Microalgae-based biofuels and bioproducts: from feedstock cultivation to end-products 9780081010235, 9780081010310, 9780124079090, 9780128179376, 9780128179383, 1051051061, 1621641651, 0081010230

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Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts

Related titles Microalgae-Based Biofuels and Bioproducts (ISBN 978-0-08-101023-5) Bioenergy Systems for the Future (ISBN 978-0-08-101031-0) Bioenergy-Biomass to Biofuels (ISBN 978-0-12-407909-0)

Woodhead Publishing Series in Energy

Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts New Technologies, Challenges, and Opportunities

Edited by

Majid Hosseini

Woodhead Publishing is an imprint of Elsevier The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom Copyright r 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: http://www.elsevier.com/ permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-817937-6 (print) ISBN: 978-0-12-817938-3 (online) For information on all Woodhead Publishing publications visit our website at https://www.elsevier.com/books-and-journals

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Contents List of Contributors Preface

1.

Microscale and Macroscale Modeling of Microalgae Cultivation in Photobioreactor: A Review and Perspective

xv xix

1

Choon Gek Khoo, Man Kee Lam and Keat Teong Lee Introduction Photobioreactor System Mathematical Models Macroscale Modeling 1.4.1 Volumetric Mass Transfer Coefficient 1.4.2 Fluid Hydrodynamics 1.4.3 Mixing Time Determination 1.4.4 Superficial Gas Velocity 1.4.5 Specific Power Input 1.4.6 Fluid Velocity 1.5 Microscale Modeling 1.5.1 Basic Kinetic Equations: Primary Models 1.5.2 Dynamics Kinetic Equations: Secondary Models 1.6 Challenges in Scale-up Microalgae Cultivation 1.7 Potential Industry Application 1.8 Conclusion 1.9 Future Outlook Acknowledgment References

1 1 2 3 5 5 6 6 6 6 6 7 9 14 14 15 15 16 16

Cell Wall Disruption: A Critical Upstream Process for Biofuel Production

21

1.1 1.2 1.3 1.4

2.

Zahra Lari, Hossein Ahmadzadeh and Majid Hosseini 2.1 Introduction 2.2 Mechanical Cell Wall Disruption Methods 2.2.1 Bead-Beating 2.2.2 Homogenization 2.2.3 High-Pressure Press 2.2.4 Ultrasonication

21 22 23 23 24 24 v

vi

3.

Contents

2.2.5 Freeze-Drying 2.2.6 Microwaves 2.3 Nonmechanical Cell Disruption 2.3.1 Osmotic Shock 2.3.2 Chemical Methods 2.3.3 Enzymatic Method 2.4 Microalgae Cell Wall Disruption Efficiency 2.5 Challenges and Prospects 2.6 Conclusion References

26 26 27 27 27 29 29 30 32 33

Enhancing Carbohydrate Productivity in Photosynthetic Microorganism Production: A Comparison Between Cyanobacteria and Microalgae and the Effect of Cultivation Systems

37

Carlos Eduardo de Farias Silva, Eleonora Sforza and Alberto Bertucco

4.

3.1 Ethanol Market and Perspective of a Carbohydrate-Rich Biomass 3.2 Microalgae and Cyanobacteria for Bioethanol Production 3.3 Microalgae and Cyanobacteria: Biological Aspects and Strains 3.4 Cultivation of Microalgae and Cyanobacteria Toward Large-Scale Applications 3.4.1 Cultivation System and Operation Mode 3.4.2 CO2 Availability 3.4.3 Nutrients Supply 3.4.4 Light Exploitation and Photosynthetic Efficiency 3.5 Conclusion 3.6 Future Outlook Acknowledgment References

45 48 51 54 57 61 61 61 62

Production of Bioethanol From Brown Algae

69

37 39 43

Marı´a Cristina Ravanal, Carolina Camus, Alejandro H. Buschmann, Javier Gimpel, A´lvaro Olivera-Nappa, Oriana Salazar and Marı´a Elena Lienqueo 4.1 Introduction 4.2 Process for Bioethanol Production From Brown Algae 4.2.1 Cultivation of Macroalgae 4.2.2 Chemical Composition of Macroalgae 4.2.3 Pretreatment and Enzymes for Brown Algae Degradation 4.2.4 Degradation Pathways of Main Macroalgae Carbohydrates

69 70 70 71 72 76

Contents

4.2.5 Genetically Engineered Microorganisms for Macroalgae Carbohydrate Utilization 4.2.6 Saccharification and Fermentation in Process Configurations 4.3 Examples of Bioethanol Production From Brown Algae 4.4 Conclusion 4.5 Future Outlook Acknowledgment References Further Reading

5.

Approaches to Improve the Quality of Microalgae Biodiesel: Challenges and Future Prospects

vii

79 80 82 82 83 83 83 88

89

Ali Parsaeimehr, Meisam Tabatabaei and Roberto Parra-Saldivar

6.

89

5.1 Introduction 5.2 Screening the Microalgae Strains for Production of Biodiesels 5.3 Metabolic Engineering for Enhanced Microalgae Biodiesel Production 5.4 Genetic Engineering for Improving Microalgae Biodiesel Quality 5.5 Impact of Additives on Biodiesel Quality 5.6 Conclusion 5.7 Future Outlook Acknowledgment References Further Reading

97 98 99 99 100 100 103

Biosequestration of Carbon Dioxide From Flue Gases by Algae

105

91 94

Jose´ C.M. Pires 6.1 Introduction 6.2 Microalgae Culture 6.2.1 Bioreactors 6.2.2 Key Culture Parameters 6.2.3 CO2 Capture 6.3 Effect of NOx and SO2 6.4 Recent Advances and Challenges 6.5 Research Needs 6.6 Conclusion 6.7 Future Outlook Acknowledgments References

105 106 107 107 108 111 113 113 114 114 115 115

viii

Contents

7.

Using Microalgae for Treating Wastewater

119

Kaushik K. Shandilya and Vikram M. Pattarkine

8.

119 120 122 122 122 123 123

7.1 Introduction 7.2 Microalgae Metabolic Pathways 7.2.1 Autotrophic Microalgae Cultivation 7.2.2 Heterotrophic Microalgae Cultivation 7.2.3 Mixotrophic Microalgae Cultivation 7.3 Biomass Productivity 7.3.1 Organic Carbon Sources for Microalgae Cultivation 7.4 Microalgae Cultivation Opportunities in Wastewater Treatment 7.4.1 Microalgae Cultivation Challenges in Wastewaters 7.5 Biomass Production Using Microalgae 7.6 Conclusion 7.7 Future Outlook References

124 127 129 130 130 131

Jerusalem Artichoke: A Promising Feedstock for Bioethanol Production

137

Jiaoqi Gao and Wenjie Yuan

9.

8.1 Introduction 8.2 Jerusalem Artichoke and Its Potential as a Biorefinery Crop 8.2.1 Basic Properties of Jerusalem Artichoke 8.2.2 Economic Value in a Biorefinery Concept 8.3 Ethanol Fermentation From Jerusalem Artichoke Tubers 8.3.1 Two-Step Ethanol Production 8.3.2 One-Step Ethanol Production 8.4 Ethanol Fermentation From Jerusalem Artichoke Stalks 8.5 Current Status, Problems, and Challenges 8.6 Enhanced Productivities for Economic Efficiency 8.7 Conclusion 8.8 Future Outlook References

137 138 138 139 139 141 144 151 152 153 154 154 154

Recent Advances and Future Prospective of Biogas Production

159

Rahulkumar Maurya, Sushma Rani Tirkey, Soundarya Rajapitamahuni, Arup Ghosh and Sandhya Mishra 9.1 Introduction 9.2 Biogas-Enhancement Strategy 9.2.1 Pretreatment Strategy 9.2.2 Enzymatic Hydrolysis 9.2.3 Microbial Strains Enhance Biogas Production 9.2.4 Phase Separation and Codigestion Strategy

159 160 160 160 161 161

Contents

9.3 Process Control and Monitoring 9.4 Anaerobic Membrane Bioreactors 9.4.1 Membrane Materials and Modules 9.4.2 Types of Anaerobic Membrane Bioreactors 9.5 Recent Advances in Biogas Purification Technologies 9.5.1 Water Scrubbing 9.5.2 Solvent Scrubbing 9.5.3 Chemical Scrubbing 9.5.4 Pressure Swing Adsorption 9.5.5 Membrane Technology 9.5.6 Cryogenic Separation 9.5.7 Biological Technologies 9.5.8 Biogas Reforming Technologies 9.6 Conclusion 9.7 Future Outlook Acknowledgments References

10. Recent Advances in Lipid Extraction for Biodiesel Production

ix 162 164 165 165 165 165 166 166 167 167 168 169 171 173 174 174 174

179

Narges Moradi-kheibari, Hossein Ahmadzadeh, Ahmad Farhad Talebi, Majid Hosseini and Marcia A. Murry 10.1 Introduction 10.2 Lipid Extraction Methods 10.2.1 Expeller Pressing 10.2.2 Solvent Extraction Method 10.2.3 Supercritical Fluid Extraction Method 10.2.4 Subcritical Water Extraction 10.2.5 Electrochemical Extraction 10.2.6 Modifications in Lipid Extraction Methods 10.3 Influence of Extraction Methods on Biodiesel Properties 10.4 Conclusion References

179 180 180 181 182 184 186 187 189 192 193

11. Synthesis of Catalyst Support From Waste Biomass for Impregnation of Catalysts in Biofuel Production

199

Sumit H. Dhawane and Gopinath Halder 11.1 Introduction 11.2 Preparation of Porous Activated Carbon From Waste Biomass 11.2.1 Hydrothermal Carbonization 11.2.2 Pyrolysis or Carbonization 11.3 Functionalization of the Activated Carbon 11.3.1 Sulfonation by Strong Acids

199 203 203 204 207 207

x

Contents

11.3.2 Metal-Doped Carbon Catalyst 11.3.3 Carbon-Supported Biocatalysts 11.4 Application of Carbon-Supported Catalyst in Transesterification 11.4.1 Carbonaceous Acid Catalysts 11.4.2 Metal-Doped Carbonaceous Catalyst 11.4.3 Carbon-Supported Biocatalyst 11.5 Conclusion 11.6 Future Outlook References

12. Heterogeneous Catalytic Conversion of Rapeseed Oil to Methyl Esters: Optimization and Kinetic Study

208 209 210 210 212 214 215 216 216

221

Basit Ali, Suzana Yusup, Armando T. Quitain, Awais Bokhari, Tetsuya Kida and Lai Fatt Chuah 12.1 Introduction 12.1.1 Catalytic Transesterification 12.2 Materials 12.3 Experimental Setup 12.4 Parametric Optimization 12.5 Analysis 12.6 Results and Discussion 12.6.1 Statistical Analysis 12.6.2 Kinetic Study 12.7 Determination of Fuel Properties 12.8 Industrial Applications 12.9 Conclusion 12.10 Future Outlook Acknowledgments References

13. Fatty Acid Profiling of Biofuels Produced From Microalgae, Vegetable Oil, and Waste Vegetable Oil

221 222 224 224 225 225 226 226 230 234 235 235 235 236 236

239

Narges Moradi-kheibari, Hossein Ahmadzadeh, Marcia A. Murry, Hui Ying Liang and Majid Hosseini 13.1 Introduction 13.2 Materials, Methods, and Experimental 13.2.1 Materials and Instruments 13.2.2 Microalgae Culture, Media, and Sample Preparation 13.2.3 Microalgae Oil Extraction 13.2.4 Transesterification 13.2.5 Aliquoting Reference Standard Mixture and Preparing Internal Standard 13.2.6 Gas Chromatography Injection

239 242 242 242 243 243 243 244

Contents

13.3 Results and Discussions 13.3.1 Method Development 13.3.2 Fatty Acid Profiling of Biofuels 13.4 Conclusion Acknowledgments References

14. The Challenges to Produce an Oxidation Stable and an Acceptable Cloud Point Biodiesel From Lipid Sources

xi 244 244 250 252 253 253

255

Ramasubramania Iyer 14.1 Introduction 14.2 Preparation of Biodiesel 14.2.1 Problems of Producing High Yield of Biodiesel From Microalgae/Fungi Lipids 14.3 Biodiesel Fuel Stability 14.3.1 Oxidation Stability 14.3.2 Choosing Between Iodine Value ,120 and Cetane Number 51 as Oxidation Stability Standard 14.3.3 Methods for Estimating Double Bond Equivalent, Allylic Position Equivalent, and Bis-Allylic Position Equivalent 14.4 Cloud Point 14.4.1 Measurement Methods of Cloud Point 14.4.2 Differential Scanning Calorimetry 14.4.3 Methods Reported for Estimating Cloud Point of Saturated Fatty Acid Methyl Esters Present in Biodiesel 14.4.4 Estimated Cloud Point Versus Reported Cloud Point Measured by DSC With Estimated Indices of Three Biodiesel Mixtures 14.4.5 Estimated Points and Estimated Indices P PCloudP DBE, APE, BAPE of Reported wt.% FAME From Lipids of a Microalga 14.5 Conclusion 14.6 Future Outlook Acknowledgments References

15. Supercritical Extraction of Value-Added Compounds From Empty Fruit Bunch: An Optimization Study by Response Surface Methodology

255 257 258 260 260 260

263 265 267 267

269

270

272 275 275 276 276

281

See Cheng Yim, Yi Herng Chan, Suzana Yusup, Khairiraihanna Johari, Armando T. Quitain and Daniel Joe Dailin 15.1 Introduction

281

xii

Contents

15.2 Materials and Methods 15.2.1 Materials 15.2.2 Supercritical CO2 Extraction With Water as Co-solvent 15.2.3 Design of Experiment, Data Analysis, and Model Fitting 15.2.4 Soxhlet Extraction 15.2.5 Extracted Oil Yield Calculation 15.2.6 Characterization of Extracted Oil by GC
MS 15.3 Results and Discussions 15.3.1 Analysis of Variance 15.3.2 Effect of Temperature, Pressure, and Particle Size on SCCD Extraction of Empty Fruit Bunch 15.3.3 Soxhlet Extraction 15.3.4 Comparison Between SCCD Extraction and Soxhlet Extraction 15.3.5 Characterization of Extracted Oil: GC
MS Analysis 15.4 Potential Application of Supercritical Extraction in Industrial Scale, Limitations, and Challenges 15.5 Conclusion 15.6 Future Outlook Acknowledgment References

16. Progress in Anaerobic Digestion of Manures

283 283 283 284 284 286 287 287 287 290 292 292 294 295 296 296 296 296 299

Willem Jan Oosterkamp 16.1 Introduction 16.2 Biofilms 16.2.1 Fixed Film Reactors 16.2.2 Settling 16.2.3 Sequencing Batch Reactors 16.2.4 Induced Blanket Reactor 16.2.5 Simplified Induced Blanket Reactor 16.2.6 Simplified Induced Blanket Reactor With Percolated Scum Layer 16.3 Cattle Manure 16.3.1 High Solids Digestion 16.3.2 Fixed Film at Cartago, Costa Rica 16.3.3 Fixed Film at Hague, FL, United States 16.3.4 Sequencing Batch Reactors at Pullman, WA, United States 16.3.5 Induced Blanket Reactor at Ogden, UT, United States 16.3.6 Straw as Support for Biofilms (Foulum, Denmark) 16.3.7 Pretreatment and Addition of Nutrients 16.4 Swine Manure 16.4.1 Manure Storage Time

299 300 300 300 301 301 301 302 302 302 302 303 303 303 303 303 304 304

Contents

16.4.2 Sequencing Batch Reactors at Lennoxville, Quebec, Canada 16.4.3 Sequencing Batch Reactors at Stillwater, OK, United States of America 16.4.4 Upflow, Downflow, Reflow Reactor at Sterksel 16.4.5 Biofilm Reactor at Foulum, Denmark 16.4.6 Thermal Pretreatment and Addition of Nutrients 16.5 Poultry Manure and Combinations With Other Manures 16.5.1 Poultry Manure Mono-digestion 16.5.2 Pretreatment 16.5.3 Poultry and Cattle Manures 16.5.4 Continuous Stirred Tank Reactor With Poultry, Cattle, and Swine Manures in Langenwetzendorf 16.6 Discussion 16.7 Conclusion 16.8 Future Outlook References

17. Thermochemical Processes Aimed at the Energy Valorization of Cow Manure from Feedlots

xiii

305 305 305 306 306 306 306 307 307 307 308 310 310 311

317

Melisa Bertero, Juan Rafael Garcı´a and Ulises Sedran 17.1 Introduction 17.2 Experimental 17.2.1 Biomass 17.2.2 Pyrolysis Experiments 17.2.3 Product Analysis 17.3 Results and Discussion 17.3.1 Composition of Cow Manure 17.3.2 Pyrolysis of Cow Manure 17.3.3 Other Thermochemical Processes Appropriate for Cow Manure Processing 17.3.4 Characterization of Liquid Products in the Pyrolysis of Cow Manure: Biooil and Tar 17.3.5 Characterization of Gases and Char: Composition and Properties 17.4 Conclusion 17.5 Future Outlook References

18. Environmental Concerns on the Production of Value-Added Bioproducts From Residual Renewable Sources

317 319 319 319 320 321 321 322 325 327 332 334 334 335

339

Sara Gonza´lez-Garcı´a, Beatriz Gullo´n, Gumersindo Feijoo and Maria Teresa Moreira 18.1 Introduction

339

xiv

Contents

18.2 Materials and Methods 18.2.1 Description of Methodology 18.2.2 Goal and Scope Definition 18.2.3 Description of the Scenarios Under Evaluation 18.2.4 Description of the Functional Unit 18.2.5 Life Cycle Inventory Data Acquisition 18.2.6 Description of the Allocation Procedure 18.2.7 Life Cycle Assessment Method 18.3 Results and Discussion 18.3.1 General Results 18.3.2 Sugar Beet Pulp
Based Scenarios 18.3.3 Woody Chips
Based Scenarios 18.4 Conclusion 18.5 Future Outlook Acknowledgments References

19. Life Cycle Assessment of First-, Second-Generation, and Microalgae Biofuels

340 340 341 341 344 344 346 346 346 346 348 350 350 350 351 351

355

Vijay Kumar Garlapati, Shweta Tewari and Rajiv Ganguly 19.1 Introduction 19.2 Steps in Life Cycle Assessment Methodology 19.3 Life Cycle Assessment Studies of First-Generation Biofuels 19.4 Life Cycle Assessment Studies of Second-Generation Biofuels 19.5 Life Cycle Assessment Studies of Microalgae Biofuels 19.6 Other Impact Indicators of Life Cycle Assessment 19.6.1 Acidification Potential 19.6.2 Environmental Impact Indicators 19.6.3 Land Usage Impact Indicators 19.6.4 Key Parameters Influencing the Implemented Methods and Results 19.6.5 The N2O Balance 19.6.6 Coproduct Allocations 19.7 Conclusion 19.8 Future Outlook References Index

355 356 358 361 361 363 364 364 365 366 366 366 367 367 368 373

List of Contributors Hossein Ahmadzadeh Department of Chemistry, Ferdowsi University of Mashhad, Mashhad, Iran Basit Ali Biomass Processing Lab, Centre of Biofuel and Biochemical Research (CBBR), Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia Melisa Bertero Research Institute on Catalysis and Petrochemistry “Ing. Jose´ Miguel Parera” INCAPE (UNL-CONICET), Santa Fe, Argentina Alberto Bertucco Department of Industrial Engineering DII, University of Padova, Padova, Italy Awais Bokhari Biomass Processing Lab, Centre of Biofuel and Biochemical Research (CBBR), Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Chemical Engineering Department, COMSATS University Islamabad (CUI), Lahore, Pakistan Alejandro H. Buschmann i~mar Centre, Universidad de Los Lagos, Puerto Montt, Chile Carolina Camus i~mar Centre, Universidad de Los Lagos, Puerto Montt, Chile Yi Herng Chan Biomass Processing Lab, Center of Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia Lai Fatt Chuah Marine Department Malaysia Northern Region, Gelugor, Malaysia Daniel Joe Dailin Biomass Processing Lab, Center of Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia Carlos Eduardo de Farias Silva Department of Industrial Engineering DII, University of Padova, Padova, Italy Sumit H. Dhawane Department of Chemical Engineering, National Institute of Technology, Durgapur, India Gumersindo Feijoo Department of Chemical Engineering, School of Engineering, University of Santiago de Compostela, Santiago de Compostela, Spain Rajiv Ganguly Department of Civil Engineering, Jaypee University of Information Technology, Waknaghat, India Jiaoqi Gao School of Life Science and Biotechnology, Dalian University of Technology, Dalian, P.R. China xv

xvi

List of Contributors

Juan Rafael Garcı´a Research Institute on Catalysis and Petrochemistry “Ing. Jose´ Miguel Parera” INCAPE (UNL-CONICET), Santa Fe, Argentina Vijay Kumar Garlapati Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, India Arup Ghosh Division of Biotechnology and Phycology, CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India; Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India Javier Gimpel Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile Sara Gonza´lez-Garcı´a Department of Chemical Engineering, School of Engineering, University of Santiago de Compostela, Santiago de Compostela, Spain Beatriz Gullo´n Department of Chemical Engineering, School of Engineering, University of Santiago de Compostela, Santiago de Compostela, Spain Gopinath Halder Department of Chemical Engineering, National Institute of Technology, Durgapur, India Majid Hosseini Manufacturing and Industrial Engineering Department, The University of Texas Rio Grande Valley, Edinburg, TX, United States Ramasubramania Iyer Department of Chemical Engineering, University of Queensland, Brisbane, QLD, Australia Khairiraihanna Johari Chemical Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia Choon Gek Khoo School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Malaysia Tetsuya Kida Department of Applied Chemistry and Biochemistry, Kumamoto University, Kumamoto, Japan Man Kee Lam Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia; Centre for Biofuel and Biochemical Research (CBBR), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia Zahra Lari Department of Biology, Ferdowsi University of Mashhad, Mashhad, Iran Keat Teong Lee School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Malaysia Hui Ying Liang Department of Chemistry, California State Polytechnic University, Pomona, CA, United States Marı´a Elena Lienqueo Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile Rahulkumar Maurya Division of Biotechnology and Phycology, CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India; Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India

List of Contributors

xvii

Sandhya Mishra Division of Biotechnology and Phycology, CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India; Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India Narges Moradi-kheibari Department of Chemistry, Ferdowsi University of Mashhad, Mashhad, Iran Maria Teresa Moreira Department of Chemical Engineering, School of Engineering, University of Santiago de Compostela, Santiago de Compostela, Spain Marcia A. Murry Department of Biological Sciences, California State Polytechnic University, Pomona, CA, United States ´ Alvaro Olivera-Nappa Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile Willem Jan Oosterkamp Oosterkamp Oosterbeek Octooien, Oosterbeek, The Netherlands Roberto Parra-Saldivar School of Engineering and Science, Tecnologico de Monterrey, Campus Monterrey, Monterrey, Mexico Ali Parsaeimehr School of Engineering and Science, Tecnologico de Monterrey, Campus Monterrey, Monterrey, Mexico Vikram M. Pattarkine PEACE USA, Mechanicsburg, PA, United States Jose´ C.M. Pires LEPABE, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal Armando T. Quitain Department of Applied Chemistry and Biochemistry, Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan; International Research Organization for Advanced Science and Technology, Kumamoto University, Kumamoto, Japan Soundarya Rajapitamahuni Division of Biotechnology and Phycology, CSIR— Central Salt and Marine Chemicals Research Institute, Bhavnagar, India; Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India Marı´a Cristina Ravanal Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile; Food Science and Technology Institute (ICYTAL), Faculty of Agricultural Sciences, Universidad Austral de Chile, Valdivia, Chile Oriana Salazar Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile Ulises Sedran Research Institute on Catalysis and Petrochemistry “Ing. Jose´ Miguel Parera” INCAPE (UNL-CONICET), Santa Fe, Argentina Eleonora Sforza Interdepartmental Centre Giorgio Levi Cases, University of Padova, Padova, Italy

xviii

List of Contributors

Kaushik K. Shandilya The University of Toledo, Toledo, OH, United States Meisam Tabatabaei Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education, and Extension Organization (AREEO), Karaj, Iran; Biofuel Research Team (BRTeam), Karaj, Iran Ahmad Farhad Talebi Microbial Biotechnology Department, Semnan University, Semnan, Iran Shweta Tewari Department of Biotechnology and Bioinformatics, Jaypee University of Information Technology, Waknaghat, India Sushma Rani Tirkey Division of Biotechnology and Phycology, CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India; Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India See Cheng Yim Biomass Processing Lab, Center of Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Perak, Malaysia Wenjie Yuan School of Life Science and Biotechnology, Dalian University of Technology, Dalian, P.R. China Suzana Yusup Biomass Processing Lab, Centre of Biofuel and Biochemical Research (CBBR), Institute of Self-Sustainable Building, Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

Preface This book highlights the novel applications of and new methodologies for the advancement of biological, biochemical, thermochemical, and chemical conversion systems required for biofuels production. The book addresses the environmental impact of value-added bioproducts and agricultural modernization, and the risk assessment of industrial scaling. The book also stresses the urgency in finding creative, efficient, and sustainable solutions for environmentally conscious biofuels while underlining pertinent technical, environmental, economic, regulatory, and social issues. It provides a basis for technology assessments, current research capability, progress, and advances, as well as the challenges associated with biofuels at an industrial scale, with insight toward expected forthcoming developments in the biofuels sector. Rapid advancements in the production of a variety of biofuels in a laboratory setting has necessitated a guide to aid in understanding the limiting factors for industrial scale-up. There is a clear gap between the application of fundamental knowledge in the development of novel conversion technologies and the commercialization of biofuels. This book explores new technologies applied to biofuels along with value-added bioproducts while discussing their developments and improvements through advanced conversion technology integration into the manufacturing processes of biofuels and explaining the practical impact of technological advances for improvement of biofuels quality and production. A compilation of current research findings is presented with an emphasis on the most recent breakthroughs in conversion technologies for biofuels and bioproducts production. Chapter 1, Microscale and Macroscale Modeling of Microalgae Cultivation in Photobioreactor: A Review and Perspective, gives a perspective on micro- and macroscales mathematical modeling that plays a significant role in optimizing the performance of a photobioreactor (PBR) for cell cultivation. The discussions in this chapter are focused on the sensitivity of the model input parameters, comparison between the predicted microalgae productivity within literature, and analysis of the influences of transport phenomenon with microalgae growth kinetics within the PBR cultivation system through model validation. The developed mathematical models are theoretically driven and statistically tractable, which is applicable to scale-up microalgae cultivation systems. The insights to the qualitative understanding of

xix

xx

Preface

PBR optimization have been achieved through improvement of modeling works, which are through the efforts of laboratory-scale experiments and field study. Chapter 2, Cell Wall Disruption: A Critical Upstream Process for Biofuel Production, presents a thorough review of possible cell disruption processes of microalgae cells, considering their advantages and disadvantages. Chapter 3, Enhancing Carbohydrate Productivity in Photosynthetic Microorganism Production: A Comparison Between Cyanobacteria and Microalgae and the Effect of Cultivation Systems, provides a comparison between cyanobacteria and microalgae based on composition, productivity, and photosynthetic efficiency, as well as the effectiveness of the continuous system as a method to exploit these organisms for carbohydrate production. The production of carbohydrates from microalgae is a promising technology that can potentially cover different fields of application, including the production of biofuels. The chapter concludes that to overcome economic issues, a deeper control of the effect of operative conditions on carbohydrate quality and quantity should be carefully considered, particularly with respect to outdoor conditions, where a fluctuating environment may affect the carbohydrate composition. Chapter 4, Production of Bioethanol From Brown Algae, presents an overview of third-generation bioethanol technology in terms of available feedstock, pretreatment technologies, and production of bioethanol from brown algae. The chapter also addresses recent advances while explaining the challenges and bottlenecks in the production of bioethanol from brown algae. Chapter 5, Approaches to Improve the Quality of Microalgae Biodiesel: Challenges and Future Prospects, discusses the current approaches and techniques (e.g., genetic and metabolic engineering, additives, emerging biorefinery approaches, etc.) for increasing the quality of microalgae biodiesel. Chapter 6, Biosequestration of Carbon Dioxide From Flue Gases by Algae, reviews the latest developments of CO2 biosequestration by algae. The chapter also describes in detail the potential of microalgae to be a sustainable technology for CO2 capture from flue gases. The chapter concludes that the optimization of the CO2 biofixation process and the valorization of achieved biomass can improve the economic competitiveness when compared to other carbon capture and sequestration technologies. Chapter 7, Using Microalgae for Treating Wastewater, highlights the environmental advantages of using wastewater as the source of nutrients for the microalgae cultivation while producing bioenergy and high-value bioproducts. The chapter discusses the challenges and opportunities of algae power and the benefit of wastewater algae and concludes that this positive symbiotic relationship can help in mitigating climate change, decreased fossil fuel availability, and degraded human and environmental health while providing

Preface

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alternative energy sources with an economic advantage. The chapter also describes heterotrophic and mixotrophic microalgae cultivation in wastewater as a flexible means to improve production economics while generating more concentrated and valuable bioproducts. The chapter suggests using indigenous microalgae species that perform well in wastewater streams or in multistage cultivation systems for specific bioproducts to ensure financial viability and overall sustainability. Chapter 8, Jerusalem Artichoke: A Promising Feedstock for Bioethanol Production, reviews the most recent advances in the production of ethanol from Jerusalem artichoke including characteristics of Jerusalem artichoke, strains for producing ethanol from inulin, fermentative processes, the current status of production, and key factors for high productivity for the construction of a cheaper, eco-friendly, and highly efficient fermentative process in future industrial endeavors. Chapter 9, Recent Advances and Future Prospective of Biogas Production, discusses recent advancements in technologies for biogas production through various biogas enhancement strategies, their process control and monitoring, anaerobic membrane bioreactors, and biogas purification. The chapter provides applications, such as capturing CO2 from biogas through its conditioning with microalgae culture and concentrating methane content. This chapter also addresses the key developments in recent years that are associated with challenges in the anaerobic digestion process. Chapter 10, Recent Advances in Lipid Extraction for Biodiesel Production, describes recent developments in the extraction methods of microalgae lipids and compares the advantages and disadvantages of each method comprehensively in terms of products and economic issues. Chapter 11, Synthesis of Catalyst Support From Waste Biomass for Impregnation of Catalysts in Biofuel Production, provides an overview of the utilization of waste biomass for the synthesis of carbonaceous support for the immobilization of the lipase enzyme and impregnation of metals to be used in the transesterification of natural feedstocks toward biodiesel production. The chapter also discusses various techniques for the preparation of highly porous carbon and different impregnation technologies. Moreover, the chapter highlights the characterization of the indigenously developed catalyst support and includes a stability study of the developed catalyst along with the optimization of the transesterification process and catalyst development process. In addition, the merits of the developed heterogeneous catalytic process is discussed in this chapter and compared with the conventional techniques of biodiesel synthesis. Chapter 12, Heterogeneous Catalytic Conversion of Rapeseed Oil to Methyl Esters: Optimization and Kinetic Study, investigates biodiesel production from rapeseed oil in the presence of impregnated bentonite with sodium hydroxide as a heterogeneous catalyst. The chapter also examines

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the transesterification reaction parametric study and optimization by response surface methodology (RSM) and gives a comparison of the fuel properties of the produced biodiesel with international standards, such as ASTM D6751 and EN 14214. Chapter 13, Fatty Acid Profiling of Biofuels Produced from Microalgae, Vegetable Oil, and Waste Vegetable Oil, analyzes biodiesel made from extracted lipids of two microalgae strains, seed plant oils, commercial biodiesel, and locally produced biodiesel from waste vegetable oil. The chapter also determines the fatty acid (FA) profiling using gas chromatography analysis of the fatty acid methyl esters (FAMEs). The chapter concludes that the analyzed microalgae contained higher percentages of saturated fatty acids (FAs), while plant biomass sources were rich in unsaturated FA. Also, the chapter suggests that the FAME content of locally produced biodiesel was higher than that of the commercially produced biodiesel. Chapter 14, The Challenges to Produce an Oxidation Stable and an Acceptable Cloud Point Biodiesel from Lipid Sources, provides a discussion on cloud points estimated by a thermodynamic equation listed with three indices, estimated for unsaturation present in three biodiesel mixtures, microalgae species exposed to replete and limited nutrient conditions, and describes the challenges in preparing an oxidation stable and acceptable cloud point biodiesel. The chapter gives detailed description of an alternative reported methodology of indices double bond equivalent, allylic position equivalent (APE), and bis-APE, estimated from reported unsaturated FAME of two microalgae, three biodiesel mixtures, and a single microalgae exposed to replete and limited nutrient conditions. The chapter explains that reinterpretation of reported data on autoxidation of methyl oleate and linoleate mixtures discounts IV , 120 as an oxidation stability standard. Chapter 15, Supercritical Extraction of Value-Added Compounds from Empty Fruit Bunch: An Optimization Study by Response Surface Methodology, investigates value-added compounds from oil palm empty fruit bunches (EFB) using supercritical CO2 and water as a cosolvent (modifier). The chapter includes an optimization study of extraction parameters using RSM with central composite rotatable design. The chapter also characterizes the supercritical extract of EFB using GC
MS and compares it with an extract obtained by Soxhlet extraction using hexane. The chapter concludes that the quantity and quality of the extract obtained by supercritical CO2 is superior to that of the Soxhlet extraction. Chapter 16, Progress in Anaerobic Digestion of Manures, reviews most recent progress in anaerobic digestion of manures. At present, most manure anaerobic digestion plants convert the methane on site into electricity. The chapter highlights different types of reactors used in the process and suggests that the methane yield of manure substrates must be increased and the costs

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for anaerobic digestion plants must be reduced for further expansion of manure anaerobic digestion by simplification and standardization. Chapter 17, Thermochemical Processes Aimed at the Energy Valorization of Cow Manure From Feedlots, discusses the pyrolysis of cow manure, with emphasis on the characterization of products. The chapter describes that the biooil contained mainly acids, ketones, and furans, typical in the pyrolysis of cellulose, hemicellulose, and lignin, while the tar contained mainly alcohols and long-chain esters, derived from depolymerization and cracking of lipids and proteins. In comparison with other raw biomass more extensively studied, cow manure produced much more tar, which could be important in, for example, bioasphalt formulations. The chapter also identifies a process that could alleviate the environmental impact produced by concentrated animal feeding operations (feedlots) by means of using solid livestock wastes as the raw material to produce energy and/or chemicals. Chapter 18, Environmental Concerns on the Production of Value-Added Bioproducts from Residual Renewable Sources, evaluates the environmental impacts of the valorization of two types of waste biomass (i.e., sugar beet pulp and woody chips) for the production of saccharides streams (pectinderived oligosaccharides and polymeric or oligomeric saccharides derived from hemicelluloses) with manifold applications in the food, nutraceutical, and pharmaceutical industries. Chapter 19, Life Cycle Assessment of First-, Second-Generation, and Microalgae Biofuels, examines a life cycle assessment (LCA) for first- and second-generation biofuels along with microalgae biofuels. The chapter also presents an overview of greenhouse gas (GHG) emissions generated from some biofuels in comparison with conventional fuels (both diesel and gasoline) assuming no change in land use pattern. The chapter concludes that certain pathways of biofuel production gave better results (e.g., ethanol from sugarcane, sugar beet, wheat) in reducing GHG emissions, wherein other cases the reverse trend was observed (e.g., corn, ethanol, soybean, and palm biodiesel). The LCA studies reported a comparison of GHG emissions in terms of different environmental impact indicators, such as acidification, eutrophication, smog, toxicity, etc. The chapter concludes that existing LCA procedures will not take full large-scale deployment of new technologies but can be combined with other assessment tools such as agro-economic market models. Hence, these existing LCA procedures have to be further improved to minimize the uncertainty issues by taking into account of other measures, such as acidification potential, environmental, and land usage impact indicators. Finally, a number of people have helped make this book possible. I hereby acknowledge Ms. Ana Claudia Abad Garcia, our Editorial Project Manager at Elsevier, Ms. Raquel Zanol, our Acquisition Editor at Elsevier, Ms. Sheela Bernardine Josy at Elsevier, Mr. Omer Mukthar, our Production

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Project Manager at Elsevier, Mrs. Megan Rohm, and all contributors and reviewers, without whose contributions and support this book would not have been written. I thank you all for all of the excellent work and assistance that has been provided in moving this book project forward. Majid Hosseini Fall 2018

Chapter 1

Microscale and Macroscale Modeling of Microalgae Cultivation in Photobioreactor: A Review and Perspective Choon Gek Khoo1, Man Kee Lam2,3 and Keat Teong Lee1 1

School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal, Malaysia, 2Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia, 3Centre for Biofuel and Biochemical Research (CBBR), Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

1.1 INTRODUCTION In recent decades, microalgae biomass has emerged as a new and sustainable energy source for third-generation biofuels production. The potential biofuels that can be derived from microalgae biomass are biodiesel, bioethanol, biohydrogen, and biomethane [1
4]. Microalgae have a simple cellular structure that allows them to grow rapidly (100 times faster than terrestrial plants) and are able to double their biomass in less than 1 day under favorable cultivation conditions. Some microalgae species are able to accumulate a significant amount of lipids within their cells, such as Botryococcus braunii (lipid content of 25%
75%), Chlorella sp. (28%
32%), Scenedemus sp. (20%
21%), and Nannochloropsis sp. (31%
68%), in which the lipid can be further converted to biodiesel [2,3]. From an environmental perspective, the cultivation of microalgae coupled with CO2 fixation and biotreatment of wastewater has evolved as a clean and green energy producer [5,6].

1.2 PHOTOBIOREACTOR SYSTEM A photobioreactor (PBR) is a closed system that is usually used for microalgae cultivation. It is designed based on several basic features, such as illumination surface area, liquid circulation, and gas exchange to supply CO2 to the system and to degas O2 [7]. A PBR that has high degree of cultivation Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00001-1 © 2019 Elsevier Inc. All rights reserved.

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process control is more suitable and reliable for large-scale microalgae cultivation [3]. In addition, it is also easier to optimize biomass productivity based on the biological and physiological characteristics of the cultivated microalgae species [7]. The general conceptual design of a PBR is that it is a solar receptor that is usually made of glass or plastic, coupled with a gas exchange vessel where CO2 and nutrients are added to the cultivation system. Excess O2 is then removed through the end of the PBR tubing. A pump or airlift system is usually used to circulate the microalgae culture in the PBR to prevent microalgae from settling and to improve the CO2
liquid mass transfer [8]. This photoautotrophic cultivation system utilizes protons (e.g., sunlight) as the main driven energy source, in which these protons are emitted through the transparent PBR walls before reaching to the cultivated microalgae cells instead of impinging directly on the culture’s surface [9].

1.3 MATHEMATICAL MODELS Mathematical models are one of the important interpretations of experimental results that provide a greater insight toward the studied biological system [10]. Throughout the decades, there has been a great deal of importance placed on predicting the productivity of the microalgae biomass and the transport phenomena in PBR through mathematic modeling [11]. The mathematical models can be conceptually categorized into macroscale and microscale, which described the operational performance in PBRs and the growth of microalgae cells, respectively [12
14]. Fig. 1.1 illustrates the phenomena

FIGURE 1.1 Phenomena occurring during microalgae cultivation within photobioreactors: (A) macroscale transport phenomena and (B) microscale kinetic growth for microalgae cells.

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occurring at the macroscale and microscale of microalgae growth in PBR. Generally, the developed mathematical modeling follows the sequences from microscale of microalgae growth to the macroscale of transport phenomena in PBR. The integration between micro- and macroscale modeling are theoretically driven, which is applicable to the scale-up the microalgae cultivation process. However, the complexity of macroscale modeling with respect to the simple expressions of microscale modeling is not widely reported.

1.4 MACROSCALE MODELING There are two types of PBR that have been widely used for microalgae cultivation, namely column and flat plate types, each with pros and cons in operation (Table 1.1) [4,8,15]. The aeration strategies in the PBR are different, in which the air can either be circulated around or blown forcefully. By categorizing the PBR according to bioprocess operation rather than

TABLE 1.1 The Advantages and Drawbacks of Different Types of Photobioreactors for Microalgae Cultivation PBR Design

Advantages

Column

G

G G G G G G G G G G

Flat plate

G G G G

G G

Drawbacks

Radial movement of fluid for improved light
dark cycle with less photoinhibition and photooxidation Greater gas hold up High mass transfer rate Lower energy consumption Easy to sterilize Readily tempered Good for algae immobilization Suitable for outdoor cultures Large illuminated surface area Good biomass productivities Cheap

G

Suitable for outdoor cultures Large illuminated surface area Good for algae immobilization Better biomass productivities than bubble columns Ease of maintenance Readily tempered

G

G G G G G

G G

G

G G

G

G

Low surface/volume Expensive Smaller illuminated surface area Required sophisticated materials Shear stress to algal cultures Large number of units required for commercial plants due to a fixed diameter to height ratio Fouling Some degree of wall growth, dissolved O2, and CO2 along the tubes pH gradients Difficulty in culturing temperature controlled Some degree of wall growth Required support materials for scale-up Hydrodynamic stress to algal strains Higher power supply cost

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by physical design features would result in a more accurate mathematical model. Perfect mixing is usually assumed when mathematical modeling an aerated PBR, to simplify the models. In macroscale modeling, the PBR characteristics are defined in terms of volumetric mass transfer coefficient (kLa), fluid hydrodynamics, mixing time determination, superficial gas velocity, specific power input, and fluid velocity [16
18]. These parameters are often used to scale-up the bioprocess in a PBR. The advancement of bioprocessing established a series of engineering characterizations for a single-use system in a PBR, which are (1) parametric, (2) experimental, and (3) computer-based numeric analysis [19]. Application of each method will further enhance the understanding of the PBR system’s performance and process optimization. Table 1.2 summarizes the general parametrical methods used for the engineering characterization of a PBR.

TABLE 1.2 General Parametrical Methods Used for Engineering Characterization of an Aerated Photobioreactor Parametrical

Mathematical Models

Nomenclature

Reference

Flow regime

Re 5 ρL Nda2 =ηL Ne 5 P =ρL N 3 da5

Re: Reynold number ρL: Fluid density (kg m23) da: Agitator diameter (m) ηL: Fluid viscosity (kg m21 s21) Ne: Newton number P: Power input

[23]

Fluid velocity

umax 5 uTip 5 πNda

umax: Maximal fluid velocity (m s21) utip: Tip speed (m s21)

[16,19,23]

Superficial gas velocity

UG 5

UG: Superficial gas velocity (m s21) VG: Flow rate of gas (m3 s21) A: Area (m2)

[19]

Power consumption

P =V 5 ððM 2 Md Þ2πN Þ=V

V: Working volume (m3)

[17,19]

VG A

(Continued )

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TABLE 1.2 (Continued) Parametrical Mixing time distribution

Volumetric mass transfer coefficient

Mathematical Models 

   H ðt Þ 5 Cτ;0 2 C ðtÞ = Cτ;0 2 Cτ;ω

 C kL a 5 C1 P =V 2 ðVG ÞC3

Nomenclature

Reference

H: Homogeneity Cτ,0: Tracer concentration at the start of experiment (kg m23) Cτ,ω: Tracer concentration at the end of experiment (kg m23) C(t): Tracer concentration at time t (kg m23)

[24,25]

kLa: Volumetric mass transfer coefficient (s21) C: Concentration (kg m23)

[21]

1.4.1 Volumetric Mass Transfer Coefficient CO2 is supplied to photosynthetic microalgae cells to grow; however, it is usually regarded as a rate-limiting factor as the cultivation scale increases [20]. The CO2 transfer rate is expressed as the overall volumetric mass transfer coefficient (kLa) [21]. According to Kadic and Heindel [22], there are a few methods to measure the kLa, which are (1) static gassing out, (2) sulfite methods, and (3) dynamic gassing out. The static gassing out is measured based on the dissolved oxygen (DO) during microalgae cultivation, where N2 gas is first introduced to the PBR system to create an inert environment before sparging with compressed air or CO2-enriched air. On the other hand, the sulfite method is measured based on chemical reaction of sulfite (SO22 3 ) to sulfate (SO22 ) in the presence of DO and is catalyzed by copper, ferric, 3 cobalt, or manganese ions. As for the dynamic gassing out method, the DO is measured during microalgae cell growth.

1.4.2 Fluid Hydrodynamics The fluid hydrodynamics in a PBR can be described though flow regimes and can be examined experimentally. The flow regimes included laminar,

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transitional, and turbulent flow. The dimensionless Reynold’s number (Re) estimation is directly correlated to the physical properties (e.g., density and viscosity) of the fluid’s flow [23].

1.4.3 Mixing Time Determination The operation of a PBR greatly depends on the microalgae cells’ productivity throughout the cultivation period. Mixing time can be determined by the acid tracer method that combines both acid injection and pH measurement over time [24,25].

1.4.4 Superficial Gas Velocity The theoretical superficial gas velocity (UG) is important in predicting the liquid dispersal of an aerated process. In the typical bioprocess operation of a PBR, the diameter of the gas bubbles is influenced by UG in the turbulent flow regime [19].

1.4.5 Specific Power Input The specific power input plays a vital role in mechanically driving a scaleup operation of a typical PBR. The power input can be correlated by temperature and a numerical dimensionless number, which enables the prediction of the Newton number (Ne) [19]. In addition, the specific power input can be correlated with the superficial gas velocity in a pneumatically mixing PBR [17].

1.4.6 Fluid Velocity Fluid velocity is generally dependent on the type and design of the PBR. The maximal fluid velocity (umax) is generated by the aeration system and is influenced by the diameter of the agitator or sparger and the microalgae cultivation vessel [16,19,23].

1.5 MICROSCALE MODELING The following sections discuss the parameters required for microalgae cell growth, which can be employed for kinetic growth modeling. Generally, the growth models can be categorized into primary and secondary models. The primary models describe the growth with the least parameters, which only provide a rough estimation of microalgae productivity. As for the secondary models, a few features are taken into consideration to enhance the accuracy of the predicted microalgae productivity, such as temperature, light, photosynthesis rate, pH, and nutrients concentration [26,27]. Besides, sensitivity of model input with microalgae productivity rate could be used to further strengthen the understanding of microalgae growth in a PBR.

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FIGURE 1.2 The empirical kinetic profiles for primary models. In applying nonlinear regression to experimental data, zero time will typically be taken as the end of the lag-phase.

1.5.1 Basic Kinetic Equations: Primary Models The main objective of the primary models is to express the microalgae cells growth with the fewest parameters at their respective growth phases [28]. There are three basic primary kinetics models: (1) Malthusian model, (2) logistic model, and (3) Gompertz models, as shown in Fig. 1.2. Among the three basic kinetic models, the logistic model is the most widely used in expressing microbial growth kinetics. This is due to the simplicity of the mathematic models, which described the entire growth curve from lag-phase until the latter stages of the cell’s death [29]. The detailed description of each basic kinetic model is discussed in the following sections.

1.5.1.1 Malthusian Model The Malthusian model is often referred to as the exponential law [30], in which the concentration of microalgae cells is expressed as exponentially increasing with time. This phenomenon indicates the potential ability of microalgae cells to boost its productivity. A direct proportional relationship between the microalgae cells concentration with biomass accumulation can be expressed by the first-order rate equation as shown in the following equation: rX 5 μX

ð1:1Þ

where rX represents the cell growth rate (kg cell m23 h21), X is the cell concentration (kg cell m23), and μ represents the kinetic growth constant,

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namely the specific growth rate (1/h). On the other hand, the cell growth over a discrete period of time (t) for a batch system can be expressed in the following equation: dX 5 μUXðtÞ dt

ð1:2Þ

This equation can be further differentiated and expressed in the following equation: XðtÞ 5 X0 UexpðμtÞ

ð1:3Þ

where X0 represents the initial cell concentration at the starting cultivation time, t 5 0 (as in Fig. 1.2). However, the Malthusian model has violated the first kinetic principle [31], where cell growth cannot achieve its saturation point as time is extended.

1.5.1.2 Logistic Model The logistic model is expressed in the following equation:   dX X 5 μX 1 2 dt K

ð1:4Þ

where K (kg cell m23) represents the maximum cell concentration that can be supported by the cultivation environment. The main drawback of logistic model is that it cannot be validated if the microorganism does not grow continuously until it reaches the death phase. The integration of the logistic model can be further expressed as follows: X ðtÞ 5

K 1 1 CKexpð 2μtÞ

ð1:5Þ

The logistic trend is illustrated in Fig. 1.2, where C 5 1/X0 2 1/K is determined by the initial condition X0.

1.5.1.3 Gompertz Model The Gompertz model is usually expressed as a sigmoid function, which is similar to the logistic curve. The Gompertz model exhibits a slower growth at the initial cultivation stage and at the end of the cells’ growth (Fig. 1.2). The Gompertz model is expressed in the following equation: X ðtÞ 5 aexp½ 2 bexpð 2ctÞ

ð1:6Þ

where a is the upper asymptotic value, b is the x displacement, and c is the growth rate, while the initial cell concentration at t 5 0 is X0 5 a exp(2b).

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1.5.2 Dynamics Kinetic Equations: Secondary Models The secondary models described the microalgae cells’ growth dynamics as a function of environmental conditions. It is usually modified based on the Monod and Droop models [32]. The Monod model [33] is a general kinetic model that describes the correlation of microalgae cell growth under a limited nutrient concentration environment as expressed in the following equation: μ 5 μmax

S ðKs 1 SÞ

ð1:7Þ

where μmax represents the maximum specific cells’ growth rate, S represents the concentration of nutrients in the cultivation medium, and KS represents the half-saturation constant (the nutrient concentration at which the specific growth rate is half of the maximum). The Monod model is preferred due to the ease of measuring the external substrate concentration. Nonetheless, the Monod model has low accuracy in predicting the microalgae cells’ growth rate due to the high uptake of nutrients by the microalgae [34]. Besides, Richmond [35] also indicated that the growth rate of microalgae is more dependent on internal cellular concentrations rather than the external quantities. In contrary, the Droop model [36] relates the microalgae cells’ growth rate with internal nutrients concentrations as shown in the following equation:   Kq μ 5 μmax 1 2 ð1:8Þ q where Kq represents the limiting cell quota for the limiting nutrient while q represents the cell quota for the limiting substrate. Factors that affect microalgae growth rates include the availability of carbon sources (measured by inorganic carbon concentration), nutrients concentration (nitrogen and phosphorus), light intensity, inhibition, temperature, and combination of multiple factors. Table 1.3 summarizes some of the commonly used microalgae kinetic models derived from the Monod or Droop models, together with models that integrate with multiple factors from Lee et al. [37] and Be´chet et al. [38]. The Monod and Droop models are basically used to express activity of microalgae growth performance in single variant, either by considering the external [39
41] or internal [42] changes. After all, the modification of the Monod models with the integration of multiple factors (preferably with the consideration of light intensity and temperature [43
46]), yield a sophisticated model that is able to attain greater accuracy in the prediction of cells productivity.

TABLE 1.3 Commonly Used Kinetic Models With Correlation Factors of Inorganic Carbon Concentration, Nitrogen Concentration, Phosphorus Concentration, Light Intensity, Inhibition, Temperature, and Combination of Multiple Factors Kinetic Model

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

(a) Monod Model—External Factor    μ 5 μmax Sc = KSc 1 Sc

a.1

μmax: Maximum specific cells growth rate Sc: Carbon concentration KS,c: Half-saturation constant

Inorganic carbon concentration

CO2 source: 13.1 mg L21 TIC Scenedesmus quadricauda pH: 7.10
7.61 T: 27 C μmax: 2.29 day21 KSc: 0.30
0.71 mg L21 Scenedesmus capricornutum pH: 7.05
7.59 T: 27 C μmax: 2.45 day21 KSc: 0.40
1.20 mg L21

[39]

   μ 5 μmax SN = KSN 1 SN

a.2

μmax: Maximum specific cells growth rate SN: Nitrogen concentration KS,N: Half-saturation constant

Nitrogen concentration

N source: 41.8
92.8 mg L21 NH4-N Chlorella vulgaris pH: 7 T: 20 C k 5 μmax/YN: 1.5 mg mg21 day21 KSN: 31.5 mg L21

[40]

(Continued )

TABLE 1.3 (Continued) Kinetic Model

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

   μ 5 μmax SP = KSP 1 SP

a.3

μmax: Maximum specific cells growth rate SP: Phosphorus concentration KS,P: Half-saturation constant

Phosphorus concentration

P source: 7.7 mg L21 PO4-P C. vulgaris pH: 7 T: 20 C k 5 μmax/YN: 0.5 mg mg21 day21 KSN: 10.5 mg L21

[41]

   μ 5 μmax I= KS;I 1 I

a.4

I: Light intensity KS,I: Saturation light intensity

Light intensity limitation

I source: 0
71.8 mW m22 C. vulgaris pH: 6.8 T: 22 C Air and CO2 flow: 200 mL min21 μmax: 0.040 h21 KI: 2.8 mW L21

[42]

b.1

μmax : Maximum specific cells growth rate Qmin: Limiting cell quota for the limiting nutrient

Phosphorus concentration

P source: 0.352
324 3 10215 mol cell21 Scenedesmus pH: 7.2 T: 12 C 0 μmax : 0.755 day21 Qmin: 5.16 fmol cell21 Chlorella pH: 7.2 T: 12 C 0 μmax : 0.842 day21 Qmin: 0.352 fmol cell21

[42]

(b) Droop Model—Internal Factor    0 μ 5 μmax 1 2 Qmin =Q

0

(Continued )

TABLE 1.3 (Continued) Kinetic Model

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

(c) Models With Consideration of Multiple Factors μ5K

00



  ðε∙al ∙X∙I0 Þ=ðX∙V Þ 2 Im ð1 2 VF Þ

      I 2 μ 5 μmax ðI Þ= I 1 μmax Iopt 21 α

00

c.1

K : A proportionality constant (kg mol21) ε: A constant al: Effective light absorption surface area of each cell (m2) X: Cell concentration (kg m23) V: Liquid volume in the reactor (m3) I0: Incident light intensity (mol m22 day21) Im: Maintenance rate (mol kg21 day21) VF: Illuminated volume fraction of the reactor

Light-limitation associated with light attenuation by cells

Chlorella pyrenoidosa Aeration rate: 0.6 vvm K: 0.8 kg mol21 X: 0.01905 kg m23 V: 0.00075 m3 Imax: 0.13 mol kg21 day21

[43]

c.2

α: Initial slope of light response curve Iopt: Irradiance for which growth is maximal with respect to light μmax: Maximum growth rate for optimal irradiance

Light-limitation and photoinhibition

C. pyrenoidosa (wild type) Topt: 38.7 C α: 0.05 Iopt: 275 μE m22 s21 μmax: 2.00 day21

[44]

(Continued )

TABLE 1.3 (Continued) Kinetic Model    S  I=ðIS 1 I Þ μ 5 μm ðS Þ=S 1 KS 1 KI      1 2 Cx =Cxm 1 2 Cp =Cpm

  2  C1 μ 5 μmax C1 = Kn 1 C1 1 Ki m  m  En = Ke 1 Enm IðT Þ where IðT Þ 5 1:066ðT 220Þ

TIC, total inorganic carbon.

Nomenclature

Correlation Factor(s)

Example of Kinetic Experimental Results

Ref.

c.3

Cpm: Maximum product concentration (mg L21) Cp: Product concentration (mg L21) Cxm: Achievable maximum cell concentration (g L21) Cx: Cell concentration (g L21)

Acetate concentration, microalgae cell concentration, and light intensity

Haematococcus pluvialis μmax: 0.5258 day21 Cxm: 2.92 g L21 Cpro,m: 55.6 mg L21 KS,OC: 0.0211 g L21 Ki,OC: 56.6813 g L21 KI: 53.26 μmol m22 s21

[45]

c.4

C1: Concentration of bicarbonate in culture (mol L21) Kn: Half-saturation constant of carbon (mol CO2 L21) Ki: Inhibition constant of carbon (mol CO2 L21) En: Average radiant energy within bulk culture medium (μE m22 s21) Ke: Half-saturation constant of light (μE m22 s21) I(T): Function to include temperature effects T 5 temperature ( C)

Inorganic carbon concentration, light intensity, and temperature

Scenedesmus sp. μmax: 1.4 day21 Ke: 131 μE m22 s21 Kn: 9.818e 2 4 mol L21 kd: 0.165 day21 Kc: 0.16 cm2 mg21 Chl a fi: 0.016 Kw: 0.001 cm21 Kni: 1 mol L21 m: 2.27

[46]

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1.6 CHALLENGES IN SCALE-UP MICROALGAE CULTIVATION Currently, the production of microalgae biomass is identified as a green and sustainable feedstock for biofuel production [2,47,48]. There are four key steps in designing and optimizing the microalgae cultivation process on an industrial scale which are (1) strain identification, (2) strain modification, (3) process design and optimization, and (4) process scale-up and validation. However, there is still a lack of evidence to support the industrial cultivation of microalgae biomass due to the challenges of economic feasibility and technology maturity [48,49]. There are a number of challenges in large-scale microalgae cultivation, which include the selection of microalgae strains, cultivation methods, nutrients, and carbon sources for microalgae growth [50]. In addition, an ideal microalgae scale-up cultivation process should be able to meet a positive energy balance, especially when the biomass is further processed to biofuels. Therefore the cultivation systems should be designed to promote the high photosynthetic activities of microalgae [1,2]. Besides, transport processes such as the loses of CO2 due to diffusion and high evaporation rates that cause high salinity values should be taken into consideration during the scale-up study [4,51].

1.7 POTENTIAL INDUSTRY APPLICATION The current trends in modern biotechnology development intend to extend the knowledge of life science to a scalable industrial production for commercialization purposes. A typical bioprocess system description can be generated through the adaption of biological, chemical, or physical analytical methods and are then recorded and interpreted for process control. Additionally, bioprocess optimization and intensification involves sophisticated data analysis, which requires data pooled from variety sources. Therefore more information on measuring, monitoring, and modeling are required to understand the actual performance in a biosystem and to further enhance production sustainability [52]. Among all these, the mathematical modeling plays a vital role in designing and optimizing the intensified bioprocesses. Quinn et al. [53] reported that the productivity of microalgae could not be directly extrapolated from laboratory-scaled to industrial production. The application of the constructed mathematical models should be validated with the respective industrial facilities under regional environmental scenarios in order to minimize the inaccuracy due to locational factors. Also, the biological
physical interaction of the microalgae with the cultivation system plays an essential role in microalgae productivity performance. For instance, the influence of hydrodynamics and mass transfer plays a critical role in determining microalgae productivity at pilot-scaled cultivation [54]. In addition,

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the use of dynamic modeling in growth kinetics enables a more precise of microalgae productivity [55
57], which includes consideration for both biotics and abiotic factors. The advancements of microalgae growth models that integrate the variability of biological function with respect to their seasonal and geographical influences are expected to make a significant scientific breakthrough at commercial scale. A study carried by Khoo et al. [54] showed the potential of model integration yielding insights into the interaction of macro- and microscale phenomena in a PBR during microalgae cultivation.

1.8 CONCLUSION By considering macro- and microscale factors when constructing a mathematical model can improve the understanding of complex microalgae growth in a PBR. Besides, mathematical descriptions on reaction-diffusion provide a clear insight on the scale-up process. Most of the available mathematical models predict the dynamic state of microalgae growth and their transport phenomenon separately by using numerical methods with certain modification. However, more theoretical frameworks on the integration of both macro- and microscale modeling associated with experimental validation are required to enhance the performance of PBRs at commercial scale and to improve the economic feasibility of microalgae biofuel production.

1.9 FUTURE OUTLOOK The main challenge of mathematic modeling in a biological process is dealing with the complicated integration of cell’s activity within the hierarchy of numerous biological levels, which include molecular, cellular, and multicellular sources [58]. In order to have a better understanding on biological process development, future mathematical models should consider the following: 1. A more realistic prediction for a microalgae cells’ kinetics study (growth and death kinetic) and transport phenomena (mass and heat transfer) at macro- and microscale operation. 2. Detailed evaluation on the energy, water, and O2 balances are required for PBR control schemes design, and to maintain the PBR temperature and water content at the optimum value for high microalgae biomass productivity. 3. Improvement in computing power for routine control of the optimized bioprocess in the PBR.

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ACKNOWLEDGMENT The authors would like to acknowledge the financial support received from the Universiti Sains Malaysia, through Short Term Grant (304/PJKIMIA/6315016) and the Ministry of Higher Education (MOHE) Malaysia, through Fundamental Research Grant Scheme Malaysia’s Rising Star Awards 2016 (FRGS MRSA 2016) (203/PJKIMIA/6071362) and Fundamental Research Grant Scheme (FRGS) with cost center 0153AB-L25. C.G. Khoo wishes to acknowledge the financial support from the MOHE Malaysia through Ph.D. scholarship scheme (MyBrain15-MyPhD) and M.K. Lam wishes to acknowledge the technical support from Centre for Biofuel and Biochemical Research (CBBR) of Universiti Teknologi PETRONAS.

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[34] U. Sommer, A comparison of the Droop and the Monod models of nutrient limited growth applied to natural populations of phytoplankton, Funct. Ecol. 5 (4) (1991) 535
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[55] J.P. Bitog, I.B. Lee, C.G. Lee, K.S. Kim, H.S. Hwang, S.W. Hong, et al., Application of computational fluid dynamics for modeling and designing photobioreactors for microalgae production: a review, Comput. Electron. Agric. 76 (2) (2011) 131
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Chapter 2

Cell Wall Disruption: A Critical Upstream Process for Biofuel Production Zahra Lari1, Hossein Ahmadzadeh2 and Majid Hosseini3 1

Department of Biology, Ferdowsi University of Mashhad, Mashhad, Iran, 2Department of Chemistry, Ferdowsi University of Mashhad, Mashhad, Iran, 3Manufacturing and Industrial Engineering Department, The University of Texas Rio Grande Valley, Edinburg, TX, United States

2.1 INTRODUCTION Cell wall disruption is an important upstream process of extracting the valuable metabolites from microalgae which must be carried out after biomass harvesting [1]. Due to the rigid and resistant protective layer surrounding microalgae cells, the liberation of cell components such as lipids and carbohydrates from their intracellular location into the external medium is prohibited [2]. For instance, triacylglycerols (TAGs), the main form of convertible lipids in biodiesel production, have been located within lipid droplets in cytoplasm. These oily particles have been surrounded by a tough cell wall which works as a barrier against the oil being dissolved into the solvents [3]. Three times higher extracted lipid from cell wall
less mutants of Chlamydomonas reinhardtii when compared to the wild-type microalgae exhibits that the use of cell wall disruption would help to improve the extraction efficiency [4]. The cell disruption process, while required to break down the cell walls, also aids in lipid liberation (i.e., enhanced extraction), reduces the content of insoluble residues (i.e., improved mass transfer), and decreases emulsifiers (i.e., enhanced recovery of solvents) [1,5]. Most microalgae species have lipids located within their cells, some with higher levels than others [5]. The easy extraction of these lipids is prevented by the microalgae’s strong cell walls and membranes, causing the need for increased energy demand during the cell disruption process and thus requiring additional consideration when creating a low-cost biofuel [1,6
9]. Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00002-3 © 2019 Elsevier Inc. All rights reserved.

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Some microalgae species such as Dunaliella are naked cells with only one outer glycocalyx layer, while a few microalgae species such as Tetraselmis are covered with a theca that consists of fused elegant crystals. The cell wall of some microalgae (e.g., Nannochloropsis and Chlorella) is composed of structural fibrils trapped in complex matrices of carbohydrates [10,11]. Generally, the complicated chemical structure of carbohydrates and glycoproteins gives the microalgae cell walls mechanical strength and chemical resistance [3]. The strength of the microalgae cell wall as well as its resistance against disruption methods makes cell wall disruption a bottleneck process for lipid extraction. Due to different cell wall types of microalgae, the best method of cell wall disruption is species specific. The overall lipid extraction efficiency for biodiesel production may be influenced by the efficiency of the disruption method. In addition, lipid quality which directly affects biodiesel performance can also be influenced by the disruption method. Therefore an optimum method for disintegration of algae cells should increase the extraction efficiency and maintain the fatty acid composition as well [12]. It should be noted that the disruption process may become economical if the improved extractability of lipids surpasses the disruption costs. Some extraction techniques (e.g., enzymatic, supercritical fluid, and Soxhlet extractions) do not require a separate stage of cell wall disruption, because during these extraction processes, the cell collapses [13,14]. Applying a disruption method is beneficial particularly for wet biomass, mainly because the water is immiscible in organic solvents [3]. Hence, most disruption methods cannot be applied for thickened microalgae containing low water content [15]. Since water is vital for the successful operation of some cell disruption methods (e.g., ultrasonication), this step must be performed before drying the biomass [15]. Mechanical and nonmechanical methods are the two general categories of cell disruption [16]. The mechanical categories include microwaves, freeze-drying, ultrasonication, highpressure press, homogenization, and bead-beating while the nonmechanical categories involve physical, chemical, or enzymatic disruption approaches [17]. These methods are typically applied separately; however, they can also be used in combination to enhance the cell wall disruption efficiency [18]. For example, the combination of mechanical and chemical methods usually leads to the enhancement of lipid yield due to increasing solvent contact with microalgae [13].

2.2 MECHANICAL CELL WALL DISRUPTION METHODS Bead-beating, pressing, ultrasonication, autoclaving, and homogenization are classified in this group, all of which are energy consuming [13]. Since mechanical methods are not accompanied with chemical contamination of microalgae biomass, the cells components remain chemically unchanged

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[13]. However, physical parameters such as high temperature generated during some mechanical methods (e.g., microwave) can change the biochemical composition of microalgae cells. Bead-beating, ultrasonication, and highpressure homogenization are among the mechanical methods that can be utilized in laboratory scale for microalgae cell disruption [19] and they usually exhibit a better efficiency at high cell concentrations [20]. Although these treatments are effective for several microalgae species, their utilization for large scale seems to be challenging [17]. They consume a lot of energy because a continuous supply of energy inputs (e.g., thermal, electrical, or mechanical) must be applied for breaking the cell wall [3].

2.2.1 Bead-Beating In the bead-beating technique, the glass or steel beads are positioned in a cylindrical compartment where the high-speed agitating movement of beads can damage the cells [13]. The microalgae cell walls are disrupted when they are physically grinded “against the solid surfaces of beads in a violent” spinning motion [21,22]. This mechanical disruptive method is considered an efficient technique. Microalgae cell disruption via bead-beating for a few strains has been reported (e.g., Scenedesmus sp., and Botryococcus sp.) [23]. Disruption efficiency of bead-beating is mainly dependent upon the shape of beads, size, composition, the contact between the cells and beads, and the resistance of microalgae cell walls [13]. Although the use of bead-beating resulted in a higher lipid extraction from Botryococcus sp., when compared to other evaluated techniques (e.g., lyophilization, French press, homogenization, and sonication), it exhibited the lowest efficiency (7.9%) in lipid extraction of Chlorella vulgaris [23]. It has also been reported that bead-beating did not increase the lipid yield of Phaeodactylum cornutum significantly; however, this technique was shown to be an effective method for extracting nonpolar lipids, the major constituents in biodiesel [24]. Bead-beating can be applied separately or in combination with other techniques such as chemical or other types of mechanical methods. It is worth mentioning that its efficiency improves especially in dense biomass concentrations (100
200 g L21) [25]. Therefore if the biomass is supposed to be processed by bead-beating, it should be concentrated after harvesting. Beadbeating has been applied successfully on both bench and industrial scales [23,26]. Although high energy input is the main challenge of using this method in industry, it is still a practical method for large-scale disruption of microalgae [13], due to its low operating cost [19].

2.2.2 Homogenization High-pressure homogenizers pump microalgae paste under high pressure and accelerate the liquid movement to high-shear forces leading to the collapse

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of cell walls [13]. High-shear homogenization systems are effective for the extraction of value-added products. As an example, disruption of Haematococcus cells using a combination of high-pressure homogenizer and acid, bases, and enzymes resulted in higher astaxanthin yield than enzymatic treatment [27]. It was also reported that high-pressure homogenization was an effective method for the disruption of Chlorococcum sp. [15]. Highpressure homogenizers are classified into two main types: rotor
stator and blade homogenizers [13]. The Potter-Elvehjem homogenizer is also applied manually for the homogenization of microalgae suspensions at small scale. It has been found that homogenization using a Potter-Elvehjem homogenizer enhanced the lipid yield of Scenedesmus dimorphus while it did not prove to increase the extractability of Selenastrum minutum and Chlorella protothecoides [28].

2.2.3 High-Pressure Press The exposure of a microalga cell to higher pressure can rupture its cell wall and release its contents [13]. Various press instruments (e.g., screw, expeller, or piston) have been applied for different microalgae species [13]. The combination of mechanical pressing and other disruption approaches may help to increase lipid extraction efficiency from microalgae [29,30]. For example, a combination of French press and microwave improved the disruption rate of Nannochloropsis salina [31].

2.2.4 Ultrasonication Transmission of sonic waves, by a method known as ultrasonication, leads to microalgae cell disruption [21]. Several questions remain unanswered about the mechanism of microalgae cell wall disruption by ultrasound [32]. In different powers and frequencies of ultrasounds, cell rupturing happens due to different mechanisms. Overall, the damages caused by this method occur through two main mechanisms: cavitation and acoustic streaming. The effects of low-power ultrasound are attributed to radiation forces and acoustic streaming in cytoplasm and accelerate the mixing of the microalgae culture [32]. Cavitation is a phenomenon in which microbubbles are created, and as they grow, they join to each other and form bigger bubbles that consequently break the microalgae cells. Furthermore, inside the bubbles, the temperature can increase up to 5000 K. These severe conditions cause cell disruption and lead to the release of intracellular materials into the liquid [33]. Hence, high-intensity-focused sonic energy can destroy the cells or tissues by introducing high pressure and heat [17]. It must be noted that “cavitation effects” may comprise both chemical and physical effects, including microjet, shear stress, shock waves, and free radical reactions [32].

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The sonic waves possess enough energy to break covalent connections within the cell wall chemicals. During high-power sonication, very reactive hydroxyl radicals are produced which may react with other biomolecules and damage the cell components. Injuries caused by “oxidative free radicals” would be lessened via the addition of some scavengers such as “cysteine” or “dithiothreitol” into the medium [13,34]. Moreover, a strong cavitation effect may change the chemical composition of cultivation media. The size of cavitation bubbles produced under ultrasound may be larger than those of microalgae cells. Therefore most energy is dissipated as heat instead of disrupting the cell walls [13]. Since ultrasonic action produces extensive heat during disruption, the temperature of the sample must be controlled. In this technique, the cells can be broken without the addition of any chemical or physical matter. It can also be applied to large scale and operated in continuous mode [17]. The effectiveness of sonic waves in controlling the bloom of deleterious microalgae has been studied [17]. Ultrasound waves have been reported to be efficient in Microcystis aeruginosa deactivation [35
38] probably via damaging gas vacuoles which are as wide as the resonance radius of the cavitation bubbles. Sonication has been widely utilized for the disruption of microbial cells [26,39]. Recently, the Bligh and Dyer procedure aided by ultrasound was shown to increase the efficiency of lipid extraction from C. vulgaris [40]. The author also stated that the cell disruption was in part due to the sonication [40]. Ultrasound-assisted extraction was also utilized to extract carotenoids from Dunaliella salina [41]. Furthermore, the lipid extraction from C. vulgaris using ultrasound-assisted homogenization was also shown to increase the yield of extracted lipids in comparison with that of only sonication or homogenization [42]. Two main types of sonicators, including horn and bath, are frequently employed in batch operations and can be applied continuously as well [43]. Vibration with the amplitude of 10
15 mm are created by horns using crystals, while in bath sonicators, ultrasonic waves are produced by transducers placed at the bottom of generator [43]. Some factors affect the disruption efficiency of microalgae via sonication. These parameters may include the size, shape, density, the age of microalgae cells, etc. The disruption response of microalgae cells through ultrasonication depends on the frequency, power, and exposure time of sonic waves. Increasing the power of sonic waves improved cell wall disruption rates of Chlamydomonas concordia [44]. As the exposure time of sonication increased, cell reduction of Chaetoceros gracilis enhanced in all applied frequencies [32]. D. salina cell wall disruption via sonication takes place easier than C. concordia. Less required energy may be ascribed to the microalgae cell wall chemical structure such as the lack of cell wall in Dunaliella cells [44]. Ultrasonic frequencies (20
100 kHz) have been utilized for induction of a chemical reaction and cell wall disruption [44]. There is a positive relation

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between the frequency of sonication and the disruption rate of microalgae. For example, 2.2 MHz frequency resulted in complete disruption of Chaetoceros calcitrans over 2 minutes, whereas almost 40% of the microalgae were damaged using 0.4 MHz within 10 minutes. However, the optimum frequency leading to maximum disruption rate is different among species and may depend on microalgae size, physicochemical properties of the cell wall, etc. As the best frequency for disruption of C. gracilis and C. calcitrans was found to be 2.2 and 3.3 MHz, cells of Nannochloropsis sp. were more resistant and disrupted with the best efficiency at 4.3 MHz [32]. Microalgae concentration is also an important parameter in the efficiency of cell disruption. Dense microalgae biomasses require more energy for disintegration due to receiving less energy by individual cells. For instance, it was shown that low-density cultures of Chlorococcum sp. were disintegrated more than high-density microalgae suspensions [15].

2.2.5 Freeze-Drying Freeze-drying has been utilized as a disruption method for microorganisms [24]. Freezing causes ice formation, some of which in the form of microneedles act as if they were internally stabbing cell walls. When the water inside the cells is frozen, it expands to a bigger space and presses the cell wall, contributing to cell disintegration [19]. Lyophilization has been widely utilized as a drying method for microalgae; however, there are not many reports about its efficiency on cell wall disruption. A few studies have revealed the low efficiency of lyophilization in increasing the lipid extractability from microalgae [23].

2.2.6 Microwaves Vibrations of polar molecules of a suspension under the influence of “oscillating electric field” create molecular erosion [45]. The movement and crashes of charged ions cause rapid increase in temperature of the substrate leading to cell membrane rupturing [46]. Fast creation of pressure and heat within the microalgae cells extricate intracellular components from their compartments [43]. Microwave-exposed microalgae have been suggested to possess greater lipid yields due to the creation of some micro-cracks in their cell walls [47]. Microwave radiation destroys the cells via high frequency shock waves and has been reported as an efficient method for cell wall disruption of oleaginous plants [48]. Based on the quantity of extracted lipid, microwave treatment was determined as an effective technique for the cell disruption of some microalgae strains (i.e., C. vulgaris, Scenedesmus sp., and Botryococcus sp.) when compared to 10% (w/v) NaCl, sonication, beadbeating, and autoclaving treatment [23]. Microwave treatment resulted in maximum lipid recovery from Scenedesmus obliquus in comparison to other

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methods (e.g., bead-beating, homogenization, lyophilization, osmotic shock, and autoclaving) [49]. Similar to other disruption methods, the efficiency of microwave treatment on rupturing the microalgae cells and increasing the extractability of desired metabolites is species-specific. Microwave treatment found to be as efficient as bead-beating for lipid extraction from Botryococcus sp., whereas sonication proved to be the worst (8.8%) [23]. For Scenedesmus sp. and Chlorella vulgaris, microwave resulted in a maximum lipid extraction and was recognized as an efficient disruption method [23]. On the contrary, microwave did not induce cell wall disruption in Nannochloropsis oculata [50]. Longer time intervals (5, 10, 15, 20, 25, 30 minutes) and higher temperature (80
95 C) via microwave irradiations were shown to increase the lipid extraction of S. obliquus [51]. However, other researchers have reported that increasing the incubation time of microwave caused a reduction of the lipid yield [50]. It was also shown that high and fast generation of heat during microwave treatment can influence the lipid quality [51].

2.3 NONMECHANICAL CELL DISRUPTION The nonmechanical methods including those of physical methods (i.e., osmotic shock) and chemical methods which can be applied for breaking microalgae cell walls are further discussed below.

2.3.1 Osmotic Shock Rapid changes in the concentration of water across the cell membrane of living organisms is known as osmotic shock [52], caused by the addition of solutes such as salts, neutral polymers, mineral substrates, etc., or dilution of solution external space of the cells. The abruption of the osmotic pressure of cells may lead to cell wall rupturing and releasing the intracellular metabolites. Osmotic shock using NaCl addition was reported as an effective method for increasing extracted lipid from C. reinhardtii [4]. Osmotic shock using 10% NaCl followed by 1 minute vortexing and 48 hours incubation time was shown to be as efficient as bead-beating in the disintegration of C. vulgaris and Scenedesmus sp. [23]. In spite of the fact that the obtained results from osmotic shock were comparable to that of bead-beating for the aforementioned microalgae strains, it however required longer treatment time (at least 48 hours) [12].

2.3.2 Chemical Methods Chemical disruption methods cause destruction of the cell wall through chemical reactions targeting the strong covalent bonds among the functional groups at the cell wall surfaces, leading to the liberation of the cell

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components [13]. Organic solvents (e.g., chloroform, ether, or alcohols) can be used for disruption of the microalgae cell wall through permeabilizing cell walls and membranes. They can be effective for extraction of hydrophobic molecules (e.g., plant pigments) because they will be collected in the solvent. Upon exposure of the microalgae cells to chemical treatments, the uptake of solvent changes the chemistry of the membrane in order to improve the material motion outward the cell. Hence, the outer layers of the cells and/or cell membrane characteristics are considered as important elements as they may influence the solvent extraction process. For instance, rough and multilayer cell wall may hinder immediate and complete exposure as well as connection between the cell membrane/solvent, thereby influencing the efficiency of extraction process [53,54]. Chemical lysis includes the addition of bases such as NaOH and/or acids such as HCl and H2SO4. Acid treatment of N. oculata for 2 hours was recognized as an effective cell wall lysis method after enzymatic hydrolysis [50]. The main disadvantage of acid/base utilization is that they may induce the corrosion of cultivation equipment. They also might interact with value-added metabolites of microalgae and destroy their composition, affecting the whole process of extraction [55]. Of other chemicals that have been utilized for microalgae cell wall lysis, dimethyl sulfoxide and methanol can be mentioned [56]. In addition, the combination of H2O2 and FeSO4 has been reported as an effective chemical method for cell disruption of C. vulgaris [3]. This treatment resulted in more than twice the lipid extraction compared to that of the control group, probably through the formation of hydroxyl groups which attack the constituents of the cell wall leading to cell disruption. It must be noted that long exposure of microalgae cells to H2O2 may have a negative effect on lipid extraction due to the oxidation of released lipids by hydroxyl radicals. Unfortunately, chemical solvents added to the biomass for disruption purposes are potentially toxic to operator and to the environment [17]. Using chemicals usually leads to higher cell disruption efficiency as compared to physical methods [3]. In comparison with physical methods, chemical methods typically require less energy and can be scaled-up provided that a continuous supply of chemicals is added to biomass. Due to the fact that in large-scale production, a continuous supply of chemicals is necessary, this may influence the economics of the process. In addition, chemical methods may impact the lipid quality. In a reported study, cell wall disruption increased the percentage of desired fatty acids and simultaneously decreased the proportions of unfavored ones which led to improved biodiesel quality [3]. The efficiency of chemicals in cell lysis to some extent depends on their concentration. For example, free nitrous acid (FNA), the protonated form of nitrite, augmented the lipid yield of Tetraselmis striata M8, in a concentration and incubation time-dependent

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manner due to its effect on cell wall disruption and increasing the cells permeability. At higher FNA concentrations and longer pretreatment time, the more lipid was achieved [57]. The efficiency of chemical disruption may be influenced by other parameters (e.g., temperature). As an example, the sulfuric acid treatment at higher temperature (160 C) was more effective than low temperature (120 C) in the cell wall disruption of Chlorococcum sp. [15].

2.3.3 Enzymatic Method Enzymatic hydrolysis relies on a chemical interaction of an enzyme such as cellulase or trypsin to loosen the connections of the cell wall and enable easy extraction of lipids after cell breakage [13]. In addition to consuming less energy, enzymatic hydrolysis can be practiced at low temperatures preventing oil oxidation and improving the biodiesel quality. Enzymatic hydrolysis of cell wall of N. oculata using cellulase was reported as an effective disruption method which led to a highest lipid yield when compared to mechanical methods [50]. In spite of the advantages of enzymatic lysis, disintegration of cell wall using enzymes is not very economic due to its high cost and low efficiency [17].

2.4 MICROALGAE CELL WALL DISRUPTION EFFICIENCY Efficiency of microalgae cell wall disruptions in laboratory scale has been evaluated via different approaches including direct observation by optical microscope, oil extraction after disruption treatment, and spectrophotometry [15,24,32]. In the microscopy technique, the disruption efficiency is achieved by counting treated cells using hemocytometer and comparing the ratio of intact cells to disrupted cells (control group). The higher ratio of disrupted to nondisrupted cells reflects a higher disrupting efficiency [58]. Direct observation of microalgae cells is a tedious and time-consuming approach; however, it is an accurate method for the estimation of ruptured cells, and the results may be generalized to large-scale application. A commonly utilized procedure is to quantify the liberation of intracellular constituents with and without the cell disruption. This approach directly exhibits the influence of disruption technique on the end-product recovery; however, as to whether the higher lipid yield is due to the rupturing of the treated cells or not sometimes is a difficult task and may be questionable. For instance, the increasing effect of osmotic shock on lipid extraction efficiency may be due to the “salting out” effect rather than the disintegration of microalgae cells. Another method for examination of the cell disruption is via the measurement of “intracellular nonpolar pigments” that are liberated into the solvent due to cell collapse [59]. In addition, to analyze microalgae

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performance, the absorbance of microalgae at the wavelengths of 630 and 680 nm can be measured. Peak height can be obtained by subtracting the absorbance of 630 nm from that of 680 nm. Higher peak height indicates the more chlorophyll liberation from intracellular location due to cell disintegration [32].

2.5 CHALLENGES AND PROSPECTS Microalgae disruption in large scale needs a detailed and calculated understanding of this stage at bench scale [60]. A practical cell disruption strategy should require a low energy input and low operating cost, while providing higher lipid recovery and quality [50]. The challenges associated with largescale application of disruption methods have limited the use of this important step in biodiesel production from microalgae due in part by the cost prohibitive nature of large-scale production along with the employment of energy intensive disruptive methods. Since cell disruption usually requires high energy input [13], it is therefore of economic importance to examine the energy requirement, the cost impact of disruption methods, and the effect of specific disruption method on overall efficiency of the lipid extraction, lipid yield, and its quality in order to achieve greater net energy output from the process [61]. It should be noted that only a few studies have evaluated the cost
benefit analysis of using this step on the whole process. For example, the energy requirement for ultrasonication and high-pressure homogenizers has been estimated to be around 33 and 529 MJ kg21, respectively [62]. If the average achievable energy by means of combustion of algae biomass is assumed to be around 29 MJ kg21, it could be concluded that mechanical disruption methods would result in a negative energy balance which may destabilize the economic sustainability of the microalgae-based biofuel production [62]. Although sometimes more lipid may be extracted after the application of cell disruption, it would be cost-effective if the price of lipid yield exceeds the cost and energy requirement of disruption methods and does not lead to negative energy balance [21]. Furthermore, chemical methods with less of an energy requirement may influence the fatty acid composition of microalgae oils. Thus before utilizing a specific disruption method on an industrial scale, its effects on the lipid profile of selected microalgae strain should be examined. Moreover, the environmental impacts of the microalgae processing chain must be evaluated. It is worth mentioning that since the most appropriate method of cell disruption is species-specific, there is no single recipe to obtain maximum lipid extraction from all microalgae strains. Genetic manipulation of algae cell walls to generate smoother or more extractable cells may be a breakthrough for easier extraction of complicated products. Hence, no disruption method

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TABLE 2.1 The Benefits and Drawbacks of Disruption Methods Disruption Method

Benefits

Drawbacks

Reference

Ultrasonication

Simple, easy to use, economical, ecofriendly, short time treatment, high productivity, efficient for small-scale application at low temperature, preventing protein denaturation

High energy input for large scale, causing free radicals in long exposure times

[33]

Bead-beating

Low operation cost, minimum “contamination from external sources,” maintaining “the chemical integrity of the substance,” “highrate cell disruption,” practical for large-scale applications

High energy input, need large amount of biomass, costly due to removal of the beads, effective for dense biomass concentrations

[43,64]

Enzymatic

Short extraction time, high-extraction yield, less decomposition of target compounds, high specificity/selectivity for better efficiency

Costly

[43,50]

Homogenization

Minimizing the “contamination from external sources,” maintaining “the chemical integrity of the substance,” largescale applications

Only utilized in value-added products recovery, energy intensive

[13,64]

Chemical

Less energy consumption, high efficiency, simple to scale-up

Continuous supply, corrosion of the equipment, interaction with product

[3]

High-pressure press

Efficient disruption

Require high energy, costly for large-scale performance

[13]

(Continued )

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TABLE 2.1 (Continued) Disruption Method

Benefits

Drawbacks

Reference

Osmotic shock

Easy handling

Costly, no large-scale applications, long incubation, lower efficiency

[13]

Microwave

Shorter reaction time, lower operation costs, efficient for algae oil extraction, easy to scale-up, simple

Damaging the products due to heat, need to cooling system, high energy input

[43]

Freeze-press

Easy to use, no solvent

High energy, low efficiency, costly

[20]

would be necessary if the cell wall
less mutants of microalgae are produced. Another option would be the manipulation of algae cells which can secrete their compounds such as wax esters free fatty acids, TAGs, and alkanes into their surrounding medium [63]. The benefits and drawbacks of several disruption methods are given in Table 2.1.

2.6 CONCLUSION In order to obtain high-quality lipids from microalgae cells and improve extraction efficiency with a lower operating cost, an appropriate cell disruption method may be necessary. Development and improvement of the microalgae cell wall technologies may lead to significant cost-savings in lipid extraction from microalgae. As discussed in this chapter, there are some techniques for microalgae cell wall disruption which can potentially improve extraction efficiency. However, a critical assessment of industrial viability of microalgae cell wall disruption techniques is necessary. The optimum cell wall disruption method is species-specific and depends on several factors like the chemical and physical structure of cell wall, algae strain, and the employed extraction method. The possibility of using cell wall disruption approaches are also influenced by their energy requirement, because energy consumption is a crucial challenge in commercialization of algae-based biofuels. Optimization of this stage may help to decrease the cost and therefore makes the process economically practical. Any future research in cell disruption methodologies must exhaustively evaluate lipid extraction in terms of scaling, energy expenditure, efficiency, and downstream compatibility.

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Chapter 3

Enhancing Carbohydrate Productivity in Photosynthetic Microorganism Production: A Comparison Between Cyanobacteria and Microalgae and the Effect of Cultivation Systems Carlos Eduardo de Farias Silva1, Eleonora Sforza2 and Alberto Bertucco1 1 2

Department of Industrial Engineering DII, University of Padova, Padova, Italy, Interdepartmental Centre Giorgio Levi Cases, University of Padova, Padova, Italy

3.1 ETHANOL MARKET AND PERSPECTIVE OF A CARBOHYDRATE-RICH BIOMASS At present, “the global energy matrix is composed of fossil carbon sources (about 87%), with about 33% of oil, 30% of coal, and 24% of natural gas.” Renewable energy accounts for only 10% of the global energy [1]. In particular, the “growing needs to expand the use of renewable energy sources in a sustainable” way has boosted biofuel production worldwide [1]. Biofuels have an important role in reducing global climate change and their impact will depend on several aspects related to the development of new and appropriate technologies, legal restrictions, international trade, land use, choice of raw materials, and management techniques [2]. In addition, the use of natural resources represents a great opportunity to boost economic activities in both developed and developing countries, especially in the industrial and agricultural sectors, with numerous studies and investments in clean technologies, resource saving, recycling, and reuse of wastes [3,4]. Moreover, there is a need to increase simultaneously energy security and Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00003-5 © 2019 Elsevier Inc. All rights reserved.

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energetic mitigation related to CO2 emissions, especially about energy efficiency and expanding the use of alternative energy (i.e., the use of renewable fuels that promotes the carbon cycle without changing the atmospheric balance and the development of CO2 neutral energy resources) [5]. Biofuel production is vigorously growing in the 21st century, and the United States along with Brazil account for about 70% of world production, usually reported as Mtoe (millions tonnes of oil equivalent). Thinking of a carbohydrate-rich biomass, bioethanol is the main current application due to its worldwide application (i.e., it is the biofuel which is most often produced). In fact, the global ethanol production is comprised between 45 and 50 Mtoe (over 100 million m3
25 billion gallons per year) (Fig. 3.1). The United States and Brazil hold hegemony in ethanol production accounting for almost 85% of worldwide production, using corn and sugarcane as feedstock, respectively. However, this amount is less than 1% of the overall volume of fuel consumed. Currently, bioethanol production is usually classified as first (e.g., raw saccharine or starch-based materials), second (e.g., lignocellulosic biomass), and third generation (e.g., microalgae biomass) types, with these three routes differing in the feedstock and the conversion process. First generation bioethanol is obtained from foods rich in soluble sugars or are starch-based; whose disadvantages are the use of large extensions of land and a seasonal production, the requirement of fertilizers and pesticides, the reduction of biodiversity, the increase of soil erosion, and the competition with food production. However, it has a well-established technology and exhibits lower industrial production costs, burning up to 0.16
0.22 US$ L21 in Brazil [7], even though its exploitation is not a viable option for all the

FIGURE 3.1 Global ethanol production by country/region and year. Adapted from AFDC Energy, Bioethanol Production. 2016. Available from: ,http://www.afdc.energy.gov/data/. (last accessed 22.11.18) [6].

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regions of the planet, in particular due to the competition with arable lands, which is one of the major societal challenges of this century. In second generation processes, lignocellulosic materials are used which are resistant to saccharification, especially due to the lignin content. This is recalcitrant, nonfermentable, and difficult to biologically degrade. Thus, strong pretreatments and the use of several enzymes are needed, increasing production costs and lowering productivities, which affect a large-scale consolidation in the market. The costs of bioethanol based on lignocellulosic materials (United States, 0.43
0.93 US$ L21) are still higher than first generation biofuels, even considering corn (United States, 0.25
0.40 US$ L21) and sugar beet (Europe, 0.43
0.73 US$ L21) [7]. As a third generation option, microalgae biomass can be exploited, as it has no lignin in its cellular structure and is characterized by higher growth rates with respect to higher plants [8,9]. Due to the lack of enough data in the literature, economic estimation of bioethanol production from microalgae biomass is relatively difficult. As an emerging technology, also the interest in fourth generation bioethanol is currently growing: in this process, ethanol is produced directly by photosynthetic microorganisms, and it is released in the cultivation media, where it can be extracted without the need to breakdown the biomass. This process, known as “photofermentation,” is based on genetically modified cyanobacterial strains and is currently exploited on an industrial scale [10]. On the other hand, the exploitation of genetically modified organisms has some environmental and legislative implications that might limit the applicability of this technique on a large scale. The advantages and disadvantages for the process considered above are summarized in Table 3.1. The exploitation of photosynthetic wild type organisms is increasing worldwide, as several biotechnological processes can utilize microalgae and cyanobacteria in fermentative applications for biofuels and high value-added chemicals. The main constraint for biofuel application is obviously linked to production costs, which can be possibly overcome by a rational approach to the process variables involved. On the other hand, the problem of fossil fuel availability cannot be postponed indefinitely; therefore, finding alternative routes to fuel supply is both urgent and strictly required from an environmental point of view.

3.2 MICROALGAE AND CYANOBACTERIA FOR BIOETHANOL PRODUCTION Ethanol production from microalgae and cyanobacteria is still a matter under investigation and the related technology has not been fully realized commercially so far. The process of obtaining ethanol from microalgae is similar of the one used when producing ethanol from starch and cellulosic biomass [11]. The cell wall of microalgae is composed by cellulosic and

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Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts

TABLE 3.1 Advantages and Disadvantages of the Process for Bioethanol Production Generation— Biomass

Advantages

Saccharine and starch biomass

G G G

Lignocellulosic biomass

G

G

Algal biomass

G

G

Microalgae e cyanobacteria (directly from sunlight by photofermentation)

G

G

Disadvantages

Stable Known technology Can be competitive to fossil fuels

G

Low or no geographical limitation Less controversial fuel versus food

G

Can be used anywhere (need essentially water, light, salts, and CO2) Does not have lignin at the cell wall

G

Low environmental impact due reduced steps to biomass conversion Composed by a twosystem cultivationdistillation, basically

G

G G

G G

G G G

G G G

Fuel versus food Geographical limitations Periods between harvests Recent industrial plants High capital costs Domestication others cultures Recent technology High cultivation costs Microalgal domestication High capital costs Emergent technology Use GMO High capital costs Need R&D

GMO, Genetically modified organisms.

hemicellulosic structures. In addition, as storage of polysaccharides, microalgae and cyanobacteria have starch and glycogen, respectively. The absence of lignin, which is a recalcitrant compound present in higher plants, allows a faster and more effective hydrolysis and conversion of sugars. In the last two decades, microalgae have been extensively studied as a complement to biodiesel production due to high levels of lipids accumulated in dry biomass (15%
70%). However, the related cultivation costs are still quite high [12,13] and most of the international research community unanimously comply with a systemic approach for microalgae production, where the production of fuels and value-added bioproducts is combined to boost the feasibility of microalgae-based processes. So, in a biorefinery concept, carbohydrates and lipids could be extracted efficiently from microalgae biomass and used to produce biofuels, with the concomitant extraction of some valuable bioproducts. It is noteworthy that the chemical rupture of the biomass to obtain fermentable sugars can improve the solvent extraction of lipids. Nannochloropsis gaditana, Chlorella sorokiniana, and Phaeodactylum tricornutum have been treated by steam explosion with sulfuric acid (0%
10%

Enhancing Carbohydrate Productivity Chapter | 3

41

H2SO4, 120
150 C for 5 minutes), so that sugars were efficiently recovered (almost 100%) and lipid extraction from the biomass was also increased [14]. Significant differences between the lipid content obtained before and after acidic hydrolysis of the microalga Tribonema sp. were also reported with an increase of about 25% of the extraction efficiency [15]. In addition, microalgae carbohydrate exploitation is not limited to ethanol fermentation, as other products such as butanol, acetone, hydrogen, methane, or value-added molecules can be obtained. For instance, butanol was produced with a concentration of 8.05 [16] and 3.74 g L21 [17] by using the residues of microalgae biomass after lipid extraction, which may still have good carbohydrate contents. Moreover, butanol was obtained from Chlorella vulgaris JSC-6 hydrolyzate (mixed of glucose and xylose) with a productivity of 0.9 g L21 day21 [18]. Hydrogen can also be obtained between 38 and 97 mL g21 versus using several microalgae species, such as Scenedesmus and Chlorella species [19], Chlorella pyrenoidosa, Nannochloropsis oceanica [20], and Arthrospira platensis [21]. In addition, methane can be produced by the anaerobic digestion of algae residues [20,22]. Concerning high value-added compounds, 7-aminocephalosporanic acid (antibiotic) was produced by C. vulgaris lipid-extracted biomass [23]. For bioethanol, productivity is mainly determined by the sugars concentration, since a high amount of fermentable carbohydrates can be converted by yeast and bacteria (e.g., Saccharomyces cerevisiae and Zymomonas mobilis). A carbohydrate-rich microalgae biomass (B50%) has a theoretical bioethanol production of around 0.26 gethanol gbiomass21 (based on GayLussac stoichiometry, 1 g monomer-glucose produces 0.5111 g ethanol), but the efficiency depends on the sugars extraction and saccharification methods, which must not degrade the sugars and ensure high rates of monomer production. Another factor is the composition of sugars present in the biomass because, for example, glucose (hexose) is easily fermentable by the strains mentioned above, but xylose (pentose) is not used by S. cerevisiae (which is sometimes present in considerable amount within the carbohydrate-based microalgae biomass (20%
30%) [9,24]) Other strains are necessary in this case, such as Pichia stipitis (aka Scheffersomyces stipitis), Pichia segobiensis, Kluyveromyces marxianus, Candida shehatae, and Pachysolen tannophilus, although the ethanol production rates are slow when compared to a Saccharomyces-glucose fermentation system [10]. In addition, glucose is the main component of the carbohydrates in the biomass, reaching values between 60% and 90% of total carbohydrates, which is good as this monomer is easily fermentable by Saccharomyces strains (Table 3.2). Some experimental values of ethanol/biomass yields obtained are 0.163 gethanol gbiomass21 (A. platensis—chemical hydrolysis) [34], 0.140 gethanol gbiomass21 (Dunaliella tertiolecta—chemoenzymatic), [35] and 0.214
0.233 gethanol gbiomass21 (C. vulgaris FSP-E—enzymatic and chemical hydrolysis), respectively [36].

TABLE 3.2 Carbohydrate Composition in Different Microalgae and Cyanobacteria Species Microorganism

Carbohydrate Content (%)

Starch or Glycogen Content (%)

Chlamydomonas reinhardtii UTEX 90

59.7

Chlorella variabilisNC64A Chlorella vulgarisP12

Monosaccharidesa

Reference

D-Glu

L-Fuc

L-Rha

D-Ara

D-Gal

D-Man

xyl

43.6

75

0.67

1.5

3.2

4.5

2.3




[25]

53.9

37.8

2.3b




42.9b

13.0b

19.1b

9.0b

13.7b

[26]




41






















[27]

C. vulgaris FSP-E

53




94.3










5.7

C. vulgaris KMMCC-9 UTEX26

17.1
22.4




54




16

4.5

27

7

4.5

[28]

Chlorococcum spp.

32.5

11.3

47










8.9

1.5

29

[24]

Scenedesmus obliquus

21
45




56







5

13

19

7.2

[29]

S. obliquus CNW-N

50




B80
















B20

[30]

Synechococcus elongatus PCC 7942

28
35




68
87



















[31]

Synechococcus sp. PCC 7002

59




60







5

4

4

5

[32]

Tetraselmis subcordiformis

44.1




B90

ara, Arabnose; fuc, fucose; gal, galactose; glu, glucose; man, mannose; rha, rhamnose; xyl, xylose. a Based in total carbohydrate content. b Based in the cell wall components only. c D-Gal1 xyl.

c

[9]

[33]

Enhancing Carbohydrate Productivity Chapter | 3

43

3.3 MICROALGAE AND CYANOBACTERIA: BIOLOGICAL ASPECTS AND STRAINS The advantages of exploiting photosynthetic organisms for industrial applications are relevant in achieving a sustainable future. Microalgae are a polyphyletic group, which includes a large typology of organisms, often also the cyanobacteria, even though they may have extremely different characteristics. The “phylum cyanobacteria or division Cyanophyta is a group of oxygenic bacteria (prokaryotic) able to obtain energy by photosynthesis. They are commonly referred to as blue-green algae, even though the term algae are usually associated with eukaryotic organisms (such as the divisions Chlorophyta, Rhodophyta, and Heterokontophyta). Most species of cyanobacteria are terrestrial” [10] or live in fresh water “but there are some marine species as well. Spirulina, Chlorococcus, Gloeocapsa, Synechocystis, and Synechococcus are examples of genera grouped in the cyanobacteria phylum” [10], which are usually studied in view of scientific or industrial perspectives. The most studied genera of eukaryotic microalgae are Scenedesmus, Chlorella, Chlamydomonas, and Nannochloropsis. The taxonomy of photosynthetic microorganisms is historically based on morphology, biochemical composition, and pigmentation but is under constant revision from a genetic and evolutionary point of view. Another key point to be accounted for is the wide species variability characterizing both eukaryotic microalgae and cyanobacteria; of the about 25,000 species known worldwide, approximately 70 are commercially exploited and play an important role in the production of foods, food additives, animal feed, fertilizer, and biochemicals (Fig. 3.2). From a cultivation perspective, both microalgae and cyanobacteria may follow many metabolisms and are capable of a metabolic shift as a response to changes in the environmental conditions. They can grow: (1) photoautotrophically (i.e., using light as a sole energy source which is converted to chemical energy through photosynthetic reactions); (2) heterotrophically (i.e., exploiting only organic compounds as carbon and energy source, in the absence of light); (3) mixotrophically or photoheterotrophically (i.e., performing photosynthesis as the main energy source for both organic compounds and CO2 consumption). Amphitrophy (i.e., the capability of organisms to live either autotrophically or heterotrophically depending on the concentration of organic compounds and light intensity available) is commonly included in mixotrophy. The photoheterotrophic and mixotrophic metabolisms are not well distinguished, but they can be defined according to a difference in the energy source required to perform growth and specific metabolite production [37,38]. Finally, certain microalgae species such as Ochromonas danica are capable of phagotrophic growth (i.e., feeding by engulfing a food cell or particle and ingesting it in a phagocytic vacuole) [39,40].

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Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts

FIGURE 3.2 Industrial applications of microalgal components.

The main metabolism is certainly crucial in view of storage material accumulation, including carbohydrates for biofuel production. In the autotrophic process, the accumulation of carbohydrates within the cells is linked to the carbon fixation pathway during photosynthesis. If the aim is a sustainable biofuel production, sunlight must be the main energy source, as autotrophic growth is preferred. However, in a global perspective, the exploitation of wastewaters for nutrient supply in biofuel processes may also affect the carbon metabolism, as they contain organic compounds and particles that can be mixotrophically or phagotrophically used by certain microalgae and cyanobacteria species. The type of carbohydrate present in the biomass has been reported (Table 3.2). Carbohydrates in microalgae are formed within the plastids as a storage product (starch) or cellular wall components, such as cellulose, pectin, and sulfated polysaccharides [41]. Conversely, cyanobacteria generally accumulate glycogen as a storage product. Glucose-based polysaccharides are more suitable since once hydrolyzed, they can be fermented easily by S. cerevisiae (the species most used at industrial level). In addition, from a chemical point of view the peptidoglycane-based cell wall of cyanobacteria can be degraded by lysozymes, as it has a lower complexity and diversity of polysaccharides than microalgae. Moreover, glycogen is soluble in water (while starch is not) which is another advantage of cyanobacteria when enzymatic systems are applied [32]. Cyanobacteria can also accumulate polyhydroxybutyrate, which is very interesting for industrial applications [42]. On the other hand, a reliable comparison about carbohydrate production between microalgae and cyanobacteria is not possible yet, as little information about the stability of cyanobacterial production in industrial systems is available at present.

Enhancing Carbohydrate Productivity Chapter | 3

45

In terms of productivity, common values of growth rate constants are between 0.3 and 1.2 day21 depending on the optimum environmental and nutritional conditions and reactor design for Chlorophytes and Cyanobacteria [43], although these values can be increased by nutritional and environmental factors adaptations [44]. Several species that may accumulate carbohydrates in a reasonable amount have been found, in some cases exceeding 50% of dry weight (Table 3.3), but most investigations are performed in batch and/ or laboratory scale and studies in continuous mode for scale-up are still missing. The reported carbohydrate productivities are between 0.1 and 0.6 g21 L21 day21 showing that Scenedesmus, Chlorella, and Synechococcus spp. are the best microorganisms available at the moment for this approach. Factors such as temperature, nutrient form and availability, and light intensity are species-dependent. The best species in terms of productivity, energy conversion, and carbohydrate accumulation require screening with respect to a specific region, and the use of native species can help to overcome acclimation problems.

3.4 CULTIVATION OF MICROALGAE AND CYANOBACTERIA TOWARD LARGE-SCALE APPLICATIONS When considering the possible exploitation of photosynthetic organisms for bioethanol production, a large-scale approach is the only way to assess the technical and economic feasibility of the whole process. Thus, moving from lab to industrial scale, the impact of variables involved in the cultivation step increases. Even not considering the technical challenges of downstream processes, which are currently under in-depth investigation, some critical aspects concerning the cultivation are not overcome yet. The main key issues still needing improvement are related to photosynthetic efficiency (PE), CO2 and nutrient supply, and the management of cultivation conditions to stimulate the accumulation of the target product of interest. Other aspects which are not considered in this chapter (i.e., the water supply, regulation of a suitable temperature, pH, salinity, and mixing) should be carefully considered as well. However, data from large-scale production is still limited, due to the high costs involved. Therefore, in order to limit investment risk, a deeper comprehension of the variables and mechanisms involved in the cultivation step is necessary. Based on lab measurements, the dependence of growth parameters on key variables can be obtained and can be then applied in reliable models useful to develop large-scale process by simulation. Of course, the experimental approach, even at lab scale, must be carefully designed and performed, if the aim is to understand the effect of variables in the view of large-scale application.

TABLE 3.3 Carbohydrate Production: Productivities and Carbohydrate Content Microorganism

Growth Conditions

Reactor Type

Operation Mode

Type of Stress

Dry Weight (g L21)

Carbohydrates Accumulation (%)

Carbohydrates Productivity (g L21 day21)

Reference

Chlamydomonas reinhardtii UTEX 90

450 μmol m22 s21, 23 C, 4 days and 130 rpm




Fed-batch with acetate

NS

2.40

59.7

0.360

[25]

Chlorella variabilis NC64A

150 μmol m22 s21, 25 C, and 2% CO2 (CO2
air)

Glass bottle

Batch 16/8 light
dark cycle

NS

0.43

53.9




[26]

Chlorella vulgaris P12

70 μmol m22 s21, 30 C and 2% CO2 (CO2
air)

Column PBR

Batch

NS and IS




41

0.199

[27]

C. vulgaris FSP-E

60 μmol m22 s21 and 2% CO2 (CO2
air)

Glass bottle

Batch

NS

5.51

51.3

0.631

[36]

C. vulgaris KMMCC-9 UTEX26

150 μmol m22 s21, 20
22 C, bubbling air

Plastic containers

Batch 16/8 light
dark cycle

NS and SS




17.1
22.4




[28]

Chlorococcum spp.

Outdoor and bubbling CO2 34.5 kPa

Bag PBR

Fed-batch







32.0




[24]

Dunaliella tertiolecta LB999

60 μmol m22 s21, 20
25 C and 2% CO2 (CO2
air)

Plate PBR










37.8




[35]

Dunaliella tertiolecta LB999

60 μmol m22 s21 and 20
25 C

Plate PBR










40.5




[45]

Scenedesmus dimorphus

50
1200, 25 C and 2% CO2

Glass column

Batch

NS

5

45
50

0.510

[46]

Scenedesmus obliquus

150 μmol m22 s21, 25 C, bubbling air

Tubular Bubble Column Open pound











0.37 0.41
0.68 0.81

24 45 29






[29]

S. obliquus CNW-N

210
230 μmol m22 s21, 28 C, 300 rpm and 2.5% CO2 (CO2
air)










4.5

51.8




[30]

S. obliquus CNW-N

220
240 μmol m22 s21, 28 C, 300 rpm and 2.5% CO2 (CO2
air)

Glass vessel

Batch fed-Batch Continuous

NS NS HRT






49.4 52.9 35.6

0.273 0.467 0.312

[47]

Synechococcus elongatus PCC 7942

200 μmol m22 s21, 28 C, and 5% CO2 (CO2
air)




Batch




2.9
3.7

28
35

0.144
0.564

[31]

Synechococcus sp. PCC 7002

400 μ mol m22 s21 and 1% CO2 (CO2
air)

Vessels

Batch

NS

3.0

59

0.590

[32]

Synechococcus sp. PCC 7002

150 μmol m22 s21 and sodium bicarbonate (5.5
88 g L21)

Glass bottle

Batch

NS

6.0

30

0.360

[44]

Tetraselmis subcordiformis

200 μmol m22 s21, 25 C and 3% CO2 (CO2
air)

Bubble column

Batch

PS

2.5
5.6

24.8
44.1

0.180
0.310

[48]

Tetraselmis subcordiformis FACHB-1751

150 μmol m22 s21, 25 C and 3% CO2 (CO2
air)

Bubble Column

Batch

Salinity and NS

1.7
4.5

30
40

0.070
0.420

[33]

Tribonema sp.
















31.2




[15]

HRT, Hydraulic retention time or residence time; IS, iron starvation; NS, nitrogen starvation; PBR, photobioreactors; PS, phosphorous starvation; SS, sulfur starvation.

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Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts

3.4.1 Cultivation System and Operation Mode The cultivation systems generally considered for the production of microalgae biomass are open and closed ones, generally ponds and photobioreactors (PBRs), respectively. Raceway ponds are the simplest alternative. These “systems are usually 30 cm deep and are relatively easy to construct and operate” [49]. Here, microalgae/cyanobacteria are grown under the same conditions of their natural environment, and the operation may be carried out continuously; however, open pounds are prone to contamination and evaporation. In PBRs, there is no direct contact with the atmosphere, so that they can be an alternative to avoid the problems of open ponds, and usually obtain a higher productivity (Fig. 3.3). Closed reactors are more technologically advanced, with a past-proposed example being “flat panel PBRs, which are rectangular reactors with light paths between 0.02 and 0.10 m, or tubular PBRs, which have usually a light path of 0.06 m” [49]. “The tubes can be placed horizontally next to each other (horizontal tubular PBR), or can be stacked vertically (resulting in a vertical tubular PBR)” [49]. Each type of reactor has a particular operation mode. Closed systems have the greatest advantage of ensuring stable process productivity, while open systems are highly dependent on external conditions. On the other hand, the costs associated with closed reactors should be carefully considered, because they strongly affect the economical

FIGURE 3.3 Operation mode of different photobioreactor configurations.

Enhancing Carbohydrate Productivity Chapter | 3

49

sustainability of the whole process. In Table 3.4, a comparison and the differences between open and closed systems is summarized. The discussion about open versus closed systems is currently fervent, but the productivity advantages of PBRs should boost the research toward cheaper solutions. Some of the currently proposed solutions are based on a concept of a “closed pond,” with a simple covering of the pond surface, or the allocation of the pond inside greenhouses [52]. Other solutions, specifically designed for marine environments, include floating permeable bags offshore (OMEGA Project) [53]. Beside the geometry of the cultivation system, the major constraint toward industrial application is the operation mode of the reactor. Batch production is not suitable in an industrial perspective, while continuous processes could noticeably improve the performances, as a steady state production is generally more efficient, has lower costs, and is easier to operate [54]. At lab scale, cultivation of microalgae in continuous systems is well established and has been studied for several species and different approaches (e.g., biomass production, lipid production, wastewater treatment, and growth modeling development) [55
58], but less information is available on carbohydrate accumulation under continuous operation [47]. As fed-batch and continuous systems for carbohydrate production from microalgae have not been sufficiently characterized so far, few outcome references are available. For instance, Scenedesmus obliquus CNW-N cultivated in fed-batch and continuous systems yielded better productivities than batch (0.467 and 0.312 g L21 day21, respectively, against 0.273 from the batch cultivation) [59]. C. vulgaris FSP-E grown in batch experiments reached a remarkable value of carbohydrate productivity of 0.631 g L21 day21 [9]. C. vulgaris was also cultivated at lab scale under continuous conditions, reaching a carbohydrate productivity of 0.37 g L21 day21 [60]. Concerning cyanobacteria productivity in continuous systems, only a few papers are available in literature [61,62], and the actual feasibility of a continuous production of these organisms is not yet proven. In addition, it is noteworthy that continuous systems working at steady-state are a viable tool to study the biomass’ physiological response to environmental condition since light intensity, residence time, and nitrogen concentration can be managed for an efficient carbohydrate accumulation [42,61,63]. In a perfectly mixed continuous system at steady state, the apparent growth rate μ is equal to the dilution rate, which is the inverse of the residence time τ [62,64]: μ5

1 τ

ð3:1Þ

Thus, by changing the residence time, different growth rates can be studied. This means that microalgae population is somehow selected by the

TABLE 3.4 Comparison Between Open and Closed Systems for Microalgal Cultivation Culture System

Open Systems

Closed Systems

Contamination control

Hard

Easy

Contamination risk

High

Reduced

Sterility

Does not have

Achievable

Process control

Hard

Easy

Species control

Hard

Easy

Mixing

Very poor

Uniform

Operating system

Batch or semicontinuous

Batch or semicontinuous

Required space

PBRs B ponds

Depend of productivity

21

Area/volume ratio

Low (5
10 m )

High (20
200 m21)

Cellular density

Low

High

Investments

Low

High

Operation costs

Low

High

Capital costs

PBRs . ponds

Ponds 3
10 times cheaper

Light use efficiency

Poor

High

Temperature control

Hard

More uniform

Productivity

Low

3
5 times more productive

Water losses

PBRs B ponds

Depend cooling

Hydrodynamic stress to algae

Very low

High
low

Culture medium evaporation

High

Low

Gas transfer control

Low

High

CO2 losses

PBRs B ponds

Depend of pH, alkalinity, etc.

O2 inhibition

PBRs . ponds

A big problem to PBRs

Biomass concentration

PBRs . ponds

3
5 times higher

Parameters reproducibility

Not, depend of the climatic conditions

Possible, depend of control conditions

Dependence of temperature

High, does not have production in rainy periods, photoinhibition

Have an efficient control

Operating period

Long, 6
8 weeks

Short, 2
4 weeks

Scale-up

Hard

Hard

PBRs, Photobioreactors. Source: Adapted from O. Pulz, Photobioreactors: production systems for phototrophic microorganisms, Appl. Microbiol. Biotechnol. 57 (2001) 287
293 [50]; T.M. Mata, A.A. Martins, N.S. Caetano, Microalgae for biodiesel production and other applications: a review, Renew. Sustain. Energy Rev. 14 (2010) 217
232 [51].

Enhancing Carbohydrate Productivity Chapter | 3

51

residence time set, and they grow with a rate which is fixed by the operating conditions. This also has another biological implication; if the residence time is low enough, all the biomass contained in the reactor is composed of living organisms, as the dead cells are continuously removed from the reactor [65]. Only the cells actually acclimated to the operating/environmental conditions can survive in a continuous system. So, working in a lab scale continuous system is essential even from a biological point of view, because once steady state is reached, all the transient acclimation phenomena are achieved, allowing a more reliable measure of physiological parameters [66]. Thus, if the crucial point is to understand the feasibility of large-scale carbohydrate production from microalgae and cyanobacteria, continuous cultivation should be recognized as the best method to deeply understand the effect of the key operating variables on microalgae acclimation and physiology. On the other hand, at industrial scale, the possibility of cultivating microalgae in a continuous industrial system is still challenging, particularly due to the variability of environmental parameters in outdoor cultivation. Productivity varies from species to species, as well as the geometry of the reactor and lighting conditions (i.e., the location in the world). In Table 3.5, some examples of outdoor productivities as a function of the countries and using different types of reactors are reported.

3.4.2 CO2 Availability Carbon may represent between 17.5% and 65% of the dry weight of microalgae and cyanobacteria biomass, usually around 50% [70]. In their natural environment, microalgae usually absorb carbon from CO2 dissolved in an aqueous solution which maintains a balance with the bicarbonate and carbonate ions (pH-dependent, pK1 5 3.6, pK2 5 6.3, and pK3 5 10.3). 

1 22 1 CO2ðaqÞ 1 H2 O 2 H2 CO3 2 HCO2 3 1 H 2 CO3 1 2H pK1

pK2

pK3

ð3:2Þ

Most microalgae and cyanobacteria are cultivated in the pH range of 6.5
10, and the dominant form of carbon, in aqueous phase, is the bicarbonate ion. Since the amount of dissolved inorganic carbon depends on pH, salinity, pressure, and temperature, microalgae have developed uptake mechanisms for each specific environment, depending on the carbon source. These mechanisms include diffusion, active transport, carbonic anhydrase, and phosphorylation [70]. The main ways to supply carbon to the culture media are pumping air, pumping air enriched with CO2, using bicarbonate salts, and using organic compounds (heterotrophy and mixotrophy). Usually, the carbohydrate-rich microalgae systems use inorganic carbon supplied by CO2 in excess (2%
5% mix air
CO2) to conduct

TABLE 3.5 Overview of Some Reported Outdoor Culture Productivities Observed During 1 Year of Production Reactor System

Location

Light Path (m)

Algae Species

Productivity (ton ha21 year21)

Raceway pond

Hawaii

0.12

Haematococcus pluvialis

37.2 [49]

Raceway pond

Tucson, Arizona

0.08
0.20

Nannochloropsis salina

12.2
12.7 [49]

Raceway pond

La Jolla, California

0.28

Phaeodactylum tricornutum

59.1 [49]

Raceway pond

Poole, England

0.40

P. tricornutum

10.6
27.3 [49]

Raceway pond

Perth, Australia

0.16

Pleurochrysis carterae

60 [49]

Raceway pond

La Mancha, Mexico

0.1
0.25

Spirulina platensis

43.1 [49]

Raceway pond

Malaga, Spain

0.30

S. platensis

23.6
30 [49]

Raceway pond

China

10

Grasiella sp. WBG-1

43.8
69 [67]

Raceway pond

Florence, Italy

25

Nannochloropsis

52.9 [68]

Horizontal tubular

Hawaii

0.38

H. pluvialis

55.1 [49]

Horizontal tubular

The Netherlands

0.03

Nannochloropsis

20 [49]

Horizontal tubular

Florence, Italy

0.06

S. platensis

30 [49]

Horizontal tubular

Hawaii

0.41

Unknown

47.5 [49]

Vertical tubular

Almeria, Spain

0.09

Scenedesmus almeriensis

90 [12]

Vertical tubular

Cadiz, Spain

0.30

Unknown

73 [49]

Flat panel

Sede-Boquer, Israel

0.10

Nannochloropsis

22.1 [49]

Flat panel

Florence, Italy

1.5

Nannochloropsis

88.69 [68]

Flat panel

Eilat, Israel

1.3
17

Nannochloropsis

20
44 [69]

Four reactor systems are considered: raceway pound, horizontal tubular and vertically stacked horizontal “vertical” tubular PBRs, and flat panels. The areal productivity is based on 1 ha ground surface area.

Enhancing Carbohydrate Productivity Chapter | 3

53

photosynthesis, increasing the rates of carbon fixation to maximum, and consequently, maximizing the conversion of CO2 in carbohydrates. Some authors applied high concentrations of bicarbonate salts (Euhalotece ZM 001 14
160 g L21) [71], while lower concentrations (,15 g L21) were studied on Chlorella protothecoides [72], Scenedesmus sp. [73], Scenedesmus obliquus [74], and Thermosynechococcus sp. [75]. Not all the species of microalgae and cyanobacteria are able to exploit large amounts of bicarbonate; despite having the necessary carriers, they are often inhibited by the substrate concentration [76]. Silva et al. [44] used sodium bicarbonate (5.5
88 g L21) to cultivate Synechococcus PCC 7002, in order to specifically produce carbohydrates, in batch conditions with automatic pH control, and noticed that these values were not efficient to obtain high amounts of carbohydrates, reaching around 30% (in dry weight) with 88 g L21 of sodium bicarbonate. In addition, growth inhibition was shown above 22 g L21 of sodium bicarbonate, so that a fraction of bicarbonate was wasted as CO2 when 88 g L21 was used. Selecting and optimizing the growth conditions of these tolerant bicarbonate species is a recent approach and can substantially reduce cultivation production costs because the cost of gaseous CO2 supply is too high [44]. When microalgae are cultivated in organic substrates (both mixotrophic and heterotrophic cultivation), they tend to grow faster with higher biomass and lipids yields than with conventional autotrophic cultivation [77]. This concept finds applications to treat several effluents and wastes, such as whey protein concentrate (Spirulina platensis using lactose 7.18% and producing 60 mg L21 day21 of carbohydrate productivity) [78], cheese whey permeate (40% of culture medium—1.9
3.0 g L21 in 13 days of cultivation) [79], dark fermentation effluents (C. vulgaris ESP6
0.25
0.3 g L21 of lactate, formate, butyrate, and acetate— 0.2 g L21 day21 of biomass productivity) [80] (Scenedesmus subspicatus GY-16, C. vulgaris FSP-E and Ankistrodesmus gracilis GY-09
2 g L21— sodium acetate—0.498 g L21 day21 of carbohydrate productivity) [81], and (C. sorokiniana—0.2
0.3 gcarbon L21 of acetate and butyrate—1.14 g L21 in 10 days of cultivation) [82], glucose (C. sorokiniana—0.5
2 to 0.1
0.6 g L21 of biomass in 10 days of cultivation) [83], human urine (S. platensis—urea 75 mg L21 and acetate 200 mg L21
1.7 g L21 of biomass in 5 days) [84] and glycerol (C. pyrenoidosa—1%—1.2 g L21 of maximum biomass production with 60% of carbohydrate content) [85]. However, this approach of carbohydrate production is not discussed in literature enough, focused more on lipid accumulation. In addition, from a biofuel perspective, autotrophic cultivation is quite essential if the process has to be sustainable, but the effect of organic source on carbohydrate accumulation could become a relevant issue in the case of waste stream exploitation.

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3.4.3 Nutrients Supply To efficiently produce microalgae at large scale, all the essential elements have to be supplied in a cultivation medium with appropriate ratios, adequate quantities, and bioavailable chemical forms, so that the growth of microalgae will not be limited by anything else but light [70]. Common phytoplankton elemental composition is based on the universal Redfield C:N:P of 106:16:1. In some conditions, algae stoichiometry may diverge from this canonical ratio, thus suggesting that the cultivation media should be flexible and must be adapted to the microalgae metabolic needs [70]. The standard biochemical composition of microalgae includes protein (30%
50%), carbohydrates (20%
40%), and lipids (8%
15%) [11,86], but several studies have shown that lipids and carbohydrates can be accumulated under stress conditions (mainly under nitrogen starvation), thus decreasing the protein content [9,30,40]. The mechanism of nutrient starvation or limitation can be applied to obtain carbohydrate-rich biomass with the potential to produce bioethanol. Nitrogen is the second most abundant element in microalgae biomass, as its fraction may vary from 1% to 14% [70]. Microalgae can use various forms of inorganic nitrogen like nitrite, nitrate, ammonia, and N2 (in some cyanobacterial species), but also organic forms such as urea and amino acids. They are essential in the formation of genetic material, pigments, and protein. Nitrate is usually used as the culture media for microalgae and cyanobacteria [70], although the preferred source of microalgae and cyanobacteria is the ammonium ion by intracellular uptake; in terms of energy expenditure, nitrate needs two more reduction steps to provide ion ammonium before being processed by the cells [42]. Nutrient starvation is a strategy for macromolecules accumulation (mainly lipids and carbohydrates) [42]. A standard growth curve for batch conditions under nutrient starvation is shown in Fig. 3.4. At the beginning, the carbohydrate content is low (5%
20%) and increases after nutrient exhaustion and CO2 in excess, driving the assimilated carbon to produce energetic substances (carbohydrates and lipids), almost as an exponential function although the biomass production does not increase significantly. Generally, maximum carbohydrate content may reach 50%
60% of dry weight. As examples, C. vulgaris FSP-E decreases the protein content from 60% to 20% of protein, while the carbohydrate content increased from 12% to 54% and lipids 11% to 20%, when the cultivation status changed from nitrogen in excess to nitrogen starvation [9]. In similar way, the composition of S. obliquus CNW-N moved from 50% to 25% of proteins, 20% to 50% for carbohydrates, and 7% to 14% for lipids [30]. A recent work where carbohydrates were produced from microalgae using nitrogen starvation (in outdoor conditions) to grow Chlorella spp. (four strains), a maximum protein, carbohydrate, and lipid content of about 25, 51, and 40, respectively, was reached

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FIGURE 3.4 Carbohydrate accumulation under nutrient starvation in batch cultivation mode.

instead of 41, 25, and 26 in nitrogen excess [87], confirming that nitrogen starvation is a great strategy to carbohydrate accumulation in closed and open systems. The disadvantage of using nutrient depletion strategies is that the viability of the process decreases substantially due to the reduced yield of biomass, even though a larger accumulation of carbohydrates is reached [88]. Moreover, carbohydrate accumulation in a batch system is not stable, and under extreme N starvation, carbohydrates content may actually decrease [88], while N limitation seems a viable alternative [89]. Again, a continuous system approach can be the solution to this issue [57], where a nitrogen limitation was applied to a continuous system, to find an optimum carbohydrates productivity without affecting the overall biomass productivity. Understanding in detail the influence of each nutrient’s availability on algae productivity is thus seminal to optimize the productivity of both biomass and carbohydrates. Studying the nutrient uptake and yield on biomass in a continuous system allows a better application of mass balance and a reliable evaluation of the capability of microalgae to adapt their composition to the environment. It was found that N and P concentrations in biomass depend on the specific growth rate and specific light supply rate [63,64,90]. Again, it is possible to investigate these phenomena by working in a continuous system at different residence times. As stated previously, different growth rates correspond to different residence times. For instance, it was observed that N
P-biomass composition increased at higher residence time values. This corresponds to the overall specific growth rate fixed by the dilution rate [64], and this appears to be a common behavior for many species.

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In Fig. 3.5, a linear correlation is highlighted between YN=X and μ, for a few species and under different conditions, suggesting that at higher growth rates an accumulation of nitrogen occurs. In fact, a change in N- and P-biomass ratio was observed by various authors [63,64,90,92]. As an example, in the case of Scenedesmus [92], a higher P/X ratio (i.e., P content in biomass), at higher growth rate was found, suggesting P is stored content as a result of increasing cellular nucleic acids and high energy compounds under highgrowth rate conditions. More complex is the effect of light on nutrient yields. Even though the relation between light and nutrient uptake under nonlimiting concentration is mostly unknown, some authors have suggested that light inhibition can affect

FIGURE 3.5 Nitrogen content in microalgal biomass as a function of specific growth rate, as a results of different residence time in continuous system for Chlorella vulgaris (dark circles), Scenedesmus obliquus (dark and open squares), and Nannochloropsis salina (open triangles). Chlorella vulgaris: Dark circles—Data from C.E.F. Silva, E. Sforza, Carbohydrate productivity in continuous reactor under nitrogen limitation: effect of light and residence time on time nutrient uptake in Chlorella vulgaris, Process Biochem. 51 (2016) 2112
2118; Scenedesmus obliquus: Dark squares—From E. Sforza, S. Urbani, A. Bertucco, Evaluation of maintenance energy requirements in the cultivation of Scenedesmus obliquus: effect of light intensity and regime, J. Appl. Phycol. 27 (4) (2015) 1453
1462, Open squares—Data adapted from E. Barbera, E. Sforza, A. Bertucco, Maximizing the production of Scenedesmus obliquus in photobioreactors under different irradiation regimes: experiments and modeling, Bioprocess Biosyst. Eng. 38 (2015) 2177
2188 [91]; Nannochloropsis salina: Open triangles—Unpublished data from experiments reported in E. Sforza, C. Calvaruso, A. Meneghesso, T. Morosinotto, A. Bertucco, Effect of specific supply rate on photosynthetic efficiency of Nannochloropsis salina in a continuous flat plate photobioreactor, Appl. Microbiol. Biotechnol. 99 (2015) 8309
8318.

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the elemental composition of microalgae [90,93]. In fact, a reduction in nutrient content under strong irradiances can be observed. The effect of light on nutrient uptake is particularly interesting in the case of P uptake, whose assimilation is directly linked to light intensity, as algae may transform P into high energy organic compounds by photophosphorylation, where light energy is transformed and incorporated into ATP [63,92]. Generally, phosphorus is an important nutrient for the growth of microalgae, and its contents in biomass ranges from 0.05% to 3.3% [70]. It participates in the synthesis of various substances including RNA and DNA, phospholipids, and ATP. Unlike the sources of N and C, it can be provided only by the fossil feedstock rocks such as sodium, ammonium, and potassium salts. Microalgae and cyanobacteria can accumulate intracellular phosphorus when it is in excess in the medium as polyphosphate granules (luxury uptake) [70]. Moving to a large-scale system, this phenomenon referred as P luxury uptake should be carefully accounted for. In fact, as P is a finite resource and luxury uptake represents an actual nutrient loss. Even when recycling nutrients, it is essential to minimize their make up to avoid luxury uptake phenomena that, from a process point of view, represent a loss. In addition, moving to large scale, where continuous production is required, the effect of residence time on nutrient uptake, resulting in different P- and light-biomass ratio, should be carefully accounted for in order to maximize the productivity and PE, as well as to minimize P consumption.

3.4.4 Light Exploitation and Photosynthetic Efficiency An efficient light exploitation is the key to any industrial process based on photosynthetic organisms. Under phototrophic conditions, the increasing turbidity associated with microalgae concentration usually becomes a significant growth-limiting factor due to the self-shading effect [54]. On the other hand, high sunlight irradiances may produce reactive oxygen species and damage the photosynthetic apparatus, thus causing growth inhibition (photoinhibition) as the photosystems are not able to efficiently exploit the high flow rate of absorbed photons. If photosaturation is a reversible process, photoinhibition causes damages in the reaction centers, mainly PSII [94], which affects process efficiency. Some cellular defenses can be activated against photooxidation, such as the synthesis of enzyme superoxide dismutase and carotenoids, although these mechanisms are not sufficient with prolonged exposition. Therefore, a suitable light supply is necessary to avoid these phenomena and to optimize biomass productivity. In addition, the natural changes in sunlight intensity along with the day and the seasons should be considered, as well as the homogeneous light distribution in a PBR or pond, which is a complex environment with different regions where light limitation, light saturation, or light inhibition may

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simultaneously occur. Effective engineering strategies to face this complex phenomenon are still lacking and should be developed as soon as possible, to allow successful biofuel production from photosynthetic organisms [64]. Providing optimal irradiation flux is also essential for the accumulation of storage compounds such as carbohydrates and lipids [41]. In general, increasing light intensity leads to a higher carbohydrate content in photosynthetic microorganisms [95], although this can happen in combination with other operative and nutritional parameters as well [41]. Generally, the positive effect of increasing light intensity on the accumulation of starch and lipids is feasible only below the saturation of photosynthesis under the given conditions of a particular species, ranging between 150 and 450 μmol photons m22 s21 for carbohydrate accumulation [10], as shown in Table 3.3. At present, biomass productivity and PE in outdoor conditions need to be improved because the instability of the climatic conditions changes the temperature in the PBR significantly, and consequently, the biomass production rate (low or excess light and rain). Outdoor data for carbohydrate production is not available at present. Some remarks can be based on the biomass productivity range for Scenedesmus sp. [12] of 47
160 ton biomass ha21 year21 (90 can be referred as a medium value). Supposing a carbohydrate content of 50%, it is possible to estimate a bioethanol productivity from 50 to 140 L bioethanol ha21 year21. This range means 5
15 times more biomass production than first and second generation biomass from sugarcane, corn, beet, and lignocellulosic material, showing a promising approach for this type of biomass. Optimizing photoconversion is the key of a sustainable process, and this can be obtained not only by selecting a proper reactor geometry (i.e., an optimal light path) but also operating the reactor appropriately. For instance, when setting the residence time, the concept of specific light supply rate should be carefully considered. As mentioned before, the possibility to account for the light actually perceived by the cells allows not only for the understanding of the physiology of adaptation but also the application of the energy balance in the photoconversion process. At steady state, a stable specific light supply per unit mass of cell rEx (mmol g21 day21) [62,64] can be calculated as rEx 5

PFDabs 3 APBR cx 3 VPBR

ð3:3Þ

Looking at the efficiency of the photoconversion from sunlight to biomass energy, the PE can be calculated as PEð%Þ 5

Cx 3 Q 3 LHV PFDabs 3 Ep 3 APBR

ð3:4Þ

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where LHV is the lower heating value (assumed equal to 20 kJ g21), Ep is the energy of photons (kJ μmol21), CX is the biomass concentration (DW) at steady state, and APBR is the irradiated surface of the reactor (m2). The photon flux density absorbed by the algae (PFDabs ) can be measured at steady state as PFDabs 5 Iin 2 BI 2 I0 22

ð3:5Þ

21

where Iin is the incident light (mmol m day ), BI the back irradiance (mmol m22 day21), and I0 the light absorbed by the medium and the panel walls (mmol m22 day21). From Eqs. (3.3) and (3.4), the following equation can be obtained: PEð%Þ 5

LHV 1 1   Ep τ rEX

ð3:6Þ

So, for a given species, LHV as well as Ep are constants, and a clear relation between PE and the specific light supply rate is found, depending on residence time as reported in Fig. 3.6A. In this figure, different data series are reported together in the cases of limiting or saturating irradiances. Clearly, both PE and rEX depend on the light actually absorbed by the biomass ðPFDabs ), which is strongly affected by the incident light, as the biomass-light yield is strongly reduced under photosaturating and inhibiting irradiances [64,96]. The effect of light intensity is clear when, at the same residence time, the rEX is modified by changing both the biomass concentrations. In one case, different biomass concentrations were obtained by reducing the N supply in the inlet and incident lights and were reported (Fig. 3.6B) [63]. Thus, a direct relation can be observed between PE and rEX. On the other hand, as reported in Fig. 3.6A, it appears that the trends obtained by changing the residence time are similar for both irradiances, suggesting that by managing this operating variable, the specific supply rate can be adjusted so as to obtain a higher photoconversion efficiency. In conclusion, the specific light supply rate is a quantitative measurement allowing for the understanding of light conversion efficiency in the cultivation of microalgae and cyanobacteria. In fact, photoconversion efficiency was found to decrease if the specific light supply rate per cell was not optimized [66]. Thus, the specific light supply rate is the main variable that affects photosynthetic productivity, as it affects both PE and the state of the photosynthetic apparatus, resulting in a lower light/biomass yield and high energy requirement for maintenance because of photoinhibition. The key factor to obtain stable biomass productivity in a continuous system is the ability to set a proper light supply rate, which depends on the incident light and residence time applied, the reactor depth and geometry, and the biomass concentration.

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FIGURE 3.6 Photosynthetic efficiency as a function of specific supply rate. (A) The trends obtained by changing the residence time under low (black squares) and high (open squares) light intensities (150 and about 650 μmol m22 s21, respectively) are reported. Data are an elaboration of those reported in [64] per Scenedesmus obliquus under no limiting nutrient condition. (B) Data obtained under limiting N supply, resulting in different biomass concentration and specific light supply rate, are reported under different incident light intensities (black triangles for 150, open circles for 300, and black squares for 450 μmol m22 s21) characteristic curve when the same residence time is used. Data are an elaboration of those reported in Ref. [63] for Chlorella vulgaris.

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3.5 CONCLUSION The interest in a carbohydrate-based microalgae and cyanobacteria biomass is growing due to the increased request of biofuel production. Light conversion, carbon fixation, and nutrient supply are the main issues that need to be resolved in order to obtain a high content of carbohydrates and to optimize biomass productivity. Some differences between microalgae and cyanobacteria were highlighted concerning mostly carbohydrate composition and biomass productivity. Cultivation in continuous systems working at steady state has been proposed as a reliable method to compare growth performances, avoiding problems of acclimation of biomass, and allowing an easier application of mass and energy balances. In particular, the effectiveness of investigating biomass productivity, photoconversion efficiency, and nutrient uptake in a continuous mode were discussed. Working in continuous may also allow researchers to understand the feasibility of possible process scale up, which is one of the major challenges of the future.

3.6 FUTURE OUTLOOK The production of carbohydrate from microalgae is a very promising technology that can cover different fields of application, including the production of biofuels. However, some aspects such as the achievable carbohydrate productivity in order to decrease the amount of energy and nutrient necessary are lacking and need further investigations, influencing directly in the production costs. At the moment these are not compatible with first/second generation biomasses used to obtain bioethanol by hydrolysis and fermentation. On the other hand, to overcome the economic issue, a deeper control of the effect of operative conditions on carbohydrate quality and quantity should be carefully considered. This is particularly true with respect to outdoor conditions, where fluctuating environment may affect the carbohydrate composition. In addition, to really understand the feasibility of the process, economic analyses should be carried out on data from continuous real system, to have a more reliable estimation of the potential of third generation process proposed in this work.

ACKNOWLEDGMENT The authors thank CNPq Brazil (National Research Council of Brazil), Process number 249182/2013-0, for resources and fellowship. C.E.F. Silva would also like to thank Silvia Romito for help with the figures.

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718.

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Chapter 4

Production of Bioethanol From Brown Algae Marı´a Cristina Ravanal1,2, Carolina Camus3, Alejandro H. Buschmann3, Javier Gimpel1, A´lvaro Olivera-Nappa1, Oriana Salazar1 and Marı´a Elena Lienqueo1 1

Department of Chemical Engineering and Biotechnology, Centre for Biotechnology and Bioengineering (CeBiB), Universidad de Chile, Santiago, Chile, 2Food Science and Technology Institute (ICYTAL), Faculty of Agricultural Sciences, Universidad Austral de Chile, Valdivia, Chile, 3iBmar Centre, Universidad de Los Lagos, Puerto Montt, Chile

4.1 INTRODUCTION The two most common biofuels are biodiesel and bioethanol, which can potentially replace conventional liquid fuel such as diesel and petrol, respectively [1]. Particularly, bioethanol can be produced from abundant supplies of starch/cellulose biomass. Although growth of feedstock crops for ethanol is feasible (i.e., sugarcane in Brazil, corn and wheat in the United States and sugar beet in Europe [2
4]), its production has raised doubts about possible impacts on food supply and security. The fuels versus food debate is aggravated by the large-scale cultivation demands and the high levels of resources required [1,5]. Accordingly, there is an urgent demand for alternative, sustainable fuels, and feedstocks in order to replace food-based feedstocks. In comparison to other feedstocks, seaweeds are a promising source for renewable energy production since they use solar energy and fix carbon dioxide from the atmosphere for assimilation, mainly in the form of carbohydrates and lipids, which can be exploited for biofuel production [4,6,7]. In addition, the use of seaweed may also reduce the use of freshwater that is necessary for crop production and transformation processes [8]. Furthermore, macroalgae have high productivity and photosynthetic efficiency, great potential for carbon dioxide fixation, low percentage of lignin, and high content of carbohydrates that are all advantageous for biofuel production [9
11]. Macroalgae are a source for a variety of chemical compounds that are of commercial interest [12], which could be separated from the polysaccharides used for bioethanol prior to using the remaining biomass for biofuel Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00004-7 © 2019 Elsevier Inc. All rights reserved.

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FIGURE 4.1 Schematic representation of the bioethanol production process.

production [8,13], allowing the inclusion of the biorefinery concept. The process for bioethanol production has multiple steps, and Fig. 4.1 summarizes this process.

4.2 PROCESS FOR BIOETHANOL PRODUCTION FROM BROWN ALGAE 4.2.1 Cultivation of Macroalgae The amount of seaweed necessary for the development of biofuels on an industrial scale is not achievable by solely harvesting natural stocks, thus making cultivation a key challenge for the feasibility of the industry. Although seaweed farming has existed in Asian countries for more than 50 years [12], it focuses on food production, primarily morphological and organoleptic characteristics as well as the quality of fronds, and not on the generation of significant volumes of biomass. Although some of these techniques may be applied in the production of feedstocks for bioenergy, the industry’s focus must be on increasing the biomass and yield. A deeper understanding of large-scale biomass production is necessary before the large-scale exploitation of biofuels. The advances in cultivation and harvest technologies can potentially increase the production three- to tenfold with a corresponding decrease in the area needed for cultivation to meet specified production goals as one of the key strategies for economical biofuel production [6]. The majority of the macroalgae biomass worldwide comes from a relatively small number of species of Phaeophyceae (Saccharina japonica and Undaria pinnatifida) and Rhodophyceae (Eucheuma, Kappaphycus, and Pyropia) [14], some of which are traditionally cultivated in Asian countries but are expanding to other continents. Commercial and/or experimental farms are found in Europe (France-Brittany [15], Spain-Galicia [16], IrelandGalway [17], Norway-Frøya [18]), Africa (Zanzibar [19]), North America (Canada-Acadian Seaplants [20]), and South America (Peru, Chile [20]). The majority of these farms produce different macroalgae for commercial use. Few countries are developing seafarming to produce biomass for biofuels.

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One example is in southern Chile, where Macrocystis pyrifera seafarming was developed at a precommercial scale mainly for biofuel production and the abalone industry [21]. During a period of 3 years, juvenile sporophytes produced in a hatchery were seeded on a 21 ha farm located on the Chiloe interior sea. Productivity reached up to 200 fresh tones ha21 year21 (with B10% solids), a productive value comparable to the 6.1
9.5 kg fresh weight m22 year21 for sugarcane, a productive land plant [7]. In addition, macroalgae biomass seems not to require fertilization and freshwater, and therefore significantly reduces competition with agriculture land, is suitable for integrating wastewater treatments; possess a high rate of CO2 fixation, as well as other advantages [4,7,10]. In addition, macroalgae farming has positive environmental externalities when cultivated with fish and invertebrates. Macroalgae are able to use inorganic dissolved waste (e.g., nitrogen and phosphorus) that, when discharged into the environment, contribute to enhanced coastal eutrophication [22]. Thus, the use of farmed macroalgae associated with fish and invertebrate cultivation has been highlighted as a relevant aquaculture strategy toward the development of a more sustainable aquaculture model [23
25]. However, macroalgae farming still faces several challenges. Besides the success of macroalgae cultivation in Asian countries, the farming industry needs to create value-added products and reduce macroalgae aquaculture development based on only commodity-type products [12]. In other words, macroalgae are highly variable in their chemical composition, which in many cases complicates the establishment of commercial processes. In order to establish a more predictable and less-variable macroalgae-based feedstock for industrial processes, strains need to be developed that provide a reliable chemical composition. However, macroalgae domestication is still in its infancy; a better integration of existing knowledge on ecology and genetic diversity of wild populations is needed. Also, the selective pressures that were exerted by cultivators to manipulate macroalgae, while shaping aquaculture environments are missing [26,27]. In order for large-scale macroalgae biomass farming to increase in the near future, several other environmental concerns such as nutrient competition with phytoplankton, introduction of soluble organic matter in water, regulation and development driver to lead macroalgae farming, and in particular sustainable strategy (e.g., integrated multitrophic aquaculture) must be addressed [22,28]. Therefore, research and development driving eco-conscious macroalgae aquaculture are still needed to assure sustainable and reliable biomass production for biofuels and coproducts. This would be in addition to technical restrictions that seaweed biomass transformation imposes prior to establishing a macroalgae-based biofuel industry.

4.2.2 Chemical Composition of Macroalgae The chemical composition of macroalgae is very important for defining the type of process required for bioethanol production. The main structural

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materials of their cell walls are alginate, cellulose, laminarin, fucoidan, agar, and carrageenan [29]. Alginate or alginic acid is composed only of uronic acid (carbohydrate where the terminal carbon has been oxidized from the alcohol to a carboxyl group). Alginate is a copolymer of α-L-guluronate (G) and its C5 epimer β-D-mannuronate (M), being arranged as homopolymeric G blocks, homopolymeric M blocks, alternating GM blocks, or random heteropolymeric G/M stretches [30]. Cellulose has a linear chain of β-1,4-linked D-glucose units and algae cellulose (Cladophora cellulose) presents a characteristic X-ray diffraction pattern similar to that of native cellulose found in land plants such as wood [31]. Laminarin or laminaran is composed solely of glucose, with the main chain containing glucopyranoses linked by β-1,3 and is substituted with β-1,6-linkages. Laminarin has a β-(1-3):β(1-6) ratio of 3:1 [32]. Fucoidan is a heterogeneous polysaccharide that has a backbone built of (1-3)-linked α-L-fucopyranosyl. It includes mainly sulfated galactofucans with backbones built of (1-6)-β-D-galacto- and/or (1-2)-β-D-mannopyranosyl units with fucose or fuco-oligosaccharide substitutions [33]. Agar or agar
agar is a linear polymer of alternating 3-linked β-D-galactopyranosyl and 4-linked 3,6-anhydro-α-L-galactopyranosyl subunits [34]. Carrageenan is a polysaccharide made up of repeating galactose units and 3,6 anhydrogalactose, both sulfated and nonsulfated. The units are joined by alternating α-1,3 and β-1,4 glycosidic linkages [34]. Brown algae have evolved a cell wall that shares features with plants [29]. Like plants, brown algae produce cellulose, but these crystalline fibers account for only a small portion of the cell wall (B1%
8% of the dry weight) [35]. The main cell wall components are alginates and fucoidans [33]. Alginates, fucoidans, and cellulose are in an average weight ratio of 3:1:1 in mature intertidal brown algae [36]. M. pyrifera is the largest brown algae on earth with a carbohydrate makeup of 60.6% alginate, 4.6% fucoidan, and 22.6% cellulose in an average ratio of 13:1:5 [37]. The approximate composition of M. pyrifera harvested in different months in the year exhibited changes in the carbohydrate content: 64% in May, 47% in November, and 37% in December [38]. In general, the cell wall and storage carbohydrate in brown algae varied strongly with season, maturity, environmental factors and are species specific, which increase difficulty for industrial processing. For example, the phaeophyceae Ascophyllum nodosum and Laminaria digitata have a composition of alginic acid, fucoidan, laminarin equivalent to ratio of 6:3:1 and 6:1:3, respectively [9].

4.2.3 Pretreatment and Enzymes for Brown Algae Degradation Usually, saccharification of brown algae is carried out by a two-step process of biomass treatment. In the first step, dewatered, dried, and milled biomass is processed with acid and subsequently treated with enzymes. The acid

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utilized in the first step is generally sulfuric acid (Table 4.1), but cases of pretreatment with hydrochloric acid have also been reported [44]. In general, conditions for acid hydrolysis with sulfuric acid vary between 0.1 and 2 wt. % at B120 C
121 C (Table 4.1) for 30
60 minutes. Considering that the acid is not selective and that both the number and variety of glycosidic linkages in the biomass are variable, it is difficult to identify the optimal parameters for acidic saccharification while controlling the experimental conditions. As a result, there is a nonnegligible risk of a partial release of some monosaccharides or production of toxins derived from the harsh treatment. Even so, acid hydrolysis is still considered to be a good option when used in combination with enzymes. The main enzymes used for saccharification of cellulose from macroalgae are endoglucanases (EC 3.2.1.4) which randomly cleaves internal glucosidic linkages of this polysaccharides, and cellobiohydrolases (EC 3.2.1.91) and β-glucosidase which hydrolyze cellobiose to glucose (EC 3.2.1.21) [52]. For the biodegradation of alginate involving alginate lyase, mannuronate lyase (EC 4.2.2.3), or guluronate lyases (EC 4.2.2.11) cleave within the chain producing unsaturated uronic acid oligomers with a double bond between the C4 and C5 at the nonreducing end. Oligoalginate lyase (EC 4.2.2.) cleaves these oligomers to produce monosaccharides (i.e., unsaturated uronate) [53]. Other enzymes for the enzymatic degradation of laminarin and fucoidan are β-1,3 glucanase (EC 3.2.1.6), β-agarase (EC 3.2.1.81), and sulfated fucan endo-1,4-fucanase (EC 3.2.1.-), respectively [54]. A review of the enzyme type and operation conditions used for saccharification of carbohydrates from brown macroalgae shows that targeted carbohydrates have primarily been those that can produce fermentable sugars, such as laminaran and cellulose (Table 4.1). As a consequence, alginate, the most abundant carbohydrate, has been relegated to second place, given the difficulty in finding a microorganism able to ferment the uronic acid that is produced. Enzymatic treatment of brown algae is best reported for Laminaria sp. For instance, Laminaria cell walls were successfully saccharified using laminarinase after acid pretreatment [39], releasing 92.5% glucose/g macroalgae. On the other hand, in order to address the heterogeneity of macroalgae carbohydrates, multienzymatic preparations containing predominantly cellulase and cellobiase were successfully applied. This was the case for S. japonica, which was saccharified with a mixture of fungal cellulase and cellobiase [43]. In the case of S. japonica, acid pretreatment and Termamyl 120L (Novozymes, Inc., Denmark), a commercial cocktail containing a thermostable amylase, released 70% of total carbohydrates in the biomass [45], but when pretreated with acid and then with cellulase and glucosidase, 84% of carbohydrates were extracted [46]. Interestingly, in the work of Jang et al. [45], the joint use of the enzyme mixture and a bacterial microorganism producing carbohydrases significantly improved the glucose yield.

TABLE 4.1 Summary of Experimental Conditions Used for Hydrolysis of Brown Algae Cell Wall for Bioethanol Production Brown Algae

Conditions

Saccharification Yield

Reference

Laminaria hyperborea

PT: pH 6, 23 C, 30 min

ND

[39]

Laminaria japonica

PT: 1 wt.% H2SO4 at 120 C, 30 min; SCF: alginate lyase

28.08 wt.% uronic acid

[40]

Nizimuddinia zanardini

PT: 7 wt.% H2SO4 at 120 C, 45 min; SCF: 15 FPU cellulase and 45 U β-glucosidase

70.2 wt.%

[41]

Alaria crassifolia

PT: 2% H2SO4, 121 C, 30 min; SCF: meicelase, 50 C, pH 5.5, 120 h

28.4% Glucose

[42]

L. japonica

PT: 0.1% H2SO4 121 C, 1 h

277.5 mg glucose/g algae (92.5%)

[43]

ND

[44]



SCF: laminarinase 0.1 U/100 g, pH 6, 32 C

21.3% Galactose

SCF: 45 FPU/g substrate cellulase 55 CBU/g substrate cellobiase 50 C, pH 4.8 Laminaria digitata

PT: 2 M HCl, 30 min 

SCF: 0.5 U Trichoderma laminarinase, 24 C

Saccharina japonica

PT: 40 mM H2SO4, 121 C, 1 h; SCF: termamyl 120L, Bacillus sp. pH 7, 30 C

69.1% Of total carbohydrate in biomass

[45]

S. japonica

PT: 0.06% H2SO4 at 170 C, 15 min; SCF: 15 FPU cellulase and 70 U β-glucosidase

84.0 wt%

[46,47]

L. digitata

SCF: 10% celluclast 1.5 L, 0.5% alginate lyase, pH 4.8, 50 C, 48 h

84.1% Glucose

[48]

L. digitata

PT: pH 4.8, autoclave at 121 C, 20 min; SCF: 40 U celluclast 1.5 L/g dm; 40 U Novozyme 188/g dm; 10 U alginate lyase/ g dm, 50 C, 48 h

78.23% Total sugars

[49]

Saccharina latissima

PT: 50 C

ND

[50]

Macrocystis pyrifera

PT: 2 vol.% H2SO4

68.4 wt.% of glucose

[37]

SCF: celluclast at pH 5.2, 4 h, 50 C

85.8 wt.% of uronic acid

SCF: 20 mg cellic CTec2/g glucan and 2 mg cellic HTec2/g glucan

Alginate lyase/oligoalginate lyase, pH 7.5, 2 h at 37 C L. digitata

PT: 40 C, pH 5. SCF: 1% (w/w) alginate lyase, 10% (v/w) Cellic CTec2

PT, Pretreatment; SCF, saccharification; ND, No data.

92% Glucose in biomass laminarin

[51]

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With the arrival of the concept of a biorefinery defined as the sustainable processing of biomass into a spectrum of marketable products and energy, the last 5-year period has seen much interest in recovery of not just glucoseproducing carbohydrates. That is how the use of alginate lyases combined with oligoalginate lyases became popular, by taking advantage of the alginate monomers. The alginate lyases have been applied either individually [40,48] or combined with cellulolytic enzymes [49,51]. The new generation of cellulolytic enzymes, represented by Cellic CTec2 and Cellic HTec2 from Novozymes, has also contributed to increasing the recovery of carbohydrate components present in brown algae [50]. The simultaneous use of enzymes coming from substantially different native environments, as is the case of marine alginate lyases and fungal cellulolytic enzymes, impose some restrictions on the application conditions. In this case, a careful design of the process conditions is required. Therefore, running the enzymatic hydrolysis in two successive saccharification steps should be considered. It is interesting to notice that the use of alginate lyase apparently improves the cellulasecatalyzed degradation of laminarin and cellulose in the material by selective removal of alginate, as was suggested by results showing that cellulase alone releases only half of the available glucose [51].

4.2.3.1 Biotechnology of Recombinant Enzymes In 2011, the use of a recombinant oligoalginate lyase from Sphingomonas sp. MJ-3 for the saccharification of alginate was proposed, obtaining 33% monomerization yield using pure alginate without the addition of an endo-acting catalyzer [55]. A year later, an Escherichia coli platform expressing four endo- and three exo-alginate lyases, along with other catabolic enzymes, was able to utilize 80% of the theoretical sugars present in M. pyrifera to produce bioethanol [56]. The sequential addition of recombinant Alg7D exo- and Alg17C endo-alginate lyases from Saccharophagus degradans 2
40 to pure alginate resulted in 45.5% production yield of reducing sugar ends (monomers and oligos) [53]. The same research group using the same enzymes demonstrated that acid pretreatment of pure alginate only improved alginate saccharification by 7.5% [57]. This is not surprising given that authors pretreated pure alginate and not the macroalgae biomass. In contrast, it was found that acid pretreatment of M. pyrifera biomass significantly enhanced the production of glucose (68% yield) and uronic acids (86% yield) after treatment with cellulases, endo-alginate lyase (A1603, Sigma-Aldrich, St. Louis, MO, United States), and recombinant Agrobacterium tumefaciens oligoalginate lyase (Atu3025) [37]. In addition, two recombinant endo-lyases from Sphingomonas sp. and Flavobacterium sp. were compared to the Flavobacterium multivorum enzyme (A6973, Sigma-Aldrich). Glucose released from L. digitata with each of the two experimental enzymes was lower than with the commercial alternative. On the other hand, production of

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unsaturated uronic acid ends was higher with each of the two “homemade” endo-lyases when treating this macroalgae [58]. Saccharina latissima has also been saccharified using a cellulose cocktail and F. multivorum endolyase (A6973, Sigma-Aldrich). The combination of these enzymes released 74 g L21 of mannitol plus glucose in a high solid-ratio reaction (250 g L21 dry matter), which is closer to industrial scale requirements. The authors also suggested the necessity of finding alginate lyases that work at temperatures closer to the celullases optimum (50 C) [59]. This would also apply for lower pHs (B5.0) that are preferred by these glucanases. Recently, alginate lyases have also been proposed as alternative for enhancing extraction yields of high-value bioactives from brown macroalgae such as carotenoids [12], fluorescent pigments, fucoidan [60], and phlorotannins [61]. Endo-alginate lyases can only be obtained from two commercial sources: alginate lyase powder 100 mg/1000 U/USD137 (Sigma-Aldrich), and recombinant Sphingomonas sp. alginate lyase solution 42 mg/5000 U/USD174 (Megazyme, Ireland). Moreover, there is no commercial source for exoalginate lyases. Given the high cost of these sources, it is not viable to include them in pilot or industrial scale processes involving alginate degradation. A number of reported studies have dealt with optimizing the production of alginate lyases from natural or recombinant sources. Most research has focused on the analytical-scale purification of a single enzyme and its characterization [62,63]. Recombinant expression optimization by means of alternative expression platforms (other than E. coli) has also been successful for alginate lyases. Secretion and/or surface-display of recombinant enzymes constitute a solution for the tendency of E. coli to accumulate overexpressed proteins as inclusion bodies, while also facilitating downstream processes. Single bacterial alginate lyases have been successfully expressed as secreted proteins in Bacillus subtilis and Pichia pastoris [64,65], and as a surface displayed active enzyme in Yarrowia lipolytica [66] and Saccharomyces cerevisiae [67]. An eukaryotic alginate lyase from abalone has also been obtained as a secretion product from the baculovirus expression system [68].

4.2.4 Degradation Pathways of Main Macroalgae Carbohydrates In contrast to cellulose in land plants, alginate monomers are more oxidized than glucose, so the yield of energy obtainable per carbon atom in biochemical oxidation/respiration processes is greatly reduced in comparison to glucose polymers such as cellulose [13,69]. Inside macroalgae cells, this misbalance is equilibrated by combining alginate synthesis with the production of further reduced compounds, such as mannitol, which is a very hydrophilic sugar alcohol that is unable to cross the cell membrane, confined

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inside the cell. As such, mannitol also serves the additional purpose of controlling intracellular osmolarity to prevent cells from dehydrating or bursting when confronted with sudden osmotic pressure changes in their surrounding environment [70,71]. However, for an organism that feeds only on oxidized macroalgae cell wall carbohydrates, there is no possibility of metabolically balancing such an oxidized substrate and so the net energy available for such an organism is very low, thus making the entire process unfavorable, slow, and cumbersome [69]. In addition, since alginate sequesters water molecules available for hydrolysis and its formal charge structures water molecules in specific positions around the carbohydrate molecule, alginate enzymatic hydrolysis is a difficult and slow process that also contributes to slowing down alginate catabolic rates. Some marine organisms have evolved enzymes that use alternative chemical pathways to degrade alginate, such as alginate lyases, but their catalytic rates are not as high as those of hydrolases, making them less-effective degradation catalysts [55,56]. Moreover, alginate lyases produce monomers (4deoxy-L-erythro-5-hexoseulose uronate, or DEHU) with the same oxidation level as the original mannuronic and guluronic moieties of alginate, thereby not allowing them to overcome the low-energy yield of such sugars if not balanced by more reduced compounds [13,69]. Taken together, the low enzymatic depolymerization rates of alginate and low-energy yield of alginate monomers make degradation of macroalgae cell walls a slow, cumbersome, and energy-limiting process. This is true for herbivores and saprophytes that feed on brown macroalgae cell walls, but from a technological point of view this is highly relevant, since these are major bottlenecks that must be tackled in all biotechnological processes in which any organism, engineered or not, is used to manufacture commercially relevant products using macroalgae as a feedstock.

4.2.4.1 Metabolic Redox Imbalance Caused by Brown Algae Carbohydrates As a whole, alginate, mannitol, and glucan (present as laminarin and cellulose) are the main carbohydrate sources available in brown algae, both for algaeconsuming organisms and for biotechnological purposes. A key criterion for the economic and efficient industrial use of these sugars implies the use of native, engineered, and/or evolved microorganisms that can metabolize these carbohydrates. It is likely that herbivores and saprophytes consuming carbohydrates from brown algae have developed metabolic strategies to cope with the redox unbalance induced by alginate. However, the well-characterized and dependable industrial biotechnology workhorse microbial species E. coli and S. cerevisiae are not metabolically adapted to these conditions. Laminarin and cellulose produce glucose when degraded, which is the preferred carbon and energy source for both industrial species. However,

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TABLE 4.2 Theoretical Energy and Reducing Equivalent Yields of Glucose, Mannitol, and Uronic Acids Reaction

ATP and NADH Yield

Catabolism to acetyl-CoA Glucose
! 2 acetyl-CoA 1 2 CO2

2 ATP 1 4 NADH

Mannitol
! 2 acetyl-CoA 1 2 CO2

2 ATP 1 5 NADH

DEHU
! 2 acetyl-CoA 1 2 CO2

ATP 1 2 NADH

Catabolism to CO2 Glucose
! 6 CO2

36 ATP

Mannitol
! 6 CO2

39 ATP

DEHU
! 6 CO2

29 ATP

DEHU, 4-Deoxy-L-erythro-5-hexoseulose urinate.

mannitol is a heavily reduced molecule and alginate is largely oxidized when compared to glucose; therefore, neither is well tolerated in E. coli’s or S. cerevisiae’s metabolism because of the large induced redox imbalances. These imbalances result in a less-efficient metabolism, which means lower productivity and yields in terms of a biotechnological process. Nevertheless, metabolic differences and preferences arise when comparing these two carbon and energy sources in the context of E. coli or S. cerevisiae metabolism. Table 4.2 summarizes theoretical energetic and redox equivalent yields from glucose, mannitol, and alginate monomers (DEHU) when degraded to acetylCoA or completely degraded to CO2 and H2O. As a polyol, mannitol generates excess reducing equivalents. Both E. coli and S. cerevisiae have native mechanisms to counteract this surplus by way of electron shunts or futile cycles [69,72]. Alginate monomers create a heavy shortage of reduced cofactors in the cytoplasm, and neither E. coli nor S. cerevisiae has the metabolic means to correct this burden. When comparing both carbohydrates from a bioenergetic point of view, mannitol may create an overload of energy (seen as a reduced cofactor excess) for the cell which must be dissipated, while alginate causes a large deficit of energy that cannot be obtained otherwise. Therefore, mannitol induces the cell to lose part of the chemical energy available in the molecule and stresses its cellular machinery but is still a substrate on which E. coli and S. cerevisiae can grow, albeit with lower biomass and/or byproduct yield. Alginate, on the other hand, directly induces cell starvation even if it can be used for cell maintenance, and this cannot be overturned. This causes a dramatic reduction in cell growth and biomass and product yields [13,69].

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As a solution, many macroalgae-feeding sea microorganisms, such as various Vibrio species including Vibrio splendidus, have developed mixed metabolic pathways in which they can use alginate and mannitol together for their needs. This is a good strategy, because the excess of reducing equivalents generated by mannitol can be used to counteract the shortage of reduced cofactors produced by alginate. The same approach could be used to maximize the productivity and yield of an industrial strain growing on macroalgae carbohydrates. However, in order to make this a successful choice, a tight and narrow redox balance mechanism has to be implemented because even a slight imbalance can greatly affect the output.

4.2.5 Genetically Engineered Microorganisms for Macroalgae Carbohydrate Utilization 4.2.5.1 Engineered Strains and Redox Imbalances Metabolic engineering plays an important role in improving product yields, efficient use, and optimizing the biomass. At present, wide efforts have been made to engineer well-characterized microorganisms to utilize alginate and mannitol as carbon sources. In particular, Wargacki et al. [56] and EnquistNewman et al. [72] have shown that bioethanol can be produced from cofermentation of mannitol and alginate/DEHU by genetically modified E. coli and S. cerevisiae, respectively. The V. splendidus alginate metabolism pathway, including membrane transporters and extracellular alginate and oligoalginate lyases, was introduced in E. coli while mannitol catabolism was left to be performed by native E. coli pathways. In S. cerevisiae, the native mannitol metabolic pathway was deregulated and the V. splendidus alginate metabolism pathway was reconstructed together with the DEHU transporter of Asteromyces cruciatus, but with no production of extracellular enzymes, which constrained the process to use depolymerized alginate as a substrate. Both strains were engineered to produce bioethanol from pyruvate, which adds a further complication for cell metabolism when fermenting carbohydrates from brown macroalgae. For bioethanol production from pyruvate, an additional two reducing equivalents per mole of bioethanol were consumed. This can partly alleviate the metabolic burden when mannitol is used as a carbon source but is disastrous when alginate is used as a feedstock, causing growth and yields to drop to near zero values if no redox counterbalance is available. For industrial applications, mannitol can be used to counteract an alginate induced redox imbalance, since both are major components in brown macroalgae. However, only a narrow window in the mannitol:alginate ratio can be used in practice without drastically affecting cell performance. In particular, it has been demonstrated that the efficiency of mannitol and alginate metabolic pathways to produce bioethanol depends tightly on redox control in the bioethanol-producing S. cerevisiae strain of Enquist-Newman

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et al. [72]. Surprisingly, glycerol was the main byproduct detected in nonoptimal fermentations and in model simulations. Glycerol was used to counterbalance the excess of reduced cofactors produced when the rate of the reaction catalyzed by mannitol dehydrogenase was higher than that of DEHU reductase. The relationship between the ratio of mannitol:DEHU consumption and mannitol uptake rate can favor a decrease or increase in growth and product formation while bioethanol production is favored by higher mannitol:DEHU ratios. The authors conclude that the full potential of brown macroalgae as feedstock for biofuel production and other more valuable chemicals depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. In a reported study by Wargacki et al. [56], an engineered E. coli BAL1611 strain was used to obtain a final bioethanol titer of B4.7% v/v with 41% yield from sugars but only when an 8:4:1 mannitol:alginate:glucose carbon source ratio was used. This is the carbohydrate proportion present in S. japonica (kombu), but it is not a representative composition for all commercially relevant brown macroalgae. In fact, many fast-growing brown macroalgae such as M. pyrifera have completely opposite average composition, 10:1:1. Experimental studies carried out by the authors, with Wargacki’s strain BAL1611, shown in Fig. 4.2 indicated that this is an exception for bioethanol production. Excluding the 5:8 alginate:mannitol ratio of S. japonica, all other bioethanol yields are barely half the reported values, which makes bioethanol production using the unoptimized E. coli strain BAL1611 an uneconomical process [13].

4.2.6 Saccharification and Fermentation in Process Configurations The saccharification and fermentation steps can be carried out via different configurations: separate hydrolysis and fermentation (SHF), simultaneous saccharification and fermentation (SSF), simultaneous saccharification and cofermentation (SSCF) of hexoses and pentoses, and consolidated bioprocessing (CBP) [73]. SHF is a process in which the hydrolysis of polysaccharides from macroalgae (e.g., cellulose, alginate, laminarin, or fucoidan) and the fermentation of C5 and C6 sugars are performed separately. The advantage is that the hydrolysis and fermentation take place under optimum conditions (pH, temperature, etc.), but the disadvantages are the increased contamination and the inhibitory effects. In this case, hydrolysis is rate-limited by the concentration of generated sugar which inhibits cellulase activity [74
76]. SSF is a process in which polysaccharides’ hydrolysis and C6 fermentation are carried out in one step [77]. The advantages are the low quantity of enzyme needed, the high bioethanol yield, reduced foreign contamination, less inhibitory effects, and lower cost but the disadvantage is that neither hydrolysis nor fermentation can be performed under optimal conditions [78,79].

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FIGURE 4.2 Biomass and bioethanol production in Escherichia coli BAL1611 at different alginate:mannitol ratios, with 2.6% w/v total carbohydrate concentration. (A) Growth rate, (B) biomass yield, (C) bioethanol yields after 12 and 48 fermentation hours. For comparison, Saccharina japonica (kombu) has a B5:8 alginate:mannitol ratio, while Macrocystis pyrifera has a B10:3 alginate:mannitol ratio.

SSCF is a process in which polysaccharide hydrolysis and C5 and C6 fermentation are performed in one reactor [73]. The main polysaccharides from macroalgae have C6 such as glucose, galactose, mannose, mannuronic acid, guluronic acid, fucose, etc. The branch chain of some polysaccharides, such as fucoidan, have xylose (C5) [6]. The advantages of this methodology are shorter processing times, high bioethanol yield, and a reduction in the risk of contamination. However, the disadvantages are that a high enzyme load is required and neither hydrolysis nor fermentation can be performed under optimal conditions. CBP is a process in which the enzymes are produced by the fermenting organisms. The advantages are cost-effectiveness and energy efficiency, but the disadvantages are a lack of suitable organisms and difficulty in process control [73,74].

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Solid state fermentation is defined as a fermentation process in which microorganisms grow on solid materials without the presence of free liquid. The advantage of this coupling of processes is the immediate consumption of sugar to produce ethanol, which leads to lower enzyme requirements, not sterile conditions, shorter process time, and smaller reactor volumes [73,75]. Choosing the optimal configuration of saccharification and fermentation depends on the composition of biomass (hexose and pentose), kinetic parameters (temperature and pH optimum) of enzymes, characteristics of the microorganisms, and the compatibility with the conditions of hydrolysis of the enzymes.

4.3 EXAMPLES OF BIOETHANOL PRODUCTION FROM BROWN ALGAE Along with a diluted sulfuric acid and hot water pretreatment step, bioethanol production from the brown algae Nizimuddinia zanardini was described. The pretreated and untreated biomass was subjected to enzymatic hydrolysis by cellulases. Hydrolysis yield of glucan was 29.8, 82.5, and 72.7 g kg21 for the untreated, hot water pretreated, and acid pretreated biomass, respectively. Anaerobic fermentation of hydrolysates by S. cerevisiae resulted in a maximum bioethanol yield of 34.6 g kg21 of the dried biomass [80]. Another example of bioethanol production from brown algae is L. digitata, where only minor pretreatment of milling was used on the biomass to facilitate the subsequent enzymatic hydrolysis and fermentation using SHF. A high conversion rate of 84.1% glucose recovery by enzymatic hydrolysis and overall bioethanol yield at maximum of 77.7% theoretical were achieved [48]. In addition, production of bioethanol from brown algae M. pyrifera was reported by Camus et al. [13]. This study used modified microorganisms that were able to convert brown macroalgae carbohydrates into bioethanol in a process modeled for scale up, including acid leaching, depolymerization, saccharification, and fermentation steps. The fermentation in a 75-L pilot-scale vessel achieved a maximum bioethanol concentration of 8.87 g L21 using DEHU, mannitol, and glucose from M. pyrifera. Using this process, 0.213 kg bioethanol per 1 kg dry macroalgae was obtained, equivalent to 9.6 m3 of bioethanol ha21 year21, reaching 64% of the maximum theoretical bioethanol yield [13].

4.4 CONCLUSION In the world, there is a fast-growing economy with an inherent increase in demand for energy. The depletion of petroleum reserves and the high level of pollution caused by fossil fuels have led to enhancing renewable energy and fuel production from biomass. Biomass is currently used mainly related

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to food crops. The idea of using macroalgae as a feedstock arises to develop a sustainable biofuels, biocomponents, and bioproducts that have lower environmental impact, a reduced freshwater consumption and a higher energetic efficiency. For these reason, this chapter provides an overview of the thirdgeneration bioethanol technology in terms of available feedstock, pretreatment technologies, and production of bioethanol from brown algae. There have been relevant advances during the past years toward the development of this goal, but still there are a large number of bottlenecks that require attention as described in this chapter.

4.5 FUTURE OUTLOOK One of the challenges for third-generation biofuels from macroalgae is that the variability of the biomass composition toward developing and standardized and optimize industrial process with the right alginate:mannitol ratio. If the breakthroughs of the described bottlenecks can be accomplished, these new technologies hold the promise of much more efficient biofuels. However, several important scientific and technical barriers remain to be overcome before the large-scale macroalgae production is established for biofuel production. Technological developments, including advances in biomass harvesting, drying, and processing, are important areas that may lead to enhanced cost-effectiveness and therefore, effective commercial implementation of the biofuel from macroalgae strategy. In addition, it is very important that the law and the government support the development of third-generation technologies for biofuel production.

ACKNOWLEDGMENT This research was supported by Centre for Biotechnology and Bioengineering (CeBiB) FB-0001.

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1622. [55] M. Ryu, E.Y. Lee, Saccharification of alginate by using exolytic oligoalginate lyase from marine bacterium Sphingomonas sp. MJ-3, J. Ind. Eng. Chem. 17 (5) (2011) 853
858. [56] A.J. Wargacki, E. Leonard, M.N. Win, D.D. Regitsky, C.N.S. Santos, P.B. Kim, et al., An engineered microbial platform for direct biofuel production from brown macroalgae, Science 335 (6066) (2012) 308
313. [57] D. Wang, E.J. Yun, S. Kim, D.H. Kim, N. Seo, H.J. An, et al., Efficacy of acidic pretreatment for the saccharification and fermentation of alginate from brown macroalgae, Bioprocess. Biosyst. Eng. 39 (6) (2016) 959
966. [58] D. Manns, C. Nyffenegger, B. Saake, A.S. Meyer, Impact of different alginate lyases on combined cellulase
lyase saccharification of brown seaweed, RSC Adv. 6 (51) (2016) 45392
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[59] S. Sharma, S.J. Horn, Enzymatic saccharification of brown seaweed for production of fermentable sugars, Bioresour. Technol. 213 (2016) 155
161. [60] H.C. Lakmal, J.H. Lee, Y.J. Jeon, Enzyme-assisted extraction of a marine algal polysaccharide, fucoidan and bioactivities, Polysaccharides: bioactivity and biotechnology, in: K. Ramawat, J.M. Me´rillon (Eds.), Polysaccharides, Springer International Publishing, Cham, Switzerland, 2014, pp. 1
11. http://dx.doi.org/10.1007/978-3-319-03751-6_46-1. [61] A. Leyton, R. Pezoa-Conte, A. Barriga, A.H. Buschmann, P. Ma¨ki-Arvela, J.P. Mikkola, et al., Identification and efficient extraction method of phlorotannins from the brown seaweed Macrocystis pyrifera using an orthogonal experimental design, Algal Res. 16 (2016) 201
208. [62] A.H. Badur, S.S. Jagtap, G. Yalamanchili, J.K. Lee, H. Zhao, C.V. Rao, Characterization of the alginate lyases from Vibrio splendidus 12B01, Appl. Environ. Microbiol. 81 (2015) 1865
1873. [63] H.J. Yoon, W. Hashimoto, O. Miyake, M. Okamoto, B. Mikami, K. Murata, Overexpression in Escherichia coli, purification, and characterization of Sphingomonas sp. A1 alginate lyases, Protein Expression Purif. 19 (1) (2000) 84
90. [64] K. Murata, T. Inose, T. Hisano, S. Abe, Y. Yonemoto, T. Yamashita, et al., Bacterial alginate lyase: enzymology, genetics and application, J. Ferment. Bioeng. 76 (5) (1993) 427
437. [65] M.M. Yue, W.W. Gong, Y. Qiao, H. Ding, A method for efficient expression of Pseudomonas aeruginosa alginate lyase in Pichia pastoris, Prep. Biochem. Biotechnol. 46 (2) (2016) 165
170. [66] G. Liu, L. Yue, Z. Chi, W. Yu, Z. Chi, C. Madzak, The surface display of the alginate lyase on the cells of Yarrowia lipolytica for hydrolysis of alginate, Mar. Biotechnol. 11 (5) (2009) 619
626. [67] T. Takagi, T. Yokoi, T. Shibata, H. Morisaka, K. Kuroda, M. Ueda, Engineered yeast whole-cell biocatalyst for direct degradation of alginate from macroalgae and production of non-commercialized useful monosaccharide from alginate, Appl. Microbiol. Biotechnol. 100 (4) (2016) 1723
1732. [68] A. Inoue, C. Mashino, T. Kodama, T. Ojima, Protoplast preparation from Laminaria japonica with recombinant alginate lyase and cellulose, Mar. Biotechnol. 13 (2) (2011) 256
263. [69] C.A. Contador, C. Shene, A. Olivera, Y. Yoshikuni, A. Buschmann, B.A. Andrews, et al., Analyzing redox balance in a synthetic yeast platform to improve utilization of brown macroalgae as feedstock, Metab. Eng. Commun. 2 (2015) 76
84. [70] K. Iwamoto, H. Kawanobe, T. Ikawa, Y. Shiraiwa, Characterization of salt-regulated mannitol-1-phosphate dehydrogenase in the red alga Caloglossa continua, Plant Physiol. 133 (2003) 893
900. [71] A. Groisillier, Z. Shao, G. Michel, S. Goulitquer, P. Bonin, S. Krahulec, et al., Mannitol metabolism in brown algae involves a new phosphatase family, J. Exp. Bot. 65 (2014) 559
570. [72] M. Enquist-Newman, A.M.E. Faust, D.D. Bravo, C.N.S. Santos, R.M. Raisner, A. Hanel, et al., Efficient ethanol production from brown macroalgae sugars by a synthetic yeast platform, Nature 505 (7482) (2014) 239
243. [73] R. Harun, B. Liu, M.K. Danquah, Analysis of process configurations for bioethanol production from microalgal, Biomass and bioenergy production, Chapter 20, Intech Science & Technology, Croatia, 2011. ISBN: 978
953-307
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[74] J.R. Pereira, Composition of Lignocellulosic Biomass for Ethanol Fuel Production Within the Context of Biorefinery, School of Chemistry, Brazil, 2008. [75] P. Santiba´n˜ez, Chemical Characterization of Forest and Agricultural Residues Pretreated with White Rot Fungi In Order to Obtain Bioethanol (Thesis), University of Chile; Chile, 2010, . 48. [76] K. Olofsson, M. Bertilsson, G. Lide´n, A short review on SSF—an interesting process option for ethanol production from lignocellulosic feedstocks, Biotechnol. Biofuels 1 (2008) 7
14. [77] S. Zinoviev, F. Mu¨ller-Langer, P. Das, N. Bertero, P. Fornasiero, M. Kaltschmitt, et al., Next-generation biofuels: survey of emerging technologies and sustainability issues, Chem. Sustain. 3 (2010) 1106
1133. [78] E.T. Pejo, J.M. Oliva, M. Ballesteros, L. Olsson, Production by xylose- comparison of SHF and SSF processes from steam-exploded wheat straw for ethanol fermenting and robust glucose-fermenting Saccharomyces cerevisiae strains, Biotechnol. Bioeng. 100 (2008) 1122
1131. [79] M. Cantarella, L. Cantarella, A. Gallifuoco, A. Spera, F. Alfani, Comparison of different detoxification methods for steam-exploded poplar wood as a substrate for the bioproduction of ethanol in SHF and SSF Process, Biochemistry 39 (2004) 1533
1542. [80] P. Yazdani, A. Zamani, K. Karimi, M.J. Taherzadeh, Characterization of Nizimuddinia zanardini macroalgae biomass composition and its potential for biofuel production, Bioresour. Technol. 176 (2015) 196
202.

FURTHER READING M. Troell, P. Ro¨nnba¨ck, C. Halling, N. Kautsky, A.H. Buschmann, Ecological engineering in aquaculture: use of seaweeds for removing nutrients from intensive mariculture, J. Appl. Phycol. 11 (1999) 89
97. E. Deniaud-Boue¨t, N. Kervarec, G. Michel, T. Tonon, B. Kloareg, C. Herve´, Chemical and enzymatic fractionation of cell walls from Fucales: insights into the structure of the extracellular matrix of brown algae, Ann. Bot. 114 (6) (2014) 1203
1216. J.W. Li, S. Dong, J. Song, C.B. Li, X.L. Chen, B.B. Xie, et al., Purification and characterization of a bifunctional alginate lyase from Pseudoalteromonas sp. SM0524, Mar. Drugs 9 (1) (2011) 109
123. S. Li, X. Yang, L. Zhang, W. Yu, F. Han, Cloning, expression and characterization of a coldadapted and surfactant-stable alginate lyase from marine bacterium Agarivorans sp. L11, J. Microbiol. Biotechnol. 25 (5) (2015) 681
686. L. Ma, Z. Chi, J. Li, L. Wu, Overexpression of alginate lyase of Pseudoalteromonas elyakovii in Escherichia coli, purification, and characterization of the recombinant alginate lyase, World J. Microbiol. Biotechnol. 24 (1) (2008) 89
96. O. Miyake, A. Ochiai, W. Hashimoto, K. Murata, Origin and diversity of alginate lyases of families PL-5 and-7 in Sphingomonas sp. strain A1, J. Bacteriol. 186 (9) (2004) 2891
2896. S.M. Swift, J.W. Hudgens, R.D. Heselpoth, P.M. Bales, D.C. Nelson, Characterization of AlgMsp, an alginate lyase from Microbulbifer sp. 6532A, PLoS One 9 (11) (2014) e112939.

Chapter 5

Approaches to Improve the Quality of Microalgae Biodiesel: Challenges and Future Prospects Ali Parsaeimehr1, Meisam Tabatabaei2,3 and Roberto Parra-Saldivar1 1

School of Engineering and Science, Tecnologico de Monterrey, Campus Monterrey, Monterrey, Mexico, 2Microbial Biotechnology Department, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education, and Extension Organization (AREEO), Karaj, Iran, 3Biofuel Research Team (BRTeam), Karaj, Iran

5.1 INTRODUCTION The technological progresses in the present century have provided the opportunity to reduce the dependence on fossil fuels as the main energy resources. Alternative energy carriers, such as hydroelectricity, nuclear energy, wind energy, solar energy, geothermal energy, and biofuels, have the potential to partially address the concerns about high carbon dioxide emissions, the key factor of global warming mainly caused by the vast utilization of fossil fuels. Microalgae, as a low input
high yielding feedstock used in third generation biofuels production, are recognized as one of the most feasible sources of alternative energies [1,2]. Based on statistics, algae biofuel can potentially replace all the petroleum fuel used in the United States using 39,000 km2 cultivation areas (0.42% of the country’s area) provided that the technological challenges currently faced will be overcome [3]. In addition, microalgae have the potential to be transformed into different types of fuels, including biodiesel, butanol, and bioethanol [4]. Studies have demonstrated that microalgae, in contrast to plants, do not need fertile lands for growth and can be cultivated in open (i.e., ponds) or closed (i.e., bioreactors) systems. Moreover, microalgae have higher growth and biomass production rates compared with plants, while indigenous production of microalgae biofuels could be of economic benefits to rural development. For instance, an algae system with the capacity of 100,000 kg year21 biomass production level (lipid content of 29.6%) has a yield of 32.9 m3 or 206.97

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FIGURE 5.1 Algae production system and its products.

barrels per year [2,5]. Fig. 5.1 shows an algae production system and its potential products. Microalgae produce different types of lipids and hydrocarbons based on species and strains. Yet, not all of the microalgae lipids have the potential to be used for biodiesel production, and different factors, such as biomass, lipid content, the ratio of saturated fatty acids (FAs) (SFAs) to poly unsaturated FAs (PUFAs), biodiesel yield, the quality of biodiesel, and the cost of the system must be considered. Biodiesel production is mostly carried out through the esterification and transesterification processes of lipids, through which different FA methyl (ethyl) esters are formed. The overall fuel properties of the generated biodiesel are strongly proportional with the profile of these esters [6,7]. Different international standards have been introduced based on which biodiesel properties are evaluated. EN 14214 (based on the former DIN 51606) was introduced as the European standard for biodiesel, although there are variations in the national versions in terms of the climate-related properties of biodiesel. ASTM D6751 was also introduced for biodiesel to be used in unmodified diesel engines in the United States [8]. It should be noted that the variations observed in biodiesel specifications are mainly attributed to the characteristics of the feedstock used. However, eventually all these standards are meant to guarantee important parameters, such as acid value, cold filter plugging point (CFPP), cloud point (CP), cetane number (CN), iodine value (IV), biodiesel lubricity, toxicity, and low level of sulfur content are met, while the product is free of glycerin, catalyst, excess alcohol, and free FAs [9]. Biodiesel analysis by using gas chromatography is usually the primary stage of biodiesel quality evaluation and additional tests typically follow thereafter. The sustainability of algae-based biodiesel industries is highly linked to the accurate selection of microalgae strains in terms of their cost-effective

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yield and lipid quality [10]. Existing literature shows that to date, biodiesels produced from microalgae species typically have low oxidative stabilities, poor cold flow properties, and inappropriate CN values. The low oxidative stability of microalgae biodiesel is ascribed to the significant amounts of PUFAs in microalgae oil and their tendency for oxidation by free radicals [11]. Nevertheless, some studies also indicate algae species containing higher SFAs and monounsaturated FAs (MUFAs) contents compared with PUFAs result in improved quality of the produced biodiesel. This has triggered a widespread search to identify new species of microalgae with higher percentages of SFAs and MUFAs [12,13]. Environmental or cultural conditions also have undeniable impacts on biomass, lipid content, and the FAs profile of microalgae, which ultimately influence biodiesel yield and quality. A study reported by Converti et al. [14] demonstrated that the lipid content of microalgae was significantly influenced by the variations in temperature and nitrogen. More specifically, the lipid content of Nannochloropsis oculata was increased from 7.90% to 14.92% by increasing the temperature from 20 C to 25 C, while the lipid contents of N. oculata and of Chlorella vulgaris were increased from 7.90% to 15.31% and 5.90% to 16.41%, respectively, by reducing the nitrogen concentration by 75%. In another study, a high-quality biodiesel [high heating value: 41 MJ kg21, viscosity: 5.2 3 1024 Pa s (at 40 C), density: 0.864 kg L21, and CFPP: 211 C] was obtained from Chlorella protothecoides by metabolic manipulation technique using corn powder hydrolysate instead of glucose as an organic carbon source in a heterotrophic system [15]. During the last decade, considerable progress in microalgae genomics has been made, and chloroplast, mitochondrial, and nuclear genomes of different microalgae species have been sequenced and reported. These achievements have opened a new path for scientists to manipulate microalgae genomes for biofuels production, more specifically for FA ester production, modification of lipid characteristics, production of straight chain alkanes, lipid biosynthesis, and lipid catabolism [16]. An interesting example was using chimeric plasmid vectors containing a bacterial antibiotic resistance gene for inserting more copies of the acetyl-CoA carboxylase (ACC) gene into Cyclotella cryptica and Navicula saprophila in order to enhance the biosynthesis level of lipids [17]. This chapter attempts to present and discuss the current approaches and techniques used for increasing the quality of microalgae biodiesel.

5.2 SCREENING THE MICROALGAE STRAINS FOR PRODUCTION OF BIODIESELS Although biomass and lipid productivity of microalgae have an important role in the economical production of biodiesel, the quality of the lipids produced by microalgae species is also of great importance as it directly

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influences the properties of microalgae biodiesel. Among the studied microalgae, the Chlorella species (e.g., C. protothecoides) have attracted much attention since the FA methyl esters (FAMEs) derived from the Chlorella species demonstrate satisfactory fuel properties (CFPP 5 213 C, IV 5 112.2 g I2 100 g21, kinematic viscosity 5 4.43 mm2 s21 at 40 C, and oxidation stability 5 4.5 hours) [18]. Nascimento et al. [19] studied the potentials of 12 microalgae species as feedstock for biodiesel production, and their results showed that the Chlorella strain with 204.91 mg L21 day21 and Botryococcus strains with 98
112.43 mg L21 day21 had the highest lipid yield in comparison with the other investigated strains. Based on FAs’ profile analyses, several microalgae species found in literature were classified into three groups with respect to their potential for biodiesel production. The first group includes Scenedesmus obliquus and Chlamydomonas sp. with the highest percentage of SFAs. The biodiesel produced from these microalgae is predicated to have higher CN (63.63
64.94), lower IV (27.34
35.28), and higher oxidation stability [20]. The second group includes Ankistrodesmus fusiformis, Ankistrodesmus falcatus, Kirchneriella lunaris, and Chlamydocapsa bacillus. Due to the high level of PUFAs in their FA profile, the resultant microalgae biodiesel is characterized by low oxidation stability, high IV (101.33
136.97), and low CN (42.47
50.52) [19]. The third class was introduced as the microalgae that the predicated biodiesel quality parameters stand approximately between those of the first and the second groups based on their oil’s SFA and MUFA contents [21]. For instance, CN is significantly correlated with the percentage of SFAs to PUFAs. CN is used to evaluate the delay between compression and ignition, and a higher CN value would result in a shorter delay in ignition and a more complete combustion of the fuel. Amphora sp., a marine and freshwater diatom with a considerable ratio of SFAs to MUFAs, produces up to 38.16% palmitoleic acid and has the potential to be considered as a suitable source for quality biodiesel production (SV 5 188.30, IV 5 57.56, CN 5 62.33, DU 5 55, LCSF 5 9.19) [21]. In a study, Phaeodactylum tricornutum, S. obliquus, Phormidium sp., Aphanothece microscopica Na¨geli, C. vulgaris, and Dunaliella tertiolecta were cultivated in a bubble photo bioreactor to be screened as industrially viable biodiesel feedstocks. The results obtained demonstrated that among the investigated strains, C. vulgaris with the biomass productivity of 20.1 mg L21 h21, lipid content of 27.0%, and the carbon dioxide sequestration rate of 17.8 mg L21 min21 was the best option. Moreover, the analysis of the FA profile of C. vulgaris showed SFA and MUFA contents of 43.5% and 41.9%, respectively. The quality of the biodiesel produced from C. vulgaris was acceptable (CN 5 56.7, IV 5 65.0 g I2 100 g21, CFPP 5 4.5 C, DU 5 74.1%), meeting the ASTM 675, EN 14214, and ANP 255 standards [22]. In a different investigation, the potential of the freshwater microalgae Scenedesmus abundans as a feedstock for high-quality biodiesel production

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was studied using a photo bioreactor. The results obtained revealed that the microalgae contained a significant amount of MUFAs (76%) which is desirable for biodiesel production. The analysis of biodiesel quality of S. abundans (CN 5 52.15, IV 5 94.06 g I2 100 g21, SV 5 202.02 mg KOH g21) showed that it could meet the European biodiesel standard (EN 14214), South African standards (SANS1935), National Petroleum Agency (ANP 255), and Germany’s standard DIN 51606 [23]. Song et al. [24] explored different algae groups, including green algae (K. lunaris, Selenastrum capricornutum, Staurastrum sp., C. vulgaris, Scenedesmus obliqnus), diatoms (Navicula sp., P. tricornutum), red algae (Batrachospermum sirodotia), blue algae (Lyngbya kuetzingii), and prymnesiophytes (Isochrysis sphacrica) for their potential as biodiesel feedstock. Among the studied microalgae, P. tricornutum, S. capricornutum, S. obliqnus, C. vulgaris, and I. sphacrica demonstrated promising lipid productivity and biodiesel properties. The highest lipid productivity (26.75 mg L21 day21), and CN (55.10), and the lowest IV (99.2 g I2 100 g21), and relatively low could point (4.47 C) belonged to the biodiesel produced from P. tricornutum oil. In another study by Doan et al. [25], 96 microalgae strains were isolated from the coast of Singapore and were screened for growth cycle period, biomass, lipid productivity, FA composition, as well as biodiesel quality parameters. Although among the studied strains, Chaetoceros sp., Skeletonema costatum, and Achnanthes sp. had the highest specific growth rates (0.87
0.99 day21), Nannochloropsis sp. showed to be the most promising source as biodiesel feedstock due to its higher lipid content (42.5%
45%), FAME yield of 16%
22% of dry weight (dwt), and a growth rate of more than 0.55 g day21. Also, the higher amount of SFAs and MUFAs compared with PUFAs demonstrated that the biodiesel derived from Nannochloropsis oil led to a higher CN and less susceptibility to oxidation during the storage period. It should be noted that undesirable biodiesel characteristics like poor CFPP could lead to increased engine maintenance costs and ultimately failure. The presence of PUFAs in biodiesel could reduce the stability of biodiesel by increasing the risk of oxidation through hydrolytic degradation by water or atmospheric oxygen. The microalgae C. vulgaris, Botryococcus braunii, Botryococcus terribilis, A. falcatus, A. fusiformis, K. lunaris, Chlamydomonas sp., C. bacillus, Coelastrum microporum, Desmodesmus brasiliensis, S. obliquus, and Pseudokirchneriella subcapitata were also screened by Islam et al. [20]. Their results revealed that the microalgae B. braunii, and B. terribilis had more lipid contents compared with the other studied microalgae, however, the total lipid content of C. vulgaris was higher (75.9 mg g21 dwt) than the other species. Moreover, investigating the biodiesel quality of the tested microalgae showed that the microalgae biodiesels had suitable properties in terms of CN (39.5
66.9), IV (34
159 g I2, 100 g21 fat) and kinematic

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viscosity (3.44
4.20 mm2 s21), meeting ASTM 6751-02 and EN 14214 standards. Chen et al. [26] investigated the quality of biodiesel from Dinoflagellate, Scenedesmus sp., and Nannochloropsis sp. and reported accepted biodiesel characteristics including density, kinematic viscosity, acid value, sulfated ash, sulfur, and phosphorous contents based on the Chinese national standards (CNS). Moreover, the heating values of the resultant microalgae biodiesels were approximately in the same range as of fossil oil; however, the oxidative stability of Scenedesmus sp., Dinoflagellate, and Nannochloropsis sp. (1.02
5.42 hours) biodiesels was significantly lower than that of the CNS (6 hours). The authors claimed that the oxidative stability was considerably increased to 11.2
60.3 hours by a hydrogenation process catalyzed with palladium or carbon (Pd/C). In another study, the biodiesel derived from C. vulgaris was analyzed for ASTM standards, and it was found out that the produced biodiesel was stable. Moreover, the water emulsified C. vulgaris biodiesel was found to have a higher heating value in comparison with cottonseed biodiesel and water emulsified cottonseed biodiesel [27]. In a report by Malcata [28], it was claimed that the lipid content in microalgae like B. braunii can reach up to 75% cell dwt, but this high level of lipid is also linked to a low biomass productivity. Other species, such as Neochloris, Nannochloropsis, Nitzschia, Phaeodactylum, Porphyridium sp., Chlorella, Dunaliella, Isochrysis, and Nannochloris, have a lipid content ranging from 20% to 50%; they also have an appropriate biomass productivity. Chlorella appears in particular to be a good option for biodiesel production since Chlorella has a rapid growth rate and its resulting biodiesel can satisfactorily meet biodiesel quality standards.

5.3 METABOLIC ENGINEERING FOR ENHANCED MICROALGAE BIODIESEL PRODUCTION Fig. 5.2 illustrates current strategies to improve the quality of biodiesel. Manipulation of microalgae metabolic pathways with an aim to achieve desirable metabolites has been applied by many researchers. For instance, by manipulating the level of nutrients and regulating the fermentation process of C. protothecoides in bioreactors with different capacities (5750 and 11,000 L), Li et al. [29] increased the lipid content from 44.3% to 48.7% (biodiesel production rate from 6.24 to 7.02 g L21). They also explored biodiesel quality from S. obliquus and Chlorella pyrenoidosa under different nitrates concentrations (0, 0.3, 0.6, 0.9, 1.5 g L21). It was found that nitrate deficiency increased lipid content to 54.5% and 47.7% for C. pyrenoidosa and S. obliquus, respectively. Also, an increase in biodiesel quality was estimated for C. pyrenoidosa grown under nitrate deficiency (SFA 5 39.5, IV , 69, and CN . 58) and for S. obliquus using nitrate at 0.3 g L21 (SFA 5 31.2, IV , 66, and CN . 59) [28]. Another study by Levine et al.

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FIGURE 5.2 Current strategies to improve the quality of biodiesel.

[30] showed culturing Neochloris oleoabundans under different nitrogen levels (0, 10, 25, 50, 100 mg L21) and using different sources (NO32, NH41) resulted in different biomass, lipid, and biodiesel quality outcomes. For instance, culturing N. oleoabundans in MBBN-NO32 and 100 mg L21 N led to the highest biomass production (158 6 25.5 mg L21 day21, lipid content 5 8.9 6 0.1 mg L21 day21). Moreover, fewer percentages of PUFAs (15-octadecatrienoic acid, 7,10,13-hexadecatrienoic acid) and greater percentages of C18:1 (9-octadecenoic acid) were obtained by decreasing nitrogen. Such findings supported the feasibility of using metabolic engineering to, for instance, decrease the PUFAs content to less than 12% in order to meet the EN 14214 standards. Moreover, by decreasing the N concentration, the viscosity of the produced biodiesel reached 3.5 Pa s, while the heat of combustion was estimated at 39 MJ kg21. Nitrogen deficiency has reportedly resulted in similar effects in cyanobacteria such as Spirulina platensis, Oscillatoria rubescens, Microcystis aeruginosa, and Anacystis nidulans while the lipid content and FA profile of these organisms were also changed by reducing the nitrogen level [31,32]. Microalgae species react differently to the limitation of other nutrients, for example, the lipid content in Nannochloris atomus and Tetraselmis sp. reportedly dropped in response to phosphate and sulfate limitations, while it

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was upregulated in Monodus subterraneus, P. tricornutum, Chaetoceros sp., Isochrysis galbana, and Pavlova lutheri as a result of phosphate and sulfate limitations. Reductions in the phosphorous concentration also demonstrated an increase in SFAs and MUFAs and a decrease in PUFAs (stearidonic acid, eicosapentaenoic acid, and docosahexaenoic acid) which are generally desirable for quality biodiesel production [33
35]. Silicon has also been reported to play an equivalent role compared with the nutrients in diatoms like C. cryptica; the lack of silicon increased the level of lipid by increasing the activity of ACC. Also, the ratio of saturated and MUFAs to PUFAs was increased by reducing the level of silicon [36]. Exogenous signaling molecules like neurotransmitters [acetylcholine (ACh)] can also play an important role in biodiesel production from microalgae. Literature indicates the presence of ACh at concentrations around 0.2 μg g21 of dwt in certain microalgae species, such as Laurencia obtuse and Micrasterias denticulata [37]. It has been mentioned that supplementation of ACh (μg L21) and its precursors and analogs, including choline, choline chloride, choline hydroxide, citicoline, and phosphatidylcholine in dosages ranging from 50 to 200 μg L21, led to increased biomass, lipid content, and biodiesel yield in Chlorella sorokiniana by 126%, 80%, and 36.5% 6 4% compared with the control, respectively. Moreover, a rise in the biodiesel yield from 17.7% 6 6% to 30.9% 6 7% was achieved by using 5 μg L21 ACh during the initial growth phase of C. sorokiniana [38]. Temperature has also been reported as a crucial factor in manipulating the lipid content and FA composition of microalgae. As a typical tendency, the SFAs percentage upregulates at higher temperature levels, while the PUFAs increase at lower temperature levels. Xin et al. [39] reported 20 C as the best temperature level for Scenedesmus sp. growth with a higher level of very long chain PUFAs (C22:3). However, at low (10 C) or higher (30 C) temperatures, the majority of the FA profile of Scenedesmus sp. was composed of C16 and C18FAs. The authors propose the usage of temperature as an essential factor to manipulate microalgae cells for biodiesels suitable for use in warm or cold regions. Light intensity has also been reported as a change stimulator in microalgae FA profile based on the examined species. Specifically, the limitations in light in Nannochloropsis sp. resulted in more eicosapentaenoic acid; however, the higher level of light intensities associated with a growth in the percentage of SFAs and MUFAs [40]. D. tertiolecta is reported as a microalga that contains C16
C18 saturated and unsaturated FAMEs and is considered a potential organism for biodiesel production, although the biomass of D. tertiolecta is relatively low (0.8
1 g L21). In a case reported by Tang et al. [41], increasing the light was associated in more rapid growth of D. tertiolecta, and different light sources (white, red light-emitting diodes, and fluorescent light) and different intensities (100, 200, and 350 μE m22 s21) were found to have no significant influence on the FA profile of D. tertiolecta. Changes in the microalgae lipid

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content and the FA profile during the microalgae growth were also stated by the researchers. For example, triacylglycerols (TAGs) in the Parietochloris incise increased to 77% in the stationary phase, with a growth in the amount of SFAs and MUFAs (indicators of biodiesel quality) and a decrease in the amount of PUFAs [42].

5.4 GENETIC ENGINEERING FOR IMPROVING MICROALGAE BIODIESEL QUALITY The rapidly growing interests in using genetically engineered microalgae in different sectors including bioenergy has triggered rapid progress in microalgae biotechnology and in line with that, the complete genome of different microalgae, such as Ostreococcus tauri, Bathycoccus prasinos, Chlamydomonas reinhardtii, Chlorella variabilis, Dunaliella salina, and Volvox carteri, were sequenced [43]. The literature has revealed that the manipulation of the genes involved in lipid biosynthesis and catabolism, as well as the genes associated with the pathways responsible for the saturation degree and length of the FAs, is the primary targets set for increasing the quality of microalgae biodiesel [44]. In this context, the acyl-ACP thioesterase enzyme that terminates the extension of the fatty acyl group through hydrolyzing the acyl groups on the FAs has attracted much attention since the shortened chain of FAs results in more favorable cold flow characteristics [45]. In a study by Radakovits et al. [46] two acyl-ACP thioesterase genes from Umbellularia californica and Cinnamomum camphora were successfully transformed into P. tricornutum using the transformation vector pPhaT1 to increase the amounts of shorter length FAs to enhance the expression level of total lipid, and finally to secrete FAs. Their results showed 6.2% increase in shorter length FAs (lauric and myristic acid), while a 15% increase in the C12:0 and C14:0 was also observed. The authors also reported that 75%
90% of the produced shorter length FAs were fused into TAGs and that the thioesterase transcript levels were proportional with the percentage of shorter length FAs. Such findings support the hypothesis of using a stronger promoter for the production of shorter length FAs in future investigations. In a study by Lei et al. [47], the key genes involved in the FA biosynthesis in Haematococcus pluvialis, including biotin carboxylase, acyl carrier protein (ACP), malonyl-CoA:ACP transacylase, 3-ketoacyl-ACP synthase (KAS), acyl-ACP thioesterase (FATA), stearoyl-ACP-desaturase, and omega-3 FA desaturase, were cloned and expressed under different stress conditions (i.e., high salinity, high and low temperature, and nitrogen deficiency) as well as separate or combined in 45 mM NaAC and 450 μM FeSO4. Their results demonstrated a significant correlation between the synthesis of MUFAs and PUFAs with the level of expression of ACP, KAS, and FATA genes. The estimation of H. pluvialis biodiesel properties under

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different stress conditions revealed, with the exception of nitrogen deficiency stress, the other types of stress could improve the CN in a range from 45.9 to 51.6. A genetically engineered Synechocystis sp. was introduced by Liu et al. [48] to produce and secrete FAs by expressing the acyl-ACP thioesterase gene. The secretion of the FAs was enhanced to 197 6 14 mg L21 of culture (cell density of 1.0 3 109 cells mL21) via fading the peptidoglycan layers. The analysis of the FAs also showed that the percentage of SFAs was upregulated from 16.5% to 47%, which is desirable for biodiesel production. Since 1990 when Roessler et al. isolated the ACC gene responsible for the production of malonyl-CoA substrate for the biosynthesis of FAs from C. cryptica, researchers have introduced and expressed the ACC gene in different species, such as N. saprophila and C. cryptica [49]. The hypothesis considered by many of these researchers was to increase the activity of ACC and to consequently upregulate the amount of lipids by incorporating additional malonyl-CoA into the lipid biosynthesis pathway, which has found little success reported in the following years. For example, regardless of the two to three times upregulation achieved in ACC expression in C. cryptica by Courchesne et al. [50], no increase in lipid production was observed. Such results could be ascribed to the multigene encoded enzyme complex and the posttranslational regulation of ACC. On the other hand, although common transformation procedures, such as using electroporation, biolistic particle delivery systems, silicon carbide whisker-mediated transformation, and Agrobacterium tumefaciens, have been used by researchers, no reliable nuclear transformation systems for microalgae have yet been found.

5.5 IMPACT OF ADDITIVES ON BIODIESEL QUALITY Another approach to improve the quality of microalgae biodiesel is the application of additives. To date, different types of additives, such as CN improvers, wax antisettling, antifoam, antivalve seat recession, diesel detergency, demulsifiers, deposit control, antistatic, lubricity improvers, antiicing, corrosion inhibitors, metal deactivators, combustion chamber deposit modifiers, antioxidants have been introduced [51,52]. Typically, a faster startup with less smoke and less NOx emissions along with an improved cold start with lower fuel consumption and increased engine performance can be obtained through the application of these additives [53]. Chemicals, such as alkyl nitrates, nitrates, nitro carbonates, and peroxides, have been introduced as CN improvers; however, alkyl nitrates (i.e., ethylhexyl nitrate, amyl nitrate, hexyl nitrate, and octyl nitrate) are traditionally used for biodiesel fuels [54]. Wax antisettling additives decrease the size of the wax crystals to smaller sizes. These additives (i.e., ethylene vinyl acetate copolymer, olefin-ester copolymers, and polymethyl acrylate) basically affect the CFPP and the pour point without influencing the CP [55,56].

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The antivalve seat recession additives (e.g., alkyl phosphate) offer a critical wear-reducing effect by covering the valve seat surfaces with a thin protective layer [57]. Detergent additives (i.e., polyisobutylene succinimide of polyethylene polyamine) are low molecular weight in an aromatic hydrocarbon diluent that are used to decrease engine deposits [51]. Demulsifiers have also been introduced as chemicals for the separation of fuel from water and are used in percentages ranging from 0.01 to 1 v/v% in crude biodiesel fuel [58]. Deposit control additives are chemicals that keep diesel engines clean in three main areas, including carburetors, port fuel injectors, and engine blow-by valves in order for biodiesel to meet the ASTM D 5598 and IFPTAE-1
87 standards [59]. Antistatics (conductivity improvers) are composed of soluble chromium and quaternary ammonium materials, as well as polymeric sulfur and nitrogen compounds. Antistatics increase the charge dissipation and electrical conductivity, thereby decreasing the static buildup of the biodiesel [60]. Issue of poor lubricity of various fuel blends can be addressed by applying lubricity additives including long chain polar compounds, which cover the metal surfaces of engines [61]. Butylated hydroxytoluene, tert-butylhydroquinone, butylated hydroxyanisole, and pyrogallol have been introduced as effective antioxidants for biodiesel [61].

5.6 CONCLUSION Microalgae for fuel applications have yet to be cultivated at large scales because the existing technology still cannot deliver competitive products required by the fuel market. However, microalgae still hold promise as one of the unique feedstocks for biodiesel production. As discussed, one of the main concerns attributed to microalgae biodiesel is the wide variations in the microalgae FA compositions, highlighting the necessity to search for microalgae species capable of producing desirable FA compositions. Bioengineering can play a constructive role in optimization and improvement of microalgae cultivation, in the terms of enhanced biomass production, lipid productivity, and production of desired FA profiles. Furthermore, the manipulation of the microalgae genome presents another efficient strategy to improve the quality of microalgae biodiesel, although much more work is still required in this field. Overall, the latest progress made in the area of genetic and metabolite engineering, as well as emerging biorefinery approaches, reveals a promising mid-term and long-term perspective for algae biodiesel.

5.7 FUTURE OUTLOOK The extensive usage of fossil fuels to secure our rapidly enhancing energy demand has caused significant environmental problems (i.e., air pollution

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and global warming). Alternatively, microalgae biodiesel with estimated yield costs of $9.84 and $20.53 per gallon using an open pond and a photo bioreactor, respectively, are demonstrating a promising avenue for sustained energy production. Yet, not all the microalgae species are favorable for biodiesel production because of low lipid yield or inappropriate profile of FAs, which is further linked to the quality of biodiesel. Currently, the scientists have used four strategies, including, metabolic engineering, genetic engineering, identification of algae with appropriate FA profile, and usage of additives to improve the quality of algae biodiesel. However, since the microalgae biodiesel costs needed to be able to compete with fossil fuel, the current and future studies should be focused on economical aspect of using these strategies, as not all the proposed tactics by the researchers are economically feasible. Indeed the environmental risk of genetic engineered algae should be well considered.

ACKNOWLEDGMENT The authors appreciate the School of Engineering and Science, Tecnologico de Monterrey, Campus Monterrey, Me´xico for supporting this study.

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1313. [57] S. Pace, A. Schilowitz, Low nitrogen content fuel with improved lubricity, United States Patent Application United States 10/930,100, 2004. [58] H. Takanashi, Method for purifying biodiesel fuel, United States Patent United States 8,062,391, 2011. [59] R.C. Tupa, C.J. Dorer, Gasoline and diesel fuel additives for performance/distribution quality-II, SAE Technical Paper, 1986. [60] G. Knothe, K.R. Steidley, Lubricity of components of biodiesel and petrodiesel. The origin of biodiesel lubricity, Energy Fuels 19 (3) (2005) 1192
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FURTHER READING [1] A. Groysman, Corrosion in Systems for Storage and Transportation of Petroleum Products and Biofuels: Identification, Monitoring and Solutions, Springer Science & Business Media, 2014.

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Chapter 6

Biosequestration of Carbon Dioxide From Flue Gases by Algae Jose´ C.M. Pires LEPABE, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Porto, Portugal

6.1 INTRODUCTION The change in climate is evident. The current warming trend presents a rate that has not been observed in the last 1300 years [1]. The cause for this phenomenon is the emission of greenhouse gases (GHGs), mainly CO2, to the atmosphere due to anthropogenic activities. The atmospheric concentrations of GHGs have increased in recent years, creating an unbalance in the Earth’s heat transfer as these compounds absorb infrared radiation emitted by the surface. For instance, the atmospheric CO2 concentration is now more than 400 ppm, corresponding to an increase of more than 40% relative to preindustrial levels (280 ppm) [2]. Evidences of climate change have been observed. Sea level is rising [3,4], mainly due to the thermal expansion of water (related with global temperature rise) and the ice melting [5,6]. This last phenomenon led to shrink ice sheets (mainly in Greenland and Antarctic) [7] and to decline the Artic sea ice (in September 2016, the ice amount in this region achieves the second lowest record). Moreover, extreme events, such as heat waves and intense rainfall events [8,9], have occurred. Associated with CO2 climate effect, its dissolution in the ocean decreases its pH (ocean acidification) [10]. Climate change has become one of the most relevant environmental topics discussed by scientific and policy communities. At the end of 2015, 195 countries adopted the first universal, legally binding global climate deal (COP21—Paris agreement) [11]. The main objective of this agreement was to avoid dangerous climate change by limiting the increase of global average temperature below 2 C above preindustrial levels. The long-term objective is to mitigate CO2 emissions to the atmosphere. The European Union (EU) has Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00006-0 © 2019 Elsevier Inc. All rights reserved.

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played an important role in the advances achieved in the climate agreement. After limited international participation in the Kyoto Protocol (COP3, 1997) [12] and the lack of agreement in Copenhagen (COP15, 2009) [11], the EU developed several efforts to persuade developed and developing countries regarding the impact of climate change on economy, society, and environment, which resulted in the success of the Paris climate conference (COP21). In addition, the EU was the first major economy that submitted its intended contribution, targeting a reduction of emissions by at least 40% by 2030. The main concern of developed countries in regards to any climate agreement is the associated negative impact to the economy. Gross domestic product is linked to energy consumption which is mainly obtained from fossil fuels that are cheaper than clean energy sources. The decarbonization of world economies should be studied if the amount of emitted CO2 per unity of consumed energy (carbon intensity of the energy system) is to be reduced. Three main strategies were considered by the countries that participated in COP21 [13]: (1) reduction of CO2 emissions by enhancing energy efficiency and conservation, (2) carbon capture and storage, and (3) carbon abandonment (changing to carbon neutral energy sources and fuels like renewables and biofuels). In this context, the cultivation of microalgae can play a dual role [14]: as photosynthetic microorganisms with photosynthetic efficiencies 10 times higher than terrestrial plants, microalgae can capture CO2 to produce biomass and this biomass can be used to produce biofuels with zero or even negative net carbon balance. Microalgae can be cultivated using atmospheric CO2 (0.04% v/v), flue gases from a wide range of industries (10%
15% v/v, with NOx and SOx), or CO2-enriched air streams (2%
100% v/v) [15
17]. Thus, the cultivation of microalgae can be one of the sustainable solutions used to mitigate climate change. The majority of research studies have focused in the assessment of microalgae culture when fed with CO2enriched air streams. However, this chapter will make an overview of the studies that evaluate the growth of these microorganisms fed with real or simulated flue gases. In these cultures, the pH decrease (i.e., dissolution of SO2 and NOx) and temperature rise are the main adverse effects to microalgae cultures. Procedures to minimize these effects are also described.

6.2 MICROALGAE CULTURE Microalgae can live in different kinds of water: fresh, sea, estuarine, and sewage water. They present several interesting features that make them ideal candidates for CO2 capture [14,18,19]: (1) fast growth (doubling time can be few hours or less than a day under suitable environmental conditions); (2) high photosynthetic rate, which corresponds to a high CO2 fixation rate; (3) CO2 uptake remains interrupted even at high temperatures, as it is transported to cells through diffusion; and (4) wide range of microalgae biomass applications (e.g., nutraceuticals, cosmetics, food and feed supplements, and biofuel).

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6.2.1 Bioreactors Microalgae can be cultivated in open ponds or closed systems [20]. Cultivation in open systems depends on the local climate. The lack of control of the critical culture variables makes it impossible to use these systems in some locations. Other disadvantages are the high risk of contamination by predators, which reduces the biomass’ productivity. On the other hand, the capital and operational costs are lower in open system than they are in closed systems. Currently, the commercial production of microalgae in open systems is limited to species that are resistant to severe culture environments [21,22]: Dunaliella, Spirulina, and Chlorella can be cultivated in high salinity, alkalinity, and excess nutrition, respectively. Closed systems, or photobioreactors (PBRs), are more interesting for biotechnology, as culture contamination can be avoided and they allow the control of culture variables, thus achieving higher biomass productivities [23]. PBRs require less space, a drawback of microalgae culture technology for CO2 capture compared to physicochemical processes, while losing less water by evaporation and CO2 to the atmosphere. The requirement of cooling and heating systems used to control the temperature increases capital and operational costs of PBRs. There are several configurations of PBRs: (1) vertical column reactors, (2) tubular reactors, and (3) flat plate reactors. Due to high capital and operational costs, the cultivation of microalgae in closed systems is restricted to the production of high-value compounds. Ultimately, choosing between open or closed systems depends on the main objective for the culture and the market value attributed to the compounds that can be achieved from the biomass.

6.2.2 Key Culture Parameters The main growth parameters of microalgae are light supply, temperature, pH, salinity, quantitative and qualitative nutrient profiles, dissolved oxygen, and the presence of certain minerals and trace elements [20,24]. As photosynthetic organisms, light supply is one of the most important parameters for microalgae growth kinetics. Light can be supplied to cultures via sunlight, artificial light, or both. Microalgae growth rates increase with light intensity until an optimal value and this value depends on the selected species [25,26]. Higher values can induce photodamage, reducing photosynthetic activity. In that case, light/dark cycles may be important to repair this damage (i.e., during dark periods) [27]. The efficiency of the light use depends on the frequency of the light/dark fluctuations and the light exposure duration. In addition, light can be efficiently provided to the culture if the culture density is taken into account. High light intensities can cause photoinhibition in low-density cultures, while low values may increase the dark volumes in high-density cultures (i.e., difficult light penetration) [28]. Therefore, light supply should be controlled according to culture density.

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6.2.3 CO2 Capture Photosynthesis is an important natural and sustainable process for CO2 capture, storage, and reuse. Due to high photosynthetic rates, microalgae can play an important role within the wide range of technological solutions for CO2 abatement. The low solubility of CO2 in water does not guarantee an adequate carbon supply from atmospheric air (0.04%) that is required for microalgae growth. Thus, an enriched CO2 air stream should be added to achieve high biomass productivities. The supply of flue gases to the culture led to sufficient mass transfer of CO2 from gaseous to the culture, avoiding stressful conditions for the microalgae due to absence of this nutrient in liquid phase. Table 6.1 presents several studies that used real and simulated flue gases in microalgae cultures. To apply this technology to the industrial scale, a culture facility should be placed near the emission source to reduce the costs of CO2 transport. As microalgae cultures require large surface areas using open systems, this requirement may constitute a disadvantage for this technology. Moreover, microalgae may experience stressful conditions in the absence of a control system in open system. Flue gases can be at high temperatures and can contain compounds such as CO2, NOx, and SO2 that reduce the pH of the culture medium. Associated with light supply, temperature is also an important culture parameter. This variable influences the kinetics of metabolic reactions (and the growth rate), cell size, biochemical composition, and nutrient requirements. Microalgae grow better in temperatures ranging from 15 C to 26 C. There a only a few known species (thermotolerant) that can achieve acceptable biomass productivities at higher temperatures (up to 50 C) [45,46]. On the other hand, temperature reduces the solubility of CO2 in the medium, limiting its availability to cells. CO2 availability is also dependent on pH [47]. Optimum value of pH is specie specific. Some microalgae species can grow at pH values of 7
9 [48], and some have been successfully grown at pH values of 4
7 [49]. The increase of pH shifts the chemical equilibrium of inorganic carbon forms to the formation of carbonates, which is not the preferred carbon source for microalgae. On the other hand, the decrease of pH shifts this chemical equilibrium to the formation of CO2. With usual aeration process, CO2 can be lost to the atmosphere, decreasing the concentration of this important element in the culture. Besides carbon, pH also influences the availability of nitrogen and phosphorus to the microalgae [50]. If ammonium is provided to the culture, high pH moves the chemical equilibrium of ammonium for the production of ammonia that can be released to the atmosphere due to the aeration process. In the case of phosphorus, high pH leads to the precipitation of different forms of phosphates and therefore limiting the availability of this nutrient to microalgae. Moreover, culture pH can directly affect the growth of microalgae. The pH of the cytoplasm is neutral or slightly alkaline. As enzymes (catalysts of metabolic reactions) are pH sensitive, they may be inactive in acidic conditions [51], reducing significantly culture growth kinetics.

TABLE 6.1 CO2 Capture From Flue Gases (Real and Simulated) by Microalgae Microalgae

Experimental Setup

Chlorella vulgaris

μ (day21)

CFR (g L21 day21)

Reference

OM: batch; R: flat vertical prism; V: 300 L; T: 30 C; LI: sunlight; AR: 1.33 m3 h21 (daylight period); RFG: 8%
10.2% CO2, 46 mg m23 NOx, 10 mg m23 SO2, and 2 mg m23 CO

0.2a

[29]

Chlorella sp.

OM: batch; R: open (55 m2); V: 400 L; T: 32 6 2 C; pH 7 6 1; LI: sunlight; AR: 7 6 1 m3 h21; RFG: 6%
8% CO2, 45 mg m23 NOx, and 3 mg m23 CO

12a

[30]

Chlorella sp.

OM: batch; R: bubble column; V: 0.6 L; T: 35 C; pH: controlled by adding CaCO3; LI: 110 μE m21 s21; AR: 0.5 L min21; SFG: 15% CO2, 240 ppm NOx, and 80 ppm SOx

0.3a

[31]

Chlorella sp.

OM: batch; R: bubble column; V: 50 L; pH 6.3
8.2; LI: 1000
1800 μmol m22 s21; AR: 0.05 vvm (daylight period); RFG: 23% 6 5% CO2, 4.2% 6 0.5% O2, 78 6 4 ppm NO, and 87 6 9 ppm SO2

0.9a

[32]

Dunaliella tertiolecta

OM: batch; R: bubble column; V: 4 L; LI: 38 W m22; AR: 150 mL min21; SFG: 15% CO2, 2% O2, 83% N2, and 300 ppm NO

0.63a

[33]

Scenedesmus obliquus

OM: batch; R: airlift; V: 100 L; T: 26 C; LI: 12
13 klux; AR: 0.1 vvm; RFG: 18% CO2, 2% O2, 200 ppm SOx, 150 ppm NOx

0.2a (67%)

[34]

NOA-113

OM: batch; R: bubble column; V: 4 L; T: 25 C; pH 6; LI: 38 W m22; AR: 150 mL min21; SFG: 15% CO2, 300 ppm NO in N2

0.98

[35]

Chlorella sp.

OM: batch; R: bubble column; V: 1200 L (24 3 50 L); T: 26 6 1 C; pH 6.8
7.4; LI: 1500
1800 μmol m22 s21; AR: 0.2 vvm; RFG (coke oven): 23%
27% CO2, 6.0%
8.0% O2, 70
80 ppm NOx, and 80
90 ppm SO2

0.827

0.31a

[36]

Chlorella sp.

OM: batch; R: bubble column; V: 1200 L (24 3 50 L); T: 26 6 1 C; pH 6.4
7.1; LI: 1500
1800 μmol m22 s21; AR: 0.2 vvm; RFG (hot stove): 24%
28% CO2, 2.0%
3.0% O2, 8
10 ppm NOx, and 15
20 ppm SO2

0.762

0.23a

[36]

Chlorella sp.

OM: batch; R: bubble column; V: 1200 L (24 3 50 L); T: 26 6 1 C; pH 6.8
7.3; LI: 1500
1800 μmol m22 s21; AR: 0.2 vvm; RFG (power plant): 22%
26% CO2, 3.0%
6.0% O2, 25
30 ppm NOx, and 15
20 ppm SO2

0.728

0.33a

[36]

(Continued )

TABLE 6.1 (Continued) Microalgae

Experimental Setup

μ (day21)

CFR (g L21 day21)

Reference

Desmodesmus abundans

OM: batch; R: bubble column; V: 1 L; T: 25 6 2 C; LI: 190 μmol m22 s21; AR: 100 mL min21; SFG: 25% CO2, 800 ppm NO, 200 ppm SO2

0.447

0.416

[37]

Scenedesmus sp.

OM: continuous; R: raceway; V: 20 m3; pH 8; LI: sunlight; AR: 100 mL min21; RFG: 10.6% CO2, 18.1 ppm CO, and 38.3 ppm NOx, and 0.0 ppm SO2

(94%)

[38]

C. vulgaris

OM: batch; R: bubble column; V: 0.3 L; T: 30 C; pH 6.5
7.5; LI: 1150 μE m21 s21; AR: 15 L h21; RFG: 10%
13% CO2, 8%
10% O2, 79%
80% N2, 5.69 mg m23 CO, 4.41 mg m23 SO2, 179.05 mg m23 NOx

4.4

[39]

Nannochloropsis oceanica

OM: continuous; R: raceway; V: 8 m3; T: 5
10 C; pH 5.5
7.5; LI: 100
350 μmol m22 s21; AR: 0.17 m3 min21 (daylight period); RFG: 13% CO2, 6.7% O2, 26 ppm CO, 115 ppm NO, 129 ppm NOx, 14 ppm NO2, and 30 ppm SO2

0.066

Chlorella emersonii

OM: batch; R: bubble column; V: 5.5 L; T: 25 C; pH 8; LI: 200 μmol m22 s21; AR: 1 L min21; RFG: 15% CO2

0.10
0.13

Spirulina platensis

[40]

0.108

[41]

OM: batch; R: bubble column; V: 1.5 L; T: 25 6 1 C; pH 8.5
9.2; LI: 90
125 μmol m22 s21; AR: 20 L h21; SFG

0.27

[42]

Tetraselmis suecica

OM: semibatch; R: airlift; V: 1000 L; T: 21.8
27.9 C; pH 7.5; LI: sunlight; AR: 900 6 150 L h21; RFG: 62% N2, 5% O2, 11% CO2, 22% H2O, 220 ppm SO2, and 150 ppm NOx

0.33

[43]

Chlorella fusca

OM: batch; R: bubble column; V: 1.8 L; T: 30 C; pH 8.0
10.8; LI: 41.6 μmol m22 s21; AR: 0.05 vvm; SFG: 10% CO2, 200 ppm SO2, and 200 ppm NO

0.18

[44]

Blank indicates no information available; values between brackets are CO2 removal efficiencies. R, Reactor; T, temperature; V, volume; LI, light intensity; CFR, CO2 fixation rate; μ, specific growth rate; OM, operation mode; AR, aeration rate; SFG, simulated flue gas; RFG, real flue gas; SFG, simulated flue gas. a Obtained from biomass productivities (ratio of 1.8 g CO2 to 1 g of biomass).

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Another possible limiting factor for microalgae growth is the mass transfer of CO2 and O2 between gaseous and liquid phases. CO2 presents a low mass transfer coefficient, which is required at high concentrations in the gaseous streams to guarantee sufficient quantities of CO2 for microalgae to perform photosynthesis. One of the products of photosynthesis is oxygen that, if not efficiently removed from medium, can achieve high concentrations, thus reducing biomass productivities [52,53]. Concerning the accumulation of oxygen, aeration with flue gas is beneficial for the microalgae culture. As this gaseous stream is poor in oxygen (less that 10%), the gradient of concentrations between gas and liquid increases, promoting the mass transport between the two phases.

6.3 EFFECT OF NOX AND SO2 Besides CO2 (5%
15%), industrial flue gas is a mixture that contains many other compounds, some being toxic to the microalgae culture, including NOx (70
420 ppm) and SO2 (50
400 ppm) [38,54]. NOx in flue gases typically contains 90%
95% of NO and 5%
10% of NO2. NO is a colorless gas with low solubility in water (0.032 g L21 at 1 atm and 25 C). Thus, to be absorbed in a microalgae culture, the contact time between air bubbles and the culture should be high in order to promote its mass transfer, a known constraint in the design of PBRs. Jin et al. [55] studied improving the solubility of NO in microalgae cultures (Scenedesmus sp.) by adding Fe(II) EDTA. Twelve days old cultures in a 1-L airlift PBR were fed with 300 ppm of NO at rate of 0.3 vvm. The NO mass transfer rate increased and the microalgae presented a constant increase of cell density. In the performed experiments, no other source of nitrogen was added to the culture. Microalgae assimilated the NO dissolved in the medium and used it as a nitrogen source. NO removal efficiency was 40%
45%. Van Eynde et al. [56] studied the combination of a photocatalytic gas pretreatment unit with a microalgae culture. This process converted NO to NO2 (with higher solubility in water: 213.0 g L21 at 1 atm and 25 C), enhancing the absorption of NOx in the medium. The experiments were performed with a culture of Thalassiosira weissflogii aerated with a simulated flue gas (1% CO2 and 50 ppm NO) and presented a NOx removal efficiency of 84%. The chemical reactions of the dissolution of NO and NO2 in water are as follows [57,58]: NOðaqÞ 1 H2 OðlÞ2HNO2 ðaqÞ

ð6:1Þ

2NO2 ðaqÞ 1 H2 OðlÞ2HNO2 ðaqÞ 1 HNO3 ðaqÞ

ð6:2Þ

3HNO2 ðaqÞ2HNO3 ðaqÞ 1 2NOðaqÞ 1 H2 OðlÞ

ð6:3Þ

1 HNO2 ðaqÞ2NO2 2 ðaqÞ 1 H ðaqÞ

ð6:4Þ

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1 HNO3 ðaqÞ2NO2 3 ðaqÞ 1 H ðaqÞ

ð6:5Þ 21

SO2 is a colorless gas with high solubility in water (100 g L at 1 atm and 25 C). For microalgae cultures, high concentration of this compound is not recommended due to its inhibitory growth effect. For flue gases with high sulfur content, a desulfurization unit should be used as preprocessing of the gaseous stream. Chemical reactions of dissolution of SO2 in water are as follows [57,58]: SO2 ðaqÞ 1 H2 OðlÞ2H2 SO3 ðaqÞ

ð6:6Þ

2SO2 ðaqÞ 1 O2 ðaqÞ22SO3 ðaqÞ

ð6:7Þ

SO3 ðaqÞ 1 H2 OðlÞ2H2 SO4 ðaqÞ

ð6:8Þ

1 H2 SO3 ðaqÞ2HSO2 3 ðaqÞ 1 H ðaqÞ

ð6:9Þ

1 H2 SO4 ðaqÞ2HSO2 4 ðaqÞ 1 H ðaqÞ

ð6:10Þ

The tolerance of microalgae to NOx and SO2 is not well studied and depends on the species. Until a certain value, the feeding of these compounds to the medium could be beneficial for microalgae growth. NOx and SO2 dissolve in water and are converted into assimilatory forms of nitrogen and sulfur to the microalgae [33,58,59]. As nitrogen is a macronutrient in microalgae cultures, the cells may assimilate the dissolved NOx forms, avoiding their accumulation in the medium. However, sulfur is a micronutrient, and the sulfates, sulfites, and bisulfites (dissolved SO2 forms) tend to accumulate in the medium when fed with flue gas [43]. Therefore, culture dilutions should be performed to avoid the inhibitory growth effect. Table 6.1 presents studies that tested simulated and real flue gases, using different compositions for NOx and SO2 in various microalgae cultures. Generally, microalgae cultures were not inhibited at the studied concentrations. According to the chemical reactions presented previously [Eqs. (6.1)
(6.10)], the dissolution of NOx and SO2 promotes the increase of H1 concentration and consequently decreases the pH of the culture. Some authors attributed that the inhibition effect associated with these compounds is mainly due to its effect on pH [31,36]. pH control along with the addition of CaCO3 or NaOH with a low or intermittent aeration rate may prevent the inhibition of microalgae growth. In addition, considering that industrial flue gas is pretreated (desulfurization unit, dust electrostatic precipitation, and heat exchanger), high initial cell concentration is applied, and acclimation of microalgae to acidic conditions is performed [60]. Microalgae culture can be considered a promising technology for cleaning flue gases, not only for CO2 but also for NOx and SO2 [33,36].

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6.4 RECENT ADVANCES AND CHALLENGES The application of flue gas as CO2 source for microalgae cultivation still has several challenges. One of the main problems is the culture pH control, which can be performed using different strategies. Duarte et al. [61] evaluated the effect of intermittent coal flue gas injection in cultures of two microalgae. Chlorella fusca LEB 111 was isolated from a coal power plant and it was able to fix 2.6 times more CO2 than Spirulina sp. Maximum biofixation rate of 360.1 6 0.3 mg L21 day21 was achieved. Kim and Lee [62] tried to minimize the inhibition effect of acid compounds of flue gas on microalgae cultures with trona (Na3(CO3)(HCO3)  2H2O), using it as a buffer chemical. This is a natural occurring mineral and it is inexpensive when compared with other sodium-based buffer chemicals. The addition of trona buffer led to the control of medium pH between 7 and 8, optimum range of the studied microalgae (Nannochloris sp.). Aeration with pure CO2 gaseous stream and with a stream with SO2, NO, and HCl gases did not significantly reduce the microalgae growth. The results showed that trona buffer can be used in microalgae cultivation for biological CO2 remediation from wet flue gas of a coal fired power plant (after desulfurization unit). Other important topic to be studied is the effect of other trace inhibitory compounds present in flue gas. Hess et al. [63] studied the effect of trace inorganic contaminants (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, Sn, V, and Zn) of coal-derived flue gas on microalgae productivity, lipid yield, and fatty acid profiles. Nannochloropsis salina was cultivated in PBRs at outdoor light levels (984 μmol m22 s21). Authors observed a negative impact of the inorganic contaminants on microalgae growth (decrease of 67.5% of biomass yield) and lipid yields (decrease of 14% of lipid content). To avoid the pretreatment costs, Moheimani [43] evaluated the effect of untreated flue gas as source of inorganic carbon for the culture of Tetraselmis suecica (1000 L industrial scale airlift PBR). The experiments were performed during 7 months, recycling the nutrient from an electroflocculation harvesting unit. Untreated flue gas and media recycling led to the increase of sulfates in the medium, achieving the concentration of 870 mg L21. However, this concentration did not show inhibitory effect on microalgae growth. Biomass productivity was 179 6 30 mg L21 day21 and carbon fixation rate was 89 6 20 mg L21 day21. The results showed the potential of growing the studied microalgae on untreated flue gas for biofuel production and CO2 bioremediation.

6.5 RESEARCH NEEDS A wide range of industries is interested in reducing their CO2 emissions in a sustainable way. A culture of microalgae fed with flue gas seems to be a

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promising technology that needs some improvements to be economically viable. Taking into account the required CO2 capture rate from industries and the areal biomass productivity of microalgae, large cultivation areas would be required to apply this technology. However, some companies are integrated in industrial complexes, and they do not have requisite area nearby. Moreover, available land is costly. Consequently, research work should be performed that focuses on increasing the areal productivity of microalgae and improving the photosynthetic efficiency of cultivation systems. In open systems, this efficiency is usually below 3% and the theoretical maximum value is about 11%. This difference may be justified by the poor distribution of light inside of the bioreactors. The effect of each different bioreactor’s design configurations and process conditions can be evaluated using computational fluid dynamics software. This powerful software coupled with mass and heat transfer models, chemical reaction, and light dynamics model may help to achieve the photosynthetic efficiencies required by the industrial sector.

6.6 CONCLUSION Microalgae have the potential to be a sustainable technology for CO2 capture from flue gases. Its application led to convert CO2 into organic forms of carbon, which can be used for different applications. The optimization of the CO2 biofixation process and the valorization of achieved biomass will improve the economic competitiveness, when compared with other carbon capture and sequestration technologies. In addition, if biomass is used for energetic applications, the requirement for fossil fuels will decrease (and also derived CO2 emissions), decreasing the carbon intensity of the energy system at same time (ratio between CO2 emissions and consumed energy).

6.7 FUTURE OUTLOOK CO2 capture from flue gases with microalgae is a promising technology to solve one of the most important environmental issues today. To be economically competitive with other CO2 capture technologies, the integration of processes (e.g., wastewater treatment) should be studied to reduce the overall costs. In the engineering point of view, the design of PBRs should be improved in terms of light distribution. The available (and free) light energy (sun) has high intensity for microalgae cultures, and these values cause growth inhibition and low biomass productivity. The design of new PBRs should promote an efficient distribution of light. Apart from the advances in PBR engineering, the application of the biorefinery concept (to exploit the full potential of commercial products derived from microalgae biomass) can make this CO2 capture process economically feasible.

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ACKNOWLEDGMENTS This work was the result of the following projects: 1. POCI-01-0145-FEDER-006939 (Laboratory for Process Engineering, Environment, Biotechnology and Energy—UID/EQU/00511/2013) funded by the European Regional Development Fund (ERDF), through COMPETE2020—Programa Operacional Competitividade e Internacionalizac¸a˜o (POCI) and by national funds, through FCT— Fundac¸a˜o para a Cieˆncia e a Tecnologia. 2. NORTE-01-0145-FEDER-000005—LEPABE-2-ECO-INNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). 3. FCT Investigator 2015 Programme (IF/01341/2015).

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97. [43] N.R. Moheimani, Tetraselmis suecica culture for CO2 bioremediation of untreated flue gas from a coal-fired power station, J. Appl. Phycol. 28 (2016) 2139
2146. [44] J.H. Duarte, L.S. Fanka, J.A.V. Costa, Utilization of simulated flue gas containing CO2, SO2, NO and ash for Chlorella fusca cultivation, Bioresour. Technol. 214 (2016) 159
165. [45] S.H. Ho, Y.Y. Lai, C.Y. Chiang, C.N.N. Chen, J.S. Chang, Selection of elite microalgae for biodiesel production in tropical conditions using a standardized platform, Bioresour. Technol. 147 (2013) 135
142. [46] C.W. Hu, L.T. Chuang, P.C. Yu, C.N.N. Chen, Pigment production by a new thermotolerant microalga Coelastrella sp. F50, Food Chem. 138 (2013) 2071
2078. [47] P.J. Hansen, Effect of high pH on the growth and survival of marine phytoplankton: implications for species succession, Aquat. Microb. Ecol. 28 (2002) 279
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1190. [50] A. Gonc¸alves, Microalgal Cultivation for Biomass Production, Carbon Dioxide Capture and Nutrients Uptake, 2017. [51] P. Chiranjeevi, S.V. Mohan, Critical parametric influence on microalgae cultivation towards maximizing biomass growth with simultaneous lipid productivity, Renew. Energy 98 (2016) 64
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[53] A.S. Miron, M.C.C. Garcia, F.G. Camacho, E.M. Grima, Y. Chisti, Growth and biochemical characterization of microalgal biomass produced in bubble column and airlift photobioreactors: studies in fed-batch culture, Enzyme Microb. Technol. 31 (2002) 1015
1023. [54] M. Olofsson, E. Lindehoff, B. Frick, F. Svensson, C. Legrand, Baltic Sea microalgae transform cement flue gas into valuable biomass, Algal Res. 11 (2015) 227
233. [55] H.F. Jin, D.E.O. Santiago, J. Park, K. Lee, Enhancement of nitric oxide solubility using Fe(II)EDTA and its removal by green algae Scenedesmus sp., Biotechnol. Bioprocess Eng. 13 (2008) 48
52. [56] E. Van Eynde, B. Lenaerts, T. Tytgat, R. Blust, S. Lenaerts, Valorization of flue gas by combining photocatalytic gas pretreatment with microalgae production, Environ. Sci. Technol. 50 (2016) 2538
2545. [57] A.D. Kroumov, A.N. Mo´denes, D.E.G. Trigueros, F.R. Espinoza-Quin˜ones, C.E. Borba, F. B. Scheufele, et al., A systems approach for CO2 fixation from flue gas by microalgae— theory review, Process Biochem 51 (11) (2016) 1817
1832. [58] S. Van den Hende, H. Vervaeren, N. Boon, Flue gas compounds and microalgae: (bio-) chemical interactions leading to biotechnological opportunities, Biotechnol. Adv. 30 (2012) 1405
1424. [59] Y.L. Jiang, W. Zhang, J.F. Wang, Y. Chen, S.H. Shen, T.Z. Liu, Utilization of simulated flue gas for cultivation of Scenedesmus dimorphus, Bioresour. Technol. 128 (2013) 359
364. [60] T. Li, G. Xu, J. Rong, H. Chen, C. He, M. Giordano, et al., The acclimation of Chlorella to high-level nitrite for potential application in biological NOx removal from industrial flue gases, J. Plant Physiol. 195 (2016) 73
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475. [62] J. Kim, J.-Y. Lee, Mitigation of inhibition effect of acid gases in flue gas using trona buffer for autotrophic growth of Nannochloris sp., Biochem. Eng. J. 117 (Part A) (2017) 15
22. [63] D. Hess, K. Napan, B.T. McNeil, E.M. Torres, T. Guy, J.E. McLean, et al., Quantification of effects of flue gas derived inorganic contaminants on microalgae growth system and end fate of contaminants, Algal Res. 25 (2017) 68
75.

Chapter 7

Using Microalgae for Treating Wastewater Kaushik K. Shandilya1 and Vikram M. Pattarkine2 1

The University of Toledo, Toledo, OH, United States, 2PEACE USA, Mechanicsburg, PA, United States

7.1 INTRODUCTION Microalgae have existed on the earth as far back as 1.2 billion years ago [1] and have contributed in creating oxygenated atmosphere, the vital component for the survival of humans [2]. Generation of wastewater (sewage) is an inherent part of human society. The development of wastewater treatment systems can be traced back to Greek civilization [3]. With increasing human population, climate change implications and water quality impairment due to wastewater release into clean water bodies warrant attention. The past decade has seen a renewed interest in using fast-growing microalgae for biofuels [4]. Carbohydrates, proteins, and oils can comprise up to 90% of these microscopic organisms, making them a top candidate for bioenergy production [5]. Moreover, microalgae are rich in high-value compounds, for instance, proteins, vitamins, pigments, and biologically active compounds used in cosmetics, pharmaceutical, nutraceuticals, food, and feed industries [6]. At present, however, the large-scale production of microalgae and related bioproducts is still too costly [7], which presents unique research challenges [8]. Present research investigations target either increase in productivity by realizing greater biomass production, growth in cellular lipid and carbohydrate content, or eliminating infrastructural costs in microalgae cultivation [9]. Replacement of traditional nutrients with wastewater through wastewateralgae treatment cycle is a way to reduce the costs associated with the production of microalgae biomass [10]. This strategy offers the microalgae-based technologies with low-cost feedstock and also favors wastewater remediation at the same time [11]. However, sustainability of microalgae biomass production using agricultural, municipal, or industrial wastewaters and progress

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of related technologies depends on the rapid growth of potent microalgae strains (e.g., Aphanocapsa sp., Botryococcus braunii, Chlamydomonas sp., Chlorella sp., Dunaliella sp., Galdieria sulphuraria, Haematococcus pluvialis, Nannochloropsis sp., Neochloris oleoabundans, Pediastrum sp., and Scenedesmus sp.) that can adapt to the physical and chemical conditions of cultivation system and produce high levels of oil, biomass, or any other anticipated product(s) [12]. Metabolic pathways optimization is an alternative path in the direction of increasing biomass yield [13]. Microalgae have many metabolic pathways which (for some species) are interchangeable under different nutrient and light conditions [14]. Microalgae metabolic pathways are vital, because these pathways control microalgae biomass formation which is the key for biofuel production [15]. Microalgae can synthesize its own food using CO2 and light. This is defined as autotrophic microalgae cultivation. When microalgae in the life cycle cannot manufacture their own food, they instead obtain food and energy by taking in organic substances. Such cultivation is defined as heterotrophic cultivation. Some microalgae species can take advantage of both autotrophic and heterotrophic sources of nutrition. More specifically, these mixotrophic microalgae can not only acquire inorganic carbon by photosynthesis but can also utilize dissolved organic carbon. Currently, research has mostly focused on autotrophic microalgae cultivation. However, the potential of heterotrophic and mixotrophic microalgae for biofuel production has also been explored [16]. It has been found that certain microalgae species can switch between these metabolic pathways over a few generations depending on the prevailing environmental conditions [17]. The comparison between different types of metabolisms used by microalgae is shown in Fig. 7.1.

7.2 MICROALGAE METABOLIC PATHWAYS Different types of metabolic pathways as shown in Fig. 7.1 can be used for microalgae cultivation using wastewater. Microalgae metabolic pathways have been studied by many research groups [18] around the world in recent years. Also, numerous microalgae species have been studied on their ability to perform fermentation anaerobically and easily switch between phototrophic and heterotrophic modes under aerobic conditions [19]. Table 7.1 shows the carbon assimilation metabolic pathways that impact productivity related to microalgae type, substrate composition, and environmental parameters. To understand microalgae cultivation potential using liquid waste streams, the three types of metabolic pathways and their relevance to biomass production must first be discussed.

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FIGURE 7.1 Autotrophic, heterotrophic, and mixotrophic microalgae cultivation. H2O, Water; CO2, carbon dioxide; H2S, hydrogen sulfide; S0, sulfur (0); SO422, sulfate ion; O2, oxygen molecule; DOM, dissolved organic matter; H2A, hydrated molecule of A (other atom).

TABLE 7.1 Dynamics of Autotrophic, Heterotrophic, and Mixotrophic Microalgae Metabolisms [20] Autotrophic Organic carbon consumption (mol g21 cells)

N/A

Heterotrophic

Mixotrophic

0.017

0.018

Inorganic carbon consumption (mol g21 cells)

2 0.04

0.06

0.07

O2 consumption (mol g21 cells)

2 0.06

0.04

0.05

0.009

0.008

21

Biomass yield (g kJ supplied)

energy

0.002

Microalgae biomass (g) 21

ATP demand (mmol g

21

h ) 21

Theoretical ATP yield (g mol ) ATP, Adenosine triphosphate.

3.8

21.1

25.5

15.6

15.9

17.5

3.1

19.3

6.6

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7.2.1 Autotrophic Microalgae Cultivation Autotrophic metabolism employs freely available sunlight and carbon dioxide to form energy and cellular carbon and, hence, has received research attention. Autotrophic microalgae play a major role in carbon sequestration on the planet. Under optimal conditions, some autotrophic species can grow as rapidly as 0.2 g L21 day21 [21]. At this rate, the conversion efficiency of absorbed photons to the adenosine triphosphate of autotrophic Chlorella sp. is reported as low as 10% [22]. This low conversion efficiency creates a need for selection and development of novel microalgae strains with higher photosynthetic efficiency.

7.2.2 Heterotrophic Microalgae Cultivation Heterotrophic microalgae obtain energy and carbon from organic sources [23]. In the presence of carbon sources such as glucose, glycerol, and acetate, some microalgae species can grow significantly faster than under autotrophic conditions. In some isolates, final biomass yields have exceeded 100 g L21 and the final lipid yields are thus much higher than those of the autotrophic microalgae [20]. Heterotrophic microalgae cultivation does not need light; however, the ability is apparently found only in certain microalgae species, so it needs selection criteria for cultivation. Moreover, heterotrophic metabolism using glucose is not shown in anaerobic conditions because of insufficient energy and required enzyme lactate dehydrogenase [23]. The ability of growing without light improves the biomass productivity for heterotrophic microalgae cultivation. The biomass productivity for unit energy input for heterotrophic growth (0.00924 g cells per kJ input) is at least five times higher than autotrophic growth (0.00177 g cells per kJ input) [22]. The common disadvantages of heterotrophic microalgae cultivation are higher cost, carbon dioxide production, higher possibility of contamination, and diminished pigmentation [24]. Using wastewater as the substrate for heterotrophic microalgae cultivation can turn these disadvantages into profit. The cost associated with the organic carbon supply can be nullified by incorporating wastewater streams. Also, the carbon dioxide production during organic carbon conversion, the potential contamination on culture vitality, and the reduced pigmentation of the microalgae cells under the dark conditions of heterotrophic growth can be solved by using a hybrid two-stage microalgae cultivation [25
29].

7.2.3 Mixotrophic Microalgae Cultivation Mixotrophy has different characteristics in different species of microalgae. In some cases, the productivity of mixotrophic microalgae cultivation can be higher than heterotrophic and autotrophic cultivation individually, but often

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within the ranges of these two. Numerous microalgae species are mixotrophic and are able to photosynthesize as well as digest organic matter. The ability to use either metabolism (autotrophic or heterotrophic) affords the mixotrophic microalgae the facility of cell growth independent of photosynthesis [30
32]. Light is not crucial for mixotrophic microalgae cultivation, so biomass can be produced using either light or organic carbon as energy source. The mixotrophic growth is influenced by the organic carbon substrate (glucose) during the light and dark phases; so, there is less biomass loss during the dark phase [16,33
37]. The mixotrophic growth rates are better than both autotrophic and heterotrophic cultivations because mixotrophic microalgae allow the integration of both autotrophic and heterotrophic components during the 24-hour cycle.

7.3 BIOMASS PRODUCTIVITY In terms of biomass and lipid productivity, many microalgae species perform better in heterotrophic or mixotrophic cultures than in autotrophic culture. Photoautotrophic metabolism results in lower biomass and lipid productivities than mixotrophic metabolism because only limited light supply is available in a day [38]. The lipid accumulation (particularly for heterotrophic conditions) depends on resource exhaustion, induced by high-density cultures and extended growth periods [39]. Similarly, in autotrophic cultures, the nitrogen starvation can induce cell-stressed lipid accumulation [40]. During heterotrophic growth, nitrogen is limited, which likely triggers lipid accumulation. As previously mentioned, energy efficiency of heterotrophic cultures is substantially better than that of autotrophic cultures, resulting in more resources available for lipid formation at terminal culture density. There is a major emphasis on finding a metabolic trigger for inducing the accumulation of triacylglycerides in microalgae growth systems, as it would increase production by decreasing total culture time. There is a need for finding better lipid triggers that can improve biomass productivity from microalgae cultivation, and heterotrophic metabolism can be the solution. Lipid accumulation (lipid trigger) under heterotrophic conditions appears to be related to resource exhaustion (selective stressing, e.g., by nutrient limitation), induced by either high-density cultures or extended growth periods.

7.3.1 Organic Carbon Sources for Microalgae Cultivation Contrary to the photosynthetic growth of photoautotrophic microalgae for which organic carbon is not essential, heterotrophic and mixotrophic microalgae metabolism require organic carbon [41]. Ready-to-be-metabolized glucose is the most widespread organic carbon substrate for heterotrophic and mixotrophic microalgae. Overall, using wastewater for the microalgae

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bioenergy production comes with a dual advantage of both providing a costeffective feedstock for microalgae cultivation while also pretreating or posttreating the wastewaters, thereby cleaning the environment [35]. A large share of cost is due to organic carbon consumption during microalgae cultivation, which can be offset if wastewater is used for cultivation [42,43]. Corn starch [44] and many other organic carbon sources have been tested to reduce the cost [45] but have not been found economical [46]. Wastewaters are rich in organic carbon, nitrogen, phosphorus, vitamins, and trace metals, which can provide an excellent substrate for heterotrophic/ mixotrophic microalgae growth [47]. Municipal wastewaters are considered for huge and stable daily intake for microalgae biomass production as they are usually introduced from the town or city areas to a wastewater treatment plant. Industrial wastewaters, for example, from pulp mills, dairy facilities, mink farms, swine and beef industries, breweries, wineries, and food and juice processing plants are also considered as potential sources of organic carbons. The treatment of wastewater using microalgae consumes nitrogen and phosphorus, hence when microalgae-treated wastewater is discharged into local rivers, streams, and lakes, the chances of eutrophication are decreased.

7.4 MICROALGAE CULTIVATION OPPORTUNITIES IN WASTEWATER TREATMENT Several articles have reported successful treatment of wastewater using microalgae [20,30,34,47
52]. A few species in the Chlorella family have been successfully used for wastewater treatment [48]. Microalgae can grow in bioreactors using freshwater, nutrient media, wastewater, marine water, or even sewage water, as shown in Fig. 7.2. A favorable method to realize cost and energy reductions for microalgae cultivation is to integrate biomass production within existing wastewater treatment plant [7]. All types of wastewater treatments require costly energy inputs [53], and the twofold paybacks of the microalgae biomass production would be cost reduction of waste treatment while cleaning the wastewater itself [54]. Using a combination of autotrophic and heterotrophic microalgae can greatly reduce the cost and resources necessary for wastewater treatment. The symbiotic relationship of both the metabolic processes can work together and can yield in increased treatment efficiency. In this relationship, heterotrophic/mixotrophic algae generate carbon dioxide, nutrients (like ammonia), and heat energy, which can be readily used by autotrophic algae. Autotrophic algae produce oxygen and organic matter which can be consumed by heterotrophic/mixotrophic algae. The full potential of this symbiotic relationship is yet to be explored. Many researchers have explored two-stage autotrophic
heterotrophic microalgae cultivation systems for

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FIGURE 7.2 Symbiotic relationship between different microalgae metabolism.

wastewater treatment [25,27,29]. Systems with more than two stages can be designed depending on resource availability and treatment requirement. Many studies have characterized the nutrient content of wastewaters for the cultivation of terrestrial crops and microalgae [52]. Despite variability in specific parameters, a consistently high concentration of the major nutrients has regularly been reported (Table 7.2). In most cases, concentrations of these nutrients are in excess in traditional microalgae culture media (like F/2). However, the wastewater allows dilution to protect against concentrations higher than those needed for specific microalgae strains. Adequate amounts of trace metals (Mn, Fe, Cu, Zn, etc.) are also readily available in wastewater, and these metals are then removed through microalgae growth. Wastewater nutrient removal efficiencies through microalgae cultivation have demonstrated an exceptional potential. Consequently, wastewaters can be used as a substrate for culturing microalgae and could simultaneously remove nutrients during tertiary wastewater treatment [55]. Microalgae biomass production and lipid accumulation depend on the sewage composition. Nitrogen (in the form of ammonia and ammonium ions) has a vital role as macronutrient and is a lipid trigger for microalgae synthesis, thus its presence in wastewater is very promising [34,35]. Potential industries that can use wastewater for microalgae biomass production (while eradicating possible environmental hazard) are dairy, swine, poultry, aquaculture, mink, wood pulp production, breweries, and wineries. The digested wastewaters have high ammonium and organic acid concentrations,

TABLE 7.2 Nutrient Concentration of Various Wastewaters and F/2 Microalgae Culturing Medium mg L21

Aquaculture [56]

NH4
N

3.3

NO3
N PO4
P

Brewery [25] 30
75a

Fish Manure Settled [58]

Domestic [59]

Mink [60]

306

30.2

12
45

74.4b

,1

0.74 7.4

Dairy [57]

c

12
20

c

303

491 0.18

Reference number corresponds to related study reporting the nutrient concentration. a Values reported as total nitrogen (mg L21). b Values indicate the upper range of manure leachate findings. c Values reported as total phosphorus (mg L21).

0
trace c

4
12

103.4 b

90.4

b

Poultry Litter [49]

Swine [61]

F/2 Medium [62]

9.2

4800

0

10.3




12.4

5.3

c

290

6.7

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which are needed for heterotrophic and mixotrophic microalgae biomass production [63]. Industrial wastewaters often pose a significant environmental hazard due to the presence of contaminants of newly emerging concern (such as pharmaceuticals and personal care products), but they also have a significant potential as a feedstock for microalgae production. Furthermore, salinity levels in certain wastewaters are harmful for traditional land application but are readily tolerated by marine microalgae [47]. Thus, based on wastewater composition, the strategies and the selection microalgae strains are a subject of further research.

7.4.1 Microalgae Cultivation Challenges in Wastewaters Autotrophic microalgae cultivation can sequester CO2 generated from wastewater treatment plants [64]. While treating wastewater using heterotrophic and mixotrophic microalgae (CO2 generation during cell respiration), the benefit of the CO2 source may be marginal [65]. Another concern for heterotrophic microalgae is that they may be overrun in wastewaters rich in other competing organisms of little value to wastewater treatment and biomass production. This competing organism problem can be addressed through sterilization. Autotrophic microalgae production facilities can be integrated with large point source CO2 emitting industries and should be integrated with heterotrophic and mixotrophic microalgae cultivation [65]. Ding et al. [50] argue that this challenge could be overcome by sterilizing the waste, excessive concentration of inoculums, or multistage cultivation systems. However, each of these solutions comes at an increased cost, especially for large-scale operations. The hybrid multistage cultivation systems (two-stage) are the solution which can make the microalgae biomass production systems not only economical but also environmentally sustainable [25
29]. The effectiveness of microalgae systems for removing nitrogen and phosphorus is well established as shown in Table 7.3. Microalgae are effective in removing phosphorus from the effluent of wastewater tertiary treatment. Similarly, microalgae can remove up to 98% of total nitrogen of wastewater obtained from aquaculture. Pires et al. [47] reported that the use of suspended microalgae cultures can be a successful way of removing nitrogen and phosphorus from wastewater. Zhang et al. [20] performed their experiments with mixotrophic microalgae strains for heterotrophic cultivation under dark conditions. Moreover, mixotrophic microalgae cultivation may prevent culture contamination in wastewaters due to a characteristic autotrophic component [69]. These result in the lowest removal efficiency, which could be considered the worst-case scenario. The potential of heterotrophic and mixotrophic microalgae is yet to be explored fully. Further research is needed to develop resilient microalgae strains for wastewater treatment.

TABLE 7.3 Removal Efficiencies of Microalgae for Various Wastewaters Percent Removal

Aquaculture [56,66] (%)

Brewery [25] (%)

Total N

95
98

80

15.8

90

100

90

Total P

32
71

87

49.9

77

100

98

Domestic (Under Dark Conditions) [20] (%)

Swine [34,51,61] (%)

Wastewater With Suspended Microalgae Cultures [67] (%)

Wastewater Tertiary Treatment [48,68] (%)

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7.5 BIOMASS PRODUCTION USING MICROALGAE Biomass production of microalgae is generally reported as dry weight (g m22 day21). Benemann and Oswald [70] reported the average growth of Scenedesmus as 15 g m22 day21 in an open pond. Biomass production varies with different microalgae species, type of cultivation, and time of the year. Garcı´a-Gonza´lez et al. [71] reported that the highest growth of Dunaliella in autumn was 3.08 g m22 day21 in an open raceway. Huntley and Redalje [72] observed average growth of H. pluvialis as high as 36 g m22 day21, while Olaizola [73] reported 15 g m22 day21 in raceway ponds. Moheimani and Borowitzka [74] noted highest growth of Pleurochrysis in summer month as 47.7 g m22 day21, similarly Olguı´n et al. [75] reported average growth of Spirulina in summer as 15.1 g m22 day21. Biomass production from microalgae cultivation is proven. The commercial application of developed technologies is critical. Production cost is becoming economical by improved production capacity over the years. For microalgae oil extraction, hydrothermal liquefaction is a proven method. Microalgae strain adaptation to local conditions is important in lowering the production costs of any bioproducts. For this, microalgae strain identification is necessary [76]. Using carbon sequestration from an industrial unit can bring up the production capacity. In nutraceuticals markets, starch and lipids obtained from microalgae are valuable and can be extracted using hydro-processing methods such as hydrothermal liquefaction or catalytic hydrothermal gasification. Microalgae transesterification (the process of exchanging the alkoxy group of an ester compound by another alcohol) is well proven for producing biodiesel, while microalgae fermentation is used for producing ethanol. Cost is the major consideration for the success of any microalgae study to be implemented on a commercial scale. Heterotrophic microalgae can yield oil through fermentation of organic carbon in dark conditions [77]. While costlier than autotrophic microalgae, high heterotrophic yield may justify the cost [78]. High oil content was found in Chlorella protothecoides [79,80], and in species of Crypthecodinium and Schizochytrium grown heterotrophically [81]. Reactor fermentation can produce biomass concentrations greater than 100 g L21 with 85% oil, compared to the autotrophic yield of only a gram per liter with 5%
30% oil [82]. However, oil production by heterotrophs is a recycled use of solar energy, since the organic carbon is provided by autotrophs in the first place. Organic carbon-waste products should be recycled [83], but they must be in a form that can be readily consumed by microalgae. Different stages of microalgae production incur different costs; however, Grima et al. [84] have shown that biomass recovery contributes 20%
30% of the total production costs. Many researchers have calculated microalgae feedstock cost, as listed in Table 7.4. Schenk et al. [85] and Chisti [83] reported cost for a kilogram of

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TABLE 7.4 Microalgae Biomass Production Cost Study

Growth Cost

Schenk et al. [85]

0.34 (USD kg21 biomass)

Chisti [83]

0.47
0.6 (USD kg21 biomass)

Benemann and Oswald [70]

135
241 (USD MT21 biomass)

Weissman and Goebel [86]

185
273 (USD MT21 biomass)

microalgae biomass varying from 0.34 to 0.6 USD, while Benemann and Oswald [70] and Weissman and Goebel [86] reported cost for a metric ton of microalgae biomass varying from 135 to 273 USD. To effectively compete with petroleum products, microalgae biomass must be produced with high efficiency at low cost. Growing microalgae using wastewater has the potential to help in achieving the goal of low-cost and highly efficient microalgae production.

7.6 CONCLUSION The unique challenges and opportunities of algae power are still not fully explored and discussed. The benefit of wastewater-algae symbiosis is also yet to be harnessed. This positive symbiotic relationship can help in mitigating climate change, decreased fossil fuel availability, and degraded human and environmental health while providing alternative energy sources with the economic advantage. Heterotrophic and mixotrophic microalgae cultivation in wastewater media may offer the flexibility to improve production economics while generating more concentrated and valuable bioproducts. These metabolic pathways should be explored using indigenous microalgae species that perform well in wastewater streams or in multistage cultivation systems for specific bioproducts to ensure financial viability and overall sustainability.

7.7 FUTURE OUTLOOK Wastewater treatment using algae is an ideal step in nutrient recycling. Algae ponds can use the wastewater from agriculture (as used in Bangladesh [87]), so rural areas have a major role to play in sustainability. Mixotrophic algae ponds require relatively low technology and are especially suited for developing countries. Mixotrophic algae ponds may also be an affordable alternative for carbon intensive economies. The choice of using algae for wastewater treatment is very logical as it can use abundant resources such as salt water and barren salt land. Halophyte algae (algae that grow in waters of high salinity) can use salt land and ocean water to produce commercial crops

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[88] in mixotrophic algae ponds. For cultivating algae in a heterotrophic way, it is necessary to design bioreactors (that allow the algae to grow within a closed system). It is very important to combine the wastewater treatment with biofuel production to innovate better techniques to harvest microalgae. A concept of sustainable township [89] is introduced which incorporates the production of algae biofuels in conjunction with domestic wastewater treatment. These townships would treat its wastewater using mixotrophic algae ponds that are used to achieve a tertiary level of municipal sewage treatment. Governmental agencies such as the United States Environmental Protection Agency (USEPA) are also helpful since they prohibit feedlots from discharging pollutants into waters [90], thus encouraging feedlots to treat their wastewater with mixotrophic algae ponds that may yield valuable byproducts in the future. Nutrient pollution is a major concern in the river and lakes around the world like in Toledo [91,92]. The potential of algae productivity for mixotrophic ponds is yet to be determined. This emerging potential of algae-based wastewater treatment technology could have a significant role in sustainable future and boost the economies of several countries with large coastlines. Not only do heterotrophic bioreactors and mixotrophic algae ponds have the potential to yield biodiesel, ethanol, biogas, and hydrogen but they can also produce valuable coproducts, while treating wastewater.

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726.

Chapter 8

Jerusalem Artichoke: A Promising Feedstock for Bioethanol Production Jiaoqi Gao and Wenjie Yuan School of Life Science and Biotechnology, Dalian University of Technology, Dalian, P.R. China

8.1 INTRODUCTION With exhausting fossil fuel resources, sustainable development of human beings and environmental protection requires novel, clean, and renewable resources. Biofuels, a potential substitution for carbon resources, represent great promise to the future energy industry and has attracted numerous researchers [1]. Bioethanol is the earliest and most mature biofuel product thus far and is widely considered as one of the most promising biofuels [2]. Bioethanol, as a kind of bulk products, is likely to be extremely sensitive to any improvements in the costs of raw materials [3]. The researchers have focused on finding the most appropriate feedstock for bioethanol production for decades. As illustrated in Fig. 8.1, first-generation bioethanol is based on agricultural cereal and sugar crops [5], which are also sources of human food. Although sugar or starch-based materials have already been applied in industrial bioethanol production, they have been proved unsuitable for largescale bioethanol production when food security and land-use issues are taken into consideration [6,7]. Consequently, the development of biofuels in the future counts on nongrain materials, which is also regarded as secondgeneration bioethanol [8]. Lignocellulose, the most typical representative of nongrain feedstock, has attracted an increasing amount of attention due to its low cost and rich sources. However, toxic inhibitors from pretreatment and difficult to utilize pentose, like xylose and arabinose require additional theoretical studies. Jerusalem artichoke (JA), similar to lignocellulose, is another nongrain substitution for the ethanol production. Recently, ethanol from JA has received increasing interest due to the advantages of JA including resistance

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FIGURE 8.1 Brief introduction to bioethanol production from various raw materials [4].

to poor soil, drought, cold temperature, and pests [9
12], and high yields of inulin in tubers. However, in spite of recent productivity improvements, numerous problems still exist in ethanol fermentation from JA such as longer processing times, low ethanol yield, and high residual sugar [13
15]. Herein, the most recent advances in ethanol production from inulin or JA are reviewed. Furthermore, its potential industrial application from the angles of strains, fermentative processes, and key factors for high productivities are discussed. The economic efficiency of the processes is also included.

8.2 JERUSALEM ARTICHOKE AND ITS POTENTIAL AS A BIOREFINERY CROP 8.2.1 Basic Properties of Jerusalem Artichoke JA (Helianthus tuberosus), which is also called sunroot or sunchoke, is a kind of herbaceous perennial plant that is native to North America which then spread to Europe in the 17th century [16]. It is now cultivated widely across the world, especially in China, due to its great economic value in various fields. JA is supposed to be a low-requirement crop with the attractive advantages. Particularly, JA grows in saline-alkali fields and fields that are not suitable for farming, which may also help to improve the soil condition. JA blooms in early September, followed by the tubers expanding gradually, and it is ripe between October and November. The economic value of JA mainly focuses on the tubers with their high sugar production, and the composition of fresh JA tubers is shown in Table 8.1 [16,17]. Among the carbohydrates, inulin is the principal storage sugar (75%
80%) [16], coupled with small amounts of free glucose, fructose, sucrose, and kestose [18]. For the dry tubers, total useful composition may account for about 80% except water and crude ash [16].

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TABLE 8.1 Nutrient Composition in the Fresh Jerusalem Artichoke Tubers [16,17] Large Component (g kg21) Water

Carbohydrate

Ash

Crude fiber

Crude protein

Fat

797.9

166.1

28.3

6

1.1

1

Fe

VC

VB1

VB2

8.4

6

0.17

0.06

21

Microconstituent (mg kg ) P

Ca

119

49

21

Indispensable Amino Acid (g kg ) Lys

Try

Met

Thr

Ile

His

Arg

Phe

0.9

2.4

0.9

8.0

0.9

0.6

1.2

1.3

8.2.2 Economic Value in a Biorefinery Concept It is known to most scientists that the economic value of JA is mainly focused on its tubers, which are rich in carbohydrates for producing various products in industries. The inulin in tubers, as a kind of functional food, can be directly applied in the food and medicine industry. Besides, as is shown in Table 8.1, a nitrogen source and biotin are involved in JA tubers except for the carbohydrates, and so it may be directly utilized to ferment some target products without adding other nutriments. Consequently, JA tubers are considered by some as an ideal substrate that can be used by microbes to produce ethanol, 2,3-butanediol [19], lactic acid [20], high fructose syrup [21], and others (Fig. 8.2). From a biorefinery concept [22], the whole JA possesses the potential to be fully utilized [10,23]. The lignocellulosic aerial part of JA has been reported to contain lower cellulose and hemicelluloses than other preferred feedstocks for lots of products [9], especially for the production of ethanol [10,23] and 2,3-butanediol [19]. Besides, the leaves of JA contain many kinds of bioactive constituents [24], and the extractions of these constituents are also important parts for a JA-based biorefinery.

8.3 ETHANOL FERMENTATION FROM JERUSALEM ARTICHOKE TUBERS Due to its potential as a promising feedstock for industrial products, bioethanol production from JA has always been regarded as one of the most practical technical routes in the long term. In fact, except for its commercial advantages and characteristics, unsterilized JA can be directly used to

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FIGURE 8.2 Potential of Jerusalem artichoke as a biorefinery crop.

ferment ethanol [13,25], which is very competitive in reducing the costs across industries. Inulin, as the main type of reserved carbohydrates in tubers for bioethanol production, is a linear polymer consisting of β-2,1-linked D-fructofuranose molecules terminated by a glucose residue through a sucrose-type linkage at the reducing end [26]. Inulin, with its high degree of polymerization, has been reported as dietetic fiber, low-calorie sweetener, fat substitute, and texture modifier [27]. However, JA grown in poor soil conditions generally contains inulin with relatively low degree of polymerization, which may not match the requirements of inulin or platform chemicals production, but the rich carbohydrates, nitrogen, and phosphorus made them even more suitable for ethanol production [13]. Considering fermentation processes, there are three technical ways that are recognized as being feasible: ethanol production by Saccharomyces cerevisiae after the acid or enzyme hydrolysis of JA (two-step method); ethanol

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FIGURE 8.3 Sketch map on ethanol production from fresh JA tubers. JA, Jerusalem artichoke.

production by the cocultures of S. cerevisiae and the inulinase-producing strains (one-step method); ethanol production by the strains that can produce both inulinase and ethanol, such as engineered S. cerevisiae or Kluyveromyces marxianus (one-step method). Fresh JA tubers may go through some simple pretreatment steps prior to ethanol fermentation by both one-step and two-step methods. Recently, JA juice [28] and JA meal [29] have been reported to be applied in ethanol production from fresh JA tubers (Fig. 8.3).

8.3.1 Two-Step Ethanol Production Though inulin for ethanol production has a relatively low degree of polymerization, many ethanol-producing strains like S. cerevisiae are not able to directly convert inulin into ethanol due to the lack of native inulinase. Consequently, bioethanol production by these kinds of strains must go through a pretreatment step, followed by inulin hydrolysis by acid or inulinase.

8.3.1.1 Acid Hydrolysis Two-step ethanol fermentation requires the hydrolysis of JA tubers in advance to yield easily used fructose, glucose, and sucrose for most

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microbes. Acid hydrolysis by HCl or H2SO4 is considered a traditional method, generally conducted over 30 minutes at low pH (pHB2) and high temperature (between 60 C and 140 C), as is shown in Table 8.2 [28,30,33]. Hydrolysis by acids is a type of efficient nonspecific method used to fully break down glycosidic bonds in inulin, releasing up to 140 g L21 of reducing sugars (mostly fructose and glucose) [30,33]. Hydrolysates are used after pH is adjusted to 4
5 to perform ethanol production by S. cerevisiae or Zymomonas mobilis with additions of some essential nutrients [28,30,33]. Generally, acid hydrolysis is intended to be a simple yet cheap process, and strains adopted in this process may achieve high-gravity ethanol production due to their high ethanol tolerance. However, similar with lignocellulose hydrolysis by acids, some specific inhibitors like 5-hydroxymethylfurfural [30] may be generated when JA tubers are hydrolyzed by acids. Though the conditions of acid hydrolysis may be optimized to reduce the concentrations of the inhibitors (i.e., less than 0.2 g L21), resulting in poor future fermentation performance.

8.3.1.2 Enzymatic Hydrolysis by Inulinase Inulin hydrolysis by inulinase is considered a moderate approach compared to acid hydrolysis. Inulinase specifically hydrolyzes β-2,1-glycosidic bond in inulin, which is a proven key enzyme that releases glucose and fructose for microbes. Inulinase can be divided into endo-inulinase and ex-inulinase according to their distinctive functions. The former randomly breaks down the glycosidic bond inside inulin to generate fructo-oligosaccharide; the latter acts on the glycosidic bond at the reducing end of inulin to release fructose and glucose one by one. Inulinase comes from alternative sources including plants, fungi, bacteria, and yeasts, with Aspergillus niger and K. marxianus being important inulinase-producing strains. Actually, ex-inulinase, which cuts inulin into glucose and fructose, has a better prospect in finding application in the field of ethanol fermentation. Existing publications indicate that the optimal reaction conditions are between 52 C and 64 C in a weak acid solution, which have also been the reaction conditions in the process of enzymatic hydrolysis for two-step ethanol production thus far. Compared to acid hydrolysis, hydrolysis by inulinase reacts in moderate conditions without inhibitors. However, there are many problems with the process of enzymatic hydrolysis. First of all, costs are relatively high. Commercial inulinase [32] or native crude inulinase [31] has been adopted in the process of enzymatic hydrolysis, both of which are more expensive than acid. Next, enzymatic hydrolysis processes are complicated. Different types of inulinase require diverse reactive conditions, which may also vary for different substrates from JA [32]. Furthermore, concentrations of reducing sugar are relatively low. Onsoy et al. [33] found that hydrolysates from

TABLE 8.2 Two-Step Ethanol Production From Jerusalem Artichoke (JA) Tubers in the Past 10 Years No.

Pretreatment Methods

Strains

Substrates

Ethanol (g L21)

References

Acid Hydrolysis 1

Hydrochloric acid hydrolysis at 126 C, hydromodule 1:1 and pH 2 for 60 min

Saccharomyces cerevisiae

JA hydrolysates

76

[30]

2

JA tuber juice, sulfuric acid hydrolysis at 80 C and pH 2 for 40 min

Zymomonas mobilis TISTR548

JA juice hydrolysates with 250 g L21 of total sugar and 0.5 g L21 of diammonium phosphate, pH 5.0

95.9

[28]

Enzymatic Hydrolysis 1

Inulinase, 38.4 U g21 inulin, 50 C for 30 min

Saccharomyces sp. W0

20% Inulin and 1% (NH4)2SO4

B115.2

[31]

2

Winter JA juice, commercial inulinase, 17 U g21, 52.5 C for 60 min

S. cerevisiae

JA juice hydrolysates

0.454 g g21 (yield)

[32]

3

Autumn JA juice, previous heating at 80 C for 15 min, and added commercial inulinase, 51 U g21, 60 C for 120 min

S. cerevisiae

JA juice hydrolysates

0.458 g g21 (yield)

[32]

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inulinase hydrolysis contained only 50 g L21 of reducing sugar, which was much lower than that found in hydrolysates from acid hydrolysis. Possible explanations for these results are that the activities of inulinase are inadequate, and that some putative inhibitors related to inulinase activities exist in substrates of JA. Though some reasonable fermentative performances were achieved in recent years, industrial-scale production of ethanol from JA requires the immediate support of additional theoretical studies.

8.3.2 One-Step Ethanol Production A one-step ethanol production form JA tubers requires both native inulinase production and ethanol production in strains, which is considered a consolidated bioprocessing (CBP) process. CBP technology combined inulinase production, inulin hydrolysis, and ethanol production into a single step, which has multiple advantages in the ethanol production process [13]. Compared with traditional fermentative processes, CBP is a promising technology that may reduce the number of unit operations and the overall capital cost of the process [35]. CBP strains may directly utilize inulin to produce ethanol; that is, the strains can secrete inulinase natively. Recently, the production of bioethanol from JA tubers by both genetically engineered S. cerevisiae and a wild-type K. marxianus via CBP technology have been widely studied.

8.3.2.1 Ethanol Production by Saccharomyces cerevisiae S. cerevisiae is able to secrete invertase, a kind of hydrolase possessing low activities of ex-inulinase, to utilize inulin with a low degree of polymerization [36]. However, S. cerevisiae cannot directly convert inulin with a relatively high degree of polymerization to produce ethanol due to its low activities of invertase and low specificity without native inulinase. Therefore, adopting S. cerevisiae as CBP strains to conduct one-step ethanol production from JA requires the introduction of a foreign inulinase gene via genetic engineering technology, which may eventually result in an entire CBP process from the native inulinase production from S. cerevisiae. The biggest strength of ethanol production from S. cerevisiae lies in the fast growth rates and high ethanol tolerance, with recent studies focused on sources of foreign inulinase gene. From Table 8.3, excellent sources of inulinase gene come from fungi [14,36] and some nonconventional yeast species [34,38], among which K. marxianus and Pichia sp. are the most popular and widely applied species. Besides, as previously discussed, ex-inulinase is potentially more suitable for ethanol production from inulin, considered a premium source of foreign inulinase genes for S. cerevisiae [37]. Though endo-inulinase may be applied in this process to achieve a reasonable ethanol production performance, lower activities and efficiencies eventually limit the entire process [25]. Wang et al. [36] coexpressed both ex- and

TABLE 8.3 Foreign Inulinase Genes Expressed in Saccharomyces cerevisiae Cells No.

Host Strain

INU1 Gene Source

INU1 Gene Type

Genotype

IMa

SIMb

Reference

1

Saccharomyces sp. W0

Pichia guilliermondii

Exo

18S rDNA integration

34.8




[37]

2

S. cerevisiae D452-2

Kluyveromyces marxianus

Exo

Plasmid pRS426

B16c

1.34

[38]

3

S. cerevisiae 6525

Penicillium janthinellum

Exo

18S rDNA integration

10.9

B1

[14]

4

Saccharomyces sp. W0

Meyerozyma guilliermondii

Exo

18S rDNA integration

43.84




[39]

5

Saccharomyces sp. W0

Arthrobacter sp.

Endo

18S rDNA integration

7.6




[25]

6

Saccharomyces sp. W0

Arthrobacter sp.

Endo

Delta-sequence

8.6




[25]

7

S. cerevisiae JZ1C

K. marxianus

Exo

Plasmid pYC230

2.73




[40]

8

S. cerevisiae JZ1C

Candida kutaonensis

Exo

Plasmid pYC230

3.46




[40]

9

Saccharomyces sp. W0

P. guilliermondii

Exo

Plasmid YCPlac33

34.2

B11

[34]

10

S. cerevisiae JZ1C

Aspergillus niger; K. marxianus

Endo; Exo

Ty1 retrotransposons and delta sequences YPRCΔ15

B26




[36]

11

S. cerevisiae 6525

K. marxianus

Exo

18S rDNA integration

22.9

B2.5

[41]

IM, Maximum inulinase activity (U mL21). SIM, Maximum specific inulinase activity (U g21 dry cell weight). c Data calculated by authors from the published articles. a

b

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endo-inulinase in S. cerevisiae cells to improve the efficiency of inulin usage and to enhance both ethanol yield and productivity, which provided new insights with potential application. Meanwhile, in most cases, foreign inulinase genes integrated into chromosomes of S. cerevisiae increased the stabilities of these genes without screening pressure like auxotroph and drug resistance, which facilitates industrial-scale application in the future. At present, activities of inulinase from genetically engineered S. cerevisiae have increased from a few units to dozens of units, which may be related to the sources of the inulinase gene, ways of gene manipulation, and measurements of inulinase activities. Liu et al. [39] integrated the inulinase gene from Meyerozyma guilliermondii into the chromosome of Saccharomyces sp. W0 and conducted related codon optimization, resulting in achieving activities up to 43.84 U mL21, which are desirable results referring to the expression of foreign inulinase gene in Saccharomyces sp. cells. S. cerevisiae embracing a foreign inulinase gene may achieve ethanol fermentation from JA tubers by CBP technology, with all of the main parameters of the past decade summarized in Table 8.4. Ethanol fermentation from JA tuber meals or inulin extracted from JA tubers, with simple nutrition (e.g., ammonium sulfate as nitrogen source) optionally added will meet the needs of subsequent industrial-scale production, which vastly reduces raw material costs. In the meantime, high-gravity ethanol production is a necessary requirement for productivity enhancement in industries, and substrate concentrations in recent studies reached up to 200 g L21, even over 300 g L21, for high-gravity ethanol fermentation. Around 30 C is a popular fermentation temperature due to relatively poor high temperature tolerance of S. cerevisiae. It is not hard to see the inhibitory effect due to high substrate concentrations results in longer fermentative time, up to 72 hours and even longer. Final ethanol concentrations typically achieve up to 100 g L21, and even 128 g L21 [42], important considering the high ethanol tolerance of S. cerevisiae. Liu et al. [39] carried out ethanol fermentation from 300 g L21 of inulin by a recombinant S. cerevisiae Y13 to obtain 108 g L21 of ethanol, with the relatively fast process achieving 2.25 g L21 h21 with the elevated inulinase activity ensuring a fast fermentation process. In addition, some new S. cerevisiae strains that are able to naturally utilize inulin with a high degree of polymerization have recently been discovered in succession [43,45]. In particular, the S. cerevisiae strain L610 may achieve ethanol production from inulin with a degree of polymerization over 15 without hydrolysis. However with this approach, only 6570 U mL21 of invertase activity can be detected in the supernatant. The inulinase protein of L610 is inferred to be positioned to cell walls because 108.7 U mL21 of activity is detected inside cells [45]. Consequently, these findings may promote further process development of ethanol production from JA. In general, genetically engineered S. cerevisiae, as a traditional ethanol producer, may achieve favorable ethanol production because of the fast

TABLE 8.4 Bioethanol Production From Jerusalem Artichoke (JA) Tubers by Genetically Engineered Saccharomyces cerevisiae in the Past 10 Years No.

Strains

IAa

Substrates

Temperature ( C)

Duration (h)

Ethanol (g L21)

Yield (g g21)

Productivity (g L21 h21)

Residual Sugar (g L21)

References

1

Saccharomyces sp. W0




50% of tuber meal

28

144

95.5

0.319

B0.66b

37

[31]

2

S. cerevisiae D452-2




20% of inulin

30

66

80.2

0.43

1.22




[38]

3

Saccharomyces sp. W0

34.8

50% of tuber meal and 0.1% (NH4)2SO4

28

120

B99.4

0.331

B0.83

24

[37]

4

S. cerevisiae 6525




240 g L21 of dry powder of JA tubers, and fedbatch 300 g L21 of powder twice

34

72

84.3

0.453

1.17

14.3

[29]

5

S. cerevisiae DQ1

19.1

35% of JA tubers

30

80

128.1

B0.374

1.6




[42]

6

S. cerevisiae 6525-BR8

10.9

30% of inulin and 1% of (NH4)2SO4

30

192

110.5

0.483

0.57

25

[14]

7

Saccharomyces sp. W0-Y13

43.84

300 g L21 of inulin

30

48

B108

B0.454

2.25

B22

[39]

8

S. cerevisiae KCCM50549




180 g L21 of JA flour

30

34

36.2

0.357

1.06




[43] (Continued )

TABLE 8.4 (Continued) No.

Strains

IAa

Substrates

Temperature ( C)

Duration (h)

Ethanol (g L21)

Yield (g g21)

Productivity (g L21 h21)

Residual Sugar (g L21)

References

9

Saccharomyces sp. W0-D5

8.6

JA tuber meal (1:3.5)

30




B79.8










[25]

10

S. cerevisiae JZ1C-inuCK

3.46

200 g L21 of JA tuber flour

30

36

B58.3

0.47

1.62

16.9

[40]

11

S. cerevisiae




JA raw extract

30

84

102.1




1.21




[44]

12

Saccharomyces sp. W0-inu-66

34.2

30% of inulin and 4% of hydrolysate of soybean cake

28

120

B108.1

0.36

0.9

3

[34]

13

S. cerevisiae L610

None

135 g L21 of JA powder

30

24

40




1.67




[45]

15

S. cerevisiae JZ1C

B26

200 g L21 of inulin

30

60

95.32

0.486

1.59




[36]

16

S. cerevisiae JZ1C

B26

250 g L21 of JA tuber powder

30

60

B80




1.33




[36]

IA, Inulinase activity (U mL21). Data calculated by authors from the published articles.

a

b

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growth rate and high ethanol tolerance, which may be influential in largescale ethanol production from JA by recombinant S. cerevisiae in the future.

8.3.2.2 Ethanol Production by Kluyveromyces marxianus K. marxianus, a sister species of Kluyveromyces lactis, is another type of “nonconventional” yeast that has been widely studied and has also been reported as a model of “crabtree-negative” yeast, in contrast to S. cerevisiae [46]. Due to its high-temperature resistance, rapid growth, and capacity to utilize various substrates, there are an increasing amount of applications utilizing K. marxianus in the fields of industrial biotechnology, such as in enzyme secretion, biofuels fermentation, and heterologous proteins production. Furthermore, K. marxianus is a key strain that can achieve ethanol fermentation from JA by CBP technology because of the high level of native inulinase secretion. Considering its high-temperature tolerance, bioethanol production from JA tubers via K. marxianus at an elevated temperature (more than 35 C) may become mainstreamed. There is a significant possibility of a faster fermentative process and a reduced contamination risk for ethanol production at high temperature, especially at 40 C [47]. Another obvious advantage of K. marxianus lies in its native inulinase production ability, and the activities of K. marxianus are dramatically higher than that of S. cerevisiae. These advantages ultimately indicate that K. marxianus may achieve a faster fermentative process and the conversion of the highly concentrated substrate was finished within 48 hours to ultimately obtain over 100 g L21 of ethanol [48]. Besides, upon comparing Tables 8.4 and 8.5, there are no significant differences in yields and productivities between K. marxianus and S. cerevisiae, but the final ethanol concentration of K. marxianus may be slightly less than that of S. cerevisiae considering the relatively lower tolerance of K. marxianus. In addition, De´nes et al. [51] proposed ethanol fermentation from a mix of K. marxianus and S. cerevisiae. The equal amounts of two strains were inoculated into an initial media, and 10.67% (v/v) of ethanol was obtained after 148 hours. At present, conditions of ethanol production using mixed culture need to be further optimized, but it may turn out to be a reasonable suggestion in the future. In brief, K. marxianus is a promising strain for ethanol production from JA because of its fast growth rate, high-temperature tolerance, and high inulinase activities, which may make significant differences to future industrial production. Consequently, both genetically engineered S. cerevisiae and K. marxianus are two excellent candidate strains in the process of ethanol fermentation from JA via CBP technology.

TABLE 8.5 Bioethanol Production From Jerusalem Artichoke (JA) Tubers by Kluyveromyces marxianus in the Past 10 Years No.

Strains

IAa

Substrates

Temperature ( C)

Duration (h)

Ethanol (g L21)

Yield (g g21)

Productivity (g L21 h21)

Residual Sugar (g L21)

References

1

K. marxianus DBKKU Y-102




JA tubers meal

37

48

94.62

0.46

B1.97b

B40

[48]

2

K. marxianus PT1

30.4

JA tubers flour

40

84

73.6

0.46

B0.86

15

[47]

3

K. marxianus Y179

B110

220 g L21 JA flour

30

144

97.1

0.444

0.67

B14

[49]

4

K. marxianus ATCC8554

52.3

200 g L21 of JA tubers meal

35

60

60.9

0.467

B1

B20

[13]

6

Recombinant K. marxianus ATCC8554 K/ INU2

114.9

230 g L21 of inulin

35

72

96.2

B0.447

1.34

15

[41]

7

Recombinant K. marxianus ATCC8554 K/ INU2

114.9

220 g L21 JA tubers meal

30

48

69.0

B0.45

1.44

22.9

[41]

8

K. marxianus Y179

B110

230 g L21 of inulin

30

60

98.0

0.43

1.63

12

[50]

IA, Inulinase activity (U mL21). Data calculated by authors from the published articles.

a

b

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8.4 ETHANOL FERMENTATION FROM JERUSALEM ARTICHOKE STALKS Ethanol production from lignocellulosic materials has attracted global attention due to its low cost. Like most lignocellulosic materials, JA stalks are composed of cellulose, hemicellulose, and lignin to form a very compact structure, requiring a more complex pretreatment step. Unlike some other materials, however, JA stalks have a relatively low content of cellulose, hemicellulose, and lignin, intended to be replaced by inulin [10,52]. It was reported that the content of inulin in JA stalks may reach 22.2% [10]. Therefore, differences in structure and chemical composition lead to a different pretreatment method that distinguishes JA stalks from others, and also makes JA stalks alone [53] or together with its tubers to be promising materials for ethanol production in industries. Khatun et al. [53] conducted ethanol fermentation using JA stalks as substrates alone. The pretreatment method was to hydrolyze stalks in a 2% NaOH solution at 121 C for 1 hour, and then wash them with water to neutralize, and were then dried. An amount of 115.8 g L21 total sugars including glucose, fructose, xylose, and inulin were achieved after adding cellulase batch wise. Finally, a recombinant S. cerevisiae with an inulinase gene was adopted to perform fermentation experiments to obtain 38.3 g L21 of ethanol, with a yield of 0.361 g g21. JA stalks alone may achieve reasonable ethanol production, but concentrations of total sugar obtained by the present pretreatment methods cannot meet the requirements of very high-gravity ethanol fermentation in industries considering the low contents of carbohydrates in the stalks. Therefore, a more suitable way is to evaluate the whole biomass of JA for ethanol production, promoting comprehensive utilization of both tubers and stalks [10,54]. Kim et al. [54] first pretreated stalks with both acids (0.5% H2SO4, 121 C, and 60 minutes) and bases (1 M NaOH, 121 C, and 60 minutes). Simultaneous saccharification and fermentation were carried out by adding cellulase from tubers and stalks with a corresponding ratio of 1:10 to achieve 70.2 g L21 of ethanol within 76 hours. Afterward, the researchers thought pretreatment using bases might not be suitable for industrial-scale production due to wasting a large amount of water. Consequently, base pretreatment step was removed. A percentage of 10 (w/v) JA stalks were pretreated with 0.5% of H2SO4 at 121 C for 60 minutes, followed by adding 8% (w/v) tubers, cellulose, and K. marxianus to perform ethanol fermentation by CBP technology. The results indicated that 45.5 g L21 of ethanol was achieved after 30 hours [10]. Actually, an improved sugar utilization rate meets the requirements of industrialization in the whole process considering the capacity of K. marxianus metabolizing xylose and arabinose. Stalks improve the utilization rate of biomass in the entire ethanol production process from JA and avoid wasting carbon sources. However, similar

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to the problems faced by other lignocellulosic materials, pretreatment processes generate inhibitors and pentose that is hard to use for most stains, even for K. marxianus that is able to consume pentose naturally. Consequently, how to optimize the pretreatment process, reduce inhibitor generation, increase the tolerance of these inhibitors, and improve the utilization of pentose will become an important direction for ethanol production from JA, and even for other lignocellulosic materials.

8.5 CURRENT STATUS, PROBLEMS, AND CHALLENGES The whole biomass of JA is certainly a promising feedstock for ethanol production. In the past 10 years, researchers have been focused on related studies to promote its industrialization. For now, ethanol production from JA may achieve expected results, including constructions of high-yield strains, reasonable parameters, and optimized processes. However, some bottlenecks require more intensive and concrete work, which is essential for the future development of ethanol production from JA. Recently, many high-yield strains have been constructed or screened for ethanol production from JA, but these strains fall far from what is required in industries. At present, two strains are promising for ethanol fermentation: recombinant S. cerevisiae with foreign inulinase gene, and wild-type K. marxianus, both of which posing their own unique challenges. The stability of the genetically reprogrammed S. cerevisiae with a foreign inulinase gene for large-scale ethanol production in industries remains in doubt and the problem of K. marxianus lies in its low tolerance of ethanol. Whether using genetically engineered S. cerevisiae or K. marxianus, each possess relatively low inulinase activity in the process of fermentation, which may be much lower than that under normal conditions due to the repression of high sugar concentration. This phenomenon eventually results in an obvious decrease in inulin hydrolysis efficiency. In the meantime, inulin hydrolysis, the rate-limited step in this process, contributes directly to the entire fermentative time. In some cases, the total duration reached up to 192 hours [14], which may not be acceptable for industrial-scale ethanol production. Unfortunately, the current state of knowledge about the mechanisms of inulinase is problematic, and a more controlled expression of inulinase may not be achieved, which significantly lowers ethanol productivities. Aeration has been reported to be beneficial in enhancing the expression of inulinase [55], which may mitigate the problem of insufficient activities during the process of ethanol fermentation. However, microaeration has been known to contribute to high ethanol yield. Therefore, this represents great challenges for the future of ethanol fermentation from JA. Finally, high residual sugar concentration in the end is a widespread problem in the process of ethanol production from high concentrations of substrates [48], no matter what the processes, strains, and substrates are. This

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problem may lead to wasting carbon sources and increased costs of sewage treatment, which can be fatal to ethanol production from JA.

8.6 ENHANCED PRODUCTIVITIES FOR ECONOMIC EFFICIENCY The problems above represent great challenges for the scale-up of ethanol production from JA. Therefore, studies on the enhancement of productivities may also make great differences in industrial production. Except for the previous discussion on fermentative processes and strains, some essential factors are vital for resolving these issues and the enhancement of productivities. On the one hand, enhanced inulinase activities, by genetic engineering or extra addition of inulinase [41,50], contribute to a faster fermentative process, thus increasing ethanol productivities. On the other hand, the positive role of an appropriate aeration strategy in fermentative performances, as discussed previously, can be attributed to an enhancement of cell viabilities during the initial stage [50]. Given this understanding, reducing costs in feedstock represents great promise to ethanol production from JA in the industrial scale. Taking data collected in 2010 as an example, which involved in data from ethanol production in each main step (Fig. 8.4), the largest costs still lie in the feedstock, which accounts for around 50% of the total costs [56]. Therefore, how to cut down costs of feedstock as much as possible makes great differences to the industrial process of ethanol production from JA in an economicefficiency perspective. Generally, JA-containing inulin with quite low degree of polymerization could not match the requirements of inulin or platform chemicals productions, but the rich carbohydrates, nitrogen, and phosphorus

16%

3% Feedstock

2%

45%

Auxiliary materials Fuel and power

17%

Wages and welfares

17%

Manufacturing Period cost

FIGURE 8.4 Preliminary calculation of costs in the process of ethanol production from JA. JA, Jerusalem artichoke.

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made them even more suitable for ethanol production [14]. Therefore, the cultivation of halophilic JA on saline-alkali soil is supposed to be economically more competitive as the alternative feedstock for fuel ethanol production.

8.7 CONCLUSION JA, as a promising feedstock for ethanol production, has recently received global attention on the fermentation strains, processes, and key parameters. In spite of achievements in two-step ethanol fermentation by acid hydrolysis, or enzymatic hydrolysis, ethanol production from inulin in JA tubers by both genetically engineered S. cerevisiae and K. marxianus via CBP technology (one-step) contributes to utmost all-round development in industries. Current efforts have been made to reveal the contributions of microaeration to shortening processing time and lowering residual concentrations. Consequently, a deep exploration that targets enhanced ethanol productivities in the future may lay a good foundation for its industrial-scale production.

8.8 FUTURE OUTLOOK According to aforementioned reviews and comments, solving the existing problems on a basis of increasing productivities will become the next critical area for research. Previous results showed the positive influences of vital factors (i.e., inulinase and aeration) on fermentative performances, thus providing some new ideas for further optimization. Integrating the whole production process into a single competitive step, followed by optimizing fermentation factors and strategies, may determine the final industrialization prospect of ethanol production from JA. With the development of omicsrelated technologies, applying genomics, transcriptomics, proteomics, and metabolomics in ethanol fermentation from JA investigate every tiny change in cells in the whole process from a global concept. In particular, sequencing technology allows the determination of changes in every important differently expressed gene during ethanol fermentation, which contributes to directed optimization of the expression of these genes. Besides, studies on mechanisms of inulinase regulation are conducive to achieve a controlled expression, which may resolve the issues fundamentally. In summary JA can be considered a promising feedstock for ethanol production and will eventually make contributions to sustainable energy strategies along with improvements of research and technologies.

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[38] S.J. Hong, H.J. Kim, J.W. Kim, D.H. Lee, J.H. Seo, Optimizing promoters and secretory signal sequences for producing ethanol from inulin by recombinant Saccharomyces cerevisiae carrying Kluyveromyces marxianus inulinase, Bioprocess Biosyst. Eng. 38 (2015) 263
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307.

Chapter 9

Recent Advances and Future Prospective of Biogas Production Rahulkumar Maurya1,2, Sushma Rani Tirkey1,2, Soundarya Rajapitamahuni1,2, Arup Ghosh1,2 and Sandhya Mishra1,2 1

Division of Biotechnology and Phycology, CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India, 2Academy of Scientific & Innovative Research (AcSIR), CSIR—Central Salt and Marine Chemicals Research Institute, Bhavnagar, India

9.1 INTRODUCTION Alternative energy sources may play a significant role in limiting the CO2 emissions. Waste materials such as crop waste, sludge, manure, municipal organic waste, wastewater sludge waste, or organic byproduct waste are the sources that will not compete with food crops and agricultural land usage [1]. Anaerobic digestion (AD) is the simplest approach to recover energy from biomass in the form of biogas. Recently, several technologies have been developed and continuously emerge on many aspects of AD. These aspects comprise pretreatment of biomass, reactor design and development, membrane bioreactors (MBRs), advanced microbiology, anaerobic process monitoring, various reactor configurations, post biogas purification, and reforming technologies. All aspects were ultimately developed to overcome problems associated with AD, and it should allow it to become economic, eco-friendly, recover the maximum amount of energy, and simplify processing while ensuring a high-quality yield. Two-phase AD, AD process monitoring and control, anaerobic MBRs (AnMBRs) along with post biogas purification and reforming is the primary field where major challenges and recent advances have been focused in recent years. This chapter addresses the key developments in recent years that are associated with challenges in the AD process. Recent advancements in the different strategies of biogas enhancement, process control and monitoring, AnMBRs, and biogas purification technologies are discussed in this chapter.

Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00009-6 © 2019 Elsevier Inc. All rights reserved.

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9.2 BIOGAS-ENHANCEMENT STRATEGY 9.2.1 Pretreatment Strategy Long-term digestion is a major drawback in AD due to its different sequential digestion steps such as “hydrolysis, acidogenesis, acetogenesis, and methanogenesis” [2]. The pretreatment hydrolysis step is the rate-limiting factor for the entire AD process [3]. However, initial substrates are typically very complex in nature; pretreatment solves this issue by altering the composition of substrate so as to be more suitable for digestion [1]. Pretreatment methods include physical/mechanical (e.g., ultrasound, high pressure, and lysis), electric pulses, wet oxidation, freeze/thaw, chemical (e.g., ozonation, alkali, acids), biological and thermal (,100 C, .100 C, microwave) techniques. Particle size reduction increases the surface area, providing a better contact between anaerobic bacteria and the substrate [4]. Mechanical pretreatments (e.g., sonication, lysis-centrifugation, collision, liquid shear, highpressure homogenization, liquefaction, and maceration) are used in order to reduce the substrate’s particle size [5]. These pretreatment methods were mostly applied at laboratory scale, with only a few methods successfully utilized at large scale. Only a few examples of the thermal hydrolysis process such as Cambi, Porteous, and Zimpro processes, and thermochemical pretreatment methods such as Synox, Protox, and Krepro have been applied on a large scale [6].

9.2.2 Enzymatic Hydrolysis Bacteria can produce two types of enzymes: endoenzymes (i.e., inside the cell) and exoenzymes (i.e., secreted outside the cell). In the case of AD, the primary step is hydrolysis of substrate. It is possible to separate the biological pretreatment of biomass/substrate before it progresses to further AD stages such as acidogenesis, acetogenesis, and methanogenesis. It can either be treated with different extracted enzymes or microbial strains that produce hydrolytic enzymes. The hydrolytic enzymes are mainly cellulase, cellobiase, amylase, xylanase, protease, and lipase. No bacterium produces all of the exoenzymes needed for the degradation of a large variety of substrates; therefore large and diverse communities of bacteria that produce different kinds of enzymes are required. Many authors have reported the employment of crude and commercial enzymes in order to treat the complex organic matters of biomass for enhancement of biogas production [7]. Many studies have been reported on the hydrolysis of lignocellulose and cellulose (i.e., primary constituents of plant materials) by lingo-cellulase and cellulase, respectively [8]. Similarly, improvement of biogas was also reported in the literature by employing lipase during the pretreatment of lipid-rich wastewaters [7]. Production of enzyme for the hydrolysis step through genetically engineered microorganisms may be helpful for biogas improvement. The

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engineered yeast Saccharomyces cerevisiae has been reported to ferment cellulose, xylose, and arabinose [8].

9.2.3 Microbial Strains Enhance Biogas Production Cellulolytic prokaryotes (e.g., Actinomycetes and other mixed consortia) were reported to enhance biogas production from the cattle dung [9]. It was found that a
c
d coculture addition (3%
9% as an inoculum) to feces fermentation was helpful in increasing gas and methane production by 56.36% and 18.09%, respectively, when compared to that of natural fermentation by feces microbes [10]. Microorganisms present in the rumen have the ability to anaerobically degrade cellulosic waste. Many researchers have attempted to isolate different cellulolytic microorganisms that possess a higher degradable activity. Sarkar et al. [11] reported 11 different microbial consortia that had different enzymatic activity and reduced the time span for degradation for organic kitchen waste. Dhadse et al. [12] studied biogas production through three different microbial consortia “A,” “B,” and “C.” Where consortia “B” contains strict (obligate) and facultative anaerobic bacteria (Bacteroides, Peptostreptococcus, Clostridium, and Propionibacterium); consortia “C” represents methanogenic bacteria (Methanobacterium formicicum, Methanobrevibacter ruminantium, Methanisarcina frisia, Methanothrix soehngenii) while consortia “A” represents all the eight isolates. Consortia “C” yielded the highest methane concentration (76%) when compared to consortia “A” (23%) and consortia “B” (1%).

9.2.4 Phase Separation and Codigestion Strategy The AD process can be separated into two phases: acidogenesis and methanogenesis. Acidogenesis, or acid fermentation, produces predominantly volatile fatty acid (VFA). Methanogenesis produces methane and carbon dioxide using the end product of the first phase. The main advantage of phase separation is the toxicity of the acid produced in one phase is not directly encountered with methanogens in the second phase. The other advantage is separate controlling of an environmental condition for a separate reactor. The two phases are very different in their microbial populations, digestion rates, and environmental conditions. Various reactor configurations that employ two-phase AD with two separate reactors include “upflow anaerobic sludge blanket (UASB)— UASB system, continuous stirred tank reactor (CSTR)—upflow anaerobic filter system, hybrid reactor, CSTR—anaerobic fluidized bed reactor system, two-phase plug-flow reactor, and anaerobic packed bed reactor” [13]. The digestion of a single substrate having a low carbon
nitrogen (C:N) ratio or inhibitory compounds may lead to low biogas production with lower quality. Low carbon leads to low methane production and high nitrogen content leads to ammonia gas or ammonium ion accumulation, which is an inhibitory methanogenic activity. In such a situation, improvement of methane

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production can be enhanced by the simultaneous codigestion of the substrate with a high carbonic substrate (high C:N ratio) which can be optimized by using a different blend ratio and types of cosubstrate because it enhances the synergism, methane production, digestate quality, and dilutes inhibitory compounds [13]. However, a higher C:N ratio leads to higher methane production but lowers buffer capacity, while a low C:N ratio leads to lower methane production and higher buffer capacity [14,15].

9.3 PROCESS CONTROL AND MONITORING AD is a very complex and multistep microbial process where microbes have a synergistic, interdependent relationship with each other. To gain a better insight into the digestion process, its development, and optimization, it is necessary to have knowledge of the microbial community. Various molecular techniques have been reported for the identification of bacterial and archaeal communities from AD. “Combinations of 16S ribosomal RNA gene clone library sequencing and dot blot hybridization” [16], “denaturing gradient gel electrophoresis” [17], “real-time polymerase chain reaction” (PCR) [18,19], “terminal restriction-fragment length polymorphism” [20], and “fluorescence in situ hybridization” [21] have all been reported for assessing microbial community structures and their shift during AD of different wastes. Morris et al. [22] hypothesized that a number of methanogens are related with the production rate of methane. Using a quantitative PCR, the researchers investigated a “number of gene copies and mRNA transcripts of the methanogenspecific gene mcrA” from DNA and RNA of different anaerobic digesters. They found “fewer copies of mcrA gene per nanogram DNA and fewer mcrA transcripts per nanogram RNA” from a low methane production digester than higher methane production digester [1]. So, a quantitative PCR was shown to be a helpful technique in monitoring methanogenic activity during AD. Palatsi et al. [23] studied the influence of the exposure of long chain fatty acids (LCFA) inhibitory pulses on microbial community structures and the “adaptation process in anaerobic thermophilic digestion.” The study showed the “improvement of hydrogenotrophic and acidogenic (β-oxidation) activity rates” without any notable change in “microbial community composition” upon LCFA exposure [23]. The interconnected nature of microbial communities in an AD system is hindered by the scientific community’s inability to culture the most of the microorganisms. In this aspect, metagenomics is an appropriate approach, which bypasses the cultivation bottleneck. Metagenomics is the extraction and sequencing the bulk DNA from an uncultured, environmental sample [24]. Metagenomics allows the metabolic potential of the microbial community to be accessed. Recently, Vanwonterghem et al. [25] studied the “genome-centric resolution of microbial diversity, metabolism, and interactions” in AD. They recovered 101 population genomes based upon “differential coverage binning” [25]. The populations comprised

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“19 phyla” with novel species and expanded the genomic coverage of rare phyla. Treu et al. [26] characterized the biogas microbiome through high throughput metagenomic sequencing. They extracted 236 genome bins including 157 new genomes [26]. They also revealed some of the microbial groups present independently formed operational conditions and comprised many “recurrent phylotypes” including Methanoculleus, Proteobacteria, Methanothermobacter, and Synthrophomonas [26]. The bacterial community was more diverse than the archaeal community due to higher functional variability. The “omics” methods are the combination of “metagenomics, metatranscriptomics, and metaproteomics” [13], which are very helpful molecular biology tools used to diagnose anaerobic digesters [3]. The AD process may be hindered by a lack of quality raw materials, interruptions in process, addition of toxic substrate by accident, and unintended organic loading [13]. Therefore, reliable monitoring and control technologies for effective process operation are essential. Process analytical technologies deal with the advanced monitoring of the AD process through “spectroscopic and electrochemical measurement principles” along with “chemometric multivariate data analysis” [13,27]. Utilizing advanced technologies such as “multivariate sensor technologies” and “electrochemical arrays” [13] has contributed to the expansion of monitoring technologies. VFAs are used as control parameters because they are produced by acidogens and acetogens during the first phase of AD and used by methanogens of the second phase. Several methods can be used in order to measure VFAs including spectroscopic, electrochemical, and chromatographic technologies. Recently, Jin et al. [28] developed a novel biosensor based upon “the principle of a microbial desalination cell” for VFAs monitoring during AD. The developed biosensor did not require any external power source or signal transducer due to the fact that the reactor can power itself from substrate oxidation and the current produced can be used directly to measure the VFA concentrations [28]. Other important parameter should be considered in developing AD process models. Such parameters include cation concentrations, nutrients availability, inhibitors’ concentration, degree of acclimation to the feedstock, startup conditions of digester, feedstocks’ solid concentration and their type, hydraulic loading, organic loading, and biogas production per unit volume [13]. Tomei et al. [29] reviewed various mathematical models for AD process improvement. The consolidation of different approaches from various models was required to develop a unique model that represented the optimal performance of AD process. In this regard, Batstone et al. [30] developed the International Water Association Anaerobic Digestion Model No. 1 (ADM1). Although it is the standard model widely used by many authors, still few issues are yet to be unfolded. In this model, the first-order kinetics was followed for disintegration and hydrolysis, which is regarded as the ratelimiting step in the AD process. In the application of ADM1, parameters of disintegration and hydrolysis were considered to be important and required

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calibration [1]. The other variables were assumed to be constant with an assigned default value [1]. The existing models do not deal with the effects of mixing the reaction mixture. In this aspect, other researchers have also studied the effects of mixing intensity along with organic loading rate on methane production using a 2D distributed model [31]. They observed that “the spatial separation of the initial methanogenic zones” from “active acidogenic zones” was significant for AD at high organic loading rates. They suggested avoiding vigorous mixing if the rate-limiting step throughout the startup duration is methanogenesis, while high mixing improves the methane production and solids degradation if the rate-limiting step is hydrolysis. Another aspect that must be taken into consideration is the microbial community data in digestion models. In this regard, Ramirez et al. [32] argued that the ADM1 model distinguishes “between different microorganisms involved in different reactions” but does not consider the species diversity between organisms with the same function. Therefore, they proposed an extended ADM1 model that includes the microbial diversity in AD. Ramirez and Steyer [33] replaced the average kinetic parameter in ADM1 model with a set of 10 kinetic parameters, each representing a separate species when modeling the AD process. The last issue is the availability of data for system identification. So many factors are involved in AD and it is not easy to measure and monitor online each independent factor or component throughout the process because it is very difficult, timeconsuming, and costly. The other model is the Siegriest model [34] in which parameters are based on experiments rather than the review consensus used in ADM1. Activated sludge models ASM1, ASM2, and ASM3 were also developed.

9.4 ANAEROBIC MEMBRANE BIOREACTORS MBRs principally work on the effective retention of methanogens in the reactor “through the decoupling of solids retention time (SRT) and hydraulic retention time” [35]. Membrane reactors are generally categorized in many ways such as conventional AnMBRs and enhanced AnMBRs [35] as well as low (,15% TS), medium (15%
20% TS), and high (20%
40% TS) rate reactors [36]. AD can be set up in various configurations such as batch and continuous modes as well as one- and two-stage methods [36]. AnMBRs are generally configured in two ways: “external/side-stream configurations and submerged/immersed configurations” [37]. External or side-stream configurations hydrodynamically control fouling are easy to replace membrane, have frequent cleaning and high-energy consumption [37]. In a submerged configuration system, membranes are placed into the liquid to drag the permeate through the membrane using either a pump or gravity. The advantages of the submerged configuration are lower energy consumption, easy cleaning procedures, and mild operational conditions [37].

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9.4.1 Membrane Materials and Modules Three types of membrane materials used for AnMBRs are polymeric, metallic, and inorganic (ceramic). Polymeric membranes were reported to be cheaper than that of metallic or ceramic membranes [37]. In recent years, polymeric membranes have become of great interest in research and commercial applications. The most used polymeric membrane materials (75% of the market) are “polyvinylidene difluoride and polyethersulfone (PES), while others are polyethylene (PE)” [38], polypropylene [39,40], and polysulfone (PSF) [41,42]. Ultrafiltration or microfiltration are used in most membrane modules with either hollow fiber, flat sheet (plate or frame), or tubular configurations [37]. Hollow fiber membranes are mostly used in submerged MBRs because of their high packing density and cost efficiency, while flat sheet membranes have good stability, ease of cleaning and replacement. Several tubular membranes are arranged next to each other, which provides low fouling, easy cleaning and replacement but have a high capital cost.

9.4.2 Types of Anaerobic Membrane Bioreactors Conventional AnMBRs include configuration with CSTR, UASB, expanded granular sludge bed reactor, anaerobic fluidized-bed MBR, jet flow anaerobic bioreactor [35,43]. Modified AnMBRs include configurations with anammox, anaerobic dynamic MBRs, anaerobic membrane distillation bioreactors (AnMDBRs), anaerobic osmotic MBRs, anaerobic membrane sponge bioreactors, gas-lifting, vibrating, and anaerobic bioentrapped MBRs [35].

9.5 RECENT ADVANCES IN BIOGAS PURIFICATION TECHNOLOGIES Biogas produced through AD contains methane, carbon dioxide, hydrogen, hydrogen sulfides, water vapor, ammonia, and siloxanes. Among them, only methane and hydrogen can be used for energy purposes. Without purification of the biogas, contaminated gases adversely affect the appliances (i.e., hydrogen sulfides are corrosive to engine or pipeline). Hence, concentrating methods have been developed. Among the contaminated gases, carbon dioxide is considered to be the major gas while others are present in trace amounts.

9.5.1 Water Scrubbing Water scrubbing works on the basis of H2S and CO2’s higher aqueous solubility compared to CH4 [44]. Through this method, 80%
99% CH4 purity can be achieved [45]. This accounts for 41% of the global biogas market upgrading due to sufficient availability of water at low cost and is less sensitive to biogas impurities [46]. To support better gas
liquid mass transfer

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efficiency, countercurrent operation is preferred in which the absorption and desorption units have random packing of Pall or Raschig rings [47
49].

9.5.2 Solvent Scrubbing Scrubbing can also be done by organic solvents such as PE glycol based absorbents (Selexol, Genosorb) with a higher affinity to CO2 and H2S than water. In this process, biogas and organic solvents are cooled down at 20 C prior to absorption [49]. The main advantage of this process is the anticorrosion nature of solvents but shares only 6% of the upgraded biogas market [46]. In order to avoid sulfur-mediated deterioration of the organic solvent, either the complete removal of H2S using activated carbon filters before organic solvent scrubbing or regeneration of organic solvent using steam or inert gas after desorption process is recommended [47]. Organic solvents such as tetradecane or Selexol can remove the siloxane at 97%
99% efficiencies [44]. Water absorption in glycols can also simultaneously remove oil and dust particles.

9.5.3 Chemical Scrubbing Chemical scrubbing is involved in the CO2 reactive absorbents such as alkanol amines (monoethanolamine, diethanolamine, etc.) or alkali aqueous solutions (NaOH, KOH, CaOH, K2CO3, etc.) [44,50]. Nowadays, a mixture of methyldiethanolamine (MDEA) and piperazine constitutes major amine absorbents, which are widely used commercially at MDEA:CO2 molar ratios of 4:7 [49]. In chemical scrubbing, intermediate chemicals such as CO22 3 , HCO2 are generated exothermally when the adsorbed CO reacts with che2 3 micals present in the scrubbing solution, resulting in higher absorption capacities [47]. This process is reported to share 22% of global upgrading market [46]. The addition of Fe21/Fe31 (FeCl2, FeSO4/FeCl3) in a digester will react with H2S, generating the insoluble salt FeS [47,51]. It can be effective at higher H2S concentrations but is not economic for concentrations lower than 100
150 ppm. Chemical adsorptions on Fe2O3, Fe(OH)3, and ZnO-based filters are often immobilized (e.g., on wood chips, red mud) and are widely used due to their simplicity, high affinity, and fast oxidation [47,51]. The residence time of the gas is 1
15 minutes at a threshold H2S concentration of approximately 100 ppm. Adsorption of H2S can also be done on nonimpregnated, catalytic-impregnated, and impregnated activated carbons in which H2S is catalyzed to elemental sulfur [52,53]. Nitrogenous compounds (e.g., urea or ammonia) are used to treat carbon for the catalytic impregnation and NaHCO3, Na2CO3, NaOH, KOH, KI, or KMnO4 are used to treat carbon for regular impregnation. Adsorption of H2S has been performed at high pressures (7
8 bar) and temperatures (50
70 C) along with air

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injection at 4%
6% for partial oxidation of H2S [47]. In the absence of oxygen, only KI and KMnO4 have the ability to partially oxidize H2S. The maximum adsorption capacities of catalytic, impregnated, and nonimpregnated carbons are 0.1, 0.15, and 0.2 g H2S g carbon21, respectively. Water absorption in hygroscopic salts is also an alternative approach in batch mode [52].

9.5.4 Pressure Swing Adsorption The selective adsorption of CO2 over CH4 on porous adsorbents with a high surface area includes examples such as activated carbon, silica gel, activated alumina, zeolite, and polymeric sorbents [44,47,54]. Molecular size exclusion and adsorption affinity are the basic separation mechanism of pressure swing adsorption (PSA) technology. The average pore size of adsorbents is ˚ , which retains CO2 (3.4 A ˚ ), excludes CH4 (3.8 A ˚ ), and makes up 21% 3.7 A of the global upgrading market [44,46]. Zeolites, activated alumina, silica gel, and activated carbon adsorption can support siloxane removal of up to 95% when treating dry biomethane. Adsorption of water has been carried out in packed bed columns with silica, alumina, magnesium oxide, or activated carbon under pressure (6
10 bar) [44,52].

9.5.5 Membrane Technology Membrane-based biogas purification is conceptually working on selectively separation or permeation of biogas component through semipermeable membranes. Different membranes selectively permeate biogas impurities such as CO2, H2S, H2O (called permeate) retain and concentrate CH4. In general, either polymeric materials or mixed matrix membranes (MMMs) make up membranes. Examples of polymeric materials include cellulose acetate, cellulose triacetate, polyimides (PI), polyetherimide (PEI), polyamide, PSF, PES, polycarbonate (brominated), polyphenylene oxide, polymethyl pentene, polydimethylsiloxane, and polyvinyltrimethylsilane [55]. MMMs are prepared by dispersing selective inorganic materials into a continuous polymer matrix to improve performance. Three different kinds of special fillers are used to prepare MMMs: (1) ordered mesoporous silicas (OMSs), (2) high aspect ratios (HARs) silica-based particles and metal
organic frameworks (MOFs). OMSs have ordered mesoporosity of the surface which allows for penetration of the polymer chain [56]. Some examples are MCM-41/(PSF/PEI), MCM-48/(PSF/PEI). HARs obtained through swelling and exfoliation of microporous materials (e.g., aluminophosphates, silicate AMH-3, titanosilicate JDF-L1) resulted in thin layers with a high area/volume aspect ratio [56]. MOFs are composed of metal
ligand complexes forming vertices of a framework that are connected with organic linkers [57]. MOFs are porous crystalline structures composed of metal
ligand complexes forming vertices of a framework that are connected with organic

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linkers to enhance molecular sieve properties [58]. They have very good adhesion with the polymer chain. MOFs are an emerging interest among researchers and very few studies have reported on their viability. Some examples are MOF-5/[PSF/PI/polyvinylacetate (PVAc)], Cu3(BTC)2/(PDMS/ PSF), Cu-BPY-HFS/PI, CuTPA-PVAc, ZIF-8/PSF, carbon nanotube (CNT)PDMS, silica nanoparticles-PSF (10% np), and single-walled CNTs (SWNT)-PSF (10% SWNT) [56]. An additional advantage of using MOFs for separation is that they can be regenerated under milder conditions than most zeolites, which require considerable heating and associated high costs [59]. Most recently, in order to improve the CO2 separation performance of polymeric membranes, an amino-functionalized MOF, NH2-MIL-53, was incorporated into PSF materials [59]. Recent breakthroughs in membrane technology are driven by nanotechnology due to their high affinity to selectivity. Membrane-based biogas-upgrading shares only 10% of the global market [46]. Membrane-based biogas upgrading also depends on the process configuration of membranes. In the single-stage gas permeation process, CH4 recovery is limited and cannot reach values of more than 95% [60]. A multistage system using various membrane modules is a good strategy to obtain high purity and high recovery of methane [60]. Four different types of membrane modules have been employed in multistage systems by applying different membrane in a cascade mode and recycle loop. In the hybrid processes, membrane-based biogas upgrading technology is combined with conventional gas separation equipment and may be superior to singular membrane technology. In another approach by Makaruk et al. [61], permeate of the membrane separation contained CH4 that can be used to drive power engine and simultaneously heat generated in engine can be used for heating the fermentation process.

9.5.6 Cryogenic Separation Different biogas components have the ability to liquefy or solidify at various low temperatures. This characteristic is utilized for the selective separation of biogas components. In cryogenic separation at constant pressure (10 bar), the temperature of biogas was decreased stepwise to 225 C (i.e., where F2O, H2S, siloxanes, and halogens are removed in the liquid phase), 255 C (i.e., where most CO2 is liquefied), and finally to 285 C (i.e., where the remaining CO2 solidifies) [44]. Even after decreasing the temperature to 2162 C to 2182 C, liquefied biomethane could be generated [44]. Allowing this process to operate at high pressure (80 bar) prevented the sudden solidification of CO2 below 278 C, avoided the clogging of pipelines, and heat exchanges [47,49,54]. This technology provided 97% pure biomethane with lower (2%) loss but cannot be commercialized and shares only 0.4% of the global market [46,50]. It was recommended to remove water, H2S, siloxanes, and halogens prior to CO2 removal to avoid pipe or heat

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exchanger clogging [49]. Investment costs and energy inputs will need to be evaluated for further operation of this technology. Tuinier and van Sint Annaland [62] proposed a cryogenic packed bed operated in a dynamic manner which would require 22% lower energy (2.9 MJ/kg CH4) than conventional PSA (3.7 MJ/kg CH4) and would only consume 5% of generated methane’s heat of combustion. Decreasing the cryogenic temperature to 270 C may remove the siloxanes through condensation (99.3%) [63]. Cooling of biogas through an electric cooler or an underground pipeline is a simple approach but is a less efficient water separation process in which condensed water droplets are removed by deminsters, cyclones, or water traps [47].

9.5.7 Biological Technologies CO2 reduction by biological means to constitute its cell component is an eco-friendly approach for biogas upgrade. H2-assisted CO2 bioconversion, microalgae-based CO2 fixation, enzymatic CO2 dissolution, fermentative CO2 reduction, and in situ CO2 desorption are biological methods for the removal of CO2 from biogas.

9.5.7.1 H2-Assisted CO2 Bioconversion CO2 (from biogas) can be served as a carbon source and an electron acceptor, and H2 can serve as an electron donor (from the external source or the biogas source) for bioconversion of CO2 to CH4 through hydrogenotrophic methanogens [44]. Even CO, H2, and CO2 from syngas can be utilized for methane bioconversion using hydrogenotrophs. This process required an extremely high gas residence time (1
208 hours) to achieve higher CH4 concentrations (90%) but had volumetrically low methane productivity (0.65
5.3 L CH4/Lr d) [44]. In this context, two-phase partitioning and Taylor flow bioreactor may be able to increase volumetric CH4 productivities [64]. Bioconversion to CH4 was favored over H2 by a preexisting gas distributing infrastructure and well-established methane combustion technology, while low density of H2 would require a larger storage facility and is highly explosive in nature with its transportation and direct utilization still under development. 9.5.7.2 Microalgae-Based CO2 Fixation CO2 present in the biogas can be reduced by microalgae to convert its biomass through acquiring electrons from photolysis of water [65]. In general, 5% CO2 concentration has been considered inhibitory to microalgal growth, but intense efforts in research were able to isolate species of microalgae that are able to tolerate high CO2 concentrations (up to 60%) [66]. The presence of H2S can inhibit the growth of microalgae if it is present in concentrations

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above 100 ppm [67]. Using H2S oxidizing bacteria and chemical oxidation for H2S in microalgal photobioreactors, researchers were able to oxidize elemental sulfur to sulfate, preventing growth inhibition of microalgae. Many studies were able to remove CO2 with up to 80% efficiency and 90% pure methane [44]. The residence time of biogas in a photobioreactor was reported to be 0.03
0.3 hours for outdoor and 0.7
96 hours for indoor reactors [44]. The high residence time of the biogas resulted in high O2 concentrations (5%
25%) in an upgraded biomethane that can be minimized by a two-stage process by recirculation of the microalgae culture through an external column downstream of the photobioreactor (i.e., biogas scrubbing) [68]. Generation of valuable microalgae biomass through the valorization of CO2 has a greater impact than a physical
chemical method where CO2 is vented to the atmosphere. A very different approach for the removal of CO2 is immobilizing enzyme carbonic anhydrase where CO2 dissolution in a bicarbonate solution resulted in 99% pure biomethane [69]. In another approach, CO2 from biogas can be utilized as a carbon source for the anaerobic fermentation of sugar to succinic acid [70]. Due to the higher aqueous solubility of CO2 over CH4, an anaerobic mixed slurry form biogas reactor can be continuously recirculated through a desorption unit in countercurrent mode with air stripping the dissolved CO2, H2S, and CH4 into the atmosphere [71,72].

9.5.7.3 Biological H2S Removal Sulfur-oxidizing bacteria (SOB) utilize H2S as an electron donor, carbon 2 dioxide as a carbon source, and O2, NO2 3 , or NO2 as an electron acceptor in 2 order to convert H2S to sulfate (SO4 ) along with elemental sulfur as an intermediate. These SOBs belong to the genera Thiobacillus, Paracoccus, Thiomonas, Acidithiobacillus, Halothiobacillus, or Sulfurimonas. Biotrickling filters (BTFs) used for desulfurization are packed bed columns (Pall rings, HD-QPAC, or polyurethane foam) which support bacterial growth by providing nutrients via a recirculating aqueous stream of a biogas reactor or wastewater treatment plant [44]. These BTFs remove H2S concentrations ranging from 500 to 10,000 ppm with efficiencies of 80%
100% at gas residence times ranging from 2 to 16 minutes. These filters have lower operating costs than physical/chemical technologies [73]. The only limitations with this technology are the clogging of packing media, their cleaning, and replacement due to the accumulation of elemental sulfur [74]. SOBs can remove H2S from the headspace of a bioreactor through microaerobically and lithoautotrophically growth on the wall of the reactor, producing elemental sulfur. This concept can be combined with microalgae to remove CO2 and H2S simultaneously where O2 is produced by the microalgae (i.e., photosynthesis) which are then consumed by lithoautotrophs to oxidize H2S to elemental sulfur.

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9.5.8 Biogas Reforming Technologies 9.5.8.1 Steam Reforming Under the presence of a catalyst (e.g., transition metals: Pt, Ni; noble metals: Rh, Pd), methane reacts with water vapor to produce CO and H2 at 650 C
850 C. CO can be separated from H2 by a water
gas shift reaction using a suitable catalyst (e.g., Cu, Fe, Mo, and Fe
Pd alloys) at 300 C
450 C [75,76]. In this process, carbon deposited on the catalyst’s surface may cause deactivation. It is only beneficial in the case of carbon deposited in the form of nanotubes that preserves longer catalyst activity. The membrane filter
based reactors used for steam reforming (SR) permits all reactions to occur in a single vessel, including separation process. Lin et al. [77] prepared mesoporous Ni2xCe1 2 xO2 catalysts through a reverse precipitation method. By using this catalyst in an SR process (500 C
900 C), they obtained a higher H2:CO ratio and a lower H2:CO2 ratio than when a commercial catalyst was used. Increasing the nickel content in the catalyst increased methane conversion to hydrogen production. Xu et al. [78] developed a polymer electrolyte fuel cell to obtain high H2 (70%) from desulfurized biogas. They utilized a steam reformer at high temperature, two water
gas shift reactors at low temperatures, a selective CO oxidizer, and a gas producer. Italiano et al. [79] prepared a nanocrystalline Ni/CeO2 catalyst for biogas conversion to hydrogen through oxy-steamreforming. They obtained above 85% H2 yield at 800 C when this catalyst was used. 9.5.8.2 Partial Oxidation Reforming Differing from the SR process, partial oxidation reforming (POR) is highly exothermic in nature in which methane is partially catalytically oxidized with O2 into syngas (H2:CO ratio is 2:1) at atmospheric pressure and 700 C
900 C in order to reduce coke formation [80]. Some catalysts such as solid solutions of Ca
Sr
Ti
Ni, NiO
MgO, Ni
Mg
Cr
La
O, and mixed metal oxides are reported as highly active catalyst for POR [80]. 9.5.8.3 Autothermal Reforming Similar to the SR process, autothermal reforming (ATR) is endothermic in nature and combines SR and POR technologies. In ATR process, heat generated during the partial oxidation of methane is utilized in parallel for the SR process. The ratio of syngas (H2:CO) is between 2.0 and 3.5 using O2:CH4 and H2O:CH4 ratios between 0.25
0.55 and 1.0
2.5, respectively [81,82]. 9.5.8.4 Dry Reforming In the dry reforming (DR) process, CH4 reacts with CO2 to produce syngas (H2, CO) at temperatures between 700 C and 900 C. A 1
1.5 ratio of CH4

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and CO2 yields up to 50% hydrogen [83]. The advantage of this process is the utilization of two greenhouse gases, while disadvantages include its endothermic nature requiring external energy as well as coke formation on the active surfaces of the catalyst. The catalysts used for this process are primarily Rh, Ru, Pt, Co, and Ni [84
86].

9.5.8.5 Dry Oxidation Reforming Dry oxidation reforming is the exo- and endothermic combination of DR and POR processes, using a parallel feeding of oxygen with methane and CO2 [80]. This method controls the deposition of coke on the catalyst as well as improves the methane conversion to higher hydrogen yield using lower energy input. The exothermic nature can be controlled to different degrees by the O2 feed’s concentration. Bimetallic catalysts have been used in this process to enhance the stability of the catalyst [85,87]. 9.5.8.6 Hot Slag in a Packed Bed Reactor Purwanto and Akiyama [88] reported passing biogas (CH4, CO2) continuously through a hot slag packed bed reactor at atmospheric pressure to produce syngas with higher H2 yields. In this process, slag acted as the thermal media as well as the catalyst for biogas component decomposition. Increasing the temperature of the slag resulted in higher H2 yields and higher methane conversions (96%). 9.5.8.7 Plasmatron-Assisted CH4 Reforming Chun et al. [89] studied plasmatron-assisted CH4 reforming in which hightemperature plasma was generated by air (5.1 L min21) and arc discharge (6.4 kW). In the parametric study, they found the optimum CH4 flow ratio (38.5%), methane conversion rate (99.2%), syngas concentration (H2, 45.4%; CO, 6.9%; CO2, 1.5%; and C2H2, 1.1%), H2:CO ratio (6.6), hydrogen yield (78.8%), and reformer thermal efficiency (63.6%). In another approach, Rueangjitt et al. [90] studied the upgrading of biogas by passing the biogas through a multistaged AC (alternating current) gliding arc system. A sudden increment in the stage numbers of plasma reactors, applied voltage, and electrode gap distance increased the conversion of CH4 and CO2 to hydrogen. The performance may be improved by using a combination of plasma reforming and POR. 9.5.8.8 Molten Carbonate Fuel Cells Bensaid et al. [91] prepared molten carbonate fuel cells (MCFCs) which generated power from landfill biogas. The MCFCs comprised a fuel cell system (FCS), an air process system (APS), and a PSA system [91]. The FCS stack had a catalytic burner, steam reformer, and a blower. The FCSs operated in vessel at 650 C, 2.6 bar, and were fed H2
CO-rich reformate gas. The

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unreacted syngas was combusted by a catalytic burner, which was thermally integrated with the steam reformer [91]. The outlet steam (rich in CO2) of a catalytic burner sent to the cathode stack along with the compressed air (from APS). Then output stream of cathode sent to PSA system. The APS system generated additional power by expanding the flue gases from FCS in a turbine. The PSA system was an equipment system that treated the FCS stream to maximize the H2 concentration through a water
gas shift reaction after cooling (200 C) via gas coolers. After further cooling (80 C), it was then compressed and fed to the PSA. There are two proposed configurations to operate this system [91]. In first configuration, the processed entire H2 at anode is conveyed to the PSA system for H2 enrichment instead of generating power [91]. While in second configuration, the outlet of the reformer is diverted to direct PSA system, which increases the separation efficiency up to 60% by increasing the high H2 concentration at PSA inlet. The residual stream (residual H2) after PSA purification is mixed with anode outlet to conveyed to the catalytic burner [91]. The net power efficiency of the two different proposed configurations of MCFC process is 56% and 55%, respectively. Meyer et al. [92] introduced intermediate CO2 capture by sorptionenhanced reforming technology. In this process, hydrogen is produced using a “high-temperature CaO-based CO2 solid sorbent together with a reforming catalyst (usually nickel based).” The process includes reforming, water
gas shift, and CO2 capture occurring simultaneously. The process is regenerative, decomposing calcium carbonate at higher temperatures through a calcination process into CaO and CO2. The regenerated CO2 can be further utilized to produce biomethane through the Sabatier process, which involves the reaction of CO2 with H2 at elevated temperatures (300 C
400 C) and pressures in the presence of nickel catalyst to produce methane and water.

9.6 CONCLUSION The biogas production through AD is the simplest way to extract energy from waste materials, but it is associated and affected by multiple factors. In last decades, many technologies for pre- and postprocess of biogas production are innovated to achieve the maximum biogas production. This may include the pretreatment, microbes, process control and monitoring, codigestion, phase separation, AnMBRs, and biogas purification. However, these technologies are needed to be integrated with each other in such a way that it enhances the biomethane generation with the economic and sustainable approach. Due to the dynamic nature of AD process, the process control and real-time monitoring of the AD in the bioreactor is the most important thing to focus on innovations. In a postprocess of biogas generation, the capture of CO2 from biogas can be achieved through conditioning with microalgae with additional benefits of biomass generation.

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9.7 FUTURE OUTLOOK Advancement of microbiology, specifically the selection and ratio of specific strains for AD, needs to be developed. Molecular tools and various sensorbased monitoring, as well as the development of models, are needed for future development. Future trends of MBR technology are mainly focused on the reduction of the energy demand and membrane fouling. Knowledge of engineering coupled with microbiology will improve AD process enhancement and optimization. Research and development at the lab-scale should be transferred to large-scale production levels without any fear of failure, boosting the exploration of problems associated with that and getting encouragement to further solution by statistical optimization of the process.

ACKNOWLEDGMENTS This manuscript has been assigned registration number CSIR
CSMCRI—165/2016. RM, SRT, and RS would like to acknowledge CSIR, UGC-NFHE, DST-INSPIRE, respectively, for awarding research fellowship as well as financial support from CSC 0203, TLP 0096 (CSIR
NMITLI), and AcSIR for their PhD enrollment. SM and AG acknowledged AcSIR as a faculty member.

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[73] M. Fortuny, et al., Operational aspects of the desulfurization process of energy gases mimics in biotrickling filters, Water Res. 45 (17) (2011) 5665
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ZrO2
CeO2
Al2O3, J. Nat. Gas Chem. 15 (2) (2006) 122
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6814.

Chapter 10

Recent Advances in Lipid Extraction for Biodiesel Production Narges Moradi-kheibari1, Hossein Ahmadzadeh1, Ahmad Farhad Talebi2, Majid Hosseini3 and Marcia A. Murry4 1

Department of Chemistry, Ferdowsi University of Mashhad, Mashhad, Iran, 2Microbial Biotechnology Department, Semnan University, Semnan, Iran, 3Manufacturing and Industrial Engineering Department, The University of Texas Rio Grande Valley, Edinburg, TX, United States, 4Department of Biological Sciences, California State Polytechnic University, Pomona, CA, United States

10.1 INTRODUCTION Over the last decade, global population growth increased by 10%, while oil consumption grew by 13%, a consequence of the industrialization of emerging countries [1,2]. Thus the search for the renewable and sustainable biofuels has recently received much attention. Among many alternative transportation fuels, biodiesel is gaining ground because normal compression ignition engines are able to consume it without adverse effects to operating performance [3]. Biodiesel can be produced from the transesterification of plant or animal oils, vegetable oil, waste cooking oil, algae oil, and fats [4
6]. Glycerides, or acylglycerols (AGs), are the energy storage form of lipids within algae and plant cells. Generally, algae lipids are classified as either nonpolar or polar [4]. Nonpolar or neutral lipids (NL) are composed of free fatty acids (FFAs) and AGs [4]. Polar lipids are mainly comprised of two subcategories, phospholipids (PLs) and glycolipids [7]. Although the lipid members of both categories can be converted to biofuels, nonpolar lipids and, especially AGs, are ideal lipid fractions for biodiesel production, because these fractions are easily transesterifiable [8]. Furthermore, the unsaturation degree of AGs is lower than other lipid types found in algae, which produces fatty acid methyl esters (FAMEs) with higher oxidation stability [7]. The chain-lengths of microalgae fatty acids (FAs) range from 12 up to 22 [9]. Biodiesel fuel quality is impacted by the specific types of lipids Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts. DOI: https://doi.org/10.1016/B978-0-12-817937-6.00010-2 © 2019 Elsevier Inc. All rights reserved.

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extracted from the algae biomass [4,10,11]. Therefore, extraction of lipids represents a major step in successful biodiesel production from microalgae feedstock. Both novel and traditional means of oil extraction have been explored at industrial and laboratory scales. For example, mechanical extraction, “accelerated solvent extraction (ASE),” “ultrasound-assisted extraction (UAE)” [12], “microwave-assisted extraction (MAE)” [13], “electrochemical extraction (ECE)” [14], “supercritical fluid extraction (SCFE)” [15], and “subcritical water extraction (SCWE)” [16] have attracted considerable research attention in the last decade, with the goal being to develop technologies for reliable oil extraction from microalgae [17]. In marine systems, members of Pyrrophyta and Chrysophyta are the main microalgae divisions represented [18]. High photosynthetic efficiencies coupled with rapid growth lead to rapid biomass production and lipid accumulation rates [7]. Microalgae lipid content is dictated by the species and environmental conditions [7,19]. Although biodiesel has many advantages in comparison with petrodiesel, its production cost is a major barrier for largescale applications of this technology [20]. New technologies for cultivation, harvesting, and oil extraction must be developed for microalgae biofuels to be cost-effective and economically feasible. As an example, cultivation of microalgae for CO2 mitigation and wastewater bioremediation can potentially reduce overall biofuels production cost [21,22]. Due to the influences of extraction methods on biodiesel quality, the extraction method should be properly selected. This chapter will provide a broad examination of currently available microalgae lipid extraction techniques along with their advantages and shortcomings. In addition, a comprehensive discussion of the recent advances in lipid extraction methods is provided.

10.2 LIPID EXTRACTION METHODS 10.2.1 Expeller Pressing First used in the 1900s to extract oils from plant seeds, expeller or screw pressing uses high mechanical pressure to crush the often tough cell wall of algae biomass to compress the lipid out from cells. One of the advantages of this method is that no chemicals are added during the expeller pressing, and the device used is not complicated. However, this method suffers from some disadvantages such as the need for a large amount of biomass [9], lower oil recovery relative to chemical extraction methods, not being selective for triacylglycerols (TAGs) [23], and the extraction of pigments along with oil. Furthermore, because of the high pressure imposed on the cell wall, mechanical friction causes the biomass to heat up. As such, the higher temperatures inherent in expeller processing negatively affect the extracted lipid’s quality [24]. There are few laboratory studies of expeller extraction of microalgae

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oils in the literature. A 75% lipid extraction efficiency was achieved from 70 kg of seaweed (macroalgae) using screw expeller press [25].

10.2.2 Solvent Extraction Method Solvent extraction (SE) is well established in the separation of lipids from other cellular components based on their relative solubility in organic solvents. Hexane, chloroform, and dichloromethane (i.e., organic solvents) easily dissolve NL and are extensively used in traditional lipid extraction from algae [17]. Combinations of solvents with different polarities have been applied successfully [4]. When the feedstock is wet due to the hydrophilic nature and hydrated surface of algae cell walls, a combined nonpolar/polar solvent system was reported to achieve higher yields of lipid extraction [26]. When the feed is wet and nonpolar solvents are used individually, the penetration of solvent into the cell wall does not occur efficiently, resulting in lower extraction efficiency. On the other hand, polar solvents can operate as a dispersive solvent which increases mass transfer and surface contact between the algae lipid and nonpolar organic solvents, reducing the extraction time and enhancing extraction efficiency [27]. Due to their lower toxicity and cost, methanol and ethanol have widely been used as polar solvents [28]. The polar solvent should be soluble in the nonpolar solvent, and addition of both polar and nonpolar solvent is preferred to be simultaneous [29]. Separation of solvents is achievable through the addition of water. Water molecules form hydrogen bonds with polar solvents and eject the nonpolar solvent containing lipids from the aqueous phase [30]. Chloroform is a commonly used solvent in batch extraction procedures [29]. Even in laboratory-scale experiments, less toxic solvents are preferred due to health concerns. When recovery of a specific lipid product is desired, the selectivity of the solvent is important. As an example, for biodiesel production, the separation of TAGs is favored [4,7] and the coextraction of pigments lower the quality of biodiesel [4,31]. A selective solvent solubilizes the target component much more efficiently than other components of the sample, is based on solvent polarity, and must be matched to that of the TAGs’ polarity [4,32]. Long-chain AGs are more soluble in solvents with low polarity. SE methods are performed in both continuous and batch process [9]. Continuous organic SE was originally designed for the extraction of solutes with low solubility from a solid material. Lipid extraction via this methodology allows for the processing of a large amount of biomass, while recycling a small amount of solvent. A Soxhlet extractor is a laboratory apparatus used for continuous extraction in which a specialized piece of glassware is placed between a flask and a condenser [33]. Continuous extraction methods have some disadvantages including that the methods cannot be applied to small amounts of biomass, the thermolabile components are at risk of being

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degraded, and high extraction efficiency is time consuming [34]. Batch extraction procedure is a commonly used approach for lipid extraction at small scales (e.g., in laboratory) due to its relative speed and that it requires only minute amounts of sample [7]. However, it is worth noting that the yield of batch SE processes for single-step extraction is typically not very high. SE is generally limited to laboratory practice and shows limited ability of extraction from intact cells. Therefore, the SE method, when combined with an effective cell disruption techniques, can be used to increase the yield of lipid extraction.

10.2.3 Supercritical Fluid Extraction Method SCFE is a methodology that employs solvents in the supercritical condition to extract lipids from biomass [7]. Supercritical fluids (SCFs) are the extracting solvent in a state that can separate lipid content from the matrix of biomass. SCF refers to a compound at ambient conditions above the critical point in which distinct liquid and gas phases of solvent are nonexistent (Fig. 10.1) [35]. Due to the compressibility of a SCF, it behaves as a gas. SCFs have the density of a liquid (between 0.1 and 1.0 g mL21) and therefore have the dissolving power of a liquid. Furthermore, supercritical viscosity values and diffusion coefficients lie between those of liquids and gases, which result in low surface tension and high mass transfer capability [36]. The intermediate characteristics of these two extremes of state grant the solvent power of liquids coupled with the transport properties common to gases.

FIGURE 10.1 Schematic of a typical SCFE system. SCFE, Supercritical fluid extraction.

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10.2.3.1 Supercritical Carbon Dioxide Extraction Method Among many solvents, supercritical carbon dioxide (SCCD) has been a frequently utilized [37]. The most common advantages of SCCD extractions are that CO2 is nonflammable, relatively inexpensive, and nontoxic [38]. Moreover, due to the gaseous state at ambient pressure, no separation step is necessary in order to remove the solvent from the extracts. In addition, the lower critical temperature of CO2 extraction does not degrade thermolabile compounds [39]. Furthermore, when carbon dioxide used is in the extraction, it can be condensed and liquefied by utilizing a compressor and eventually returned to the extraction cycle [40]. Due to the low-processing temperatures and solvent-free technique, the residual biomass maybe utilized as fertilizer and animal feed [41], which is not possible with solvent-based processing. Accordingly, the use of supercritical carbon dioxide extraction (SCCDE) has been the subject of many investigations recently [7,42]. SCCD in the extraction process represents unique features. First of all, due to the low polarity of SCCD, nonpolar or partially polar compounds are more efficiently extracted. SCCD also has a higher extraction efficiency of low molecular weight solutes. In addition, as SCCD pressure increases, the solubility of solutes with high molecular weight is enhanced. SCCD has high solubility of oxygenated organic compounds of medium molecular weight. Finally, varying critical conditions (i.e., pressure/temperature combination) could change the dissolving power and selectively of SCCD, allowing for the sequential extraction of different lipids [43]. Optimization of extraction parameters, including temperature, pressure, flow rate, time, and sample size, improves the extraction efficiency of specific compounds [44]. Generally, increasing pressure up to about 30
50 MPa enhances the extraction efficiency while raising the temperature above 50 C exhibits a reduction in oil extraction form some green algae [15]. Furthermore, the physiochemical properties of SCCD can be altered by using modifiers (i.e., cosolvents). The addition of a modifier can cause enhancement in the SCFE efficiency through enhanced separation of more polar compounds [43]. The results reported by Tang et al. [45] showed considerable improvement in the lipid yield obtained using ethanol as cosolvent. Moreover, addition of EtOH or CH2Cl2 as cosolvents in SCCDE improved the extraction of TAGs during lipid extraction from Nannochloropsis oculata [46]. A typical SCFE apparatus is depicted in Fig. 10.1. The essential features include a CO2 source, a means to pressurize the gas (i.e., pump), an extraction vessel within an oven, an extraction line restrictor in order to maintain high pressure, as well as an analyte-collection device [47]. The material of construction is usually stainless steel because it is chemically inert and resists high pressures [41]. The extraction process starts with flowing the liquid CO2 toward the pump. The pressure rises above the critical pressure, and by using a heater, the temperature of the fluid reaches the critical

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temperature. The SCCD passes through the extraction vessel’s loaded sample and, upon exit, enters a collection vessel. Here, the fluid is quickly depressurized by a micrometering valve (restrictor). Before the extraction vessel, a modifier can be added to the SCCD via an external pump, directly added to the extraction cell’s sample [47]. To prevent the SCCD flow from carrying away the sample biomass, a set of frits are situated at both the extraction vessel’s ends. Upon complete depressurization, the SCCD becomes a gas, precipitating the extracted crude lipids in the collection device [7]. Pressure-reducing valves are usually equipped with heaters to prevent the line blockage due to freezing water as well as the formation of dry ice and/or deposition of heavy molecules inside the pipes [35]. SCFE can be accomplished in dynamic, static, or coupled mode (i.e., static/dynamic). By combining an initial static period with a subsequent dynamic extraction where SCCD flow is maintained through the extraction chamber, an optimized quantitative approach can be obtained [36]. SCCDE is an environment-friendly method which has no negative effects on the residual biomass and extracted lipid. The main advantages over SE methods include SCCDE’s nonexplosive, nontoxic, and nonflammable properties as well as its ability to be reclaimed. CO2 can easily evaporate, facilitating separation of the analyte from the solvent, and allows for the CO2 to be recycled using a condenser. Although SCCD was first discovered in the early 1800s, new practical applications of this discovery have only recently appeared and have been embraced by industrial communities in the last few years. However, instrumentation necessities for SCCDE contribute to the high capital cost of commercially available equipment when evaluated against typical extraction processes.

10.2.4 Subcritical Water Extraction SCWE is another example of an extraction process that uses pressurized liquid water at temperatures above its boiling point while remaining below its critical point [48]. At these conditions (i.e., subcritical, below 374 C/647 K, 22.1 MPa), water is superheated rather than supercritical [49]. At high temperature and pressure, water exhibits very different behavior than water at ambient conditions because water’s hydrogen-bonded lattice is disrupted. Liquid water at subcritical conditions dissolves compounds with low polarity [50]. Applying pressure has a minimum impact on the characteristics of subcritical water, and it is utilized for the purpose of maintaining water in its liquid form [51]. However, when temperatures exceed 300 C, the physical properties of SCWE (e.g., density) change significantly with high pressure [49]. The increased extraction rate with increasing pressure at temperatures below 300 C may be due to effects on the substrate, particularly in plant and algae feedstocks, rather than changing physical properties of water. Varying temperature also imposes greater changes to water behavior than would be

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expected. Properties such as viscosity, dielectric constant, density, dissociation constant, and surface tension as well as diffusivity of water, all vary with temperature which can modify solubility of less polar compounds in SCWE [48]. At 275 C, liquid water’s dielectric constant and saturation pressure are nearly equal to that of CH3OH and C2H5OH at ambient conditions, which increases solubility for less polar compounds [52,53]. Water’s dissociation constant (Kw) increases with temperature and causes pH decreases to about 5.5. Since hydronium ions act as a catalyst, decreased pH increases the risk of hydrolysis [50]. Increasing the temperature also causes lipid degradation and causes oxidation reactions to occur in the system if oxygen is not purged from the water before the extraction [54]. Other parameters that can influence extraction efficiency include extraction time, flow rate, addition of modifiers (e.g., surfactants, light alcohols), as well as the biomass’ particle size and moisture content [49]. Several studies have examined microalgae lipid extraction using SCWE [55
57]. Reddy et al. [16] used static SCWE to extract lipids from Nannochloropsis salina and optimized extraction temperature (i.e., 215 C), time (i.e., 25 minutes), and biomass loading. A significant enhancement was observed in the total lipid yield using SCWE compared to the Folch method, while TAGs content did not show significant differences. However, diacylglycerols (DAGs) and monoacylglycerols (MAGs) showed an enrichment over the lipid content of extracts produced using the Folch method, indicating glycerol lipid acyl chains have been partially hydrolyzed at subcritical conditions [16]. In a reported study, a comparison between the total lipid yield and composition extracted from Scenedesmus sp. using SCWE and Bligh and Dyer methods [58] indicated that while the methods delivered identical total lipid yields, analysis of the lipid composition showed lipid extracted using the SCWE method contained more TAGs and DAGs relative to the Bligh and Dyer method [58]. The equipment needed for SCWE are similar to those used in SCFE, and both techniques can be run in dynamic, static, or a combination of the two modes [49]. Dynamic SCWE also requires an extraction vessel, a collection vessel, a heater, a pump, and a pressure restrictor. The extraction vessel and the tubing are typically composed of stainless steel [54]. Frits are typically installed at both ends of the extraction vessel to hinder sample losses and line plugging. A pressure restrictor before the collection vessel is necessary to avoid pressure drop and boiling of water [51]. Decrease in temperature and pressure in the outlet pipe can cause deposition of the analyte, blocking the system, which is avoided by using a second pump to inject a solvent into the tube lines after the extraction and by keeping the vessel warm using a heating tape [49]. In static SCWE mode, pump and pressure restrictor is not obligatory. Manual water addition to the extraction vessel is accomplished via an

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apparatus and mechanism resembling an autoclave. As the vessel is closed and the temperature increases, the pressure rises and subcritical conditions are created [59]. A major disadvantage to the static mode of extraction is that the residence time of analytes is increased when compared to that of the dynamic mode, causing degradation of thermally labile analytes [60].

10.2.5 Electrochemical Extraction ECE of lipid from microalgae cells is simple, clean, cheap, and quick [61]. The method disrupts cells using a pulsed electric field (PEF) providing a single-step extraction and, as such, is gaining attention. A schematic of ECE is depicted in Fig. 10.2. The use of an external electric field, above the critical threshold of biological cells, can structurally alter the cell membrane along with the PL layers. The cell membrane loses its gatekeeping function and becomes permeable [62]. Based upon the electric field’s intensity, pulse width, and a variety of taxa-specific algae cell characteristics, reversible or irreversible permeabilization occurs [63,64]. Irreversible destruction of cell membranes allows the cell contents to release into the medium [62,65]. Teissie et al. [66] have reviewed the theory of cell membrane electropermeabilization. A typical PEF system includes a “pulse generator” that allows for steady and continual pulse treatment, electrode filled flow chambers, and a fluidhandling system [61]. The extraction conditions for maximum extraction efficiency are species specific and vary based on the algae cells characterization and compounds of interest [63]. Several parameters may influence the extraction and electrochemical performance efficiency including the distance between electrodes, surface area of the electrodes, pulse duration, number of pulses, field strength [62], the type of materials used for anode electrodes, and the conductivity between the anode and cathode electrodes, with increased salt levels enhancing cell disruption [67]. Furthermore, electroporation efficiency needs to be optimized while considering the algae cell’s geometry and size. Smaller microorganisms need greater electric field strength [64,68]. For example, an electric field strength of 10
15 kV cm21 may be optimal for the electroporation of microbial cells that are 1
10 μm in diameter [64].

FIGURE 10.2 Schematic of ECE method: (1) applying pulsed electric filed, (2) rupturing the cell wall by applying electric field, and (3) phase separation. ECE, Electrochemical extraction.

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Although PEF has been typically used as a pretreatment to enhance the extracted lipid in the laboratory scale, its application as a lipid extraction method is relatively new [62]. The major issue is the separation of the lipid layer after extraction from the aqueous surface layer. Flisar et al. [61] evaluated a continuous flow PEF system for extraction of lipids from Chlorella vulgaris, in which 22% lipids from dry mass were extracted. The PEF approach is promising for commercial operations to extract microalgae lipids since it is a nonthermal and environment-friendly method which neither inserts additional impurities into the products nor induces any adverse changes in the target analyte [62,67]. Furthermore, the PEF methods are applicable for wet biomass and dewatering steps, a major expense in algae feedstock is avoided [68].

10.2.6 Modifications in Lipid Extraction Methods 10.2.6.1 Microwave-Assisted Extraction MAE is an extraction technique that speeds up the process of extraction. The kinetics of extraction will be faster when it is performed under microwave exposure. Microwave’s energy enables the solvent penetration into layers of the samples and, therefore, increases the rate of SE [9]. An oscillating electric field causes “vibrations of polar molecules” along with inter- and intramolecular friction [69]. The friction of all charged ions in the sample causes a very rapid heating of the whole sample (volumetric heating) [69]. Intracellular water evaporation increases pressure resulting in cell disruption. Subsequently, the maximal partition of lipids into the solvent phase is observed at the same solvent ratio, increasing the efficiency of extraction [24,69]. The mechanism of microwave extraction has been discussed comprehensively [70]. Recent studies of MAE in lipid extraction from algae biomass have demonstrated the enhancement in the extraction yield relative to conventional methods [69,71,72]. Balasubramanian et al. [69] and Iqbal and Theegala [71] demonstrated a 40% and 8% improvement in lipid yield, respectively, in comparison to Soxhlet extraction. It is worth noting that over longer periods of time, raising the vessel’s temperature can cause more conversion of TAGs into MAGs and DAGs. However, in a MAE, the recovery of biodiesel from the reaction mixture was reported to take about 15
20 minutes, which is faster than that of conventional heating method (6 hours) [24,73]. MAE techniques benefit from several advantages over traditional methods including feasibility of lipid extraction from wet biomass, eliminating of dewatering steps, and shortened extraction time due to the rapid cell warming reducing energy consumption [24]. Therefore, MAE was proposed as a rapid and economical approach for lipid extraction [13,74]. The factors affecting the efficiency of MAE techniques include time, temperature,

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dielectric properties of the sample mixture, and solvent type [69]. The need for special equipment, low selectivity, and unavoidable reaction in high temperature is regarded as MAE drawbacks [75]. Oxidation and hydrolysis of AGs during MAE affect biodiesel production yield and quality [76].

10.2.6.2 Ultrasonic-Assisted Extraction UAE uses ultrasonic waves throughout the extraction procedure, thus improving the efficiency of extraction. Ultrasound involves the generation of mechanical waves which can be spread in an elastic medium at frequencies above the human threshold of hearing while also at submicrowave frequencies (20 kHz
10 MHz) [77,78]. Pulsing ultrasonic waves with high frequencies ranging from 20 to 1000 kHz through a liquid induces consecutive compression/decompression cycles [79]. The rapid variation in pressure creates cavitation through the liquid [80]. Each cavitation bubble creates a high temperature spot (around 4000
6000 K) with high pressures (roughly 100
200 MPa) [79]. The cavitation effect can disrupt cell membranes, aiding in the release of extractable compounds and enhancing mass transfer for lipid extraction [77]. UAE has been used to accelerate both SE [81] and SCFE [82] processes enhancing extraction efficiency. UAE can take place in an ultrasonic bath or through the use of an ultrasound instrument/probe [80]. In SCFE, an ultrasound probe is practical as it also agitates the sample in the extraction vessel to improve the yield [82]. UAE improves lipid extraction from intact cells by providing a greater contact surface between the extracted compounds and solvent that reduces the extraction times and does not require addition of chemicals [17]. Furthermore, UAE provides enough energy to break firmly bound lipids [77]. Nevertheless, extending the duration of ultrasonication may result in free radical formation, thus reducing lipid quality [24]. In some cases, UAE has no effect on the cell wall disruption and shows no improvement in the lipid yield. Widjaja et al. [83] used UAE on C. vulgaris which showed no improvement in efficiency of the lipid extraction and time [83]. Therefore, the effect of ultrasound on extraction yield is species specific and is influenced by the cell wall composition. It was reported that using UAE resulted in an improvement in both extraction efficiency and extraction time when compared with Soxhlet extraction [84,85]. Moreover, Bermu´dez Mene´ndez et al. [86] demonstrated the beneficial aspect of an ultrasound-assisted approach on the efficiency of lipid extraction in the microalga Nannochloropsis gaditana and its operating cost. 10.2.6.3 Accelerated Solvent Extraction In ASE method, organic solvents in subcritical conditions are utilized. ASE is quite similar to the SCWE method [34]. There are several advantages of

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using hot organic solvents compared to the low-temperature extractions. High temperature of solvent increases the lipid solubility [9], and a lower amount of solvent is needed [87]. Temperature increases the diffusion rates of solvent, which reduces the viscosity of solvents and lipid and the surface tension, increases the mass transfer, and lowers extraction time [88,89]. Furthermore, disruption of the strong noncovalent bonds increases lipid extraction efficiency [88]. However, this method suffers from some disadvantages including high energy requirements to supply the subcritical condition. Moreover, the probability of lipid decomposition increases with temperature [88]. A comparison of ASE and SE in algae lipid extraction showed changes in the FA composition, especially those with longer carbon chains, in comparison with conventional SE method [87]. The result reported by Pieber et al. [90] showed a high lipid extraction yield from Nannochloropsis oculata by using pressurized hot ethanol, which was attributed to a decrease in selectivity and extraction of nonesterifiable lipids. The apparatus used in ASE is similar to that used in SCWE and SCFE as it can be operated under dynamic, static, or a combination of both modes. The parameters affecting the extraction yield include temperature, sample size and matrix, solvent type, and flow rate in the case of dynamic mode [34].

10.3 INFLUENCE OF EXTRACTION METHODS ON BIODIESEL PROPERTIES The FA composition of extracted lipid influences the produced biodiesel’s physicochemical properties [4,91]. Carbon chain length influences the physical features of FAME molecules and the number of double bonds [92]. These features influence kinematic viscosity, cold filter plugging point, higher heating value, cetane number, and density, all determining factors for biodiesel quality [91]. The quality criteria are specified by international standards, including for example, ASTM 6751-3 or EN 14214, and widely used to establish the standard quality of biodiesel. These quality specifications are monitored experimentally and can be predicted using empirical equations based on the FAME profile of the biodiesel product [18,93,94]. Recently, in silico approaches have been developed to accelerate this evaluation process [95]. The extraction method may also influence FAME profiles, quantity, quality of extracted lipid fractions, and the cost of the produced biodiesel. The specific algae strains and the cultivation conditions dictate FAME profiles and the quantity of derived biodiesel [4,18,96]. For example, polar lipids are relatively proportional to cell numbers as opposed to storage of TAGs which depend on physiochemical conditions of cultivation. These conditions include but not limited to light levels, carbon availability, and essential nutrient contents especially nitrogen and phosphate [19]. Since high concentrations of phosphorus

TABLE 10.1 Methods and Results Summary of Some Microalgae Lipids Extraction Techniques Microalgae

Extraction Method

Solvents

Pretreatment

Extraction Condition

Lipid (%)

References

Chlorococcum sp.

Soxhlet

n-Hexane

Oven Drying (85 C, 16 h)

4 g Algae, 300 mL solvent, 330 min

3.2

[99]

Pavlova sp.

Soxhlet

n-Hexane

Bead-beating

2 g Algae, 450 mL, 900 min

15.5

[38]

Chlorella vulgaris

Soxhlet

Acetone

Oven drying (60 C, 24 h)

5 g Algae, 110 mL solvent, 480 min

1.8

[84]

Nannochloropsis oculata

Soxhlet

Ethanol

Freeze
drying

10 g Algae, 300 mL solvent, 960 min

40.9

[46]

Nannochloropsis sp.

SE

Chloroform/methanol (1/1 v/v)

Freeze
drying

0.1 g Algae, 8 mL solvent, vortexing

30.2

[104]

N. salina

SE

Chloroform/methanol (5.7/3 v/v)

Freeze
drying

1 g Algae, 15 mL solvent, 25 C, 120 min vortexing

47.7

[105]

N. salina

SE

Chloroform/methanol (1/2 v/v)

Drying

0.1 g Algae, 5 mL solvent, 65 C, 60 min vortexing

35

[58]

Scenedesmus sp.

SE

Chloroform/methanol (2/1 v/v)

Drying

0.1 g Algae, 2 mL solvent, 25 C, 30 min vortexing

20

[16]

Pavlova sp.

UAE

Ethyl acetate/ methanol (2/1 v/v)

Bead-beating

10 g Algae,72 mL, 180 min

44.7

[38]

C. vulgaris

UAE

Chloroform/methanol (1/2 v/v)

Oven drying (60 C, 24 h)

5 g Algae,37.5 mL, 20 min sonication

52.5

[84]

N. oculata

UAE

Petroleum ether

Oven drying (105 C, 48 h)

Frequency 40 kHz, 60 min

3.3

[106]

Nannochloropsis gaditana

UAE

Methanol

Drying

Frequency 40 kHz, 50
60 C, 20 min

38.1

[86]

N. gaditana

MAE

Methanol

Drying

Frequency 2.45 GHz, 60 C, 20 min

39.6

[86]

Chlorella sorokiniana

MAE

Ionic liquid [BMIM] [HSO4]

Vacuum drying oven

1 g Algae, 5 g solvent, 120 C, 60 min

23

[13]

C. sorokiniana

MAE

Chloroform/methanol (1/1 v/v)

Vacuum oven drying

1 g Algae, 10 mL solvent, 120 C, 60 min

9

[13]

N. salina

MAE

Ionic liquid [BMIM] [HSO4]

Vacuum oven drying

1 g Algae, 5 g solvent, 120 C, 60 min

10

[13]

Chlorococcum sp.

SCCDE

CO2

Oven drying (85 C, 16 h)

60 C, 30 MPa, 80 min

5.8

[99]

Pavlova sp.

SCCDE

CO2

Bead-beating

60 C, 30 MPa, 360 min

17.9

[38]



Shizochytrium limacinum

SCCDE

CO2 1 Ethanol




40 C, 35 MPa, 30 min

33.9

[45]

Nannochloropsis sp.

SCCDE

CO2

Freeze
drying

55 C, 55 MPa, 100 min

25.2

[107]

Scenedesmus sp.

SCWE

Water/[HNEt3] [HSO4] (100/1 v/v)

Drying

110 C, 1 MPa, 60 min

35.7

[58]

N. salina

SCWE

Water

Drying

Batch, 220 C, 25 min

30

[16]

MAE, Microwave-assisted extraction; SCWE, subcritical water extraction; SE, solvent extraction; SCCDE, supercritical carbon dioxide extraction; UAE, ultrasoundassisted extraction.

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and sulfur are present in polar (membrane) lipids, lipids insulated from strains with few storage TAGs will have relatively higher concentrations of these elements which reduce the quality of final biodiesel [97,98]. Again, implementation of an approach with high selectivity and solubility for TAGs and low solubility for polar lipid is more appropriate in production of biodiesel with higher quality. Applying high temperature during the extraction methods (e.g., SCWE, MAE, and ASE) may increase the risk of TAGs’ decomposition [50,76,88], which decreases the biodiesel production efficiency. In a similar trend, raising the temperature in SCCDE resulted in the reduction of lipid extraction yield [15,99]. Heating up the wet feedstock can cause hydrolysis of TAGs and producing FFAs [50,76]. As a matter of fact, high FFA content influences the transesterification process for biodiesel production [5,6,100]. The extraction method could also affect the FAME content and its profile. The FA profiles containing a high amount of oleic and palmitic acids fall within the optimal parameters set by international standards [10,92]. The effect of the lipid extraction process on the FAMEs profile has to be evaluated early when developing production methodologies. For instance, a comparison of the Bligh and Dyer method and SCCDE showed a lower total lipid yield and less extractability of longer chain FAs by the SCCDE method [101]. However, when oleic and palmitic acid are present in large quantities in the FAME profile of a lipid extracted by SCCDE, this method resulted in a better quality biodiesel than the former [101]. When the SCWE method was used for lipid extraction from macroalgae feedstock, raising the temperature resulted increased the total lipid extraction yield; however, the amount of the FAME fraction was reduced significantly [102]. According to a reported study, despite the lower total lipid yield for SCCDE in comparison with SE, SCCDE extracted a greater fraction of FAMEs [103]. A summary of some commonly used microalgae lipid extraction techniques utilized for biodiesel production are presented in Table 10.1.

10.4 CONCLUSION Lipid extraction is a crucial step which will affect the biodiesel quality, quantity, and cost efficiency of the operation in algae-based biodiesel. An ideal extraction method needs to be solvent free, low cost, selective, and efficient. Currently, the search to find novel lipid extraction techniques, which are more environment friendly, less energy and labor intensive, timely, costeffective, scalable, and sustainable, is still in progress. The methods such as SE and SCCDE have exhibited excellent results in terms of extraction yields. But the challenges in large-scale application should be addressed. The SCCDE method may be considered as a scalable and environment-friendly method where CO2 is used as the solvent. Since the CO2 can be recycled during extraction, SCCDE method may be considered as a technique that inflicts less damage on the environment. Furthermore, the residual biomass

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is solvent free and is suitable for agricultural use (e.g., fertilizer, feed). The production of coproducts may be essential, at least for the short term to allow for the economic viability of algae biodiesel. The ECE approach has low efficiency, but due to the low cost of required facilities and the resulting solvent free residual biomass, lipid extraction by this method is gaining more attention. SCWE and ECE methods omit the harvesting and drying of microalgae biomass and may be tied directly to the microalgae culture facilities. Many studies have evaluated extraction methods as a downstream process for biodiesel production at the laboratory scales, but industrial-scale production of algae biodiesel continues to be hindered by inadequate scale up of extraction procedures.

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386.

Chapter 11

Synthesis of Catalyst Support From Waste Biomass for Impregnation of Catalysts in Biofuel Production Sumit H. Dhawane and Gopinath Halder Department of Chemical Engineering, National Institute of Technology, Durgapur, India

11.1 INTRODUCTION In the present era of oil-driven economies, developing countries are facing difficulties in their sustainable development due to the maximum importation of crude oil. The exhaustive consumption of fossil fuels due to ever increasing energy demands has led to the depletion of conventional sources of energy. The excessive increment in energy demand and lack of energy supply have drawn a major gap in between. To mitigate this gap and to avoid the problems arising due to combustion of fossil fuels such as global warming, air pollution, and health issues, researchers across the globe are working on the development of a sustainable source of energy that can overcome all the demerits of fossil fuels. Among fuel commodities, automobile fuels such as petrol and diesel are in great demand. Moreover, diesel additionally contributes to higher costs and is primarily responsible for inflation as it is used heavily in the transportation of goods. The excessive consumption of petrodiesel, growing concern over global warming, and emissions of toxic gases have drawn significant attention on finding a nonconventional alternative to nonrenewable sources of energy. Biodiesel, or alkyl esters of fatty acids, has emerged as a potential substitute to conventional petro-diesel due to its properties such as renewability, biodegradability, and sustainability [1] to name a few. It is noteworthy to mention that CO2 emitted from the combustion of biodiesel is consumed by the plants that are grown for the production of feedstock during photosynthesis. Thus, the net effect of the CO2 on the

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Advances in Feedstock Conversion Technologies for Alternative Fuels and Bioproducts

environment is nullified by the utilization of the biodiesel as a fuel produced from natural resources such as plants. The qualitative production of biodiesel at optimized conditions can lead to the applicability of B100 in existing diesel engines without any modifications. Hence, in order to improve the quality of biodiesel so that it can be fully utilized as an alternative, an intensified process needs to be established. At present, several new techniques have been introduced for the effective production of biodiesel such as microwave irradiation, ultrasonication, superheated vapor injection, etc. [2,3]. However, biodiesel in general is produced conventionally and commercially by the transesterification process. In this reaction, triglyceride molecules react with alcohol molecules in the presence of a catalyst to produce three moles of ester molecules along with the notable by-product glycerol. Stoichiometrically, 3 mol of alcohol is required for the transesterification reaction to take place, but an excess amount of alcohol is always preferred to drive the equilibrium on the product side so as to enhance productivity per unit time. There are many sources from which triglyceride can be obtained as a feedstock including vegetable oils, animal fats, microalgae lipids, and recycled oil such as waste cooking oil [4]. The use of edible oil as a feedstock is causing a great threat to food security of developing countries such as India because vegetable oil is the second highest commodity imported after raw petroleum crude. In addition, production of edible oils requires an abundant amount of fertilizers and conventional energy sources, which would lead to the addition of CO2 in the environment, ultimately increasing the cost of the process [5,6]. Thus, all attention has been focused on the utilization of nonedible oils as a feedstock for the production of biodiesel. In general, biodiesel can be produced commercially by base transesterification process, but the limitation of the process is due to high soap and emulsion formation when higher free fatty acid (FFA) contents are present in the feedstock. The general process for the use of different feedstocks in the transesterification process is well explained by the schematic presentation shown in Fig. 11.1. The oils with higher FFA content undergo esterification process and catalyzed by acid catalysts such as concentrated H2SO4, phosphoric acid, HCl, etc. [8]. However, homogeneous alkaline metal hydroxides are the widely accepted commercial catalysts used in the production of biodiesel due to their higher rate of reaction as compared to acid catalysts [9], whereas the limitation of homogeneous catalysts in the transesterification reactions has restricted its commercialization with higher productivity. In general, homogeneous alkaline catalysts are faster compared to that of acid catalysis and require a lower amount of alcohol in the reaction [10]. However, it possesses several detriments. The presence of FFA in the feedstock can hamper the productivity of esters due to higher soap formation. Any moisture present in the alcohol can lead to formation of soap and emulsions. Also, the separation of the product and its purification is complicated and become most costly due to the presence of emulsions and

Synthesis of Catalyst Support Chapter | 11

Carbon catalyst derived from waste biomass

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Vegetable oil or animal fats If FFA >2

If FFA