Process Systems Engineering for Biofuels Development 9781119580317, 9781119580270

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Process Systems Engineering for Biofuels Development

Wiley Series in Renewable Resources Series Editor: Christian V. Stevens, Faculty of Bioscience Engineering, Ghent University, Belgium

Titles in the Series: Wood Modification: Chemical, Thermal and Other Processes Callum A. S. Hill Renewables-Based Technology: Sustainability Assessment Jo Dewulf, Herman Van Langenhove Biofuels Wim Soetaert, Erik Vandamme Handbook of Natural Colorants Thomas Bechtold, Rita Mussak Surfactants from Renewable Resources Mikael Kjellin, Ingegärd Johansson Industrial Applications of Natural Fibres: Structure, Properties and Technical Applications Jörg Müssig Thermochemical Processing of Biomass: Conversion into Fuels, Chemicals and Power Robert C. Brown Biorefinery Co-Products: Phytochemicals, Primary Metabolites and Value-Added Biomass Processing Chantal Bergeron, Danielle Julie Carrier, Shri Ramaswamy Aqueous Pretreatment of Plant Biomass for Biological and Chemical Conversion to Fuels and Chemicals Charles E. Wyman Bio-Based Plastics: Materials and Applications Stephan Kabasci Introduction to Wood and Natural Fiber Composites Douglas D. Stokke, Qinglin Wu, Guangping Han Cellulosic Energy Cropping Systems Douglas L. Karlen Introduction to Chemicals from Biomass, 2nd Edition James H. Clark, Fabien Deswarte Lignin and Lignans as Renewable Raw Materials: Chemistry, Technology and Applications Francisco G. Calvo-Flores, Jose A. Dobado, Joaquín Isac-García, Francisco J. Martin-Martínez Sustainability Assessment of Renewables-Based Products: Methods and Case Studies Jo Dewulf, Steven De Meester, Rodrigo A. F. Alvarenga

Cellulose Nanocrystals: Properties, Production and Applications Wadood Hamad Fuels, Chemicals and Materials from the Oceans and Aquatic Sources Francesca M. Kerton, Ning Yan Bio-Based Solvents François Jérôme and Rafael Luque Nanoporous Catalysts for Biomass Conversion Feng-Shou Xiao and Liang Wang Thermochemical Processing of Biomass: Conversion into Fuels, Chemicals and Power, 2nd Edition Robert C. Brown The Chemical Biology of Plant Biostimulants Danny Geelen, Lin Xu Chitin and Chitosan: Properties and Applications Lambertus A.M. van den Broek and Carmen G. Boeriu Biorefinery of Inorganics: Recovering Mineral Nutrients from Biomass and Organic Waste Erik Meers, Evi Michels, Rene Rietra, Gerard Velthof Process Systems Engineering for Biofuels Development Adrián Bonilla-Petriciolet, Gade Pandu Rangaiah Forthcoming Titles: Waste Valorization: Waste Streams in a Circular Economy Carol Sze Ki Lin, Chong Li, Guneet Kaur, Xiaofeng Yang Biobased Packaging: Material, Environmental and Economic Aspects Mohd Sapuan Salit, Rushdan Ahmad Ilyas High-Performance Materials from Bio-based Feedstocks Andrew J. Hunt, Nontipa Supanchaiyamat, Kaewta Jetsrisuparb, Jesper T.N. Knijnenburg

Process Systems Engineering for Biofuels Development Edited by

ADRIÁN BONILLA-PETRICIOLET Instituto Tecnológico de Aguascalientes, México

GADE PANDU RANGAIAH National University of Singapore, Singapore and Vellore Institute of Technology, India

This edition first published 2020 © 2020 John Wiley & Sons Ltd. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah to be identified as the authors of the editorial material in this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Editorial Office The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Names: Bonilla-Petriciolet, Adrián, editor. | Rangaiah, Gade Pandu, editor. Title: Process systems engineering for biofuels development / edited by Adrián Bonilla-Petriciolet, Dept. Chemical Engineering, Instituto Tecnológico de Aguascalientes, Aguascalientes, México, Gade Pandu Rangaiah, National University of Singapore, Singapore. Description: First edition. | Hoboken, NJ : John Wiley & Sons, Inc., [2020] | Series: Wiley series in renewable resources | Includes bibliographical references and index. Identifiers: LCCN 2020016306 (print) | LCCN 2020016307 (ebook) | ISBN 9781119580270 (cloth) | ISBN 9781119580317 (adobe pdf) | ISBN 9781119580331 (epub) Subjects: LCSH: Biomass energy. | Chemical processes. | Systems engineering. Classification: LCC TP339 .P753 2020 (print) | LCC TP339 (ebook) | DDC 662/.88–dc23 LC record available at https://lccn.loc.gov/2020016306 LC ebook record available at https://lccn.loc.gov/2020016307 Cover Design: Wiley Cover Image: © Jim Barber/Shutterstock Set in 10/12pt, TimesLTStd by SPi Global, Chennai, India. Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY 10 9 8 7 6 5 4 3 2 1

Contents List of Contributors Series Preface Preface 1 Introduction Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah 1.1 Importance of Biofuels and Overview of their Production 1.2 Significance of Process Systems Engineering for Biofuels Production 1.2.1 Modeling of Physicochemical Properties of Thermodynamic Systems Related to Biofuels 1.2.2 Intensification of the Biomass Transformation Routes for the Production of Biofuels 1.2.3 Computer-Aided Methodologies for Process Modeling, Design, Optimization, and Control Including Supply Chain and Life Cycle Analyses 1.3 Overview of this Book References 2 Waste Biomass Suitable as Feedstock for Biofuels Production Maria Papadaki 2.1 Introduction 2.1.1 The Need for Biofuels 2.1.2 Problem Definition 2.1.3 The Biomass Pool 2.2 Kinds of Feedstock 2.2.1 Spent Coffee Grounds 2.2.2 Lignocellulose Biomass 2.2.3 Palm, Olive, Coconut, Avocado, and Argan Oil Production Residues 2.2.4 Citrus 2.2.5 Grape Marc

xiii xv xvii 1 1 3 4 5

7 9 11 15 15 15 17 18 20 21 22 25 33 36

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2.3

3

4

2.2.6 Waste Oil and Cooking Oil 2.2.7 Additional Sources Conclusions Acknowledgment References

Multiscale Analysis for the Exploitation of Bioresources: From Reactor Design to Supply Chain Analysis Antonio Sánchez, Borja Hernández, and Mariano Martín 3.1 Introduction 3.2 Unit Level 3.2.1 Short Cut Methods 3.2.2 Mechanistic Models 3.2.3 Rules of Thumb 3.2.4 Dimensionless Analysis 3.2.5 Surrogate Models 3.2.6 Experimental Correlations 3.3 Process Synthesis 3.3.1 Heuristic Based 3.3.2 Supestructure Optimization 3.3.3 Environmental Impact Metrics 3.3.4 Safety Considerations 3.4 The Product Design Problem 3.4.1 Product Design: Engineering Biomass 3.4.2 Blending Problems 3.5 Supply Chain Level 3.5.1 Introduction 3.5.2 Modeling Issues 3.6 Multiscale Links and Considerations Acknowledgment Nomenclature References Challenges in the Modeling of Thermodynamic Properties and Phase Equilibrium Calculations for Biofuels Process Design Roumiana P. Stateva and Georgi St. Cholakov 4.1 Introduction 4.2 Thermodynamic Modeling Framework: Elements, Structure, and Organization 4.3 Thermodynamics of Biofuel Systems 4.3.1 Phase Equilibria 4.3.2 Thermodynamic Models 4.4 Sources of Data for Biofuels Process Design 4.5 Methods for Predicting Data for Biofuels Process Design 4.5.1 Group Contribution Methods for Biofuels Process Design 4.5.2 Quantitative Structure–Property Relationships for Biofuels Process Design

37 38 40 40 40

49 49 50 50 51 56 56 56 59 60 60 61 65 66 66 66 68 68 68 70 71 74 74 75

85 85 86 88 88 90 98 102 103 105

Contents

4.6 4.7 4.8

Challenges for the Biofuels Process Design Methods Influence of Uncertainties in Thermophysical Properties of Pure Compounds on the Phase Behavior of Biofuel Systems Conclusions Acknowledgment Exercises References

5 Up-grading of Waste Oil: A Key Step in the Future of Biofuel Production Luigi di Bitonto and Carlo Pastore 5.1 Introduction 5.2 Physicochemical Pretreatments of Waste Oils: Removal of Contaminants 5.3 Direct Treatment and Conversion of FFAs into Methyl Esters 5.3.1 Homogeneous Catalysis: Brønsted and Lewis Acids 5.3.2 Heterogeneous Catalysis 5.3.3 Enzymatic Biodiesel Production 5.3.4 ILs Biodiesel Production 5.3.5 Use of Metal Hydrated Salts 5.4 Future Trends of the Pretreatments of Waste Oils 5.5 Conclusions Acknowledgment Abbreviations References 6 Production of Biojet Fuel from Waste Raw Materials: A Review Ana Laura Moreno-Gómez, Claudia Gutiérrez-Antonio, Fernando Israel Gómez-Castro, and Salvador Hernández 6.1 Introduction 6.2 Waste Triglyceride Feedstock 6.3 Waste Lignocellulosic Feedstock 6.4 Waste Sugar and Starchy Feedstock 6.5 Main Challenges and Future Trends 6.6 Conclusions Acknowledgments References 7 Computer-Aided Design for Genetic Modulation to Improve Biofuel Production Feng-Sheng Wang and Wu-Hsiung Wu 7.1 Introduction 7.2 Method 7.2.1 Flux Balance Analysis 7.2.2 Flux Variability Analysis 7.2.3 Minimization of Metabolic Adjustment

ix

109 112 114 114 114 115

121 121 124 125 125 127 128 130 133 139 140 141 141 142 149

149 150 159 164 165 167 167 167

173 173 175 175 176 176

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7.3 7.4

7.5

8

9

7.2.4 Regulatory On-Off Minimization 7.2.5 Optimal Strain Design Problem Computer-Aided Strain Design Tool Examples 7.4.1 E. coli Core Model 7.4.2 Genome-Scale Metabolic Model of E. coli iAF1260 Conclusions Appendix 7.A: The SBP Program References

Implementation of Biodiesel Production Process Using Enzyme-Catalyzed Routes Thalles Allan Andrade, Massimiliano Errico, and Knud Villy Christensen 8.1 Introduction 8.2 Biodiesel Production Routes: Chemical versus Enzymatic Catalysts 8.2.1 Chemical Catalysts 8.2.2 Enzymatic Catalysts 8.3 Optimal Reaction Conditions and Kinetic Modeling 8.3.1 Evaluation of the Reaction Conditions 8.3.2 Kinetic Modeling 8.4 Process Simulation and Economic Evaluation 8.5 Reuse of Enzyme for the Transesterification Reaction 8.5.1 Recovery of Eversa Transform by Means of Centrifugation 8.5.2 Recovery of Eversa Transform by Means of Ceramic Membranes 8.6 Environmental Impact and Final Remarks Acknowledgments Nomenclature References Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design Bruna Ricetti Margarida, Wanderson Rogerio Giacomin-Junior, Luiz Fernando de Lima Luz Junior, Fernando Augusto Pedersen Voll, and Marcos Lucio Corazza 9.1 Introduction 9.1.1 Homogeneous-Based Reactions 9.1.2 Heterogeneous-Based Reactions 9.1.3 Enzyme-Catalyzed Reactions 9.1.4 Supercritical Route Reactions 9.1.5 Methanol or Ethanol for Biodiesel Synthesis 9.2 Getting Started with Aspen Plus V10 9.2.1 Pure Compounds 9.2.2 Mixture Parameters 9.3 Kinetic Study 9.3.1 Esterification Reaction

177 177 179 181 181 183 185 187 187

191 191 194 195 196 198 199 201 205 210 210 211 215 217 217 217

221

221 222 223 224 224 224 224 225 229 232 232

Contents

9.4

9.5 9.6

10

11

9.3.2 Experimental Reaction Data Regression 9.3.3 Transesterification Reaction 9.3.4 Supercritical Route Process Design 9.4.1 Esterification Reaction 9.4.2 Methanol Recycling 9.4.3 Transesterification Reaction 9.4.4 Biodiesel Purification 9.4.5 Additional Resources Energy and Economic Analysis Concluding Remarks Acknowledgment Exercises References

Process Development, Design and Analysis of Microalgal Biodiesel Production Aided by Microwave and Ultrasonication Dipesh S. Patle, Savyasachi Shrikhande, and Gade Pandu Rangaiah 10.1 Introduction 10.2 Process Development and Modeling 10.3 Sizing and Cost Analysis 10.4 Comparison with the WCO-Based Process of the Same Capacity 10.4.1 Biodiesel Process Using WCO as Raw Material 10.4.2 Comparative Analysis 10.5 Comparison with the Microalgae-Based Processes 10.6 Conclusions Acknowledgment Appendix 10.A Exercises References Thermochemical Processes for the Transformation of Biomass into Biofuels Carlos J. Durán-Valle 11.1 Introduction 11.2 Biomass and Biofuels 11.3 Combustion 11.4 Gasification 11.4.1 Fixed Bed Gasification 11.4.2 Fluidized Bed Gasification 11.4.3 Dual Fluidized Bed Gasification 11.4.4 Hydrothermal Gasification 11.4.5 Supercritical Water Gasification 11.4.6 Plasma Gasification 11.4.7 Catalyzed Gasification 11.4.8 Fischer–Tropsch Synthesis

xi

234 236 238 239 239 243 244 245 248 252 254 255 255 256

259 259 262 272 277 277 277 280 280 281 281 282 282

285 285 288 289 290 291 292 292 293 294 294 295 295

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11.5 11.6

11.7 11.8

12

13

Liquefaction Pyrolysis 11.6.1 Slow Pyrolysis 11.6.2 Fast Pyrolysis 11.6.3 Flash Pyrolysis 11.6.4 Catalytic Biomass Pyrolysis 11.6.5 Microwave Heating 11.6.6 Product Separation Carbonization Conclusions Acknowledgments References

Intensified Purification Alternative for Methyl Ethyl Ketone Production: Economic, Environmental, Safety and Control Issues Eduardo Sánchez-Ramírez, Juan José Quiroz-Ramírez, and Juan Gabriel Segovia-Hernández 12.1 Introduction 12.2 Problem Statement and Case Study 12.3 Evaluation Indexes and Optimization Problem 12.3.1 Total Annual Cost Calculation 12.3.2 Environmental Index Calculation 12.3.3 Individual Risk Index 12.3.4 Controllability Index Calculation 12.3.5 Multi-Objective Optimization Problem 12.4 Global Optimization Methodology 12.5 Results 12.6 Conclusions Acknowledgments Notation References Present and Future of Biofuels Juan Gabriel Segovia-Hernández, César Ramírez-Márquez, and Eduardo Sánchez-Ramírez 13.1 Introduction 13.2 Some Representative Biofuels 13.2.1 Bioethanol 13.2.2 Biodiesel 13.2.3 Biobutanol 13.2.4 Biojet Fuel 13.2.5 Biogas 13.3 Perspectives and Future of Biofuels References

Index

296 296 297 297 297 303 304 304 305 308 309 309

311

311 316 317 319 319 320 322 323 324 325 335 335 335 336 341

341 344 344 347 348 349 351 352 354 357

List of Contributors Thalles Allan Andrade Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Odense M, Denmark Luigi di Bitonto Istituto di Ricerca Sulle Acque (IRSA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy Adrián Bonilla-Petriciolet Mexico

Instituto Tecnológico de Aguascalientes, Aguascalientes,

Georgi St. Cholakov Department of Organic Synthesis and Fuels, University of Chemical Technology and Metallurgy, Sofia, Bulgaria Knud Villy Christensen Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Odense M, Denmark Marcos Lucio Corazza Department of Chemical Engineering, Federal University of Paraná, Polytechnic Center (DEQ/UFPR), Curitiba, Brazil Carlos J. Durán-Valle Departamento de Química Orgánica e Inorgánica, Universidad de Extremadura, Badajoz, Spain Massimiliano Errico Department of Chemical Engineering, Biotechnology and Environmental Technology, University of Southern Denmark, Odense M, Denmark Wanderson Rogerio Giacomin-Junior Department of Chemical Engineering, Federal University of Paraná, Polytechnic Center (DEQ/UFPR), Curitiba, Brazil Fernando Israel Gómez-Castro Departamento de Ingeniería Química, Universidad de Guanajuato, Guanajuato, Guanajuato, México Claudia Gutiérrez-Antonio Facultad de Química, Universidad Autónoma de Querétaro, Querétaro, Querétaro, México Borja Hernández Salamanca, Spain

Department of Chemical Engineering, University of Salamanca,

Salvador Hernández Departamento de Ingeniería Química, Universidad de Guanajuato, Guanajuato, Guanajuato, México

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List of Contributors

Luiz Fernando de Lima Luz Junior Department of Chemical Engineering, Federal University of Paraná, Polytechnic Center (DEQ/UFPR), Curitiba, Brazil Mariano Martín Salamanca, Spain

Department of Chemical Engineering, University of Salamanca,

Ana Laura Moreno-Gómez Facultad de Química, Universidad Autónoma de Querétaro, Querétaro, Querétaro, México Maria Papadaki Agrinio, Greece

Department of Environmental Engineering, University of Patras,

Carlo Pastore Istituto di Ricerca Sulle Acque (IRSA), Consiglio Nazionale delle Ricerche (CNR), Bari, Italy Dipesh S. Patle Chemical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, India Fernando Augusto Pedersen Voll Department of Chemical Engineering, Federal University of Paraná, Polytechnic Center (DEQ/UFPR), Curitiba, Brazil Juan José Quiroz-Ramírez CONACyT – CIATEC A.C. Centro de Innovación Aplicada en Tecnologías Competitivas, León, México Bruna Ricetti Margarida Department of Chemical Engineering, Federal University of Paraná, Polytechnic Center (DEQ/UFPR), Curitiba, Brazil César Ramírez-Márquez Departamento de Ingeniería Química, Universidad de Guanajuato, Guanajuato, México Gade Pandu Rangaiah Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore and School of Chemical Engineering, Vellore Institute of Technology, Vellore, India Antonio Sánchez Salamanca, Spain

Department of Chemical Engineering, University of Salamanca,

Eduardo Sánchez-Ramírez Departamento de Ingeniería Química, Universidad de Guanajuato, Guanajuato, México Juan Gabriel Segovia-Hernández de Guanajuato, Guanajuato, México

Departamento de Ingeniería Química, Universidad

Savyasachi Shrikhande

School of Chemical Engineering, VIT, Vellore, India

Roumiana P. Stateva Sciences, Sofia, Bulgaria

Institute of Chemical Engineering, Bulgarian Academy of

Feng-Sheng Wang Department of Chemical Engineering, National Chung Cheng University, Chiya, Taiwan Wu-Hsiung Wu Department of Chemical Engineering, National Chung Cheng University, Chiya, Taiwan

Series Preface Renewable resources, their use and modification are involved in a multitude of important processes with a major influence on our everyday lives. Applications can be found in the energy sector; paints and coatings; and the chemical, pharmaceutical, and textile industry, to name but a few. The area interconnects several scientific disciplines (agriculture, biochemistry, chemistry, technology, environmental sciences, forestry), which makes it very difficult to have an expert view on the complicated interaction. Therefore, the idea to create a series of scientific books, focusing on specific topics concerning renewable resources, has been very opportune and can help to clarify some of the underlying connections in this area. In a very fast-changing world, trends are not only characteristic of fashion and political standpoints; science too is not free from hypes and buzzwords. The use of renewable resources is again more important nowadays; however, it is not part of a hype or a fashion. As the lively discussions among scientists continue about how many years we will still be able to use fossil fuels – opinions ranging from 50 to 500 years – they do agree that the reserve is limited, and that it is essential not only to search for new energy carriers but also for new material sources. In this respect, the field of renewable resources is a crucial area in the search for alternatives for fossil-based raw materials and energy. In the field of energy supply, biomass- and renewables-based resources will be part of the solution alongside other alternatives such as solar energy, wind energy, hydraulic power, hydrogen technology and nuclear energy. In the field of material sciences, the impact of renewable resources will probably be even bigger. Integral utilization of crops and the use of waste streams in certain industries will grow in importance, leading to a more sustainable way of producing materials. Although our society was much more (almost exclusively) based on renewable resources centuries ago, this disappeared in the Western world in the nineteenth century. Now it is time to focus again on this field of research. However, it should not mean a “retour à la nature,” but should be a multidisciplinary effort on a highly technological level to perform research toward new opportunities, to develop new crops and products from renewable resources. This will be essential to guarantee an acceptable level of comfort for the growing number of people living on our planet. It is “the” challenge for the coming generations of scientists to develop more sustainable ways to create prosperity and to fight poverty and hunger in the world. A global approach is certainly favored.

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This challenge can only be dealt with if scientists are attracted to this area and are recognized for their efforts in this interdisciplinary field. It is, therefore, also essential that consumers recognize the fate of renewable resources in a number of products. Furthermore, scientists do need to communicate and discuss the relevance of their work. The use and modification of renewable resources may not follow the path of the genetic engineering concept in view of consumer acceptance in Europe. Related to this aspect, the series will certainly help to increase the visibility of the importance of renewable resources. Being convinced of the value of the renewables approach for the industrial world, as well as for developing countries, I was myself delighted to collaborate on this series of books focusing on the different aspects of renewable resources. I hope that readers become aware of the complexity, the interaction and interconnections, and the challenges of this field, and that they will help to communicate on the importance of renewable resources. I certainly want to thank the people of Wiley’s Chichester office, especially David Hughes, Jenny Cossham and Lyn Roberts, in seeing the need for such a series of books on renewable resources, for initiating and supporting it, and for helping to carry the project to the end. Last, but not least, I want to thank my family, especially my wife Hilde and children Paulien and Pieter-Jan, for their patience, and for giving me the time to work on the series when other activities seemed to be more inviting. Christian V. Stevens, Faculty of Bioscience Engineering Ghent University, Belgium Series Editor, “Renewable Resources” June 2005

Preface Biofuels (e.g. biodiesel, bioalcohols, and biojet fuel) are alternative energy solutions to the environmental and safety problems related to the use of petroleum-based fuels. This renewable energy can be generated from a wide variety of low-cost feedstocks and transformation routes that also imply a spectrum of process units based on different technologies. During the past two decades, significant developments and improvements have been achieved to increase the commercial production of biofuels worldwide. However, the creation and operation of sustainable biofuel production chains have imposed new challenges to the field of Process Systems Engineering (PSE). The analysis, modeling, design, optimization, intensification, and control of individual units (e.g. reactors and separators) and the entire facilities to produce biofuels have generated drivers for PSE research and development, which should be addressed via theoretical, computational, and experimental studies. The PSE of biofuel production schemes demands advances and novel contributions to handle the challenges associated with the diversity of physicochemical properties of available feedstocks, biofuel processing routes, operating conditions, and characteristics of technologies applied in pretreatment units, reactors, separators, and other process equipment. The opportunities of PSE in the production of renewable biofuels include (i) development of a reliable thermodynamic framework for estimating the properties of pure components and mixtures that are required in the design, control, and intensification of biomass transformation routes; (ii) intensification and optimization of the processing routes to handle a wide variety of feedstocks for obtaining biofuels and other high-value-added by-products; (iii) implementation of realistic and proper models for PSE analysis; (iv) application of reliable global and multiobjective optimization techniques for solving design problems and improving the performance of biofuel production schemes; and (v) utilization of computer-aided methodologies for process controllability, mass and energy integration, and other tasks associated with PSE. Therefore, theoretical, computational, and experimental studies in these and other topics are required to develop a sustainable biofuel production chain. The present book is the first one specifically devoted to PSE for the production of biofuels. It covers a wide range of topics associated with the process engineering of biofuel production including the thermodynamic modeling, process design and control, reaction engineering, separation, and purification of biofuels obtained from different biomass feedstocks and transformation routes. In all, this book contains 13 chapters devoted to PSE

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for biofuel production. It provides an overview of the subject and covers the portfolio of available biomass feedstocks for biofuel production, multiscale analysis of bioresources, challenges in modeling thermodynamic properties and phase equilibrium calculations, the production and separation of biofuels, computer-aided design, enzyme-catalyzed biodiesel production, process analysis of biodiesel production (including kinetic modeling and simulation), and the use of ultrasonification in biodiesel production, as well as thermochemical processes for biomass transformation and production of alternative biofuels. It is a collection of contributions from leading researchers in PSE and biofuels. Every chapter in this book has been reviewed anonymously by at least two experts and then thoroughly revised by the respective contributors. This review process has been attempted to provide high-quality and educational value for all chapters. This book will provide researchers and postgraduate students with an overview of the recent developments and applications of some state-of-the-art technologies and PSE for biofuel production. We consider that this book is a useful resource for researchers in renewable energies and practitioners working on the production of biofuels. We are grateful to all the contributors and reviewers of the chapters for their cooperation to meet the requirements and schedule to finalize this book. We would like to thank the book publishing team of John Wiley & Sons, Ltd, for their support and assistance during the preparation of this book. Adrián Bonilla-Petriciolet Instituto Tecnológico de Aguascalientes, México Gade Pandu Rangaiah National University of Singapore, Singapore June 2020

1 Introduction Adrián Bonilla-Petriciolet1 and Gade Pandu Rangaiah2,3 1

Instituto Tecnológico de Aguascalientes, Aguascalientes 20256, Mexico of Chemical and Biomolecular Engineering, National University of Singapore,117585, Singapore 3 School of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, India

2 Department

1.1

Importance of Biofuels and Overview of their Production

The relevance and importance of biofuels are recognized worldwide, mainly due to the problems caused by fossil fuel depletion and environmental pollution (e.g. climate change) arising from the generation and consumption of traditional energy sources (Li et al. 2019; Raud et al. 2019; Quiroz-Perez et al. 2019). Biofuels belong to the category of sustainable energy that can be obtained from biological (e.g. anaerobic digestion and fermentation), physicochemical (e.g. transesterification), and thermochemical (e.g. liquefaction, gasification, and pyrolysis) processing routes, which can involve the application of conventional and intensified technologies (Gutierrez-Antonio et al. 2017; Li et al. 2019; Quiroz-Perez et al. 2019). Several researchers have concluded that biomasses can be regarded as a primary source for obtaining green and renewable energy because they are distributed and generated worldwide (Li et al. 2019; Quiroz-Perez et al. 2019; Wei et al. 2019). In fact, it has been estimated that the biomass-based fuel sources can account for 70% of all renewable energy production (Raud et al. 2019). Diverse processes have been studied and implemented to perform the transformation of biomass-based feedstocks to solid, liquid and/or gaseous products that contain energy-enriched chemicals (Quiroz-Perez et al. 2019). Lignocellulosic materials, food crops, urban wastes, animal fats, vegetable oils, starch-rich compounds and non-edible Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

2

Process Systems Engineering for Biofuels Development

Table 1.1 Classification of biofuels based on the biomass feedstock and its transformation route. Type of biofuels according to their processing routes Primary

Secondary

1st generation 2nd generation 3rd generation 4th generation Bioethanol, Biofuels produced Biodiesel or Firewood, wood, Bioethanol or butanol from biobutanol or using genetically bioethanol from pellets, chips, fermentation of synthesized biofuels microalgae, modified forest and starch or sugars made from microalgae or agricultural seaweed or contained in food non-food microorganisms. residues, gas. microorganisms. crops. lignocellulosic biomass. Source: Raud et al. 2019. Reproduced with permission of Elsevier.

biomasses like algae and microorganisms (with and without genetic modifications) can be utilized as feedstocks to produce renewable fuels (Sawangkeaw and Ngamprasertsith 2013; Loman and Ju 2016; Stephen and Periyasamy 2018). Biofuels include end products known as biodiesel (a mixture of long-chain alkyl esters), biojet fuel (a mixture of C8–C16 alkanes, iso-alkanes, naphthenic derivatives, and aromatic compounds), biogasoline (C6–C12 hydrocarbons), and bioalcohols (e.g. bioethanol and biobutanol) (Hassan et al. 2015; Gutierrez-Antonio et al. 2017; Wei et al. 2019). Table 1.1 shows a common and simple classification of biofuels based on the biomass used as the starting material and its processing route (Raud et al. 2019). The production of biofuels comprises several process units that should be analyzed, modeled, designed, optimized, intensified, and controlled. In general, conventional processes employed in biofuels production rely on unit operations that are performed independently without mass and/or energy integration, whose process conditions are not optimized and the tradeoff between process efficiency and cost may not be the best (Quiroz-Perez et al. 2019). On the other hand, intensified process operations outperform their conventional counterparts in terms of energy consumption, profitability, and effectiveness. Process intensification generally reduces the equipment number, sizes and/or energy consumption, to increase the productivity and to enhance other performance metrics via the synergy obtained from multifunctional phenomena at different spatial and time scales (Stankiewicz and Moulijn 2000; Tian et al. 2018). It allows the integration of two or more operations in multitasking units, the development of alternative configurations and design of process equipment, besides the application of optimization tools and reliable process synthesis methodologies to improve the pathways for obtaining biofuels (Nasir et al. 2013; Quiroz-Perez et al. 2019). Sustainable development of biofuels supply chains from the variety of available feedstocks and process routes imply new challenges for Chemical Engineering. In particular, there are key process design aspects of biofuels production to be improved and intensified (Nasir et al. 2013; Oh et al. 2018; Raud et al. 2019). They include the collection (harvesting/production or recovery) of the biomass, feedstock pretreatment, biomass transformation routes, end-products separation and purification, and the corresponding logistic tasks that are linked to the elements of the supply chain. All these factors impact the economic feasibility of the specific pathway to produce the biofuel. For instance, some authors have highlighted that the production of 4th generation renewable fuels could imply expensive

Introduction

3

and energy intensive operations thus limiting its current commercialization (Darda et al. 2019). The application of sustainable technologies in each process stage is paramount to reach the goal of a green and feasible large-scale production of bioenergy. In terms of process modeling, there is also the necessity of improving the thermodynamic framework and conceptual design approaches employed in the biofuels process engineering. It is clear that biofuels production creates new applications for process system engineering (PSE) in terms of biomass valorization, green chemistry, thermodynamics, catalysts, reaction engineering, separation units, process modeling, optimization, design, and control. Although several developments have been achieved in this direction, there are still technical limitations and barriers to be overcome with the objective of minimizing costs and energy requirements of commercial biofuels production facilities utilizing affordable feedstocks and consequent protection of the environment via energy efficiency and waste reduction. This book aims to contribute to the development of sustainable production of renewable biofuels. Specifically, it covers different topics associated with PSE of biofuels production. The remainder of this chapter is organized as follows: Section 1.2 provides an overview of relevant issues of PSE associated with biofuels production. Examples of gaps and current challenges in the production of biofuels are briefly discussed. Finally, Section 1.3 outlines the scope of all the chapters in this book.

1.2

Significance of Process Systems Engineering for Biofuels Production

PSE is devoted to analyzing the elements associated with the creation and operation of chemical supply chains (Grossmann and Westerberg 2000). This implies the development of systematic procedures that can be applied in the discovery, design, manufacture and distribution of chemical products starting from the microsystem level until reaching the industrial scale applications (Grossmann and Westerberg 2000); see Figure 1.1. Undoubtedly, these

Time scale Month

Enterprise

Week

Site

Day

Plants

Hour

Process units

Minute

Single and multiphase systems

Second

Chemical scale Small Intermediate Large

Particle, thin film

ms

Molecule cluster

ns

Molecules

ps 1 pm

1 nm

1 μm

1 mm

1m

1 km Length scale

Figure 1.1 Conceptual description of a chemical supply chain considering the time, length and chemical scales. Source: Grossmann and Westerberg 2000. Reproduced with permission of John Wiley & Sons.

4

Process Systems Engineering for Biofuels Development

PSE elements can be extrapolated to the development of biofuels supply chains, and they include theoretical, computational and experimental studies. As stated by Grossmann and Westerberg (2000), research and development in PSE comprise the process and product design, process modeling, integration, control and operation, supporting design methods and numerical tools. The feasible and environmentally friendly production of biofuels also need advances in these PSE areas (Nasir et al. 2013). The existence of diverse processing routes for the biomass transformation and the incorporation of novel technologies with the corresponding discovery of alternative feedstocks are the main drivers of PSE research in biofuels production. This section provides an overview of opportunities of PSE areas for the production of renewable fuels. Many of these topics are analyzed in detail in the other chapters in this book. 1.2.1

Modeling of Physicochemical Properties of Thermodynamic Systems Related to Biofuels

Thermodynamic modeling of properties of pure components and their mixtures, including the prediction of the phase equilibrium behavior, is paramount for the engineering of biofuels production because it is the basis of process design. Reliable prediction of thermodynamic properties is fundamental to calculate the type and size of different equipment, profiles of state variables (e.g. concentrations and temperature) of separation units and energy consumption for separation and purification tasks, and also to identify optimal operating conditions of reaction systems and other process units. For example, knowledge of density and viscosity of a given thermodynamic system is relevant for vessel design, piping, and calculation of mass transfer rates. As stated, feedstocks for the production of biofuels include biomass by-products (e.g. forest residues, sugar cane bagasse, cereal straw), urban wastes (e.g. organic compounds present in industrial and municipal solid and liquid wastes), animal fats, vegetable oils, insect lipids and dedicated materials as energy crops (Sawangkeaw and Ngamprasertsith 2013; Loman and Ju 2016; Stephen and Periyasamy 2018; Kumar et al. 2020). Consequently, mixtures involved in the process operations to synthetize biofuels are characterized by the presence of a wide spectrum of organic (e.g. lipids, dyes, aromatic hydrocarbons and biopolymers) and inorganic compounds (e.g. electrolytes and heavy metals). This complex composition imposes different challenges in the development and application of suitable thermodynamic models to predict correctly physicochemical behavior. For instance, the fatty acid profile of feedstocks can affect the physicochemical properties of biodiesel, and this profile could change substantially depending on biomass origin (Sawangkeaw and Ngamprasertsith 2013). On the other hand, mixtures present in biofuels production usually show non-ideal phase behavior with complex phase diagrams that could be very sensitive to changes in pressure, temperature and composition. Consequently, thermodynamic calculations required to predict phase behavior/diagrams of biofuels-based systems usually pose computational challenges. These calculations involve multivariable and nonlinear problems that are characterized by the potential of multiple solutions due to the complexity of thermodynamic models. Phase equilibrium calculations must be performed numerous times in the process simulators for the design, optimization and control of process units. These include Gibbs free energy minimization to estimate phase compositions, phase stability analysis to verify

Introduction

5

the reliability of solutions obtained for phase equilibrium problems, prediction of bubble and dew points, critical conditions, azeotropic points, etc. Reliable determination of parameters of thermodynamic models employed in phase equilibrium calculations is an additional issue that should be resolved. These adjustable parameters can be obtained from the regression analysis of experimental data, whose (un)availability limits the implementation of some thermodynamic models for the study of biofuels-related systems. Therefore, the application of predictive models and computer-aided methodologies is necessary to estimate the required physicochemical properties. Fortunately, there are scientific databanks of experimental physical and chemical properties of many compounds (Su et al. 2017). However, they usually contain limited information for the molecules involved in the mixtures associated with biofuels systems. This issue also highlights the importance of developing a robust thermodynamic framework for the process design and modeling of the biofuels supply chain. Computational chemistry approaches, group contribution methods and equation of states can be utilized to estimate the properties required in biofuels process design at different modeling scales (i.e. atomic, group, and molecular) (Su et al. 2017). The conventional thermodynamic models (e.g. cubic equations of state or local composition models) could fail to predict the physicochemical behavior of biofuels-based systems (Reynel-Avila et al. 2019). Consequently, reliable predictive methods are required to calculate the physical and chemical properties of pure components and their mixtures in the processing routes of biofuels. Application of artificial intelligence tools such as artificial neural networks and deep learning can be an interesting option to improve the available models for predicting the physicochemical performance of biofuels systems (Reynel-Avila et al. 2019). Reliable and improved numerical methods for solving nonlinear equations and global optimization problems should be developed to resolve, robustly and efficiently, the mathematical problems arising in the phase equilibrium modeling of biofuels. In summary, development of robust and flexible models with improved capabilities, effective solution methods and software tools for predicting the thermophysical behavior and properties of biofuels-related systems (from molecular to macroscopic level) is one of the challenges in PSE for biofuels production. 1.2.2

Intensification of the Biomass Transformation Routes for the Production of Biofuels

Process intensification is a relevant area of PSE to enhance the performance of biofuels production routes (Nasir et al. 2013; Quiroz-Perez et al. 2019). Classical schemes for biofuels production imply the operation of process units that work independently without the integration of mass and energy, where their performance metrics are usually not optimum. Strategies to intensify the biofuels processing routes have increased substantially allowing significant reductions in the production cost and environmental impact. Overall, process intensification principles have been applied in different stages of the pathways for the transformation of biomasses to biofuels (Nasir et al. 2013; Quiroz-Perez et al. 2019; Wong et al. 2019). The diversity of transformation routes for biofuels production has promoted advances in catalytic and non-catalytic processes, biotechnology, separation and reaction technologies. For instance, catalyst-based transformation routes are very common to obtain biofuels (Wong et al. 2019). Transesterification-based processes can be used to convert edible and

6

Process Systems Engineering for Biofuels Development

non-edible fats and oils into biodiesel, where homogeneous and heterogeneous (acid, base, or enzymatic) catalysts are employed (Rezania et al. 2019). This processing route may require a pretreatment stage (e.g. esterification reaction) if the feedstock contains high fatty acids (Nasir et al. 2013). The need to reduce costs in these processes has led to the synthesis and application of novel catalysts (Trombettoni et al. 2018), the study of novel reaction media such as supercritical fluids (Deshpande et al. 2010) and the proposal of alternative reactor technologies (Tabatabaei et al. 2019; Wong et al. 2019). On the other hand, some authors have concluded that microbial fermentation for obtaining bioalcohols is a simple and promising approach to produce bioenergy (Bhatia et al. 2017). In particular, alcohols with two or more carbon atoms (e.g. ethanol and butanol) have been considered as interesting alternatives to conventional petroleum-based fuels. However, fermentation processes utilized in the production of these alcohols have several disadvantages that limit their large-scale industrial applications. The process intensification of this route should address the inhibition of competitive pathways that affect the alcohol productivity due to by-products formed, the genomic adaptation of strains to enhance the substrate utilization capability to use low cost feedstocks (e.g. lignocellulosic wastes), the genetic diversification of microbes with improved alcohol producing capabilities to intensify specific metabolic performance for obtaining the desired end-products and to design synthetic biofuels pathways (Shanmugam et al. 2020). Indeed, advances and developments in metabolic engineering have contributed to the process intensification of biofuels production via the optimization of bioprocess yields and productivities (Shanmugam et al. 2020). Microbial genome engineering can be utilized to maximize the efficiency of fermentation processes via the improvement of the genomic characteristics of biofuels producing microorganisms to direct the metabolic flux toward the generation of desirable bioproducts (Shanmugam et al. 2020). Several authors have analyzed and discussed these and other advances in metabolic engineering and synthetic biology for biofuels production (e.g. Bilal et al. 2018; Majidian et al. 2018). Separation units also represent an important area for process intensification in the production of biofuels. Separation technologies are utilized in the pretreatment and preprocessing stages of biomass transformation due to the heterogeneous composition of feedstocks and in the purification of process streams to recover biofuels and their by-products. Both non-intensive and intensive energy separation methods have been applied in biofuels production. Distillation, extraction, adsorption and membrane-based methods are part of the spectrum of technologies for obtaining renewable fuels (Atadashi et al. 2011; Levario et al. 2012; Abdehagh et al. 2014; Li et al. 2019). The application of intensified non-reactive separations such as heat-integrated and membrane-based distillation, has been explored in the production of biofuels (Diaz and Tost 2017; Kumar et al. 2019). Also, intensified schemes that combine reaction and separation units (e.g. reactive distillation and extraction) (Plesu et al. 2015; Poddar et al. 2017; Gor et al. 2020), and purification systems assisted with microwave, ultrasound and supercritical fluids (Patil et al. 2018; Li et al. 2019; Mahmood et al. 2019) have been reported to produce biofuels. Separation and purification methods applied in biofuels production show different limitations and advantages in terms of energy consumption and product(s) recovery. For example, extraction techniques are relevant for biofuels processing that usually require low energy consumption (Li et al. 2019). Extraction is a key step to carry out the recovery of the desired bioproducts and to reduce the content of undesired substances in the intermediate

Introduction

7

stream to be processed. Fatty acids, hydrocarbons, lipids and biosolids can be extracted from extractable feedstocks for biofuels production such as animal fats, energy crops, agricultural residues and microalgae. The selection of the extraction technique is constrained by the characteristics of the feedstock to be processed and the specific components to be recovered or concentrated, which impact the separation efficacy and selectivity. Mechanical, physical and chemical extraction methods have been utilized in the production of different generation biofuels (Li et al. 2019). Extraction techniques can be intensified via the application of microwave, ultrasound and supercritical fluids. Also, novel extractive agents such as ionic liquids and green solvents have been explored to intensify the recovery of the target compound(s). Li et al. (2019) have analyzed in detail the advantages and limitations of extraction techniques utilized in biofuels production. These extraction processes may generate residues that could cause health hazards and environmental pollution, which is an issue to be resolved as part of PSE challenges. With respect to energy intensive separation methods, distillation is the primary method in chemical process industries but its application in the recovery of biofuels depends significantly on the characteristics of the streams to be purified. However, conventional distillation is not an effective approach for the purification of bioalcohols from fermentation broths due to the occurrence of homogeneous azeotropes (Abdehagh et al. 2014). Therefore, hybrid and intensified distillation schemes have been applied to recover these and other biofuels. For example, Nagy et al. (2015) reported that the combination of distillation and pervaporation can decrease the energy demand for downstream separation of fermentation broths. Several studies have also reported the application of reactive distillation for the production of biofuels. Reactive distillation allows simultaneous transesterification and separation of products within the same equipment (Poddar et al. 2017). Several improvements to this reactive separation scheme to produce different renewable fuels have also been reported (Gutierrez-Antonio et al. 2018; Gao et al. 2019). See Singh and Rangaiah (2017) for a review of advances in separation processes for bioethanol recovery and dehyration. Overall, it is required to develop improved process units that should be flexible and robust for the transformation of feedstocks with changing physicochemical characteristics to biofuels. Advanced and less energy-intensity separation techniques are needed to increase the sustainability of biofuels production. The development of green technologies for the purification and recovery of biofuels and by-products is considered a relevant PSE issue. The application of intensification technologies based on supercritical fluids, microwave, ultrasound, and ionic liquids opens new opportunities for the development of improved processes for biofuels production. Research on these technologies should be increased to establish their benefits and limitations for industrial applications. Efforts should also be focused on the recovery and use of value-added compounds generated during biomass transformation such as glycerol. These and other shortcomings should be addressed with the aim of developing cost-effective separation and purification schemes for the production of biofuels. 1.2.3

Computer-Aided Methodologies for Process Modeling, Design, Optimization, and Control Including Supply Chain and Life Cycle Analyses

Process design of biofuels production facilities should consider performance metrics and objectives related to environment, economics, and safety. In particular, current and

8

Process Systems Engineering for Biofuels Development

anticipated regulations for environmental protection impose additional restrictions to this design stage. Biofuels process design requires the application of proper models that accurately represent the characteristics and properties of the systems, units and all elements involved in the supply chain ranging from the microscopic to macroscopic level (Figure 1.1). Therefore, development of realistic models to be used in process design is an important PSE challenge for biofuels production. Note that high level physicochemical description in the design problem formulation is challenging. For instance, Quiroz-Perez et al. (2019) have highlighted the importance of computational fluid dynamics for process design and modeling of equipment involved in biofuels production where transport phenomena are paramount to ensure correct scale up and industrial operation. Also, kinetic and thermodynamic data of the reacting systems involved in biomass transformation routes are fundamental for reliable design of reactors including fermenters. Processing routes for biofuels production can be optimized via the formulation of design problems with one or more objectives to be minimized or maximized simultaneously. Indeed, multi-objective optimization (MOO) has found numerous applications in chemical engineering and related areas (Rangaiah and Bonilla-Petriciolet 2013; Rangaiah et al. 2015; Madoumier et al. 2019). Optimization can be employed to improve the performance of specific process units and the entire processing route for producing biofuels. Biofuels process optimization is not an easy task and robust numerical methods are required to solve the design problems, which are usually multivariate, nonlinear and with equality/inequality constraints. Deterministic and stochastic optimizers have been applied to solve design problems in the biofuels production. In particular, stochastic optimizers (metaheuristics) have shown several advantages for solving both global optimization and MOO problems of biofuels production due to their easy implementation, computational efficiency and ability to handle both discrete and continuous design variables. Optimization has been used for the design of intensified separation sequences for biofuels purification (Sanchez-Ramirez et al. 2019; Gor et al. 2020), for the improvement of processing routes to obtain biofuels (Woinaroschy 2014), for the integrated design of biorefineries to produce biodiesel from different feedstocks (Prieto et al. 2017), to identify processing paths for obtaining biofuels from different feedstocks (Eason and Cremaschi 2014), and for biodiesel plant design (Patle et al. 2014a; Alvaraes et al. 2019). Biofuels production facilities comprise a large set of operating variables that should be manipulated and regulated. Therefore, process controllability is an important issue for the implementation, operation and safety of biofuel production. The control problem of a complete biofuels production process is large with nonlinear functions of states, many inputs and outputs, and a reduced number of degrees of freedom (Bildea and Kiss 2011; Prunescu et al. 2017). Consequently, nonlinear control concepts and plantwide control are required to achieve flexible and stable operation of process units in biofuels production. For example, plantwide control has been studied for a complete biodiesel plant (Patle et al. 2014b). Life cycle analysis (LCA) is desirable for comprehensive assessment of the sustainability of biofuels processes in terms of environmental, social, energetic and economic indicators (Collotta et al. 2019). Several authors have reported LCA of the production of renewable fuels using different levels of details, methodologies, analytical boundaries, and impact metrics (e.g. Mu et al. 2017; Liu et al. 2018). However, it has been pointed out that the standardization of methodologies utilized for LCA of biofuels, including life cycle inventory data, is a relevant issue to be addressed for performing reliable comparison and supporting

Introduction

9

the decision-making process to identify the best options for biofuels production (Mayer et al. 2020). Finally, biofuels supply chains include all the activities related to the transformation of biomasses into renewable fuels and their delivery to the end-users (An et al. 2011; Awudu and Zhang 2012). The biofuels supply chain is affected by several uncertainties in terms of prices, demand and supply of feedstocks and end-products, transportation and storage issues, performance of processing facilities, among other factors (Awudu and Zhang 2012). Consequently, the design of a reliable and sustainable biofuels supply chain requires the application of the latest computer-aided methodologies to optimize the operational, tactical and strategic decisions. In summary, PSE contributions and developments are fundamental to consolidate, optimize and operate the biofuels supply chains to achieve the economic, environmental and social benefits of this type of renewable energy.

1.3

Overview of this Book

After this chapter, this book contains 12 chapters that describe and analyze different applications of PSE for biofuels production. Chapters 2–13 are briefly summarized in this section. Chapter 2 provides an overview of different biomasses that can be utilized for biofuels production. It highlights the relevance of feedstock composition for biofuels production. Biomasses analyzed in this chapter include spent coffee grounds, different lignocellulosic materials, residues of oil production from palm, olive, coconut, avocado and argan, residues from crops such as citrus and grapes, and waste oil and waste cooking oil. This chapter ends stating the importance of developing new methods and technologies to exploit the variety of available feedstocks for producing biofuels. In Chapter 3, analysis and discussion of PSE contributions for the process design of biorefineries and biomass-based infrastructure are presented. This chapter describes methods for the design of process units and approaches for process synthesis. The product design problem for biomass processing, supply chain modeling and the importance of multiscale analysis are also discussed. The challenges of thermodynamic properties and phase equilibrium calculations in biofuels process design are covered in Chapter 4. Elements of the thermodynamic modeling framework for the prediction of properties required for process design of biofuels are described. The formulation of phase equilibrium problems and a survey of available thermodynamic models for phase equilibrium calculations are presented. A brief analysis of property databanks for biofuels process design and the impact of uncertainties of thermophysical properties are also provided in this chapter. Finally, some methods for the prediction of thermodynamic properties of compounds involved in biofuels production are described. Chapters 5 reports pretreatment methods and processing routes to transform waste oil into biodiesel. Capabilities and limitations of homogeneous and heterogeneous catalysis, enzymatic-, ionic liquid- and hydrated salts-based conversions for processing waste oils are discussed. The authors of this chapter have highlighted the technical limitations and challenges to intensify biodiesel production from waste oil. Biojet fuel production from wastes and residues is reviewed in Chapter 6. This chapter discusses the importance of biofuels development for the aviation sector. It contains the

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Process Systems Engineering for Biofuels Development

state of the art in the processing of triglyceride-containing wastes, lignocellulosic materials, sugar and starchy residues for the production of biojet fuel. The authors of this chapter have analyzed the challenges and future trends to potentiate this renewable fuel for the aviation industry. Computer-aided design is important to develop new processes for the production of biofuels. Therefore, Chapter 7 focuses on the development of a simulation platform for biological models associated with biofuels systems. The modeling approach involves an optimization problem subject to specific constraints. Two examples are reported to show the application of this modeling approach. Results show that this approach can be used to assist the industrial production of biofuels via mutated strains. Different aspects of PSE of biodiesel production via enzyme-catalyzed routes are analyzed in Chapter 8. A comparison of the biodiesel production routes with chemical and enzymatic catalysts is performed. The authors have discussed the optimal reaction conditions and the kinetic modeling in biodiesel production routes catalyzed by liquid enzymes. Details of process simulation and economic evaluation of this type of transformation route, including the reuse of the enzymes, are also included in this chapter. Chapter 9 deals with simulation and design of process scenarios for biodiesel production. In particular, this chapter describes the application of the Aspen Plus® simulator to model the biodiesel process. Examples are described for the calculation of thermodynamic properties of both pure components and mixtures, required for process design. Also, the authors have discussed some aspects of utilization of Aspen Plus to model reactions involved in biodiesel production. Case studies related to the process design of esterification and transesterification reactions with different reactor models are described. Finally, use of Aspen Plus for energy and economic analyses is illustrated. This chapter provides a simple and handy guide for students and practitioners in the use of Aspen Plus for biofuels process design. Chapter 10 also describes the modeling and simulation of a continuous biodiesel process from microalgae using Aspen Plus. For this, process parameters and reaction kinetic data were based on reported experimental results. A sensitivity analysis was performed to analyze the impact of some design variables of process units. The sizing and cost analysis of the equipment utilized in the biodiesel process simulation were carried out. The authors compared the performance of this biodiesel process from microalgae with the results for a biodiesel process using waste cooking oil. Based on this, some research topics to reduce the cost of biodiesel production from wet microalgae are suggested. A state of the art of thermochemical methods for the production of renewable fuels is given in Chapter 11. Thermochemical methods that are utilized to obtain solid, liquid and gaseous biofuels are described in this chapter. First, a simple classification of thermochemical methods is provided. Combustion, gasification, liquefaction, pyrolysis, and carbonization are analyzed. Advantages, limitations, energy requirements and equipment used in these thermochemical methods are also covered in this chapter. A perspective of the present and future of biofuels is presented in Chapter 12. The importance, implications and advantages of utilizing biomass to produce renewable energy are analyzed. Characteristics of some biofuels feedstocks and their processing routes are provided. This chapter includes a detailed discussion of bioethanol, biodiesel, biobutanol, biojet fuel, and biogas. It concludes that biofuels production from some specific feedstocks will be commercially attractive in the next decade.

Introduction

11

Finally, Chapter 13 deals with the design of intensified purification options to produce methyl ethyl ketone, which has been suggested as a biofuel that can be produced by the fermentation route. Purification of mixtures that contain this biofuel is challenging due to the presence of azeotropes. Hence, this chapter analyzes some intensified schemes to improve methyl ethyl ketone purification. Separation schemes based on distillation and liquid–liquid extraction were designed via the MOO approach considering economic, environmental, controllability and safety indexes. Results show that the intensified process requires lower energy compared with the separation scheme based on distillation alone. In short, the contents of this book expand and cover developments and contributions of PSE for biofuels production. Students of Chemical Engineering, Environmental Engineering, Energy Engineering and related areas will find the chapters useful in their studies on biofuels. The editors and authors of this book hope that its contents will contribute to further research and development of PSE for biofuels production in both academia and industrial practice. As stated, biofuels are at the forefront of new energy solutions to the environmental and safety problems related to the use of petroleum-based fuels. Consequently, consolidation of biofuels production and supply chains are important to support the sustainable human development of future generations.

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Introduction

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2 Waste Biomass Suitable as Feedstock for Biofuels Production Maria Papadaki Department of Environmental Engineering, University of Patras, Agrinio, 30100, Greece

2.1 2.1.1

Introduction The Need for Biofuels

Babu (2008) defines biomass as a term used to describe all Earth’s living matter. It is a general term for material derived from growing plants or from animal manure (which is effectively a processed form of plant material), while, according to Jessup (2009), biofuels are solid, liquid, or gaseous energy sources derived from renewable biomass sources. On the other hand, in article 2 of the European Community Directive “On the promotion of the use of biofuels” specific definitions of a narrower spectrum are given. As such, the term “biomass” is used to express the biodegradable fraction of products, waste and residues from agriculture (including vegetal and animal substances), forestry and related industries, as well as the biodegradable fraction of industrial and municipal waste. The term “biofuels” is exclusively referred to liquid or gaseous fuel for transport, i.e. the directive focuses in fuels which can partially replace fossil-origin fuels employed in transport. It describes specific characteristics of “bioethanol,” “biodiesel,” “biogas,” “biomethanol,” “biodimethylether,” “bio-ethyl tert-butyl ether,” “bio- methyl tert-butyl ether,” “synthetic biofuels,” “biohydrogen,” “pure vegetable oil” which are the referred biofuels, which naturally originate from processed biomass. In this work, the broader definitions are preferred, although a great deal of the chapter focuses on biomass which can provide liquid and gas biofuels. Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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Biomass has always been used for the production of energy. Chemicals and pharmaceutical products have been obtained from biomass; biomass has been burnt to produce energy since fire was harnessed. Since the energy crisis of 1973, considerable interest has developed in biomass use toward meeting the energy needs of the world. Furthermore, the interest in valorization of biomass was awakened as the awareness of the finite nature of fossil liquid and gaseous hydrocarbons and their ultimate depletion rose. There was much argument over when this would occur (Goldstein 2018). However, in the mind of society the search for alternative energy sources had begun. A drop in oil prices in the 1990s kept bioenergy markets apart from the production. But the reliance on fuel supplies from a few major producers and the need to reduce greenhouse emissions imposed the search for renewable sources of energy; valorization of biomass was a good option. Special attention was given to the replacement of transport fuels as their numbers were steadily rising (Karp and Halford 2010). The European Union in particular in 2008 decided that by 2020, one fifth of the totally consumed energy should be obtained from a renewable energy reservoir. Great weight was also given to the energy consumed for transportation. As such, biofuels were expected and resolved to comprise at least 10% of the energy used in that sector by the end of the second decade of the twenty-first century (Rechberger and Lötjönen 2009). Renewable forests and field crops appeared to be attractive and suitable sources of biomass in processes aiming at generating biofuels. Moreover, it was expected that existing technology could function efficiently and that it was a feasible solution toward meeting set targets of gradual fossil fuel replacement. In fact, by the end of the first decade of the twenty-first century a high production of biofuels was reached in both North and South America. Brazil, Argentina, and the United States produced and consumed large amounts of bioethanol. A very high percentage of liquid biofuels mainly in the form of bioethanol was also employed in Australia, China, and Canada. Europe was predominantly producing and utilizing biodiesel (Azadia et al. 2017). Appropriate for biofuel production biomass includes in principle, food-crops containing sugars and starch or crops which have high oil content; those can be converted to appropriate fuels via a sequence of transformation and reaction paths. Biomass of lignocellulosic content is also a very good candidate. It can be either obtained as the side or waste product of other processes, such as agricultural and forestry residues. It can also be obtained via purposefully cultivated plants in the form of the so-called energy crops. Additional sources for biofuel production are: used and waste oils, other organic wastes, algae (micro- or macroalgae, encountered or grown in open or pond waters or in photobioreactors) (Stafford et al. 2017). Every year, the plants on Earth produce and store up to four times the energy needed by humanity per annum (Guo et al. 2015). As such, shrewd, systems-approach planning for the valorization of the available biomass can substantially reduce our dependency on fossil fuels. However, to reach such a goal, a number of parameters have to be accounted for prior to investment in new processes. The aim of producing replacement fuels has to be harmonically enlaced with objectives such as CO2 reduction, independence from a few energy providers in the world, that fossil fuels replacement does not impact on food production and availability, that deforestation is not a consequence, etc. In other words, the urge for biofuel production will not be shifting the problems associated with energy elsewhere, thus forming alternative types and kind of dependencies. Moreover, new, fit-for-purpose technologies have to be developed and the industrial size production of large quantities of desired commodities may have to be questioned and probably partially shifted or be

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replaced by smaller, specialized-goods production. Alternative methods of production that are mobile, transformable, or of extendable or shrinkable capacities may need to be invented and evolve. Alternative products of a functionality similar to the one offered by existing commodities, or entities with completely new properties and potential uses may also arise as side or by-products of the alternative energy production processes. The selection of good or adequately suitable feedstocks for biofuels production is a question with multiple answers. Many of the answers which have been given so far have created more important questions. Much progress is being made however, thanks to the substantial research already conducted and which is continuously expanding. In the search of such feedstocks, one has to wind through a multi-stage and multi-scale labyrinthine pathway in order to first identify the primary qualities required in the feedstock, to subsequently devise the processes which have the potential to provide expected outputs and to finally implement a multi-objective optimization process in order to guarantee the viability of the venture. There are multiple issues which have to be addressed and the primary ones lie in the definition of the problem which is to be solved. The top-to-bottom approach is traditionally implemented by the chemical engineers during the conceptual process design. The problem is relatively static, with well defined raw materials and desired products; the engineer searches for the best route to take from the starting to the end point. Biomass valorization and the production of biofuels from biomass form an unprecedented opportunity for the implementation and development of the-top-to-bottom, bottom-to-top coupled method, described in detail by Pham and El-Halwagi (2012). The method can be also coupled with the atomic targeting and design (El-Halwagi 2017). Furthermore, in their book Sengupta and Pike (2012) present how different chemicals including biofuels can be obtained from waste biomass. They focus on processes which can contribute toward a sustainable economy. They present the advances of chemistry and technologies which can be used toward biomass valorization and how non-renewable feedstocks can be replaced by renewable ones. Furthermore, given the dynamics and the volatility of the market in the globalized world an embedded flexibility in the kind of the products, the methods of production, and the capacity may counterbalance any costs which may affect the economics and the viability of the process. Thus, space should be sought for versatile designs to replace traditional rigid requirements where stability of the selected feedstock and product prices are the key factors governing the process economics. 2.1.2

Problem Definition

Until the end of the first decade of the twenty-first century the raw materials for biofuel production were carefully selected and they were obtained through cultivations dedicated to that very purpose. Associated with this objective, agricultural cultivation involved starchy, sugary and oily plants which would otherwise have been used for the production of food. Moreover, the land employed for their growth would have normally been used for the production of vegetables for feeding people. This had its impact on agricultural products, raising their prices, with tropical deforestation due to the expansion of food and non-food crop cultivation in those areas. Thus, the apparent solution of the energy problem was causing a number of new problems. As such, the net result was not moving the situation forward; the

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energy availability problem was just transformed to another entity. However, the technologies which have been developed, the research which has been conducted, and the wisdom which has been acquired can be implemented in the valorization of biomass of a different origin, such as waste and residual biomass and perennial grasses. Such an application will provide additional economic and environmental benefits. Naturally, the exploitation of these alternative sources of biomass will not be as straightforward as the collection of the waste biomass; the variability of its composition, its potential degradation before treatment, and discontinuity in availability pose different technological challenges. However, sustainability is a parameter which always has to be taken into consideration. Cyclic economy is also gaining ground in the engineering thinking and objectives for future development. As such, the value of bioenergy, instead of being measured merely in terms of the quantity of the replaced fossil fuels, should be holistically assessed and the impact that it has on the food production, on the forests and the potential deforestation, on the spent water resources and their pollution, on its effects on the wildlife, the soils, and society should be “measured” and accounted for (Union of Concerned Scientists 2012). Furthermore, the initial approach of employing corn, and other crops rich in sugars and starch, traditionally used to cover needs of human and animal food, is proven incapable of reaching the required needs for bioethanol as transport fuel (Sarkar et al. 2012). On the other hand, there is a great quantity of organic wastes produced and accumulated via human activities (city, kitchen, agricultural, animal-farm wastes, wastes of lignocellulosic origin such as forest residues or residual ligocellulosic mass from other processes) which can be exploited for the production of biofuels via a number of appropriately designed processes (Stephen and Periyasamy 2018). Amongst those, excellent quality lignocellulosic waste biomass can be accumulated from the residuals of crops production (wheat, corn, barley, rye, oats, and others), from a number of different agricultural activities as well as from the residual biomass of food industry branches which employ processing of freshly collected fruits and vegetables, such as the juice industry for instance. The option of plant-origin waste biomass also consists of an environmentally friendlier way of disposal (Rivas-Cantu et al. 2013). 2.1.3

The Biomass Pool

The waste biomass which can be used for biofuels production ranges from anything which can be burnt, producing thermal energy as a solid fuel, to more sophisticated specialized waste, which can provide high efficiency and high quality liquid biofuels. Waste biomass which can be used for the production of biofuels can be: 1. Waste biomass which is generated as a by-product or residual product following agricultural activities for the production of food. Typical examples are branches of fruit trees after pruning, fruit tree leaves, husks of legumes and their stems and leaves, wild vegetation trimmings, corn residues, like rice, wheat, barley, oat, rye, millet straws, sorghum stalks, cassava peels and stalks, yam straws, and nut shells. 2. Forest material which involves branches and leaves of forest trees, willow, poplar, bamboo, canes, sawdusts, firewood, and woodchips.

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3. Uncontrolled growth wild type vegetation like perennial grasses and shrubs such as switchgrass, miscanthus, jatropha, algae, micro- and macroalgae and vascular land plants. 4. Waste energy cane. Energy cane refers to high biomass sugarcane. 5. Natural cosmetics industries waste biomass like seeds, roots, stems and peels of fruits and vegetables. 6. Food industry waste: remains of plant or animal origin, fish remains, bones, skins, fermentations waste biomass, frying oils and other fats, corn cobs, coffee and other beverages of plant origin waste biomass, sugarcane and sugar beet waste, kernels and shells of nuts, etc. 7. Waste from human activities, city and general waste, plastics, paper, waste food, waste tires, greenhouses plastic sheets, and crop protection plastic nets. 8. Other organic waste like animal and poultry manure, and energy industry waste of oil or lignocellulosic origin (see Figure 2.1). Such biomass feedstocks can be used to produce biofuels via a number of treatment methods. The methods of treatment and their value as biofuel feedstocks depends on their chemical composition and on their content in cellulosic compounds, sugars and carbohydrates and their content in oils. However, a great variety of those also contain small quantities of compounds which have valuable properties as natural medicinal, food or cosmetic agents. Furthermore, their potential use as biofuel feedstock often solves the problem of their disposal as a waste. There are different ways that this biomass can be treated so as to provide valuable products. The main methods of biofuel production employ processes such as anaerobic fermentation, pyrolysis and co-pyrolysis, gasification, transesterification, fermentation, acid or base hydrolysis, solvent extraction supercritical or not, simple mechanical processes of grinding and pelletizing, simple thermal treatment such as drying, direct biomass combustion, or combinations of the above.

Figure 2.1 Sugar canes. Source: Truncated photo from https://www.pexels.com/search/agricultural %20waste.

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The chemical composition of the biomass is a very important factor when seeking the most appropriate methods of treatment which could potentially be employed. However, the uniformity of biomass in terms of composition, the variation of its availability during the year, the potential need of collection, transportation and storage are factors which define the kind of process which will make its exploitation economically viable and the quantity and quality of the products which can be obtained. As such, before selecting a specific type of biomass, the following type of questions have to be answered. What secondary biomass is already available? Is the selected biomass accessible? In what quantities? What are their qualities (characteristics)? How is its distribution through time? What is its spatial distribution? How long can it be stored for before degrading or suffering an alteration of its composition? In which form should it be stored? Does it need to be transported? How easy will that be? Can transport fuels be obtained? Can they be used locally? Is their quality better or worse than that of the fossil fuels (i.e. what is their nitrogen and/or sulfur content?). Furthermore, the importance of its valorization as a biofuel has to be assessed in additional terms which involve safety, environment, and society. For example, does its use for biofuel production solve any other problems? Does it result in a substantial reduction of the environmental impact that it would otherwise provoke as waste? Does it contribute toward local or national energy independence? Does it reduce the energy footprint by reducing transportation of fossil fuels in the area? Does it produce a safer alternative to employed energy sources? Does it contribute to the economic development of the area without shifting problems to other places in the world? Does it help the preservation of biodiversity? In a preliminary evaluation of the biomass valorization, the geography and the population of the area of the industrial process are of crucial importance for this type of enterprise and the collection and transportation of residual biomass can constitute an important expense while the long term storage of biomass is of crucial importance for its quality. As such, mobile units of treatment or multiple establishments of small units versus one large facility may be worth considering. Moreover, the potential of extraction of high added value compounds prior to biorefining may substantially increase the financial potential of the process. This often implies that the biomass under question has already been well and reliably characterized. In the following paragraphs a number of promising feedstocks is presented. Amongst the huge multitude of potential feedstocks a few selected biomasses are discussed. Their primary common characteristic is their relative abundance and a substantial amount of completed research on their characteristics. The reason for the selection of each specific biomass is explained in the relevant sections (Figure 2.2).

2.2

Kinds of Feedstock

A number of potential feedstocks are presented in the following sections. Each feedstock is accompanied by a description which intends to answer as many of the above questions as possible.

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Figure 2.2 Equisetum. A plant with important medicinal and pesticide properties, abundantly encountered in wet soils. Potential biofuels precursor following extraction of added value compounds.

2.2.1

Spent Coffee Grounds

According to the European Coffee Federation (ECF 2016), a few billions of coffee cups are consumed daily all over the world thus making it the most extensively preferred beverage worldwide: the USA consumed about one and a half million tons of coffee each year for the period 2011–2013. Mexico consumes two hundred thousand tons, Brazil over one million tons, while the EU consumes about two and a half million tons of coffee; however, it imports around three million tons, part of which is re-exported. The amount of coffee produced in 2015 was approximately nine million tons; from each kilogram of coffee, 0.91 kg of solid waste is produced, thus the importance of a further valorization of this residue becomes obvious. As Murthy and Naidu (2012) confirm, coffee is a very popular drink and as such its trade is extensive. In a comprehensive review, Campos-Vega et al. (2015) report a detailed catalog of compounds which can be exploited by spent coffee grounds, as the cited research articles in the review suggest. In the mentioned review, it is proposed to use spent coffee grounds in order to extract several added value products. More specifically they state that the residual mass after the initial coffee extraction contains numerous organics like different kinds of polysaccharides and fatty acids, which can be extracted following further treatment. As presented in their review, research is being conducted toward biofuel generation from the exploitation of coffee residues. Moreover, its capacity as a source of sugars, as absorbent of pollutants and in particular heavy metals, and as a primary source for the production of activated carbons are also being investigated. At least 10–15% lipids, measured on a dry basis, have been found to remain in the used coffee grounds. Different coffee residual biomass samples have been analyzed and approximately 90% lipids were found to remain in the solid residue after coffee extraction. Spent coffee biomass oil consists mainly of triglycerides and small amounts of diglycerides, free fatty acids, terpenes, sterols, and tocopherols. Therefore, it represents an important source of raw material for a variety of products including biofuels. Cholakov et al. (2013) applied

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extraction methods on spent coffee grounds to evaluate their potential as a source of biofuels and activated carbon absorbents. Their results were promising in both aspects but not conclusive, so they recommended the need for further research. Coelho et al. (2018) examined the influence of three co-solvents, namely ethanol, isopropanol and ethyl lactate, on the yield and composition of the oil extracted from the above spent coffee grounds. The highest yield (12.4%) was obtained at a temperature of 333.2 K with 5% ethyl lactate as a co-solvent (Georgieva et al. 2018). A great proportion of the overall, worldwide spent coffee waste biomass production is generated at the industrial sites where instant coffee is manufactured. Therefore, a further treatment for added value compounds, for biofuels or any additional application will be free of costs of collection and transportation of the raw material, and their management. The weight that these costs carry in processes which target the valorization of waste biomass constitute a major issue; they can be such that they can actually make or break the respective processes (Iervolino et al. 2018). Consequently, expansion of existing instant coffee industries to incorporate processes for the extraction of added value compounds from the remains of the spent coffee may be a venture worth considering. 2.2.2

Lignocellulose Biomass

Sindhu et al. (2016) present an overview of lignocellulosic biomass, which is high in cellulose, hemicellulose, lignin and as such it can serve as an excellent source for biofuel generation. And of course, an important virtue of this type of biomass is that it is renewable. Second generation lignocellulosic biomass in particular, in addition to being an excellent candidate for bioethanol production, is an amply available and low cost feedstock, which is often accumulated in places favoring its treatment and as such, minimalizing the otherwise high costs of transport and collection process as analyzed in the previous subsection for spent coffee grounds. Such biomass can be the straw of cereals cultivated for human or animal consumption, i.e. field residues or process residues such as residual biomass produced during the collection of wheat, rye, barley, oats, corn and other cereals, which are traditionally used for animal feed, as natural absorbent of water, and as insulation material in certain constructions. The collection of cereals takes place in the summer months and as such this type of biomass can be collected in a dry form and can be transported to biorefineries simultaneously with the collection of the edible crop. Such an option reduces the management needs associated with the residual biomass collection. Husks of legumes, rice and corns can be also used in a similar way and the same methods can be applied for their collection. Additional source of lignocellulosic origin is the bagasse disposed of in bioethanol production units, which with further treatment can produce additional biofuels. Moreover, they are readily available in the sites of bioethanol production and unless they are further treated for further added value products generation, they form a waste which has to be disposed of according to environmental requirements at additional costs (Figure 2.3). According to Eurostat, cereals in the EU are aiming to be used as food or food derivatives by humans and/or animals. Cereals are extensively produced in Europe and all over the world. During 2015 cereals and rice production in Europe reached around three hundred and twenty million tons, half of which were different kinds of wheat and approximately 20%

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Figure 2.3 Hay residues. Source: Truncated photo by Petar Starˇcevi´c (https://www.pexels.com/photo/ hay-field-under-clear-sky-2389122).

was barley (KFE 2017). In addition to cereals, Europe cultivates plants with oil-generating seeds, such as turnip-rape, rape, soya, and sunflower, the production of which was approximately 30 million tons in 2016 with rapes accounting for two thirds of the quantity and sunflower approaching one third. Soya production, which was only two and a half million tons at the time, has increased (EAF&F 2017). These seeds are primarily used for oil and/or bioethanol production. Therefore, the waste biomass produced after the treatment of those seeds could always be further utilized for the extraction or formation of value-added products, while appropriate further treatment can provide additional biofuels. According to Zabed et al. (2016), the world availability of such biomass is approximately three to four billion tons per year while 2–5 tons of such biomass could produce approximately 1–2 m3 of ethanol. In their review, amongst others, they provide the content of cereals of different origin (i.e. from rice or wheat, or barley or oat) in cellulose, hemicellulose and lignin as measured by Saini et al. (2015), Ludueña et al. (2011) (rice husk), and Sánchez (2009) (oat and rye straw). Bioethanol production has well advanced over the last decade. The processes for its production were designed to employ primary biomass produced for that very purpose. As such, the composition and properties of the raw materials were assumed to be adequately similar. However, the biomass properties are subject to numerous conditions such as variety of the crop, season, temperature variations during growth, soil properties, irrigation frequency, type of fertilizer and quantities used. Additionally, as these were industrial size continuous processes, the constant supply of biomass of the same quantity and composition is of key importance for their efficient operation which also ensures good and within the required specification product quality (Abraham et al. 2016). Second generation biomass and generally waste biomass of a varying origin and composition will require inventive actions and the design of processes versatile enough to adapt to the expected variations. This may be proven a very challenging task. Abraham et al. (2016) discuss the potential of rice straw as a second-generation source of biofuel so that this waste with a high annual volume can be exploited. While this type of biomass can be used for the production of bioethanol, the solid waste of the process can be further treated for the production of other type of biofuels. The formation of biopolymers can also be feasible from this feedstock. According to Saini

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et al. (2015), the annual universal production of rice residues is over 700 tons. Useful waste biomass from rice straw consists of the rice stems, the leaf-sheaths and their blades. Much research has been conducted on this waste biomass and data on its composition are available in the literature. Wheat straw is the residue of harvested wheat with an estimated annual yield of 1–3 tons per acre. Talebnia et al. (2010) report research on wheat straw as a biofuel source. This raw material, as many others of its kind, mainly contains cellulose (of strains very highly attached to each other), hemicellulose and lignin in mass proportions of around one third to 40% for the former, one fifth to one forth hemicellulose, and 15–20% for the latter, as well as a smaller proportion of extractives. This kind of biomass resource is encountered in ample quantities everywhere in the world. Those remains of agricultural produce after all useful food parts have been collected can be exploited for direct bioethanol production without the need for any additional investment. The United States alone generates over 400 million tons of biomass which comes from agricultural wastes usable for bioethanol production, while an additional amount of approximately 0.4 billion tons of energy crops is also available (Saini et al. 2015). The production of corn, wheat, rice and sugar cane can provide the majority of agricultural waste biomass which with appropriate management can form a precious raw material for biofuel production (Kim and Dale 2004). The production of biofuels from dedicated crops is straightforward because the employed biomass composition is more or less the same. Waste biomass on the other hand has a wide variability of composition. Consequently, its use as as an alternative feedstock for the production of biofuels introduces several technical challenges. An effective way to resolve this shortcoming is the implementation of appropriate pretreatment stages. Such stages can for instance increase the biomass content in fermentable sugars. Alternatively, new fermentation technologies can be developed to ensure viability and efficiency of the production process (Sarkar et al. 2012). Corn waste biomass, which consists of stalks and leaves, the empty cobs and the husks, is a promising biomass for bioethanol production, because of its quantity (1 g per kg of produced corn grains or about four tons of waste biomass per acre according to Kim and Dale (2004)) and its composition (Sarkar et al. 2012). A study conducted by Ayeni and Daramola (2017) considered the exploitation of corn waste biomass for the production of biofuels and other products of everyday use. For that purpose, they utilized different pretreatment methods involving, amongst others, alkaline hydrolysis with or without hydrogen peroxide and dilute acid hydrolysis. They used these processes in order to characterize the corncob, to enhance the cellulose, to remove the lignin, to solubilize the hemicellulose and overall to evaluate the economics of the aforementioned exploitation of this type of biomass on the basis of the above separations and characterizations and they proposed methods which they demonstrated can ensure process viability (Ayeni and Daramola 2017). Shariff et al. (2016) have also conducted research on corncob residual biomass and in other kinds of feedstocks like palm wastes, rice husk, wheat straw, wood sawdust, corncob (which is abundant in Malaysia throughout the year) for the slow pyrolysis process. Corncob waste biomass was characterized for its cellulose, hemicelluloses and lignin content; the mass fractions of which were found to be approximately 0.46, 0.4, and over 0.11, respectively (Shariff et al. 2016). Corncob has low nitrogen and

Waste Biomass Suitable as Feedstock for Biofuels Production

Figure 2.4

25

Perennial plants.

sulfur contents and a high proportion of volatile matter, thus it was demonstrated that slow pyrolysis is a suitable method for its valorization. Bagasse from a multiplicity of processes can form an excellent feedstock for the generation of biofuels, simultaneously solving the environmental issues associated with their disposal or cutting costs from their further treatment. According to Sánchez (2009), the quantity of bagasse estimated at the time from all over the world was over a third of a million tons. Similarly, perennial biomass can be exploited for the same objective (Mantziaris et al. 2017). However, for both categories of these biomasses, the variation in quantity, quality (composition of feedstock) and availability of feedstock are posing new technological challenges imposing the need for versatile and inventive approaches for their valorization. At the same time however, they can act as a make-up feedstock to counterbalance seasonal and compositional variations of biomass (Figure 2.4). In summary, lignocellulosic residual biomass is abundant on the planet, it is of low cost and often a waste which has to be environmentally disposed of, concentrated and accessible. It has a composition which is suitable for biofuel production; much research has been conducted already and technology for its treatment is available. 2.2.3

Palm, Olive, Coconut, Avocado, and Argan Oil Production Residues

Edible oil production from trees serves the biofuel biomass supply in different ways. One is that the oil production trees have a considerable life span and as such they are providing yearly biomass from pruning and leaves. The trees producing oily fruits have a smaller but non-negligible content of oils in their leaves and branches; as such, the valorization of residual biomass from trimmings toward biodiesel production in particular, is appropriate. They are evergreen trees and as such they produce a continuous leaf supply. Their leaves have valuable compounds, the extraction of which can be considered a means of improving the economics of processes, thus valorizing their residual biomass for biofuel. And, of course, the used cooking oil resulting at a later stage is an excellent biodiesel source.

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For some, the residual biomass has been already well characterized and their exploitation technologies have been developed or are in the process of development. Moreover, the need for oil production from all the aforementioned plant fruits is in increasing demand and plantations are rapidly increasing. Therefore, the future and present abundance of this type of biomass is also an important factor for which this type of feedstock is worth considering. 2.2.3.1

Olive Oil Production Residues

The olive tree is a very long living tree. It is not uncommon to find trees as old as a few or even several ages, while the existence of trees over a thousand years old can also be found. They are evergreen trees and their fruit, the olive, is either mildly treated and its flesh is eaten or it is crashed to provide its juice, olive oil. The great majority of olive oil production comes from trees grown around the Mediterranean (Christoforou and Fokaides 2016). Recent global olive oil production amounts to approximately two million tons. Olive oil production involves mechanical separation of the fruit from the leaves that are gathered together with the fruit during collection, the mechanical crashing of the olives, the separation of the oil from the solid mass with or without the addition of water as an assisting extraction solvent, and the subsequent separation of the olive oil from the solid (olive mill solid wastes) and the liquid wastewaters. Depending on the method of treatment, and whether there has been addition of water during treatment or not, subsequent wastewaters can be removed separately or together with the solid residue. Both those waste streams are produced in very large quantities and their pollution potential is high. Christoforou and Fokaides (2016) present a comprehensible review of techniques employed for the olive mill solid waste to energy utilization. The treatment and disposal of olive mill wastewaters however, forms a much greater problem, the solution of which is a really pressing necessity. The applied techniques have not given satisfactory results (Haddad et al. 2017). In addition to the above, olive leaves, olive tree branches from pruning and bark can be utilized toward that direction. Although extensive research has been done on the reduction of the pollution caused by olive mill wastewaters, substantially less work has been conducted toward the valorization of the solid and liquid wastes associated with olive oil production. There are however studies on added value products from olive leaves; studying important effects of olive leaves as antihypertensive, anticarcinogenic, anti-inflammatory, hypoglycemic, antimicrobial and hypocholesterolemic (Talhaoui et al. 2014). Additional medicinal properties have been reported. Extracts of olive leaves can also serve as cosmetic precursors and also as food preservatives (Figure 2.5). Altogether, every year a great amount and variety of waste biomass is generated from the olive cultivation (e.g. pruning of branches) and the olive treatment processes to obtain edible olives and olive oil. These residues involve olive leaves, olive branches from pruning, olive mill wastewaters and olive mill solid wastes. The latter two, as mentioned above, form an undesirable and difficult to treat waste. The leaves of the former two are occasionally used as animal food. Branches are usually burned on site as they are difficult to be moved and transported (Talhaoui et al. 2015). In addition to being in the pruned branches, olive leaves, are collected in large amounts in the olive mills. This is because leaves are also falling during the collection of the olives. Leaves collected together with the olives are approximately one third of the volume or one tenth of the weight of the collected olives which are taken to the olive mill (Herrero et al. 2011). The leaves are separated from the

Waste Biomass Suitable as Feedstock for Biofuels Production

(a)

(b)

(c)

(d)

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Figure 2.5 Typical olive trees before pruning (a, c). Leaf-load of the tree before trimming (a). Typical branch-load of the tree before trimming (c). Typical pruned trees (b, d).

olives by suction, immediately before milling. Erbay and Icier (2010) analyzed olive leaves and found that they contained oil (about 7%), carbohydrates (about 30%), protein, fiber, ash, and water. In a subsequent study, Erbay and Icier (2010) characterized olive leaves and provided a list of typical compounds which can be found in and also extracted from the leaves. These compounds are worth knowing and considering for improving the economics of a facility which aims at using olive cultivation waste biomass for the production of biofuels, as mentioned in Section 2.1.1. As Velazquez-Martı et al. (2011) and Talhaoui et al. (2015) report in their review, the leaves from a typical olive tree pruning amount to about 25% w/w of the trimmings; the thin branches constitute approximately half of the total weight of the pruned biomass and the thicker branches or wood the remaining 25%. Naturally, this is only an example and the proportions vary depending on a number of factors such as the variety of olives, the kind of cultivation and the protocols followed, the size and the age of the trees, and the local pruning practice. However, rainfall during the year may also be an important factor. The pruning of olive trees can produce around 10 ton/ha of residual biomass depending on the country, the variety, the morphology of the area, as well as the local climate conditions and cultivation practices. However, these figures can substantially change as pruning frequency and extent can be substantially altered because of the transition of the farming activities to complementary ones (i.e. to complement agricultural activities with animal pasture or with involvement in tourism). Velazquez-Martı et al. (2011) give a comprehensive and detailed quantification of the pruning residual biomass as a function of tree kind and conditions.

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Olive bark and branches possess properties similar to those of the leaves and as such they can be used for the extraction of valuable compounds. After the extraction of such compounds, the solid waste can be further treated for biofuel production (Cara et al. 2007; Ballesteros et al. 2011; Romero et al. 2010; Sequeiros and Labidi 2017); the waste leaves can be also used for the same purpose. Therefore, olive oil production wastes have the potential to be utilized for biofuel production. Their production can be combined with that of olive oil and that of valuable products from olive leaves. There are multiple methods to employ in order to minimize costs, increase profitability and minimalize environmental impact on the way toward a cyclic economy and sustainability. For example, Iervolino et al. (2018) have integrated the olive wood waste supply chain with a thermocatalytic reforming process in which biofuels are produced and optimally distributed. They have assessed the environmental gains of the venture using carbon dioxide equivalency as an indicator and they identified the conditions under which such a process can be self-sustained and eco-friendly. Haddad et al. (2017) proposed a new method for olive mill wastewaters valorization. Their method involved mixing olive mill wastewaters with sawdust to produce biofuels and to transform a polluting entity to valuable products. 2.2.3.2

Biomass Wastes from Palm Oil Production

Palm oil production comes mainly from Indonesia and Malaysia in particular. Smaller quantities are produced in West Africa and in Latin America. The tree life span is 25–30 years, they are harvested every 10 days and they are pruned twice a year; the waste biomass from pruning is often left to decay and fertilize the soil of the farm. According to the Department for Environment, Food and Rural Affairs (DEFRA) in the UK (DEFRA 2012) palm oil is the most used vegetable oil in the world and it has a great number of uses in addition to being inexpensive. Moreover, the palm tree is a very efficient producer of oil, although its lifespan does not extend beyond 30 years (DEFRA 2012). Palm oil is extracted from the flesh of the fruits of the oil palm tree. The quantity of palm oil which can be produced from the flesh corresponds to approximately one third of the mass of the fruit. The stone of the fruit is crashed and pressed to produce the palm kernel oil. Those two oils are usually refined and further processed to produce a number of derivatives for a number of different applications in the food sector (biscuits, margarine, bakery products, frying oil), and also in the production of biodiesel, animal foods, soaps, and cosmetics. Its use as a first-generation biofuel is also increasing. Similarly to olive oil production, after the extraction of the palm oil there is a substantial amount of biomass left, the management of which is very problematic. Consequently, a great deal of effort is put toward its valorization via the production of fuels. However, most of this waste mass is continuously produced and therefore abundantly available and also concentrated at the place of oil production. Therefore, the economics of a subsequent treatment will not be burdened by the costs of collection; moreover, if additional treatment is planned in the same area via an extension of the oil production facility, transport costs will be also negligible. The energy content of those wastes can be exploited following different treatments employing a number of existing technologies, ranging from direct combustion, combustion to electricity generation, pyrolysis, esterification, torrefaction, gasification, up to anaerobic

Waste Biomass Suitable as Feedstock for Biofuels Production

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digestion and fermentation. Research is advancing in biomass characterization, factors affecting economic viability, optimal existing technologies and improved methods of treatment, economics, life cycle analysis, and others (International Institute for Sustainable Development 2014). Lee et al. (2017) investigated the slow pyrolysis products obtained by the relevant palm oil production wastes (palm kernel shell, empty fruit bunch and palm oil sludge) as feedstock. Their findings implied that the former two had higher lignin contents; moreover they appealed as precursors for biochars while displaying promising potential for biofuel generation. As mentioned above, Malaysia is a leading country as a palm oil producer. Chiew and Shimada (2013) have applied a life cycle analysis, in order to make a comparison of biofuel production using different technologies and a waste biomass consisting of empty bunches of palm-oil fruits as feedstock. The research also involved other potential uses of this waste. They found that the best option of biofuel was for combined heat and power production. In a later study, Kurnia et al. (2016) compare and discuss developed and in the process of being developed technologies which are used for the valorization of palm oil wastes toward biofuel production. They have furthermore discussed the challenges that future research and development has to address. They classify the biomass treatment and conversion methods as thermochemical, biological or physical, each category having its pros and cons. The thermochemical processes are more suitable for large scale applications, although they are more energy intensive compared with the other two categories. The studies comparing the different technologies back up their arguments by means of relevant life cycle analysis studies via which the sustainability aspects of the different scenarios are evaluated. In the whole process of palm oil generation, the plantation stage appears to carry a heavy environmental weight. However, in terms of costs and on the basis of economics, biofuels from the treatment of palm oil waste do not appear attractive, especially when the cost of fossil fuels is dropping or stays at the levels of 2016. Therefore, aspects which affect the economics of waste treatment such as transport and distribution of biofuels, design of conversion methods, have to be improved. Better improved metrics for a more representative evaluation of the environmental impact of the processes via life cycle analysis have to be devised. It is worth mentioning here though, that the plantation costs and environmental impact have to be considered in connection with the life span of the tree. Palm oils become productive four years after they are planted, and they are productive for 20–30 years. Trees with a longer life span may form a better alternative toward edible oil production and subsequent exploitation of their residual biomass. As such, if a comparison was to be made, olive trees are superior to palms, although they grow in different geographical areas. The aforementioned findings regarding the methods of exploitation of energy using palm oil waste biomass are also supported by a review conducted by Sukiran et al. (2017). According to them, the hygroscopic nature, the low calorific value and the high quantity of oxygen and moisture contained in those wastes restricts their potential as fuels. As such, they propose to enhance the properties of those wastes toward the formation of fuels employing torrefaction processes; they present an overview of factors associated with such a treatment. They also show characterization results of these waste biomasses. They discuss different views resulting from previous research (which is also projected to the present) on oil palm solid wastes torrefaction; potential applications related to the yield obtained via those methods of treatment were shown. Moreover, discussion was conducted around

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the potentials and the evolution of methods for the valorization of palm oil wastes. They conclude that the torrefaction technology can form an excellent method of biomass valorization toward energy production. Finally, they evaluate the relevant costs of the technique and they characterize it as potentially feasible and economically viable. Asibey et al. (2018) have conducted research on the valorization of waste biomass in Ghana toward the production of electricity. They acknowledge the impact which waste collection and management has on the economics of waste valorization, but also the health risks from the untreated waste, especially in African cities. Palm oil waste biomass is an excellent feedstock for the production of electricity according to their research; it is also a better source or raw material for combustion compared with the rest of the waste produced by the local agro-industry. The volumes of waste biomass produced in the process of palm oil production are very large and have already been used for the generation of electricity as a means of energy sources for the needs of the relevant industrial processes; the potential of such a valorization in sub-Saharan Africa is great. However, in their view, there is a hurdle due to the existing local policies, which have to be reconsidered together with the current institutional arrangements so as to take advantage of this potential. Their work also refers to the need to give consideration to maintaining biodiversity and to increase the safety of the processes (Asibey et al. 2018). Obviously, the appropriateness of biomass for biofuel production strongly depends on the area, the alternative energy resources of the local community, the variety of potential feedstocks, and as such, valorization of palm oil wastes in Africa is unlikely to have the same efficacy as in Malaysia or Indonesia or in South East Asia in general. Consequently, studies which deal with the economics of such processes, especially when based on life cycle analyses, have to refer to the local conditions, because owing to the different prices and costs of labor, all such findings are area specific. A study conducted in Malaysia by Hansen and Nygaard (2014) examined the operational status and the evolution of a number of plants which were using or were planning to use palm oil waste biomass for the production of energy; the study examined both completed plants and plants under construction. Amongst a continuing increase of construction of such plants, 39 were advancing toward full operation. Plants accounted for in the study involved plants producing electricity either as stand alone or in connection with others. The produced electricity was then directly used by the industries of the area, by plants connected to the national grid, and by combined power and heat generation plants. During 1990–2010, substantial pressure was exerted from the international community because the industry of palm oil production was expanding in a way that could provoke serious environmental problems in the future. As a consequence, Malaysia was forced to adopt environmentally friendlier practices including the valorization of palm oil effluents and waste biomass. A simultaneous fossil-fuel price rise encouraged energy-intensive industries like those of cement and rubber gloves, refineries, and power generators to use energy produced by sources like palm oil waste biomass to meet their respective needs. However, as in the case of Ghana, progress on waste mass valorization had been hindered, amongst others, by problems in the implementation of energy policy, cost increases, and obstacles in network formation. In addition to palm oil waste, date palm residues can be utilized for biofuel production. Bensidhom et al. (2018) have studied this type of lignocellulosic biomass, which is extremely abundant in Tunisia. For that purpose, they used a fixed-bed reactor to pyrolize the respective biomass, i.e. date palm wood, leaves, empty bunches of fruit and similar

Waste Biomass Suitable as Feedstock for Biofuels Production

31

remains; they produced bio-oil (yield 17–26%), biochar (yield up to 37%), and syngas (yield over 40%). They characterized the respective biofuels and found that they have a great potential to function as alternatives to the usual fossil fuels. This biomass however, before being exploited for biofuel production, needs to be aggregated and transported to appropriate places for treatment. 2.2.3.3

Biomass Wastes from Coconut Oil Production

Besides palm oil waste biomass, coconut oil residues are also an available source for biofuel production. The pros and cons of the management of its valorization have analogies with those of palm oil, as the issues of transportation and aggregation of biomass residues are of a similar kind, while at the same time, they are produced in similar areas. In Malaysia for example, coconut is extensively cultivated; however, after the extraction of coconut milk the residual biomass is usually used as fertilizer or as animal feed or it is left to decay. Research on the characterization of this biomass has been conducted by several researchers and a brief account is given below. Abigor et al. (2000) studied biofuel production by catalytic (PS30 lipase) transesterification of palm stone oil and coconut oil and examined the effect of tert-butanol, 1-butanol, n-propanol, and iso-propanol on the overall outcome. They characterized the oils and reported the efficiency of the reaction in each case. They found that they could produce fuel of a quality consistent with biodiesel specifications. Biodiesel fulfilling the requirements of quality was also produced by Thushari and Babel (2018), in a high efficiency process which employed methanol and a catalyst produced by coconut meal waste in an open reflux reactor. Sulaiman et al. (2013) considered the valorization of coconut waste by means of a reactive extraction method recovering coconut milk from the coconut solid waste in order to produce biodiesel. Different temperatures, potassium hydroxide concentration and agitation speeds were employed in their experimental study. They then employed response surface methodology to identify the range of optimum conditions (reported in their work) with a yield of approximately 90% toward biodiesel. In a subsequent study, Talha and Sulaiman (2018) produced biodiesel using solid coconut waste and a CaO-waste derived catalyst, in a packed bed reactor; they conducted an in situ transesterification reaction obtaining biodiesel yield of 95% at 61 ∘ C with 2.3% w/w catalyst loading and methanol. The mass of methanol was 12 times that of the solid. A refinery waste coconut oil employed as raw material, was treated with ethanol, with and without water, for the production of biodiesel, by Oliveira et al. (2010). The conversion was over 99% on a molar basis. They found that when water adsorption was simultaneously conducted with the esterification, a lower molar quantity of alcohol was needed, thus improving the process costs and mass economy. 2.2.3.4

Biomass Wastes from Avocado Oil Production

The consumption of avocado is very high, and it has been increasing worldwide in recent years. Avocado is eaten raw, but it is also extensively used in large quantities in the food industry. It is also used in the production of cosmetics.

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The seeds are high in starch and polyphenols. Avocado seeds were characterized in terms of thermal and physical properties as well as chemical composition and technologies for their valorization as fuel are shown by Domínguez et al. (2014). Avocado is in fact, a fruit which is extensively traded in the world and its world-wide production was estimated to be nearly five million tons in 2014, which corresponded to an increase of 140% from 1995 to 2015. A great amount of valuable biomass is the residual products (skin, stone) of guacamole manufacturing. This industry generates a large amount of such waste biomass products which are good for energy production, because they possess high calorific value (Perea-Moreno et al. 2016). Durak and Aysu (2014) studied experimentally, by means of slow pyrolysis, the potential of avocado stones for biofuel production. Avocado stones were used in a fixed-bed plug-flow reactor with and without catalyst; bio char, oil and gas were obtained. Potassium hydroxide and alumina were used as individual catalysts, at three temperatures ranging from 400 to 600 ∘ C. In addition, the same group also produced bio-oil from the same biomass, employing ethanol and acetone with potassium hydroxide and zinc chloride as individual catalysts, as well as without catalyst. Avocado stones were put in an autoclave and were subjected to supercritical treatment using ethanol or acetone as solvents. The processes were conducted under high pressure, at temperatures of 250, 270, and 290 ∘ C with or without the aforementioned catalysts. The highest conversion (liquid+gaseous products) of nearly 77% was obtained at 290 ∘ C with acetone via a zinc chloride 10% catalytic process (Aysu and Durak 2015). Furthermore, Díaz-Muñoz et al. (2016) and Bazzo et al. (2015) have also researched valorization of avocado stones toward absorbents and adsorbents formation. Perea-Moreno et al. (2016) described the enthalpy of combustion properties of avocado stones. Their calorific value was similar to that of olive stones and almond shells, and it was concluded that the avocado stones may be used as a solid fuel for heating. Rachimoellah et al. (2009) investigated the capacity of oil from avocado seeds to produce biodiesel. They conducted a series of measurements at different temperatures with different alcohols by changing their molar ratios to the employed quantity of oil; they also modified the method of removing impurities from the end product. They used sodium hydroxide as a catalyst (1% w/w). They found that at 60 ∘ C, using six times more alcohol than oil and a subsequent product dry washing process, a biodiesel 85% in methyl ester was formed. 2.2.3.5

Biomass Wastes from Argan Oil Production

Argan oil is produced in small quantities at present and its main production is centered at the country of its origin, i.e. Morocco. However, as its properties are gaining increasing recognition and publicity, its production in large scale is advancing fast. The argan oil exports from Morocco have dramatically increased in the last 20 years; importers employ it for a number of uses, cosmetics production being the most common one. As Charrouf and Guillaume (2008) report, argan oil is an advanced product with excellent, valuable dietary compounds. For that reason, interest in it has been increasing and there is a continuous rise and expansion of the extent of plantations and of the trees producing this oil, both in Morocco and in other countries where the trees can grow. In a subsequent publication of theirs, Charrouf and Guillaume (2018) report on the progress of a project which holistically addresses production, social, environmental and other issues associated with sustainable processes. In their work the seven stages in argan oil production are explained.

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Argan oil is produced via inefficient production methods which however do not harm its physicochemical properties. More efficient processes are currently being developed, while their application and industrial implementation are also advancing. As already mentioned, argan oil production and argan oil tree plantations are rapidly expanding. Given the experience from other oils presented in this section, where the problem of waste first became significant and then consideration was given to the development of methods for its management, it would be prudent to consider valorization of waste biomass prior to the industrialization of production so that integrated, energy independent, self-sustained argan oil production units can be developed. Such units can make allowance to incorporate the production of added value products and the valorization of their waste in their original process design. Naturally, intensive research is required in this field as the characteristics of the materials involved are far from well known. 2.2.4

Citrus

Citrus is the most important fruit crop in the world with a production estimated at approximately 90 million tons in 2014. According to the Food and Agriculture Organization of the United Nations, in 2012, the production of orange, lemon and grapefruit worldwide was around 95 million tons. Nearly 26% of citrus fruits are used for the production of juice. The biomass residues from the juice production is estimated to be 15 million tons, and it consists of seeds, peels, and pulp. Citrus peel is rich in antioxidants and multiple other valuable compounds with anti-inflammatory, anti-cancer, anti-proliferative, anti-viral and other activities which may contribute to the prevention of disease (Mhiri et al. 2017; Geraci et al. 2017). The largest share of citrus juice production is orange juice, followed by grapefruit, lemon and lime. Other citrus fruit such as mandarins, tangerines, and pomelos are produced and traded in much smaller quantities. The food industry is expanding very fast, especially in the most developed countries, where the time spent in food preparation by families and individuals is continuously shrinking. Consequently, the food industry is expected to generate more by-products, which in many cases is merely waste biomass of plant origin, such as fruit skins, vegetable stems, fruit stones and seeds, pomace, nut shells, etc. Amongst those, the citrus juice industry is producing a great deal of waste biomass very high in added value compounds (Sharma et al. 2016). The orange (Citrus sinensis) is a very common and highly consumed crop. It is part of the citrus family, and it is one of the highest worldwide crops produced. The production of concentrate for the juice industry is one of the primary applications. The orange peel is a part of the residual biomass generated by the processing of citrus. The quantity of orange juice production residuals is estimated to be around 16 million tons per year (Ayala et al. 2017). Oranges are extensively cultivated in America (Florida and California in the United States, Mexico, and Brazil), in Asia (China, India, Pakistan, Iran, and Mediterranean countries), as well as in all European, Asian and African Mediterranean counties (Siles-López et al. 2010). More than 50% of the world orange production is generated in the American continent, where Mexico makes a significant contribution. Mexican orange production was over four million tons in 2013, which represents over 6% of the worldwide orange production. Mexico accounts for 5% of the world grapefruit production and about 15%

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of the lime–lemon global production. In Mexico, the citrus waste generation consists of approximately 750 thousand tons of orange, 70 thousand tons of grapefruit, and around 350 thousand tons of lemon and lime (Ayala et al. 2017). According to the 2007 data of the Statistical Database of the Food and Agriculture Organization of the United Nations, Brazil produces over approximately 28% of the world orange production; it is followed by the United States which produces over 10%, while Europe is in third place with a production of just under 10%. Over 50% of the processed fruit becomes waste. This waste biomass consists mainly of citrus peel (flavedo and albedo), seeds, membranes of the segments and the crushed endocarp. The peel can be used for extracting added value products for preserves, to produce marmalade for food or the peels are even used to produce animal food. However, in many cases it is just treated as a problematic waste and it has often just been dumped in deserted areas close to the juice industries. In such cases, the decomposition resulted in a serious pollution threat for the underground water, a health and safety threat due to uncontrolled methane production, and a visual sore and odor-repellant for the area. However, the valuable properties of the citrus peels and seeds have driven research and technologies development aiming at valorization of this waste in multiple ways and to a great range of applications from the extraction of pharmaceuticals, food preservatives and flavoring agents to cosmetics and biofuels production. Of course, in order to develop such alternative valorization methods a good knowledge of their composition is required; Siles-López et al. (2010) report in their article of publications providing information and characterization data of different varieties of citrus fruits. According to those, in citrus peel, sugars, cellulose, hemicellulose, starches, lignin, organic acids and proteins are contained in proportions which depend on variety, climate conditions, soil and other factors. Moreover, in this study, Siles-López et al. (2010) report how to produce methane from citrus industry waste biomass; they also state the problems associated with such a production. Erukainure et al. (2016) highlight that the use of orange peel waste can be utilized for the extraction of high purity essential oils to decrease the purchase of high-cost raw materials using water as a solvent. Orange and citrus peels in general can be used to produce numerous quality added value products the production of which can compensate for the costs of less profitable or even non-viable synergistic processes aiming at waste exploitation or at environmentally oriented transformation processes. For example, Raga and Lopes (2003) studied the chemical composition of a fivefold sweet orange oil obtained via vacuum distillation. They found that the quantitative composition of the obtained products demonstrated that by appropriate adjustment of the conditions of the fractionator, high quality oils could be obtained. John et al. (2017) conducted a detailed literature survey on bioethanol production using citrus waste. They provide the composition of lignocellulose biomass of different feedstock used for bioethanol production and compare it with that of citrus peel waste which contains low lignin and high carbohydrates (fructose, sucrose, cellulose, hemicellulose, and pectin), and as such it can favor bioethanol production. Citrus peels contain polysaccharides and soluble sugars. They found that it is possible to make citrus peel waste more susceptible to saccharification with enzymatic treatment. They employed pretreatment methods to reduce the formation of compounds restricting its effectiveness. The pretreatment involved dilute acid hydrolysis and steam explosion. A great incentive for the valorization of citrus peel waste comes from the understanding that extraction of added value products prior to biofuel production can greatly improve the economics of the venture, so scale up efforts are under

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way. Extraction of D-limonene as an added value product can also facilitate the bioethanol production via saccharification because D-limonene inhibits the enzymatic fermentation. The economics of the saccharification can be improved by the selection of enzyme type, function or method of application. Options include the immobilization of the enzymes for example, the use of cell-recycle reactors, and co-culture fermentation (John et al. 2017). Bicu and Mustata (2011) worked with cellulose extraction from orange peel, the pulping of which was achieved employing sodium sulfite and sodium metabisulfite. They evaluated how the addition of the pulping precursor, the reaction residence time and the temperature affected the result. They found conditions at which good quality cellulose could be recovered with over 40% w/w yield. The obtained cellulose was good for a number of uses, such as water absorbents or fillers, including availability of cellulose as raw material for production of further derivatives. Lanfranchi (2012) conducted an evaluation of the economics of different methods of production of energy using citrus waste biomass which resulted after removing the juice of the fruits, i.e. what has been reported before as juice production residual biomass such as peel, seeds, membranes of the segments, residual juice and pulp. Solid fuel can be produced via drying of the waste and produce solid pellets for combustion. However, as previous experience has shown, it has to be done via very well-designed processes and employ only the less moist parts of this residual. This biomass has occasionally been used as a fuel. However, it was highly uneconomical. This is because its water content is very high (more than 90%). In these applications, the residual biomass, as a whole, was dried before it was burnt. However, this incurred high energy costs. As a result, it ended up being a very inefficient energy source, requiring more energy for its drying than the energy provided by its combustion. Biofuels (bioethanol and biogas) production from those wastes is a much better option. Rivas-Cantu et al. (2013) have conducted a study to present the best at the time technologies which can be employed for the valorization of citrus waste biomass toward the production of energy, using laboratory data obtained from the pretreatment and enzymatic hydrolysis of industrial waste. Their work involves results obtained from an extensive study and long experience in the relevant work. They examined the production of sugars and value-added compounds. They also studied how different methods of pretreatment, including mechanical and chemical methods, could be used to increase the yields toward the most valuable sugars. The key processing steps of the waste were the following: shredding and grinding below 1.25 cm particles to form a suspension in water, which was partially hydrolyzed by steam-, flash distillation or centrifugation for fermentation-inhibitor limonene removal, enzymatic hydrolysis of the resulting slurry under a controlled temperature and pH range, fermentation, or simultaneous saccharification and fermentation to produce ethanol and finally distillation to separate the produced biofuel from the waste. They also report problems associated with the implementation of this technology. Such technologies have not yet found their way to an efficient large scale, although much progress is being made. In summary, citrus peel waste can form an excellent second-generation non-food lignocellulose feedstock for biofuel production. Prior to its utilization as a biofuel raw material, it can provide other added value products following extractive processes. All citrus wastes are good for those applications but orange peel has a higher concentration of carbohydrates and as such it can provide the desired products at higher yields (Joshi et al. 2015).

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Grape Marc

According to the literature review conducted by Brunerova and Brozek (2017), the vineyards of the world extend to approximately 8 billion ha of land. The residual biomass from grapes and wine production consists of pruning and trimming the vines producing an average of 5 kg per waste biomass per vine and later from the pomace, left over after the grapes are crushed for the production of wine. Corbin et al. (2015) report on the usage of grape pomace which is either further fermented and distilled for the production of alcoholic drinks in the form of grapa, or is used as animal feed, as a raw material for added value products like antioxidants or it can be used as a promising source for biofuel (bioethanol or biogas) production. According to them, the pomace contains a high portion of water soluble compound which upon fermentation can provide ethanol equal to approximately 25% of the pomace waste or be subjected to acid pretreatment followed by enzymatic hydrolysis to produce 0.4 l of ethanol per kilogram of pomace. The environmental impact of the burying of untreated grape pomace is highlighted by Mendes et al. (2013), water contamination and soil erosion being amongst the most important. In their review they report on the composition of the residual biomass following wine production. High concentrations of phenolic compounds hinder the natural degradation of this waste biomass; extraction of such compounds is something that should be aimed for because they are very valuable and can find diverse applications such as for the formation of food supplements or cosmetics or for other uses in the pharmaceutical industry, as adhesives or as compounds whose range of applications can expand if the necessary research is conducted. In their study, Mendes et al. (2013) focus on the grape skins and in particular on red ones; after having assessed their composition, they evaluated their chemical structure with attention on the main macromolecular constituents; the target was to develop strategies for their valorization toward formation of compounds for biocomposite applications and insulation materials. Typical composition identified was: cellulose, hemicelluloses, proteins, tannins, sugars, aliphatic compounds, and ash in approximate percentages of 21, 12, 19, 14,12, 14, and 8%, respectively, a great proportion of which are water soluble. Following this analysis, they considered it plausible to employ a multistage process for the treatment of grape skins; they proposed pre-extraction of polar compounds, subsequent polar compound extraction with hot water, and finally valorization of grape skin solid part as biocomposite. In their work “Extraction and purification of high added value compounds from by-products of the winemaking chain using alternative/nonconventional processes/ technologies,” Yammine et al. (2018) considered the valorization of winery waste products. Their literature review provided quantitative data (approximately 65 million tons world production of grapes in 2014, around 50 million tons of which were used for wine production with an estimated maximum of over 15 million tons of valuable waste biomass left over), which can be used for the production of multiple value-added products. They also compared different technologies for the extraction methods, but their comparison could not provide any solid results (Yammine et al. 2018). Prado et al. (2012) and Coelho et al. (2018) studied the supercritical fluid extraction of grape seeds. The collated literature review of Prado et al. (2012) highlighted the benefits of grape seeds for food supplement and their antimicrobial activity, which resulted in the commercialization of relevant applications. The researchers focus on the extraction

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of those valuable compounds by means of supercritical carbon dioxide, but they do not focus on any further treatment of the waste biomass from this process. Grapes employed in wine production are crashed and the produced pomace is left to ferment. The fermented pomace is subsequently subjected to distillation. The batch or semi-batch distillation of those fermented residues provides pisco, a grapa-like alcoholic drink. Farías-Campomanes et al. (2013) conducted an experimental study for the process of extraction of polyphenolic compounds from such fermented grape bagasse. Bagasse of this type contains approximately 50% seeds, while the remaining 50% is equally shared between grape stems and skins. They employed supercritical carbon dioxide extraction with ethanol and water to enhance extraction of polar compounds and they found that it is viable to establish an industrial supercritical fluid extraction plant to recover phenolic compounds from grape bagasse. They found that this is viable even for small scale production units. Sanahuja-Parejo et al. (2019) have produced bio-oil via the co-pyrolysis of polystyrene and grape seeds achieving high conversions and good oil properties, i.e. with a higher pH value than the acidic bio-oils obtained from lignocellulosic mass, a lower content of phenols and oxygen and an increased quantity of aromatics, owing to the hydrogen-donor contribution of polystyrene in the process. Xu et al. (2009) subjected winery waste to pyrolytic treatment in a bubbling fluidized bed reactor. The calorific value of the produced gases and bio-oil were evaluated and optimum conditions for the process were identified in order to ensure a positive net energy operation. Using self-produced biogas and bio-oil to cover the thermal needs of the pyrolysis, they achieved sustainable bio-oil production. Zhang et al. (2017) designed and compared two methods of valorization of grape pomace toward energy uses; combustion for electricity and pyrolysis for biofuels, i.e. for bio-oil and bio-gas. They found that the latter had a better positive impact on wineries in terms of economics as well as environmental aspects. According to this study, small scale wineries (80%) by the price of the starting vegetable oil (Gebremariam and Marchetti 2018; You et al. 2008) to the point that it compromised the entire profitability of the industrial production chain with respect to fossil fuel, as well as for an ethical concern, due to the land for fuel use. Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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O

. cat. FAMEs Triglycerides

Figure 5.1

Glycerol

General equation to obtain biodiesel from triglycerides.

In any case, production of FAMEs still remains one of the main important European targets in terms of renewables in liquid transport fuels for the future (Ajanovic 2013). In fact, a value of 10% of renewables was fixed by the European Parliament as a final objective to be achieved by 2020. In addition, new forthcoming rules have been introduced which also consider the sustainability criteria. More specifically, in order to limit the environmental impact correlated with the use of “first-generation” feedstock, and to better regulate the indirect land use change due to the cultivation of crop-for-fuel, a cap for this specific fuel was fixed to 7% (meaning 7 Mton/yr). On the other hand, the use of alternative waste-oily sources and renewable cleaner energy sources (renewable electricity for rails and cars) (Directive 2009/28/EC 2009) are going to be privileged by introducing the “double counting” rule/concept. Such a decision actually “forces” 1.5 Mton of less valuable feedstocks to be used to produce biofuel for promoting a cleaner mobility. Waste cooking oils (WCO), waste animal grease (WAG) and non-edible oils were definitively recognized as the most important alternative feedstocks (“second-generation feedstocks”) for a sustainable biodiesel production (Ashraful et al. 2014; Atabani et al. 2013). In Italy, the total annual consumption of vegetable oils is around 1.4 Mton, and it was estimated that over one forth (0.26 Mton) ended up as waste (CONOE 2018): 94 000 ton (36%) from restaurants, bars, hotels, etc., and 166 000 ton from domestic users. Only a very limited amount (72 000 ton in 2018) was effectively and properly collected and recycled, while over 60% of waste oil was dispersed into the environment, most presumably into the water collection system. Besides WCO, in Italy, over 0.25 Mton WAG, which should be disposed of through incineration (cat1 and cat2, as defined by European Regulation No. 1069/2009), is collected annually and could be potentially used for producing liquid biofuels. These considerations of consumption and effectiveness of collection, can be easily extended to most of the rich and developed American, Asiatic, and European countries. In any case, most uncollected oils can be potentially recovered as brown grease, trap grease, sewer grease, and sewage scum at different points of the water collection systems depending on the specific country considered. For example, in European countries, where grease traps are hardly installed, most of the WCO is supposed to be potentially collected in wastewater treatment plants (WWTPs), as sewage scum or sewer grease, even if it is not ever collected and separated (di Bitonto

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et al. 2016). In the US or in some developed Asiatic countries, where grease traps are more widely installed, a huge amount of yellow grease is effectively collected and even found to be convertible into biodiesel (Montefrio et al. 2010). All these second-generation feedstocks normally contain high levels of FFAs (1–90 wt%) and could be efficiently converted into biodiesel by adopting appropriate strategies. In fact, the presence of FFAs, water, or other contaminants, makes the direct application of the conventional base-catalyzed process more complicated. Specifically, the addition of a base to oil which contains FFAs, leads to the formation of soaps and water (Figure 5.2). This reaction not only leads to the neutralization of the catalyst, but also complicates the industrial operations in recovering both the products (FAMEs and glycerol), since the resulting soaps and water may cause emulsification, with an increase in separation costs. In general, two main strategies have been considered for the conversion of waste oils into biodiesel (Figure 5.3): • Physicochemical pretreatments of waste oils for the removal of FFAs. • Direct treatment and conversion of FFAs into esters (FAMEs or glycerides). Evaporation, steam injection, filtration, and dewatering (Figure 5.3, route a) represent the most common physicochemical operations adopted to remove impurities from waste oils (water, FFAs, etc.) with the aim of obtaining refined glycerides to be transesterified under the conventional route. Alternatively (Figure 5.3, route b), FFAs can be directly converted in situ in the relevant FAMEs. To this end, homogeneous mineral acids represent

RCOOH + MOH

RCOO⊝M⊕ + H2O

M = Na, K, etc. Figure 5.2

Reaction for saponification of FFAs.

Waste Oil WCO, Animal grease, Brown grease, Low-quality oils FFAs, water, glycerides

(a)

FFAS or Soaps

(b)

Refined Oils

Pretreated Oils

Biodiesel Figure 5.3

Rationale of conversion of waste oils into biodiesel.

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the conventional choice. Recently, several further catalysts have been investigated and proposed: heterogeneous acids, ionic liquids (ILs), and enzymes. In this chapter, a critical overview of these different approaches has been assessed, by reporting the respective advantages and drawbacks.

5.2

Physicochemical Pretreatments of Waste Oils: Removal of Contaminants

Different types of pretreatments have been studied with the aim of reducing the presence of contaminants and for improving the quality of an oil to be converted into biodiesel: steam injection, neutralization, vacuum evaporation, and vacuum filtration represent the most simple procedures investigated (Kulkarni and Dalai 2006). When not considerable differences were determined between waste oils (as could happen for some WCO) and virgin oils (Knothe et al. 1997), with FFA content less than 3 mg KOH/g, heating and removal by filtration of solid particles are often sufficient as pretreatments for the subsequent conventional transesterification reaction. In fact, heating WCO by steam to 65 ∘ C, and allowing sedimentation, reduction of the water (from 1.4 to 0.4%) and FFA contents (from 6.3 to 4.3%) were observed, which allowed higher yield of FAMEs to be obtained (from 67.5 to 83.5%) (Supple et al. 2002). In some other cases, water and suspended solids were efficiently removed by mixing oily feedstock with magnesium sulfate (Felizardo et al. 2006), or 10% silica gel (28–200 mesh) (Issariyakul et al. 2007), or calcium chloride (Predojevi´c 2008), and using vacuum filtration. However, considering that waste oils often partially decompose and/or deteriorate, the reduction of the final methyl ester yield and the co-formation of undesired products were verified during biodiesel production. So, in most of cases, pretreatment methods were focused on reducing FFAs (up to 3 mg KOH/g), water (up to 0.1 wt%) and polymers, before the conventional transesterification process. Typically, FFAs can be removed by neutralization and separated as soaps (caustic washing), while activated charcoal was found to efficiently remove polymers through adsorption (Cvengroš and Cvengrošová 2004). In this way, industrial production can be maintained without the great problems of separations and emulsification. For lower quality feedstocks, such as yellow grease (FFA content up to 15 wt%) or brown grease (FFA content up to 99%), the preliminary treatment can result in a steam stripping (Anderson 2014), in order to obtain FFAs, water and light contaminants on one side and glycerides ready to be converted conventionally on the other side. Although the removal of impurities such as water, FFAs, and polymers prior to transesterification can improve the quality of waste oil for producing FAMEs, pretreatments tend to increase biodiesel production costs, and specific and detailed economic analysis needs to be conducted. For this purpose, very limited studies were carried out to estimate and quantify the additional costs correlated to the pretreatment steps. However, a significant improvement of the economic viability of the process from waste oils was determined compared with virgin oils (Yaakob et al. 2013). In some cases, reduction of about 45% of direct production costs, even including the additional costs due to pretreatment (Zhang et al. 2003a), was achieved. In any case, removal of FFAs as soaps, prevent a huge amount of starting feedstock from being converted into the desired FAMEs, by lowering the final proficiency of the whole

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production chain. This approach could be feasible only on those oily feedstocks which contain low FFA content.

5.3

Direct Treatment and Conversion of FFAs into Methyl Esters

As an alternative to the removal of FFAs from waste oils, a more profitable strategy is the direct conversion of FFAs in the relevant methyl esters in situ (Figure 5.3, route b). In this case, through the use of a two-step reaction, acid oil can be more efficiently converted into biodiesel: in the first step an acid catalyst is used to promote the direct esterification of FFAs, whereas alkaline catalysts are adopted in a second stage for the transesterification of glycerides (Deng et al. 2010; Canakci 2007). The most challenging part of this process is the first conversion of FFAs in FAMEs: homogeneous and heterogeneous Brønsted and Lewis acids, ILs and enzymes represent possible alternatives. 5.3.1

Homogeneous Catalysis: Brønsted and Lewis Acids

Industrially, sulfuric acid (H2 SO4 ) is the most used catalyst for promoting direct esterification of FFAs. It allows the reaction to be effectively carried out at low temperatures (60–120 ∘ C), atmospheric pressure and in a short time (two to four hours). The direct esterification suffers from several thermodynamic and kinetic constraints, since the co-formation of water inhibits the reaction, requiring a double pretreatment step (Canakci and van Gerpen 2001) or the use of a large excess of MeOH (up to 40:1 MeOH:FFAs) (Chai et al. 2014). In principle, the use of MeOH in this step allows FFAs to be converted into FAMEs, by obtaining a final mixture of FAMEs and glycerides, which can eventually be reacted under alkaline condition to produce biodiesel. However, a series of drawbacks connected to the use of H2 SO4 is also included: (i) the acid is partially soluble in the resulting esterified oils/fats; (ii) its recovery is only partial; (iii) its recycle and/or reuse in a new pretreatment cycle is quite complicated; and (iv) the use of costly materials (resistant to the acid treatment) for reactors is compulsory. In detail, the partial dissolution of H2 SO4 in the resulting oily mixture needs costly downstream operations. Washing procedures with solvents (MeOH [Kawahara and Ono 1977] or glycerol [Brunner et al. 2000]) or chemical neutralization with bases (potassium methoxide or sodium hydroxide, sodium hydroxide in MeOH [Garcia 2015]) are necessary. Besides large consumption of the alkaline catalyst, a concomitant production of a new salt, which needs to be disposed of at the end, characterizes such a pretreatment. Such drawbacks negatively affect the economic balance sheet of the overall production of biodiesel from waste grease, pushing forward the study and the optimization of alternative solutions. For example, the use of sulfamic acid as a catalyst demonstrated elevated potential for an environmentally friendly means of biodiesel production. In fact, it results in a safe chemical, cheap and sufficiently active in promoting the direct esterification: about 90% of the starting acidity was converted in the relevant ethyl esters under mild conditions. Finally, it was even found potentially recoverable and recyclable. However, its separation from the reacted mixture needs unpracticable operations for an industrial scale: (i) addition of hexane in order to induce precipitation of most of the catalyst; and (ii) washing of the organic phase

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was necessary to remove any traces of sulfamic acids from the pretreated organic mixture FAMEs/glycerides (de Oliveira et al. 2016). Besides conventional mineral Brønsted acids, Lewis acids have also been found active in promoting vegetable oil transesterification. Several metal acetates and stearates (calcium, barium, plumb, zinc, cobalt, and nickel) were found to be capable of producing esters from low-quality feedstock, even in the presence of high FFA contents. In fact, they do not produce soaps, but if used in large excess, they precipitate as salts. In addition, they need the use of high temperature (200–250 ∘ C) and high pressure (400–600 psi). Stearates were found more active than the respective acetates, due to the relevant higher solubility in the oils, showing better performance than Brønsted acids with lower concentration of catalysts and lower oil/alcohol molar ratio (Di Serio et al. 2005). Glycerolysis may be adapted to reduce FFA content in acidic and low-cost feedstocks for biodiesel production (Figure 5.4): reduction of FFA content from 50 to 5% after three hours of reaction at 200 ∘ C, even without the addition of any catalyst, was observed (Felizardo et al. 2011). Zinc salts were tested as efficient catalysts. Once a mixture of monoacylglycerol (MAG), diacylglycerol (DAG), and triacylglycerol (TAG) was obtained, the conventional transesterification could be proficiently applied. Such an approach was really promising, specially if carried out by directly using crude glycerin coming from a conventional biodiesel plant. Produced in substantial excess as a by-product from the alkaline transesterification process to make biodiesel, the direct utilization of crude glycerin with minimum processing can significantly benefit the biodiesel industry. Low quality grease (30% FFAs) was efficiently converted into MAG, DAG, and TAG using crude glycerin, which could be obtained from a conventional biodiesel plant, evaporating methanol from the glyceric phase. Working under a 1:1 M ratio of crude glycerin and FFAs at 230 ∘ C and for 150 minutes, the wt% FFAs was reduced to below 1 wt%. Such a biodiesel production, based on glycerolysis-treated oil, resulted in a less energy intensive route (0.251 MJ/kg biodiesel produced) than the conventional “esterification and transesterification” route (0.534 MJ/kg biodiesel produced) (Tu et al. 2017).

First Step MeOH

Waste Cooking Oils

Glycerol

FFAs + Glycerides

FAMEs + Glycerides

Glycerides Animal Grease

Biodiesel Second Step Figure 5.4

Scheme of two-step up-grading of waste oil and grease to biodiesel

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Actually, the evaluation of the overall sustainability of this process needs to take into account the effective recoverability of glycerol at the end of the whole process (glycerolysis and transesterification). The presence of different contaminants in raw grease could promote and induce side reactions of polymerization or decomposition of glycerol, even into toxic compounds, such as acrolein. With respect to the environmental issue (check of the emissions and treatment of toxic gaseous compounds), an economic constraint could be associated in particular for brown grease, which has low glycerides content. In fact, degradation of glycerin could be a critical aspect, since a partial refilling of fresh glycerol could be always necessary, by compromising the economic profitability. 5.3.2

Heterogeneous Catalysis

In order to take advantage of an easy recoverability, several heterogeneous acid catalysts were widely investigated. In fact, the use of “solid” acids allows an easy separation of the catalyst from the final products at the end of the chemical pretreatment, which makes the overall downstream simpler. Strong acid cation exchange resins (Lewatit K2621, K2620, Amberlyst 15, 20BD, 35, Dowex M 31, Tulsion T-42, Indion 130 [Sani et al. 2014; Park et al. 2010]) represent one of the most investigated species. Largely available for several different industrial purposes, they were reported as adequately active in promoting the direct esterification, under mild conditions. Acceptable reduction of acidity, below 1 wt%, was obtained even starting from pure acids, after two to six hours of reaction at 50–80 ∘ C and working with an oil:MeOH molar ratio of 1:6. Specific surface analysis, number of acidic sites and the size of average pore diameters play key roles in determining better performances (Özbay et al. 2008). Another class of heterogeneous catalysts are H-form zeolites: microporous crystalline solids with well-defined structures containing Si, Al, and O in their framework and cations. Already used in oil refining, petrochemistry, and in the production of fine chemicals, they can be prepared with strong Brønsted acid sites (also Lewis acid ones) with a quite good resistance to high reaction temperatures. However, diffusion of the reactant molecules to the active sites can be a limitation due to their small pore size (lower than 2 nm). In the production of biodiesel, H-ZSM-5, H-MOR, H-BETA, and H-USY are found to show poor esterification or transesterification catalytic activity, as a consequence of internal diffusion limitations of the bulky reactant molecules (FFAs and glycerides) into the micropores of zeolites (Kiss et al. 2006; Peters et al. 2006). In addition several mixed metal oxides were found to be active in promoting direct esterification and, in some cases, the transesterification also, in a one-pot conversion of acid oils (Dibenedetto et al. 2014). In particular, bifunctional oxides, namely basic and acid ones, can promote the two main reactions in a single step. High temperatures and oil:MeOH molar ratio are however necessary. It is worth specifying that a surface modification of some simple metal oxides (such as ZrO2 , Ta2 O5 , Nb2 O5 , TiO2 ) through sulfuric acid, yield the relevant sulfated metal oxides (SO4 2− /ZrO2 , SO4 2− /Ta2 O5 , SO4 2− /Nb2 O5 , and SO4 2− /TiO2 ), resulting in new typical solid superacids. Since they possess both Brønsted and Lewis acid sites, they can promote under relatively mild conditions direct esterification and transesterification of FFAs and glycerides, respectively. Transesterification of tripalmitine (TP) with MeOH

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was investigated under a N2 atmosphere at 65 ∘ C for 24 hours. TP (0.55 mmol) was dispersed into 49 mmol of MeOH, and 5 wt% catalyst (referred to the overall reaction mixture). A yield of 25% MP was obtained, but the use of some appropriately modified catalysts (SO4 2− /ZrO2 –SiO2 (Et) hybrid material) produced more than 75% under the same conditions (W. Li et al. 2010, 2012). The use of some magnetic nano-sized solid acid catalysts has been recently proposed. Basically, to a magnetic iron-based core may be grafted different functionalized arms (carbonaceous or also glycidylic), ending with sulfonic acid groups. In this way, to the acidity of sulfonic groups, the easy recoverability of magnetic iron oxide was coupled, by guaranteeing good activity and recoverability. Direct esterification of FFAs (16 wt%) in grease with MeOH using this kind of catalyst (4 wt%) gave 96% conversion to FAMEs within two hours. At the end of a reactive run, the catalyst was easily separated under a magnetic field and showed no loss of productivity during 10 cycles (Tan and Li 2012). Direct production of biodiesel from Jatropha oil with high acid value (17.2 mg KOH/g) was also positively tested at 200 ∘ C. In addition to a good (90.5% at 200 ∘ C) biodiesel yield, there was also a very efficient recovery of the catalyst (Zhang et al. 2015). Finally, very simple FeSO4 represents an effective example of heterogeneous catalysts, even if it was demonstrated that the direct esterification reaction was promoted by the soluble part of this salt and catalysis did not take place on the solid surface (Wang et al. 2007). However, heterogeneous catalysis in general was quite expensive, often less active than conventional mineral acids, and reusable for only limited cycles. The surface of catalysts was subjected to passivation and/or deactivation phenomena (especially when very contaminated oils were treated) and the relevant reactivation requires costly procedures not compatible with industrial constraints. In fact, washing operations with pure alcohol and/or thermal treatment (up to calcination) were proposed as recovery procedures. 5.3.3 5.3.3.1

Enzymatic Biodiesel Production The Use of Lipases for Biodiesel Production

A wide range of lipases and other esterases have been tested as catalysts for biodiesel production (Jothiramalingam and Wang 2009; Fjerbaek et al. 2009). The alkaline catalysts are able to convert glycerides as well as FFAs into biodiesel in the same mild conditions without the formation of soaps. In addition, they offer several additional benefits: • The utilization of low-quality and non-edible oils without a negative impact on the environment (Tan et al. 2010). • Fewer process steps with the reduction of energy consumption and wastewater volumes. • Improved phase separation to obtain a higher quality of glycerol (Kumari et al. 2007; Meher et al. 2006; Fukuda et al. 2001). However, they present some drawbacks that limit the use on an industrial scale: • Low reaction rate (Zhang et al. 2003b). • Their costs (Andrade et al. 2019; Haas et al. 2006; Jaeger and Eggert 2002). • Loss of activity. Lipases are widely employed to catalyze hydrolysis, alcoholysis, esterification, and transesterification of carboxylic esters.

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Several literature reports describe biodiesel synthesis with a range of enzymes, using different feedstocks and alcohols under different conditions (Mata et al. 2017; Mulalee et al. 2016; Yan et al. 2012). The conversion of oils and fats into biodiesel is a complex reaction in which several parallel reactions take place. The conversion of glycerides occurs through the sequential transesterification reaction of TAG to DAG and subsequently to MAG and finally to glycerol, by using MeOH, ethanol, propanol, and butanol as nucleophiles. At the same time, FFAs can be esterified into the corresponding alkyl esters. For several lipases, the esterification reaction is significantly faster with respect to the transesterification process, since the smaller nucleophilic part of the former fits better in the active site of the enzyme (Nordblad et al. 2016). The Candida antarctica lipase B (CALB) has been extensively investigated in biodiesel applications for the high activity and selectivity shown in the conversion of substrates rich in FFAs such as spent oils and animal fats (Syrén and Hult 2010; Deng et al. 2005; Shimada et al. 2002; Watanabe et al. 2001, 2000). However, several papers describe the benefits of other lipases, Candida rugosa lipase (CRL; Kaieda et al. 1999), Candida sp. 99–125 (C sp.; Lu et al. 2010; Nie et al. 2006), Rhizomucor miehei lipase (RML; Nelson et al. 1996), Eversa Transform (Andrade et al. 2017), Burkholderia cepacia (BCL) and Thermomyces lanuginosa lipase (TLL; Li et al. 2006) that can be used alone or combined to catalyze both the processes (see Table 5.1). Many of these enzymes are commercially available, also in immobilized form, in order to facilitate the relevant recovery and re-use. CALB, which is commercially known as Novozym 435, has been used as a catalyst for the conversion of different vegetable oils and WCO to obtain yields of FAMEs over 90–95% (Mulalee et al. 2016; Royon et al. 2007; Du et al. 2004). Candida sp. 99–125 lipase immobilized on textile membrane can catalyze the conversion of several waste oils, lard, and various vegetable oils with yield higher than 90% (Lu et al. 2007; Lee et al. 2002). Generally, enzymes show a higher yield and a longer lifetime in substrates rich in FFAs than in substrates rich in triglycerides, but the presence of phospholipids in the crude oil could inhibit the activity of lipases (Lai et al. 2005; Wei et al. 2004). Therefore, preliminary treatments of degumming and filtration of starting oils are often required before the process (Watanabe et al. 2002). Optimal temperature is in the range between 50 ∘ C and 70 ∘ C, in relation to the nature of the enzyme and the thermal stability of the carrier used. Generally, a stoichiometric amount of alcohol is required for the complete conversion of glycerides and FFAs into the corresponding alkyl esters. However, considering that an excess of alcohol causes inactivation of the enzyme, and that the smaller the alcohol, the more important the inhibition effect, the stepwise addition of MeOH during the process was the best strategy. Since the solubility of MeOH in the alkyl esters is greater than in the oil, with the gradual addition of alcohol it is possible to limit the enzyme deactivation (Shimada et al. 1999). This strategy was also effective for other lipases such as C sp. (Lu et al. 2007), Rhizopus oryzae (Chen et al. 2006), and Pseudomonas fluorescens (Soumanou and Bornscheuer 2003). Another approach to resolve the lipase inactivation caused by MeOH, is the use of organic solvents. Royon et al. (2007) have studied the enzymatic methanolysis of cotton seed oil in the presence of solvents. Lipase inhibition was eliminated by using t-butanol as reaction medium, by obtaining a noticeable increase of reaction rate and ester yields (97%). Other solvents such as 1,4-dioxane (Iso et al. 2001) and ILs (Ha et al. 2007) might solve the problem of lipase inactivation caused by MeOH, but other problems like environmental

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Table 5.1 Enzymatic biodiesel production using various lipases. Enzyme

Substrate

CALB

Alcohol

Sunflower oil

MeOH

Pretreated tallow (15% FFAs)

MeOH

Restaurant grease (8.5% FFAs)

EtOH

Waste oil (2.5% FFAs)

MeOH

Rice bran oil (85% FFAs)

MeOH

CRL

Soybean oil

MeOH

C sp.

Soybean oil

MeOH

Waste oil (46.7% FFAs)

MeOH

RO and CALB

Soybean oil

MeOH

TLL and CALB

Waste oil (70% FFAs)

MeOH

Eversa Transform

Castor oil

MeOH

Reaction conditions 3:1 M alcohol 10 wt% cat. based to oil 40 ∘ C, 24 h 3:1 M alcohol 10 wt% cat. based to oil 30 ∘ C, 72 h 3:1 M alcohol 5 wt% cat. based to oil 30 ∘ C, 24 h 3:1 M alcohol 1 wt% cat. based to oil 30 ∘ C, 24 h 3.5:1 M alcohol 5 wt% cat. based to oil 30 ∘ C, 7 h 3:1 M alcohol 10 wt% cat. based to oil 45 ∘ C, 90 h 3:1 M alcohol 10 wt% cat. based to oil 40 ∘ C, 12 h 3:1 M alcohol 5 wt% cat. based to oil 40 ∘ C, 90 h 4.5:1 M alcohol 30 wt% cat. based to oil 45 ∘ C, 21 h 4:1 M alcohol 3 wt% cat. based to oil 35 ∘ C, 12 h 6:1 M alcohol 5 wt% cat. based to oil 35 ∘ C, 8 h

Yield

References

93.2%

Deng et al. (2005)

74%

Lee et al. (2002)

>96%

Wu et al. (1999)

90%

Watanabe et.al. (2001) Lai et al. (2005)

>98%

90%

Kaieda et al. (1999)

83.2%

Lu et al. (2010)

92%

Nie et al. (2006)

>99%

Lee et al. (2006)

95%

Li et al. (2006)

94%

Andrade et al. (2017)

RO, Rhizopus oryzae.

issues (toxicity, emissions) and economic sustainability (due to recovery and losses of solvents) are introduced and need to be taken into consideration. 5.3.4 5.3.4.1

ILs Biodiesel Production ILs and Supported ILs Catalysts in Biodiesel Production

ILs are organic salts generally composed of an anion and an organic cation. With respect to the inorganic salts, these are liquid at low temperature (even room temperature) and show unique and advantageous properties (Greaves and Drummond 2008): • Low vapor pressure. • Good chemical and thermal stability.

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• Ability to dissolve a wide range of inorganic and organic compounds. • Easy separation by obtaining products with high purity. ILs can be used in various fields such as enzymatic catalysis (Zhao et al. 2002), organic synthesis (Forbes et al. 2006), Diels–Alder cycloadditions (Janus et al. 2006), Friedel–Crafts alkylations and acylations (Baleizão et al. 2004), spectroscopy and electrochemistry (Liu et al. 2010), polymerizations (Lu et al. 2009), nanomaterials (Tunckol et al. 2012), and extraction and separation processes (Nakashima et al. 2005). In recent years, ILs have received much attention for their potential applications for biodiesel production in replacing the conventional homogeneous and heterogeneous catalysts. In fact, they are very effective in the direct esterification and transesterification processes, due to the presence of Brønsted acid sites. In addition, they have a poor solubility in the biodiesel phase, which facilitates the operations of separation at the end of the process and reuse for several cycles of reaction (De Diego et al. 2011). N-triethylammonium sulfate ([Et3 NH][HSO4 ]), N-methyl-2-pyrrolidonium methyl sulfonate ([NMP][CH3 SO3 ]), and 1-butyl-3-methylimidazolium hydrogen sulfate ([BMIM][CH3 SO3 ]) have been positively tested as effective promoters in the conversion of non-edible oils and animal fats, as well as several –SO3 H functionalized strong Brønsted acidic ILs, e.g. 1-(4-sulfonic acid) butyl-pyridinium hydrogen sulfate ([BSPy][HSO4 ]), 1-(4-sulfonic acid) butyl-pyridinium trifluoro methane sulfonate ([BSPy][CF3 SO3 ]) and 1-(3-sulfonic acid) propyl-3-methylimidazolium hydrogen sulfate ([C3 SO3 H–mim][HSO4 ]) (Figure 5.5) (Muhammad et al. 2015; Ullah et al. 2015; Su and Guo 2014, Liu et al. 2012; K.X. Li et al. 2010). Zhang et al. (2009) have investigated the use of ILs containing imidazolium and pyrrolidonium groups in biodiesel production, especially starting from pure FFAs or their mixtures. [NMP][CH3 SO3 ] showed the best catalytic behavior in the esterification of oleic acid with ethanol, with a yield of 95.3%, into ethyl esters after eight hours at 70 ∘ C (molar ratio of oleic acid:ethanol:IL = 1:12:0.2). In addition, the catalyst can be recovered at the end of the process and reused up to eight times, maintaining a conversion of above 90%.

–

HSO4 N

+

–

H

[Et3NH][HSO4] –

HSO4

SO3H

N + N

H

[BMIM][CH3SO3]

–

CF3SO3

N

[BSPy][HSO4]

+

[NMP][CH3SO4]

+

Figure 5.5

HSO4

O N

N

–

CH3SO3

+

[BSPy][CF3SO3]

–

HSO4

SO3H N + N

SO3H [C3SO3H–min][HSO4]

Chemical structure of –SO3 H functionalized strong Brønsted acidic ILs.

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Process Systems Engineering for Biofuels Development

Han et al. (2009) reported biodiesel preparation from WCO using novel Brønsted acidic ILs with an alkane sulfonic group (R-SO3 H) as catalysts, with yields of over 93.5% after four hours at 170 ∘ C (molar ratio of oil:MeOH:IL = 1:12:0.06). ILs containing pyridine rings were studied by K.X. Li et al. (2010, 2013) in the esterification and transesterification of Jatropha oil, which presents a high content of FFAs. A FAMEs yield of 95.1% was obtained after six hours at 100 ∘ C under a molar ratio of oil:MeOH:IL = 1:18:0.15. The product was easily recovered from the reaction medium, and [BSPy][CF3 SO3 ] in particular maintained its catalytic activity for seven reuses. Olkiewicz et al. (2016) studied biodiesel production from sludge lipids catalyzed by different acidic imidazolium and long chain ammonium liquids with an alkane sulfonic acid group and different anions. The FAMEs yield reached 90% at 100 ∘ C after five hours, with a molar ratio of MeOH to saponifiable lipids of 10:1 and 7 wt% of catalyst with respect to lipids. However, in some cases ILs present some disadvantages such as partial catalyst loss during the process, limited solubility with organic compounds (especially polar molecules), and high viscosity that limits their application on an industrial scale. To overcome these problems, the immobilization of ILs (on porous silica, polystyrenebased polymers, polydivinylbenzene, and inorganic material supports) has been adopted (Claus et al. 2018; Pârvulescu and Hardacre 2007; Mehnert 2005). Karimi and Vafaeezadeh (2012) developed a supported 1-methyl-3-octylimidazolium hydrogen sulfate on SBA-15-functionalized propylsulfonic acid ([MOIm]-HSO4 @SBA15-Pr-SO3 H) that shows a high activity in the direct esterification of different FFAs with ethanol at room temperature (yields = 91–87%, molar ratio of FFAs:ethanol:catalyst = 1:0.6:0.1). The catalyst can be reused for four cycles without significant decrease of activity. Liang (2013) reported the use of a novel liquid polymer based on the copolymerization of IL acid oligomers with divinylbenzene (PDVB-[SO3 H(CH2 )3 VPy]HSO4 ), able to catalyze the conversion of WCO with high FFAs content, obtaining a FAMEs yield of 99.1% after 12 hours at 70 ∘ C (molar ratio of oil:MeOH = 1:15, 1 wt% of catalyst with respect to oil). The catalyst was easily recovered by filtration and recycled six times without loss of activity. Zhang et al. (2012) investigated the efficiency of Brønsted acidic IL supported onto Fe-incorporated SBA-15 (Fe-SBA-15) for esterification of oleic acid with short-chain alcohols. Using a molar ratio of oleic acid to MeOH = 1:6 and 5 wt% of catalyst, a yield of 87.7% was obtained after three hours at 90 ∘ C. The reusability of IL/Fe-SBA-15 catalyst was also evaluated with a yield of 80.8% after six reaction cycles. He et al. (2013) synthetized and tested long-chain Brønsted acid ILs, 3-(N,N-dimethyl dodecylammonium) propane sulfonic acid p-toluene sulfonate ([DDPA][Tos]), for synthesis of biodiesel from FFAs. FAMEs were obtained in good yield (92.5–96.5%) at 60 ∘ C and three hours with molar ratio of FFAs:alcohol:catalyst = 1:1.5:0.1. The catalysts could be recycled up to nine times. 5.3.4.2

Combined Use of Lewis Acids with ILs in the Conversion of Non-edible Oils

Bifunctional catalysts with Brønsted and Lewis acid sites can improve the catalytic activity in biodiesel production from non-edible oils due to their synergistic effect. The Brønsted acid functionality promotes the direct esterification of FFAs, while the Lewis acid site facilitates the transesterification of glycerides (Di Serio et al. 2005).

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A preliminary study of the effect of Lewis acids in the transesterification of soybean oil with MeOH was conducted by Liang et al. (2009). Choosing triethylammonium chloride ([Et3 NH]Cl) as the model ammonium salt, different anhydrous metal chlorides were added and the catalytic activity in the transesterification process was evaluated. [Et3 NH]Cl–AlCl3 showed the highest activity with a yield of 98.5% at 70 ∘ C after nine hours of reaction (molar ratio of oil:MeOH:catalyst = 1:12:0.7). Conversely, Lewis acids such as Mg2+ and Zn2+ showed lower yields (32.1 and 64.2%, respectively), due to the characteristics of the metal centers. Liu et al. (2013) tested Brønsted–Lewis acidic ILs [HO3 S-(CH2 )3 -NEt3 ]Cl-xFeCl3 (molar fraction of FeCl3 , x = 0.67) in the conversion of waste oil with high level of FFAs (acid value = 34.6 mg KOH/g). A yield of over 95% was obtained at 120 ∘ C after four hours of reaction, due to the synergetic effects of the Brønsted and Lewis acid catalytic sites. Li et al. (2014) prepared various Brønsted–Lewis catalysts using Brønsted acidic IL 1-sulfobutyl-3-methylimidazolium hydrogen sulfate ([BSO3 HMIM]HSO4 ) with a series of metal sulfates that were applied in the Camptotheca acuminata seed oil (acid value = 23.7 mg KOH/g). [BSO3 HMIM]HSO4 -Fe2 (SO4 )3 showed the best catalytic activity with respect to the other metal sulfates with a FAMEs yield of 95.7% (molar ratio of oil:MeOH = 1:5, 5 wt% of catalyst with respect to oil). The order of activity was as follows: Fe3+ > Cu2+ > Zn2+ > Mg2+ > Ca2+ in line with the respective capability to accept electrons. Finally, Casiello et al. (2019) proposed a very cheap binary catalytic system ZnO/TBAI (where TBAI = tetrabutylammonium iodide) for the simultaneous transesterification and esterification of vegetable oils, animal fats, WCO, and municipal sewage scum. A maximum yield of 96% was achieved at 65 ∘ C after seven hours of reaction. Also, in this case, the catalytic system can be separated and reused for five cycles without significant decrease of activity. 5.3.4.3

Drawbacks Related to the Use of ILs

As already mentioned for enzymatic processes, IL systems suffer from economic viability. In fact, as already stated above, ILs not only need high methanol:oil ratio, which bring high costs in recovering final products, but they are really expensive in themselves. In addition, their effectiveness may be negatively influenced by the presence of water and/or other contaminants contained in waste oils, compromising the reuse, and needing a proper final treatment. Different from the enzymes, they are very toxic compounds and have a problematic disposal in terms of the environmental impact. In fact, at the end of their use they need to be oxidized to more innocuous products. All these aspects made the use of ILs for the production of biodiesel inconvenient and a long way from being industrially applied. 5.3.5

Use of Metal Hydrated Salts

Most recently, new sustainable alternatives were found in using hydrated chlorides and nitrates (Pastore and di Bitonto 2017), due to their cheapness and environmental safety.

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Process Systems Engineering for Biofuels Development

Even commonly used as coadjuvants in wastewater treatment in sedimentation processes, they were sufficiently active in promoting FFAs conversion into FAMEs under very mild conditions. Real WCOs, having an acidity of 8.04 mg KOH/g, were reacted with MeOH in the presence of 7 mol% of different hydrated chlorides and nitrates. The residual acidity was found to be linearly correlated with the pH of the corresponding aqueous solutions prepared by using their reaction concentration. Only some cases did not follow this general trend. H2O MeOH

5

pH

CaCI2·2H2O MgCI2·6H2O

4 AlCI3·6H2O

3

6

CuCI

H2O MeOH

5 CoCI2·2H2O

MnCI2·4H2O

4

Cu(NO3)2·2.5H2O

0

3 2

Fe(NO3)3·9H2O

1

Fe(NO3)3·9H2O Al(NO3)3·9H2O

0 0.5 1 1.5 2 2.5 Residual acidity (wt%)

Cd(NO3)2·4H2O

Al(NO3)3·9H2O

SnCI2·2H2O FeCI3·6H2O AlCI3·6H2O SnCI2·2H2O 1 FeCI3·6H2O 2

0

Zn(NO3)2·6H2O Ni(NO3)2·6H2O

pH

6

3

0

(a)

0.5 1 1.5 2 Residual acidity (wt%)

2.5

(b)

Figure 5.6 Correlation between acidity (pH) and efficacy in promoting direct esterification of FFAs in WCO expressed as residual acidity of (a) hydrated metal chlorides and (b) hydrated metal nitrates.

On the other hand, once these particular metal salts were dissolved into MeOH, the correlation of the reactivity versus acidity was found better addressed (see the specific points in Figure 5.6). Such a behavior can be attributed to the formation of new super-acid species in an alcoholic environment, in particular for cases of aluminum, iron, and tin dissolved in MeOH. After the formation of partially solvated species, obtained from the substitution of coordinated water with MeOH, a rearrangement into dimeric (oligomeric) chains with bridging water molecules was observed through electrospray ionization-mass spectrometry experiments. The acidity of such hydrogen was clearly improved with respect to that of the original hexa-aquo complex (Figure 5.7; Pastore et al. 2015a). This provided evidence that, despite aluminum chlorides being typically identified as Lewis acids, when dissolved in MeOH they act as Brønsted acids. This point was also further experimentally demonstrated by studying in detail the reaction of direct esterification of FFAs into FAMEs through the use of AlCl3 ⋅6H2 O (Eq. (5.1))) (Pastore et al. 2014).

AlCl3 • 6H2 O RCOOH + MeOH

kFAMEs kFFAs

ROOMe + H2O

(5.1)

Kinetic experiments were carried out with the aim of evaluating the effect of the temperature (40, 55, and 70 ∘ C) on the conversion of FFAs in FAMEs. The reduction of acidity

Up-grading of Waste Oil: A Key Step in the Future of Biofuel Production

H

135

H O

(H2O)n–1(MeOH)6–nAI3

AI3 (H2O)n–1(MeOH)6–n 6 CI O

H

H (b)

MeOH –H2O

H

3

H O

2

AI3 (H2O)n–1(MeOH)6–n 6 CI

(H2O)n–1(MeOH)6–nAI3

3CI

O

H (a)

H

(c)

Figure 5.7 Mechanism of formation of alumino-aquo-MeOH super-acid species: (a) structure of starting AlCl3 ⋅6H2 O; (b) dimeric chains with bridging water molecules, obtained from the partial substitution of coordinated water with MeOH; and (c) super-acid species generated in an alcoholic environment.

and the concomitant formation of FAMEs were evaluated as a function of time with a MeOH:FFAs ratio of 10:1. The direct esterification was modeled as a second-order reversible reaction and considering the disappearance of the reagents. The rate of the reaction was studied by using Eq. (5.2): ν=− CFFAs = CFFAs =

dCFFAs m = FFAs (kFFAs CFFAs CMeOH − kFAMEs CFAMEs CH2 O ) dt Mmix

nFFAs,t

(5.3)

Mmix nMeOH,t Mmix

(5.2)

=

CH2 O = CFAMEs =

nMeOH,t0 − nMeOH,react nFAMEs,t Mmix

=

nMeOH,t0 − (nMeOH,t0 − nFFAs,t )

Mmix nFFAs,t0 − nFFAs,t = Mmix

Mmix

(5.4) (5.5)

Under conditions defined by Eqs. (5.3–5.5), Eq. (5.2) became a nonlinear differential equation in which the reaction time was the independent variable, while kFFAs and kFAMEs were the variables to be determined. Figure 5.8a shows the experimental points at 40, 55, and 70 ∘ C and the relevant fitting curves were calculated through mathematical modeling, while in Figure 5.8b the lnKeq , ln(kFFAs ), and the ln(kFAMEs ) were plotted versus 1/T. The expected linear trends (with an R2 higher than 0.998) were confirmed and the activation energies for the production of FAMEs and the back-reaction of hydrolysis were appropriately calculated (43.9 and 24.7 kJ/mol) and found to be in good agreement with values reported for direct esterification catalyzed by homogeneous strong acids, namely

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Process Systems Engineering for Biofuels Development

Concentration (mmolFFAs/g solution)

1.5 40°C 55°C 70°C Fitting 40°C Fitting 55°C Fitting 70°C

1

0.5

0

1

0

2

3

4 Time (h) (a)

5

6

7

8

0

In(kFFSs or FAMEs)

–1 –2

In kFFAs In kFAMEs

–3

In Keq –4 –5 –6 2.90

2.95

3.00

3.05 1/T (K–1)

3.10

3.15

3.20 × 10–3

(b) Figure 5.8 (a) FFAs kinetic profiles obtained at 40, 55, and 70 ∘ C by reacting MeOH:FFAs:AlCl3 ⋅6H2 O 10:1:0.02. (b) Van’t Hoff plot and representations of ln(kFFAs ) and ln(kFAMEs ) with respect to 1/T.

sulfuric acid. The experimental evidence not only shows that AlCl3 ⋅6H2 O is as active as sulfuric acid, but also suggests that it works as a Brønsted acid. Such an activity in promoting direct esterification was confirmed with real samples of WCO and WAG (di Bitonto and Pastore 2019). WCO with a starting acidity of 8.04 mg KOH/g was brought to 0.77 mg KOH/g after four hours at 70 ∘ C. AlCl3 ⋅6H2 O works in the homogeneous phase, since it is completely

Up-grading of Waste Oil: A Key Step in the Future of Biofuel Production

137

soluble in MeOH, but at the end of the pretreatment, it induced a very advantageous separation. In the upper MeOH phase, most of the catalyst was dissolved, together with most of the residual FFAs, water, and contaminants salts, present in the initial feedstocks. However, in the bottom organic layer, most of FAMEs and glycerides were solubilized, resulting in a mixture ready to be directly transesterified using conventional alkaline catalysis, without any further treatments. Such a separation, not only allows the catalyst to be efficiently recoverable and re-usable for new pretreatment cycles, but also a series of inconvenient pretreatments and downstream processes became unnecessary (such as washing or neutralization of the residual acid catalyst onto the pretreated oil before conventional alkaline transesterification, and preparation of starting feedstocks, etc.). This behavior was confirmed in the pretreatment of WAG, where its conversion into biodiesel was known to be hampered by certain limitations that have to be overcome. The huge presence of proteins, phosphoacylglycerols, water, FFAs, pathogens etc., usually requires some preliminary operations to be conducted on raw WAG, in order to obtain a more suitable substrate. For example, protein and phosphoacylglycerols (so-called gums) need to be removed through a degumming process before biodiesel production. Also, the huge presence of water and salts require some complicated and appropriate operations for the relevant removal (Bankovi´c-Ili´c et al. 2014). AlCl3 ⋅6H2 O was also positively tested for the pretreatment of WAG. In detail, when a real sample of WAG was pretreated with AlCl3 ⋅6H2 O, not only were 84% of the initial FFAs converted to FAMEs, but also the substantial removal of Na (62.7%), Ca (54.1%), Mg (83.5%), and P (90.2%) was specifically revealed in the pretreated oily phase. At the end, the residual ash content was reduced to one fourth of its initial value (437–612 ppm versus 1718 ppm). If from one side this one-pot removal of water and salts from a waste oily feedstock associated with the efficient conversion of FFAs to FAMEs can be seen as an advantage, some specific drawbacks need to be considered. In particular, direct reuse of the methanolic phase (Pastore et al. 2015a) could become more complicated by the increasing presence of water and further contaminants. In fact, when the methanolic phase after a cycle of pretreatment of WCO was recovered and directly reused for a second and a third reaction cycle with fresh WCO, the residual acidities of the resulting oily phases after treatment at 70 ∘ C and four hours were 1.51 and 4.35 mg KOH/g, respectively. These values were higher than the 0.77 mg KOH/g which was obtained after the first cycle under the same conditions. In order to obtain residual acidities within 1 mg KOH/g, longer reaction times (8–12 hours) were required, evidencing a kinetic inhibition. The effect of water on this reduction in reaction rate of the direct esterification was specifically investigated. Considering the biphasic nature of the reactive system in the pretreatment, where a methanolic phase was well separated by an oily layer, the distribution of FFAs among these two phases was monitored in the presence of different amounts of water. The higher the water content in the reactive system, the lower the amount of FFAs dissolvable in the methanolic layer. For instance, reactive mixtures containing 20 000 ppm of water, which can be achieved for the development of water from the direct esterification only, the maximum concentration of dissolved FFAs in the MeOH phase resulted in a halved value (21 mmol/l) with respect to that in anhydrous media (43 mmol/l). The almost complete dissolution of the catalyst in MeOH showed that the direct esterification reaction should occur in this specific part of the reactive system. For that reason, the slowing down of the migration of FFAs from the waste oil/grease to MeOH due to the presence of water, may have a negative influence on the overall kinetics of the process.

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Process Systems Engineering for Biofuels Development

In any case, dewatering of the methanolic phase recovered from direct esterification was positively achieved through the use of molecular sieves (3 Å) and silica. In fact, after the dewatering treatment with these drying agents, the water content in the regenerated methanolic phase dropped to less than 100 ppm. When recycling and reuse was carried out by using this regenerated methanolic phase, the residual acidities of the resulting treated oils were always lower than 1 mg KOH/g after four hours of treatment. AlCl3 ⋅6H2 O has good capability in promoting direct esterification. It is completely soluble in the MeOH layer during and after reaction. It induces the efficient separation of an oily phase prompt to be directly reacted through alkaline transesterification. All these properties allowed a new configuration of biodiesel plant to be designed (Figure 5.9). Figure 5.9 shows the two-step process to obtain biodiesel from waste grease (WCO, WAG, etc.) through the use of AlCl3 ⋅6H2 O as a catalyst in the pretreatment. It consists of the following operations: i. Waste raw grease having FFAs content of 1–90 wt% can be directly used as a feedstock without any further preliminary treatment. ii. AlCl3 ⋅6H2 O is solubilized in MeOH and added to the feedstock (i) in a batch reactor (R1). iii. Direct esterification can be operated at 70 ∘ C, for 2–16 hours. iv. Two distinguished phases can be separated using a static separator (S1): a methanolic layer (up) and an oily phase (down). v. The oily phase can be directly transesterified with MeOH under alkaline catalysis, for example using sodium hydroxide 0.5 wt% at 70 ∘ C for one hour in a new reactor (R2). vi. A biphasic system is obtained in this case: FAMEs can be easily recovered in the upper phase from glycerol in S2. MeOH Direct esterification (pretreatment) S1

dw

R1 AICI3·6H2O in MeOH Raw grease

oil

R2

S2

MeOH DC

MeOH/base

Biodiesel Transesterification Glycerol Figure 5.9 Scheme of the process to obtain biodiesel from WCO and WAG using AlCl3 ⋅6H2 O as a catalyst in the pretreatment.

Up-grading of Waste Oil: A Key Step in the Future of Biofuel Production

139

vii. Unreacted MeOH can be distilled, recovered, and eventually recycled back to the reaction in R2, by residuing crude biodiesel. viii. Crude biodiesel can be washed through water and then dried through evaporation. ix. The methanolic phase separated from step (iv) in which the AlCl3 ⋅6H2 O was mostly dissolved can be treated with drying agents (dw) and reused for new cycles of direct esterification in R1 with fresh raw grease. The present scheme of production results in a more simplified process with respect to the conventional two-step configuration which uses H2 SO4 and NaOH as catalysts. In fact, pretreated oils can be directly transesterified under alkaline conditions and without any further washing, and the catalyst can be efficiently recovered into the methanolic phase, which can be recycled back for pretreating fresh raw grease in a new reactive cycle. Furthermore, brown grease, sewage scum or substrates poor in glycerides can be easily converted into biodiesel through the use of a simple direct esterification, without adopting the transesterification step (Pastore et al. 2015b). In this context, new sources of grease, namely sewage sludge, could further implement the actual scenario of possible second-generation feedstock (Pastore et al. 2013). In particular, lipids that are in urban primary sludge could be also conveniently converted into biodiesel through the abovementioned process scheme. In any case, the use of waste oils, in which “inert and heavy” compounds are also present, strongly needs a further final purification step, likewise distillation of FAMEs as pure products, in order to satisfy EN14214 specifications (FAMEs over 96.5 wt%). The robustness of the catalyst was clearly demonstrated for different typology of feedstocks having very different starting compositions.

5.4

Future Trends of the Pretreatments of Waste Oils

In the future, improvements will be necessary to consistently decrease the overall economic and environmental impact of the current production of biodiesel. Regarding the reaction conditions, several innovative solutions have been already proposed. Enzymatic catalysts, ILs, and hydrated salts resulted in very efficient promoters for the direct production of biodiesel, or for the pretreatment of the starting waste oil. However, currently they are not ready for industrial application because of economic limits. Actually, these limits are not directly related to the reactive step, but more principally to the costs of lipid feedstocks and the downstream purification of the final biodiesel. For this reason, improvement of the present state of the art in biodiesel production can be achieved more by developing new side technologies (i.e. recovering lipids from innovative feedstocks, purification processes, water separation from alcohol, etc.) than by improving the reactive step. The identification of new feedstocks represents a really challenging objective for the future of this field. For example, in the European context, satisfaction of the economic constraints could be more easily achievable by using second-generation feedstock, for which the double-counting rule could be applied. Besides the well-known WAG and WCO, sewage grease, trap grease and grease recoverable from municipal waste (di Bitonto et al. 2018) could represent profitable alternatives which satisfy the viability criteria. The use of these biomasses can have a great economic potential: according to the literature data (Pastore and di Bitonto 2017), over 5 Mton/yr of biodiesel could be obtained

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Process Systems Engineering for Biofuels Development

annually in Europe from sewage sludge only. This massive amount would represent half of the present continental demand. For these specific lipids, aluminum hydrated salts were very effective in promoting the direct esterification reaction with methanol (di Bitonto et al. 2016; Pastore et al. 2014, 2015a). AlCl3 ⋅6H2 O not only satisfied the criterion of economic viability, but also guaranteed a high robustness in reactivity of these waste greases, which are very contaminated by salts, water, polymers, etc. Finally, even improvement of technologies for conducting side operations (separation of water from methanol and/or purification of products in general) through integration and intensification of processes may significantly influence the overall economic scenario of biodiesel production. Development of these processes may make proficient some technologies that are already available but not usable currently due to some drawbacks and limits related to side processes.

5.5

Conclusions

In the present chapter, a critical overview of the current biodiesel production scenario was reported. The industrial production is nowadays based on the application of simple homogeneous acid and alkaline catalysts. However, they present several drawbacks, especially in terms of bad recoverability and generation of waste. Heterogeneous catalysts have been proposed as alternatives: in some cases they resulted in very active compounds, but very rarely can they be considered economically viable. In addition, they suffer from the problem of contamination of the active surface and further costs need to be considered for their regeneration. Lipase-catalyzed esterification and transesterification can be carried out in the presence of relatively high water content, at low temperature, and convert more feedstock to biodiesel in a single step. Nevertheless, it is costly in particular due to the expense of the enzymes (especially for immobilization) and for the long reaction time to have good yields. Conversion of waste oils/grease mediated by ILs resulted in easier separations of the final products, due to formation of two phases, which reduces process costs. In addition, properties of catalysts can be modulated during the relevant preparation: high catalytic activity, excellent stability and easy separability and reusability are the most important qualities. On the other hand, they often require relatively more alcohol for producing effective yields, which increases the overall production cost for the corresponding increase in the separation costs of the final pure product. They are too expensive in themselves, especially if the final treatment and disposal of spent residues have been taken into account. Cheap and safe hydrated salts efficiently convert FFAs into methyl esters. They mediated the direct esterification of FFAs into the methanolic phase. Hydrated metal chlorides and nitrates are solubilized in MeOH (homogeneous catalysis), and for the same reason, they can be easily reused several times, by simply recycling the methanolic phase after a reaction cycle. They resulted in very effective and robust catalysts, to the point to be efficiently applied for pretreating WCO, WAG, and sewage grease to obtain biofuel. Considering that the final use of exhausted aluminum- and iron-based hydrated salts may be as coagulant in WWTPs, they actually result in a “zero-waste” process.

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141

Acknowledgment This research was partially supported by FESR “PON Ricerca e Innovazione 2014–2020. Progetto: Energie per l’Ambiente TARANTO—Cod. ARS01_00637”.

Abbreviations BCL CFAMEs CFFAs CH2O CMeOH C sp. CRL DAG DC dw FAMEs FFAs ILs Keq kFFAs kFAMEs MAG mcat Mmix MP MeOH nFFAs,t nMeOH,t nFAMEs,t nFFAs,t0 nMeOH,t0 nMeOH ,react R1 R2 RML RO TAG TLL TP 𝜈 WAG WCO WWTP

Burkholderia cepacia Concentration of FAMEs as mmol/gsolution Concentration of FFAs as mmol/gsolution Concentration of H2 O as mmol/gsolution Concentration of MeOH as mmol/gsolution Candida sp. 99–125 Candida rugosa lipase Diacylglycerol Distillation column Dehydration of methanolic phase Fatty acid methyl esters Free fatty acids Ionic liquids Equilibrium constant Kinetic constants referring to the direct esterification reaction Kinetic constants of the hydrolysis Monoacylglycerol Amount of catalyst used in a batch reaction Total mass of the solution Methyl palmitate Methanol Number of moles of FFAs at time t Number of moles of MeOH at time t Number of moles of FAMEs at time t Number of moles of FFAs at time t = 0 Number of moles of MeOH at time t = 0 Number of moles of MeOH reacted at time t Batch reactor for direct esterification Batch reactor for transesterification Rhizomucor miehei lipase Rhizopus oryzae Triacylglycerol Thermomyces lanuginosa lipase Tripalmitine Rate of reaction Waste animal grease Waste cooking oil Wastewater treatment plant

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6 Production of Biojet Fuel from Waste Raw Materials: A Review Ana Laura Moreno-Gómez1 , Claudia Gutiérrez-Antonio1 , Fernando Israel Gómez-Castro2 , and Salvador Hernández2 1 Facultad

de Química, Universidad Autónoma de Querétaro, Querétaro, 76010 Querétaro, México de Ingeniería Química, Universidad de Guanajuato, Guanajuato, 36050 Guanajuato, México

2 Departamento

6.1

Introduction

In the transport sector, aviation has the highest growth. The forecasts of the International Air Transport Association, IATA, indicate that in the next two decades aviation will have a compound annual growth rate of 3.5%, which implies that the number of passengers could double to 8200 million in 2037 (IATA 2018a,b). As consequence, there will be an increase of both fuel usage and carbon dioxide emissions. Therefore, in order to guarantee the sustainable development of aviation sector, the Four-Pillar strategy was proposed (IATA 2009); this strategy considers technological improvements in engines and aircraft structures, operational improvements through online optimization of flight paths, market-based measures, and development of alternative fuels (Gutiérrez-Antonio et al. 2016a). From these alternatives, the IATA and the International Civil Aviation Organization, ICAO, agree that renewable aviation fuel is the one that contributes most to the sustainable development of the aviation sector. Fossil aviation fuel consists of hydrocarbons in the boiling point range from C8 to C16, including paraffinic, naphthenic and aromatic compounds. On the other hand, renewable Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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aviation fuel contains paraffinic and naphthenic compounds, but it may or may not contain aromatic compounds, depending on the processing pathway. The absence of aromatic compounds could cause leaks in the tank fuels, since aromatic compounds interact with the elastomers in the tank seals. Due to this, renewable aviation fuel must be used blended with fossil jet fuel, with a maximum composition of renewable fuel of 50%, according to the standard ASTM D7566 (ASTM 2018). Renewable aviation fuel is also known as biokerosene, biojet fuel or synthetic paraffinic kerosene; and it can be generated from triglyceride, lignocellulosic, sugar and starchy feedstock. The feedstock is transformed in biojet fuel through different pathways, which include hydroprocessing of triglyceride feedstock, thermochemical processing of biomass, and alcohol-to-jet (Gutiérrez-Antonio et al. 2016a). At present, five pathways have been certified under ASTM D7566 standard: gasification followed by Fischer–Tropsch synthesis, hydroprocessing of esters and fatty acids, hydroprocessing of fermented sugars, Fischer–Tropsch synthesis with aromatics, and alcohol-to-jet (IATA 2018a,b). However, another seven pathways are in the evaluation stage to reach certification (CAAFI 2019). The certified conversion routes allow the production of renewable aviation fuel that meets or even exceeds the quality standards. Despite these pathways being different, three possible scenarios are observed: raw material of high cost with low processing costs, raw material of low cost with high processing costs, and both raw material and processing with intermediate costs. The reduction in the processing costs can be reached through several strategies, such as process intensification or energy integration; several works have reported the application of these tools to the production processes of biojet fuel (Gutiérrez-Antonio et al. 2016a,b, 2018a,b). On the other hand, the raw material costs depend on several factors, such as the variety of the crops, the automation of the cultivation task, along with the transportation distances; due to this an optimal supply chain must be established, and some works are reported on this topic (Reimer and Zheng 2017; Domínguez-García et al. 2017a,b; Leila et al. 2018). Another alternative is the use of waste raw materials, which are abundant and have low cost; some of them even constitute a pollution problem, due to the high volumes in which they are generated. In this way, a problem can be transformed into an energetic solution. Therefore, the focus of this work is the review of the use of waste raw materials to produce biojet fuel. As shown in Figure 6.1, there are three main waste feedstocks: (i) triglyceride-containing materials, (ii) lignocellulosic, and (iii) sugar and starchy. Any of this feedstock can be transformed into biojet fuel through different processing pathways. Moreover, the main challenges found in these production processes are analyzed, and future research trends are discussed. The chapter is organized as follows: the conversion of waste triglycerides through hydroprocessing is discussed in Section 6.2. The transformation of lignocellulosic residues, through different technologies, to produce renewable aviation fuel is presented in Section 6.3. The production of biojet fuel from sugar and starchy residues is shown in Section 6.4. Finally, the main challenges and future trends in the use of residues to produce aviation biofuel are discussed in Section 6.5.

6.2

Waste Triglyceride Feedstock

Waste triglyceride feedstock includes waste cooking oil, animal fats, oil extracted from agroindustrial residues, and bio-oil obtained from pyrolysis. Waste cooking oil and animal

Production of Biojet Fuel from Waste Raw Materials: A Review

Lipids Bio Processing

Waste sugar and starchy feedstock

Waste lignocellulosic feedstock

Waste triglyceride feedstock

Figure 6.1

Saccharification

Sugar

Pyrolysis

Bio-oil

Gasification

Syngas

151

Catalytic Hydrothermolysis

Alcohol Thermo Processsing

FischerTropsch ThermoProcessing

H y d r o p r o c e s s i n g

Biojet fuel

Pathways to process waste feedstock to produce renewable aviation fuel.

fats are usually a by-product of food industries and/or residues from residential and commercial sectors; they represent a contamination problem due to the high volume in which they are generated, and usually there is a non-appropriated disposal of such residues. For instance, in the United States the availability of 10 million tons of waste cooking oil has been reported (Gui et al. 2008). In Mexico, the information is scattered; there are some reports that indicate nearly 8300 and 60 000 l/yr of waste cooking oil in the Puerto Interior Industrial Park, in Guanajuato (IEEG 2016), and Emiliano Zapata, in Tabasco (Gasca González 2017), respectively. In Europe, the potential of waste cooking oil was estimated as 3.55 million tons in 2015, with 1.748 million tons generated in the domestic sector (EUBIA 2015). Other interesting waste material is the spent coffee ground, which represents a good source of triglycerides to produce biojet fuel. According to McNutt and He (2019), in 2016 the worldwide production of coffee was around 9.3 billion kilograms, and it is possible to recover between 10% and 20% of oils from spent coffee grounds by conventional extraction methods. Therefore, there is a great amount of these polluting residues to produce biojet fuel through hydroprocessing technology. The first production process to obtain biojet fuel through hydroprocessing was proposed by UOP Honeywell (Vera-Morales and Schäfer 2009), and it consist of two consecutive reactors and a distillation train (Figure 6.2). In the first reactor, the triglyceride feedstock is converted, at high pressure and temperature, to long lineal chain hydrocarbons; these hydrocarbons are cracked and isomerized in the second reactor, which also operates at high pressure and temperature. In both reactors, hydrogen is used as reactant along with solid catalysts. The reactor outlet stream contains renewable hydrocarbons that include light gases, naphtha, biojet fuel and green diesel; these products are purified through a distillation sequence. The UOP Honeywell’s technology was proposed as an extension of a green diesel production process, in which a selective cracking stage was added (Regalbuto 2010; Verma et al. 2015); therefore, the

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Process Systems Engineering for Biofuels Development

CO2 H2O pretreatment deoxygenation

cracking/ isomerization

light gases

naphtha Waste triglyceride feedstock

Physical Chemical Thermal Biological

biojet fuel

green diesel Water Reactants Figure 6.2

H2

H2

Distillation

Hydrotreating process to produce renewable aviation fuel from waste triglyceride feedstock.

maximum selectivity to biojet fuel is 36%, with total conversion to hydrocarbons of 70% (Regalbuto 2010; Verma et al. 2015). Another hydrotreating process is BioSynfining™, in which renewable aviation fuel is produced from fatty acids and triglycerides. The fatty acid chains are converted to n-paraffins through deoxygenation, and the long-chain paraffins are hydrocracked to short-chain paraffins. The hydrocracked products have boiling points in the ranges of kerosene and naphtha. The BioSynfining process was successfully used to produce about 600 gal of biojet fuel (Liu et al. 2013), and it is very similar to that proposed by UOP Honeywell. The hydrotreating process can employ diverse catalysts, raw materials and different processing conditions (Vásquez et al. 2017). In the hydrotreating process different catalysts have been utilized, either nickel-based or bifunctionals, as well as different types of reactors such as the fixed bed reactor. In most of the studies, a large amount of hydrogen is required to perform the conversion to biojet fuel. Moreover, almost all the studies report the use of vegetable oils, both edible and inedible, such as soybean, Jatropha curcas, castor and microalgae oil (Gutiérrez-Antonio et al. 2017). However, the use of waste oils represents a good alternative to reduce the price of biojet fuel, and, at the same time, to solve a contamination problem due to the accumulation of these materials. Next, we present a review of the works where waste triglyceride feedstock is treated to generate biojet fuel. The paper published by Tian et al. (2008) reported the production of different biofuels through the cracking of animal fats. They obtained a high yield for liquefied petroleum gas (45%) and olefins (47%), but a low yield for naphtha (approximately 4.17%). In the reactor, CoRh was used as catalyst, and the hydrogen requirement was not reported. Additionally, aromatic compounds in the range C7–C10 were obtained. Other works (Bezergianni et al. 2009; Bezergianni and Kalogianni 2009) point out that cracking is a prominent technology for biojet fuel production. These works compare the yield of products and the quality of hydrocracking at three different temperatures, considering non-used oil and waste cooking oil as raw materials. The evaluation indicates that, for both raw materials, a high diesel production is observed, while the yield for kerosene and gasoline is low. They also noted that as temperature increases, the selectivity toward diesel also increased for both raw materials; however, at 390 ∘ C the selectivity toward kerosene was favored (22.24%).

Production of Biojet Fuel from Waste Raw Materials: A Review

153

Later, Bezergianni et al. (2012) reported the conversion of waste cooking oil toward biojet fuel using three catalysts: a hydrotreating catalyst, a mild-hydrocracking catalyst and a severe hydrocracking catalyst. They used conditions of high temperature (330–390 ∘ C) and pressure (8.27–13.79 MPa). As products, hydrocarbons in the range of gasoline and diesel were obtained. The highest yield was 80% for diesel at 370 ∘ C and 8.27 MPa, with a hydrotreating catalyst. Shi et al. (2014) showed a new path for converting bio-oils, prepared from cornstalks liquefaction, to diesel and hydrocarbons in the boiling point range of biojet fuel. The reaction was carried out using Ni/ZrO2 as catalyst in the presence of supercritical cyclohexane at 573 K and 5 MPa of hydrogen. They obtained a hydrocarbon yield of 81.6%, with 90% of hydrocarbons in the range of diesel and biojet fuel, and 7% in the range of gasoline. Mosisa et al. (2018) studied the cracking of waste cooking oil for the production of liquid fuels in a semi-batch reactor with a nitrogen atmosphere; they used zirconia oxide (ZrO2 ) as catalyst. It is important to mention that the raw material was cracked without the need of a pretreatment using the mentioned catalyst. A yield of 83% for organic liquid products was obtained. An important aspect of this process is that it does not require high energy consumption, and the catalyst is easily regenerated and recycled, being environmentally friendly. Next, we present the review of the works where waste triglyceride feedstock is deoxygenated and cracked to generate biojet fuel. Charusiri et al. (2006) studied the conversion of waste vegetable oils into liquid fuels over sulfated zirconia, HZSM-5 and HZSM-5 hybrid catalysts; their experiments were performed in a batch micro-reactor at temperature range of 380–430 ∘ C, the hydrogen initial pressure was in the range of 10–20 bar, and the reaction time was 45–90 minutes. Most of the obtained products were liquids (gasoline, kerosene), gases and a small amount of solids. Waste cooking oil, which contains a high acidity value (28.7 mg KOH/g oil), is converted through the hydrocracking process over a ruthenium catalyst to obtain diesel as product (Liu et al. 2012); the advantage of obtaining diesel is that it can be cracked and then converted to biojet fuel. The temperature, hydrogen pressure, retention time and H2 /oil ratio were 350 ∘ C, 2 MPa, 15.2 h, and 400 ml/ml, respectively. Free fatty acids and triglycerides present in oil were deoxygenated at the same time to form hydrocarbons; the liquid hydrocarbons had a yield of 98.9%, with octadecane, heptadecane, hexadecane, and pentadecane as the predominant ones. I.H. Choi et al. (2015) performed a study to obtain biojet fuel through deoxygenation, isomerization, and cracking in a single stage. In order to carry out such reaction 300–420 m3 of H2 per m3 of waste oil were required. With this proposal, they achieved simplification of the process, and simultaneously they decreased the consumption of hydrogen and energy. In such study, they prepared a Pd catalyst supported in beta-zeolite. As raw materials, waste soybean oil and palm fatty acid distillate were used. Moreover, the work proposed by Hanafi et al. (2016) reported the production of hydrocarbons in the boiling point range of jet fuel, as well as naphtha and light gases; this work is important because fatty acids are deoxygenated and cracked in a single reactor, which allows a reduction in the investment costs. Furthermore, waste chicken fats, obtained from a company, are employed as raw material. In their work, the physical characteristics of the raw material and activation energy are provided; the yield toward kerosene/diesel fraction is 53%. The operating conditions for temperature, pressure, retention time and H2 feed are 400 ∘ C, 6 MPa, 4 h, and 450 v/v H2 /oil, respectively.

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Process Systems Engineering for Biofuels Development

The study reported by Zhang et al. (2017) showed the production of fuel for airplanes in a one-stage process; they used animal fat as raw material and Pt/SAPO-11 as catalyst in a micro-reactor that operated at high pressure. According to their experimental results, the optimum operation conditions to produce a bigger amount of hydrocarbons in the C8–C16 range are 4 MPa, 400 ∘ C, and 1000 ml H2 /ml oil; under such conditions there is a 96.6% conversion and the selectivity toward C8–C16 hydrocarbons is 50.25% with selectivity to their corresponding isomers around 35.68%. It should be noticed that the retention time of the micro-reactor is 1.2 hours. In that study, they mentioned that isomerization gradually increases with the rise of temperature, but when the temperature reaches 380 ∘ C the isomerization of the hydrocarbons decreases gradually. The increase in the H2 /oil ratio is beneficial for the hydrocarbon conversion and for the isomerization of alkanes; however, when the H2 /oil ratio is too large, the hydrocarbon selectivity decreases, and thus the isomerization percentage reduces. In the following paragraphs, we present a review of the works where waste triglyceride feedstock is deoxygenated to generate biojet fuel. Zhang et al. (2014) carried out a kinetic study of the hydrodeoxygenation of waste cooking oil with a CoMoS catalyst. Their results show that the hydrodecarbonylation/decarboxylation (HDC) are the predominant reaction paths for oxygen elimination; the catalyst activity decreases as the amount of sulfur in the catalyst also reduces. Moreover, the hydrodecarbonylation of fatty acids controls the HDC path; while through the hydrodeoxygenation path fatty acids are transformed to aldehydes/alcohols, and subsequently to C18 hydrocarbons as final product. The difference between the C18/C17 ratio with supported and unsupported catalyst shows that the acid Lewis sites are related to the selectivity for the hydrodeoxygenation path, thus giving as a result a product of high quality. The experiment was performed in a batch reactor using 0.6 g of catalyst and 120 g of waste oil, with catalyst to oil ratio of 1:200 w:w. Three types of zeolites (Meso-Y-SAPO-34, y HY) mixed with nickel were used to convert waste cooking oil to aviation fuel (Li et al. 2015). The mesoporous-Y zeolite exhibited 53% selectivity and 13.4% of selectivity to aromatic compounds in the liquid products; this zeolite showed a 40.5% yield at 400 ∘ C, but the yield for aromatic compounds decreased 2.1%. The experimental results show that the deoxygenated raw materials tend toward heptadecane and pentadecane, through a decarbonylation path during the three first hours. In this experiment, the raw material was previously dried in order to eliminate the water content (Li et al. 2015). The direct conversion of waste oil to biojet fuel was researched by Zhang et al. (2018); in this process a zeolite support with a core-shell structure USY-AL-SBA-15 and NiMo as catalyst were used. The use of this support and catalyst contributed significantly to improve the selectivity toward biojet fuel from 9.3% over NiMo/USY to 35.7% over NiMo/USY-AL-SBA-15, with a high isomerization ratio (iso-n/n-paraffin = 2.7) and 18.7% of aromatic compounds. The authors mention that through a single path, using either NiMo or NiW catalysts supported on ZSM-5, it is possible to obtain high yields to biojet fuel, 40–45%, with an excellent isomer/alkane ratio in the range 2–6. An interesting work proposes the one-step hydroprocessing of bio-oil generated in the hydrothermal liquefaction of microalgae cultivated in wastewater (Ranganathan and Savithri 2019). In this process, renewable hydrogen is also obtained from the biogas generated from the anaerobic digestion of the sludge from the wastewater treatment.

Production of Biojet Fuel from Waste Raw Materials: A Review

155

However, renewable hydrogen can also be obtained from the electrolysis of water (IEA 2017). Fu et al. (2015) proposed the direct conversion of microalgae lipids in water to renewable aviation fuel. The catalyst used was Pt/C, and the optimal conditions were 360 ∘ C with a reaction time of 45 minutes; in these conditions, a total conversion was observed with a selectivity of 90% to heptadecane. There are several works in the literature where the hydroisomerization of model compounds is reported, mainly n-hexadecane and n-dodecane. These works are important since in most cases the hydroprocessing consists of two stages; in the first one the hydrodeoxygenation is performed, while the hydroisomerization and hydrocracking are carried out after that. These works are described next. The first work in this category was presented by Zhang et al. (2000). They reported the activity, selectivity and long-term stability of platinum-promoted tungstate-modified zirconia (Pt/WO3 /ZrO2 ), under mild conditions; in the hydroisomerization reaction, n-hexadecane was used as a model compound. A trickle bed reactor was used for the experiments. The best results were a conversion of n-hexadecane of 79.1 wt%, while for iso-hexadecane a selectivity of 89.9 wt% and a yield of 71.1 wt% i-C16 were reported. Later, Zhang et al. (2001) studied the effect of activity and selectivity of tungstated zirconia (8% w) on the isomerization of n-hexadecane. The study was carried out in a trickle bed continuous reactor. The results showed that temperatures between 300 ∘ C and 400 ∘ C, for three hours, were slightly beneficial for achieving high yields of iso-hexadecane. Gomes et al. (2017) studied the performance of bifunctional Pt/alumina-beta zeolite catalysts for the hydroisomerization of n-C16. A biphasic micro-reactor of plug flow was used to study the effect of nC16 isomerization on the pour point of the products. They found that as the pour point decreases at constant rate, the formation of cracked products was small; products are essentially composed of mono- and disubstituted C16 isomers, while 50% of n-C16 was converted. Moreover, the hydroisomerization of n-hexadecane was studied in order to evaluate the activity of Pt/AlSBA-15 catalysts (Jaroszewska et al. 2017); they determined that the catalyst AlSBA-15 using aluminum isopropoxide showed better isomerization selectivity than AlSBA-15 using aluminum sulfate. De Lucas et al. (2005) studied the performance of palladium and platinum beta zeolite-based catalysts with or without binder in the hydroisomerization of n-octane, along with the influence of Si/Al ratio. As result, they found that the catalytic activity of beta zeolite catalysts decreased when the Si/Al ratio increased, in samples with or without binder. In addition, the isomer selectivity rose from 54.3 to 67.8% in samples without binder when the Si/Al ratio increased. Wang et al. (2008) investigated the hydroisomerization of n-dodecane over Pt supported in ZSM-22 unmodified and ZSM-22 modified with two treatments. The ZSM-22 unmodified showed low activity in the hydroisomerization of n-dodecane. The catalyst treated with NH4 + ion and (NH4 )2 SiF6 showed a high selectivity for iso-dodecane (88.0%) and good conversion (87.5%) at 300 ∘ C. The experiments were carried out in a fixed bed flow reactor with an internal diameter of 12 mm. Other studies were reported for the hydroisomerization of n-butane (Adeeva et al. 1998), n-hexane and n-octane (Amanza et al. 1999), n-hexane, n-octane, and n-hexatriacontane (Calemma et al. 2000), and n-hexane, n-heptane, and n-octane (Dhar et al. 2017). To summarize, Table 6.1 presents the reported articles where waste triglyceride feedstock is used to produce biojet fuel.

Table 6.1

Summary of the use of waste triglyceride feedstock to obtain biojet fuel.

Raw material Animal Fats

Catalyst CORH LTB-2

Non used oil

DMDS∗∗ TBA∗∗∗

Waste cooking oil

Temperature(∘ C) 500 (first stage); 520 (second stage) 350 370 390

Pressure

Hydrogen requirements

Reactor type

Yield (%)

References

1 atm

Not mentioned

TSRFCC∗

LPG: 47; liquid total: 77.6

Tian et al. (2008)

13 789.5 kPa

1068 nm3 /m3 H2 /oil

Fixed bed

Waste oil: 20.04; fresh oil: 22.24

Gas oil: 15; diesel: 79 Hydrocarbon: 81.6; diesel-biojet fuel: 90; gasoline: 7 Liquid organic product: 83 Gas oil: 6.5; gasoline: 26.57; kerosene: 10.65; light gases: 23.62; residues: 12.88 Liquid hydrocarbons: 98.9 Biojet fuel: 40

Bezergianni et al. (2009), Bezergianni and Kalogianni (2009) Bezergianni et al. (2012)

Waste cooking oil

HDT MID-HDC HDC

330–390

8.27–13.79 MPa

3000 l at NTP

Fixed bed

Bio-oils

Ni/ZrO2

573

5 MPa

Not mentioned

Waste cooking oil Waste vegetable oil

Zirconia oxide 400–500 (ZrO2 ) Sulfated Zirconia 380–430 (HZSM-5); Hybrid catalyst (HZSM-5)

Not mentioned

Not mentioned

Nantong Huaxing Petroleum Semi-batch

10–20 bar

Not mentioned

Batch micro-reactor

Waste cooking oil Non-edible oil

Ru supported on (Al13-Mont)† Pd/ beta-zeolite

350

2 MPa

400 ml H2 /ml oil

Fixed-bed

270

15 bar

300–420 m3 H2 /m3 oil

Not mentioned

Shi et al. (2014)

Mosisa et al. (2018) Charusiri et al. (2006)

Liu et al. (2012) I.H. Choi et al. (2015)

Waste Chicken

NiW/SiO2 -Al2 O3

400

60 atm

450 v/v, H2 /oil

Fixed-bed

Animal Fat

Pt/SAPO-11

400

4 MPa

1000 ml H2 /ml oil

Micro-reactor

Waste cooking oil Waste cooking oil Waste oil n-Hexadecane

CoMoS

375

88.4 atm

Not mentioned

Batch

Meso-Y SAPO-34 HY USY-AL-SBA-15 NiMo Pt/WO3 /ZrO2

400

3 MPa

350 ml/min

Batch reactor

Aromatics: 13.4; jet fuel: 40.5

n-hexadecane

Jet fuel: 39.7; aromatic fraction: 18.7 Selectivity i-C16: 89.9; total conversion: 79.1 Iso-hexadecane: 87

n-Hexadecane

n-Hexadecane

380

30 atm

250 ml H2 /ml oil

Fixed bed flow

218

160 psig

Trickle bed

Tungstated zirconia (8% w)

300–400

Bifunctional Pt/alumina-beta zeolite AlSBA-15

260–320

500 psig; pressure drop 73 psig 50–100 bar

H2 /n-C16 mole ratio = 2 Not mentioned

320–360

5 MPa

Trickle bed continuous

Total conversion: 94; selectivity to kerosene: 40 Conversion: 96.6; selectivity C8-C16: 50.25; isomers: 35.68 Naphtha: 10; diesel: 81

Hanafi et al. (2016) Zhang et al. (2017) Zhang et al. (2014) Li et al. (2015)

Zhang et al. (2018) Zhang et al. (2000) Zhang et al. (2001)

500 NTP l/l H2 /reactant

Micro-reactor of plug flow

Conversion of n-C16: 50 Gomes et al. (2017)

H2 :CH = 350 NTP m3 /m3

Pressure fixed bed microreactor

Conversion to iso-C16: 61

Jaroszewska et al. (2017)

Table 6.1

(continued)

Raw material n-dodecane

Catalyst Pt/ZSM-22 unmodified Pt/ZSM-22 modified Platinum

n-octane n-hexane n-hexadecane 0.3% platinum/ n-octacosane amorphous n-hexatriacontane silica–alumina (MSA/E) n-hexane n-heptane n-octane

∗ Two-stage

Pt doped on gamma alumina

Temperature(∘ C) 480

∗∗∗ TBA,

Hydrogen requirements

Reactor type

Yield (%)

References

6.0 MPa

H2 /n-C12 = 600:1

Not mentioned

Unmodified ZSM-22 showed low activity. Modified ZSM-22: 88.0

n-Hexane: 52; n-octane: Amanza et al. 27 (1999) Iso-hexadecane: 58–62; Calemma et al. iso-octacosane: (2000) 49–46; iso-hexatriacontane: 39–32 At 180 ∘ C Dhar et al. n-hexane: 85; (2017) n-heptane: 68; n-octane: 40

548

2.0 MPa

24 H2 /n-C6

Not mentioned

345–380

2–13.1 MPa

Not mentioned

Stirred microautoclave

140 160 180

20 bar

Not mentioned

Batch

riser fluid catalytic cracking. dimethyl disulfide. tert-butylamine. † Aluminum-polyoxocation-pillared montmorillonite. ∗∗ DMDS,

Pressure

Wang et al. (2008)

Production of Biojet Fuel from Waste Raw Materials: A Review

6.3

159

Waste Lignocellulosic Feedstock

Waste lignocellulosic feedstock includes wood waste, agricultural residues, textile residues, solid urban waste, among others. Lignocellulosic materials such as agricultural wastes are attractive feedstock for biojet fuel production, since they are abundant and renewable. In Mexico during 2011, 52% of the solid urban waste included waste food and waste organic materials (SEDESOL 2012); also, 75.73 millions of tons of agroindustrial residues were generated in Mexico in 2006 (Saval 2012). On the other hand, Brazil generates large amounts of agricultural residues from sugarcane cultivation; this residue could be used as raw material to produce biojet fuel (Nicodème et al. 2018). This type of feedstock is transformed through different types of processing technologies, such as gasification followed by Fischer–-Tropsch synthesis, pyrolysis followed by hydroprocessing, or alcohol production plus oligomerization. A review of the works where alcohol is generated as intermediate for the subsequent production of biojet fuel is presented next. Waste lignocellulosic feedstock can be converted to ethanol; however, a pretreatment (hydrolysis) is needed in order to extract the sugar contained. Sun and Cheng (2002) did a review of the hydrolysis of lignocellulosic materials for ethanol production. The studied materials include agricultural residues and wastes such as nutshells, grasses, paper, and newspaper. In this context, waste papers from chemical pulps are an interesting and new raw material, which have been little explored to obtain bioethanol, and later biojet fuel. Once bioethanol is produced, it can be processed to obtain biojet fuel (Figure 6.3). In this context, a review for the production of biojet fuel from agricultural residues through the production of alcohol as intermediate is not available in the literature. However, Sarkar et al. (2012) did a comprehensive review of bioethanol production from agricultural waste. The review includes different processes and methods to increase the concentration of fermentable sugars. In addition, information about the conversion of glucose and xylose to ethanol through fermentation technologies is discussed. Other works reported the production of bioethanol from kitchen waste (Tang et al. 2008), grape and sugar beet pomaces (Rodríguez et al. 2010), lignocellulosic agro-waste (Mutreja et al. 2011), date wastes (Acourene and Ammouche 2012), non-marketable dates (Louhichi et al. 2013), sugarcane bagasse (Lin et al. 2013), oil palm fronds (Ofori-boateng and Lee 2014), and a mixture of waste fruits juice (Mansouri et al. 2016). An important aspect to the selection

light gases pretreatment

hydrolysis

fermentation

oligomerization hydrogenation naphtha

Waste lignocellulosic feedstock

Physical Chemical Thermal Biological

biojet fuel

green diesel Water Reactants

H2

Distillation

Figure 6.3 Alcohol production plus oligomerization process to produce renewable aviation fuel from waste lignocellulosic feedstock.

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Process Systems Engineering for Biofuels Development

of the waste to produce bioethanol is the lignin content. The best lignocellulosic feedstocks to produce bioethanol are those with reduced amount of lignin, since it is hydrophobic in nature and is tightly bound to cellulose and hemicellulose (Sarkar et al. 2012). After the generation of the alcohol, it is possible to obtain biojet fuel through thermoprocessing, as shown in Figure 6.1. The thermoprocessing includes several stages: dehydration, oligomerization, hydrogenation, and fractionation. In the dehydration stage, the water molecule is removed from ethanol using a catalyst and heat. According to Sakthivel (2018), the thermal decomposition of ethanol takes place in a temperature range of 400–450 ∘ C and 11 bar, and the catalyst is alumina or transition metal oxides. The oligomerization process is the conversion of short chain into linear 𝛼-oleofins (long chain); this step needs a catalyst such as chromium diphosphine and zeolites, in the case where acidic zeolites are used the temperature range is 100–300 ∘ C at high pressure. Brooks et al. (2016) showed a summary of biojet fuel production processes from alcohol, considering different intermediates. The best processes were those where butane and carbonyl are intermediates, both with conversions of 70–90%; in comparison with a direct alcohol to jet process with 30–70% of ethanol converted. Recently, in a review article about biojet fuel production processes, a summary of oligomerization processes for biojet fuel production was reported (Gutiérrez-Antonio et al. 2017). In that work the authors reported that the catalyst and conditions depend on the monomer; however, these researches do not conclude with the production of biojet fuel. In this context, the first research was realized by Harvey and Quintana (2010); the raw material employed was 2-ethyl-1-hexene, the catalyst was montmorillonite K-10 and sulfated zirconia, and the yield was 90% with a mixture of diesel and jet fuel. The oligomerization of propene, on solid phosphoric acid as catalyst, was studied by Sakuneka et al. (2008). The operating conditions were 3.8 MPa and 160–240 ∘ C; also, the alkylation of benzene and toluene with propene was analyzed. The results show that it is possible to realize both reactions in the same catalyst, producing a synthetic jet fuel that meets Jet-A1 specifications. Olcay et al. (2013) studied the conversion of C5 sugars derived from lignocellulosic feedstock to produce hydrocarbons. The yield was 55%, and the hydrocarbons included gasoline, jet fuel, diesel fuel, and fuel oil. The operation conditions were 80–140 ∘ C, 5.5–8.27 MPa and Ru/Al2 O3 catalyst for the hydrocycloaddition stage, and for the hydrodeoxygenation stage NaOH and Pt/SiO2 –Al2 O3 were employed. In all the proposed studies the obtained products need additional processing, at least one additional distillation stage. Next, a review on the production of biojet fuel through pyrolysis and hydroprocessing of waste lignocellulosic feedstock is presented. In the pyrolysis, the waste lignocellulosic feedstock is heated in a special process to produce an oily product, bio-oil, which subsequently is refined to obtain biojet fuel (Air Transport Action Group 2011), as shown in Figure 6.4. The pyrolysis is the thermal cracking of biomass in the absence of oxygen (Jenkins et al. 2016); the product yield and distribution depends on the operating conditions, such as temperature, pressure, and residence time. Jenkins et al. (2016) presented an extensive review of different pyrolysis technologies, operating conditions and obtained yields of the pyrolysis products; the conversion technology was classified as slow (300–700 ∘ C; 5–500 mm), fast (400–800 ∘ C; Selection, there is an option called “User Defined.” This option allows the insertion of a new compound. All that is needed is to write the desired compound’s name and follow the steps. All property information available should be inserted in Aspen Plus. After drawing the compound, the simulator will ask if the user wants to search for the molecule in the NIST data bank or estimate it with Aspen Plus standard correlations. Therefore, there are three different sources of information provided by Aspen Plus: search the parameters with the NIST extension, use the default in the Aspen library, or estimate them with the available standard correlation. It is possible to edit and correct the data input information. These options allow some flexibility, but can lead to a problem: Is the Aspen Plus standard library reliable? The answer is “sometimes.” Table 9.1 presents three columns with data (thermodynamic properties) from different sources. There are some differences between the experimental values and those in the Aspen® data bank, and these differences may lead to a wrong conclusion in the simulation or an under design as well. Thus, it is always important to pay attention to which values are being used for simulations. For an estimation, Aspen uses as default the Joback Table 9.1 Parameter comparison for methyl oleate. Property

Aspen® estimation

Aspen® standard

−622.4 −121.0 768.0 1122 0.164

−626.0 −117.0 764.0 1280 1.049

ΔHf (kJ/mol) ΔGf (kJ/mol) Tc (K) Pc (kPa) w ∗ Brands

et al. (2002). et al. (2006). ∗∗∗ NIST databank (n.d.). ∗∗ Bucalá

Experimental −720.1∗ −117.0∗∗ 777.0∗∗∗ 1200∗∗∗ 0.919∗∗∗

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method for the Gibbs free energy of formation, critical temperature, and critical pressure, and the Benson method for the heat of formation, and the definition of acentric factor to calculate it. The Aspen standard is defined as the values from the Aspen data bank without any modification after selecting a molecule. By comparing some properties values presented in the Aspen Plus data bank with values available in the literature, some contradictions can be noticed. In the “Review” section, the user has access to a table of properties. Figure 9.1 shows the values of some properties that are present in the software and those disagree with values presented in the literature. It is essential to identify that DGFORM, DHFORM, OMEGA, PC, and TC correspond to the Gibbs free energy of formation, the enthalpy of formation, the acentric factor, critical pressure, and critical temperature, respectively. Comparing these values to the ones presented by Bucalá et al. (2006) and Brands et al. (2002) and others predicted with the Benson, Constantinou and Gani, Ambrose, and Lee-Kesler methods (Poling et al. 2001), it is possible to identify differences among the values reported for some of these properties, which are then corrected in Figure 9.2. For the enthalpy, Gibbs energy, critical pressure and temperature, and acentric factor, the use of these properties as they are set in the Aspen Plus data bank may bring significant errors to the simulation results. Therefore, if necessary, it is possible to modify any component property to the values desired by following the steps shown above. The following example is shown to exemplify this.

Figure 9.1 Aspen Plus table of properties for methyl oleate (METHY-01), ethyl oleate (ETHYL-01), oleic acid (OLEIC-01), methyl palmitate (METHY-02), and palmitic acid (N-HEX-01).

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Figure 9.2 Aspen Plus table of corrected properties for methyl oleate (METHY-01), methyl oleate (ETHYL-01), oleic acid (AC-OLEIC), methyl palmitate (METHY-02), and palmitic acid (AC-PALMIT).

9.2.1.1

Verifying and Calculating the Heat Capacity Values: An Example

As seen before, some properties must be revised, so the simulation can run with corrected data and generate more reliable results. For this reason, it is crucial to verify another important component property, the heat capacity for gas and liquid phases. The software itself presents the coefficients for the calculation of the ideal gas heat capacity for the components shown before, but for the liquid heat capacity the values are missing, indicating that the user should input them manually. The coefficients acquired for the ideal gas heat capacity come from the DIPPR equation 107 (NIST n.d.), as shown in Eq. (9.1). ( ig

CP = C1 + C2

C3∕T

sinh ( C3∕T )

(

)2 + C4

C5∕T

cosh ( C5∕T )

)2 (9.1)

The values for the coefficients of each component are presented in Figure 9.3. Coefficients 1–5 correspond to Eq. (9.1), and the other two correspond to the temperature range of the equation in Kelvin.

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Figure 9.3

Coefficients for the calculation of the heat capacity.

For liquid heat capacity, a few more steps are necessary to include the experimental parameters for regression. The liquid heat capacity values were obtained from Pauly et al. (2014). So, first, go to Methods > Selected Methods > Routes sheet and, on Subordinate Property, change the DHL route to DHL09, as it will change how the liquid heat capacity is calculated. The default mode (DHL00) uses ideal gas enthalpy and the heat of vaporization values for the liquid heat capacity, but once changed to DHL09, it will use the DIPPR equation for calculation, based on the experimental data. It is important to say that the parameter name of the DIPPR equation is CPLDIP, and its base equation is (Eq. (9.2)): CPl = C1 + C2 T + C4 T 2 + C4 T 3 + C5 T 4

(9.2)

Remember that columns 6 and 7 represent the temperature range of the equation tested. Therefore, if experimental data are available or an appropriate equation to estimate a specific parameter, it is possible to insert them on the software and calculate the missing elements. The next step is to insert the data itself. For this, go to the Data file and choose the liquid heat capacity property for the desired component, in this case methyl oleate (as the example considered). Thus, insert the data for each condition and go to the Estimation mode. Select the “Estimate all missing parameters” option and, on the T-dependent file, the CPL property must be chosen, determining the component, the Data method, as the experimental data considered for regression, and the temperature range for the analysis. Finally, run the program and go back to the Analysis mode. Then, a new folder is created with the CPLDIP-1 label with the regressed parameters in it. Later, it is possible to verify a value or the parameters’ behavior by clicking on the Pure button in the analysis sheet. Therefore, choose the CP property, check the liquid option and select the component. After running the analysis, Aspen will show a graph and a table with the property values. The difference between the values associated with the regression can be visualized and compared in Table 9.2. These new values for the liquid heat capacity are slightly different from the original ones calculated with the ideal gas enthalpy and the heat of vaporization, giving an error of up to 6.5%, which could impact the estimation of other related properties and simulations. For this reason, it is essential to compare the Aspen Plus property values with available experimental ones for more accurate estimation and simulation. The example above illustrates the drawbacks that the user can face when working with and considering compounds related to biodiesel processing, such as fatty acids, fatty acid alkyl esters, and acylglycerols. For these

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Table 9.2 Comparison between experimental and regressed values for liquid heat capacity using Aspen Plus. Temperature (K)

Experimental data (kJ/kmol K)

Regressed data (kJ/kmol K)

270 280 290 300 310 320 330 340 350 360 370 380 390

564.63 572.02 579.76 587.84 596.27 605.05 614.18 623.65 633.47 643.64 654.15 665.01 676.22

564.63 572.02 579.76 587.84 596.27 605.05 614.18 623.65 633.47 643.64 654.15 665.01 676.22

types of compounds, a careful analysis of pure compounds must be performed to make sure that the simulator is set and using reliable property values. 9.2.2

Mixture Parameters

Mixtures are an essential topic in any simulation. One needs to know exactly how the compounds interact with each other and therefore their behavior in a mixture or solution. In order to illustrate the importance of setting the correct parameters, different properties of example systems will be compared. 9.2.2.1

Phase Equilibrium: Binary System Oleic Acid (1) + Ethanol (2) as a Case Study

For this system, three different thermodynamic model options, and one set of experimental data of vapor–liquid equilibrium (VLE) are considered: the following thermodynamic models of UNIFAC, UNIQUAC using UNIFAC model to estimate, and UNIQUAC using experimental data regression are evaluated comparing with the VLE data experimental presented by Eduljee and Boyes (1981), considering the temperature of 318.14 K. The results are shown in Table 9.3. Since UNIQUAC and UNIFAC are using the same binary interaction parameters, the results must be quite similar (for the phase equilibrium calculation). The mean deviation was 6.3% for UNIQUAC and 4.8% for UNIFAC; also, UNIFAC was slightly better and had a smaller maximum relative error. For this particular system, the maximum error was small, but this is not standard behavior. Taking, for example, oleic acid (1) + methanol (2) VLE data, the magnitude of the error comparing the calculated values with the pressure experimental data, pressure increased from around 12 to 30%, for the UNIFAC predictions. Therefore, it is essential to know how well the parameters and thermodynamic model can describe the system. Aspen Plus allows the use of experimental data to regress binary interaction parameters for the thermodynamic models and mixing rules available. For this, it is necessary to insert

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Process Systems Engineering for Biofuels Development Table 9.3 Comparison between VLE pressure data from oleic acid (1) + ethanol (2) using different parameter estimators at 318.138 K. Relative error (%)

x1 P (kPa) 0.000 0.064 0.186 0.300 0.385 0.507 0.624 0.714 0.805 0.869 0.917 0.948 ∗ UNIQUAC

23.00 21.67 19.68 17.89 16.35 13.95 11.21 8.96 6.49 4.52 2.96 1.89

UNIFAC

UNIQUAC∗

−0.3 −1.4 −4.1 −4.9 −5.0 −3.4 −1.6 1.3 5.5 8.0 10.5 11.8

−0.3 −1.3 −3.7 −4.2 −3.9 −1.5 1.2 4.8 9.7 12.6 15.4 16.9

UNIQUAC∗∗ −0.3 0.4 2.0 2.2 1.3 0.3 −1.3 −1.3 ∼ 0.0 0.4 1.5 1.9

using UNIFAC as estimator. using experimental data.

∗∗ UNIQUAC

the data set in the data folder and change the run mode to regression. In this case, the regression will estimate the binary interaction parameters for the UNIQUAC model and use ordinary least squares (OLS) as the objective function. The Aspen default objective function is maximum likelihood; however, it is possible to get better results by changing that for OLS. With the experimental data regression, it is always expected that the model will present better results. Considering the last example above and regressing the UNIQUAC parameters, the mean error in pressure changed from 4.8 to 1.1%, and the maximum error observed decreased from 16.9 to 2.2%. 9.2.2.2

Excess Molar Enthalpy: Ethyl Oleate (1) + Ethanol (2) Mixture

The experimental data of excess molar enthalpy was obtained by Aissa et al. (2017) for a temperature of 310.14 K, and for the regression, the OLS was used to estimate the binary interaction parameters for the UNIQUAC model. Table 9.4 presents the results obtained, considering this example of prediction versus regression. Regarding the maximum error, it is possible to highlight that the OLS method still presented the smallest relative maximum error; however, between UNIQUAC using UNIFAC as estimator and UNIQUAC regressed to the experimental data, the best parameters cannot be chosen immediately; this will depend on the molar fraction of ethyl oleate that is used. It is worth mentioning that UNIFAC presented the worst results. 9.2.2.3

Biodiesel Density: Methyl Oleate, Methyl Palmitate, and Methyl Linoleate

In order to estimate the biodiesel density, it is necessary to determine how many compounds will be considered, since the biodiesel is a multicomponent mixture of different fatty acid alkyl esters. First, biodiesel is known as a mixture of methyl esters or ethyl esters and some of these esters can be found in higher concentration than others, depending on the raw

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

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Table 9.4 Comparison of excess molar enthalpy of ethyl oleate (1) + ethanol (2) using different parameter estimators at 310.14 K. Relative error (%) ΔHex

x1 0.0912 0.1430 0.2011 0.3021 0.4194 0.5094 0.5427 0.6335 0.7343 0.7817 0.8828 ∗ UNIQUAC

(kJ/mol)

0.588 0.896 1.139 1.435 1.618 1.680 1.677 1.659 1.524 1.406 0.995

UNIFAC

UNIQUAC∗

23.6 27.5 26.6 25.0 24.2 25.6 26.1 30.3 36.3 39.6 48.2

9.3 9.0 4.4 −0.5 −1.9 0.8 2.0 9.1 18.9 24.0 36.6

UNIQUAC∗∗ 4.8 3.4 2.9 10.8 15.4 14.6 14.1 7.8 1.7 7.1 21.0

using UNIFAC as estimator. using experimental data.

∗∗ UNIQUAC

material originating the biodiesel (different vegetable oils and animal fats present different fatty acid profile). For convenience, in this work, the biodiesel will be considered biodiesel from palm oil and methyl oleate, methyl palmitate, and methyl linoleate as the representative compounds. These three compounds together represent 94 wt% of the biodiesel from palm oil: 0.4245 methyl palmitate, 0.4192 methyl oleate, and 0.098 methyl linoleate, in mass fraction. These data were obtained by Pratas et al. (2011). With the experimental data, the binary parameters can be calculated from the Rackett liquid molar volume model. This parameter can be calculated for all the binary groups, but since there are two compounds that represent 84% of our sample, a two-compound approach should be chosen. Table 9.5 shows the different results for both approaches (two and three compounds). There is not much gain in choosing a three-compound estimation; in this case, after the estimation, even calculating Rackett without any experimental data, the error dropped only 0.2%. In general, the binary regression data had almost no effect on the system. 9.2.2.4

Ternary Mixtures (LLE): Methyl Oleate (1) + Glycerol (2) + Methanol (3)

The first step is to insert the data in Aspen Plus. The ternary data used in this topic were obtained by Andreatta et al. (2008), and the temperature of 333 K was chosen for the binary interaction parameter estimation. The regression was run with the default method of Aspen Plus. Figure 9.4 depicts a comparison among the experimental data and calculated values using the thermodynamic model. Figure 9.4d shows the experimental data, and Figure 9.4c the regressed model (UNIQUAC). Note that UNIFAC (Figure 9.4b) and UNIQUAC using UNIFAC parameters (Figure 9.4a) presented almost the same results and did not represent the ternary system well. However, after the regression, the system could be better predicted, mainly with conditions at high concentrations of methanol.

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Table 9.5 Density estimation for biodiesel at different temperatures (288.15–363.15 K). Relative error (%) Two compounds Temperature (K)

Density(kg/m3 )

288.15 293.15 298.15 303.15 308.15 313.15 318.15 323.15 328.15 333.15 338.15 343.15 348.15 353.15 358.15 363.15

877.9 874.1 870.4 866.7 863.0 859.4 855.7 852.1 848.5 844.9 841.2 831.6 834.0 830.4 826.8 823.2

Calculated 0.7 0.7 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 −0.3 0.4 0.4 0.4 0.4

∗ Rackett

liquid molar volume model using experimental data.

9.3

Kinetic Study

Regressed∗

Three compounds Calculated

1.2 1.1 0.9 0.8 0.6 0.5 0.3 0.2 0.0 −0.1 −0.2 −1.1 −0.5 −0.7 −0.8 −1.0

0.5 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2 −0.5 0.2 0.2 0.2 0.2

Regressed∗ 1.2 1.1 0.9 0.8 0.6 0.5 0.3 0.2 0.0 −0.1 −0.2 −1.1 −0.5 −0.7 −0.8 −1.0

Correct representation of the reaction kinetics is a key factor for the analysis, simulation, optimization, and mainly designing a chemical plant. For the biodiesel case, as mentioned before, the reactions involved are the acylglycerol transesterification and fatty acid esterification with short-chain alcohols. Therefore, in this section, we will discuss some aspects of using the Aspen Plus simulator to deal with these reactions. 9.3.1

Esterification Reaction

As this chapter focuses on biodiesel production from waste oil, the presence of FFA in a considerable amount is common. As previously discussed, the direct transesterification reaction cannot be used for raw material with high acidity (over 0.5% as stated by Clark et al. [1981] or above 1% as claimed by Canakci and Van Gerpen [1999]). For this reason, a preliminary step must be performed. The esterification consists of a reaction to transform the FFA into esters, also reducing the solution acidity (FFA content), enabling further oil transesterification. Thus, as stated before, the esterification consists of a reaction between one mole of an organic acid with one mole of alcohol, forming one mole of ester and one mole of water. The general esterification reaction equation is represented in Eq. (9.3). rAGL = −k1 [AGL] ⋅ [ROH] + k2 [Est] ⋅ [H2 O]

(9.3)

In the experiments carried out by Murad et al. (2017), ethanol was used as the alcohol for esterification and it was suggested that for the correct kinetic modeling the catalyst to ethanol mass ratio must be inserted in the model. This equation complementation is

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

x2

(a) 0.05

0.05

x1

0.15

0.95

0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

0.25

0.85

0.15

0.95

0.35

0.75

0.25

0.85

0.45

0.65

0.35

0.75

0.55

0.55

0.45

0.65

0.65

0.45

0.55

0.55

0.75

0.35

0.65

0.45

x3

0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

x2 (c)

0.05 0.15 0.25 0.35 0.45 0.55 0.65

0.75 0.85

0.05 0.15

0.85

0.25

0.75

0.35

0.65

0.45

0.55

0.55

0.45

0.65

0.35

0.75

0.25

0.85

0.15 0.05

0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

x1

0.95 0.85 0.75 0.65 0.55 0.45 0.35 0.25 0.15

0.95

x3

x1

x2

(d)

0.95

0.95

x3

0.85

0.25

0.75

0.35

0.95

0.15

0.85

0.25

x3

x2

(b) 0.95

0.15

233

0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

x1

Figure 9.4 Comparison between thermodynamic models/parameters of the ternary system methyl oleate (1), methanol (2), and glycerol (3) at 333 K using Aspen Plus for (a) UNIQUAC using UNIFAC parameters, (b) UNIFAC, (c) UNIQUAC with regressed parameters, and (d) experimental data.

performed by adding a parameter in the kinetic constants for the forward and backward reactions (k1 and k2 ), as shown in Eqs. (9.4) and (9.5). ( E ) − RT1

(9.4)

( E ) − RT2

(9.5)

k1 = a1 ⋅ e k2 = a2 ⋅ e

where E1 and E2 are the activation energy, and a1 and a2 the model parameters that depend on the catalyst to ethanol mass ratio, as shown in Eqs. (9.6) and (9.7). ( ) mH2SO4 (9.6) a1 = A1 ⋅ m ( EtOH ) mH2SO4 a2 = A2 ⋅ (9.7) mEtOH In Eqs. (9.6) and (9.7), A1 and A2 are the adjusted constants by regression. The reaction constants obtained by Murad et al. (2017) using 0.33 wt% of H2 SO4 as the catalyst, are presented in Table 9.6.

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Process Systems Engineering for Biofuels Development Table 9.6 Kinetic parameters for the esterification reaction with ethanol. Parameter A1 (l/mol/s) A2 (l/mol/s) E1 /R (K) E2 /R (K)

Value 9.1 × 107 3.9 × 104 6.56 × 103 3.87 × 103

Source: Murad et al. 2017. Reproduced with permission of Springer.

Table 9.7 Kinetic parameters for esterification with methanol at 333.15 K. Parameter

Value

a1 a2 E1 (J/mol) E2 (J/mol)

2.869 × 106 37.068 50 745.2 31 007.3

Source: Berrios et al. 2007. Reproduced with permission of Elsevier.

Similarly, a kinetic study using methanol was presented by Berrios et al. (2007). This time, a catalyst concentration of 5 wt% was used. The parameters obtained by their research are presented in Table 9.7, where a1 and a2 are the frequency factors. These parameters will be used in the simulation for the biodiesel production process in Section 9.3.2. 9.3.2

Experimental Reaction Data Regression

Although some Aspen Plus estimations are reliable, experimental data may improve results. It is possible to adjust many properties in the Aspen Plus data bank by changing the calculation parameters. For the reaction properties, for example, it is possible to regress experimental data to adjust an adequate equation describing the reaction behavior. The first step is to create the reactor block in the simulation and its inlet and outlet streams. First, if the data were obtained in a batch reactor, use the Rbatch model. Once finished, go to Model Analysis Tool > Data Fit > Data Set files and create a new Profile-Data. In this new folder, insert the reactor model and name it. In the Measured block variables space, choose a variable name and enter what this variable will be, in this case, to obtain the liquid molar fraction of lauric acid (representing our FFA) through time, choose the MOLEFRAC-L variable and the lauric acid component. Next, go to the Data file and insert the obtained data (Table 9.8) and the desired standard deviation value. Once finished, click on the “Initial conditions” file and add the working temperature (here consider an isothermal reaction), pressure, and feed composition. In this case study, the conditions were 343.15 K and 1 bar, and the inlet flow was 118.486 kg/h of ethanol, 11.6102 kg/h of water, 42.882 kg/h of lauric acid, and 0 kg/h of ethyl laurate.

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Table 9.8 Experimental data for the lauric acid esterification with ethanol. Time (minutes)

Lauric acid mol fraction

0 15 30 60 120 180 240 300 360

0.0678 0.0610 0.0533 0.0427 0.0303 0.0221 0.0186 0.0169 0.0159

Source: Murad et al. 2017. Reproduced with permission of Springer.

With this done, go to the Regression folder and create a new file, in the Specification file, in case more data are available, different weight values can be inserted. Thus, the main data would be given priority or maintain everything at unitary value for a general regression with no priority data. In the Vary file, choose four variables for the regression, activation energy and pre-exponential factor for both direct and inverse reactions. These properties are reaction variable type and the reaction number is the same as will be specified in the Reaction folder. Then, go to Reaction folder and create a new Powerlaw reaction. In the newly created reaction, insert a name, and select the reactants and products involved in the reaction, remembering that reactants have a negative value for coefficients. It is important to remember that four types of classes are available: Equilibrium, Powerlaw, Langmuir–Hinshelwood–Hougen–Watson (LHHW), and Generalized Langmuir–Hinshelwood–Hougen–Watson (GLHHW). In the Equilibrium class, the user can choose to calculate the equilibrium constant through Gibbs energy or by a built-in temperature-dependent expression. Powerlaw has the rate expression based on the kinetic factor and driving force, where the kinetic factor can be obtained through Eq. (9.8) and the driving force by choosing the concentration basis. LHHW is identical to the Powerlaw equation, but with an adsorption term. Finally, the GLHHW is the same as LHHW, but the adsorption term can be manually inserted and customized. ( )n ( −E )( 1 1 ) − T e R T T0 (9.8) r=k T0 The Powerlaw class and mole fraction as concentration basis are used in this work to illustrate the model fitting. As the regression values are not available yet, a reasonable initial value of the activation energy and a pre-exponential factor in the file can be considered. Finally, go back to the batch reactor. There, insert the specifications, the kinetics with the same name initially chosen, and the batch operation. As the Stop Criteria, set the time or another parameter of interest. At last, in Operation Time, put the total cycle time as one hour, which means the software will multiply the flowsheet stream, representing the batch charge by cycle time. The maximum calculation time to be inserted is at least the same time on the stop criteria file. The time interval between profile points will depend on how many points in the profile are wanted to be generated. Finally, run the simulation and see the results.

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Process Systems Engineering for Biofuels Development

0.090

Estimated value

0.075

0.060

0.045

0.030

0.015 0.015

0.030

0.045

0.060

0.075

0.090

Measured value Figure 9.5 Comparison between measured and estimated values for an esterification reaction using the Powerlaw model.

In the Regression file results, a comparison between the experimental data set and regressed values is available; it is also possible to verify this difference graphically, as shown in Figure 9.5. On Profiles, available in the batch block, it is possible to access the reaction behavior by analyzing the composition change of reagents over time. Finally, regressed parameter values can be obtained by going to the Results file in the created reaction folder. It is essential to have in mind that for a more accurate parameter regression, new values of the parameters should be substituted in the Reaction file, and then the program can be run once again. By doing this procedure until a constant value of the properties is achieved, a more precise result can be obtained. Therefore, this presented resource can be used whenever experimental data are available instead of using a simple conversion value. 9.3.3

Transesterification Reaction

Transesterification is the most common way to produce biodiesel. This reaction (Figure 9.6) can occur just by mixing the reactants and waiting until the system reaches equilibrium. Nevertheless, a process with fast conversion and high purity is always desired. Therefore, catalyst and excess alcohol are usually used. It is possible to use either acid or alkaline catalyst, and either ethanol or methanol as the reactant. In this section, the kinetics of alkali-catalyzed transesterification of soybean oil is discussed. Much research has already been done in recent years, and now the scientific community is focusing on the use of waste oil, non-edible oils, and animal fat, mainly because of the

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

H2C

RCOOR′

COOR′

H2C

OH

HC

OH

H2C

OH

237

+ HC

COOR″

+

Catalyst 3 ROH

RCOOR″

+

+ H2C

RCOOR‴

COOR‴

Triacylglycerols

Alcohol Figure 9.6

Triacylglycerol

+

ROH

Diacylglycerol

+

ROH

Monoacylglycerol +

ROH

Mixture of alkyl esters

Glycerol

Transesterification reaction.

k1 k2 k3 k4 k5 k6

Diacylglycerol

+

RCOOR′

Monoacylglycerol +

RCOOR″

Glycerol

+ RCOOR‴

Figure 9.7 Reactions involved in the transesterification reaction for biodiesel production, where ROH represents a short-chain alcohol and RCOOR the fatty acid alkyl esters.

eco-friendly and economic aspects. Still, there are three steps behind the main reaction, and all three are reversible. Monoacylglycerols and diacylglycerols are the intermediates in these reactions (Figure 9.7). Concerning the catalyst, there are several options. Alkaline metal alkoxides have shown the best performance, high purity (>98%) and short reaction time (30 minutes) at a low molar concentration (0.5 mol%) (Schuchardt et al. 1998). Moreover, alkaline metal hydroxides have a better cost–benefit ratio. However, the use of hydroxides with alcohol will form water that can hydrolyze esters and form soap. Even with soap formation being an undesirable effect that makes the recovery of the glycerol difficult, it remains reasonable due to the cost–benefit aspect. For the kinetic analysis, experimental data obtained by Noureddini and Zhu (1997) will be used in this work, where methanol was used as the short-chain alcohol at a soybean-to-methanol molar ratio of 6:1, as well as sodium hydroxide as catalyst precursor. In their research, it was stated that the shunt reaction did not improve the fit of the kinetic parameters. This means that for simulation criteria, it will not be necessary to include the overall reaction as an extra step to our simulation. To simulate, the activation energy and the rate constants are needed. Table 9.9 presents the data of activation energy (in cal/mol) that should be used in the Arrhenius equation Eq. (9.9) and the average reaction rate constants at 323.15 K. The data were retrieved from Noureddini and Zhu (1997). k = A ⋅ exp(E∕RT)

(9.9)

238

Process Systems Engineering for Biofuels Development Table 9.9 Activation energy and rate constants at 323.15 K for transesterification with methanol. Reaction

Activation energy (cal/mol)

Rate constants at 323.15 K

13 145 9932 19 860 14 639 6421 9588

0.050 0.110 0.215 1.228 0.242 0.007

TAG → DAG DAG → TAG DAG → MAG MAG → DAG MAG → GLY GLY → MAG

TAG, triacylglycerol; DAG, diacylglycerol; MAG, monoacylglycerol; and GLY, glycerol.

Table 9.10 Activation energy and equilibrium constants at 323.15 K for sunflower oil transesterification with ethanol. Reaction TAG ↔ DAG DAG ↔ MAG MAG ↔ GLY

Activation energy (kJ/mol)

Equilibrium constants at 323.15 K

48.7 49.3 53.9

3.21 3.18 72.77

Reyero et al. (2015) have obtained data for the transesterification of sunflower oil with ethanol using sodium hydroxide catalyst. They have also considered soap formation.1 These data are shown in Table 9.10. It is worth mentioning that the tables present the equilibrium constants. 9.3.4

Supercritical Route

Reactions at supercritical condition of the alcohol allow simultaneous triacylglycerol transesterification and FFA esterification by working at high temperatures and pressures (above the critical point of alcohol). The mechanisms in this type of reaction are similar to those of the traditional transesterification, involving a three-stage reaction, having tri-, di-, and monoacylglycerols and forming esters from the acylglycerols and alcohol reactions and production of glycerol as by-product. Thus, one of the main advantages of this method is that oil pretreatment to remove the FFA is not necessary, as well as the catalyst. In order to determine the reaction kinetics, Cheng et al. (2008) proposed a transesterification reaction of peanut oil under a temperature range of 250–310 ∘ C, pressure of 10 and 16 MPa, and oil-to-methanol ratio of 1:30 and 1:40 through six hours of reaction. Some conditions enabled the reaction to reach the chemical equilibrium condition in much shorter time. Also, higher molar ratios are used particularly when triacylglycerols contain high FFA content, where up to an alcohol-to-oil molar ratio of 45:1 can be used (Sprules and Donald (1950)). As seen in the experiments presented by Cheng et al. (2008), although the pressure influence was little, the temperature and the reactant molar ratio were important factors to 1

The data are available at: https://doi.org/10.1016/j.fuproc.2014.09.008.

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increase both reaction rates and yield. Therefore, for the kinetics calculation Eq. (9.10) was used. 𝛽 𝛼 ⋅ Calcohol (9.10) rA = k1 ⋅ Coil where concentrations are in mol/l, and Calcohol can be considered constant as excess alcohol is used. In addition, Coil may be rewritten as Coil 0 ⋅ (1 − X), where X is the conversion as a function of time. By manipulating these variables, Eq. (9.11) is obtained, where the conversion was regressed giving Eq. (9.12). k dx = 𝛼 ⋅ ln[Coil 0 ⋅ (1 − X)] + ln 1 dt Coil 0

(9.11)

X(t) = 7 ⋅ 10−8 t3 − 5 ⋅ 10−5 t2 + 0.0114t

(9.12)

ln

After substituting the experimental data, a table with the calculated rate constant and reaction order was obtained for different temperatures. With the value of the rate constant found along the measured temperatures, the activation energy (kJ/mol) and factor frequency (A) can be obtained. Finally, the kinetic constants are acquired and substituted in Eqs. (9.9) and (9.10), resulting in Eq. (9.13). 1.5 rA = 12.45 ⋅ exp(28.85∕RT) ⋅ Coil

(9.13)

The reaction has an increasing conversion at higher temperatures, but above 400 ∘ C decomposition reactions start due to temperature degradation. Therefore, it is recommended to work around 350 ∘ C, allowing the process to have higher efficiency without degradation (Kusdiana and Saka 2001).

9.4

Process Design

All biodiesel production process design here presented was developed using Aspen Plus using the correct experimental property parameters, as shown and discussed before. Methanol was the alcohol considered for both esterification and transesterification reactions; the oil stream was considered to be 95 wt% triolein and 5 wt% oleic acid. Our goal in this section is to simulate a process capacity to reach 10% of the Brazilian national demand estimated to 2025. Therefore, the inlet oil stream will be approximately 48 m3 /h. The property method used in most of the simulation was UNIQUAC, as very little quantity of electrolytes is present, and the properties were already corrected. The final simulation can be observed in Figure 9.8, where the left-hand side shows the esterification section, the middle shows the transesterification section, and the right-hand side shows the biodiesel purification section. The explanation of the process is presented in detail next. 9.4.1

Esterification Reaction

Seven reactor models are available in Aspen Plus. One may wonder which one should be used. The stoichiometric reactor uses a given reaction and its partial conversions. However, an equilibrium reactor can calculate the product conversion. Different options can be selected, and each case has its own individual details. Here two of them will be discussed and compared for esterification: the Gibbs equilibrium reactor and the batch reactor.

25 1,44 1,20

MKUP

59

JOIN

MIXX

1,74

1,44

NAOHIN

NAOH

REC

25

RECY

45 TC01

R01

TRANSIN

NEUTRA

233

60

65 68

TOTC

LIGHT

1,44

2,24

0,84

TOSEP

P01

P03

1,04

P04

CSTR

D02 DIN2 60

BIOD

SEP01

P05 65 1,44 SALT

BIO 174

50

0,44

1,14

LIQ

EXTIN

D03

EXT1

0,13 WOIL

51 HEAVY

1,04

46

25 1,04

NEUT2

SULF

Flowsheet of the process for biodiesel production simulation with Aspen Plus.

1,04 GLYCEROL

P08

Figure 9.8

298

P07

TC02

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ESTOUT

50

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1,44

1,44

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0,10

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1,74 NEUT

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234

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ESTIN

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25

30

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60

234

74

60

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9.4.1.1

241

Equilibrium Reactor Based on Gibbs Free Energy (RGibbs)

This option minimizes the Gibbs free energy using the atom balances from the specified components for the reaction. Thus, for a first try, it may give significant base results, but it is important to notice that it may also drive the reaction to incorrect products due to those atom balances, which can lead to the occurrence of reactant production instead of products or different undesirable reactions. In this reactor, the user can choose the “best” reaction by setting it to calculate only phase equilibrium and chemical equilibrium on Calculation Option, although the change between reagents and unexpected formations of products can happen when using this option. For this reason, the ways to work the correct reactions out will be presented. For this example, two types of alcohols, methanol and ethanol, were used to test the esterification and transesterification process. If the option mentioned above was selected, at 60 ∘ C and 1 bar, considering that the oil stream has 95 wt% triolein and 5 wt% oleic acid, and a 9:1 alcohol-to-oil molar ratio was used, it would produce methyl/ethyl esters, glycerol, and water stoichiometrically. Possibly due to the energy of formation, much more water and less glycerol are produced, indicating that part of the triolein or glycerol formed may be transformed into oleic acid. Considering that esterification and transesterification take place at the same time, the Gibbs reactor gives contradictory results including possible triacylglycerol hydrolysis to form fatty acids. One possible way to avoid this problem is to separate the simulation in two reactors and specify which components participate in the reaction and which ones should be formed. As mentioned before, let us force the desired reaction to occur by using two reactors for each type of alcohol, one for esterification and one for transesterification. As the procedure is the same, differing only in the alcohol and ester names, only the step-by-step process for ethanol will be given in the following. For the first reactor (esterification) set the Calculation Option as Calculate phase equilibrium and chemical equilibrium and in the Products file set the option to identify possible products, as wanted to restrict to the esterification reaction. Then, put all the desired components in the product stream: ethyl ester, glycerin, triolein, water, and ethanol (as it enters in excess in the reaction). In the Inerts file, put all the non-participating components and the ones that are unreacted as yet (triolein), setting all of them to a unitary fraction. Here, if the user wants to set how much of the reagents will actually react, it is possible to settle the fraction or mole flow of feed component that does not participate in the reaction. When selecting the fraction, it is directly attributed to the feed stream, but the mole flow can be attributed to the feed stream or from a secondary product formed along with the reaction, without the need to be present in the feed stream. For the esterification reactor, this is all that must be done. However, for the transesterification reaction, a few more steps are needed. By connecting the first reactor exit stream to a new Gibbs reactor, first, change the Calculation Option to Restrict chemical equilibrium – specify temperature approach or reactions. With this option, Aspen will force the desired transesterification reaction to happen. Then, in the Products file, specify the components that will leave the reactor (the same ones as for the esterification except for the triolein that will react in this stage). In the Inerts file, set the same components as for the first reactor, excluding the triolein and adding water and glycerin.

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Table 9.11

Stream results using the Gibbs reactor. Inlet (kmol/h)

Outlet (kmol/h)

Components

Alcohol

Oil

Esterification

Ethanol Triolein Oleic acid Ethyl oleate Water Glycerol

450 – – – – –

– 47.5 2.5 – – –

447.5 47.5 – 2.5 2.5 –

Transesterification 305 – – 145 2.5 47.5

Finally, enter the transesterification reaction on the Restricted equilibrium file in Individual reaction topic, but remember that three moles of alcohol react with one mole of triolein. The user can set the temperature approach to the default, as the reaction will occur at a temperature of 60 ∘ C. Finally, it is just needed to run the program and observe the results, which are presented in Table 9.11. Thus, by analyzing the results and the procedure to obtain these results, it is possible to conclude that the Gibbs reactor may be very useful to predict some reactions involved in biodiesel production, although it should be very carefully studied as it could show incorrect behavior. When this happens, the user may force the desired reaction to occur, as seen before. For this reason, it is recommended to know how the reaction behaves to be more critically aware about the results before using the Gibbs reactor. As an alternative, the REquil model could be used. With this model, only the user specified reactions occur. Both RGibbs and REquil use the standard Gibbs energy of formation. Therefore, this property should be carefully verified and compared with those available experimentally in the literature. 9.4.1.2

Batch Reactor (RBatch)

After discussing the Gibbs reactor, it is possible to realize that the Gibbs minimization reactor can be considered an odd approach, mostly because we do not know if the values for the Gibbs energy of formation are right or not. Thus, it may end with the full conversion of one reactant, or it may produce some random compound that has low Gibbs energy of formation. Therefore, it is better to know the kinetics of reactions and open up a range of possibilities: for example, a batch reactor (RBatch) or a continuous stirred-tank reactor (CSTR) can be used. In this section, a batch reactor for the reaction kinetics presented in Section 9.3.1 is presented and discussed. For both methanol and ethanol streams, the alcohol-to-oleic acid molar ratio of 9:1 was maintained, and the sulfuric acid ratio was set as 5 wt% and 0.33 wt% for the reactor using methanol and ethanol, respectively. Unlike the Gibbs reactor, the only new information that should be added is the Operation Time, which in this case, will be set as six hours as used by Murad et al. (2017). After running the simulation, the performance of both alcohols in the esterification (Table 9.12) can be compared. A visible difference in the performance concerning the two different alcohols is observed in Table 9.12, where methanol gave a much better result compared with ethanol. It is now possible to change the reaction time from the reaction with methanol by setting the desired conversion to 99% (as Stop Criteria). That should give a reaction time of 1.7 hours.

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Table 9.12 Oleic acid conversion in the esterification (6 hours of reaction). Alcohol Ethanol Methanol

9.4.2

Oleic acid conversion (%) 87.1 12.8

Methanol Recycling

In this stage, recovering the excess of methanol that remained after the esterification reactor is a key factor. For that, use a distillation column, which will separate the methanol for recycling from the rest of the components that will follow in the process. To model the distillation column, first, use a shortcut distillation design using the Winn–Underwood–Gilliland method (DSTWU) to have an estimation for the use of the rigorous two- or three-phase fraction for single columns (RadFrac). To design a DSTWU column, insert the light key component, methanol, and the heavy key, where the user can put the lightest heavy key so that it will not go to the top of the column with the alcohol. In this case, use water as the heavy key component. As for the recovery, use a high number in the light key part, once we are interested in recovering most of the methanol and use a low number in the heavy key part, as this component is not desired in the distillate stream. Set the desired working pressure and column number of stages or reflux ratio. If there is no information about these last options, it is common to set the estimation reflux ratio to −1.5, as the minus symbol is read on the Software as how many times the minimum reflux ratio will be used. In order to use vapor as the heating utility, let us work with a column under vacuum (for example 0.8 bar). By analyzing the results obtained, it is possible to use them as an estimation for the RadFrac design. It is important to mention that there is no need to always make a DSTWU column before working with the rigorous column, but if there is no idea of how the separation will be, it may be very helpful for the RadFrac design. In the RadFrac design, use the minimum number of stages estimated from the DSTWU column or suppose a reasonable number and modify it later for better adjustments. A kettle was used as the reboiler type, a total condenser, and the valid phases were set to vapor–liquid. In the last options, Reflux ratio and Distillate to feed ratio specifications were used. Here, do the same for the number of stages, use DSTWU results or estimate. In the stream file, input the feed stage (estimated or not), and in the pressure file insert the working pressure and, if wanted, the pressure drop along the column. Then, set the equipment under a pressure of 0.8 bar and pressure drop of 0.007 per stage. This pressure drop will also be used for the other columns along the process. It is crucial to remember that it is possible to have more adequate operation conditions to achieve the desired specification. For that, the Design Specification file is a very useful distillation column tool to reach the desired specification. Using this option, set the desired goal and use the Vary folder to modify properties. Therefore, starting with the Design Specification, create two blocks, one for the methanol mole purity, and the other for methanol mole recovery in the top stream. In the simulation, a 99.9% methanol mole recovery and 99.7% mole purity for this component as specification were used, as from other results, the conditions became more operationally expensive. Then,

244

Process Systems Engineering for Biofuels Development Table 9.13

Methanol recycle distillation column results.

Property Number of stages Feed stage Molar reflux ratio Mole distillate to feed ratio Methanol mole recovery Methanol mole purity

Value 7 3 (on stage) 0.595 0.131 99.9% 99.7%

go to the Vary file and set to change the reflux ratio and the distillate to feed ratio to achieve these specifications. After running the simulation, it will modify our initial number for these variables and present the found fittable values. The column properties are presented in Table 9.13. In addition, the Stage Wizard button can be used to modify the total number of stages, while the optimum feed stage, which minimizes the reflux ratio maintaining both specifications cited above, will vary according to the number of stages modified. Sometimes, although the specification is achieved, the column results appear with errors, especially after resetting the simulation. In this case, deactivate the Design Specification after substituting the correct values in the column, so that it will return the results obtained in the design and it will not be necessary to go through all the iterations for the specification achievement, avoiding possible problems. After the column design, it is necessary to give the recovered methanol back to the esterification reaction. As not all the methanol needed will be available only in the recycle stream, insert a makeup stream to compensate for the methanol reacted and lost in distillation. To achieve the correct proportion of methanol entering the reactor, use a Design-Spec block. In the block, create two variables, one representing the methanol that enters the reactor and the other the oleic acid flow for esterification. For each one, set the stream category and select the Mole-Flow type for both, as it will be compared directly with the reactor inlet flow, then input the stream and component name, and the units desired. In the Spec file, specify the goal; in this case, select that the methanol mole flow entering the reactor must be nine times that of the oleic acid entering. It is important to remember to use the same words for the variables created before, including capital letters. Finally, go to the Vary file and choose to change the methanol flow in the makeup stream to attain the desired specification. At this point, some attention is required to choose a feasible range for that variable, as it should embrace the actual result. After this, the recycling distillation column is ready, returning the remaining methanol to the reactor, reducing the costs of reagents. 9.4.3

Transesterification Reaction

In contrast to the esterification, the transesterification needs an alkaline catalyst. Sodium hydroxide has the best cost–benefit ratio as the catalyst precursor in this process. For that reason, it is necessary to neutralize the remaining sulfuric acid from the esterification reaction and insert some amount of sodium hydroxide to act as the catalyst. However, there is still a problem: how should the sulfuric acid be neutralized without adding a solution

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with water and sodium hydroxide? Following Noureddini and Zhu (1997), a stream of sodium hydroxide dissolved in methanol can be used. Knowing that the solubility of sodium hydroxide in methanol is 238 g/l, it is possible to work with a concentration of 230 g/l (NPCS Board 2012). In this case, since UNIQUAC is the model used, it will be easier to simulate the neutralization with a reactor block, avoiding the creation of a new thermodynamic section. To do that, create an RStoic reactor block and add the reaction of neutralization. If the heat of reaction is known, go to Heat of Reaction and select one of the three options for the calculation. Once the reaction and the entire block are defined, the last challenge is to adjust the flow of the neutralization stream. There are many options, and the user can opt for the easiest one: to create a Calculator adding the amount of sulfuric acid as base compound (see Additional Resources), or using a design spec. The sodium sulfate formed in the reaction has low solubility in methanol, and even with a small amount of water the separation of most of it is possible with a filter (Okorafor 1999). Thus, in Aspen Plus, a good option is to use a Sep block. The Sep block allows the user to specify the split outputs manually, which means there is no need to simulate this separation, but instead define the split fraction according to the solubility. In this case, consider the entire separation of the sulfate by the filter. That cannot be true, we know; however, considering all the sulfate left (1.7%) in solution is in ionic form, and at low concentration (approximately 3 wt% in this case), it should not interfere in our simulation results. The transesterification reactor is the “heart” of the biodiesel production process. For convenience, methanol was chosen as the reagent. It is important to mind the reaction conditions. First, the reactor will work isothermally at 333.15 K, avoiding the boiling temperature of methanol and seeking the best conversion. Secondly, the right amount of methanol must be present. Noureddini and Zhu (1997) showed that a ratio of 6:1 of methanol-to-triacylglycerols is a good start, and that the catalyst (sodium hydroxide) should represent 0.907 wt% of the methanol stream. Those specifications can be easily reached by using a Design Spec. By knowing the kinetics, the user must choose between different reactor blocks: RCSTR, RBatch, or RPlug. In this chapter, RCSTR, a reactor block for simulation of CSTRs, will be used. Inside the RCSTR block, select the valid phase (liquid only), the residence time (100 minutes), pressure, and temperature. After choosing the standard values suggested by Noureddini and Zhu (1997), it should be possible to play with these values and to seek an optimal point. Following the same steps as for the esterification reactor, add the set of reactions for the transesterification and run the simulation (Section 9.3.3). Once all those steps are done, it should be possible to reach around 90% conversion for triolein in products and sub-products. 9.4.4

Biodiesel Purification

After the transesterification, there is interest in recovering the excess methanol and purify our future biodiesel. In this first part, a distillation column was used. The strategy used is the same one as for the first column, setting the specification to have high methanol recovery for recycling, and high water recovery for the bottom, as water prejudices the transesterification reaction. The only thing needed to be aware of in this section is that it is

246

Process Systems Engineering for Biofuels Development Table 9.14

Second methanol recycle distillation column results.

Property Number of stages Feed stage Molar reflux ratio Mole distillate to feed ratio Methanol mole recovery (top) Water mole recovery (bottom)

Value 6 4 (on stage) 1.54 0.423 98.2% 84.8%

a three-phase column, as the system has two liquid phases (glycerol-biodiesel) and a vapor phase (mainly methanol). The detail in this part is to set the input of the column on the 3-Phase file to test. Thus, set such assumption from the first stage to the last, and then choose the key component of the second phase as the biodiesel. If there is any doubt about the three-phase system, it is possible to go into the stream results and observe if it contains the first and second liquid phase flows. The column specifications are presented in Table 9.14. The methanol is recovered from the distillation column and then it returns to the transesterification reactor, where there will be a Design-Spec block for the makeup, regulating the methanol to enter the reactor is six times with the triolein present in the main inlet reactor stream. The next step is to separate the water, its ions, and the glycerol from the biodiesel. This is a critical step, and it must be deeply analyzed. In the previous sections, the pure compound parameters and some other proprieties were discussed and updated. However, the glycerol–water–biodiesel interaction parameters were purposely not changed. This means analysis of the ternary diagram of this mixture is necessary to see if it can describe the mixture well. Thus, in Figure 9.9, there are three ternary diagrams: (a) with the experimental data retrieved from Bell et al. (2013), (b) using UNIQUAC, and (c) using UNIF-DMD. It is possible to conclude that UNIQUAC cannot describe this system well with the actual parameters. Therefore, it is necessary to use other thermodynamic models or update these parameters. In this case, use UNIF-DMD for the extraction of ions, water, and glycerol. To do that, change the Property method in the Block Options of the equipment. The separation of these three compounds can be accomplished by using a decanter or an extractor. The extractor will often have a better result; however, it will require a new feed stream to work. In this simulation, an adiabatic extractor with six stages and a water feed of 200 kg/h as the top stream was used. As there is the presence of ions and no electrolyte model was used, the user needs to tell Aspen Plus to treat the sodium hydroxide as water. One option is to change the sodium hydroxide for water in the Components > Specifications in the Properties environment, but it would crash the neutralization reactors. Thus, the best option is to create a functional group for the hydroxide under Components > Molecular Structure > NAOH > Functional Group tab. Then, select the method (in this case, UNIF-DMD), and the group number that should be 1300 (group number for water) and one occurrence. After separating the compounds, it is necessary to neutralize the heavy key component of the extractor with sulfuric acid or phosphoric acid. It will allow us to reach the specification

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

x2

(a) 0.05

0.05

0.95

0.15

0.85

0.25

0.45

0.25

0.25

0.75

0.15

0.85

0.35

0.65

0.35

0.75

0.45

0.55

0.45

0.65

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0.85

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0.35

0.65

0.95

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0.25

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0.05 0.95

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x3

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0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

0.05

x1 x3 0.05

0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

x1

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(c)

0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85

0.95 0.85 0.75 0.65 0.55 0.45 0.35 0.25 0.15

0.95

x3 0.05

0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95

x1

Figure 9.9 Comparison between thermodynamic models/parameters of the ternary system methyl oleate (1), water (2), and glycerol (3) at 333 K, and 0.088 MPa using Aspen Plus for (a) experimental data, (b) UNIQUAC, and (c) UNIF-DMD.

and to obtain a crude glycerin stream, as well. Finally, the mainstream is directed to a distillation column to both recover methanol/water and triolein, and to obtain the main product, biodiesel. In this stage, it is possible to follow the same steps as accomplished in the first and second columns. The main difference between this column and the other ones is the methanol and biodiesel removal in a partial condenser, as methanol and water leave the equipment in the vapor phase, while biodiesel remains liquid, requiring the modification of the condenser type to partial vapor–liquid. Another critical detail in this column is that as the components are very different, the simulation may have some difficulties in finding the correct values. In this case, set the type of convergence in the Configuration file as Strongly non-ideal liquid instead of Standard, an option that is recommended when slow convergence is encountered for the Standard option. In the case of using Standard, it would be necessary to first change some specifications for the software to achieve similar estimates, giving the error that it could not converge with the limit number of iterations, and then put the correct values for such properties (which

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Process Systems Engineering for Biofuels Development

can be used only after really finding the optimal configuration). This error is common when trying to optimize a column by changing little by little some properties and then reset and run the simulation, losing the closer estimative. However, all the effort is simplified by changing the convergence type. Thus, for this column, use three specifications, one for water recovery, one for biodiesel recovery, and one for biodiesel purity, as wanted to achieve the ANP purity specification of 96.5 wt% and maximum water content of 200 ppm. In these specifications, the first two should be the highest number possible, while the third one can be set close to the ANP number (a little more significant to ensure good results), so there will not have to be a much more expensive column but one that can do the work. As there are three specifications and three variables, choose to vary reflux ratio, distillate to feed ratio, and distillate to vapor fraction in order to achieve those specifications. After running the simulation, the results can be obtained as shown in Table 9.15. It is important to remember that the esters present in biodiesel start to degrade around 400 ∘ C, so it is necessary to be careful not to surpass this value. In this simulation, to have a good recovery and a consistent bottom temperature, set the column under a vacuum of 0.1 bar. With these conditions, the bottom temperature remains at 298 ∘ C, avoiding the degradation problem, although the use of steam will not be suitable as the heating utility. So, with this process, we could produce and purify our biodiesel to ANP’s specification, maintaining a suitable temperature (without the risk of considerable ester degradation), and recovering a good amount of the desired product. 9.4.5

Additional Resources

A lot of block and options were discussed in the previous sections, but sometimes the user can come across something different and need a functional solution. To assist users, Aspen Plus has manipulators such as Calculators and Design Specs, and analysis tools. This section summarizes three resources that may be needed at some point during the simulation. 9.4.5.1

Calculator

Calculator is a type of manipulator, just like Design Spec, that can define some variable in our simulation. To create one, click in Manipulators in the model palette and select Table 9.15 Biodiesel purification distillation column results. Property Number of stages Feed stage Molar reflux ratio Mole distillate to feed ratio Distillate to vapor fraction Water presence in biodiesel Biodiesel mole recovery Biodiesel mass purity

Value 5 2 1.25 0.956 0.055 195 ppm 90.0% 99.4%

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

Figure 9.10

249

Calculator tool interface from Aspen Plus.

Calculator. After creating the block, the interface must be as in Figure 9.10. Then, create a new variable and assign its definition. The most important information about this block is the information flow. The user can choose between import, export, and tear variable. Importing a variable means that Aspen Plus will be able to use this variable to compute other variables. However, exporting a variable will overwrite the calculated value to this variable. After defining the variables, go to the Calculate tab. This tab has a white box to execute FORTRAN statements. By clicking with the right mouse button in the variable list, a smart box will appear, allowing the user to drag and drop the names of the variables already defined. One last hint: start writing the Fortran statement by the seventh column. Columns 1 to 6 are reserved for comments and statement labels. Example: Calculating the Amount of Sodium Hydroxide In this example, calculate the amount of sodium hydroxide/methanol stream necessary to completely neutralize the sulfuric acid from the esterification reactor. The first step is to define the sampled variables. It will need two or three, depending on how the sodium hydroxide concentration was defined in the stream. This case requires two import variables and one export variable. The import variables are the mole flow of sulfuric acid in the stream that enters the neutralization reactor and the molar fraction of sodium hydroxide in the stream with methanol. The export variable will be the total mole flow for the stream methanol/sodium hydroxide. After that, starting at column 7, write a code defining the export variable as two times (reaction stoichiometry) the amount of sulfuric acid divided by the molar fraction of sodium hydroxide. Now, run the simulation, and the Calculator will do its job. As an exercise, try to calculate the amount of sulfuric acid used as catalyst necessary for the esterification reactor. By now, it should be easy.

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Figure 9.11

9.4.5.2

Sensitivity tool interface.

Sensitivity Analysis

“Sensitivity” is a model analysis tool. It is handy when having an already simulated process, and the best configuration is being searched. Try, for example, evaluating the change of reflux ratio or the feed stage. The interface is shown in Figure 9.11. As done for the Calculator resource, the first step is to define the variables. However, in the Sensitivity tool, the user needs to define the variables that are to be varied and the results to be seen, which means it can manipulate the number of stages for a distillation column and have, as results, the recovery of some compound. The Tabulate tab shows how the results will be displayed. Example: Evaluating a Distillation Column In a quick example, let us investigate the feed stage for the defined distiller. The first step is defining the variable; in other words, create a block variable in the manipulator that will vary the feed stage (FEED-STAGE). In the next step, define the sampled variables. By choosing to work with the first distiller in the process, a good variable will be the methanol recovery in the recycle stream. Thus, define the amount of methanol in the top stream and the feed stream of the distiller. Now, write in the Tabulate tab, the column number (1) and the recovery expression: the methanol in the top stream divided by the methanol in the feed stream. Sometimes the manipulated variable is discrete, so, make sure to define the limits having this in mind. As an exercise, try this with the second distiller, varying the distillate to feed ratio. 9.4.5.3

Optimization Tool

The last additional resource is the Optimization model. Like all the previous resources, first, define the variables in the Define tab, and then the desired variables range in the Vary tab.

Process Analysis of Biodiesel Production – Kinetic Modeling, Simulation, and Process Design

Figure 9.12

251

Optimization tool interface.

The difference between this tool and the other two is its use. In the Objective & Constraints tab, it is possible to add the objective (maximize or minimize a variable) and activate some constraints. The most important detail in this tool is the optimization expression. Since the model tries to reach the best point, having weak constraints or an opened variable can easily crash the simulation. That is why the optimization tool can be a double-edged sword. Figure 9.12 shows the Optimization tool interface. Example: Optimizing a Distillation Column In the first distillation column, the most natural idea is to optimize the recovery of methanol and triolein. Also, it is desired that more triolein goes to the bottom, and more methanol goes to the top of the distiller. Thus, divide this expression into three stages in the Fortran tab. Starting in row 1, column 7, the methanol recovery is the amount of methanol that goes to the top divided by the fed one. In the same way, in row 2, column 7, define the triolein recovery as the bottom amount divided by the fed amount of triolein. Finally, in row 3, column 7, write that the objective function is the sum of the methanol and triolein recoveries. It is also possible to create a constraint if the amount of water, temperature, or some other variable needs to be specified. Aspen Plus enables the manipulation of up to 20 variables in the Vary tab. For this example, only two variables are tested: the distillate to feed ratio and the molar reflux. To complete all the steps, go into the Objective & Constraints tab, write the name of the objective function, and select maximize (in this case). If there are any constraints, select them. Now, run the simulation for the optimization to take place and try to find the optimal point. Finally, go to the second distiller and vary the same variables, try to optimize the recovery for the triolein, methyl oleate, methanol, diolein, and monoolein. Note: the second column must have a temperature constraint, temperatures higher than 380 ∘ C should not be reached.

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9.5

Energy and Economic Analysis

For any project in the chemical industry, it is vital to optimize and observe how the process will behave. Therefore, saving energy and economic evaluating is important for an appropriate estimation. Nowadays, the economic feasibility and the financial return are of increasing interest to investors. Besides, not only the equipment itself but also the raw materials and utilities must be included. Thus, in this section, the Aspen Energy Analyzer and Economic Analyzer will be discussed for the correct estimation and optimization of the operational costs. The Aspen Energy Analyzer is an extension of Aspen Plus and is designed to improve how the energy flows in our plant. Once the simulation is finished, activate the Energy Analyzer by clicking on the button located in the Activation Dashboard. It may take a while, but after processing the simulation, Aspen Plus will return the estimated energy savings. By changing the view from Simulation to Energy Analysis, the user will get access to a new project. Thus, select the process type that, in this case, will be the user specified option and in the Customize box, a temperature approach of 15 ∘ C (Aspen’s® defaults) should be inserted. Clicking on Analyze on the Home menu, Aspen will generate once more the data with a new temperature approach. After that, possible designs need to be evaluated. To access the Energy Analyzer interface, click on Details in the home menu. Aspen will redirect the user to a new window with a base case. The standard calculation option of Energy Analyzer is the cost approach, and the base case calculates the cost with energy after choosing to work without any recycle and only with utility streams. It is also possible to generate the best energy design by selecting the scenario that was created and then, in the bottom menu, the Recommend Design option. After that, click on Solve. This simulation has four optimized scenarios. To interpret how it works, we have defined a scenario. As shown in Figure 9.13, the whole concept of this analyzer is the use of the energy that is already present in the process and stored as temperature. In the scenario exemplified in Figure 9.13, the stream of the condenser from second distiller column splits and part goes to an exchanger that uses the stream of the reboiler from the first distiller and part goes to the reboiler of the first distillation column. The black lines connecting the black dots represent the integration plan proposed by Aspen Energy Analyzer. Also, each line represents a heat exchanger and can be interpreted

341°C

244°C

HOT STREAM 239°C

239°C

COLD STREAM

Condenser (2nd Distiller)

186°C 203°C

Exchanger (After 1st Distiller)

186°C

115°C

Reboiler (1st Distiller)

99°C 133°C

Figure 9.13

124°C

60°C

Exchanger (After CSTR)

Energy scenario obtained by Aspen Energy Analyzer.

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in the same way as explained before. There are also lines connecting utilities that were suppressed in our figure but are represented by the white dot with a black outline. Moreover, each layout has its configuration, and each configuration has its exchangers. There are plenty of resources available in Energy Analyzer. By generating the scenario, Aspen also generates an estimative of the total exchange area and its associated costs. It is also possible to retrofit the Energy Analyzer simulation by clicking on the chosen design and then on Enter Retrofit Mode; it will export the data to the Energy Analyzer Module in the Simulation tab. After running the Energy Analyzer, an overview of what is recommended to be done can be obtained. By the Energy Analyzer tab in Aspen Plus, it is possible to evaluate some scenarios with new heat exchangers. Go to Add Scenario on the top menu and then click on Add Exchanger. Aspen Plus will give some potential changes with additional costs and payback time to be chosen. Our simulation target is 47.3% of energy cost reduction, and it can be accomplished by including four exchangers. This process helps to validate the changes, and what is necessary or what is recommended in the simulation. It is worth noting that Aspen Plus will not change the simulation, which means the economic analysis will not consider it, and therefore the changes must be done manually. After completing the simulation, it is possible to know if the project is economically viable. As Aspen’s Economic Analysis automatically calculates the equipment price (with the correct set of equipment types available in the Software to choose), the biggest issue is to search the correct and current stream product and reagent prices. In Table 9.16, all the components available in the process, and the price for each one, are presented. As the waste oil can have different prices depending on the source and purity; half of the soy oil price was considered for the estimation. The same thing was assumed for triolein. For the process utilities, Aspen’s default cost values were used. Remember that part of the values shown will be the cost, and another part will be income, as it is produced, and it may be sold for other purposes.

Table 9.16 Current prices for the used/produced components in the simulated industry. Component Soy oil∗ Waste oil (considered) Sulfuric acid∗∗ Sodium hydroxide∗∗ Biodiesel∗∗∗ Methanol† Glycerin‡ Triolein (considered) Sodium sulfate‖ ∗ Markets

Insider (n.d.). (n.d.). ∗∗∗ NESTE (n.d.). † Methanex (n.d.). ‡ Landress (2018). ‖ ICIS (2007). ∗∗ ECHEMI

Price (US$/ton) 730 370 61 650 1040 432 220 370 105

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Process Systems Engineering for Biofuels Development Table 9.17

This is shown as from Aspen Plus.

Data Total Capital Cost (USD) Total Operating Cost (USD/Year) Total Raw Materials Cost (USD/Year) Total Product Sales (USD/Year) Total Utilities Cost (USD/Year) Desired Rate of Return (Percent/Year) P.O. Period (Year) Equipment Cost (USD) Total Installed Cost (USD)

Value 20 699 300 323 495 000 291 635 000 596 838 000 5 608 870 20 1.96 4 252 900 10 326 100

In order to insert these values in the simulation, go to Setup > Stream Price, and in the input section, select to add both feed and product streams. Once added, it is necessary to exclude the stream without any expense or profit, and then put the correct values for each inlet and outlet stream. The most important detail in this part is that some streams have different components with distinct price values, so it is necessary to verify precisely which components are present and the proportion, so that the stream price may be adapted to its content. Finally, with these values, go to the next step, the process full economic evaluation. The first step is to activate the Economic Analyzer. As already done for the Energy Savings, click on the Activation Dashboard (at the View tab), and then on the green box. For this procedure, it is essential that the Energy Savings is still activated or the savings from the energy recycle will not be accounted for. After activating the green box, go to the Economics tab and press Evaluate. Aspen Plus will ask the user for the mapping options and, usually, it is a good idea to check both boxes: size equipment and evaluate cost. By mapping, choose the distiller configurations, and if desired, change one piece of equipment for another (like a shell and tube exchanger for a furnace). In the Summary of the Economic Analyzer, the user has access to a list of information as shown in Table 9.17. In this case, the plant seems economically viable, since the period of return is less than two years. The Total Capital Cost includes the instrumentation, buildings, installation, and construction of the plant, meaning all the one-time payments needed to make the plant ready for startup, the Total Operation Cost includes the labor and maintenance costs, and the P.O. Period is the return time of the investment.

9.6

Concluding Remarks

In this chapter, we have proposed and discussed some aspects related to the process simulation and design of a process for biodiesel production using the Aspen Plus simulator. Some specific aspects about using biodiesel related compounds (acylglycerols and fatty acid-based molecules) in the simulations were focused on, and how biodiesel can be obtained with different types of reaction, reagents, and operational conditions. By correcting the pure and mixture components parameters present in Aspen Plus, it was possible to rely more on our further tests and simulation. Knowing about the most common reactions to obtain the desired product and their advantages and disadvantages, an important

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decision could be made on which would be our biodiesel production route. Using the traditional transesterification route to produce biodiesel from acid oil, where a pretreatment step for FFA removal is necessary (esterification), and by having the correct kinetics, it was possible to design the entire reaction and purification process using Aspen Plus. Once all the preparation and project design were implemented, the next step was to demonstrate how to optimize a simulation by using the Aspen Energy Analysis tool, where we were able to have 47.5% less energy cost with its use. Then, the most important factor to evaluate the feasibility of an industry was run: the Economic Analysis. In this part, all the reagent and product prices were searched, and utilities and equipment costs were estimated to have a reliable source of information. Finally, besides knowing how to work with Aspen Plus tool, we were able to obtain biodiesel under ANP’s standards (Brazilian Agency) for both ester and water presence and with a return period of two years.

Acknowledgment Reference and screen images from Aspen Hysys®, Aspen Plus®, Aspen Plus Dynamics®, Aspen Economics Evaluation®, Aspen EDR®, Aspen Energy Analyzer®, and Aspen Properties® are reprinted with permission from Aspen Technology, Inc. AspenTech®, Aspen Hysys®, Aspen Plus®, Aspen Plus Dynamics®, Aspen Economics Evaluation®, Aspen EDR®, Aspen Energy Analyzer®, and Aspen Properties®, Aspen EDR®, aspenONE®, and the AspenTech leaf logo are trademarks of Aspen Technology, Inc. All rights reserved.

Exercises 1. Use the paper of Bell et al. (2013) to estimate and regress the UNIQ parameters for the ternary methyl ester, glycerin, and water mixture as described in Section 9.2.2 and discuss why UNIQUAC with our given parameters (without this regression) cannot describe the ELL equilibrium. 2. Compare the overall performance when changing the extractor (Section 9.4.5) for a decanter with and without the water feed. 3. Use the Aspen regression tool to estimate the reaction parameters: activation energy, and pre-exponential factor using POWERLAW for the direct and reverse reactions. Conditions: 343.15 K and 1 bar. Inlet flow: ethanol 108.66 kg/h, water 10.53 kg/h, lauric acid 51.94 kg/h, and ethyl laurate 0 kg/h. Use Table 9.18 for the data regression. 4. Use methanol instead of ethanol to build a new simulation and compare the total biodiesel production for both alcohols and its energy use. 5. From Section 9.3.3 we have found different energy saving designs. Choose one and use it to adjust and rerun the simulation. Then, rerun the Energy Analyzer and try to improve energy savings. 6. CHALLENGE. Build a simulation of biodiesel production using a supercritical reactor (plug flow reactor) and evaluate economic feasibility.

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Process Systems Engineering for Biofuels Development Table 9.18 Molar fraction (lauric acid) versus time for the data regression (Exercise 3). Time (minutes)

Molar fraction (lauric acid)

0 15 30 60 120 180 240 300 360

0.0809 0.0712 0.0633 0.0511 0.0348 0.0258 0.0212 0.0188 0.0176

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10 Process Development, Design and Analysis of Microalgal Biodiesel Production Aided by Microwave and Ultrasonication Dipesh S. Patle1 , Savyasachi Shrikhande2 , and Gade Pandu Rangaiah2,3 1 Chemical

Engineering Department, Motilal Nehru National Institute of Technology, Allahabad 211 004, India 2 School of Chemical Engineering, Vellore Institute of Technology, Vellore 632 014, India 3 Department of Chemical and Biomolecular Engineering, National University of Singapore,117585, Singapore

10.1

Introduction

Owing to the increasing population and ever-changing lifestyle, total primary energy consumption (TPEC) is increasing day by day and is projected to increase by 57% from the year 2010 to 2040, which impacts available fossil fuels since they are non-renewable (Lee et al. 2010; Kumar and Sharma 2016). The universal energy crisis and environmental concerns have led to extensive research and development programs on biomass utilization (Li et al. 2015). In the context of the 2012 “International Year for Sustainable Energy for All” (SE4ALL), the International Renewable Energy Agency launched a global roadmap, named REMAP 2030, in a bid to double the share of renewable energy by 2030 (Li et al. 2015). The Chinese National Energy Administration has carried out a “National Twelfth Five-Year Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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Plan” on biomass energy (CNEA 2012). In this project, consumption of biofuel (mainly ethanol and biodiesel) is expected to reach 12 million metric tons by 2020. Also, the U.S. Department of Energy has set a goal to generate 20% of the transportation fuel from biomass by 2030 (USDA 2013). Thus, energy security, petroleum price, depletion of fossil fuels, and environmental concerns have prompted considerable interest in research and development of biomass-derived fuels such as biodiesel and bioethanol. Of these, biodiesel production is the topic of this chapter. Transesterification of oil using alcohol and catalyst yields monoalkyl esters, known as biodiesel (Khiratkar et al. 2018). First-generation biodiesel is derived from feedstock such as pure vegetable oil (e.g. soybean oil, corn oil, and palm oil). Second-generation biodiesel is derived from feedstock such as inedible oil (e.g. jatropha oil and waste/used cooking oil), and third-generation biodiesel is derived from feedstock such as algae. First-generation biodiesel synthesis is not feasible and sustainable as it has three significant drawbacks: high cost of oil, limited availability, and food versus fuel issue. Second-generation biodiesel production has potential; however, availability of raw material for it is uncertain. Biodiesel processes based on first- and second-generation feedstock are well researched (e.g. Gogate 2008; Sharma and Rangaiah 2013; Patle et al. 2014) and are also in industrial practice (Lurgi 2019). In general, more than 75% of the cost of biodiesel production is for the feedstock or raw materials (Atabani et al. 2012). At present, plant seed oil (i.e. first- and second-generation feedstock) is the major source of biodiesel production (Naik et al. 2010). Biodiesel production from algae, i.e. third-generation biofuel, needs significant research and development as it can potentially contribute to the desired biodiesel production. It can be produced from either wet or dry algal biomass. Production from wet biomass is still under research whereas production from dry biomass has the drawback of the high energy consumption required to remove water content. Sara et al. (2016) reported that microbial oil with accumulated lipids, which in turn acts as a source of energy, is considered as the best alternative, mainly because it does not alter the food chain leading to reduced pressure on the land as well as the environment. Algae, especially microalgae, have better growth rate as compared with terrestrial crops, and oil yield from algae is 7 to 31 (approximated to be from 20 000 to 80 000 l/acre/yr) times higher than the most widely used source, i.e. palm oil (Demirbas and Demirbas 2011). Further, after oil extraction, microalgae produce high cellulose waste biomass that can be hydrolyzed to form ethanol (John et al. 2011). Approximate water required for microalgal biomass production varies considerably depending on the cultivation systems; Jorquera et al. (2010) reported biomass concentration of 0.35, 2.7, and 1.02 g/l of water used for raceway ponds, tubular photobioreactors, and flat-plate photobioreactors, respectively. Considering the above advantages, the last decade has seen significant research on biodiesel production from third-generation feedstock. Various algal species have been cultured and investigated for their lipid content. The lipid extraction techniques have also seen great advancements with the inclusion of ultrasonication. Recent research interest has also been on increasing the concentration of lipids in microalgae and optimization of the biodiesel process. The cost of biodiesel production from microalgae varies due to the large range of algae and their lipid content. It is from $10.87 to $13.32/gal of biodiesel (Sun et al. 2011).

Process Development, Design and Analysis of Microalgal Biodiesel Production

261

Despite the obvious potential benefits, use of algal biomass for commercial biodiesel production poses several challenges such as its high water content, wide range of lipid content depending on the algal species, and cost of production. High water content in wet microalgae (up to 98% in certain cases) makes lipid extraction more difficult as the water around algal cells generates a hydrated shell, which acts as an obstacle for both energy and mass transfer (Martinez-Guerra et al. 2018). In a two-step process for biodiesel, microbial oils (i.e. single cells oils) are first extracted from the algae and subsequently transesterified to produce biodiesel. Of late, several researchers (Sara et al. 2016; Zhang et al. 2016; Martinez-Guerra et al. 2018) have worked on direct, i.e. in situ, transesterification of oils in the algal biomass or sludge derived biomass, where oil extraction and biodiesel synthesis occur concurrently. Combining these two steps into one step is expected to reduce the cost as well as equipment footprint. As in situ processing means concurrent lipid extraction and its transesterification, it is clear that the process would have better biodiesel yield if the lipid extraction from the algal biomass is maximized and the rate of transesterification is enhanced. Such enhancements are possible using intensification by microwave (MW) radiation and/or ultrasonic irradiation. In microwave intensification, the radiation helps in enhancing the lipid extraction, whereas the intensification due to ultrasound (US) is due to the traveling of acoustic, i.e. sound waves through the solvent, resulting in the phenomenon called cavitation. The disruption of cell walls and enhanced mass transfer are the direct consequences of these intensifications. The continuous development of bubbles generates microturbulence, interparticle collisions at higher velocity and turbulence in particles of the algal biomass (Paniwnyk et al. 2009). Therefore, MW power, US power, US frequency, and ultrasonication cycle play crucial roles in the process enhancement. Although biodiesel production from algal biomass has significant potential to be used as renewable fuel, there are many aspects, which need researchers’ attention for it to be technically and commercially feasible. Ultrasonication–MW intensified in situ synthesis of biodiesel may be one such way to produce good quality renewable fuel with fewer processing units as conventional alternatives require more processing units. The major challenge lies in the scale up of MW and US reactors, downstream separation of reactants and products, and the optimization of process parameters. This includes effective transmission of the acoustic energy, i.e. cavitation into large process volume as well as the engineering aspects of the design (Gogate 2008). No article in the open literature has focused on these aspects so far. In the present study, a continuous process involving an intensified in situ transesterification of wet microalgae to produce biodiesel on industrial scale is developed and simulated in Aspen Plus v8.8. It is based on the process studied experimentally in the laboratory by Martinez-Guerra et al. (2018). More details on this experimental study are given in the next section. To the best of our knowledge, the present study is the first that investigated the complete process development for the in situ biodiesel production from wet microalgal biomass. Cost analysis is performed for the developed continuous process to understand its economic feasibility. Comparative analysis is then presented between the developed process based on microalgae and other processes using waste cooking oil (WCO) as feedstock. Later, these processes are discussed to portray the merits and demerits of the developed process.

262

10.2

Process Systems Engineering for Biofuels Development

Process Development and Modeling

The continuous process developed and studied in this chapter is based on Martinez-Guerra et al. (2018), who studied experimentally the synergic effect of MW and US radiations for biodiesel production from microalgae biomass (Nannochloropsis sp.) as raw material. They also studied reaction kinetics for in situ transesterification of algal biomass to produce algal biodiesel (i.e. fatty acid methyl ester, FAME). To optimize the process variables and to understand their parametric interdependence, response surface methodology along with central composite design was used by Martinez-Guerra et al. (2018). The microalgae paste used by Martinez-Guerra et al. (2018) in their study, contained 18.4% of biomass (by dry weight) and the rest water, and dry biomass composition was 52% protein, 0.89% chlorophyll, 16% carbohydrates, and 27% lipids. Components in the remaining fraction (100 − 52 − 0.89 − 16 − 27 = 4.11%) are not stated in the original reference. Hence, in our simulation, the remaining fraction is equally distributed among proteins and carbohydrates to make it 100%. In the present work, all lipids in the above microalgae paste are represented as triolein, for simplicity. This representation was employed by several researchers such as West et al. (2008), Morais et al. (2010), and Sharma and Rangaiah (2013), who have used triolein to represent lipids in WCO. This assumption is justified as the reaction kinetics followed in this study is for complete microalgal oil. Hence, the product produced will still be realistic. Although the esters produced from these lipids may have different properties such as density, viscosity, etc. (and hence different separability as well), they all will separate into the same light phase in phase separators (PS-1 and PS-2). Hence, assuming all lipids as triolein is reasonable for process simulation. However, it may show some discrepancy in the heat duties of reactors and distillation columns owing to different properties. In addition, properties related to combustion, extent of (un)saturation etc. will affect the biodiesel quality. However, given that the focus of the present study is not on the biodiesel quality but on the overall process design and techno-economic analysis, this assumption is reasonable. Further, this study can be modified for more accurate simulation and analysis if contents of microalgae lipids are available. The experimental study in Martinez-Guerra et al. (2018) was carried out in the presence of a base catalyst, namely, NaOH (sodium hydroxide). Twenty grams of microalgae paste was added to a homogenous mixture of catalyst and methanol (MeOH, CH3 OH), which was then subjected to the synergic effect of MW and US radiations. The transesterification reaction with MeOH is described in Eq. (10.1), wherein triglycerides react with alcohol to form esters and glycerol. Triglycerides + 3 CH3 OH ↔ 3 R′ COOCH3 + Glycerol

(10.1)

where R’ signifies the alkyl group in the triglycerides. In Martinez-Guerra et al. (2018), reaction time, US and MW power along with catalyst and methanol flow rates were varied. A reaction time of seven minutes, MW and US power of 140 W each, wet algal biomass to methanol ratio of 20 g to 30 ml and a catalyst concentration of 1 wt% were found to be the optimum parameters. The obtained product mixture was washed with 20 ml of hexane and 10 ml of deionized water. Then, the methanol-hexane phase containing FAME was separated and the procedure was repeated.

Process Development, Design and Analysis of Microalgal Biodiesel Production

263

The reaction kinetics for the reaction (Eq. (10.1)) was found to be first order, and the rate constant is given as (Martinez-Guerra et al. 2018): k=

ln(Xt ) − ln(X0 ) t

(10.2)

where Xt is FAME yield at any time t and Xo is FAME yield at t = 0. Activation energy is found using the Arrhenius equation as: k = Ae−Ea ∕RT

(10.3)

where A is the frequency factor (min−1 ), Ea is the activation energy (J/mol), R is the universal gas constant (8.314 J/mol/K), and T is the reaction temperature (K). The frequency factor and activation energy were determined to be 70.52/minutes and 17 298 J/mol, respectively (Martinez-Guerra et al. 2018). Based on the experimental procedure in Martinez-Guerra et al. (2018) and our previous studies on the biodiesel process (Sharma and Rangaiah 2013; Patle et al. 2014), a continuous process for biodiesel production from microalgae is developed in the present study, and then it is simulated in Aspen Plus version 8.8. Figure 10.1 depicts the major steps involved in the present study. A non-random two-liquid (NRTL) model was selected and used for property estimation of liquid phases and the ideal gas model for vapor phases. Note that this model was also used in Piemonte et al. (2016). Figure 10.2 shows the flowsheet of the proposed process, where F is the mass flow rate of the stream in kg/h, T is the temperature in ∘ C, and square brackets represent the composition of the stream in terms of mass fraction in the order [xLIPID , xMeOH , xWATER , xHEXANE , xFAME , xGLYCEROL ]. As stated earlier, lipids in the microalgae were approximated as trioleins (triglycerides of oleic acid); further, proteins are taken to be L-phenylalanine and carbohydrates as sucrose from the Aspen Plus database. The kinetic parameters (given above) for the transesterification reaction are taken from Martinez-Guerra et al. (2018). The extraction of lipids from microalgal oil is not modeled separately in Aspen Plus simulation as it was not studied separately in Martinez-Guerra et al. (2018), based on which the current process simulation and design are carried out. Note that kinetics in this reference is for in situ transesterification and so they include extraction kinetics. Suitable unit operation modules, as given in Table 10.1, were chosen for simulating the process shown in Figure 10.2. Then, process parameters and reaction kinetics were added. After obtaining simulation results for reactor similar to those in Martinez-Guerra et al. (2018), optimum design parameters such as number of stages, feed stage, and column pressure of distillation columns, were determined to minimize total annual cost (TAC) of the respective distillation column. Thus, the developed process is sufficiently realistic and optimal. Since the entire concept of in situ suggests the simultaneous extraction and transesterification of biomass, microalgae are fed to the continuous reactor “RTRANS” (simulated by RCSTR block in Aspen Plus, which is based on the model for a continuous stirred-tank reactor) at a flowrate of 50 322 kg/h, along with fresh methanol at 663.972 kg/h and catalyst (NaOH) at 26 kg/h (Figure 10.2). Wet microalgae flowrate of 50 322 kg/h was chosen to process 2500 kg/h of lipids based on the lipid content (= 0.184 × 0.27 × 50 322 kg/h, as mentioned above). Both extraction and reaction in the continuous reactor are aided by MW

264

Process Systems Engineering for Biofuels Development

Start Component Addition

Module Selection Process Simulation

Process Parameters and Reaction Kinetics

Property Method Selection

Results Validation

NO

YES Variation of Process Parameters targeting min. TAC such as: Number of stages, Feed stage and Pressure of a Distillation Column

Optimality (min. TAC)

NO

YES

Cost Estimation of Optimal Process Model

End Figure 10.1

Flow chart depicting the steps followed in the present study.

Process Development, Design and Analysis of Microalgal Biodiesel Production METHANOL F = 663.997 kg/h T = 25°C [0,1,0,0,0,0]

RTRANS

CATALYST

SEP–1

265

SEP–2

H3PO4

M–1 RNEUT SALT

PRO-CARB

H–1 F = 32.02 kg/h T = 30°C [0,0,0,1,0,0]

Qc = –11.72 MW Qb = 8.41 MW

M–2

P–1

5

M-4

PS–1

9

Split

10

FRAC–1

H–2 F = 2.66 kg/h T = 30°C [0,0,0,1,0,0]

M–3

PS–2 P–2

Qc = –47.96 MW Qb = 48.75 MW

HX–1 Qc = –0.35 MW Qb = 1.02 MW

FRAC–2 P–3

4

HX–2

7 8

GLYCEROL F = 283.01 kg/h P–4 T = 143.25 °C [0,0,0.041,0,0.05,0.908]

METH-REC F = 43983 kg/h T = 63°C [0,0.89,0.04,0.07,0,0]

7

P–5

FRAC–4

HX–3

13 14

Qc = –26.83 MW Qb = 31.28 MW

FRAC–3

7 HEX-REC F = 101277 kg/h T = 56°C [0,0.027,0,0.973,0,0]

HX–4

12 13

WAT-OUT F = 41462.8 kg/h T = 30 °C [0,0.009,0.99,0,0,0]

M–5

P–6 TG-REC F = 2297.13 kg/h T = 40°C [0.998,0,0,0,0.002,0]

FRESH ALGAE F = 50322 kg/h T = 30°C [0.05,0,0.816,0,0,0] WATER OUT BIODIESEL F = 2492.81 kg/h T = 25 °C [0.02,0,0,0,0.98,0]

WATER IN

WASH TOWER

Figure 10.2 Biodiesel production from microalgae “Nannochloropsis sp.” Values in square brackets are the stream composition in mass fraction in the order [xLIPID , xMeOH , xWATER , xHEXANE , xFAME , xGLYCEROL ].

266

Process Systems Engineering for Biofuels Development Table 10.1

Process parameters of the proposed biodiesel process.

Unit operation and parameters Distillation columns FRAC-1 (RADFRAC) No. of stages Feed stage Pressure (atm) Reflux ratio (molar) Qr (MW) Qc (MW) FRAC-2 (RADFRAC) No. of stages Feed stage Pressure (atm) Reflux ratio (molar) Qr (MW) Qc (MW) FRAC-3 (RADFRAC) No. of stages Feed stage Pressure (atm) Reflux ratio (molar) Qr (MW) Qc (MW) FRAC-4 (RADFRAC) No. of stages Feed stage Pressure (atm) Reflux ratio (molar) Qr (MW) Qc (MW) Reactors RTRANS (CSTR) Temperature (∘ C) Pressure (atm) Residence time (min) RNEUT (RStoic) Temperature (∘ C) Pressure (atm) Phase separators PS-1 (DECANTER) Duty (kW) Pressure (atm) Volume (m3 ) PS-2 (DECANTER) Duty (kW) Pressure (atm) Volume (m3 ) Wash tower (EXTRACT) No. of stages Pressure (atm) Heat exchangers (HEATX) Temperature approach of HX-1, HX-2, HX-3, and HX-4

Design value

10 5 0.05 0.0143 8.414 −11.728 8 4 0.4 0.1 48.75 −47.96 13 7 1.0 1.0 31.82 −26.83 14 7 0.06 0.77 1.02 −0.35

55.0 1.0 7.0 30.0 1.0

0.0 1.0 149 0.0 1.0 105 5 1.0 10.0

Process Development, Design and Analysis of Microalgal Biodiesel Production

267

and US. The reactor is maintained at 55 ∘ C and 1 atm, and the residence time of reaction mixture in the reactor is specified as seven minutes. The reactor outlet mixture contains FAME (biodiesel), catalyst, proteins, carbohydrates, chlorophyll, methanol, glycerol, hexane, and unreacted lipids. There are many methods for separation of proteins/carbohydrates from biomass such as enzymatic hydrolysis (Fleurence et al. 1995a; Joubert and Fleurence 2008; Harnedy and FitzGerald 2013), physical treatment (Barbarino and Lourenço 2005; Harnedy and FitzGerald 2013), and chemical extraction (Fleurence et al. 1995b; Harnedy and FitzGerald 2013; Kadam et al. 2017). For simplicity, a component separator block (SEP-1) is used in the present simulation (Figure 10.2), to separate proteins and carbohydrates from the other components. After SEP-1, the catalyst (NaOH) is neutralized in the neutralization reactor “RNEUT” (simulated by RStoic block in Aspen Plus, which is based on reaction stoichiometry) by addition of phosphoric acid (H3 PO4 ). The salt thus formed is removed in SEP-2, which is also modeled as a component splitter in the present simulation. Component splitter is used to model both SEP-1 and SEP-2 for simplicity. Complete separation of proteins, carbohydrates, etc. in SEP-1, and of salts in SEP-2 is assumed. Modeling the separation of proteins, carbohydrates, etc. in SEP-1 is difficult due to lack of required properties of components. The remaining liquid mixture from SEP-2 containing FAME, water, methanol, unreacted lipids, glycerol, and hexane, is sent to phase separators, PS-1 and PS-2 (each modeled as a decanter block, i.e. separation of aqueous and organic phases by density difference) operating at 25 ∘ C and 1 atm. A certain amount of hexane is fed to each phase separator, for facilitating FAME extraction into the organic phase. The lighter/organic phase with FAME, unreacted lipids and hexane is collected from both PS-1 and PS-2, and then sent to a distillation column, FRAC-1, for further separation. The Aspen Plus block used for FRAC-1, FRAC-2, FRAC-3, and FRAC-4 in Figure 10.2 is RADFRAC, which rigorously models a distillation column via mass, energy and vapor–liquid equilibrium balances. FRAC-1 has a total of 10 stages with feed entering on the 5th stage. Note that stages throughout this chapter refer to ideal or equilibrium stages, and the number of stages includes condenser and reboiler of the column. Hexane is recovered as the distillate of FRAC-1 for recycling to PS-1 and PS-2. The bottoms stream of FRAC-1 is fed to the 7th stage of FRAC-4 having 14 stages. The distillate of FRAC-4, containing primarily FAME with a mass purity of ∼98%, is cooled using the hexane stream at −12 ∘ C from FRAC-1 and then sent to a wash tower to remove any other water-soluble impurities. The unreacted lipids are recovered in the bottoms stream of FRAC-4 and cooled to 40 ∘ C using the hexane stream (used earlier for cooling the distillate stream), for recycling to the extraction/transesterification reactor, RTRANS. The heavier/aqueous phase from PS-1 is fed to PS-2 for recovering the remaining FAME in it by adding some more hexane. The heavier phase from PS-2 containing methanol, water, and glycerol (plus traces of FAME and hexane), is fed to the 4th stage of FRAC-2 having eight stages. Glycerol is obtained from the bottoms of FRAC-2 along with a small quantity of FAME (∼17 kg/h out of 2463 kg/h of biodiesel produced from 2500 kg/h of lipids). A mixture of water, methanol, and traces of hexane is recovered as the distillate, and it is further separated in another distillation column, FRAC-3, having 13 stages. Methanol with traces of hexane is obtained as the distillate of FRAC-3, and it is recycled back to the upstream process units. Water leaves the bottoms of FRAC-3 and also the process.

268

Process Systems Engineering for Biofuels Development

Three distillation columns in the process, namely, FRAC-1, FRAC-2, and FRAC-4 in Figure 10.1, are operated under vacuum (0.1–0.25 atm) in order to avoid deterioration/decomposition of FAME and glycerol at high temperatures. FRAC-3 does not have any such restriction, and so it is operated at atmospheric pressure. Note that FAME decomposes at 250 ∘ C and glycerol decomposes at 150 ∘ C (Morais et al. 2010). Owing to vacuum operation, the overhead temperature of FRAC-1, FRAC-2, and FRAC-4 is, respectively, −12, 22, and 212 ∘ C. The high temperature in the condenser of FRAC-4, i.e. 212 ∘ C, allows the use of energy removed in its condenser in some reboiler. However, the small flow rate of the distillate from FRAC-4 may not provide significant energy. Nevertheless, this heat integration is considered beyond the scope of the present work. However, recycled hexane at −12 ∘ C would result in unnecessary heating requirement in PS-1 and PS-2; to avoid this heating, recycled hexane is used to cool three streams, namely, TG-REC, Biodiesel, and WAT-OUT in HX-2, HX-3, and HX-4 respectively, to room temperature, which results in recycled hexane heated to 56 ∘ C. Refrigerant is used in the condenser of FRAC-1, whereas chilled water is used in the condenser of FRAC-2 and cooling water is used in the condenser of FRAC-3 and FRAC-4. On pumping the distillate of FRAC-1, FRAC-2, and FRAC-4 to atmospheric pressure from vacuum conditions (0.1–0.25 atm), a temperature increase of 1–2 ∘ C was observed. Taking algal biomass flowrate as the base, flow rates of all inputs (hexane, water, and methanol) are defined by the calculator block in Aspen Plus. Data for these and other important streams are given in Table 10.A1 in the Appendix whereas process parameters used for simulation are listed in Table 10.1. The liquid composition and temperature profiles of all the four columns are depicted in Figure 10.3. In FRAC-1, which is used for the recovery of hexane from the lighter phase collected from PS-1 and PS-2, the highest temperature is near 250 ∘ C (Figure 10.3a). Note that this is required to be maintained at/below 250 ∘ C to avoid biodiesel decomposition. About 99% of hexane is recycled from the distillate, and a mixture of FAME and unreacted triglycerides is obtained from the bottoms of FRAC-1. In FRAC-2, the maximum temperature is lower than the temperature at which glycerol starts decomposing, i.e. 150 ∘ C. It can be seen from Figure 10.3b that glycerol is obtained from FRAC-2 bottom with 90% mass purity along with 6% FAME and 4% water, while a mixture of water and methanol is obtained from the top of FRAC-2. FRAC-3, which separates a mixture of water and methanol, is operated at atmospheric pressure (Figure 10.3c). Methanol with 89% mass purity is obtained from the top of FRAC-3, whereas water is removed from this column bottom. FRAC-4 separates biodiesel and unreacted lipids, which are fairly easy to separate due to the large difference in their volatilities; however, it operates at relatively high temperatures despite vacuum pressure because of the high boiling point (∼847 ∘ C at atmospheric pressure) of lipids. Biodiesel i.e. FAME is obtained as the distillate from the top with 98% purity (as required to meet ASTM and EN standards). The unreacted lipids are obtained in the FRAC-4 bottoms stream, which is then cooled and recycled back along with fresh microalgae to the transesterification reactor. Number of stages in each of the columns (FRAC-1, FRAC-2, FRAC-3, and FRAC-4) was decided by performing sensitivity analysis. Targeting minimum reboiler duty and TAC (defined in Section 10.3) of the column, the number of stages was varied in Aspen Plus simulation. For this, inlet stream flow rate, temperature and composition to each column is fixed based on preliminary simulations.

Process Development, Design and Analysis of Microalgal Biodiesel Production

269

Figure 10.4 shows the trend of reboiler duty and TAC with number of stages. For FRAC-1, reboiler duty decreases and TAC increases as the number of stages increases. For FRAC-2, reboiler duty and TAC are practically constant within the range of number of stages examined. With increasing number of stages in FRAC-3, both reboiler duty and TAC decrease to a certain value and then increase. In the case of FRAC-4, both reboiler duty and TAC decrease and reach a certain plateau with increasing number of stages. These trends are due to the combined effect of reboiler duty decrease and capital cost variation (increase due to a taller column or decrease due to a smaller diameter) with increasing number of stages. The optimum number of stages is chosen to minimize TAC, ensuring that height to diameter ratio of the column is below 20 (Seider et al. 2009). Accordingly, the optimal number of stages for FRAC-1, FRAC-2, FRAC-3, and FRAC-4 is 5, 6, 11, and

Composition

1.00 0.75

TG FAME HEXANE Temperature

0.50 0.25

Temperature(°C)

0.00 213 142 71 0 2

4

6

8

10

No. of stages (a)

Temperature(°C)

Composition

1.0 0.8

MEOH WATER FAME GLYCEROL HEXANE Temperature

0.6 0.4 0.2 0.0 140 120 100 80 60 40 20

2

4

6

No. of stages (b) Figure 10.3 Liquid composition (mass fraction) and temperature profiles: (a) FRAC-1, (b) FRAC-2, (c) FRAC-3, and (d) FRAC-4.

Process Systems Engineering for Biofuels Development

Composition

1.0 0.8 MEOH WATER HEXANE Temperature

0.6 0.4 0.2

Temperature(°C)

0.0 100 90 80 70 2

4

6

8

10

No. of stages (c)

Composition

1.00 0.75 0.50 0.25

TG FAME HEXANE Temperature

0.00 300 Temperature(°C)

270

275 250 225 2

4

6

8

No. of stages (d) Figure 10.3

(Continued)

10

12

Process Development, Design and Analysis of Microalgal Biodiesel Production

271

TAC (Million $) Reboiler duty (MW)

12, respectively, excluding condenser and reboiler. Although the optimal number of stages is 5 for FRAC-1 in Figure 10.4a, it is chosen as 10 for subsequent analysis as the height to diameter ratio is unusually small (i.e. 374 ∘ C, pressure >22.1 MPa). This prevents water phase changes, which require a large consumption of energy. Supercritical water has several roles in the gasification process: it can participate as medium, reactant, or as catalyst. 11.4.6

Plasma Gasification

Plasma is a mixture of electrons, ions, and neutral molecules, electrically neutral as a whole. The reactivity of plasma depends on the degree of ionization, which is the proportion of ions in the gas. Plasma is created by an electrical arc that applies an electric current to a dielectric gas. The heat generated in this process, together with the reactivity of plasma, produces the gasification of biomass.

Thermochemical Processes for the Transformation of Biomass into Biofuels

11.4.7

295

Catalyzed Gasification

One of the problems in gasification is the removal of methane and tars. Methane can affect the use of syngas, and tars can cause blockages and corrosion. Therefore, the use of catalysts is an area of interest. The catalyst selection criteria can be summarized as follows (Sutton et al. 2001) and, with small changes, these criteria can be adapted to other catalysts mentioned in this chapter: – – – –

The catalysts must be effective in the removal of tars. If the desired product is syngas, the catalysts must be capable of reforming methane. The catalysts should provide a suitable syngas ratio for the intended process. The catalysts should be resistant to deactivation as a result of carbon fouling and sintering. – The catalysts should be easily regenerated. – The catalysts should be strong. – The catalysts should be inexpensive. Some catalysts for gasification can be mixed with the biomass, whereas others can be placed in other reactors, where the gas stream generated in the gasification is directed to undergo a reforming process. Some of the most used catalysts for gasification are dolomite, alkali metals, and nickel (Sutton et al. 2001). An example of an external catalysis, which is one of the most commonly used reforming processes, is employed in the purification of syngas (Damartzis and Zabaniotou 2011). It consists of reacting the tar formed as an unwanted product with steam to form CO and H2 at temperatures around 700 ∘ C. Carbon monoxide reacts with steam to produce CO2 and more H2 . Catalysts commonly used in this reaction are combinations of metals with carbonaceous or silicate minerals. An alternative to this procedure is the cracking of hydrocarbon tar chains, which is done at high temperatures, using a catalyst such as olivine or dolomite. Carbon monoxide and H2 are also obtained, which enrich the syngas mixture. 11.4.8

Fischer–Tropsch Synthesis

A brief description of this well-known industrial process has been included since a large part of the syngas produced in gasification is used for this purpose. It was developed in the 1920s and includes several reactions to convert the syngas into medium-mass hydrocarbon molecules. The product is similar to a synthetic petroleum. The reactions are carried out at temperatures of 250–300 ∘ C, and high pressures (2–3 MPa) with catalysts based on transition metals such as cobalt or iron. These catalysts are very sensitive to sulfur, and the syngas must be desulfurized. The use of moderate temperatures and high pressures hinders the formation of hydrocarbons of low molecular mass, such as methane. A mixture of hydrocarbons is always obtained, the composition of which depends on the conditions of the process. The final objective is usually to obtain liquid fuels. Therefore, the formation of volatile hydrocarbons such as methane is not desired, but neither is the formation of compounds with a high molecular mass. In order to avoid the former, moderate temperatures and high pressures are used. In the latter case, a hydrocracking reaction is employed, which causes the large molecules to break down to produce the desired products.

296

11.5

Process Systems Engineering for Biofuels Development

Liquefaction

Liquefaction produces mainly liquid from biomass and the process occurs in an environment where water or organic solvents are employed. The temperature used is moderate (200–400 ∘ C) and so is the pressure applied (4–20 MPa) (Jiang et al. 2018). Alkaline catalysts are commonly used. Sometimes, a reactant, such as CO or H2 , improves the process performance. The catalyst hydrolyzes the macromolecules present in the biomass, such as lignin and cellulose, into smaller molecules, which undergo decarboxylation, dehydration, dehydrogenation, and other reactions to yield smaller compounds (Verma et al. 2012). When water is used as solvent, it can be considered as a hydrothermal process. The main advantage of this method is that it can use wet biomass when water is used as solvent. Liquefaction was initially applied in coal liquefaction, in the 1920s in Germany. Later, biomass was used as raw material. In the 1990s, hydrothermal liquefaction was developed, and nowadays it is the most common process used in liquefaction. However, there are some limitations in the use of liquefaction. The yield of bio-oil is lower than that obtained in the pyrolysis method, the quality is bad (similar to tar), and the cost is high due to the fact that it requires higher reaction temperature and pressure, catalysts, and reactants. Thus, other methods (moderate acid-catalyzed liquefaction or MACL) have attracted interest, as they can be carried out at atmospheric pressure and lower temperature, using organic solvents and a catalyst. The bio-oil yield is low when the biomass has a high lignin content as it is difficult to degrade and a substantial part remains as solid waste and ash content, since there is less organic matter and more solid waste. Catalysts, either acid or alkaline, can be used in hydrothermal liquefaction. The former are more effective, but are more corrosive to the equipment. The most used solvent is water, although the product obtained has some drawbacks: bio-oil and water are insoluble, the yield is low, the bio-oil has a high oxygen content and the heating values are low. Therefore, organic solvents are used (Jiang et al. 2018). Hydrothermal liquefaction can be used as an upgrade treatment of biomass for use with other methods. For example, better gasification performance is achieved.

11.6

Pyrolysis

Pyrolysis is the process that occurs when heat is applied to a material (biomass) in an inert atmosphere. The products obtained are solids (e.g. charcoal), liquids (e.g. bio-oils), and incondensable gases (e.g. methane, H2 , and others). We will tackle those processes carried out in the absence of gasifying agents or liquids and which are studied in other sections of this chapter. The products and ratios formed vary depending on the reaction parameters (Table 11.1).

Table 11.1 Product Solid Liquid Gas

Relationship between products and parameters in pyrolysis. Temperature

Heating rate

Vapor residence time

Low Moderate High

Low Moderate High

Long Short Moderate

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A common problem is the high moisture content of the biomass. It requires a high amount of energy to produce evaporation. If a liquid fuel is desired, it may contain a high amount of water, which decreases its energy capacity. Pyrolysis is an endothermic process in which most of the energy is used to raise the temperature of the material, especially in the evaporation of water. Some common methods to provide heat to the reactor are: – Heating the external surface of the reactor, which is done when they are small in size. – Heating the carrier gas. – Heating a solid carrier, such as inert sand or a solid catalyst. 11.6.1

Slow Pyrolysis

This has been used traditionally for the production of charcoal, and today it is utilized in traditional, semi-industrial and industrial furnaces. The global production of wood charcoal was estimated at 52 million tons in 2015 (FAO 2017). This method is characterized by slow heating rates, low-to-medium temperatures, and long processing times (in some cases, several days). The main product is charcoal. Due to its importance, and its usual manufacturing method, which is an intermediate between slow pyrolysis and combustion, it is studied in this chapter as an independent method. 11.6.2

Fast Pyrolysis

This method is characterized by short times and high heating rates. To achieve a faster process, generally the biomass is supplied dry ( 𝜎 3 … 𝜎 m > 0. The product obtained by dividing the maximum singular value by the minimum singular value is known as the condition number of the gain matrix. The condition number is a measure of the relative difficulty that a decoupled multivariable control problem can present (Klema and Laub 1980). The condition number is calculated as follows: 𝛾 = 𝜎max ∕𝜎min

(12.7)

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In terms of the dynamics of a process, a high value of condition number means difficulty to meet all control objectives (regardless of the appropriate strategy). A large condition number is evidence of the relative sensitivity of a case study in a multivariable direction being very weak (Moore 1986). SVD methodology does not predict or solve all the dynamic problems in real chemical plants, however, it is relatively easy to understand and identify basic control difficulties. For the control analysis, each purification alternative provides a relative gain matrix in its nominal state. To obtain this matrix, the schemes are subjected to a step change in a manipulable variable (reflux ratio, reboiler duty, etc.). The magnitude of the disturbance is small enough (0.5%) that a first-order behavior can be assumed according to many previous works (Murrieta-Dueñas et al. 2011; Segovia-Hernández et al. 2002). To avoid the SVD dependence of the system unit used (variables limited between 0 and 1, and high values for reflux ratio and reboiler heat duties), the approach of the proposal used here is to limit the variables described. Since the maximum opening of the control valves can be twice the nominal value, the valves are theoretically open by 50%. In this way, to generate the relative gain matrix, a step change must be applied in the manipulated variable and subsequently, this change must be divided by two. With this consideration, you get the same range of variation when opening and closing the control valves. The consequence of this consideration is to relate both the amount of change and the magnitude of change in a range of 0–100%. Moreover, with this form of scaling, and with the term 1/2P in Eq. (12.8), the manipulated variables are simultaneously dimensionless standardized. For example, a relative gain matrix for the purification of three components could be stated as: v v v sp sp sp ⎡ xC11 −xC1 xC12 −xC1 xC13 −xC1 ⎤ 1 1 ⎢ 12 P P P ⎥ 2 2 ⎥ ⎢ ⎡K11 K12 K13 ⎤ v3 v1 v2 sp sp sp ⎢K21 K22 K23 ⎥ = ⎢⎢ xC21−xC2 xC21−xC2 xC21−xC2 ⎥⎥ (12.8) P P P ⎥ ⎢ 2 2 ⎥ ⎣K31 K32 K33 ⎦ ⎢ v 2 ⎢ x 1 −xsp xv2 −xsp xv2 −xsp ⎥ ⎢ C31 C3 C31 C3 C31 C3 ⎥ P P ⎦ ⎣ 2P 2 2 where all elements Kij are the relative gain matrix. The elements of the first row on the right-hand side correspond to the differences among the mass purity of component A in V sp the nominal state xA , and the mass purities after disturbance p. xA1 is the mass purity of a V chemical compound after a disturbance in manipulated variable 1, xA2 is the mass purity of V a chemical compound after a disturbance in manipulated variable 2, xA3 is the mass purity of a chemical compound after a disturbance in manipulated variable 3. In this work, the relative gain matrix was built as N × N, according to the N output streams of the separation scheme. 12.3.5

Multi-Objective Optimization Problem

The objective function would take into account those four targets already mentioned, however, since the previous work of Penner et al. (2017) showed some issues about recoveries, the maximization of the recovery was also introduced as a target in the complete objective

324

Process Systems Engineering for Biofuels Development Table 12.6 Range and type of variables used in the calculation of objective functions. Type of variable Column stages Feed stages Side stream Reflux ratio Distillate rate Diameter

Search range Discrete Discrete Discrete Continuous Continuous Continuous

5–100 4–99 4–99 0.1–75 10–248 (kmol/h) 0.9–5 (m)

function. So the objective function is described as: Min(TAC, EI99, IR, 𝛾, −Rec) = f (Ntn , Nfn , Rrn , Frn , Pcn , FCcn ) → > y→ Subject to xm m

(12.9)

where TAC is the total annual cost, EI99 is the eco-indicator 99, IR is the individual risk, 𝛾 is the condition number, and Rec the recovery for all chemical compounds. Ntn are column stages, Nfn is the feed stage, Rrn is the reflux ratio, Frn is either distillate or bottoms flux. Moreover, for IR calculation it is necessary to consider other properties such as explosive limits (Pcn , Fcn ), LC50 , vapor density and so on; ym and xm are the vectors of obtained and required purities for the mth component, respectively. In this multi-objective optimization exercise, about 25 variables, continuous or discrete, were considered. The flows of the compounds of interest and their respective purities were considered as constraints. Table 12.6 shows the type of variables used and the search range in the optimization process. The variables related to a physical aspect of the distillation columns considered average limits of industrial distillation columns (Górak and Olujic 2014). For the control study, the variables to be controlled were the purities of 2,3-BD, IBA, MEK, and water. Additionally, the distillate flows, and heat duties associated with the output currents of said products, were considered as manipulable variables.

12.4

Global Optimization Methodology

Once the objective functions are modeled, the optimization procedure was carried out. To develop the optimization we can use a stochastic hybrid algorithm, Differential Evolution with Tabu List (DETL). Initially the concept of evolution difference was introduced by Storn and Price (1997), however, it was applied only for a single objective. Later Madavan (2002) adapted it to solve problems from a multi-objective perspective. This evolutionary method uses the classical stages of differential evolution and improves the optimization process with the Tabu List (TL) concept. The TL concept avoids the revision of previously evaluated points (Glover 1989). A fairly complete description of this algorithm can be consulted in the work by Sharma and Rangaiah (2013). The application of the method was performed by means of a hybrid platform, which involves the interaction among Microsoft Excel, Aspen Plus, and Visual Basic. As a brief description, the vector of decision variables (initially proposed by the algorithm) is sent

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to Microsoft Excel, which attributes those values to the process variables in Aspen Plus to simulate the model. After the simulation is completed, Aspen Plus returns to Microsoft Excel the results in the form of a vector. Finally, with those data, Microsoft Excel evaluates the objective function and proposes new values of the decision variables in concordance with the stochastic optimization method. For the optimization task, the following parameters were used in the DETL method: 200 individuals, 1000 generations, a TL of 50% of total individuals, a tabu radius of 2.5*10−6 , and 0.80 and 0.6 for crossover and mutation fractions, respectively. These parameters were obtained from the literature and previous tuning process (Srinivas and Rangaiah 2007).

12.5

Results

In the next paragraphs, all results obtained after robust optimization are discussed and presented. First, the results obtained from analyzing the pure distillation process are presented, then a comparison between the best scheme and the intensified process is performed. Note, despite the fact that four objective functions were jointly evaluated, the results in Figures 12.5–12.10 are presented in a conventional 2D figure for better understanding. All Pareto fronts were obtained after 200 000 evaluations. Subsequently, no substantial improvement was observed, so it was considered that under the evaluation criteria, the DETL method reached convergence. Thus the results reported here correspond to the best solution obtained. As purity constraints of the process, we considered 99.5 wt% MEK, 99.5 wt% 2,3-BD, and 99 wt% IBA. As an initial analysis, the objective function of the pure distillation schemes is evaluated (S1–S4), having the economic criteria as an initial comparative index. Figures 12.5–12.10 show the difference of this objective function when is evaluated with the other three objective functions. At first sight, it would be easy to select the best alternative among those four

200000000 180000000 160000000

TAC ($/yr)

140000000 120000000 S4

100000000

S2

80000000 60000000

140000000

S3

130000000

S1

120000000 110000000

40000000

100000000

20000000 0 0.0013

90000000 0.0016715 0.0016715 0.0016715 0.0016715 0.0016716

0.00135

0.0014

0.00145

0.0015

0.00155

0.0016

0.00165

IR (probability/yr)

Figure 12.5

Pareto front between TAC and IR for the schemes.

0.0017

326

Process Systems Engineering for Biofuels Development 180000000 160000000 140000000

TAC ($/yr)

120000000 100000000

S4 S2 S3 S1

130000000

80000000

125000000 120000000

60000000

115000000

40000000

110000000 105000000

20000000

100000000 2800000 2900000 3000000 3100000 3200000 3300000

0 0

2000000

4000000

6000000

8000000 10000000 12000000 14000000 16000000 18000000 20000000

EI99 (points/yr)

Figure 12.6

Pareto front between TAC and EI99 for the schemes.

200000000 180000000 160000000

TAC ($/yr)

140000000 120000000

S1

S3

S2

S4

100000000 80000000 60000000 40000000 20000000 0

0

500

1000

1500

2000

2500

Condition number Figure 12.7

Pareto front between TAC and condition number for the schemes.

schemes. The cheapest alternative is the S4, and the most expensive is the S2. Moreover, the TAC difference is high by several magnitude orders. However, taking into consideration the amount of MEK recovered in the separation process, the view changes completely. In other words, despite the objective function including a target to maximize the recovery of all schemes, after optimization, the recoveries were not completely equal. After the optimization process, the MEK recoveries obtained, for such purities already mentioned, were 99.9, 65.4, 56.6, and 44.9 wt% for S2, S1, S3, and S4, respectively. For the by-products 2,3-BD/IBA the recoveries were 99.9/61.3, 99.9/74.4, 99.9/99.7, and 99/98.8 wt% for S2, S1, S3, and S4, respectively.

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20000000 18000000 16000000 EI99 (points/yr)

14000000 S4

12000000

S2

10000000

19000000

8000000

S3 S1

17000000

6000000

15000000

4000000 13000000 0.0013322

2000000

0.0013323

0.0013324

0 0.0013 0.00135 0.0014 0.00145 0.0015 0.00155 0.0016 0.00165 0.0017 IR (probability/yr) Figure 12.8

Pareto front between EI99 and IR for the schemes.

5000 4500

Condition number

4000 3500 3000 S1

2500

S3 S2

2000

S4

1500 1000 500 0

0

Figure 12.9

5000000

10000000

15000000 20000000 EI99 (points/yr)

25000000

30000000

Pareto front between condition number and EI99 for the schemes.

With this in mind and analyzing Figure 12.5, the difference in both TAC and IR values are clear for all schemes. Note in Figure 12.5, beyond the clear difference in TAC values because of the recovery, the difference in IR is reflected immediately because of the difference in the number of columns. Since schemes S2 and S3 are designed with only four distillation columns, it is evident that IR decreases due to the number of columns as well. In other words, the IR of the fifth column is avoided. Moreover, the interesting aspect of this

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200000 180000

Condition number

160000 140000 120000 S4

100000 80000

S2 60000 50000

S3

60000

40000

S1

40000

20000

30000

10000

20000

0 0.0016715 0.0016715 0.0016715 0.0016715 0.0016716

0 0.0013 0.00135 0.0014 0.00145 0.0015 0.00155 0.0016 0.00165 0.0017 IR (probability/yr) Figure 12.10

Pareto front between condition number and IR for the schemes.

multi-objective approach is the behavior of both objectives. Figure 12.5 shows the tendency of both objectives; after optimization, the Pareto front shows the zone where those two targets find their minimum values. Since the IR calculation considers the instantaneous and continuous release of chemicals, the higher the number of internal flows, the higher the IR value as well. However, also consider that the calculation of IR would include some physicochemical properties such as LC50 and vapor density. With this consideration, if most of the compounds are in solution, the greater the amount of water, the more the IR value decreases. For example, for the initial four case studies, the first column is mainly responsible for separating a mixture composed mainly of water, which decreases the concentration of the other compounds as well as the associated risk. Furthermore, in scheme S2, 2,3-BD is separated which also promotes the decreasing of IR values since a flammable component is released from the process. Besides, note that the other three schemes increase the reflux ratio in order to compensate for the IR value because of the presence of 2,3-BD in the first column. Consequently, all those modifications in design values such as increase reflux ratio and diameter have as a consequence an increase in TAC values. In this manner, the best solution is located at the place where both objective functions reach their minimum values. In the Pareto front of Figure 12.5, the highlighted point accomplishes those requirements and recommendations by Wang and Rangaiah (2017) and its design values are shown in Tables 12.7–12.10. Regarding the evaluation of TAC and EI99 values, the antagonist behavior of both objective functions is shown in the Pareto front of Figure 12.6. In the upper zone of the Pareto front, commonly, there are designs with columns of large size but relatively low heat duties. This combination produces high TAC values in conjunction with small EI99 values. The lower zone, on the other hand, is mainly composed of columns of reduced size but large heat duties. Finally, in the middle of both zones, the objective functions reach their minimum values (Sánchez-Ramírez et al. 2015b). Some variables affect the trend in both functions.

Intensified Purification Alternative for Methyl Ethyl Ketone Production

Table 12.7

Design parameters and performance indexes for scheme S1.

Number of stages Reflux ratio Feed stage Column diameter (m) Operative pressure (kPa) Distillate flowrate (kmol/h) Condenser duty (kW) Reboiler duty (kW) TAC ($/yr) Eco-indicator (points/yr) Condition number IR (probability/yr)

Table 12.8

329

C1

C2

C3

14 34.175 2 1.501 101.353 117.146 39 983 40 497

13 3.02 11 1.545 101.353 117.811 5149 5331

28 6.321 7 1.527 101.353 151.735 10 568 10 981 104 719 750 2 993 581 413 3.8 0.001671555

C4

C5

33 6.232 25 1.429 101.353 14.061 933 934

21 5.034 5 1.177 101.353 8.298 172 454 177 767

Design parameters and performance indexes for scheme S2.

Number of stages Reflux ratio Feed stage Column diameter (m) Operative pressure (kPa) Distillate flowrate (kg/h) Condenser duty (kW) Reboiler duty (kW) TAC ($/yr) Eco-indicator (points/yr) Condition number IR (probability/yr)

C1

C2

C3

29 1.93154 11 1.34118097 101.353 235.02 7137 7834

25 32 19.476 3.384 17 14 1.1 1.213 101.353 101.353 8.451 5.84518 1588 235 1593 255 153 136 510 16 200 579 4.78 0.00133414

C4 36 1.02469 4 0.84682939 101.353 3325 60 043 65 806

Table 12.9 Design parameters and performance indexes for scheme S3.

Number of stages Reflux ratio Feed stage Column diameter (m) Operative pressure (kPa) Distillate flowrate (kg/h) Condenser duty (kW) Reboiler duty (kW) TAC ($/yr) Eco-indicator (points/yr) Condition number IR (probability/yr)

C1

C2

C3

20 25.063 4/2 1.273 101.353 123.38 31 713 31 191

15 31 4.021 30.112 12 7 1.825 1.039 101.353 101.353 133.13 89.665 8175 26 803 8270 27 011 31 011 553 14 669 116 3.99 0.0013323

C4 59 34.568 25 101.353 7.67 2504 2669

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Table 12.10

Design parameters and performance indexes for scheme S4.

Number of stages Reflux ratio Feed stage Column diameter (m) Operative pressure (kPa) Distillate flowrate (kg/h) Condenser duty (kW) Reboiler duty (kW) TAC ($/yr) Eco-indicator (points/yr) Condition number IR (probability/yr)

C1

C2

50 2.142 22 1.138 101.353 117.146 2432 2956

8 0.416 7 1.555 101.353 117.811 2335 2508

C3 39 2.523 28 1.684 101.353 151.735 461 693 4 435 273 891 801 275 147.56 0.001665872

C4

C5

51 1.995 22 1.147 101.353 69.197 1945 1952

43 2.711 17 1.229 101.353 8.298 332 367

For example, the reflux ratio plays an interesting role for such effects; high reflux ratios increase directly the reboiler heat duty, cost of services, and the environmental impact. Regarding the evaluation of TAC and condition number, Figure 12.7 shows the Pareto front for both objective functions. So far, the TAC values are known, however, in the Pareto fronts, there is selected a point where both objective functions reach their minimum values. Note in Figure 12.7 and Tables 12.7–12.10, schemes S1, S2, and S3 present relatively the same condition number, quite different to scheme S4. Even though the condition number is not a quantitative measure, the condition number can let us know the expected controllability properties of those analyzed schemes. With this in mind, it is easy to claim that schemes S1, S2, and S3 have relatively good properties in comparison with scheme S4. Even though the sizing of the columns is one of the design variables that is used to correlate with the condition number (Vázquez-Castillo et al. 2015), during the optimization, the design variable that shows significant effect on the condition number was the reflux ratio. As long as reflux increases, the condition number decreases, and so high TAC values are related to low condition number values (meaning that built-in controllability has its cost). Observing the parameters from Tables 12.7–12.9, schemes S1, S2, and S3 have larger reflux ratios and largest heat duty and this allows the design to reject larger disturbances. When the EI99 is evaluated jointly with IR, a similar tendency is observed. Note in Figure 12.8 the Pareto front for both objective functions. The behavior observed may be understood considering that a key parameter for measuring both EI99 and IR is the reboiler duty. In other words, according to Table 12.4, the heat duty possesses a bigger weighting in comparison with the other categories, which consequently generates the biggest impact on EI99. It is the same with IR measurement, to handle a process with high reboiler heat duties elevate the risk associated with that process. In this manner, to compensate both objectives it is necessary to eventually obtain a process which accomplishes all constraints with as low as possible heat duties. Note that this behavior also promotes reducing TAC values as mentioned before. The Pareto front in Figure 12.9 shows the tendency observed when EI99 is evaluated jointly with the condition number. The tendencies observed so far show a clear connection between the condition number and some design variables such as reflux ratio and heat

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duties. In the same way, the EI99 values can be understood from the perspective of heat duty. With this in mind, the parabola observed with these two objective functions may be explained. In other words, the low zone of the Pareto front contains designs with high reflux ratios and consequently high heat duties, as a result, the condition number decreases otherwise the EI99. On the other hand, the upper zone contains schemes designed preferably with low reflux ratio, diameters, and heat duties. These parameters promote lower EI99 values but higher condition number. That is why, according to Tables 12.8 and 12.9, schemes S2 and S3 show the biggest EI99 values but the lowest condition numbers. So far, it has been denoted that both schemes S2 and S3 showed lower IR values in comparison with the other two schemes, explaining this behavior because of the design variables (highlighting a lower number of columns and low reflux ratio). On the other hand, it has been described that condition number is directly affected by low reflux ratio and low heat duties. With this in mind, it is easier to understand the curve of the Pareto front in Figure 12.10. Note, as long as the IR decreases, condition number increases. Transporting this result to the design variables, an immediate correlation is that the lower part of the Pareto front is formed by designs with high reflux ratios and heat duties, promoting high IR values but low condition number, otherwise the upper zone is mainly formed by designs with both low reflux ratios and heat duties, generating a safer process but not well conditioned for operative process under disturbances. With the results shown so far, scheme S2 can be considered as the most balanced purification process among S1–S4 in Figure 12.3. With this initial point, the separation scheme S5 was evaluated from the same optimization point of view, and the same objective functions were considered. Unlike schemes S1–S4, the alternative S5 was able to recover 99.29, 99.28, 99.99, and 99.99% wt of IBA, MEK, 2,3-BD, and p-xylene, respectively. Table 12.11 shows the objective functions and design parameters obtained for the hybrid process. Note, the reduction in both TAC and EI99 is huge. Even though the condition number of scheme S5 is higher than for the rest of the schemes (S1–S4) it is worth analyzing the role of the mass entrainer in order to reduce the energy requirements in MEK purification. Note, for

Table 12.11

Design parameters and performance indexes for the intensified scheme. Liquid–liquid extraction

Number of stages Reflux ratio Feed stage Column diameter (m) Operative pressure (kPa) Distillate flowrate (kmol/h) Condenser duty (kW) Reboiler duty (kW) TAC ($/yr) Eco-indicator (points/yr) Condition number IR (probability/yr)

10 1, 10 1.455 101.353

C2

C3

33 3.483 4 1.285 101.353 111.997 5776 6354

45 45 0.529 16.636 27 5 1.407 1.544 101.353 101.353 1693 19.193 2335 3191 4125 3202 7 903 251 1 338 593 88 121 0.0014087

C4

C5

C6

43 1.995 23 1.098 101.353 11.537 375 408

38 18.674 27 1.324 101.353 1.386 236.5 236.8

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Process Systems Engineering for Biofuels Development

example, the total energy invested in S2 is about 271 764 MJ/h, on the other hand, the best point obtained for the hybrid process requires 51 580 MJ/h, which represents an energy reduction of 220 184 MJ/h. An alternative scenario would be to maximize the purification. It would be interesting to know what purities would be obtained. However, the impact is direct for those higher concentrations on the TAC, specifically to the cost of services. Additionally, since the reboiler duty increases for higher purities, the eco-indicator will also increase. So, an interesting view would be to know the behavior of evaluating together in a Pareto front an economic or environmental objective function and the purities of interest. In this case, it would be possible to locate a point where because of the purities the process becomes economically unfeasible. Moreover, many results of the process would be compromised, for example, the recovery of the components. Considering the MEK production, scheme S2 consume 35 MJ/kgMEK and scheme S5 consumes 6.7 MJ/kgMEK. In the hypothetical scenario where all MEK produced as fuel was burned, with 31.45 MJ/kg as energy density, the energy profit of S2 and S5 was −31 266 and 188 918 MJ/h, respectively. Additionally, the reduction in EI99 is also remarkable. For example, S2 presents an environmental impact of 16 200 579 points/yr, however, scheme S5 presented 1 338 593 points/yr, a reduction above 90%. Regarding inherent safety, scheme S2 is safer than scheme S5 by about 5%. Note as aforementioned, the inherent safety is affected by many circumstances, in this case the increase in scheme S5 is initially due to the increase in distillation columns (four columns for scheme S2 and six for scheme S5). Moreover, note in scheme S2 the presence of water in many columns reduces the concentration of dangerous components and consequently the inherent safety. However, in scheme S5, most of the water was initially removed by the mass agent. Finally, a quite interesting analysis. Some design variables have a key role in the objective functions. Nowadays, the role of some design variables in the economic and environmental indexes, such as TAC and EI99, has been previously studied (Errico et al. 2016). However, another interesting perspective is the role of those design variables in the objective functions. Through the optimization process some tendencies were observed. Note in Figure 12.11 how it is only possible to obtain low IR values and low condition number when the reflux ratio increases notably. This behavior is understandable since a high reflux ratio mitigates the disturbances and increase in some columns to dissolve the flammable compounds. However, it should be noted that probably this combination affects directly another performance index such as TAC and EI99. In other words, as long as the reflux ratio increases, the amount of flow within the column increases, so this amount of matter allows an increase in disturbance mitigation in comparison with a column with low internal flows where the disturbance easily affects all the flow. Figure 12.12 shows the role of the stages for both objective functions. Note, even though a high number of stages probably promotes further good dynamic behavior and low heat duties to decrease IR values, it is possible to obtain low IR and condition number values with a low number of stages as well. This behavior also affects directly the TAC and EI99 values since the sizing of the column is a key parameter for TAC calculation and EI99 because of the contribution of the steel. Finally, Figure 12.13 shows a complete mass balance of schemes S2 and S5 considered as the best alternative for pure distillation schemes and the hybrid process.

Figure 12.11

0

be

m nu n

6 40 0

di tio

60

20

on

10 2 0 3 Reflu x ratio 0 40 50 (kmol/ kmol)

1 1 20 80 00

C

IR (probability/yr)

46 13 0 0 0. 344 01 0.0 342 01 0.0 340 01 0.0 338 01 0.0 336 01 0.0 334 01 0.0 332 01 0 0.0

r

Intensified Purification Alternative for Methyl Ethyl Ketone Production

> 0.0013 < 0.0013 < 0.0013 < 0.0013 < 0.0013 < 0.0013

Correlation generated among reflux ratio, IR, and condition number.

45

133

0.00

44

133

IR (probability/yr)

0.00

43

133

0.00

42

133

0.00

41

133

0.00

40

133

46 44 42 40 38 36 34 32 30 28 26 24 22 20 18

0 00 0 35 000 0 0 3 Co 50 00 nd itio 2 200 00 nn 0 50 um 1 000 00 1 be 50 r

ges

Sta

0

0.00

> 0.0013 < 0.0013 < 0.0013 < 0.0013

Figure 12.12

Correlation generated among number of stages, IR, and condition number.

333

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Process Systems Engineering for Biofuels Development Water 2117 kg/h IBA 823.5 kg/h MEK 7647.06 kg/h 2,3-BD 0.3 kg/h

Water 31.04 kg/hr IBA 484.69 kg/h MEK 0.48 kg/h C3

Water 2117.64 kg/h IBA 823.5 kg/h MEK 7647.06 kg/h 2,3-BD 1176.4 kg/h

C1

2,3-BD 1176.1 kg/h 7834 kW

IBA = 823.529 kg/h MEK = 7647.06 kg/h Water = 2117.65 kg/h 2–3 BD = 1176.47 kg/h

IBA 482.56 kg/h MEK 0.48 kg/h 255 kW

C2

Water 2086.6 kg/h IBA 338.81 kg/h MEK 7646.57 kg/h 2,3-BD 0.3 kg/h 1593 kW

C4 IBA 0.5 kg/h MEK 7639.24 kg/h 2,3-BD 0.145 kg/h 65806 kW

IBA = 99.691 kg/h Water, MEK (traces)

IBA = 718.027 kg/h Water = 28.4 kg/h

C4

Water = 72.67 kg/h IBA and MEK traces

C3

C5

C6

MEK = 7489.97 kg/h Water = 3.8 kg/h IBA = 1.1 kg/h

p-Xylene = 36998.1 kg/h

MEK = 155.885 kg/h IBA (traces)

p-Xylene = 36994.2 kg/h 2,3-BD, Water (traces)

C2

Water = 2012.67 kg/h 2,3-BD = 19.91 kg/h 2,3-BD = 1146.94 kg/h

Water, p-Xylene (traces)

Make Up p-Xylene = 3.9 kg/h

Figure 12.13

Complete mass balance for the selected schemes S2 and S5.

Specifically, regarding IR, reflux ratio plays an interesting role, either by helping to improve or worsen the IR. For example, when the column is associated with a water-diluted mixture, occasionally if the reflux ratio increases, the water flow may increase too. Considering the amount of water within the column, the concentration of flammable compounds decreases which minimizes risk. On the other hand, if the column is separating only flammable components, high reflux ratios increase internal flows and consequently IR also increases. The column diameter also plays a main role in all objection functions. Besides the well-known relation for diameter-control properties (as long as the diameter increases, the dynamic properties increase), note that if the size associated with each column increases, the internal flows also increase. The result obtained for IR depends on the kind of chemicals used. Note, a similar case is found with the stages; as long as the number of stages increases, the IR is affected because of the internal flows associated with the column.

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Regarding more design variables, the impact of other variables is clear; for example, for both the reboiler heat duty and column pressure, a bigger duty and pressure will always be associated with an increase in the IR.

12.6

Conclusions

In this work, an intensified process to separate MEK once the optimization process was finished was evaluated; the energy requirement of the intensified process was lower than the process based on conventional distillation columns. Additionally, this work proposes the evaluation of the inherent risk (IR) and the condition number (jointly with TAC and EI99) in early design stages in order to generate separation alternatives which accomplish current global needs. After the optimization process, those separation schemes showed very interesting results. Scheme S5, a hybrid process based on liquid–liquid extraction, was the most promissory since it was the only alternative able to recover and purify the entire feed mixture. Unlike pure distillation schemes, which were not energetically viable, the hybrid process improves energy consumption and energy profit in comparison with the scheme based on distillation. Additionally, it showed huge energy savings which are consequently observed in performance parameters such as TAC, EI99, and IR. In this manner, process intensification seems the correct alternative for improving the energy requirements and all indexes evaluated here.

Acknowledgments The authors acknowledge the financial support provided by CONACYT, Universidad de Guanajuato and the SEP (UGTO-PTC-668).

Notation 2,3-BD BLEVE CN DETL EI99 HAZOP IBA IEA IR LC50 LCA MEK QRA SVD TAC TL TS

2,3-Butanediol Boiling liquid expanding vapor explosion Condition Number Differential Evolution with Tabu List Eco-indicator 99 Hazard and operability study Isobutyraldehyde International Energy Agency Individual risk Lethal concentration, 50% Life cycle analysis Methyl ethyl ketone Quantitative risk analysis Singular value decomposition Total Annual Cost Tabu List Tabu Search

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UVECE 𝛾 𝜎 Σ

Unconfined vapor cloud explosion Condition number Singular value Diagonal matrix

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13 Present and Future of Biofuels Juan Gabriel Segovia-Hernández, César Ramírez-Márquez, and Eduardo Sánchez-Ramírez Departamento de Ingeniería Química, Universidad de Guanajuato, Noria Alta s/n, Guanajuato, 36050, Guanajuato, México

13.1

Introduction

In today’s society and industry, crude oil is the most important source of energy since it contributes approximately 35% to global energy consumption. Given the growing demand for oil, and according to several reports, it has been estimated that reserves reached their maximum production in 2010. Notwithstanding the above, it is expected that petroleum products will continue to be the main source of energy until 2030, at least. On the other hand, despite the outlook regarding oil reserves, renewable products have certainly been revalued and economic income in the countryside has been encouraged (Fortman et al. 2008). In the same way, research groups have increased their interest in clean and sustainable energies that come from renewable sources. Thus, technological advances have been stimulated and the profitability of many renewable energies has been improved. In addition, environmental protection has benefited, as well as the sustainability of conventional processes. For example, the transport sector has shown the greatest resistance in the effort to reduce carbon dioxide emissions due to its great dependence on fossil fuel. In fact, petroleum derivatives represent approximately 95% of the energy consumed in this sector (Demirbas 2007). With these considerations, research groups have focused their attention on other sources of energy, biofuels for example. Additionally, international agreements have been decreed to favor these trends, for example, the 2 ∘ C proposal is promoted by the Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

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International Energy Agency (IEA) to mitigate climate change. Briefly, the main objective of this proposal is to reduce approximately 70% of carbon dioxide emissions compared with the emissions of 2014. This proposal has as its main objective the transport sector that currently represents 23% of the total annual global emissions. On the other hand, SO2 and NOx emissions caused by fuel combustion are expected to be the same (Eggleston et al. 2006). Although electricity stands out as a very promising alternative to reduce carbon dioxide emissions, biofuels are considered responsible for reducing carbon dioxide emissions. Since the predictions of the IEA, the automotive sector could use at least 30.7% of the fuel, which demonstrates the need to replace fossil fuels in the short term and, at the same time, reduce oil production. According to several studies, it could be exhausted in a period of 50 years with the current consumption rate (Demirbas 2007; Reijnders 2006; Ture et al. 1997). In developed countries, there is a growing trend to use modern and efficient technology in the field of biofuels, which has led to these compounds becoming more economically competitive compared with fossil fuels. In general, biofuels have been considered a very interesting proposal given the advantages they offer, highlighting sustainability, reducing greenhouse gas emissions, and generating tangible social benefits for regional development and agriculture. In the same sense, due to the growing interest, biomass is an attractive raw material for three main reasons: (i) it is up to now a renewable resource; (ii) it seems to have a positive impact on the environment; and (iii) it seems to have a positive economic impact if the prices of fossil fuels increase in the future (Demirbas 2000a,b). In recent times, biofuels have been in vogue. However, this does not mean that they are a product of new technology, but rather their development and applications have increased. For example, according to data generated by the IAE, in 2009 the total supply of primary energy was close to 12 150 toe (tonne of oil equivalent), which represents 10.2% previously produced from renewable resources (Fortman et al. 2008). It is expected that the United States and the European Union will replace at least 6% of fossil fuel consumption by biofuels in the coming years (Demirbas 2000a). Biofuel can be defined as a fuel of biological origin, i.e. the term covers all fuels derivative of vegetal biomass. So, biofuel is obtained in a renewable way from organic remains. The vegetal origin fuels must have similar characteristics to fossil fuels, allowing use in spark engines without having to make significant modifications (Demirbas 2007). Biofuels can originate as liquids, gaseous and solid fuels produced mainly from biomass. They can be produced from a variety of fuels from biomass, such as bioethanol, biobutanol, bioethanol, biodiesel, and biohydrogen (Cadenas and Cabezudo 1998). Biofuels can be obtained from several raw materials and are usually used in different traditional petroleum-based fuel blends (Puppan 2002). The future of biofuels is in finding solutions and it taking advantage of their benefits. The success of biofuels resides in the economy and their easy access for everyday use. Although there are several important factors that must be taken into account for the successful implementation of biofuels in our society, there is a certain development level that is already helping to verify the potential of biofuels in our lives. The biofuels derived from biomass can be classified as primary, secondary, and so on. Primary biofuels are used in a way not involving processing, i.e. solid material from forest waste for use as fuel. Secondary biofuels come from a biomass process and can be used as fuels in vehicles or in an industrial process. Secondary biofuels are divided into first-, second-, third- and fourth-generation

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Biofuels Feedstock and examples

First generation

Second generation

Third generation

Fourth generation

Sugar, Starch, Vegetable oils, Animal Fats

Wheat Straw, Corn, Wood, Solid Waste, Energy Crop, NonFood Crops

Algae

Vegetable oil, biodiesel

Bioalcohols, vegetable oil, biodiesel, biosyngas, biogas

Bioalcohols, bio-oil, bioDMF, biohydrogen, bioFischer-Tropsch diesel, wood diesel

Vegetable oil, biodiesel

Biogasoline

Figure 13.1 Elsevier.

Classification of secondary biofuels. Source: Jefferson 2006. Reproduced with permission of

biofuels depending on the feedstock used for their production (Figure 13.1). Biofuels are also classified according to the source and type. They can come from agricultural sources, municipal waste or waste originated from industry. Biofuels can be solids (firewood, charcoal, and pellets), liquids (bioethanol, biodiesel, etc.), or gaseous (biogas) (Ture et al. 1997). Nowadays, there are difficulties in biofuels production development. These difficulties are technological, political, economic, and/or involving storage and safety. The path sought in the industrial and academic sector is to reduce these difficulties, and significantly promote and develop the conventional use of biofuels. Two of the biggest drawbacks are the production costs and the temporality of the raw materials (Difiglio 1997). At present, there are two main ways of processing lignocellulosic raw material, namely biochemical and thermochemical. Certainly, there is no clear candidate for a better technological pathway than the biochemical and thermochemical pathways (Figure 13.2) Biomass

Raw Material Processing

Thermochemical Conversion

Biochemical Conversion

Gasification

Liquefaction

Pyrolysis

Biogas

Biochemicals

Syn-oil

Figure 13.2

Bioethanol

Biodiesel

Main biomass conversion process.

Biobutanol

Biojet fuel

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(Tanger et al. 2013). This is why there still needs to be better investment, research, and development, to guarantee that future biomass raw material production can be carried out in a sustainable way and that high conversion technologies are identified. Once it has been tested, there will be a constant transition of biofuels. Accordingly, several routes for the production of renewable energy have been proposed. From the alternatives, it is possible to highlight four categories: (i) integration of solar and wind power to produce fuels and chemicals; (ii) biomass conversion; (iii) a polygeneration process which can produce simultaneously transportation fuels, energy, and high value added chemicals; and (iv) a combination of syngas and hydrogen to produce chemicals (Faaij 2006).

13.2

Some Representative Biofuels

A biofuel is produced through contemporary biological processes, such as agriculture and anaerobic digestion, instead of being produced by geological processes such as the processes involved in the formation of fossil fuels. In the particular case of biofuels, if the biomaterial can grow rapidly, this biofuel is considered as renewable energy. Biofuels can be derived from plant grains (for example corn) or indirectly from agroindustrial or domestic waste. Additionally, renewable biofuels provide a certain degree of carbon fixation due to the process of photosynthesis that occurs in all plant matter (Parikka 2004). Biofuels can be considered as theoretically neutral carbon since the carbon dioxide that is absorbed due to photosynthesis is also released when the fuel is burned. Other renewable biofuels can be produced using biomass (considering biomass as living organisms rather than material derived from plants). In general, biomass can be converted into energy by two different methods: thermochemical conversion and biochemical conversion. The converted biomass can result in fuels of solid, liquid or even gaseous nature. In the following, there is a brief description of the generalities of the most promising biofuels today (Hoekman 2009). 13.2.1

Bioethanol

Bioethanol or ethyl alcohol is a colorless, biodegradable and low toxicity liquid, so that the environmental pollution caused in the case of spillage is really small. In the case of combustion, bioethanol produces carbon dioxide and water. The octane rating of bioethanol is high; it can even be mixed with gasoline to oxygenate the mixture and promote more complete combustion to reduce gas emissions. Currently, ethanol is widely sold in the United States for its mixture with gasoline; the most common mixture is 10% bioethanol and 90% gasoline (E10). Gasoline vehicles do not require modifications to run under the E10 mixture and their warranty is also not affected. Only flexfuel vehicles can operate with mixtures above 85% ethanol and 15% gasoline (E85) (McMillan 1997). Bioethanol can be produced by biological pathways; however, it can also be obtained by the reaction of ethylene and vapor. Mainly, sugar is considered as raw material to produce bioethanol from grains, for example wheat crops, sawdust, corn, etc. (Gray et al. 2006). The material considered as feedstock is in principle, any plant; in practice, the selection of the feedstock depends on the speed of growth of the plant, as well as the sugar content and its ease of availability in the plant. As a result, a wide variety of raw materials and consequently

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the production processes come from sugar- and starch-containing raw materials. However, various available types of lignocellulosic biomass such as agricultural and forestry residues, and herbaceous energy crops could serve as feedstocks for the production of bioethanol, energy, heat, and value-added chemicals. Globally, the largest amount of bioethanol is obtained in Brazil from sugar cane. In the United States it is produced from molasses and corn; however, other sources of sugar can also be used, for example wheat, barley, and even rye can be used. Grains containing starch must first be converted to sugar. Approximately 3 tons of grains are required to produce 1 ton of bioethanol. In Europe, the most common matter for producing bioethanol is wheat and sugar beet. Sugar beet grows in most of the European Union countries and has a higher growth per hectare than wheat (Chen and Qiu 2010). Currently, research and development activities in the field of bioethanol are focused on lignocellulosic material and woody materials. Within said material may be included willow, miscanthus and eucalyptus, agricultural residues and municipal solid waste. About 2–4 tons of woody material are required to produce 1 ton of bioethanol (Cardona et al. 2010). There are many reasons to switch to bioethanol production of lignocellulosic material. Lignocellulosic material is more abundant and less expensive than the aforementioned grains due to the direct competition with the food market. In addition, the energy balance is larger, making the environmental impact lower. In fact, lignocellulosic material has the potential to accumulate up to 90% of greenhouse gas emissions; on the other hand, this type of biomass has greater difficulty in converting sugar due to its relative low accessibility in the biomass structure. The most used technology to purify and produce bioethanol is fermentation and distillation. Fermentation is a process of biochemical conversion in which matter is decomposed by microorganisms. The microorganisms can act in different types of raw material. Among the microorganisms used, bread yeast (Saccharomyces cerevisiae) is the most used since it requires only monomeric sugar as raw material. Conventional bioethanol fermentation can produce 0.51 kg of bioethanol from 1 kg of any six-carbon sugar. However, not all raw materials contain sugars of that nature. Starch and lignocellulose are polymers, so hydrolysis is required to break the bonds between the monomers to generate six-carbon sugars. For example, with grain for the production of bioethanol, the first step for the conversion is a mechanical process that includes grinding the grain to release its starch. Subsequently the mass generated must be diluted to adjust the amount of sugar. Adjusting the amount of sugar allows to manipulate the mass generated in the grinding. Then you must cook to dissolve all the soluble starches in the aqueous solution. The starch is converted into sugars by enzymatic action or acid hydrolysis. In the case of acid hydrolysis, dilute mineral acid is added to the suspension before cooking. The short chain hydrocarbons that result from this process can be fermented by microorganisms. For the microorganisms to carry out the biological process, the solution must be slightly acidic, that is, a pH between 4.8 and 5.0. During the fermentation, bioethanol dissolved in water as well as carbon dioxide is produced. Subsequently, such effluent must be treated in a purification process to obtain concentrated solutions (Segovia-Hernández et al. 2014). The lignocellulosic material can be converted to bioethanol only differing from the process described above in the sugars to be fermented. Hydrolysis of this kind of material is more difficult than for energy drops (for example starch) since lignocellulosic material is

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composed of biopolymers (cellulose 40–60%, hemicellulose 20–40% dry weight). On one hand, cellulose is composed of long chains of glucose linked together. On the other hand, hemicellulose is composed of arabinose, galactose, mannose or xylose. However, the main problem is that both biopolymers are not soluble in water. The remaining fraction (lignin 10–25% dry weight) cannot be fermented for it is resistant to biodegradation. However, lignin can be used for the production of electricity and/or heat (Rass-Hansen et al. 2007). To be used as fuel, ethanol must have a purity of almost 100%. This means that the water content must be much lower compared with the bioethanol produced by current industrial technology. For the purification of bioethanol, there are several technologies, for example the use of molecular sieves and membranes. Despite its great advantages, bioethanol does not possess a higher energy density in comparison with gasoline. In practical terms for a car, this means that a tank full of bioethanol will provide less movement in proportion to the energy density. Bioethanol has a higher octane rating than gasoline; hence, bioethanol has better antiknock characteristics. This characteristic of bioethanol can be exploited in motors with slight modification. This would increase the fuel efficiency of the engine. The oxygen content of bioethanol also generates greater efficiency, resulting in an eco-friendlier combustion process at relatively low temperatures. Reid’s vapor pressure, a measure of fuel volatility, is very low for bioethanol, which indicates a slow evaporation. This reduces the risk of explosions. However, the low vapor pressure of bioethanol is disadvantageous in comparison with starting the engine at low temperatures. Without any help, engines that use bioethanol cannot start at temperatures below 20 0∘ C. The difficulty of cold start is a main problem for future application of alcohols such as biofuels (Torres-Ortega et al. 2018). In summary, the general process for bioethanol production is shown in Figure 13.3. Bioethanol can be used as: • • • • •

Fuel for possible fossil fuels substitution. Fuel for power generation. Fuel for fuel cells used in thermochemical reactions. Fuel in systems of energy cogeneration. Raw material in the chemicals industry.

Biomass Handling

Enzyme Production

Biomass Pretreatment

Cellulose Hydrolysis

Bioethanol

Glucose Fermentation

Bioethanol Recovery

Glucose Fermentation Figure 13.3

Bioethanol production process diagram.

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Bioethanol has better results in spark ignition engines due to its high octane rating. However, it is not recommended for use in diesel engines. In general, it is not practical to use pure bioethanol in spark ignition engines because of its low vapor pressure and high latent heat of vaporization which makes cold starting problematic. 13.2.2

Biodiesel

This fuel is from triglyceride transesterification; its nature is very similar to fossil diesel. The elementary raw material for its production is vegetable or animal oil and tallow, being a conventional source from the residual oils from culinary work (restaurants and industrial food plants). Vegetable oils commonly used are from oil crops especially sunflower, canola, soy and palm. The UK represents one of the largest biodiesel producers in Europe, with the canola seed being the most used. The percentage of biodiesel production would be much higher, except for the high cost of raw material of some oils. For the above, used oil recycling is an attractive option, which despite requiring special treatment to eliminate impurities, competes adequately with fossil diesel. Barnwal and Sharma (2005) present work on the cost of biodiesel production and information on its production. Biodiesel, like most biofuels, being a renewable fuel that does not originate from fossil sources results in high benefits for the environment. Biodiesel aims to reduce the amount of carbon dioxide emitted. Nevertheless, there are a number of elements that increase and unbalance the carbon dioxide production and absorption in biodiesel production: the first aspect to consider is the carbon dioxide originated from fertilizer production for cultivation. The other aspects that result in a high percentage of carbon dioxide production are: the processes of esterification, the oil extraction with solvent, and the refining, drying and transportation. A methodology that results in adequate estimation of contaminants of this fuel source is life cycle analysis, which basically evaluates from the biofuel origin until its final use. The biodiesel results are environmentally friendly since the toxicity is very low and in the case of spillage it does not represent a danger to the environment (Demirbas 2009). Biofuel production is based on three conventional routes starting from oils and fats. The first two routes involve oil transesterification, with the first route being catalyzed by a base, and the second route catalyzed by an acid. The third route involves oil conversion into fatty acids for further transformation into biodiesel. It should be mentioned that higher production of biodiesel is carried out by the base-catalyzed transesterification, since is a much less expensive process, with affordable operating conditions and with very high conversion yields (above 98%) (Leung et al. 2010). Therefore, it is useful to describe this process. For the base-catalyzed transesterification, through the esterification process, triglycerides are reacted with alcohols (bioethanol is commonly used), using a catalyst (alkaline), such as sodium hydroxide or potassium hydroxide. Once the reaction is done, monoesters are formed, commonly called biodiesel, and in turn the production of glycerol occurs (Figure 13.4). The most recommended catalyst for ethyl ester biodiesel production is potassium hydroxide since it has the better properties. Likewise, for methyl ester biodiesel production, either sodium hydroxide or potassium hydroxide are convenient (Leung et al. 2010). The transesterification reaction can be said to be successful, if it gives the separation

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Oil and Fats

Methanol and Catalyst

Pretreatment

Transesterification

Biodiesel

Purification

Washing and Drying

Glycerin

Figure 13.4

Biodiesel production process diagram.

of glycerol from the esters, after the reaction time. Glycerol is much heavier, and begins to separate naturally, settling in the background. Once separated, glycerol can be sold to other industries, for example the cosmetics industry (Leung et al. 2010). Biodiesel is compatible with fossil diesel, which can be mixed in any proportion (0 to 100%), making it suitable for use in any diesel engine or in any oil-fired furnace. There are many studies on the effect of biodiesel on these engines, and the conclusion is that it works the same and many times better than the fossil fuel ones. The transesterification process of the oil is beneficial for the engine, giving the following characteristics to biodiesel: low viscosity, complete elimination of glycerides, and the boiling, swelling and fluidity points are reduced (Gerpen 2005). Certain analyzes are required for the use of biodiesel as a commercial fuel, to ensure that it meets the required specifications. The most important points to ensure trouble-free operation in diesel engines are: that there is complete elimination of glycerin, catalyst, and alcohol; that the reaction is carried out completely; and that there are no free fatty acids. 13.2.3

Biobutanol

This compound is an alcohol, colorless, and flammable. Not only is it used as a biofuel, but some industrial sectors use it as a solvent. Like all biofuels, it is expected that biobutanol could be an adequate substitute for fossil fuels thus causing a reduction in greenhouse gases. It is evident that many biofuels cannot be used directly in internal combustion engines, but that does not prevent them from being used in mixtures with fossil fuels, such is the case of biobutanol. To convert butanol as an additive to fossil fuel and improve its conditions, there are the following useful properties to consider: the energy density (29.2 MJ/dm3 ), the low melting point (−89.5 ∘ C), the adequate boiling point (117.2 ∘ C), the low flash point (36 ∘ C), and a beneficial autoignition temperature (340 ∘ C). Currently there is no suitable motor for using only bioalcohols, this is why all research is carried out with the use of biobutanol as a powerful additive to fossil fuels (No 2016). Biobutanol production can be carried out in different ways. An elemental process in biobutanol production is fermentation. This process is usually carried out with bacteria of the genus Clostridium acetobutylicum under anaerobic conditions. It is called the ABE

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process, in which acetone, butanol, and ethanol are formed, in typical proportions of 3:6:1, with a final approximate biobutanol concentration of 3% (Quiroz-Ramírez et al. 2017). If it is decided to produce biobutanol by a fermentative route, the following factors should be considered: pretreatment cost, raw material cost, process profitability, biobutanol purification cost (very low amount with respect to the other compounds), and the toxicity of the process. The raw material that is better for biobutanol production is waste of agricultural origin (straw, grass, grains and fruits in poor condition, etc.), since it is cheaper making the process more profitable. The above is in comparison with grain in good condition for the fermentation. Another alternative that has been proposed in recent years is the use of vegetable origin biomass, as in the case of algae, since it is not labor intensive and does not have high production costs. Some microalgae contain a high percentage of sugars (Chlorella contains around 30–40% sugars), which increase the biobutanol production. This has led to the genetic alteration of some bacteria, such as Clostridium acetobutylicum and Clostridium beijerinckii, where the resistance to the concentration of biobutanol in the fermentation broth was increased (Sánchez-Ramírez et al. 2017a). Once the fermentation broth with a low biobutanol concentration is obtained, the distillation method required for the purification is expensive. This has led to biobutanol not being economically competitive in comparison with other biofuels. Therefore, other separation techniques have been explored, such as membrane use, adsorption, liquid–liquid extraction, pervaporation, and reverse osmosis. Especially, pervaporation is a promising technology in the recovery of biobutanol, since it allows the separation and concentration of the product during a single process (Figure 13.5) (Sánchez-Ramírez et al. 2017b). 13.2.4

Biojet Fuel

This biofuel, like its name suggests, is used by the aviation industry (to boost gas turbine engines). Therefore, it has to conform to certain requirements, more strictly than for other fuels. On par with the other biofuels, biojet fuel is thought to reduce the dependence on fossil fuels and the greenhouse gas emissions that these generate. It is estimated that the commercial air industry contributes around 6% of total carbon emissions globally (Krammer et al. 2013). Biojet fuel offers the possibility to reduce greenhouse gas emissions in the aviation industry. Biomass Handling

Biomass Pretreatment

Biobutanol

Hydrolysis

Fermentation

Lignin

Recovery

Solids Acetone and Ethanol

Figure 13.5

Biobutanol production process diagram.

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Biojet fuel

Biomass

Deoxygenation

Figure 13.6

Isomerization/ Hydrocracking

Recovery

Biojet fuel production process diagram.

There are several technologies for aviation fuel production from biomass. Some of these technologies are in the research stage and others are already available for use on a commercial scale. The production processes are dependent on the raw material. Hydroprocessing technologies, such as hydrotreating, deoxygenation, and isomerization and hydrocracking, are based on oils being converted into biojet fuel. Likewise, there are processes such as catalytic hydrothermolysis to treat the oils based on triglycerides (Nygren et al. 2009). Technologies such as biomass gasification are often used on solid raw material, transforming the matter in alcohols (with the use of biochemical and thermochemical processes), in bio-oils (through pyrolysis processes), in sugars (through biochemical processes), and in synthesis gases (Figure 13.6). The previous raw materials can be transformed into biojet fuel, through synthesis processes, catalytic or fermentative. Nowadays, the biomass synthesis processes by Fischer–Tropsch to biojet fuel are approved by the D7566 ASTM International Method, achieving successful mixes up to 50%. At a commercial level (large scale), only hydroprocessing using vegetal oils and waste is documented, being the only cost-effective conversion path (Nygren et al. 2009). Fuel use in the aviation sector for the United States constitutes consumption of 20 billion gallons of fuel per year. This is why a minimum cost reduction in such fuel, results in a large financial saving. At the beginning of 2012, it was estimated that the annual cost of fuel was around 50 billion dollars. Nevertheless, for the year 2030, it is estimated that aviation fuel cost can be reduced by introducing biojet fuel at $2.5/gal. This is due to the improvement in fuel production technologies for aviation, making it possible to reduce the total annual cost up to 30%. The parameters to improve the cost of biojet fuel are: raw material costs, the improvement of the production equipment, the increase in the conversion and product performance, the generation of high value-added products, and generation of adequate energy integration systems (Hari et al. 2015). Agricultural and forestry raw materials, as well as algae biomass, are important raw materials for production of alcohol fuels and are able to provide large quantities of alcohols to convert into biojet fuel (Hari et al. 2015). Other specifications to comply with biojet fuel are: an acceptable minimum energy density; a maximum freezing temperature allowed; a maximum allowable viscosity; a maximum permissible sulfur content; a minimum aromatic compound content; a minimum electrical conductivity of the fuel; and a minimum allowable flash point. At the same time, biojet fuel must have a lower freezing point for flight purposes (Chuck 2016). Among the biofuels used for the aviation sector are the following: • Alcohol-to-Jet (ATJ) Fuel: This is the fuel coming from alcohols (bioethanol, biobutanol, and the long chain fatty alcohols). The percentage permitted in the mix with other fuel is approximately 15% (Chuck 2016).

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• Oil-to-Jet (OTJ) Fuel: There are three processes for this route: hydroprocessed renewable jet (HRJ), catalytic hydrothermolysis, and pyrolysis (also known as hydrotreated depolymerized cellulosic jet [HDCJ]). Nowadays, just the HRJ route products are considered for mixtures and have according to ASTM specification (Chuck 2016). • Gas-to-Jet (GTJ) Fuel: This route details the conversion processes of biogas, natural gas, or synthesis gas into biojet fuel (Chuck 2016). • Sugar-to-Jet (STJ) Fuel: There are two ways to generate biojet fuel starting from intermediate sugar raw materials. The first consists in the catalytic improvement of sugars and hydrocarbons. The second is linked to the biological conversion of sugars into hydrocarbons (Chuck 2016). Agricultural and forestry raw material, as well as algae biomass, are important raw materials for the production of alcohol fuels and may provide a considerable quantity of alcohols to convert into fuel for aircraft. Vegetable oils, animal fats, waste cooking oils, algae oil and pyrolysis oils are the predominant raw materials for the conversion processes related to oil (Nair and Paulose 2014). 13.2.5

Biogas

Global warming and its effects are of major concern today. The main purpose of the climate objectives is to limit the average warming to a temperature of 2 ∘ C compared with the average temperature in the nineteenth century. Biogas originates from biogenic material and it is a type of biofuel, typically referred to as a gas produced by bacterial fermentation of organic material under anaerobic condition. In general, biogas is a contributor toward the growing interest in the wider use of biofuels. The composition of biogas is mostly methane and carbon dioxide, typically 40–95% and 5–55% of the blend, respectively. Biogas is approximately 20% lighter than air. In terms of physical properties, it has an ignition temperature between 50 ∘ C and 750 ∘ C; the calorific value of 1 m3 is approximately 22 MJ; and it is an odorless and colorless gas that burns with a clear blue flame. Biogas can be produced from a wide variety of available organic materials; for example, wastes, including animal manure, sewage sludge, and municipal organic waste. Anaerobic digestion is a simple technology widely used for processing the biodegradable, organic waste for biogas production (Figure 13.7). Animal manure is used as inoculum, pretreatment of a substrate. The estimation of anaerobically digested substrate growth is around 25%. In this sense, the biogas industry has the potential to generate a substantial amount of energy. Upon completion of the anaerobic digestion process, the biomass is converted into biogas (Wieland 2010). Biogas

Biomass

Hydrolysis

Figure 13.7

Acidogenesis

Acetogenesis

Biogas production process diagram.

Methanogenesis

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13.2.5.1

Biochemical Process

Anaerobic digestion can be divided into four stages: (i) hydrolysis, (ii) acidogenesis, (iii) acetogenesis, and (iv) methanogenesis. These four stages are developed by different microorganisms in different conditions. The microorganisms that hydrolyze and ferment are responsible in the first stage for attacking polymers and monomers and produce hydrogen and acetate and several volatile fatty acids (i.e. propionate and butyrate, for example). Cellulase, cellobiase, xylanase, amylase, lipase, and protease (hydrolytic enzymes) are excreted by hydrolytic microorganisms. Most bacteria are anaerobic, but some facultative anaerobes, for example, Enterobacteria and Streptococci, participate in the process. The fatty acids with higher volatility are converted into acetate and hydrogen by the acetogenic bacteria. In this process some important details can be highlighted; the accumulation of hydrogen plays an important role, since it can inhibit the microorganism’s own metabolism. For this reason, the concentration of hydrogen remains relatively low. When the degradation stage ends, two groups of methanogenic bacteria generate methane from acetate or hydrogen and carbon dioxide (Woon and Lo 2016). In addition, with the decomposition of organic or biological materials, several gases are discharged. Organic decomposition can occur in two ways: aerobic decomposition (in the presence of oxygen) and anaerobic decomposition (oxygen is not present). The decomposition products are quite different: (i) carbon dioxide, ammonia and some other gases in small amounts are produced in aerobic decomposition or fermentation. Also, very little heat and a final product (with a higher nitrogen content) is produced in this step; (ii) methane, carbon dioxide and traces of other gases are produced in anaerobic decomposition. Anaerobic decomposition is a two-stage process. In the first step, acid bacteria break down complex organic molecules into peptides, glycerol, alcohol and the simplest sugars. When these compounds are produced in sufficient quantities (second step), these simpler compounds are converted into methane with a second type of bacteria. The methane-producing bacteria are influenced by environmental conditions, which can slow or finish the process (Achinas et al. 2017). The resulting biogas contains 55–80% methane, depending on the type of waste. The main gases produced are methane and carbon dioxide. The composition of the gas varies with the raw material used. The typical composition is: methane, 50–75%; carbon dioxide, 25–50%; nitrogen, 0–10%; hydrogen, 0–1%; hydrogen sulfide, traces; steam from water, traces; oxygen, 0–2% (Kaparaju et al. 2009). Anaerobic digestion is a simple and efficient technology that has been used for a long time for the production of biogas and it is ready for use in domestic and agricultural applications. This technology can contribute substantially to the generation of energy from organic waste, particularly that from agriculture and municipal waste. This type of waste is generated in very large quantities worldwide. The use of anaerobic digestion technology, in addition to being an important source of energy generation, also allows the integral use of biomass residues and impacts on agriculture, livestock, forestry and fishing, thus controlling pollution and protecting the environment.

13.3

Perspectives and Future of Biofuels

According to the discussion in this chapter, only 1% of energy is generated from biomass. With this in mind, it is clear that there is a great opportunity for the use of renewable fuels (Wieland 2010). In recent years there has been an increase in the use of fuels in cars and

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39 444 33 214

Coal

27 222

Oil

23 318

Gas Nuclear Hydro Renewables (Biofuels)

2013

Figure 13.8 2015 .

2020

2030

2040

Electricity generation mix worldwide (TWh)

urban transport. Currently, the transport sector represents about 20% of the total emissions in the atmosphere. Additionally, it is estimated that more than 650 million tons of carbon dioxide have been released per year, with an emission equivalent to that released by 136 million cars. This behavior predicts a constant increase in carbon dioxide emissions and greenhouse gases; adding to what is already issued by air transport that is often neglected in investigations (Hill et al. 2015). With this in mind, biofuels from renewable sources are considered a good alternative to reduce carbon emissions. Several biofuels have been considered for conventional use in the United States and the European Union with several intentions: to reduce the emissions, to reduce the use of fossil fuels, and to increase the inherent safety of fuels. Several countries are giving this transition impetus. The leaders of the Group of Seven (G7) countries declared in 2015 to totally decarbonize their economies by dispensing with fossil fuels by 2040 and using other alternatives (biofuels) as a universal source of energy (Figure 13.8). Likewise, in the United Nations Climate Change Conference in Paris, countries approved a new climate protection policy to complement the Kyoto Protocol. It is a vital step in the right direction. Nevertheless, countless efforts will still be required to get a change in emissions by 2040 so that climate change does not surpass the 2 ∘ C aim. Therefore, biofuels are an opportunity for the human race, nature, and the economy. It is time to take action (United Nations 2016). The main obstacle to biofuels is the current price of fossil fuels, and the main incentive for biofuels is the growing global population and the need to increase food supplies (Becken 2002). The biofuels industry helps greatly in the reduction of greenhouse gases, as well as reducing the dependence on petroleum derivatives, and increases diversity in renewable sources of energy, and in the generation of jobs. For these reasons, the value of biofuels is beyond their use as substitutes for fossil fuels; their economic and environmental impact must also be considered. At present, the long-term success of biofuels requires essential information. By the year 2030, there is major potential for biofuel production from inedible vegetable and waste. In addition to bioethanol, other chemicals such as biobutanol, levulinic acid and various carboxylic acids have an enormous potential to increase the value and usefulness of biofuels (Eggleston and Lima 2015; Ding et al. 2016; Ramos et al. 2016).

Demand of power. Electricity generation mix worldwide (TWh) Source: Adapted from Birol

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Index Ablative pyrolyzer, 300 Ablative reactor, 300 Acentric factor, 91, 99, 101, 226 Acid catalysts, 125, 127, 128, 137, 194–196, 223, 278 Activity coefficient, 89, 90, 92, 94–97 Adaptive evolution, 175, 186 Added value products, 21, 22, 26, 33–36, 69, 86, 110 Adsorbents, 32, 38–40, 291 Agricultural activities, 18 AlCl3 ⋅ 6H2 O, 134–140 Alcohol production plus oligomerization, 159 Alcohol-to-jet, 150, 350 Algal biomass, 260–262, 268, 282 Alkaline catalysts, 125, 128, 140, 195, 223, 236, 244, 296 Anaerobic digestion, 1, 38, 54, 55, 154, 344, 351, 352 ANP (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis), 221, 248, 255 Antoine equation, 55, 273 Arabinose, 183, 346 Artificial neural network, 5, 58, 106 Aspen Energy Analyzer, 222, 252 Aspen Plus, 10, 50, 206, 222, 224–229, 231–234, 239, 240, 245–249, 251–255, 261, 263, 267, 268, 272, 273, 277, 281, 317, 324, 325

Aspen Plus Economic Analyzer (APEA), 273 ASTM D7566, 150 Aviation sector, 9, 149, 167, 350 Bagasse, 4, 22, 25, 37, 159 Bare module cost, 273, 274 Batch reactor, 138, 141, 153, 154, 157, 234, 235, 239, 242 Bifunctional oxides, 120 Bi-level optimization, 178 Bioalcohols, 2, 6, 7, 111, 343, 348 Biobutanol, 2, 10, 54, 312, 314, 315, 342, 343, 348–350, 353 Biochemical conversion, 343–345 Biodiesel, 2, 10, 15, 16, 25, 28, 31, 32, 37, 38, 54, 55, 63, 67, 71, 87, 88, 100–103, 109–111, 113, 121–133, 137–140, 166, 191–201, 205–217, 221–224, 228, 230–232, 234, 236, 237, 239–241, 245–248, 253–255, 259–263, 265–268, 271–281, 286, 289, 312, 342, 343, 347, 348 decomposition, 268 first-generation, 121, 260 process using waste cooking oil, 10 production cost, 124 second-generation, 260 third-generation, 260

Process Systems Engineering for Biofuels Development, First Edition. Edited by Adrián Bonilla-Petriciolet and Gade Pandu Rangaiah. © 2020 John Wiley & Sons Ltd. Published 2020 by John Wiley & Sons Ltd.

358

Index

Biodiesel from microalgae alternate processes, 280 process flowsheet, 265 process parameters, 266 Biodiesel plants, 8, 126, 138, 215, 223, 279 Biodiesel process, 10 Biodiesel production process design, 239 Biodiesel purification, 205, 208–210, 223, 239, 245, 248 Biodiesel yield, 31, 128, 196–198, 200, 208, 261, 281 Bioenergy, 3, 6, 16, 18, 109, 165, 192, 291 Bioethanol, 2, 7, 10, 15, 16, 18, 22–24, 35, 36, 54, 62, 67, 71, 159, 160, 165–167, 174, 183, 192, 260, 289, 312–315, 342–347, 350, 353 common mixture, 344 fermentation, 345 Biofuel perspectives and future of, 352 climate change, 342, 353 transport sector, 341, 342, 353 producing microorganisms, 6 supply chains, 3–5, 9 used for aviation sector, 350 Biofuel feedstocks, 19, 38 Biofuel process design, 99, 101, 102, 104, 109, 110, 112 Biofuel production, 2–11, 16–20, 24, 26, 28–32, 34, 35, 40, 62, 68, 86, 88, 94, 114, 193, 313, 343, 347, 353 Biogas, 10, 15, 35–38, 40, 52, 54, 73, 109, 154, 192, 216, 343, 351, 352 anaerobic decomposition, 352 biochemical process, 352 composition, 351 Biogases, 111 Biogasoline, 2, 343 Biojet fuel, 2, 149–156, 159–162, 164–167, 343, 349–351 cost, 350 production, 9, 152, 159–161, 350 specifications, 350 Biokerosene, 150 Biomass, 1, 2, 5, 9, 20, 25, 29, 101, 139 feedstocks, 2, 19, 86 transformation routes, 2, 5, 8 Biomass feedstock 1st generation, 2 2nd generation, 2

3rd generation, 2 4th generation, 2 Biorefinery, 8, 9, 22, 50, 56, 59, 65, 67, 68, 72, 74, 86, 101, 109, 112, 161, 165, 166 smart, 86, 109, 112 Biotechnology, 5, 173, 174 Blending problem, 68 Byproducts green diesel, 151, 152, 159, 160, 164, 165 light gases, 151–153, 156, 159, 161, 164, 165 naphtha, 151–153, 157, 159, 161, 164, 165 CAPCOST program, 273, 279 Carbon dioxide emissions, 56, 149, 191, 289, 312, 341, 342, 353 Carbonization, 10, 287, 303, 305–307 Carbonization furnaces, 306 Castor oil, 130, 193, 197–201, 204–210, 212, 214, 216, 217 methanolysis, 199 transesterification, 204 Catalytic biomass Pyrolysis, 303 Catalyzed gasification, 295 Cavitation, 261 CCEP program, 273 Cell-recycle reactors, 35 Centrifugation, 35, 199, 210–217 Ceramic membrane, 210–213, 215 tubular membrane, 212 Chemical biodiesel production, 196 Chemical Engineering, 2, 8, 11, 98, 99, 103, 112, 273 Chemical Engineering Plant Cost Index (CEPCI), 273 Chemical equilibrium, 87, 238, 241 Chemical potential, 89 Chicken fat, 38, 153 Citrus waste, 34, 35 Co-culture fermentation, 35 Cold flow properties, 38 Combustion, 10, 19, 28, 30, 32, 35, 37, 51, 68, 262, 286–290, 292–294, 297–300, 305, 306, 313, 314, 342, 344, 346, 348 Computational fluid dynamics (CFD), 8, 55 Computer-aided design, 10, 173, 180 Computer-aided methodologies, 5, 7, 9 Constraint-based modeling, 175, 176, 179, 185 controllability and inherent safety, 316 Corn waste biomass, 24

Index

Cost biodiesel production, 10, 124, 260, 275, 347 raw materials, 34, 275, 276 Cost analysis, 10, 261, 272, 281 Cost of manufacture, 173, 275 Critical properties, 99–101, 104, 112 Design Institute for Physical Properties (DIPPR), 99, 227, 228 Diacylglycerols, 194, 237 Diafiltration, 212–215, 217 Differential evolution, 173, 174, 179, 324 Direct esterification, 125, 127, 128, 131, 132, 134–141 Discrete element method, 55 Distillation, 6, 7, 11, 34, 35, 37, 54–56, 62, 63, 103, 113, 139, 151, 152, 159–161, 164, 165, 206–210, 216, 223, 243–248, 250–252, 262–264, 266–268, 272–277, 279, 281, 313–315, 317, 320–322, 324, 325, 327, 332, 335, 345, 349 D-limonene, 35 Double-counting rule, 120 DSTWU, 243 Dual fluidized bed gasification, 292 Dual fluidized bed reactor, 293 Duality theory, 178, 179 Economic analysis, 124, 164, 195, 223, 224, 252, 253, 255, 262 Economic evaluation, 10, 86, 88, 205, 254, 319 Economic impact, 216, 315, 342 Energy crops, 4, 7, 16, 24, 86, 343, 345 Energy integration, 2, 61, 65, 150, 166, 167, 350 Enterprise wide optimization, 68 Enthalpy, 32, 50, 51, 102, 222, 226, 228, 230, 231 Entrained flow, 301, 302 Environmental impact, 5, 20, 28, 29, 36, 61, 65, 86, 122, 133, 139, 215, 289, 315, 317, 319, 330, 332, 345, 353 Enzymatic biodiesel production, 128, 130, 215 Enzymatic catalysts, 6, 10, 139, 194, 196 Enzymatic transesterification, 193, 194, 196, 197, 200, 206 Enzyme-catalyzed routes, 10, 191, 198, 215 Enzyme inhibition, 197, 199, 214 Enzyme recovery, 198, 211, 214

359

Enzymes, 10, 35, 54, 59, 60, 124, 125, 129, 130, 133, 140, 178, 181, 184, 186, 191, 193, 194, 196–203, 206–208, 210–217, 222, 224, 346, 352 Equation of state, 5, 90 Equilibrium reactor, 239, 241 Escherichia coli, 181, 183–187 Esterases, 128 Esterification, 6, 10, 28, 31, 125–129, 131–140, 194–196, 198, 199, 202, 204, 215, 216, 222–224, 232, 234–236, 238, 239, 241–245, 249, 255, 277, 278, 281, 347 Ethanol, 6, 22, 23, 31, 32, 35–37, 59, 62, 67–69, 101, 115, 129, 131, 132, 159, 160, 174, 181–186, 194, 197, 222, 224, 229–236, 238, 241–243, 260, 286, 289, 290, 305, 313, 344, 346, 349 Evaporation, 123, 124, 139, 297, 313, 314, 346 Eversa transform, 129, 130, 199, 200, 205, 208, 210–213, 216, 217 Evolutionary algorithms, 178 Extraction, 6, 7, 246, 260, 261, 267, 271, 285, 286, 313–315, 317, 320, 331, 335, 347, 349 Fatty acid alkyl esters (FAAEs), 194, 228, 230, 237 Fatty acid methyl esters (FAMEs), 121–126, 128, 129, 132–135, 137–139, 194–197, 199–202, 204–206, 208–211, 214–217, 262, 263, 267, 268, 271, 275 Feedstock biomass-based, 1 first-generation, 122, 260 second-generation, 122, 123, 139, 260 Fermentation, 1, 2, 6, 7, 11, 19, 29, 35, 36, 40, 55, 59, 62, 69, 159, 165, 173, 174, 183, 312, 315, 345, 346, 348, 349, 351, 352 Filtration, 123, 124, 129, 132, 197, 210, 212, 215 First-generation biofuels, 29, 289 Fischer–Tropsch synthesis, 150, 161, 164, 295 Fixed bed gasification, 291 reactors, 30, 152, 291, 292, 298 Fluidized bed gasification, 292, 298 reactor, 37, 292, 293, 299 Flux balance analysis, 175 Flux envelop analysis, 180 Flux variability analysis, 176

360

Index

Food and non-food crop, 17 Food industry waste, 19 Fossil fuel consumption, 342 replacement, 16 Fossil fuels, 1, 16, 18, 20, 29–31, 49, 68, 85, 86, 121, 191, 192, 259, 260, 287, 312, 320, 341, 342, 344, 346, 348, 349, 353 Four-pillar strategy, 149 Free fatty acids (FFAs), 21, 121–129, 131, 132, 134, 137, 138, 140, 153, 194–201, 208, 210, 211, 214–216, 223, 224, 232, 234, 238, 255, 277, 348 Fugacity, 89–91, 93–95 Fuzzy equal metabolic adjustment (FEMA), 181

Hydrolysis, 19, 24, 34–36, 55, 62, 69, 128, 135, 159, 183, 194–196, 199, 201, 202, 204, 214–216, 224, 241, 267, 285, 286, 345, 346, 349, 351, 352 Hydroprocessing, 150, 151, 154, 155, 159–161, 350 cracking, 151–153, 158, 160, 161, 164, 295, 298, 299, 350 deoxygenation, 152–154, 161, 350 Hydropyrolysis, 298, 304 Hydrothermal, 154, 286–288, 293, 294, 296, 305, 307 Hydrothermal carbonization, 287 Hydrothermal gasification, 287, 293, 294 Hydrothermal liquefaction, 154, 287, 294, 296

Galactose, 183, 346 Gasification, 1, 11, 19, 28, 52, 56, 62, 67, 101, 150, 151, 159, 164, 286–288, 290–296, 298–300, 344, 350 General algebraic modeling system (GAMS), 180, 206 Genetic modulation, 173, 180 Genome-scale metabolic network, 179, 180, 187 Gibbs energy, 87, 93, 102, 226, 235, 242, 303 Glucose, 67, 159, 164, 181, 183–185, 288, 346 Glycerol decomposition, 206 Glycerolysis, 126, 127 Grape pomace, 36, 37 Grape skins, 36 Greenhouse gases, 52, 65, 173, 191, 192, 342, 345, 348, 349, 353 Group contribution methods, 5, 103, 115 Growth-coupled Strains, 178, 181, 183–186

Indexes/objectives condition number, 317, 322–324, 326–332, 335 controllability, 316, 317, 319, 322, 330 economic performance, 304 environmental impact, 315, 317, 319, 330, 332 individual risk, 317, 320, 324 minimum singular value, 322 relative gain matrix, 322, 323 tree of events, 320, 321 Instant coffee, 22 Integrated process and product design, 109 Intensification microwave, 6, 7, 259, 261, 304 ultrasound, 6, 7, 261 Intensified process, 2, 11, 314, 317, 325, 335 International Energy Agency (IEA), 192, 342 Ionic liquids, 7, 9, 124 Isomerization, 152–155, 161, 164, 224, 350

Hazard and Operability Study (HAZOP), 66, 320 Heat exchanger networks (HEN), 62 Heterogeneous azeotropes, 316 Heterogeneous catalysis, 9, 127, 128, 194, 223 cation exchange resins, 127 FeSO4 , 128 H-form zeolites, 127 metal oxides, 127, 160 Higher level simulation algorithms, 94 Homogeneous catalysis, 125, 140 metal acetates, 126 stearates, 126 sulfamic acid, 125, 126 Husks, 18, 22–24, 38, 39, 162

Kinetic modeling, 10, 198, 201, 202, 221, 232 Kinetic models, 53, 54, 175 Kinetic parameters, 201–204, 206, 216, 234, 237, 263 Kriging, 57–59 Lewis acids, 125, 126, 132–134 Life cycle analysis, 8, 29, 65, 319, 347 Lignocellulosic material, 1, 9, 10, 159, 161, 166, 288, 289, 315, 345 Linear programming, 62, 176, 177 Lipases, 128–130, 196–199, 213–217, 224

Index

Liquefaction, 1, 10, 153, 154, 161, 286–288, 294, 296, 343 Liquid–liquid equilibria (LLE), 88, 92, 95, 97, 231 Macauba, 38 Mango seeds, 39 Mannose, 183, 346 Mass and energy balances, 50, 51, 55, 71 Mathematical modeling, 135 Mechanistic models, 51 Metabolic engineering, 6, 174, 185 Metaheuristics, 8 Metal hydrated salts, 133 Methane from citrus, 34 Methanol, 31, 38, 52, 67, 68, 71, 101, 115, 121, 126, 133, 140, 194–197, 199, 201–203, 205, 206, 208, 210–214, 216, 217, 222, 224, 229, 231, 234, 236, 237, 239, 241–247, 249–251, 262, 263, 267, 268, 276, 277, 279–281, 290, 305, 348 Methyl ethyl ketone, 11, 100, 115, 311, 313, 314 Microalgae, 2, 7, 10, 55, 152, 154, 155, 260–263, 268, 276, 277, 279–281, 288, 349 Microbial fermentation, 6 Microbial genome engineering, 6 Microbial oils, 260, 261 Microorganisms, 2, 6, 174, 178, 180, 183, 185, 289, 345, 348, 349, 352 Minimization of metabolic adjustment, 176 Mixed integer linear programming (MILP), 62, 177 Mixed integer programming (MIP), 187 Monoacylglycerols (MAGs), 126, 194, 237, 238 Multi-effect columns, 63 Multi-objective optimization (MOO), 8, 17, 69, 173, 317, 323, 324 Multiscale, 9, 49, 71–74 Nano-sized solid acid, 128 Nested hybrid differential evolution (NHDE), 179 Nonlinear programming, 62, 65, 206 Non-random two-liquid (NRTL) model, 92, 95, 263, 317 Oil residues, 31 Oligomerization, 159, 160, 165 Olive tree, 26, 27, 29

361

Optimization, 2–8, 10, 50, 54, 56, 59–70, 86, 90, 92, 93, 97, 98, 104, 125, 149, 166, 173, 175–181, 184, 185, 193, 202, 205, 208, 215, 216, 232, 250–252, 260, 261, 276, 281, 316, 317, 323–326, 328, 330–332, 335 Optimization methodology differential evolution with tabu list, 324 Pareto front, 325–328, 330–332 purities, 326, 332 purity constraints, 325 recoveries, 326 Orange peel, 33–35 Organic wastes, 16, 18, 19, 351, 353 Parameter regression, 236 Perennial biomass, 25 Petroleum, 6, 11, 121, 152, 156, 221, 224, 260, 290, 295, 315, 341, 342, 352 Phase equilibrium, 4, 5, 9, 54, 55, 85, 93, 94, 100, 102, 114, 222, 229, 241 Phase separators, 206, 262, 267, 274, 277–279 Physicochemical pretreatments, 123, 124 Physicochemical properties, 4, 5, 33, 328 Pinch technology, 61 Plantwide control, 8 Plasma gasification, 294 Polyphenolic compounds, 37 Primary biofuels, 342 Process controllability, 8 Process design, 2, 4, 5, 7–10, 17, 33, 50, 63, 66–68, 73, 85, 86, 98, 99, 101–105, 109–113, 185, 221, 239, 262, 277 Process development, 173, 174, 185, 259, 261, 262, 281 Processing pathways, 150 Process intensification, 2, 5, 6, 50, 150, 166, 167, 314, 335 Process simulation, 10, 114, 193, 205, 216, 222, 254, 262, 263 Process synthesis, 2, 9, 50, 60, 66, 99 Production rate, 174, 177, 178, 181–183, 185 Product separation, 193, 216, 304 Pyrolysis, 1, 10, 19, 24, 25, 28, 29, 32, 37, 56, 69, 101, 150, 151, 159–161, 286–288, 291, 296–307, 343, 350, 351 fast, 160, 297, 298, 306 flash, 288, 297, 298 hydroprocessing, 160 slow, 25, 29, 32, 297, 305

362

Index

Quadratic constrained programming (QCP), 187 Quadratic programming (QP), 59, 177 Quantitative structure-property relationships (QSPRs), 103, 105 Reaction data regression, 234 Reaction kinetics, 232, 239, 242, 262, 263, 280 Reaction mechanism, 201, 216 Reaction rates, 121, 128, 129, 137, 195, 197, 198, 200, 237, 239, 300, 305 Reaction temperature, 127, 198, 204, 206, 208, 263, 296 Reactor models, 10, 234, 239 Recirculating fluidized bed, 299 Regulatory on–off minimization (ROOM), 177 Renewable aviation fuel, 149, 150, 152, 155, 161, 165–167 Renewable energy, 1, 9, 10, 16, 191, 192, 259, 344 Renewable sources, 16, 308, 341, 353 Rice residues, 24 Rotating cone, 299, 300 Route reactions, 224 Rules of thumb, 56, 60, 61 Screw reactor, 301, 307 Secondary biofuels, 342, 343 Second-generation biofuels, 289 Separation, proteins/carbohydrates, 267 Sewage scum, 122, 133, 139 Sewage sludge, 139, 140, 289, 351 Sewer grease, 122 Short cut methods, 50, 55, 206 Six-tenths rule, 279, 280 Sizing, 10, 225, 272, 273, 306, 330, 332 Soap formation, 196, 223, 237, 238 Stage wizard, 244 Stochastic optimizers, 8 Stoichiometry Matrix, 175 Straw of cereals, 22 Sulfuric acid, 125, 127, 136, 195, 196, 216, 242, 244–246, 249, 253, 277 Supercritical fluid extraction, 36, 37 Supercritical water gasification, 294 Superstructure, 59, 61, 62, 64, 65, 70, 71 Supply chain, 2–5, 7–9, 11, 28, 49, 50, 68–74, 150, 166 Surrogate models, 56–59, 70, 71 Sustainable development, 2, 29, 49, 65, 149, 167

Syngas, 31, 55, 62, 67–69, 71, 151, 164, 286, 290, 293, 295, 344 Synthetic paraffinic kerosene, 150 Systems biology, 174, 175, 180, 187 makeup language, 179 Thermochemical conversion, 288, 289, 304, 343, 344 Thermochemical processes, 29, 285–289, 293, 304, 308, 350 Thermodynamic modeling framework-TMF, 9, 86 Thermodynamic models, 4, 5, 9, 87, 88, 90, 92–94, 114, 229, 231, 246, 277 binary interaction parameters, 92, 93, 97, 98, 113, 229, 230 equation of state (EoSs), 5, 90 gamma-phi method, 89 perturbed-chain statistical associating fluid theory (PC-SAFT), 92 phi-phi method, 89, 92 Soave–Redlich–Kwong (SEK), 90, 99 statistical associating fluid theory (SAFT), 92 Thermodynamic properties, 4, 9, 10, 85, 94, 111, 225, 315, 317 Thermophysical properties, 9, 86–88, 94, 98, 99, 103, 112–114 Third-generation biofuels, 260, 289 Top-to-bottom, bottom-to-top coupled method, 17 Torrefaction, 28–30, 307 Total annual cost, 263, 315, 319, 324, 350 Total capital cost, 254 Total module cost, 273 Total operation Cost, 254 Transesterification, 1, 5, 7, 10, 19, 31, 55, 63, 87, 101, 103, 121, 124–129, 131–133, 137–140, 193–199, 201–206, 208, 210, 212–217, 222–224, 232, 236, 238, 239, 241, 242, 244–246, 255, 260–263, 267, 268, 271, 277, 280, 289, 347, 348 Transport and distribution of biofuels, 29 Transport fuels, 16, 20, 122 Trap grease, 122, 139 Triacylglycerols, 126, 194, 222–224, 238, 241, 245 Triolein, 113, 115, 239, 241, 242, 245–247, 251, 253, 262, 277 Triple-level mixed-integer linear optimization problem, 179

Index

Ultrafiltration, 211 permeate, 212, 214, 215 Ultrasound, 6, 7, 261 UNIQUAC, 95–97, 229–231, 239, 245, 246, 255, 277 Urban primary sludge, 139 Vacuum reactors, 301 Valorization of biomass, 16, 18 Value-added functional products, 86 Value of bioenergy, 18 Van der Waals mixing rule, 91 Vapor catalysis, 303 Vapor–liquid equilibria (VLE), 88, 92, 95, 100, 229, 267 Vegetable oils, 1, 4, 15, 28, 109, 121, 122, 126, 129, 133, 152, 153, 192–194, 221–223, 231, 260, 277, 289, 343, 347, 351 Waste animal grease, 122 Waste collection and management, 30 Waste cooking oils, 9, 10, 38, 69, 122, 150–154, 156, 166, 167, 195, 222, 223, 261, 351 Waste lignocellulosic feedstock agricultural residues, 2, 7, 159, 345

363

solid urban waste, 159 textile residues, 159 Wood waste, 28, 159 Waste mass valorization, 30 Waste sugar and starchy feedstock Fruit waste, 164 Waste bakeries, 165 Waste candy, 165 Waste sweets, 165 Waste triglyceride feedstock animal fats, 1, 4, 7, 129, 131, 133, 150, 152, 154, 167, 193, 194, 221, 222, 231, 236, 343, 351 bio-oil, 31, 32, 37, 69, 150, 151, 192, 286, 296–298, 303, 304, 343, 350 cooking oil, 9, 10, 25, 37, 38, 69, 109, 111, 122, 150–154, 156, 157, 166, 167, 193, 195, 222, 223, 260, 261, 351 Water networks, 64 Wheat straw, 24, 343 Xylose, 67, 183–185, 346

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