point, then, the molar fraction of the final concentrated ethanol production which is separated by distillation will not exceed the mole fraction of ethanol at the azeotrope point. Ethanol concentration in the liquid output from the bottom of the distillation column would approach to 0% (mol/mol). Correspondingly, in cases that the feed stream falls on the right of the azeotropic point, the molar fraction of ethanol in bottom of column would not be lower than the ethanol concentration at the azeotrope point. Ethanol concentration in the vapor output from the top of the column would approach 100% (mol/mol). The molar fraction of ethanol at the azeotrope is sensitive to the pressure. As can be seen from Figures 37.1 and 37.3 (calculated with Aspen plus, based on NRTL model), ethanol molar fraction at the azeotrope point is 87.2%, when the system is under the atmospheric pressure. If the pressure of the system increases to 10 atm, the molar fraction of ethanol at the azeotrope point would be decreased to 79.4%. Based on this phenomenon, distillation units with different column pressures can be conducted to separate bioethanol from fermentation broth. In this process, ethanol concentration and the azeotrope point can be moved to the opposite direction. In cases when the ethanol concentration moves to the right of the azeotrope point, ethanol concentration exceeding 95 wt% can be realized. Figure 37.4a is an example for bioethanol production based on variable pressure distillation (Bessa et al., 2013). 5–10 wt% of ethanol fermentation broth is fed into the first distillation column under atmospheric pressure. Ethanol concentration in the top stream of the column is about 87.2%. Then, the stream is pumped into the second column with higher pressure (10 atm). Under this pressure, the azeotrope point of the ethanol-water binary mixture is 79.4 %, which is lower than the ethanol concentration (87.2%) of the feed stream. Therefore, in the vapor liquid equilibrium diagram, ethanol concentration in the feed stream falls on the right of the azeotrope point. According to the aforementioned phenomenon, dehydrated ethanol product that exceeds 99 wt% can be obtained in the bottom outlet of the second column with higher pressure. The liquid overhead stream of this column is fed back to the first column. 37.4.1.2  Extractive Distillation In order to change the thermodynamic characteristic of ethanol-water azeotrope, an entrainer is always introduced into the bioethanol distillation process that disrupts the ethanol-water azeotrope 185

P=10 atm

180 175

T (ºC)

170 165 160 79.4 mol%

155 150 145

0.0

0.1

0.2

0.3

0.4

0.5

x.y Ethanol

0.6

0.7

0.8

0.9

1.0

FIGURE 37.3  Vapor-liquid equilibrium (VLE) diagram for water-ethanol mixture at 10 bar. This is the 10 atm version of Figure 37.1. The azeotropic composition of ethanol-water at 10 atm is 79.4% mol of ethanol.

399

Distillation Separation of Bioethanol Degassing Dehydrated Ethanol

(a)

(b)

Entrainer

(c)

EG

THF

Recycled EG

Ethanol

Water Organic Phase

Water

Aqueous Phase

Feed

Ethanol/Water Binary Solution Water

Dehydrated Ethanol

Low-Pressure Column

High-Pressure Column

Phlegm Feed

Vinasse

Steam

(d)

(e)

(f)

(g)

Feed

Hydrated Ethanol

Hydrated Ethanol

Feed

Water

Ethanol

Water

entrainer

Feed

Water

Dehydrated Ethanol

Compressor

Feed

EG Water

FIGURE 37.4  Alternative distillation processes for bioethanol separation. (a) Variable pressure distillation process; (b) extractive distillation process; (c) multieffect distillation process; (d) typical HIDiC distillation process; (e) HIDiC and variable pressure distillation process; (f) heat pump distillation process; (g) extractive dividing-wall column. In all distillation processes, fermentation broth in entered to the distillation system and dehydrated ethanol is obtained as a product.

(Gil et al., 2012; Li and Bai, 2012; Ravagnani et al., 2010). Thus, a higher purity ethanol production can be obtained based on the alternate distillation process named azeotropic distillation or extractive distillation. An entrainer can be added into the distillation process from the top of column, where the concentration distribution of ethanol and water in vapor and liquid phase is closer to the azeotrope. An ideal entrainer should exhibit the following properties: (i) the entrainer does not form a new azeotrope with the original components in the solution; (ii) the activity coefficient of ethanol and water can be significantly changed after adding the entrainer, and further improve the relative volatility and destroy the azeotrope of ethanol and water; and (iii) boiling point of the entrainer should be higher than that of ethanol and water (Garcia-Herreros et al., 2011; Ligero and Ravagnani, 2003; Pinto et al., 2000; Su et al., 2020b; Yang et al., 2019). In extractive distillation process, the mixtures on the trays near the top of distillation column will no longer be an azeotrope after the injecting of entertainer, so the purity of ethanol can be close to 100 wt% (Luo et al., 2015; Pan et al., 2019; Shang et al., 2019). The most commonly used entrainers for extractive distillation of bioethanol are dimethyl sulfone (DMSO), ethylene glycol (EG) and ionic liquid (e.g., 1-methylimidazolium chloride, 1-butyl-3-­ methylimidazolium chloride) (Dai et al., 2014; Ramirez-Corona et al., 2015; Zhu et al., 2016). Since the entrainer is neither the product nor the components in the feed, it needs to be recycled in the distillation process. A common extractive distillation sequence should have an additional column for entrainer recovery compared with the basic distillation sequence (Ma et al., 2019; QuijadaMaldonado et al., 2014). For instance, when using EG as an entrainer, the sequence shown in Figure 37.4b can be used for ethanol separation from the model fermentation broth that contains low boiling point impurities (Jaime et al., 2018). First, the fermentation broth is fed into the column for separating the low boiling point impurities. The impurities can be separated on the top of the column, while the ethanolrich stream at the bottom of this column is fed into the second column for ethanol separation. At the top of the second column, EG as entrainer is injected into the column, so that the ethanol-water azeotrope can be destroyed. Ethanol that >99.5 wt% can be obtained, while EG and water as the

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higher boiling point components are collected at the bottom of the column and further fed into the third column to recover EG. Water with lower boiling point is obtained at the top of EG recovery column, while nearly pure EG is recycled at the bottom of the column and is fed back to the top of ethanol separating column.

37.4.2 Energy-Saving Directions 37.4.2.1  Multieffect Distillation Multieffect distillation is a commonly used technique to reduce the distillation energy requirement for bioethanol production. In the multieffect distillation process, different distillation columns are operated under different pressures; the condenser temperature of one distillation column can be higher than that of the reboiler of the other distillation column. Thus, the cooling (energy removal) stream in one column can be used as heating (energy input) stream in another column. Consequently, the thermodynamic efficiency of multieffective distillation can be improved (Han and Park, 1996; Singh and Rangaiah, 2019). When adopting the multieffective distillation in bioethanol separation, the single column for ­ethanol-water separation can be replaced by a series of columns that with different pressures, while the primary separation process can be decomposed into multiple separation process. Each column that contains multiple separation processes refers to one effect. The temperature of the high-pressure column condenser should be 10°C–15°C higher than that of the low-pressure column reboiler to transfer energy. On this basis, steam in the head of the front column can be used as the heat source for bottom reboiler of the following column. Thus, steam is only required in the first column of the distillation sequence, while cooling water is only loaded into the last column. Other columns in the middle of distillation sequences do not provide any extra energy. Therefore, energy can be saved (Diaz and Tost, 2016; Engelien and Skogestad, 2005; Palacios-Bereche et al., 2015). Bessa et al. (2013) reported a typical case of double-effect bioethanol distillation (Figure 37.4c), which consisted of a higher-pressure column (A, 1.18 bar) and a lower-pressure column (B, 0.29 bar). The ethanol fermentation broth is fed into both of the two columns separately. Because of the differences in pressure, vapor stream at the top of column A has a higher temperature than the bottom of column B, resulting in enough temperature difference (96.3°C vs 68.1°C). Therefore, after integration, heat is not required in column B while column A no longer required external cooling water. After optimization of the parameters including the flow rates, feed position, number of trays, etc., the operation cost of the double-effect distillation process is reduced by 32.4% compared with the conventional process, though the equipment cost is little higher. 37.4.2.2  Heat-Integrated Distillation Column (HIDiC) In the conventional distillation process, heat stream is required in the bottom of the column, while cooling is needed to condense the stream in the condenser of the column. Thus, if partial heat of the rectifying section or condenser is transferred to the stripping section or reboiler, the heat and cooling demand of the distillation column can be reduced. HIDiC is one of the alternative distillation techniques according to this heat-integration strategy. Several research have been suggested to take energy-saving and cost-effective bioethanol distillation using HIDiC (Bruinsma et al., 2012; Nakaiwa et al., 2003; Suphanit, 2011). As can be seen from Figure 37.4d (Ponce et al., 2015), in a typical HIDiC process based on the basic distillation sequence, the rectifying and the stripping sections (the boundary of the two sections is the feed tray) of the distillation column in conventional process can be divided into two individual columns. The pressure of the second column (corresponding to the rectifying section of the distillation column in basic sequence) is increased. Therefore, temperatures of trays in the second column are higher than those in the first column (corresponding to the stripping section). Thus, sufficient temperature difference for the heat transfer is realized. After heat transfer, the redundant

Distillation Separation of Bioethanol

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heat with higher grade is reused in the first column. During the process, a compressor is required to transfer the steam in the head of first column to the second column with higher pressure. Compared with the basic single-column-based distillation, the HIDiC process could save ~77% of heat. In another example, HIDiC is applied in a variable pressure distillation process (Kiran and Jana, 2015). The vapor overhead outlet in the column with lower pressure is partially condensed (Figure 37.4e). The residual vapor stream is compressed and injected into the higher-pressure column. At the same time, since the operation temperature in each tray of the higher-pressure column is always higher than those of the low-pressure column, several mini heat-transfer units are conducted between the two columns. Based on this strategy, the cooling energy and heating energy requirements of the high-pressure column and low-pressure column, respectively, can be simultaneously reduced. Drawback, however, is the extra investment of the equipment. The adoption of HIDiC could decrease 54% of the total energy requirement in the distillation process. Meanwhile, 3.59% of total annual cost (TAC) can be reduced. 37.4.2.3  Heat Pump Distillation Another lower energy consumption distillation process based on heat-transfer is heat pump (Jana, 2014; Yang et al., 2016). The principle of heat pump distillation is generally similar to the heatintegration process. Among different types of heat pump that showed potential in distillation process, vapor compression, the classical heat pump, is favorable to be used for bioethanol purification (Kiss et al, 2012). Distillation of bioethanol based on vapor compression type of heat pump can be realized as follows (Diez et al., 2009; Kazemi et al., 2018): (i) selection of a column from the distillation sequences. Normally, the most energy-intensive column in sequence is selected because of the high efficiency. (ii) Replacing the cooling water by a circulating medium. After heat transfer, the circulating medium is compressed and heated, and is used for heating the reboiler at the bottom of the column. (iii) The medium after heating the bottom of the column is pumped back to the condenser in the head of column. Thus, the circulation of medium is realized, which is also named reverse Carnot cycle. According to this process, heat in the vapor overhead outlet of column can be transferred into the reboiler at the bottom of the column. Kazemi et al. (2017) carried out heat pump in a regular bioethanol distillation process (Figure 37.4f). Energy requirement can be reduced by ~50%, while the operation cost is decreased by 10.7%. 37.4.2.4  Dividing-Wall Column Dividing-wall column is an advanced process intensification and integration technique (Dejanovic et al., 2010). Thermodynamic principle of dividing-wall column is similar with the fully h­ eat-integrated distillation (Asprion and Kaibel, 2010; Petlyuk, 1965). In a typical single-shell dividing-wall column, the middle section of a single column split into two sections by inserting a vertical wall, thus two fully heat-integrated distillation units are formed. After heat integration, multiple distillation units can be integrated into a single multishell dividing column or a new sequence that hybrid the conventional columns with dividing-wall column (Rong, 2011). Owing to the heat integration, the equipment investigation and the energy requirement of the distillation process can be significantly dropped when using dividing-wall column for bioethanol separation (Isopescu et al., 2008). As shown in Figure 37.4g, the second and the third columns in an extractive distillation sequence can be combined into a dividing-wall column with single shell after full thermal-coupling (the basic extractive distillation sequence referred in Figure 37.4g). More specifically, the first step for the designation of the dividing-wall column is to remove the reboiler of the second column, the bottom output stream is pumped into the third column of the sequence. At the same time, stream from the feed section is fed back to the bottom of the second column, which is used as vapor that is generated from the original reboiler. Accordingly, heat from the condenser of the third column is partially used for heating the second column. Thus, the thermodynamic efficiency of the distillation process can be improved. The second step is to integrate the process by a vertical wall in the middle section of a single column according to the topology principles (Ramapriya et al., 2018a,b). Therefore,

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two columns can be finally combined into a single dividing-wall column, which would significantly reduce the equipment investment. It is estimated that after replacing the conventional distillation sequence by the dividing-wall column, the energy requirement of ethanol distillation is reduced by 16.7%, while the TAC is decreased by 16.96% (Kiss and Ignat, 2012; Kiss et al., 2012).

37.5 METHODS FOR THE EVALUATION OF THE BIOETHANOL DISTILLATION PROCESS The techno-economic analysis (TEA) of the bioethanol distillation for early decision-making of the proposed processes is important, in which the cost of production is emphasized. The total energy requirement in a typical bioethanol distillation system is composed of heating and fluid transportation. Steam loaded into the distillation process is used for heating the bottom of columns, while electricity is used for fluid transport by powering the pumps. Based on the simulation of the distillation process, the overall energy requirement can be calculated. Except for the energy cost, fixed investment is also included in the production cost, which can be divided into investment in production facilities, infrastructure, loans, and operating costs (Bessa et al., 2013). Models for calculation of the fixed investment are described in detail in literatures (Douglas, 1988; Errico and Rong, 2012). Parameters required in the TEA model, e.g., the diameter and height of column, and the heat transfer of the heat exchanger are able to be obtained from tools in software (e.g., Aspen tray sizing tool). The bioethanol production from renewable biomass resources is proven to be a clean alternative fuel which contribute to the reduction of greenhouse gas emission and generate green benefits (Sun et al., 2013). Thus, except for taking the technoeconomic analysis (TEA) to evaluate the energy requirement and economic feasibilities of the distillation process by commercial process simulation software, the environmental influences should also be taken into account. Actually, the environmental analysis is an extraordinary vital indicator in the early decision-making of the biorefineries to realize clean, energy-saving, and sustainable bioethanol production (Ingrao et al., 2021; Safarian et al., 2019; Zarte et al., 2019). Life cycle assessment (LCA) is a comprehensive environmental impact analysis method for the evaluation of chemical production from cradle to grave, which have been extensively applied in the biorefineries of fuels and chemicals (Hannon et al., 2020; Lee et al., 2020; Levasseur et al., 2017). As a necessary step for the bioethanol production, the distillation separation unit can be isolated to take an objective environmental impact assessment. Although numerous studies have been conducted to take LCA for distillations separation of petrochemicals and the manufacturing of the distillation columns (Brodani et al., 2020; Luis et al., 2014; Meng et al., 2020; Zhu et al., 2021), literatures on the LCA of bioethanol distillation alone is still remained limited (Silva et al., 2018). Normally, for the bioethanol production, researches always carried out to evaluate the whole biorefinery with a system boundary from biomass collection to the transport of the final fuel guide ethanol product, in which process the entire distillation process is included (Caldeira-Pires et al., 2018; Pereira et al., 2019; Wang et al., 2012). The typical LCA comprising the distillation unit contains four steps: goal and scope definition, inventory analysis, impact assessment, and interpretation (ISO, 2006). For a typical LCA of ethanol distillation, the system boundary should be defined in the goal and scope definition step. Except for the process description, the energy provider, cooling water, extractant, etc., should be also included. The output bioethanol product and wastewater slurry are also required in this step. As for the inventory analysis, the storage and transportation of the feed streams are calculated by simulation, while the steam and cooling water product and transportation costs can be generated by the equipment manufacturer’s information. Electricity is obtained according to the local power composition. Processing parameters, energy consumption, and COD in wastewater can be also obtained from either the process simulation of the distillation sequences or the realistic processes.

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Based on the above analysis, evaluations can be carried out from the following aspects: GHG emissions, agricultural land occupation (ALO), natural land transformation (NLT), water resource depletion (WD), mineral resource depletion (MRD), fossil resource depletion (FD), and the proportion of renewable energy (Baral et al., 2012; Cavalett et al., 2013; Carneiro et al., 2017; Luo et al., 2009; Wang et al., 2020). On this basis, Silva et al. (2018) took multiobjective optimization of an industrial ethanol distillation system for the reduction of vinasse generation. LCA results pointed out that the use of reboiler in the distillation process would reduce 15.59% of vinasse production with the payback period. The early decision-making analysis guided the improvement of the realistic bioethanol distillation sequences. LCA for the whole bioethanol production chain is not discussed in detail in this chapter. There are a plenty of researches devoted into this field. Nevertheless, there is still a big gap between the insufficient data of the process and the methodological issues for understanding the life cycle performance (Gerbrandt et al., 2016). For example, the enzymes used in the bioethanol production process have high greenhouse gas intensities. Nevertheless, the influence of enzymes on life cycle emissions is still not accurately quantified in most of the existed commercial models (Dunn et al., 2012; Hong et al., 2013). As for the case of distillation unit, changes in the upstream process would materially impact the energy intensity, while the differences of the distillation sequences should also be considered (Gerbrandt et al., 2016; Pourbafrani et al., 2014). The solution for the weakness is to feed back the basic parameters and database from industrial plants, which improves the accuracy of LCA.

37.6 CONCLUSIONS As an indispensable unit with low thermodynamic efficiency and large energy requirement in the downstream process of the bioethanol industry, the technical innovation and process optimization for an energy-saving and cost-effective distillation is a permanent improvement direction. As a still young and breakthrough technique, there has been significant technological advances in recent years in the direction of dehydrated ethanol production and energy-effective processes. Ongoing technical development and refinement in the distillation process will facilitate better downstream process, which would further improve the economic competitiveness of bioethanol as a biofuel.

REFERENCES Ahlgren, S. and L. di Lucia. 2014. Indirect land use changes of biofuel production - a review of modelling efforts and policy developments in the European Union. Biotechnology for Biofuels 7:35. Asprion, N. and G. Kaibel. 2010. Dividing wall columns: Fundamentals and recent advances. Chemical Engineering and Processing: Process Intensification 49:139–146. Azhar, S. H. M., R. Abdulla and S. A. Jambo, et al. 2017. Yeasts in sustainable bioethanol production: A review. Biochemistry and Biophysic Reports 10:52–61. Balat, M. and H. Balat. 2009. Recent trends in global production and utilization of bioethanol fuel. Applied Energy 86:2273–2282. Baral, A., B. R. Bakshi and R. L. Smith. 2012. Assessing resource intensity and renewability of cellulosic ethanol technologies using Eco-LCA. Environmental Science & Technology 46:2436–2444. Batista, F. R. M., L. A. Follegatti-Romero and L. Bessa, et al. 2012. Computational simulation applied to the investigation of industrial plants for bioethanol distillation. Computers & Chemical Engineering 46:1–16. Bessa, L. C. B. A., M. C. Ferreira and E. A. C. Batista, et al. 2013. Performance and cost evaluation of a new double-effect integration of multicomponent bioethanol distillation. Energy 63:1–9. Brodani, M., J. S. de Oliveira and F. D. Mayer, et al. 2020. Life cycle assessment of distillation columns manufacturing. Environment, Development and Sustainability 22:5925–2945. Bruinsma, O. S. L., T. Krikken and J. Cot, et al. 2012. The structured heat integrated distillation column. Chemical Engineering Research and Design 90:458–470.

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In situ Bioethanol Separation from Fermentation Broth Di Cai, Zhihao Si, Hanzhu Wu, Yan Zhuang, and Peiyong Qin Beijing University of Chemical Technology

38.1 INTRODUCTION In facing the global climate change and environmental degradation, replacing the conventional unsustainable fossil fuels by alternative biofuels is advocated in world’s major economies (Alizadeh et al., 2020). In this direction, the global market for renewable biofuels is estimated at $26 billion, in which bioethanol plays a leading role and has been widely used as fuel additive for decades (Su et al., 2020a). Bioethanol exhibited inherent properties of low calorific value and high latent heat of vaporization (Vanzela et al., 2017). When used as the fossil fuel additive, the gasoline/dieselethanol blend fuels can significantly reduce the NOx emission, promote the charge atomization, and improve the cold flow properties (Dhande et al., 2021). More attractively, while using the ethanol blend fuels, the low-particulate matter formation can be sharply reduced, abating haze pollution at source (Verma et al., 2019). Currently, the commercial bioethanol is mainly obtained from sugar-based crops or starchy based materials (Zhang et al., 2018). Because of the competition for food with humans, the cellulosic bioethanol production from lignocelluloses is encouraged (Zhang et al., 2019a, 2020). Nevertheless, a series of technical barriers such as high inhibition to yeast cells, ineffective conversion of pentoses in hydrolysis, difficulties in biomass fractionation, and high cost of cellulase all limit the commercialization of bioethanol from lignocelluloses (Li et al., 2016a,b; Mohapatra et al., 2017; Su et al., 2020b). To improve the competitiveness of bioethanol as renewable fuel, reducing the production cost is the constant theme, regardless of the type of feedstocks. Except for well-disposal the above limitations, another potential way is to reduce the energy demand of the downstream purification process. In fact, the separation of bioethanol is the most energy-intensive unit in bioethanol production, because a large amount of water that with high specific heat should be removed (Hegely and Lang, 2020). It is revealed that the separation cost accounts for 10% of the overall cellulosic bioethanol production cost (Tao et al., 2014), and energy demand for conventional distillation process ranges from 7 to 10 MJ/kg when 3–10 wt% of ethanol contain in the feed broth (Ragauskas et al., 2006). Distillation is a classic bioethanol purification method, which is the predominant and preference technique in industry. Although distillation has the advantages of high solvent recovery (Zentou et al., 2019), drawbacks are also obvious. The ethanol-water binary mixture in fermentation broth is an azeotrope. Therefore, for fuel grade ethanol production, an additional costly dehydration process for trace water separation is required (Karimi et al., 2021). Moreover, due to the high operating temperature of distillation, the input of the high-grade heat is required, to which the valuable yeast strains and proteins are deactivated during the distillation process, also leading to a poor continuity of the bioethanol fermentation process (Vane, 2008). As a consequence, the long dead time of batch or semicontinuous fermentation would negatively influence the productivity and capital investments of bioethanol plants.

DOI: 10.1201/9781003226499-48409

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Developing alternative separation techniques with low cells toxicity and high continuity became a hot topic in recent decades (Cai et al., 2018a; Outram et al., 2017). Among the nondistillation-based bioethanol separation techniques, some of the gradually matured methods are able to applied for ex situ product recovery (ESPR) from the end of bioethanol fermentation broth, with high efficiency and low energy requirement (Wang et al., 2018; Zhang et al., 2021). Correspondingly, because of the avoidance of heating process, cells and proteins activities can be guaranteed when adopting several types of alternative separations (Cai et al., 2016a; Kujawska et al., 2015). On this basis, ethanol separation can be conducted from the fermentation broth as it is produced by in situ product recovery (ISPR) (Van Hecke et al., 2014). The superiorities of the ISPR-based ethanol fermentation are obvious. Since ethanol is separated from the vicinity of cells as soon as it is formed, the inhibitory effect of the final product can be reduced. Consequently, the solvent productivity can be improved, allowing the reduction of bioreactor size and capital expenditure (Cai et al., 2015, 2017a; Chen et al., 2018a; Wen et al., 2018). Several reports point out that the yield of solvents from sugar can also be improved by ISPR, owing to the enhancement of the carbon metabolic pathways for solvents production (Cai et al., 2016b; Ding et al., 2012; Fan et al., 2014a). Besides, with the concentration effect of ISPR, the recovered streams from fermentation always contain high-titer solvents products. Therefore, energy requirement in the separation process can be reduced (Cai et al., 2016c, 2017b; Chen et al., 2019). Fed-batch or continuous fermentation can be also conducted, with high concentration of sugars in feed and low wastewater effluent (Chen et al., 2012). Hence, the reduction of waste stream amount in ISPR-based fermentation process can significantly decrease the wastewater treatment cost (Van Hecke et al., 2018; Zhang et al., 2019b). Several studies also came up with ISPR-catalysis integration process for upgrading bioethanol into value-added biochemicals, e.g., butadiene, which significantly improve the economic benefits of the biorefinery process (Cai et al., 2018b). In this chapter, the commonly used ISPR techniques for bioethanol production are summarized. Basically, based on the principles, these ISPR techniques can be classified into three categories: techniques based on vapor liquid equilibrium, techniques based on polarity differences, and the membranebased techniques. Relative merits of the selected ISPR techniques are compared and discussed. The current advances of hybrid multiple stage ISPR for consolidate bioethanol separation are also included.

38.2  ISPR BASED ON VAPOR LIQUID EQUILIBRIUM 38.2.1 Gas Stripping The experimental setup of gas stripping is shown in Figure 38.1a. In brief, the carrier gas or the off-gases from fermentation process is sprayed by a distributor from the bottom of bioreactor. The bubbles are raised into the vapor phase of the bioreactor and transferred into the condenser that connected with the bioreactor for concentrated ethanol recovery (Ezeji et al., 2005). During the process, after passing through the condenser, the gases with little ethanol content can be recycled for next cycle of operation (Taylor et al., 1995). Bioethanol, the volatile product, is diverted from the aqueous fermentation broth into the vapor phase by vapor-liquid equilibrium (Cai et al., 2016d), while in the condensation unit, the phase-change of ethanol from gas to liquid is also obeying the vapor-liquid equilibrium. The most obvious advantage of gas stripping is convenient to operate (Lu et al., 2012). Meanwhile, gas stripping can be conducted in fermentation temperatures, nutrients remaining in the fermentation broth can be well retained owing to the mild operation conditions. Another advantage of gas stripping is that no additional chemicals are introduced; thus the ISPR can be conducted without toxic effects to the microorganisms (Ezeji et al., 2003; Qureshi and Blaschek, 2001). On the contrary, because of the decrease of ethanol concentration in broth, the production of bioethanol can be promoted in ISPR process based on gas stripping (Rodrigues et al., 2018a; Sonego et al., 2018). Niu et al. (2015) clarified the facilitation of bioethanol metabolism of yeast cells in a gas

In situ Bioethanol Separation from Fermentation Broth (a)

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(b) Gas circulation

Medium reservoir Separation section

Condenser

Feed

out

Chilled water in

F0,S0,X0,P0

Vacuum pump

Reaction column

Fermentor S,X,P Distillate product

Fresh medium Substrate Tank (600g/L glucose)

Fermentor (Spinner flask)

Condensate Cold trap

Water bath

Receiver flask F1,S,X,P

FIGURE 38.1  Experimental setup of the cells-immobilization fermentation process integrated with (a) gas stripping (Cai et al., 2015); (b) vacuum fermentation (Nguyen et al., 2011). The experimental setup of the cell immobilization fermentation process integrated with gas stripping (a) involves substrate tank, fermentor, and condensate cold trap. The experimental setup of the cell immobilization fermentation process integrated with gas stripping (b) involves medium reservoir, reaction columns, vacuum pump, and receiver flask.

stripping-assisted fermentation process. Besides, due to the heat-removal effect of the bubbling gas, except for a lower energy-demand downstream distillation process, the gas stripping separation of bioethanol also significantly reduced the cooling energy requirement of bioreactor. 63.1% of cooling water consumption can be reduced after coupled fermentation process with gas stripping in a ISPR process (Almeida et al., 2021). The ISPR via gas stripping for bioethanol production has been extensively investigated for decades. For instance, Ponce et al. (2016) used gas stripping to in situ separate ethanol from sugarcane fermentation broth and found that ethanol concentration was maintained below the toxicity threshold. Farias and Maugeri-Filho (2021) integrated the gas stripping with sequential cell recycle fed-batch fermentation process, because of the improvement of fermentation performances, 0.48 g/g and 9.5 g/L h of ethanol yield and productivity could be obtained, respectively. High-temperature gas stripping was also suggested in several papers. The clouds of hot microbubbles by fluidic oscillation significantly improve the overall bioethanol separation rate because of the improvement of vapor partial pressure (Calverley et al., 2021; Kumar et al., 2015). Gas stripping is also able to couple with solid-state fermentation processes. As stripping hybrid with heat pump technology using CO2 as carrier gas afforded to 77.5% of ethanol stripping efficiency (Chen et al., 2014, 2015). Nevertheless, the large-scale applications of gas stripping are still challenging. Reports have been pointed out that the equipment cost by industrial facilities (i.e., compressors) is high, and the excessive foaming of fermentation broth will be occurred in the ISPR process based on gas stripping (De Vrije et al., 2013). Besides, compared with other ISPR process, the solvent separation efficiency by gas stripping is relatively low, resulting in less attractive of applying gas stripping in bioethanol production that always contains a higher titer production in fermentation broth in comparison with other types of fermentations, e.g., ABE fermentation that with higher end-product inhibition and the biobutanol concentration in broth is always below 2 wt% (Chang et al., 2014; 2016; Chen et al., 2018b). Therefore, up to now, gas stripping has not been applied in commercialization-scale biorefinery plants for bioethanol production (Taylor et al., 2010; Wang et al., 2015a). Schlafle et al. (2017) have summarized the directions for technical improvement of gas stripping. Increasing the bioethanol concentration in broth, heat-integration, and replacement of energy-intensive condensation by compressor or adsorption dryer will increase the competitiveness of gas stripping technique in bioethanol separation.

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38.2.2 Vacuum Fermentation The principle of vacuum fermentation is generally similar with gas stripping. In this process, the pressure of bioreactor is decreased, and the evaporation of ethanol to vapor phase is therefore promoted by the changes of as vapor liquid equilibrium in vacuum. Bioethanol can be recovered from the vapor phase by condensation (Pereira et al., 2017). The experimental setup of vacuum fermentation is shown in Figure 38.1b. Several studies are conducted focusing on the ISPR of bioethanol from fermentation broth by vacuum fermentation. Cysewski and Wilke (1977) developed the cell cycle and vacuum fermentation systems for continuous ethanol production. The ethanol productivity is increased by 12-fold compared with the conventional continuous fermentations. The amount of wastewater discharge was also significantly reduced. The energy-saving effect of vacuum fermentation is confirmed by Palacios-Bereche et al. (2014) study, in which process thermal integration is conducted in the consolidate bioethanol process using ISPR. 36% of steam is reduced while the ethanol production increased by 3.3%–4.8%. Junqueira et al. (2009) compared the conventional fermentationdistillation process, vacuum fermentation-double effect distillation coupled process, and vacuum fermentation-conventional distillation process by process simulation. Their result indicated that the double-effect distillation assistant process could significantly reduce the energy requirement of ISPR based on vacuum fermentation, though the ethanol recovery rate in this process is less competitive. After the adoption of triple-effect distillation sequential with the front vacuum fermentation, process energy demand can be further reduced (Dias et al., 2009). Dias et al. (2012) study has proved that the vacuum fermentation process presents lower steam consumption compared with the conventional fermentation-distillation process. Nevertheless, electricity demand in vacuum fermentation is slight increased. Vacuum fermentation can also be used for the detoxification of volatile inhibitory components from the substrate before inoculation and the following bioethanol fermentation processes. Ramalingham and Finn (1977) maintain the pressure of bioreactor at 0.04 Mpa and regularly replenish oxygen into the fermentation system. Results show that the yeast cells are under high activities due to the constantly excrete volatile inhibitors. The yeast cell concentration in vacuum fermentation process is 1.5 times than that in the normal-pressure fermentation process. The popularization of vacuum fermentation is also limited by some disadvantages. For example, microorganisms are easily affected by the vacuum environment in which the cell morphology and cell behavior can be obviously changed (Belloch et al., 2008). Furthermore, the bioethanol separation rate by vacuum fermentation is always difficult to match well with the production rate in fermentation unit (Ishida and Shimizu, 1996). Besides, the vacuum environment also increases the risk of infection with other microbes. Consequently, intermittent vacuum separation for batch bioethanol fermentation is always conducted for ISPR of bioethanol from vacuum fermentation process (Diaz et al., 2019).

38.3  ISPR BASED ON PHASE TRANSFER 38.3.1 Adsorption Adsorption is a common separation technology for biofuels separation (Staggs et al., 2017). In typical adsorption process for bioethanol production, adsorbent is directly contact with the fermentation broth. Ethanol can be selectively adsorbed on the surface of adsorbent by polarity differences with other components in fermentation broth. The adsorbent enriched with bioethanol can be easily recycled from fermentation broth. Then, ethanol is desorbed from the adsorbent by heating, while the regenerated adsorbents can be used in the following cycles of adsorptions (Xue et al., 2016a; Zhang and Yang, 2015). Figure 38.2 shows the experimental setup of ISPR for bioalcohols production based on adsorption.

In situ Bioethanol Separation from Fermentation Broth

Activated carbon

Broth circulation

Cold trap

Feed Heat oven

Stirred tank bioreactor

413

Vapor circulation

Fibrous matrix

Fermentation with adsorption

Desorption by heat treatment

FIGURE 38.2  Experimental setup for ISPR of bioalcohols based on adsorption (Xue et al., 2016a). This setup involves activated carbon, stirred tank bioreactor, heat oven, and cold trap. Fermentation with adsorption on activated carbon takes place in the bioreactor, and the vapor circulates between heat oven and cold trap.

Compared with other ISPR technologies, adsorption possesses good biocompatibility and nontoxicity to microorganisms (Raganati et al., 2018). The core for effective adsorption of bioethanol from fermentation broth is the selection of suitable adsorbent with optimal structure the properties. Besides, the reusability, reusability, and the cost of adsorbent should also be taken into account. Numerous types of adsorbents have been suggested to ISPR of bioethanol. Among them, activated carbon and zeolites attached much attention owing to the high hydrophobicity and low cost (Delgado et al., 2013, 2015). Hashi et al. (2010) studied the adsorption property of four activated carbons and two hydrophobic zeolites toward ethanol. Results show that wv-b1500 activated carbon has the strongest adsorption capacity for ethanol. Jones et al. (2011) took ISPR of ethanol by activated carbon as adsorbent. The glucose in the fermentation broth can be completely exhausted, and the conversion rate is significantly improved. Nevertheless, some technical barriers hamper the development and application of adsorption for ISPR of bioethanol. First, extensive adsorbent is demanded in the ISPR process because of the limited adsorption capacity toward organics (Outram et al., 2017). Second, adsorbent contamination caused by the nonspecific adsorption of cells debris, proteins, and second generation by-products in fermentation broth resulted in the decrease of absorbability and reusability of the adsorbent (Xue et al., 2016a). As a compromise solution to the bottlenecks of directly adsorption of ethanol onto the hydrophobic adsorbent from the dilute aqueous, the adsorption dehydration of water from the ethanol-water azeotrope by hydrophilic materials after distillation is more attractive. The adsorptive dehydration for fuel-guide bioethanol production has been already under commercialization (Morales et al., 2020; Tang and Tanase, 2020).

38.3.2 Liquid-Liquid Extraction Liquid-liquid extraction is preformed according to the different solubility of bioethanol between the extractant and aqueous solution, where the hydrophobic solvent is often used as a extractant (Salas‐Villalobos et al., 2021). In the process, ethanol in the fermentation broth is transferred from

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Ethanol

Solvent

Ethanol/solvent Separation unit

Centrifuge

Organic phase Fermentation vat

Extraction unit

FIGURE 38.3  The experimental equipment of liquid-liquid extraction (Lemos et al., 2017). This setup involves fermentation vat, extraction unit, solvent, centrifuge, and ethanol/solvent separation unit. Fermentation broth is fed into the extraction unit together with solvent, and it passes through the centrifuge and separation unit to obtain ethanol.

the aqueous phase to the extractant that with higher distribution coefficient, while the yeast cells and substrates remain in the aqueous phase (De los Rios et al., 2017). The experimental equipment of liquid-liquid extraction is shown in Figure 38.3. Liquid-liquid extraction has the advantages of the large treatment capacity, easily continuous operation, and control (Habaki et al., 2016). In view of the practical application, the ideal extractants should have great biocompatibility, not easy to emulsify and insoluble in water, high ethanol distribution coefficient, and easy recycling (Huang et al., 2014; Lemos et al., 2018). Offeman et al. (2008) have observed that the organic matter with 8–12 of carbon content as the extractant damages the activity of yeast cells, while the organic matter with the >14 of carbon content is nontoxic to yeast cells. The similar phenomenon is founded in Lemos et al. (2017) work, in which oleic acid showed better biocompatibility. Except for organic solvents as extractant, several other studies explored the feasibility of using ionic liquids (ILs) or deep eutectic solvents (DESs) as extractants, owing to the advantages of high thermal stability, low viscosity, and low vapor pressure (Neves et al., 2011; Sharepour et al., 2021; Verma and Banerjee, 2018). Nevertheless, when using ILs and DESs as extractants, disadvantages of poor biocompatibility, high cost, and environmental pollution should be taken into account (Ha et al., 2010; Dezhang et al., 2019).

38.4  ISPR BASED ON MEMBRANE-BASED TECHNIQUES 38.4.1  Pervaporation Pervaporation is a membrane-based technology for bioethanol separation from fermentation broth, in which process the bioethanol molecular is first dissolved into the active layer, followed by diffusion on the permeate side of the membrane. A comprehension-diffusion model is proposed to explain the gas transport through the diaphragm. The solution-diffusion model is first proposed to explain gas transport through the diaphragms. In this model, three consecutive steps in pervaporation: (i) the volatile component is selectively adsorbed into membrane from the feed solution; the molecules are (ii) diffused through selective layer; and (iii) desorbed from the membrane with the vapor phase (Figure 38.4) (Jose et al., 2014; Smitha et al., 2004). Key factors, based on its physicochemical interaction with membrane materials (Liu et al., 2021; Yang et al., 2020) and the transport channels of membrane layer (Guan et al., 2021; Li et al., 2019; Si et al., 2019a), are adsorption

In situ Bioethanol Separation from Fermentation Broth

415

Pervaporation=Permeation + Evaporation Retentate

Liquid

Vapor Species 1 Species 2 Permeate

Feed

Membrane selective for Species 1

FIGURE 38.4  Schematic diagram of pervaporation process (Vane, 2005). The pervaporation process involves both permeation and evaporation. The broth is fed into the liquid where species 1 pass through the species 1 selective membrane into the vapor with species 2.

selectivity and diffusion selectivity of the permeating molecules. The strong interaction favors the adsorption selectivity determined by the solubility parameter of the membrane and the penetrants (Liu et al., 2012, 2014a). The transport channels determine the diffusion selectivity depending on the free volume and the possible filler (Liu et al., 2011a, 2015; Petzetakis et al., 2015). As the rapidly developed separation technology, pervaporation is promising to be coupled with the fermentation process (Liu et al., 2014b). It has not only higher selectivity towards ethanol but also lower energy consumption and less effect on microorganisms (Abdehagh et al., 2014). Different from the conventional distillation, the pervaporation process does not need to introduce any evaporation or superheat, and the operation temperature is suitable for the microbial growth (Cai et al., 2013). Furthermore, no additional chemicals are needed in pervaporation process (Qin et al., 2020). The efficiency of pervaporation is highly depending on the polymer material of the active layer. Compared with the pervaporation membranes prepared by other materials, polydimethylsiloxane (PDMS) membrane exhibited good separation performances and high stability in bioethanol separation under ISPR mode. Fu et al. (2016) have studied the fermentation-pervaporation integrated process by using polydimethylsiloxane (PDMS) membrane. Results show that the ethanol productivity is enhanced compared to conventional process when integrated with continuous ethanol fermentation. Meanwhile, high titer ethanol production of 446.3 g/L is obtained in permeate. Yong et al. (2005) study showed that the composite PDMS membrane offers the excellent separation for the separating ethanol from fermentation broth by pervaporation, where 7.7 of separation factor and 406 g/m2 h of flux were achieved. Other organic membranes for ethanol recovery have also been evaluated, such as polymethylphenylsiloxane (Liu et al., 2011b), poly(1-trimethylsilyl-1-propyne) (Kang et al., 1994), etc. However, the permeability of organic membrane is not satisfied because of lacking of high-efficiency diffusion channel (Wang et al., 2015b). In fact, the membrane permeability always negatively correlated with the cost of the pervaporation membranes (Si et al., 2018). Therefore, most of the current researches plan to prepare mixed matrix membranes in which porous fillers are doped into the organic matrix (Fan et al., 2014b). In this case, the separation performance of pervaporation could be facilitated by the exceptional transport properties of porous fillers. Hu et al. (2017) further develops the immobilized ethanol fermentation coupled to pervaporation using the advanced silicalite-1/PDMS mixed matrix membrane. In the long-term of operations, the ethanol field, separation factor, total flux, and osmotic ethanol concentration of ISPR process can be greatly improved. Other types fillers, such as covalent organic frameworks (COFs) and metal-organic frameworks (MOFs) (Mao et al., 2019), are also considered loaded into the mixed matrix membranes for ethanol recovery.

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In addition to the organic membranes and mixed matrix membranes mentioned above, the inorganic membranes are receiving a wide attention. Shu et al. (2012) prepared silicalite-1 membranes on hollow fiber substrate made of yttria-stabilized zirconia without aluminum. After optimizing the seed size, synthesis parameters, and substrates, the high throughput of 7.4 kg/m2 h is achieved on the prepared membrane, and the separation factor is 47. Compared to developed and commercially available ethanol dehydration hydrophilic membranes, pervaporation membranes for ethanol enrichment from fermentation broth are still under development due to bottlenecks in membrane preparation and application (Qin et al., 2020). For instance, taken the widely used PDMS membrane as the benchmark of alcohols-selective membranes, first, the reported curing time for PDMS is generally >180 min (Si et al., 2019b). The time-consuming membrane-forming process seriously limits the scale-up preparation of such membranes. UV-induced polymerization of PDMS membrane has an ultrafast process (Si et al., 2020a, b), where the curing time is shortened to 30–200 s. This is 2–3 orders of magnitude faster than the traditional thermal cross-linking. Second, the volatilization of organic solvents in membrane making process increases the operation difficulty and production cost, which poses a threat to the environment (Li et al., 2021). Third, in the long-term pervaporation process, the pollution of microorganisms or other biological pollutants to the membrane will lead to the temporary or permanent deterioration of membrane performance and finally shorten the service life of the membrane (Zhu et al., 2020a). The fluorinated PDMS membranes were reported exhibiting significant antifouling properties (Si et al., 2021; Zhu et al., 2020b). Compared with the methyl of traditional PDMS membrane, the interaction between fluoroalkyl and microorganisms in fermentation broth is weak due to low polarization, which inhibits biological pollution.

38.4.2  Perstraction Perstraction is actually a membrane-assisted solvent extraction process, where both membrane separation technology and liquid-liquid extraction are executed in one operating unit (Schlosser et al., 2005). In the extraction process, the extractant and fermentation broth are separated by membrane. Ethanol molecules diffuse through the membrane and are extracted by the extractant, while other components are retained (Huang et al., 2014). The defects in liquid-liquid extraction are effectively overcome like rag layer formation, phase dispersion, toxicity to cells, and emulsion formation, since the solvent is not in direct contact (Qureshi and Maddox, 2005). The perstraction-fermentation integration process involving a polytetrafluoroethylene membrane and the extractant 1-dodecanol has been studied (Tanaka et al., 2012).

38.4.3  Membrane Distillation Membrane distillation is a thermally driven separation process in which only steam molecules are transferred through microporous hydrophobic membranes. In the membrane distillation process, the driving force through the hydrophobic membrane is the vapor pressure difference (Alkhudhiri et al., 2012). The hydrophobicity of the membrane prevents the aqueous solution from penetrating into the pores. Therefore, in the process of membrane distillation, only the volatile components in feed can be transferred through the membrane (Gryta, 2005). Generally, there are four types of membrane distillation processes: direct contact membrane distillation, air gap membrane distillation, scavenging membrane distillation, and vacuum membrane distillation. Membrane distillation can theoretically 100% reject ions, macromolecules, colloids, cells, and other nonvolatiles (Huang et al., 2008). Another advantage of membrane distillation is that the operating temperature is significantly lower than that of traditional distillation, and the operating pressure is also lower than that of traditional pressure-driven membrane separation process (Tomaszewska and Białonczyk, 2016).

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Similar to other types of ISPR techniques, when coupled fermentation with membrane distillation, the bioethanol production can also be improved (Li et al., 2018; Zhang et al., 2017). Lewandowicz et al. (2011) have reported that ethanol production increased by 15.5%. During the process, sugars were the used-up, which further lowered the osmotic pressure. Besides, glycerol metabolism by yeast cells was significantly reduced while the number of viability yeast cells was increased. Loulergue et al. (2019) report the effect of operating parameters of the air-gap membrane distillation on membrane properties. Whether the membrane is contaminated or not, the separation process is robust to membrane wetting. However, the ISPR of membrane distillation bioethanol needs to be further improved to meet the needs of industrialization. Compared with other separation processes, the biggest technical obstacle of membrane distillation is the mass transfer resistance of trapped air in the membrane, which makes the permeation flux of the membrane low. Therefore, the processing capacity per unit of time is limited. Moreover, the heat lost by conduction is quite large in the membrane distillation process, resulting in energy-intensive bioethanol separation (Alkhudhiri et al., 2012). Solar-driven or microwave-assisted membrane distillation would be lowering the energy consumption to some extent (Gupta et al., 2019; Kumar et al., 2019; Woldemariam et al., 2018).

38.5  MULTISTAGE ISPR PROCESSES Even though the above alternative separation techniques show great promise for ISPR of bioethanol from fermentation broth, realizing a higher selectivity, easier operation, lower investment, lower energy consumption, and easier to scale-up process is still encouraged (Outram et al., 2017). On this manner, adopting multistage ISPR systems gain increasing attention in recent years (Pyrgakis et al., 2016). The most commonly used multistage ISPR process is integration of extraction with distillation as a consolidated separation process for bioethanol production. Energy requirement for bioethanol separation can be sharply reduced in the extractive-distillation process (Chen et al., 2018c; Yang et al., 2019). Moreover, with the addition of extractant to the ethanol-water mixture, the azeotrope would break down. Thus, dehydrated bioethanol production can be directly obtained (Meirelles et al., 1992). Stripping-adsorption is another hybrid separation technique for ISPR of bioethanol. This process is actually a gas stripping-adsorption hybrid process, in which the ethanol separation efficiency can be improved because of the enrichment of ethanol in the vapor phase to be adsorbed onto the adsorbents. The contamination of adsorbent in conventional process can also be eliminated because the low volatile and nonvolatile impurities in fermentation broth are not contacted with the material (Rodrigues et al., 2018b; Seo et al., 2018). Guided by the stripping-adsorption process, several researches suggest gas stripping-vapor permeation (GSVP) process, which is also referred as vapor stripping-vapor permeation (VSVP) process. GSVP process is generally similar to conventional pervaporation. The only difference is the vapor phase of bioreactor passed through the membrane module, rather than the fermentation broth in conventional pervaporation process. The GSVP process adopts the good points and avoids the shortcomings of the single-stage gas stripping and pervaporation. Therefore, similar to the advantages in the stripping-adsorption process, membrane fouling in GSVP process can also be eliminated, and the poor separation efficiency of gas stripping that is limited by vapor liquid equilibrium is significantly improved by GSVP (Hu et al., 2015; Si et al., 2018). In the case of recovering bioethanol from the actual fermentation broth, after hundreds of hours of operation, the total flux and separation factor generally remain unchanged, which proves that GSVP process has no membrane pollution and performance degradation. GSVP process can be extended to other types of ISPR process for bioalcohol production. Xue’s group has a great contribution to the GSVP process for acetone-butanol-ethanol (ABE) separation from dilute fermentation broth. Fermentation using lignocellulosic hydrolysate, the effect of genetic

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Feed Tank

Stripper Overhead Vapor

Main Pump

Feed Liquid

T6

T

Heater Feed Peristaltic Pump

Steam

T1

P1

T3

T2

Bottoms Reservoir

Permeate Vapor

T5 T4

Bottom Liquid

(collected or returned to feed tank)

T9

P4

P2

T7

Filters

Feed Vapor

vaccum control value

Vent

T10

Vapor Permeation Membrane Module (in hot box)

P3 vaccum regulator and Gauge Retentate

Vapor

Light Duty Vaccum Pump

Chiller

Retentate Condensate Reservoir

Retentate Condensate (collected or returned to feed tank)

FIGURE 38.5  Experimental setup of the MAVS process for bioethanol purification (Vane et al., 2010). The ethanol–water vapor was separated by hydrophilic membrane in the MAVS process where ethanol-enriched retentate vapor stream is recovered by condensation. Heat integration of the permeate and retentate of the membrane takes place.

strains, the novel MMMs, and the wastewater effluent reduction strategies within the GSVP process are investigated in detail (Du et al., 2021; Xue et al., 2016b, 2017). Rochon et al. (2020) have coupled intermittent isopropanol-butanol-ethanol (IBE) fermentation with in situ gas strippingpervaporation process. IBE concentration in the condensate reached 712 g/L. Nevertheless, in ISPR process based on GSVP, the problem of copermeation of the off-gases from fermentation process calls for well-disposal; otherwise, the changes of pressure in bioreactor would negatively influence the metabolism of microorganisms and the pass-through of bioalcohol toward the pervaporation membrane. Membrane-assisted vapor stripping (MAVS) is another potential method for high-titer bioethanol production from the fermentation broth (Figure 38.5). Unlike the GSVP process that integrated gas stripping with hydrophobic membrane, in MAVS process, the ethanol–water vapor was separated by hydrophilic membrane. Ethanol-enriched retentate vapor stream is recovered by condensation (Vane and Alvarez, 2015; Vane et al., 2013). Nearly 80 wt% ethanol is obtained from dilute model solutions. By heat integration of the permeate and retentate of the membrane, energy requirement of MAVS is decreased to 2.2 MJ/kg, which is much lower than the traditional distillation process (Vane et al., 2010).

38.6 CONCLUSIONS This chapter introduces and reviews seven ISPR techniques for effective bioethanol separation from fermentation broth. It has been successfully domenstrated that the integration of ISPR with bioethanol fermentation could significanly promote the productivity, yield, and separation efficency, affording to an energy-saving downstream process. Nevertheless, there are still inherent bottlenecks that hindered the industrial applications of these ISPR techniques. Continuous invenstigation is

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recommended to improve the competitiveness of the ISPR techniques in future works. The application of mutistage hybrid ISPR processes to eliminate the weakness of each technique is a critical R&D trend. The membrane-based techniques show great potential coupled with other techniques for separation of bioethanol. Overall, with the persistent development and improvement of the ISPR techniques, a new era for more energy-efficent and low-cost bioethanol production will be coming soon.

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Advances and Challenges in the Production and Purification of Bioethanol Using Intensified Processes Fernando Israel Gómez-Castro, Juan Gabriel Segovia-Hernández, and Ricardo Morales Rodríguez University of Guanajuato

Carolina Conde Mejía Juarez Autonomous University of Tabasco

39.1 INTRODUCTION The world population is currently facing several challenges that directly affect the wellness of a great sector of human beings. Thus, in 2012, the United Nations proposed 17 Sustainable Development Goals to be accomplished in 2030. To reach these goals, the development of affordable clean energy, clean water, and sanitation, together with innovation in industry and infrastructure, responsible consumption and production, and climate action, are key factors fully related to the search and expansion of the energetic matrix. This would increase the share of renewable energy, promoting the improvement of energy efficient processes, the enhancement of international cooperation to facilitate the access to clean energy research and technology, among others (United Nations, 2015). These goals boosted and reinforced the research actions to increase more sustainable energetic products and continue with the progress of early investigations. Among the several renewable energy sources such as biofuels, ethanol has been identified as a promising option because its physicochemical characteristics are comparable and compatible with gasoline. Moreover, ethanol production can be done employing diverse natural raw materials through a fermentation pathway. In addition, the production of biofuels and high value-added products from lignocellulosic biomass has gained special interest due to the possible reduction of crude oil production and the greenhouse effects in the environment (Morales-Rodriguez et al., 2021). Bioethanol production has taken place mainly in some parts of the world such as China, Canada, European Union, Brazil, and USA. The total bioethanol production in 2019 and 2020 was 29.03 and 26.06 billion of gallons, respectively. The fall ethanol production in 2020 was primarily due to the COVID-19 pandemic (U.S. Department of Energy, 2021). The ethanol production in the world for fuel purposes has been widely implemented in diverse countries, especially in European countries, Brazil, and USA. In this last country, ethanol is widely used and more than 98% of gasoline contains ethanol. The most common blends found in the world are E10, which means 10% ethanol and 90% gasoline, E5 (5% ethanol, 90% gasoline), E15, E85 (also known as flex fuel), and others with higher ethanol content in the blend, from 51% to 83% (U.S Department of Energy, 2015). Some projections in 2011 estimated that the use of coarse grains (mostly maize) in the USA would increase in 2015. On the other hand, the use of sugarcane to produce ethanol would increase almost 40% from 2007 to 2019. This production would mainly occur in Brazil, aiming to meet both domestic and USA 426

DOI: 10.1201/9781003226499-49

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demand. The research and development for bioethanol production from agro-industrial residues have been highly studied, but only 7% of total bioethanol in the world is produced using that kind of feedstock. Thereby, the impact of the use of lignocellulosic raw materials is still not somehow significant (The Crop Site, 2010). Currently, the major bioethanol production in the world belongs to USA with 56%, Brazil with 28%, followed by European Union (5%), China (4%), and Canada (2%). It is expected that bioethanol might increase substantially in the global market, particularly because the USA and European Union biofuel policies encourage the progress of the commercialization of cellulose-based biofuels worldwide (Karagoz et al., 2019; Medina and Magalhaes, 2020; Robak and Balcerek, 2020). The production of bioethanol employs sugars obtained from different natural resources, which are subsequently transformed by some microorganisms in ethanol. The raw material used for bioethanol production can be classified according to the source, such as crops (corn, soybean, sugarcane, sugarbeet, fruits, etc.), lignocellulosic biomass (wood, organic waste, agroindustrial residues, paper, and paper pulp, etc.), marine (macroalgae and microalgae), and advanced microorganisms. These materials are also known as first-, second-, third-, and fourth generation feedstocks, respectively (Alonso-Gomez and Bello-Perez, 2018; Bajpai, 2021; Vasic et al., 2021). Second generation biomass has been widely studied since it is a residue-based material and does not compete with food. Lignocellulosic biomass is predominantly constituted by lignin, cellulose, and hemicellulose, where the composition can vary according to the raw material. Lignocellulosic biomass has some recalcitrant characteristics making it difficult to handle. For example, cellulose crystallinity has been identified as one of the main challenges to tackle. To that end, the transformation of lignocellulosic raw materials has been carried out by assessing diverse technologies. The conventional bioethanol production process from lignocellulosic biomass includes four main sections: pretreatment, enzymatic hydrolysis, fermentation, and downstream section. The pretreatment is employed to break down the lignocellulosic matrix, reduce cellulose crystallinity, and degrade and solubilize the lignin content. The pretreatment can be performed using different technologies such as mechanical (example.g., extrusion, microwave, mechanical comminution, ultrasound), hydrothermal (like autohydrolysis, liquid hot water, steam explosion, and hot compressed water), and chemical (employing acids, alkali, organosolv, ionic liquids, and natural deep eutectic solvents). The next step is the enzymatic hydrolysis, which employs an enzymatic cocktail mainly composed of cellulases such as endoglucanase, exoglucanase, and β-glucosidase. These enzymes release the glucose content in the polysaccharides that were exposed due to the pretreatment action. The sugar molecules composed principally by glucose and sometimes xylose (released by the action of certain pretreatment techniques) can be metabolized by microorganism in the fermentation section, where Saccharomyces Cerevisiae and Zymomonas Mobilis are the most common microorganisms employed to produce ethanol. The fermentation broth is passed to a beer column to remove water and remaining sugars; then, the product is concentrated in a distillation column until the azeotrope composition is reached. The concentrated ethanol stream can be sent to a molecular sieve to get concentrated ethanol with the desired specifications (Morales-Rodriguez et al., 2011). The solid fraction that is not liquefied after enzymatic hydrolysis can be employed to produce pellets to be combusted at an industrial scale, and C5 molasses can be employed for biogas production or cattle feed (Larsen et al., 2012). Nevertheless, the conversion of biomass into bioethanol still has areas of opportunity. One of the tools to enhance the conventional production route is process intensification, which will be discussed in the next section.

39.2  PROCESS INTENSIFICATION The transformation of raw materials into products usually occurs by chemical or biochemical reactions, followed by several separation steps to obtain the products with the desired purity, recovering the excess reactants and/or the external mass agents. Nevertheless, the efficiency of each step may be lowered by limitations as chemical equilibrium or by energy losses, among other phenomena.

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Process intensification aims to overcome such limitations by developing advanced technologies that considerably enhance the performance of a given operation. Among the first definitions given for process intensification, the one given by Ramshaw (1995) introduces it as ‘a strategy for making dramatical reductions in the size of a chemical plant so as to reach a given production objective’. This relation between process intensification and the reduction of the dimensions of the equipment in a process remains, but other aspects have been added to the concept of process intensification. Stankiewicz and Moulijn (2000) mention not only the reduction on the equipment size but also reductions on energy consumption and waste production. Besides, the application of process intensification may also enhance process safety and control properties (Corona and Rosas, 2019). Ponce-Ortega et al. (2012) proposed classifying process intensification into two categories: unit intensification and plant intensification. Van Gerven and Stankiewicz (2009) described four principles on which process intensification relies: Maximize the effectiveness of intra- and intermolecular events; give each molecule the same processing experience; optimize the driving forces at every scale and maximize the specific surface area to which these forces apply; and maximize the synergistic effects from partial processes. Moreover, Van Gerven and Stankiewicz (2009) mention four domains on which process intensification can be applied: spatial (i.e., developing structures which allow a better control on the occurring phenomena), thermodynamic (i.e., making a better use of the available energy, avoiding energy lost as much as possible), functional (i.e., performing various tasks in a single unit), and temporal (i.e., reducing the time required to perform a given task). Bioethanol production processes have several areas of opportunity that can be addressed by applying process intensification, by combining different types of technologies aiming to reduce the operating and capital cost in a plant, the use of resources, additives, and processing time. For instance, the combination of the enzymatic hydrolysis and fermentation steps in a single unit (also known as simultaneous saccharification and fermentation, SSF) has been previously assessed having some improvements, especially in the processing time, capital and operating cost, and higher yields. Based on the same principle, the simultaneous saccharification and cofermentation (SSCF) has also been evaluated; the SSFC differs from the SSF because in SSCF both glucose and xylose are transformed by the action of microorganisms into ethanol. Both SSF and SSCF have demonstrated the possibility of improving the performance of the process; of course, it should be noticed that this kind of operation requires more caution because this technology could fail and generate almost the complete shutdown of the process. The in-situ product removal of ethanol in the fermentation step is another possibility that can be considered to improve the process performance, where the use of membranes in the fermentation unit can remove ethanol, reducing the product inhibition effect and increasing the total amount of bioethanol (Prado-Rubio et al., 2016). Regarding the purification section, some studies have evaluated the use of extractive distillation in the purification of bioethanol that permits breaking the water-ethanol azeotrope (Garcia-Garcia et al., 2018). Certainly, there are more possibilities and well-established methodologies to enhance the performance of the process, such as the process intensification approach.

39.3 PROCESS INTENSIFICATION IN THE CONVERSION FROM BIOMASS TO BIOETHANOL Second generation (2G) bioethanol production has received significant attention since lignocellulosic feedstock does not compete with food sources. Two principal platforms have been proposed in lignocellulosic feedstock conversion: 5 and 6 carbon sugars (biochemical route) and syngas (thermochemical route). Gonzalez-Contreras et al. (2021) present information about installed large-scale 2G biorefineries for bioethanol production and the conversion in these facilities is based on the biochemical pathway. Biochemical conversion has a significant success because it operates under moderated temperature and pressure (Haro et al., 2014). However, compared to first generation bioethanol, the conventional lignocellulosic transformation process includes more steps. Typically,

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conditioning, pretreatment, hydrolysis, and fermentation are the main steps, and other intermediate operations could be solid separations and neutralization. Conditioning consists of solid size reduction. Pretreatment is used for lignin breaking to access hemicellulose and cellulose. In hydrolysis, cellulose and hemicellulose are separated to obtain simple sugars (5 and 6 carbon sugars). Finally, in fermentation, yeast produces bioethanol from sugars. Several pretreatment alternatives can be adapted to the lignocellulosic material type, but in many cases, pretreatment is a major energy-consuming step in lignocellulosic bioethanol conversion. Ojeda et al. (2011) reported an exergy analysis of bioethanol production based on dilute acid pretreatment, showing that the pretreatment energy consumption is only lower than that for the separation step. Hydrolysis can be acid or enzymatic. Moderate operation conditions of enzymatic processes became attractive since they allow simultaneous implementation with fermentation; however, enzyme high costs are the principal limitation. Although fermentation is an old process, low product concentration and inhibition problems are still its main disadvantages. Due to the importance of bioethanol as biofuel, the interest in improving the conversion of the conventional process is a current issue. Process intensification developments can effectively help to overcome bioethanol conversion problems. An essential aspect on bioethanol production from lignocellulosic materials involves consuming the 5 and 6 carbon sugars released in hydrolysis. Today, thanks to the developments in biotechnology and microbiology, it is possible to integrate the fermentation of 5 and 6 carbon sugars in a single reactor, using one microorganism (cofermentation) or one group of microorganisms (cocultivation) (Prado-Rubio et al., 2016). Moreover, it has been reported that simultaneous operation of enzymatic hydrolysis and fermentation improves bioethanol yields (Permatasari et al., 2020; Zhang and Lynd, 2010). These developments have proposed two intensified processes by integration between fermentation and enzymatic hydrolysis to increase the 2G bioethanol production. These processes are separate hydrolysis and cofermentation (SHCF) and simultaneous saccharification and cofermentation (SSCF). They have been implemented in large scale in some countries (Gonzalez-Contreras et al., 2021). In order to reduce the enzyme cost, consolidated bioprocessing (CBP) has emerged. This intensified process integrates the enzyme production by fungi, the hydrolysis of cellulose and hemicellulose, and yeast fermentation (Yoon et al., 2019). The long residence time for CBP has limited its large-scale implementation. According to Prado-Rubio et al. (2016), the hybrid membrane bioreactors are the most important intensification application on bioprocesses. Two types of hybrid membrane bioreactors have been reported: The immerse membrane and the external loops systems. Mahboubi et al. (2016) described the reverse membrane bioreactor. This technology consists in submerged membrane modules housing microorganisms in between membrane layers. It combines the cell encapsulation techniques with the conventional immersed membrane bioreactor. Compared with conventional systems, microorganism immobilization generates high local cell density, benefiting bioconversion rate and productivity. Mahboubi et al. (2020) developed a study where a double-stage immersed membrane bioreactor was implemented to the intensified 2G bioethanol production. They found the best flow conditions to reduce the process energy requirements after the step of production. Kumakiri et al. (2021) presented recent developments on thermotolerant yeast and membrane-based technologies to improve the fermentation performance. Based on chemical, physical, or biological methods, several pretreatments have been studied for lignocellulosic materials in bioethanol production (Rezania et al., 2020). Recently, ultrasonication and microwave-assisted strategies emerge as promising alternatives to intensify bioethanol yields. These technologies can assist conventional pretreatments improving the delignification and sugar recovery, reducing inhibitor formation, residence time, and temperature conditions. Ultrasonication (US) consists of acoustic waves that oscillate at frequencies above 16 kHz, when US is applied in a liquid medium produces compression and rarefaction cycles (Flores et al., 2021). For some kind of materials, US can work as a single pretreatment assisting the hydrolysis. Suresh et al. (2020)

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compare the ultrasonic-assisted acid hydrolysis and enzymatic hydrolysis in bioethanol production from waste potatoes. They found that both processes improve the bioethanol yields, resulting US-acid hydrolysis being more efficient than US-enzyme hydrolysis. Subhedar et al. (2018) presented delignification by US-assisted alkali pretreatment on three different materials, groundnut shells, coconut coir, and pistachio shells. They compare with the conventional pretreatment, showing that delignification significantly increases when US-assisted pretreatment is used. In microwave-assisted heating (MAH), the transferring of electromagnetic waves from 0.3 to 300 GHz with wavelengths from 1 to 1,000 mm occurs; when the materials have dipolar properties, heat is generated inside (Aguilar-Reynosa et al., 2017). Ocreto et al. (2021) presented an extend review about microwave-assisted heating (MAH) for 2G and 3G bioethanol production. These authors point out that MAH pretreatment for the 2G and 3G bioethanol production is still in the early development stage. Two important issues to be attended are: knowing the dielectric properties of lignocellulosic feedstocks and the suitable design for industrial-scale microwaveheating reactors.

39.4 PROCESS INTENSIFICATION IN THE PURIFICATION OF BIOETHANOL According to many countries’ legislation, bioethanol is mixed with gasoline, pursuing environmental sustainability by reducing the use of fossil fuels. Bioethanol is produced by fermentation of many organic waste or biomass resources in diluted aqueous media. Unfortunately, bioethanol for fuel use must have a low content of water and its recovery is an energy-intensive operation (Popescu et al., 2021). Therefore, the main challenge in the purification process of bioethanol produced through fermentation is to isolate it from the aqueous medium with low energy consumption, low cost, and minimal environmental impact. The intensification of processes has been a tool that has supported different proposals for the sustainable purification of bioethanol (Segovia-Hernandez et al., 2014). In this section, applications of process intensification to the purification step in bioethanol production will be discussed. The separation of a dilute mixture of ethanol and water was studied in a pioneering paper by Hernandez (2008). This work analyzes three complex extractive distillation structures: An extractive distillation column and two thermally coupled extractive distillation sequences. According to the results, the fully thermally coupled extractive column can reduce the energy requirements by 30% compared to the extractive distillation system. Ethanol is obtained in the top of the column with a purity of 99.5 wt%. Sun et al. (2011) investigated the design and optimization of a dividing wall column for heterogeneous azeotropic distillation using cyclohexane as solvent, starting from near-azeotropic feed of ethanol and water. Simulation results indicate that the azeotropic dividing wall column has thermal energy savings of 42% and a 35% lower total annual cost over the azeotropic conventional distillation sequence, using the same solvent. Moreover, azeotropic dividing wall column eliminates backmixing of ethanol, thus improving thermodynamic efficiency by 1.57%. Mulia-Soto and Flores-Tlacuahuac (2011) studied the modeling, simulation, and control of an internally heat integrated pressure-swing distillation process to separate the azeotropic mixture ethanol/water, obtaining ethanol with high purity. According to the results, the proposed separation process can be operated smoothly with an array of PI controllers. Additionally, the control structure allows maintaining the purity of ethanol. Kiss and Suszwalak (2012) proposed dividing wall systems with azeotropic and extractive distillation for bioethanol dehydration. The conventional and dividing wall-based columns were optimized through the sequential quadratic programming methodology. According to their results, the dividing wall configurations allowed obtaining energy savings of 10% and 20%, respectively. Kiss and Ignat (2012) proposed extensions to the conventional extractive dividing wall column studied by Kiss and Suszwalak (2012). The new configuration implies the integration of three columns in a

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single dividing wall column. It has been reported that the proposed system allows reducing energy requirements by 17%, while the total annual cost is reduced by 16% in total annual cost in comparison with the conventional sequence. Aviles-Martinez et al. (2012) proposed a variety of hybrid configurations to separate ethanol from an ethanol/water mixture obtained from the fermentation of biomass. The reported configurations imply the use of liquid-liquid extraction/extractive distillation systems. For the liquid-liquid extraction columns, n-dodecane is used as entrainer. On the other hand, glycerol is used in the extractive distillation columns. According to the reported results, the hybrid scheme presents lower total energy consumption and lower total annual cost when compared to the distillation/extractive distillation scheme. In the same line, Vazquez-Ojeda et al. (2013a) presented the design and optimization of a dehydration process for ethanol. A hybrid arrangement based on liquid-liquid extraction is analyzed and compared with the conventional separation sequence. Three extraction solvents were evaluated for the hybrid design: octanoic acid, octanol, and ethylhexanol. An approximate 32% savings in total annual cost are obtained with the hybrid systems for a feed stream with 22 wt% of ethanol. The potential intensified distillation configurations for separating pure ethanol from the fermentation broth are considered in the work reported by Errico and Rong (2012). They extend the concept of thermally coupled structures and column sections recombination to obtain new distillation sequences for azeotropic mixtures, as the ethanol/water mixture. The proposed arrangements have lower energy requirements and lower capital cost compared to the conventional sequence. RamirezMarquez et al (2013) established that those novel intensified configurations provide operational advantages over the conventional schemes. Vazquez-Ojeda et al (2013b) presented a technoeconomic analysis for the separation train in bioethanol production. The advantages of process integration are described. The overall energy required in the reboilers is reduced through energy integration, while mass integration reduces the required amount of fresh entrainer. According to the reported results, the integrated hybrid separation sequence is the best sequence in terms of utility costs. Tututi-Avila et al. (2014) studied the design and control of an extractive dividing wall column, locating the dividing wall at the top of the column. This system is designed to obtain ethanol with a purity of 99.5 wt% from a 93 wt% ethanol/water mixture, using ethylene glycol as an entrainer. The studied scheme is optimized through a genetic algorithm. The extractive dividing-wall column allows savings of 12.4% in TAC in comparison with the conventional extractive distillation configuration. Moreover, the extractive dividing-wall column showed a closed-loop performance similar to the conventional extractive distillation sequence. Two processes for recovery and purification of bioethanol from fermentation broth are studied by Loy et al. (2015). The schemes are integrated in terms of energy and optimized. The first scheme is pressure swing adsorption, which is commonly employed in the industry. The second analyzed system is an extractive dividing wall column. Results indicate that while extractive dividing wall column has advantages over pressure swing adsorption in terms of capital cost and thermal energy demand, which is consistent with the anticipated benefits and results reported by Kiss and Ignat (2012), pressure swing adsorption has 33% lower manufacture cost. This is mainly due to solvent losses and the need for high-pressure steam in the extractive dividing-wall column. Maleta et al. (2015) are the first to report the performance of a pilot-scale distillation column for bioethanol-water separation, operated in a cyclic mode. Cyclic distillation is an intensified method based on separate phase movement that leads to key advantages: increased column throughput, reduced energy requirements, and better separation performance. In the work of Maleta et al. (2015), a comparative study is made between the cyclic distillation column and an existing industrial beer column used to concentrate bioethanol. According to the results, the cyclic distillation column required 2.6 times fewer trays than the classic distillation system, with energy savings of approximately 30%.

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Torres-Ortega and Rong (2016) proposed a strategy to synthesize hybrid units and divided wall columns in bioethanol purification from lignocellulosic fermentation broth. The proposed intensified alternatives allowed savings on total annual cost ranging from 17% to 23%, while energy requirements were reduced by 18%–28%. Additionally, the number of separation units is considerably reduced. The energy optimization of thermally coupled distillation sequences for bioethanol purification using glycerol as entrainer has been analyzed by Oseguera-Villasenor et al. (2018). The analysis revealed a region where three solutions for the heat duty supplied to the reboiler can be found, all of them accomplishing the desired separation. These multiplicities are attributed to nonlinearities in the model, physical properties, and interactions between the phenomena. This is an important finding since it indicated the need for rigorous optimization techniques to analyze intensified sequences to ensure obtaining the design with the lowest energy requirements. Guzman-Martinez et al (2019) developed alternative methods for ethanol dehydration, including ethylene oxide/propylene oxide hydration and azeotropic distillation using benzene and cyclohexane as separation agents. These reactive methods are coupled with organic Rankine cycles, allowing the generation of ethylene glycol or propylene glycol and electric power. According to the reported results, the reactive dehydration scheme allows obtaining a higher net profit, higher ethanol yield, and lower carbon dioxide emissions compared to azeotropic distillation. Errico et al. (2020) reported the use of reactive distillation and membrane-assisted reactive distillation as potential separation schemes alternative to the extractive distillation systems. The reactive distillation system involves the hydration reaction between ethylene oxide and water to produce ethylene glycol. It is mentioned that the membrane/reactive distillation combination allowed obtaining low total annual costs, a lower ethanol recovery in comparison with other separation alternatives. On the other hand, the reactive distillation scheme showed the highest profit. The application of membranes in ethanol recovery after fermentation is studied in the paper by Kumakiri et al. (2021). The analysis is applied to the concentration of an ethanol/water mixture with 10 wt% of ethanol to a final ethanol concentration of 99.5 wt%. According to the results, the use of membranes to perform the ethanol dehydration allows considerably reducing the energy requirements of the process. Nevertheless, among the areas of opportunity for this technology, there is a need to remove potential contaminants from the ethanol stream before entering the membrane, since those components may affect the membrane performance. Other proposal is developing new materials which are not affected by those contaminants. On the other hand, there is still the need of scaling-up the systems, which may affect the economy of the process. In the future, new intensified alternatives for bioethanol separation should be studied extensively for an accurate comparison to overcome the limitations of the current technologies. Intensified processes involving distillation and membrane separations hold promise with potential to reduce the energy and operating costs of bioethanol separation. Developing these intensified processes will contribute to achieve sustainable processes for bioethanol production. Of great relevance is the development of membranes with higher selectivity, permeability, greater resistance to fouling, longer life, and lower cost (Singh and Rangaiah, 2017).

39.5  CHALLENGES AND FUTURE TRENDS In the past decade, technological advancements have been seen in motor vehicles that run on renewable energy sources. The increasing threat of fossil fuel depletion coupled with the need of maintaining renewable sources continues to push for the demand for bioethanol. We live in a world where the global market for biofuels and renewable sources continues to grow to fulfill the growing population’s energy needs. Reliance on energy is a global necessity as governments attempt to mitigate climate change as a direct result of increased demand for automobile fuel. The

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most obvious benefit of replacing fossil fuels is related to the environmental impact, particularly reducing carbon emissions. Since bioethanol burns faster and cleaner than fossil fuels, it will release greenhouse gases at a lower rate. Second, the use of bioethanol will allow the economy to reap its benefits. As noted throughout this chapter, the grand challenge in the production and purification of liquid bioethanol is clearly defined: the production of more fuels with significantly lower carbon emissions, minimum energy consumption, and maximum economic profitability. An important relationship exists between the design and operation of upstream units and downstream separation systems. Producing more bioethanol with lower carbon emissions will require energy-efficient capacity additions. In the case of the biomass’ conversion stages, it is clear that the challenge on increasing the ethanol yield with lower residence times remains. Modern technologies, such as microwave-assisted methods, have been shown effective to enhance bioethanol production. Nevertheless, the challenge is related to the proper scaling of such technologies to the industrial scale. Additionally, there is a need to reduce the need for fresh water in bioethanol production, since water has become a resource with depletion risk in the last years. In the case of downstream operations, advanced separations play a significant role. First, advanced separations such as membranes and adsorption are thermodynamically advantaged relative to traditional separations such as distillation. However, traditional separation systems provide fundamental advantages in terms of product purity and recovery, as equilibrium stages can be added with minimal additional energy costs, whereas the advanced separation systems often require substantial additional energy inputs for each new stage. Beyond energy, there are capital cost-scaling advantages inherent in distillation columns’ design and construction. However, limits exist on column diameters and hydrodynamics, and these limitations contribute to the inflexibility of throughput expansion for distillation. These factors suggest that the path forward involves hybridization of existing separations technology with incremental capacity additions in the form of advanced separations systems. Process Intensification seems to be one of the most viable strategies for the generation of advanced separation systems that allow the production and purification of biofuels in a sustainable way. Process intensification is a rapidly growing area within chemical engineering, and one of the key unit operations thought to intensify chemical processes is the reactive separator (e.g., a membrane reactor, reactive distillation, and reactive L-L extraction). These all-in-one operations have advantages in certain applications such as breaking equilibrium limitations and a smaller overall footprint. Finally, the COVID-19 pandemics caused a drop of 8.5% in global transportation fuel use in 2020 with respect to the previous year, due to restrictions on people’s movements and disruption in trade logistics around the globe. Consequently, biofuel use fell by 8.7% in 2020 with respect to 2019. Once the postpandemic is installed, global biofuel use is expected to grow in the next 10 years. Blending mandates (mixtures of fuels-biofuels) are expected to evolve over the projection period for some emerging economies. In general, new intensified alternatives for liquid biofuel production should be studied consistently and comprehensively for accurate comparison. Developing hybrid processes and intensified technologies will help move into a sustainable path for biofuel production, allowing to cover the fuel needs on industrial and automotive sectors in a more sustainable way through economically competitive fuels.

39.6 CONCLUSIONS Bioethanol is a biofuel that can help to reduce the use of fossil fuels in the transportation sector. Its production currently occurs in industrial scale, but there are still challenges to address to reduce its costs and the environmental impact associated with its production. Process intensification is a tool that can be useful to achieve such goals. Through process intensification, yields can be increased, and energy requirements can be reduced, which allows reducing the environmental impact associated with the generation of heat and electricity. Among the challenges on the application of process

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intensification on the production of ethanol, the need for proper scaling of modern technologies can be mentioned. Additionally, to promote the use of bioethanol as fuel, there is the need for support from the governments through mandates and incentives on its use.

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Index acetic acids 13, 61, 101, 143, 227, 313 acid hydrolysis 100, 102 acid hydrolysis of cellulose 186, 202–205, 209 acid hydrolysis of straw 144, 167–168, 172 acid hydrolysis of wood 125 acid pretreatments of straw 161, 170 acid pretreatments of wood 120–121, 123–124 acids 61, 100, 185 adsorption 61, 101, 185 adsorption-based ISPR 412–414 agricultural residues 85, 228, 254, 272, 294, 314, 338, 356, 383 alcohol 60, 100, 142, 185, 227, 313, 355 alcohol dehydrogenase 313 alcohol production 227, 355 aldehyde reductase 313, 354 aldehydes 101 algae 228, 254, 272, 294, 314, 338 algal bioethanol fuels 15, 40 algal fermentation 287–291 alkaline hydrolysis 102 alkaline hydrolysis of cellulose 186, 208 alkaline hydrolysis of straw 144, 168 alkaline hydrolysis of wood 125 alkaline pretreatments of straw 157, 170 alkaline pretreatments of wood 120 alternative fermentation processes 160–279, 284–298 ammonia 61 ammonia hydrolysis of cellulose 186, 208 ammonia hydrolysis of straw 144, 168 ammonia hydrolysis of wood 125 ammonia pretreatments of straw 161, 171 anaerobic fermentation 312 anaerobiosis 227, 313 arabinose 228, 254, 312, 314, 338, 356, 383 arabinose fermentation 357, 381, 385 aspen 103 authors 5–6, 17–18, 52–54, 64–65, 92–94, 104–106, 134–136, 146–148, 177–179, 188–189, 219–220, 230–232, 261–263, 274–275, 303–306, 316–317, 347–348, 358–360 autohydrolysis 100, 102 autohydrolysis of straw 144, 168 autohydrolysis of wood 125 Bacillus subtilis 314 bacteria 13, 226, 312, 354 bacterial genes 313 bacterial growth 226, 312 bacterial proteins 313 bacterial strains 226, 312 bagasse 13, 60 bamboo 100, 103, 126 barley straw 169, 272, 294 basidiomycota 13 beech 103, 126 bioconversion 61, 101 bioengineering 226, 312

bioethanol fuel distillation 16, 41, 393–403 bioethanol fuel distillation process: evaluation of 402–403 bioethanol fuel evaluation 16, 41 bioethanol fuel extraction 101 bioethanol fuel fermentation 226, 354 bioethanol fuel production 3–24, 30–44, 61, 101, 143, 227, 260–279, 284–298, 302–322, 327–341, 345–365, 369–387, 426–434 bioethanol fuel purification 426–434 bioethanol fuel separation 393–403 bioethanol fuel utilization 16, 41 bioethanol fuel yield 255 bioethanol fuels 14, 61, 101, 143, 186, 227, 313 bio–ethanol production see bioethanol fuel production bio-ethanol see bioethanol fuels biofuel production 227 biofuels 14, 61, 101, 143, 186, 227, 313, 355 biomass 13, 60, 85, 100, 142, 185, 226, 272, 312, 354 biomass constituents 13, 60, 85, 226, 228, 254, 312, 314, 338, 354 biomass fermentation 357, 376–379, 384–386 biomass hydrolysis 16, 34–37, 41–43, 51–69, 75–88 biomass pretreatments 16, 31–34, 41–42 bioreactors 13, 226 biosyngas 228, 254 biosyngas–based bioethanol fuels 15, 40 biotechnology 101, 142, 312, 354 biotransformation 61, 101, 227 birch 103, 126 carbohydrate metabolism 226 carbohydrates 13, 60, 100, 142, 185 carbon dioxide hydrolysis of cellulose 186, 208 carbon dioxide hydrolysis of straw 144, 168 carob 356, 383 cellobiose 185, 228, 254, 338, 356, 383 cellobiose fermentation 357, 385 cellulases 13, 60, 100, 142, 185, 227 cellulose 13, 60, 85, 100, 142, 176–193, 198–211, 226, 228, 254, 294, 312, 314, 338, 354, 356, 383 cellulose derivatives 61, 185 cellulose fermentation 287–291 cellulose hydrolysates 186 cellulose hydrolysis 60, 62, 176–193, 198–211 cellulose pretreatments 185 cellulose–based bioethanol fuels 15, 40 cellulosic bioethanol fuels 355 cellulosic biomass 272 chemical hydrolysis 102 chemical hydrolysis of cellulose 186, 202–207, 209–210 chemical hydrolysis of straw 144, 168 chemical hydrolysis of wood 125 chemical pretreatments of straw 157, 168 chemical pretreatments of wood 119–120, 127 clostridium 226, 312, 314 consolidated bioprocessing (CBP) 230, 253, 273 continuous ethanol fermentation (CEF) 273, 295 continuous ethanol production (CEP) 273, 295

437

438 corn 13, 272, 294, 356, 383 corn silage 228, 254 corn stover 60, 85, 228, 254, 272, 287–288, 294, 356, 383 corn stover fermentation 287–288, 295–296 corn stover hydrolysis 62 corn straw 144, 169 corncob fermentation 287–291 corncobs 272, 294, 356, 383 countries 11, 20–21, 58, 67, 98–99, 107–108, 140, 149, 182–183, 191, 224–225, 233–234, 267–268, 276–277, 309–310, 318–319, 352, 361 cyanobacteria 335–336, 340 degradation 61, 101, 143 delignification 13, 61, 101, 142 detoxification 61, 101, 226, 230 digestibility 60 dissolution 13, 101 dividing–wall column 401 documents 4–5, 17, 52, 63–64, 92, 104, 134, 145–146, 177, 187–188, 218, 229–230, 261–262, 273, 303–304, 315–316, 346–347, 358 Douglas fir 100, 103, 126 enzymatic activity 185 enzymatic digestibility 60, 100 enzymatic hydrolysis 13, 60, 100, 102, 227 enzymatic hydrolysis of cellulose 185–186, 199–202 enzymatic hydrolysis of straw 144, 157–166, 168, 170–172 enzymatic hydrolysis of wood 116–124, 126–128, 142 enzymatic pretreatments of straw 168 enzymatic saccharification 100, 142 enzyme activity 14, 60, 100, 142, 185, 227, 313, 354 enzyme binding 185 enzyme inhibition 60, 185 enzyme kinetics 60, 142 enzymes 13, 60, 100, 142, 185, 227, 313, 354 enzymology 61, 101, 313 enzymolysis 14, 60, 142, 185 Escherichia coli 13, 226, 314, 332–334 ethane 314, 338 ethanol see bioethanol fuels ethanol fermentation see bioethanol fuel fermentation ethanol production see bioethanol fuel production ethanol see bioethanol fuels ethanol stress 357, 384–385 ethanol tolerance of microorganisms 230, 253 ethanol yield see bioethanol fuel yield eucalyptus 100, 103, 126 extractive distillation of bioethanol fuels 398–400 fermentable sugars 61, 101, 143 fermentation 13, 61, 101, 142, 185, 217–236, 242–257, 312, 354 fermentation broth 394–395, 409–419 fermentation inhibitors 230, 245–247, 253, 255, 357, 376–379, 384, 385–386 fermentation microorganisms 230, 253 fermentation processes 357, 384 fermentation technique 226 food waste fermentation 287–291 food waste hydrolysis 186 food waste–based bioethanol fuels 15, 40 food wastes 228, 254, 272, 294, 356, 383

Index forestry waste–based bioethanol fuels 15, 40 funding bodies 8–9, 19–20, 56–57, 66, 96–97, 106–107, 138–139, 148, 181, 190, 222–223, 232–233, 265–266, 276, 307–308, 317, 350–351, 360–361 fungal genes 313, 354 fungal strains 226, 312 fungi 13, 60, 100, 142, 185, 226, 312, 354 fungus culture 354 fungus growth 226, 312, 354 furfural 61, 101, 143 galactose 383 galactose fermentation 37 gas stripping–based ISPR 410–412 gene deletion 313 gene expression 226, 312, 354 gene expression regulation 313 gene overexpression 312, 354 genes 312, 354 genetic engineering 12, 226, 312, 354 genetics 13, 226, 312, 354 glucan 101 glucan synthase 185 glucose 13, 61, 101, 143, 186, 226, 228, 254, 272, 294, 312, 314, 338, 354, 356, 383 glucose fermentation 357, 376–379, 384–386 glucosidase 142, 185 glycerol 226, 228, 254, 312, 314, 338, 354 grass 85, 228, 254, 272, 294, 314, 338, 356, 383 grass fermentation 287–291 grass hydrolysis 186 grass–based bioethanol fuels 15, 40 hardwood 100, 103, 126 heat pump distillation of bioethanol fuels 401 heat–integrated distillation column (HIDIC) 400–401 hemicellulose 13, 60, 85, 142, 185, 314 hemicellulose hydrolysis 62 hexose 228, 254, 314, 338 hot compressed water (HCW) hydrolysis 102 hot compressed water (HCW) hydrolysis of cellulose 186, 208 hot compressed water (HCW) hydrolysis of straw 144, 168 hot compressed water (HCW) hydrolysis of wood 125 hydrogen peroxide hydrolysis 102 hydrogen peroxide hydrolysis of straw 144, 168 hydrogen peroxide hydrolysis of wood 125 hydrolysate detoxification 247–249, 253, 255–256 hydrolysate fermentation 16, 41, 217–236, 357, 384 hydrolysates 226, 228, 254, 272, 312, 314, 338, 354, 356, 383 hydrolysis 13, 60, 62, 91–110, 115–129, 133–151, 156–173, 176–193, 198–211, 227, 354 hydrolysis of agricultural residues 62 hydrolysis of biomass 62 hydrolysis of straw 144, 168 hydrolysis of the agricultural residues 77–81, 86 hydrolysis of the biomass constituents 76–77, 85–86 hydrolysis of the grass 83–84, 87 hydrolysis of the wood 81–83, 86–87 hydrothermal hydrolysis 102 hydrothermal hydrolysis of cellulose 186, 208 hydrothermal hydrolysis of straw 144, 168 hydrothermal hydrolysis of wood 124–128

439

Index

in situ bioethanol fuel separation 409–419 in situ product recovery (ISPR) 410–413 industrial microbiology 313 industrial waste fermentation 287–291 industrial waste–based bioethanol fuels 15, 40 industrial wastes 226, 228, 254, 272, 294, 356, 383 institutions 7–8, 19, 54–56, 65–66, 94–96, 137–138, 180–181, 190, 221–222, 232, 264–265, 275–276, 306–307, 317, 348–350, 360 intensified processes 426–434 inulins 272, 294, 383 ionic liquid (IL) hydrolysis of straw 144, 168 ionic liquid (IL) hydrolysis 102 ionic liquid (IL) hydrolysis of cellulose 186, 205–206, 209 ionic liquid (IL) hydrolysis of wood 118, 124–125, 128 ionic liquid (IL) pretreatments of straw 160, 170 ionic liquids (ILs) 13, 60, 185

metabolic engineering of Pichia stipitis 339 metabolism 226, 312 microbial engineering 226, 312 microbiology 226, 313 microcrystalline cellulose 60, 185 microorganisms 314 microwave (MW) hydrolysis 102 microwave (MW) hydrolysis of cellulose 186, 208 microwave (MW) hydrolysis of wood 125 microwave (MW) pretreatments of straw 162–163, 171 milling hydrolysis 102 milling hydrolysis of cellulose 186, 208 milling hydrolysis of straw 144, 168 milling hydrolysis of wood 125 milling pretreatments of straw 162, 168, 171 molecular cloning 313 molecular sequence data 313 multi-effect distillation of bioethanol fuels 400 multi-stage ISPR processes 417–418 mutation 312

Jerusalem A-artichoke 356, 383

nucleotide sequence 312

klebsiella 314 kraft pulp 100

olive tree 103, 126 oxidoreductase 313, 354 ozone hydrolysis of straw 144, 168 ozone pretreatments of straw 162, 171

hydrothermal pretreatments of straw 164, 171–172 Hypocrea jecorina 13, 60, 101, 185

lactic acid 227 lactobacillus 226 lactose 228, 254, 314, 338, 356, 383 lignin 13, 60, 85, 100, 142, 185, 226, 228, 254, 312, 314, 338, 354 lignin hydrolysis 62 lignin–based bioethanol fuels 15, 40 lignocellulose 13, 60, 85, 100, 142, 185, 226, 294, 312, 314, 338, 354 lignocellulose hydrolysis 62 lignocellulosic biomass 13, 60, 100, 142, 185, 226, 272, 294, 294, 356, 383 lignocellulosic biomass–based bioethanol fuels 15, 40 liquid hot water (LHW) hydrolysis 102 liquid hot water (LHW) hydrolysis of cellulose 186, 208 liquid hot water (LHW) hydrolysis of straw 144, 168 liquid hot water (LHW) hydrolysis of wood 125 liquid hot water (LHW) pretreatments of straw 164, 172 liquid hot water (LHW) pretreatments of wood 119 mahula 356, 383 mannose 101 mechanical hydrolysis 102 mechanical hydrolysis of cellulose 186, 208 mechanical hydrolysis of straw 144, 168 mechanical hydrolysis of wood 125 mechanical pretreatments of straw 162–164, 171–172 membrane distillation–based ISPR 416–417 metabolic engineering 226, 312, 354, 357, 383 metabolic engineering of Bacillus subtilis 339 metabolic engineering of clostridium 339 metabolic engineering of Escherichia coli 339 metabolic engineering of klebsiella 339 metabolic engineering of microorganisms 339 metabolic engineering of microorganisms and substrates 230, 249–253, 256, 302–322, 327–341 metabolic engineering of Saccharomyces cerevisiae 339 metabolic engineering of Zymomonas mobilis 339

pentose 226, 228, 254, 312, 314, 338, 356 pentose fermentation 257 perstraction-based ISPR 416 pervaporation–based ISPR 414–416 pH 13, 61, 101, 142, 185, 227, 313, 354 phosphoric acid 185 Pichia stipitis 226, 312, 314, 354 pine 103, 126 plant hydrolysis 186 plants 186 plasmid 312 polysaccharides 61, 101 poplar 100, 102, 126 potato 272, 294 pretreatments 13, 60, 100, 142, 227, 313, 354 pre–treatments see pretreatments process intensification of bioethanol fuel production 427–432 protein engineering 313 protein expression 312 rapeseed straw 144, 169 recombinant proteins 312, 354 recombination, genetic 313 research fronts 14–16, 22–23, 62, 68, 84–85, 102–103, 108–109, 124–126, 144–145, 150, 168–169, 186–187, 192, 207–208, 226–229, 234–235, 253–255, 271–273, 278, 293–295, 311, 314–315, 320, 337–339, 355–357, 362–363, 382–385 research output 5, 7, 19, 54–55, 65, 94–95, 106, 136–137, 179–180, 189–190, 219, 221, 232, 262–264, 275, 304–305, 317, 348–349, 360 rice 142 rice straw 85, 142, 144, 169, 228, 254, 272, 285–287, 294, 356, 383 rice straw fermentation 285–287, 295–296

440 rice straw hydrolysis 62 rye straw 144, 169 saccharification 14, 60, 100, 142, 185, 227, 354 saccharification of cellulose 185 Saccharomyces cerevisiae 13, 61, 101, 142, 186, 226, 312, 314, 328–332, 337–339 scientometric overview 3–24, 51–69, 91–110, 133–151, 217–236, 260–279, 302–322, 345–365 Scopus keywords 12–14, 21–22, 59–61, 67–68, 99–101, 108, 141–143, 149–150, 184–186, 191–192, 225–227, 234, 268–271, 277–278, 311–313, 320, 353–355, 362 Scopus subject categories 12, 21, 59, 67, 99, 108, 140–141, 149, 183–184, 191, 225, 234, 268–269, 277, 309–310, 319, 353, 362 separate hydrolysis and fermentation (SHF) 273, 295 simultaneous saccharification and co–fermentation (SSCF) 273, 295 simultaneous saccharification and fermentation (SSF) 61, 143, 226, 230, 253, 273, 295 sodium hydroxide 61, 101, 142 softwood 100, 103, 126 solid–state fermentation 226 solvent hydrolysis 102 solvent hydrolysis of cellulose 186, 208 solvent hydrolysis of straw 144, 168 solvent hydrolysis of wood 125 solvent pretreatments of straw 161, 170 sorghum 356, 383 sorghum bagasse 272, 294, 356, 383 sorghum bagasse fermentation 281–291 sorghum straw 144, 169 source titles 10–11, 20, 57–58, 66, 97–98, 107, 139, 148, 182–183, 191, 223–224, 233, 266–267, 276, 308–309, 317–318, 351–352, 361 soybean 356, 383 soybean straw 144 spruce 100, 103, 126 SSF see simultaneous saccharification and fermentation starch feedstock residues–based bioethanol fuels 15, 40 starch feedstock–based for bioethanol fuels 15, 40 starch feedstocks 228, 254, 356, 383 steam explosion 100, 142 steam explosion hydrolysis 102 steam explosion hydrolysis of cellulose 186, 208 steam explosion hydrolysis of straw 144, 168 steam explosion hydrolysis of wood 125 steam explosion pretreatments of straw 172 steam explosion pretreatments of wood 117–119 straw 13, 60, 85, 133–151, 156–173 straw hydrolysis 133–151, 156–173 substrate fermentation 217–236 sugar 13, 61, 101, 143, 186, 226, 228, 312, 354 sugar beet 356, 383 sugar cane see sugarcane sugar feedstock residues–based bioethanol fuels 15, 40 sugar feedstock–based bioethanol fuels 15, 40 sugar feedstocks 228, 254 sugarcane 60, 272, 294, 356, 383 sugarcane bagasse 60, 85, 254, 272, 294, 383 sugarcane bagasse hydrolysis 62 sugarcane leaves 272, 294 sugarcane straw 144, 169 sulfite hydrolysis 102

Index sulfite hydrolysis of wood 125 sulfite pretreatments of wood 119–120 sulfuric acids 13, 60, 100, 142, 185 surfactant hydrolysis 102 surfactant hydrolysis of cellulose 186, 208 surfactant hydrolysis of straw 144, 168 surfactant hydrolysis of wood 125 switchgrass 335–336, 340 temperature 13, 60, 100, 142, 185, 227 trees 100 Trichoderma reesei 13, 60, 101, 185 Triticum aestivum 60 ultrasound hydrolysis 102 ultrasound hydrolysis of cellulose 186, 208 ultrasound hydrolysis of straw 144, ultrasound hydrolysis of wood 125 urban waste–based bioethanol fuels 15, 40 utilization of Saccharomyces cerevisiae 345–365, 369–387 vacuum fermentation–based ISPR 412 variable pressure distillation 397–398ü water 13, 61, 100, 142, 185 water hyacinth 228, 254 water hydrolysis 102 water hydrolysis of cellulose 186, 208 water hydrolysis of wood 125 water vapor 100 wet oxidation hydrolysis 102 wet oxidation hydrolysis of straw 144, 168 wet oxidation hydrolysis of wood 125 wet oxidation pretreatments of straw 164, 172 wheat 142 wheat straw 62, 142, 144, 169, 228, 254, 272, 285–287, 294, 356, 383 wheat straw fermentation 285–287, 295–296 willow 103, 126 wood 13, 60, 85, 103, 126, 115–129, 185, 228, 254, 272, 294, 314, 338, 356, 383 wood chips 100 wood fermentation 288–291, 296 wood hydrolysate detoxification 121–123, 128 wood hydrolysis 62, 91–110, 115–129 wood–based bioethanol fuels 15, 40 xylan 60, 100, 142, 356, 383 xylitol 313, 355 xylitol dehydrogenase 354 xylose 14, 61, 101, 143, 226, 228, 254, 272, 294, 312, 314, 338, 354, 356, 383 xylose fermentation 354, 357, 370–375, 384–385 xylose isomerase 313 xylose reductase 313, 354 xylosidase 142 xylulokinase 313, 354 yeast 13, 61, 101, 142, 226, 312, 354 Zea mays (corn, maize) 13, 60, 185, 226 Zymomonas mobilis 226, 312, 314, 332 β glucosidase 60