Production Processes of Renewable Aviation Fuel: Present Technologies and Future Trends 0128197196, 9780128197196
Production Processes of Renewable Aviation Fuel: Present Technologies and Future Trends presents the available productio
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English Pages 266 [264] Year 2021
Title-page_2021_Production-Processes-of-Renewable-Aviation-Fuel
Production Processes of Renewable Aviation Fuel
Copyright_2021_Production-Processes-of-Renewable-Aviation-Fuel
Copyright
Contents_2021_Production-Processes-of-Renewable-Aviation-Fuel
Contents
1---Biojet-fuel--Driving-the-aviation-se_2021_Production-Processes-of-Renewa
1 Biojet fuel: Driving the aviation sector to sustainability
1.1 Motivation
1.2 Basic concepts
1.3 ASTM standards
1.4 Combustion and flight tests
1.5 Summary
References
2---Renewable-feedstock-and-its-conversio_2021_Production-Processes-of-Renew
2 Renewable feedstock and its conversion routes to biojet fuel
2.1 Introduction
2.2 Raw materials
2.2.1 Triglyceride feedstock
2.2.2 Sugar and starchy feedstock
2.2.3 Lignocellulosic feedstock
2.3 Production pathways
2.3.1 Certified pathways
2.3.2 Advances in the certification of new pathways
2.4 Summary
References
3---Production-processes-for-the-conversio_2021_Production-Processes-of-Rene
3 Production processes for the conversion of triglyceride feedstock
3.1 Introduction
3.2 Conversion processes of the triglyceride feedstock
3.3 Conventional processes: state of the art
3.4 Combustion tests for biojet fuel from triglyceride feedstock
3.5 Case of study: hydroprocessing of a mixture of vegetable oils
3.5.1 Problem statement
3.5.2 Modeling of the hydrotreating of the mixture of oils
3.5.3 Simulation of the hydrotreating process
3.5.4 Economic assessment
3.5.5 Estimation of price of biojet fuel
3.5.6 Environmental assessment: CO2 emissions
3.6 Conclusion
References
4---Production-processes-for-the-conversion_2021_Production-Processes-of-Ren
4 Production processes for the conversion of sugar and starchy feedstock
4.1 Introduction
4.2 Conversion of sugars to biojet fuel
4.2.1 Alcohol-to-jet pathway
4.2.2 Sugar-to-jet pathways
4.3 Technologies on separation zone
4.4 Conventional processes: state of the art
4.5 Combustion tests for biojet fuel from sugar and starchy feedstocks
4.6 Case of study: conversion of sugar and starchy feedstocks
4.6.1 Problem statement
4.6.2 Modeling of sugar and starchy feedstocks
4.6.3 Production process: conceptual design
4.6.4 Simulation of reactive and separation zones
4.6.5 Economic assessment
4.6.6 Estimation of price of biojet fuel
4.6.7 Environmental assessment: CO2 emissions
4.7 Conclusions
References
5---Production-processes-from-lignocel_2021_Production-Processes-of-Renewabl
5 Production processes from lignocellulosic feedstock
5.1 Introduction
5.2 Pretreatment technologies
5.2.1 Physical pretreatments
5.2.2 Physicochemical pretreatments
5.2.3 Chemical pretreatments
5.2.4 Biological pretreatments
5.3 Conversion processes of the lignocellulosic feedstock
5.3.1 Alcohol-to-jet process
5.3.2 Sugar-to-jet process
5.3.3 Thermochemical route
5.3.4 Lignin as a source for aromatics
5.4 Technologies on the separation zone
5.5 Conventional processes: state of the art
5.6 Combustion tests for biojet fuel from lignocellulosic feedstock
5.7 Case study: conversion of lignocellulosic waste
5.7.1 Problem statement
5.7.2 Modeling of lignocellulosic waste
5.7.3 Production process: conceptual design
5.7.4 Simulation of the overall process
5.7.5 Economic assessment
5.7.6 Estimation of price of biojet fuel
5.7.7 Environmental assessment: CO2 emissions
5.8 Conclusion
References
Further reading
6---Process-intensification-and-integration-_2021_Production-Processes-of-Re
6 Process intensification and integration in the production of biojet fuel
6.1 Introduction
6.2 Process intensification
6.3 Process integration
6.4 Techno-economic analysis of alternatives
6.5 Application of process intensification to a hydrotreating process
6.5.1 Conceptual design of the intensified process
6.5.2 Simulation of the intensified process
6.6 Application of process integration to a hydrotreating process
6.6.1 Conceptual design of the energy integration
6.6.1.1 Step 1. Data input and ΔT definition
6.6.1.2 Step 2. Adjustment of temperatures
6.6.1.3 Step 3. Temperature intervals
6.6.1.4 Step 4. Heat balance per interval
6.6.1.5 Step 5. Heat cascade
6.6.1.6 Step 6. Energy integration network
6.6.2 Simulation of the hydrotreating process with energy integration
6.6.3 Economic assessment
6.6.4 Estimation of price of biojet fuel
6.6.5 Environmental assessment: CO2 emissions
6.7 Conclusion
References
7---Supply-chain-for-the-production-o_2021_Production-Processes-of-Renewable
7 Supply chain for the production of biojet fuel
7.1 Introduction
7.2 Elements of the supply chain to produce biojet fuel
7.3 Data generation
7.4 Standards for product certification
7.5 Modeling and optimization of the supply chain
7.5.1 The generalized disjunctive programming representation
7.5.2 Relaxation of a generalized disjunctive programming model
7.5.2.1 The Big-M approach
7.5.2.2 The convex hull approach
7.6 Case study: optimization of the biojet fuel supply chain in Mexico
7.7 Importance of the life cycle analysis
7.8 Conclusion
References
Appendix A
8---The-future-trends-in-the-productio_2021_Production-Processes-of-Renewabl
8 The future trends in the production of biojet fuel
8.1 Introduction
8.2 Opportunity areas for raw materials
8.2.1 Triglyceride feedstock
8.2.2 Sugar and starchy feedstock
8.2.3 Lignocellulosic feedstock
8.3 Opportunity areas for processing routes
8.3.1 Chemical pathways
8.3.2 Biochemical pathways
8.3.3 Thermochemical pathways
8.4 Opportunity areas for supply chain
8.5 Worldwide industrial projects to produce biojet fuel
8.6 Summary
References
Index_2021_Production-Processes-of-Renewable-Aviation-Fuel
Index
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PRODUCTION PROCESSES OF R E N E W A B L E AV I AT I O N FUEL
PRODUCTION PROCESSES OF R E N E W A B L E AV I AT I O N FUEL Present Technologies and Future Trends CLAUDIA GUTIE´RREZ-ANTONIO Chemistry Faculty, Universidad Auto´noma de Queretaro, Quere´taro, Mexico
ARACELI GUADALUPE ROMERO-IZQUIERDO Chemical Engineering Department, Universidad de Guanajuato, Guanajuato, Mexico
FERNANDO ISRAEL GO´MEZ-CASTRO Chemical Engineering Department, Universidad de Guanajuato, Guanajuato, Mexico
SALVADOR HERNA´NDEZ Chemical Engineering Department, Universidad de Guanajuato, Guanajuato, Mexico
Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier B.V. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www. elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-819719-6 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals
Publisher: Joe Hayton Acquisitions Editor: Kostas KI Marinakis Editorial Project Manager: Rachel Pomery Production Project Manager: Kumar Anbazhagan Cover Designer: Victoria Pearson Typeset by MPS Limited, Chennai, India
Contents 1 Biojet fuel: Driving the aviation sector to sustainability...........................1 1.1 Motivation............................................................................................. 1 1.2 Basic concepts ...................................................................................... 3 1.3 ASTM standards................................................................................... 6 1.4 Combustion and flight tests .............................................................. 17 1.5 Summary ............................................................................................ 21 References ................................................................................................. 21
2 Renewable feedstock and its conversion routes to biojet fuel.............33 2.1 Introduction ........................................................................................ 33 2.2 Raw materials ..................................................................................... 34 2.2.1 Triglyceride feedstock ...............................................................35 2.2.2 Sugar and starchy feedstock .................................................... 37 2.2.3 Lignocellulosic feedstock.......................................................... 41 2.3 Production pathways ......................................................................... 44 2.3.1 Certified pathways.....................................................................47 2.3.2 Advances in the certification of new pathways ......................49 2.4 Summary ............................................................................................ 50 References ................................................................................................. 51
3 Production processes for the conversion of triglyceride feedstock ..............................................................................................................55 3.1 3.2 3.3 3.4 3.5
Introduction ........................................................................................ 55 Conversion processes of the triglyceride feedstock ........................ 56 Conventional processes: state of the art .......................................... 60 Combustion tests for biojet fuel from triglyceride feedstock ......... 63 Case of study: hydroprocessing of a mixture of vegetable oils ..... 64 3.5.1 Problem statement ....................................................................64 3.5.2 Modeling of the hydrotreating of the mixture of oils.............65 3.5.3 Simulation of the hydrotreating process.................................75 3.5.4 Economic assessment...............................................................79 3.5.5 Estimation of price of biojet fuel..............................................83 3.5.6 Environmental assessment: CO2 emissions............................84 v
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Contents
3.6 Conclusion .......................................................................................... 86 References ................................................................................................. 86
4 Production processes for the conversion of sugar and starchy feedstock ...............................................................................................93 4.1 Introduction ........................................................................................ 93 4.2 Conversion of sugars to biojet fuel................................................... 94 4.2.1 Alcohol-to-jet pathway.............................................................. 94 4.2.2 Sugar-to-jet pathways...............................................................98 4.3 Technologies on separation zone ................................................... 102 4.4 Conventional processes: state of the art ........................................ 103 4.5 Combustion tests for biojet fuel from sugar and starchy feedstocks ......................................................................................... 105 4.6 Case of study: conversion of sugar and starchy feedstocks ......... 106 4.6.1 Problem statement .................................................................. 106 4.6.2 Modeling of sugar and starchy feedstocks ........................... 108 4.6.3 Production process: conceptual design.................................108 4.6.4 Simulation of reactive and separation zones........................ 112 4.6.5 Economic assessment............................................................. 118 4.6.6 Estimation of price of biojet fuel............................................ 119 4.6.7 Environmental assessment: CO2 emissions.......................... 120 4.7 Conclusions ...................................................................................... 121 References ............................................................................................... 121
5 Production processes from lignocellulosic feedstock...........................129 5.1 Introduction ...................................................................................... 129 5.2 Pretreatment technologies .............................................................. 130 5.2.1 Physical pretreatments ........................................................... 130 5.2.2 Physicochemical pretreatments ............................................. 132 5.2.3 Chemical pretreatments.......................................................... 133 5.2.4 Biological pretreatments......................................................... 134 5.3 Conversion processes of the lignocellulosic feedstock................. 134 5.3.1 Alcohol-to-jet process ............................................................. 136 5.3.2 Sugar-to-jet process ................................................................ 136 5.3.3 Thermochemical route............................................................ 136 5.3.4 Lignin as a source for aromatics............................................ 138 5.4 Technologies on the separation zone............................................. 140
Contents
vii
5.5 Conventional processes: state of the art ........................................ 141 5.6 Combustion tests for biojet fuel from lignocellulosic feedstock ........................................................................................... 142 5.7 Case study: conversion of lignocellulosic waste ........................... 144 5.7.1 Problem statement .................................................................. 144 5.7.2 Modeling of lignocellulosic waste ......................................... 145 5.7.3 Production process: conceptual design.................................146 5.7.4 Simulation of the overall process .......................................... 153 5.7.5 Economic assessment............................................................. 159 5.7.6 Estimation of price of biojet fuel............................................ 160 5.7.7 Environmental assessment: CO2 emissions.......................... 161 5.8 Conclusion ........................................................................................ 162 References ............................................................................................... 163 Further reading ....................................................................................... 169
6 Process intensification and integration in the production of biojet fuel .......................................................................................................................171 6.1 6.2 6.3 6.4 6.5
Introduction ...................................................................................... 171 Process intensification ..................................................................... 171 Process integration .......................................................................... 178 Techno-economic analysis of alternatives ..................................... 181 Application of process intensification to a hydrotreating process .............................................................................................. 182 6.5.1 Conceptual design of the intensified process ....................... 182 6.5.2 Simulation of the intensified process .................................... 184 6.6 Application of process integration to a hydrotreating process .... 186 6.6.1 Conceptual design of the energy integration........................ 186 6.6.2 Simulation of the hydrotreating process with energy integration................................................................................ 191 6.6.3 Economic assessment............................................................. 193 6.6.4 Estimation of price of biojet fuel............................................ 194 6.6.5 Environmental assessment: CO2 emissions.......................... 194 6.7 Conclusion ........................................................................................ 195 References ............................................................................................... 196
7 Supply chain for the production of biojet fuel .........................................201 7.1 Introduction ...................................................................................... 201 7.2 Elements of the supply chain to produce biojet fuel..................... 201
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Contents
7.3 Data generation ................................................................................ 203 7.4 Standards for product certification ................................................. 204 7.5 Modeling and optimization of the supply chain ............................ 207 7.5.1 The generalized disjunctive programming representation ......................................................................... 208 7.5.2 Relaxation of a generalized disjunctive programming model ............................................................... 212 7.6 Case study: optimization of the biojet fuel supply chain in Mexico .......................................................................................... 214 7.7 Importance of the life cycle analysis .............................................. 226 7.8 Conclusion ........................................................................................ 232 References ............................................................................................... 233 Appendix A.............................................................................................. 235
8 The future trends in the production of biojet fuel ...................................241 8.1 Introduction ...................................................................................... 241 8.2 Opportunity areas for raw materials............................................... 242 8.2.1 Triglyceride feedstock ............................................................. 242 8.2.2 Sugar and starchy feedstock .................................................. 243 8.2.3 Lignocellulosic feedstock........................................................ 243 8.3 Opportunity areas for processing routes ....................................... 244 8.3.1 Chemical pathways ................................................................. 244 8.3.2 Biochemical pathways ............................................................ 245 8.3.3 Thermochemical pathways .................................................... 245 8.4 Opportunity areas for supply chain ................................................ 246 8.5 Worldwide industrial projects to produce biojet fuel.................... 246 8.6 Summary .......................................................................................... 252 References ............................................................................................... 252 Index........................................................................................................... 255
Biojet fuel: Driving the aviation sector to sustainability 1.1
1
Motivation
In the transport sector, the aviation industry has the greater growth rate. According to the International Air Transport Association, in 2017 the world trade growth of the aviation industry was 5.4%, which represented 787 billion dollars, due to the travel by plane of 4.1 billion of passengers and the growth of 9.7% for the air cargo business (International Air Transport Association, 2018a,b). Moreover, forecasts indicate that consumers could spend 1% of world gross domestic product on air transport in 2019 (International Air Transport Association, 2018a). In order to provide all these transport services, the aviation fuel requirements will also increase. In 2017 the worldwide airline industry used 341 billion liters of fuel, and this amount is expected to increase to 368 billion liters of fuel in 2019 (International Air Transport Association, 2018a). As can be observed in Fig. 1.1, the growth of aviation sector has been sustained over the years. It is worth mentioning that fuel represents 24.2% of the average operating costs of the aviation industry (International Air Transport Association, 2018a). Therefore the availability of fuels to fulfill the demand at competitive prices is key in the development and growth of the sector. In addition, the emissions of carbon dioxide, derived from fuel usage, will also increase as a consequence of its high growth rate. As reference, in 2017, the civil aviation emitted around 859 million tons of carbon dioxide, which represent 2% of anthropogenic carbon emissions (International Air Transport Association, 2018c). In this context, the aviation sector recognized the necessity of having a sustainable growth, setting ambitious objectives to reduce its carbon footprint. The proposed goals included a reduction of 50% in carbon dioxide emissions by 2050, with respect to 2005 emission levels, and a neutral growth in carbon dioxide emissions from 2020 (International Air Transport
Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00001-8 © 2021 Elsevier B.V. All rights reserved.
1
2
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Figure 1.1 Growth of the aviation sector in terms of fuel consumption and number of travelers.
Figure 1.2 Four-pillar strategy of the aviation sector.
Association, 2009). Thus a four-pillar strategy (Fig. 1.2) was established to reach these objectives, which included: 1. Technological improvements in engines and aircraft structures 2. Operational improvements through optimization of flight paths 3. Market-based measures 4. Development of alternative fuels The first pillar contemplates an increase in the efficiency of engines of 1.5% each year until 2020. This will help to reduce the fuel usage, and as a consequence the operating costs and the
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
carbon dioxide emissions; in addition, the application of nano coatings to airplanes to reduce its weight is also considered. The second pillar includes the minimization of fuel requirements by using online optimization strategies, which consider the actual weather conditions. On the other hand, the third pillar takes into account the trading of carbon dioxide emissions. Finally, the fourth pillar looks upon the development of alternative fuels for the aviation sector, which must be renewable and sustainable. In addition, the development of these alternative fuels will help to have independence of fossil fuels, at least partially. This is expected to occur since the raw materials used to produce such renewable fuels can be obtained in a local scale, making use of the available materials in each region. In particular, the International Air Transport Association points out that the development of alternative fuels is the option that contributes the most to the reduction of carbon dioxide emissions in the aviation sector. Unlike other alternative fuels, aviation fuel must be drop-in, which means that the chemical composition and physicochemical properties must be, at least, the same of the jet fuel. This is because redesigning the airplane engines is not a feasible alternative for the manufacturers, due to the high complexity of these systems; additionally, any change in the airplanes will require a recertification, which is a timeconsuming and expensive process. Thus alternative aviation fuels represent a viable option to begin the energy transition of the aviation sector, simultaneously guaranteeing its sustainable development without needing a recertification process of the aircraft infrastructure; other alternative energies, such as solar or wind energies, are not directly contemplated for aviation sector, since they are not compatible with the existing infrastructure. Because of all the previously exposed reasons, the development of alternative fuels for aviation has received a lot of interest in the last 11 years, and several books have been published in topics such as logistics, markets, policies, and sustainability. This book focuses on the detailed analysis of the production process for renewable aviation fuel from a variety of sources, including the application of intensification and energy integration strategies as well as the study of the supply chain. In the next section, basic concepts of the alternative aviation fuels are presented.
1.2
Basic concepts
The aviation fuel is known as jet fuel, and it consists of hydrocarbons in the range of C8 to C16. Jet fuel is obtained
3
4
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
from the hydroprocessing of one cut of crude oil, called kerosene, and it is composed of approximately 20% paraffins, 40% isoparaffins, 20% naphthenes, and 20% aromatics (Bernabei et al., 2003). There are several types of jet fuels. For commercial airplanes there are Jet A and Jet A-1; the main difference between them is that Jet A-1 is ultralow in sulfur content. For military use there are JP-5 and JP-8 fuels. The main difference between commercial and military aviation fuel is that the last ones contain corrosion and freezing inhibitors as well as lubricants and antistatic agents. In the search of a fuel to replace either partially or completely the fossil jet fuel, several alternatives have been proposed, such as hydrogen, bioacohols, and biodiesel. Nevertheless, none of these previous alternative fuels have the adequate properties (freezing point, thermal stability, volatility, among others) to be used at the regular operating conditions of the turbine system of the planes. As mentioned before, renewable aviation fuel must be drop-in. Therefore compounds known as synthetic paraffinic kerosene (SPK) have been developed, which contains hydrocarbons both lineal and branched, just like fossil jet fuel. Due to this, the physicochemical properties of SPK are equal, and in some cases superior, to those of fossil jet fuel. The SPK has been established as the most viable alternative to replace fossil jet fuel. Biojet fuel has other names as SPK, renewable aviation fuel, aviation biofuel, biokerosene, or sustainable aviation fuel. Table 1.1 shows the main properties of fossil and renewable aviation fuel (Agosta, 2002; Chevron, 2007).
Table 1.1 Some physicochemical properties of fossil and renewable jet fuel (Agosta, 2002; Chevron, 2007).
Boiling range ( C) Freezing temperature ( C) Flash temperature (minimum, C) Density at 15 C (kg/m3) Viscosity at 220 C (maximum, mm2/s) Energy content (MJ/kg)
Fossil jet fuel
Renewable jet fuel produced from
Jet A
Jet-A1
Camelina
Jatropha curcas
170 300 240 38 775 840 8.0 43.28
170 300 247 38 775 840 8.0 43.28
188 263 263.5 42.0 751 840 3.33 44.0
172 243 257 46.5 751 840 3.66 44.3
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
One important advantage of renewable aviation fuel is that it contains small amounts of sulfur, due to its renewable nature, in comparison with fossil jet fuel; this means less contaminant emissions. Moreover, the carbon dioxide emissions per Mega Joule associated with the production and use of the renewable jet fuel is between 12% and 56% lower than the ones reported for fossil jet fuel (Holmgren, 2009). At this point, it is important to remark that all the carbon dioxide emissions generated during the use of the renewable jet fuel are the same that are absorbed by the crops during its growth; therefore the lifecycle greenhouse gas emissions of the renewable aviation fuel can be 80% lower than those of fossil jet fuel, as shown in Fig. 1.3 (International Air Transport Association, 2018c,d). Therefore important reductions in carbon dioxide emissions are observed, where specific value depends on the type of raw material and the production pathway; these two factors play a key role in the sustainability of the aviation fuels. Biojet fuel can be produced from all types of biomasses through several production pathways (Fig. 1.4). Also, SPK can be produced from carbon and natural gas; however, these sources are not renewable. Depending on the production pathway, biojet fuel can contain or not aromatic compounds. The absence of aromatic compounds does not affect the main properties such as freezing temperature,
Figure 1.3 Lifecycle greenhouse gas emissions of the renewable aviation fuel.
5
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Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Figure 1.4 General production process to convert biomass to biojet fuel.
viscosity, or energy content; however, it could cause leaks in the fuel distribution circuit, since aromatic compounds expand the elastomers (Gutie´rrez-Antonio et al., 2016). Due to this, in commercial airplanes biojet fuel can be used in mixtures with fossil jet fuel up to 50% in volume, according to ASTM-D7566 standard (ASTM, 2019a). In addition to the content of aromatic compounds, biojet fuel must complain with the same properties and tests of fossil jet fuel, which are presented in the next section.
1.3
ASTM standards
To be acceptable to Civil Aviation Authorities, aviation turbine fuel must meet strict chemical and physical criteria (International Air Transport Association, 2012). Therefore the certification of aviation fuels is regulated through standards, being the main reference those emitted by the American Society for Testing and Materials (ASTM). There are five standards related to aviation fuels: ASTM D1655, ASTM D7566, ASTM D7223, and ASTM D4054. The ASTM D1655 standard, Specification for Aviation Turbine Fuels, describes the required properties for the certification of aviation fuels at the time and place of delivery (ASTM, 2019b). This standard applies to derived fuels from conventional sources, mainly Jet A and Jet A-1. The properties that need to be determined for the certification of aviation fuels include composition, volatility, fluidity, combustion, corrosion, thermal stability, contaminants, and additives (ASTM, 2019b); the respective test methods for each one of these properties are presented in Table 1.2. On the other hand, ASTM D7566 standard, Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons, includes the required properties for the certification of
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
7
Table 1.2 Test methods to determine the properties of aviation fuels according to ASTM D1655 standard (ASTM, 2019b). Test method
Description
References
ASTM D56 ASTM D86 ASTM D93 ASTM D130 ASTM D156 ASTM D240 ASTM D323 ASTM D381 ASTM D445 ASTM D613 ASTM D1266 ASTM D1298 ASTM D1319 ASTM D1322 ASTM D1405 ASTM D1840 ASTM D2276 ASTM D2386
Test Method for Flash Point by Tag Closed Cup Tester
ASTM (2016a) ASTM (2018a) ASTM (2018b) ASTM (2018c) ASTM (2015a) ASTM (2017a) ASTM (2015b) ASTM (2017b) ASTM (2018d) ASTM (2018e) ASTM (2018f)
Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure Test Methods for Flash Point by Pensky Martens Closed Cup Tester Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip Test Test Method for Saybolt Color of Petroleum Products (Saybolt Chromometer Method) Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter Test Method for Vapor Pressure of Petroleum Products (Reid Method) Test Method for Gum Content in Fuels by Jet Evaporation Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity) Test Method for Cetane Number of Diesel Fuel Oil Test Method for Sulfur in Petroleum Products (Lamp Method) Test Method for Density, Relative Density, or API Gravity of Crude Petroleum and Liquid Petroleum Products by Hydrometer Method Test Method for Hydrocarbon Types in Liquid Petroleum Products by Fluorescent Indicator Adsorption Test Method for Smoke Point of Kerosene and Aviation Turbine Fuel Test Method for Estimation of Net Heat of Combustion of Aviation Fuels Test Method for Naphthalene Hydrocarbons in Aviation Turbine Fuels by Ultraviolet Spectrophotometry Test Method for Particulate Contaminant in Aviation Fuel by Line Sampling Test Method for Freezing Point of Aviation Fuels
ASTM (2017c) ASTM (2018g) ASTM (2018h) ASTM (2013a) ASTM (2017d) ASTM (2014a) ASTM (2018i) (Continued )
8
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Table 1.2 (Continued) Test method
Description
References
ASTM D2622 ASTM D2624 ASTM D2887 ASTM D2892 ASTM D3120 ASTM D3227 ASTM D3240 ASTM D3241 ASTM D3242 ASTM D3338 ASTM D3343 ASTM D3701 ASTM D3828 ASTM D3948 ASTM D4052 ASTM D4176 ASTM D4294 ASTM D4529 ASTM D4625
Test Method for Sulfur in Petroleum Products by Wavelength Dispersive X-ray Fluorescence Spectrometry Test Methods for Electrical Conductivity of Aviation and Distillate Fuels
ASTM (2016b) ASTM (2015c) ASTM (2018j)
Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography Test Method for Distillation of Crude Petroleum (15-Theoretical Plate Column) Test Method for Trace Quantities of Sulfur in Light Liquid Petroleum Hydrocarbons by Oxidative Microcoulometry Test Method for (Thiol Mercaptan) Sulfur in Gasoline, Kerosine, Aviation Turbine, and Distillate Fuels (Potentiometric Method) Test Method for Undissolved Water in Aviation Turbine Fuels Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels Test Method for Acidity in Aviation Turbine Fuel Test Method for Estimation of Net Heat of Combustion of Aviation Fuels Test Method for Estimation of Hydrogen Content of Aviation Fuels Test Method for Hydrogen Content of Aviation Turbine Fuels by Low-Resolution Nuclear Magnetic Resonance Spectrometry Test Methods for Flash Point by Small Scale Closed Cup Tester Test Method for Determining Water Separation Characteristics of Aviation Turbine Fuels by Portable Separometer Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter Test Method for Free Water and Particulate Contamination in Distillate Fuels (Visual Inspection Procedures) Test Method for Sulfur in Petroleum and Petroleum Products by Energy Dispersive X-ray Fluorescence Spectrometry Test Method for Estimation of Net Heat of Combustion of Aviation Fuels Test Method for Middle Distillate Fuel Storage Stability at 43 C (110 F)
ASTM (2018k) ASTM (2014b) ASTM (2016c) ASTM (2015d) ASTM (2019c) ASTM (2017e) ASTM (2014c) ASTM (2016d) ASTM (2017f) ASTM (2016e) ASTM (2018l) ASTM (2018m) ASTM (2014d) ASTM (2016f) ASTM (2017g) ASTM (2016g) (Continued )
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
9
Table 1.2 (Continued) Test method
Description
References
ASTM D4737 ASTM D4809 ASTM D4952 ASTM D4953 ASTM D5001 ASTM D5006 ASTM D5191 ASTM D5452 ASTM D5453
Test Method for Calculated Cetane Index by Four Variable Equation
ASTM (2016h) ASTM (2018n) ASTM (2017h) ASTM (2015e) ASTM (2014e) ASTM (2016i)
ASTM D5972 ASTM D6045 ASTM D6379 ASTM D6751 ASTM D6866 ASTM D6890 ASTM D7042 ASTM D7153 ASTM D7154
Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter (Precision Method) Test Method for Qualitative Analysis for Active Sulfur Species in Fuels and Solvents (Doctor Test) Test Method for Vapor Pressure of Gasoline and Gasoline-Oxygenate Blends (Dry Method) Test Method for Measurement of Lubricity of Aviation Turbine Fuels by the Ball-onCylinder Lubricity Evaluator (BOCLE) Test Method for Measurement of Fuel System Icing Inhibitors (Ether Type) in Aviation Fuels Test Method for Vapor Pressure of Petroleum Products and Liquid Fuels (Mini Method) ASTM (2019d) Test Method for Particulate Contamination in Aviation Fuels by Laboratory Filtration ASTM (2012a) Test Method for Determination of Total Sulfur in Light Hydrocarbons, ASTM (2016j) Spark Ignition Engine Fuel, Diesel Engine Fuel, and Engine Oil by Ultraviolet Fluorescence Test Method for Freezing Point of Aviation Fuels (Automatic Phase Transition Method) ASTM (2016k) Test Method for Color of Petroleum Products by the Automatic Tristimulus Method ASTM (2017i) Test Method for Determination of Aromatic Hydrocarbon Types in Aviation Fuels and Petroleum Distillates High Performance Liquid Chromatography Method with Refractive Index Detection Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels Test Methods for Determining the Biobased Content of Solid, Liquid, and Gaseous Samples Using Radiocarbon Analysis Test Method for Determination of Ignition Delay and Derived Cetane Number (DCN) of Diesel Fuel Oils by Combustion in a Constant Volume Chamber Test Method for Dynamic Viscosity and Density of Liquids by Stabinger Viscometer (and the Calculation of Kinematic Viscosity) Test Method for Freezing Point of Aviation Fuels (Automatic Laser Method) Test Method for Freezing Point of Aviation Fuels (Automatic Fiber Optical Method)
ASTM (2011a) ASTM (2018o) ASTM (2018p) ASTM (2016l) ASTM (2016m) ASTM (2015f) ASTM (2015g) (Continued )
10
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Table 1.2 (Continued) Test method
Description
References
ASTM D7170 ASTM D7224 ASTM D7344 ASTM D7345 ASTM D7524
Test Method for Determination of Derived Cetane Number (DCN) of Diesel Fuel Oils Fixed Range Injection Period, Constant Volume Combustion Chamber Method Test Method for Determining Water Separation Characteristics of Kerosine-Type Aviation Turbine Fuels Containing Additives by Portable Separometer Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure (Mini Method) Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure (Micro Distillation Method) Test Method for Determination of Static Dissipater Additives (SDA) in Aviation Turbine Fuel and Middle Distillate Fuels High Performance Liquid Chromatograph (HPLC) Method Test Method for Sizing and Counting Particles in Light and Middle Distillate Fuels, by Automatic Particle Counter Test Method for Determination of Derived Cetane Number (DCN) of Diesel Fuel Oils Ignition Delay and Combustion Delay Using a Constant Volume Combustion Chamber Method Test Method for Determination of the Fatty Acid Methyl Esters Content of Aviation Turbine Fuel Using Flow Analysis by Fourier Transform Infrared Spectroscopy Rapid Screening Method Test Method for Determining the Concentration of Pipeline Drag Reducer Additive in Aviation Turbine Fuels Test Method for Determination of Dynamic Viscosity and Derived Kinematic Viscosity of Liquids by Constant Pressure Viscometer Test Method for Chloride Content Determination of Aviation Turbine Fuels using Chloride Test Strip Test Method for Determination of Water Separation Characteristics of Aviation Turbine Fuel by Small Scale Water Separation Instrument
ASTM (2016n) ASTM (2018q) ASTM (2017j)
ASTM D7619 ASTM D7668 ASTM D7797 ASTM D7872 ASTM D7945 ASTM D7959 ASTM D8073
ASTM (2017k) ASTM (2015h) ASTM (2017l ASTM (2017m) ASTM (2018r)
ASTM (2018s) ASTM (2016o) ASTM (2016p) ASTM (2016q)
commercial aviation fuels that contain synthetic components (SPK) at the manufacture point. This standard applies to mixtures of Jet A and Jet A-1 with SPK produced from alternatives sources, such carbon, natural gas, and biomass, along with hydrogenated fats and oils through Fischer Tropsch synthesis (FT-SPK), hydroprocessing (HEFA-SPK), synthetic iso-paraffinic kerosene (SIP), synthetic paraffinic kerosene plus aromatics (SPK/A), and alcohol to jet (ATJ) (ASTM, 2019a); the previous processing pathways are the ones included in the annex of the standard to the date. In this
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
11
standard, the maximum blending ratio in volume is specified for each conversion pathway. For FT-SPK, HEFA-SPK, and SPK/A processes it is possible to mix biojet fuel with fossil jet fuel until 50% in volume; however, this percentage is 30% in volume for ATJ and 10% for SIP. ASTM D7566 standard includes all the test methods indicated in ASTM D1655 standard, plus some additional ones that are presented in Table 1.3. Basically, a fuel that satisfies the ASTM D7566 standard also fulfills the ASTM D1655 standard. Moreover, ASTM D7223 standard focuses on the Specification for Aviation Certification Turbine Fuel; this standard describes the required properties for the certification of aviation fuels, derived from conventional sources (ASTM, 2017o), excluding those included in ASTM D1655 (ASTM, 2019b). The test methods listed in ASTM D7223 standard are presented in Table 1.4. Finally, ASTM D4054 standard, Practice for Evaluation of New Aviation Turbine Fuels and Fuel Additives, contains a guide for the
Table 1.3 Test methods to determine the properties of aviation fuels according to ASTM D7566 standard (ASTM, 2019a) additional to those established in ASTM D1655 standard (ASTM, 2019b). Test method
Description
References
ASTM D129 ASTM D2425 ASTM D2710 ASTM D5291 ASTM D6304 ASTM D7111 ASTM D7539
Test Method for Sulfur in Petroleum Products (General High Pressure Decomposition Device Method) Test Method for Hydrocarbon Types in Middle Distillates by Mass Spectrometry
ASTM (2018t)
ASTM D7974
Test Method for Bromine Index of Petroleum Hydrocarbons by Electrometric Titration Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants Test Method for Determination of Water in Petroleum Products, Lubricating Oils, and Additives by Coulometric Karl Fischer Titration Test Method for Determination of Trace Elements in Middle Distillate Fuels by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) Test Method for Total Fluorine, Chlorine and Sulfur in Aromatic Hydrocarbons and Their Mixtures by Oxidative Pyrohydrolytic Combustion followed by Ion Chromatography Detection (Combustion Ion Chromatography-CIC) Test Method for Determination of Farnesane, Saturated Hydrocarbons, and Hexahydrofarnesol Content of Synthesized Iso-Paraffins (SIP) Fuel for Blending with Jet Fuel by Gas Chromatography
ASTM (2019e) ASTM (2018u) ASTM (2016r) ASTM (2016s) ASTM (2016t) ASTM (2018v) ASTM (2015i)
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Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Table 1.4 Test methods to determine the properties of aviation fuels according to ASTM D7223 standard (ASTM, 2017o). Test method
Description
References
ASTM D56 ASTM D86 ASTM D130 ASTM D381 ASTM D445 ASTM D1266 ASTM D1298 ASTM D1319 ASTM D1322 ASTM D1840 ASTM D2386 ASTM D2622 ASTM D2624 ASTM D2887 ASTM D3227 ASTM D3241 ASTM D3242 ASTM D3338
Test Method for Flash Point by Tag Closed Cup Tester
ASTM (2016a) ASTM (2018a) ASTM (2018c) ASTM (2017b) ASTM (2018d) ASTM (2018f)
Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip Test Test Method for Gum Content in Fuels by Jet Evaporation Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity) Test Method for Sulfur in Petroleum Products (Lamp Method) Test Method for Density, Relative Density, or API Gravity of Crude Petroleum and Liquid Petroleum Products by Hydrometer Method Test Method for Hydrocarbon Types in Liquid Petroleum Products by Fluorescent Indicator Adsorption Test Method for Smoke Point of Kerosene and Aviation Turbine Fuel Test Method for Naphthalene Hydrocarbons in Aviation Turbine Fuels by Ultraviolet Spectrophotometry Test Method for Freezing Point of Aviation Fuels Test Method for Sulfur in Petroleum Products by Wavelength Dispersive X-ray Fluorescence Spectrometry Test Methods for Electrical Conductivity of Aviation and Distillate Fuels Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography Test Method for (Thiol Mercaptan) Sulfur in Gasoline, Kerosine, Aviation Turbine, and Distillate Fuels (Potentiometric Method) Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels Test Method for Acidity in Aviation Turbine Fuel Test Method for Estimation of Net Heat of Combustion of Aviation Fuels
ASTM (2017c) ASTM (2018g) ASTM (2018h) ASTM (2017d) ASTM (2018i) ASTM (2016b) ASTM (2015c) ASTM (2018j) ASTM (2016c) ASTM (2019c) ASTM (2017e) ASTM (2014c) (Continued )
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
13
Table 1.4 (Continued) Test method
Description
References
ASTM D3828 ASTM D3948 ASTM D4052 ASTM D4294 ASTM D4529 ASTM D4809 ASTM D4952 ASTM D5001 ASTM D5006 ASTM D5453 ASTM D5972 ASTM D6378 ASTM D7042
Test Methods for Flash Point by Small Scale Closed Cup Tester
ASTM (2016e) ASTM (2018l)
Test Method for Determining Water Separation Characteristics of Aviation Turbine Fuels by Portable Separometer Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter Test Method for Sulfur in Petroleum and Petroleum Products by Energy Dispersive X-ray Fluorescence Spectrometry Test Method for Estimation of Net Heat of Combustion of Aviation Fuels
ASTM (2018m) ASTM (2016f) ASTM (2017g) ASTM (2018n) ASTM (2017h) ASTM (2014e) ASTM (2016i)
Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter (Precision Method) Test Method for Qualitative Analysis for Active Sulfur Species in Fuels and Solvents (Doctor Test) Test Method for Measurement of Lubricity of Aviation Turbine Fuels by the Ball-onCylinder Lubricity Evaluator (BOCLE) Test Method for Measurement of Fuel System Icing Inhibitors (Ether Type) in Aviation Fuels Test Method for Determination of Total Sulfur in Light Hydrocarbons, Spark Ignition ASTM (2016j) Engine Fuel, Diesel Engine Fuel, and Engine Oil by Ultraviolet Fluorescence Test Method for Freezing Point of Aviation Fuels (Automatic Phase Transition Method) ASTM (2016k) Test Method for Determination of Vapor Pressure (VPX) of Petroleum Products, ASTM Hydrocarbons, and Hydrocarbon-Oxygenate Mixtures (Triple Expansion Method) (2018w) Test Method for Dynamic Viscosity and Density of Liquids by Stabinger Viscometer ASTM (and the Calculation of Kinematic Viscosity) (2016m)
approval process of new fuels or additives for commercial or military use (ASTM, 2017p). The testing covers basic specification properties, expanded properties called fit-for-purpose properties, engine rig and component testing, and if necessary, full-scale engine testing (International Civil Aviation Organization, 2017). The test methods included in this standard are presented in Table 1.5. Nowadays, there are six pathways that are approved for the production of biojet fuel and its use in commercial aviation. The ASTM D7566 standard was approved in 2009 to consider SPK derived from gasification of biomass through Fischer Tropsch synthesis; later, in 2011 this specification was expanded to include
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Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Table 1.5 Test methods included in ASTM D5054 standard (ASTM, 2017p). Test method
Description
Reference
ASTM A240/ A240M ASTM B36/ B36M ASTM B93/ B93M ASTM D56
Specification for Chromium and Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels and for General Applications Specification for Brass Plate, Sheet, Strip, And Rolled Bar
ASTM (2018x) ASTM (2018y) ASTM (2015j) ASTM (2016a) ASTM (2018a) ASTM (2018b) ASTM (2014f) ASTM (2018z) ASTM (2016u) ASTM (2018d) ASTM (2016v) ASTM (2017q) ASTM (2015k) ASTM (2019f) ASTM (2018g) ASTM (2018h) ASTM (2014g) ASTM (2013b) ASTM (2015l)
ASTM D86
Specification for Magnesium Alloys in Ingot Form for Sand Castings, Permanent Mold Castings, and Die Castings Test Method for Flash Point by Tag Closed Cup Tester
ASTM D93
Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure Test Methods for Flash Point by Pensky Martens Closed Cup Tester
ASTM D257
Test Methods for DC Resistance or Conductance of Insulating Materials
ASTM D395
Test Methods for Rubber Property Compression Set
ASTM D412
Test Methods for Vulcanized Rubber and Thermoplastic Elastomers—Tension
ASTM D445
Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity) Test Method for Rubber Property Effect of Liquids
ASTM D471 ASTM D790
Test Methods for Flexural Properties of Unreinforced and Reinforced Plastics and Electrical Insulating Materials ASTM D924 Test Method for Dissipation Factor (or Power Factor) and Relative Permittivity (Dielectric Constant) of Electrical Insulating Liquids ASTM D1002 Test Method for Apparent Shear Strength of Single-Lap-Joint Adhesively Bonded Metal Specimens by Tension Loading (Metal-to-Metal) ASTM D1319 Test Method for Hydrocarbon Types in Liquid Petroleum Products by Fluorescent Indicator Adsorption ASTM D1322 Test Method for Smoke Point of Kerosene and Aviation Turbine Fuel ASTM D1331 Test Methods for Surface and Interfacial Tension of Solutions of Paints, Solvents, Solutions of Surface-Active Agents, and Related Materials ASTM D1405 Test Method for Estimation of Net Heat of Combustion of Aviation Fuels ASTM D1414 Test Methods for Rubber O-Rings
(Continued )
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
15
Table 1.5 (Continued) Test method
Description
ASTM D1655 Specification for Aviation Turbine Fuels ASTM D2240 Test Method for Rubber Property Durometer Hardness ASTM D2386 Test Method for Freezing Point of Aviation Fuels ASTM D2425 Test Method for Hydrocarbon Types in Middle Distillates by Mass Spectrometry ASTM D2624 Test Methods for Electrical Conductivity of Aviation and Distillate Fuels ASTM D2887 Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography ASTM D3241 Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels ASTM D3242 Test Method for Acidity in Aviation Turbine Fuel ASTM D3338 Test Method for Estimation of Net Heat of Combustion of Aviation Fuels ASTM D3359 Test Methods for Rating Adhesion by Tape Test ASTM D3363 Test Method for Film Hardness by Pencil Test ASTM D3701 Test Method for Hydrogen Content of Aviation Turbine Fuels by Low-Resolution Nuclear Magnetic Resonance Spectrometry ASTM D3703 Test Method for Hydroperoxide Number of Aviation Turbine Fuels, Gasoline and Diesel Fuels ASTM D3828 Test Methods for Flash Point by Small Scale Closed Cup Tester ASTM D3948 Test Method for Determining Water Separation Characteristics of Aviation Turbine Fuels by Portable Separometer ASTM D4052 Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter ASTM D4066 Classification System for Nylon Injection and Extrusion Materials (PA) ASTM D4529 Test Method for Estimation of Net Heat of Combustion of Aviation Fuels ASTM D4629 Test Method for Trace Nitrogen in Liquid Hydrocarbons by Syringe/Inlet Oxidative Combustion and Chemiluminescence Detection ASTM D4809 Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter (Precision Method)
Reference ASTM (2019b) ASTM (2015m) ASTM (2018i) ASTM (2019g) ASTM (2015c) ASTM (2018j) ASTM (2019c) ASTM (2017e) ASTM (2014c) ASTM (2017r) ASTM (2011b) ASTM (2017f) ASTM (2018aa) ASTM (2016e) ASTM (2018l) ASTM (2018m) ASTM (2013c) ASTM (2017g) ASTM (2017s) ASTM (2018n) (Continued )
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Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Table 1.5 (Continued) Test method
Description
ASTM D5001 Test Method for Measurement of Lubricity of Aviation Turbine Fuels by the Ball-onCylinder Lubricity Evaluator (BOCLE) ASTM D5291 Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants ASTM D5304 Test Method for Assessing Middle Distillate Fuel Storage Stability by Oxygen Overpressure ASTM D5363 Specification for Anaerobic Single-Component Adhesives (AN) ASTM D5972 Test Method for Freezing Point of Aviation Fuels (Automatic Phase Transition Method) ASTM D6304 Test Method for Determination of Water in Petroleum Products, Lubricating Oils, and Additives by Coulometric Karl Fischer Titration ASTM D6378 Test Method for Determination of Vapor Pressure (VPX) of Petroleum Products, Hydrocarbons, and Hydrocarbon-Oxygenate Mixtures (Triple Expansion Method) ASTM D6732 Test Method for Determination of Copper in Jet Fuels by Graphite Furnace Atomic Absorption Spectrometry ASTM D6793 Test Method for Determination of Isothermal Secant and Tangent Bulk Modulus ASTM D7042 Test Method for Dynamic Viscosity and Density of Liquids by Stabinger Viscometer (and the Calculation of Kinematic Viscosity) ASTM D7111 Test Method for Determination of Trace Elements in Middle Distillate Fuels by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES) ASTM D7171 Test Method for Hydrogen Content of Middle Distillate Petroleum Products by LowResolution Pulsed Nuclear Magnetic Resonance Spectroscopy ASTM D7566 Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons ASTM E411
Test Method for Trace Quantities of Carbonyl Compounds with 2,4Dinitrophenylhydrazine ASTM E681 Test Method for Concentration Limits of Flammability of Chemicals (Vapors and Gases) ASTM E1269 Test Method for Determining Specific Heat Capacity by Differential Scanning Calorimetry
Reference ASTM (2014e) ASTM (2016w) ASTM (2015n) ASTM (2016x) ASTM (2016k) ASTM (2016y) ASTM (2018ab) ASTM (2015o) ASTM (2012b) ASTM (2016m) ASTM (2016t) ASTM (2016z) ASTM (2019a) ASTM (2017t) ASTM (2015p) ASTM (2018ac)
aviation fuel derived from the hydroprocessing of plant oils and fats (International Renewable Energy Agency IRENA, 2017). The third processing pathway approved was the synthetic iso-paraffinic kerosene, also known as Direct Sugar to Hydrocarbons, in 2014. In 2015 the SPK plus aromatics route was approved and annexed to
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
the standard (International Civil Aviation Organization, 2017), while in 2016 the alcohol-to-jet route was incorporated to ASTM D7566 standard (International Renewable Energy Agency IRENA, 2017). Recently, the coprocessing of renewable lipids with crude oil-derived middle distillates in petroleum refineries was approved by the Committee (CAAFI, 2019); this pathway will be added to the annex in the following edition of the standard. Besides these pathways, there are others in the process of approval by ASTM (International Air Transport Association, 2018c,d). One of them is the catalytic hydrothermolysis jet/high freeze point HEFA, whose possible feedstocks are bio-oils, animal fats, and recycled oils (International Civil Aviation Organization, 2017). Another conversion route is the coprocessing of bio-oils (coprocessing) with conventional middle distillates of petrorefineries. Moreover, the route ATJ-SPK is also in approval process; this process considers alcohol production, usually isobutanol, from biomass. Another pathway is ATJ-SKA, where the fuel includes bio-aromatics looking for its use in higher percentages. Finally, the process HEFA Plus (Green Diesel) is under evaluation, and the first test flight with 15% of this new fuel already took place (International Civil Aviation Organization, 2017). It is worth mentioning that the certification of a new fuel usually takes between 3 and 5 years, since it is a multistage and multifactor process and requires up to 890,000 liters of blended jet fuel to be completed (Pavlenko and Kharina, 2018). Thus it is necessary to simplify and standardize the approval process, in order to allow further diversification of conversion processes and feedstocks to be used for aviation alternative fuels production (International Civil Aviation Organization, 2017).
1.4
Combustion and flight tests
As mentioned before, the renewable aviation fuel needs to be tested in order to evaluate the fulfillment of the properties established in the standards ASTM D1655 (ASTM, 2019b) and ASTM D7566 (ASTM, 2019a). Once that the alternative fuel has approved this evaluation, combustion tests in jet engines must be realized. The combustion performance of the renewable aviation fuel will depend mainly on its composition and properties such as heat of combustion, smoke point, and density. At the same time, these properties will depend on the raw material and production process. Therefore each of the alternative jet fuel can exhibit its own unique behavior during combustion due to its properties
17
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Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
(Zhang et al., 2016). Due to this, it is necessary to perform combustion tests of these alternative fuels in jet engines. The combustion tests are oriented to evaluate the performance of the alternative fuel inside the engine (lean blowout, atomization, ignition, and altitude relight), and the combustion products (emissions, smoke and carbon deposit) (Zhang et al., 2016). In addition, long-term studies are required in order to analyze the effect of the use of alternative fuels in the mechanical integrity of the jet engine. These studies must be performed first in jet engines in the ground, and later on flight tests. The ground engine tests allow to evaluate the reliability and safety of alternative jet fuels; also, the combustion products are measured (Zhang et al., 2016). The first time where biojet fuel was used in a ground test by the Argentina’s Air Force was in 2006; the test was realized at Buenos Aires using 20% of biojet fuel produced from soy and rapeseed oils. From that test to the date many other studies have been realized, and the findings indicate that the engine performance is not affected by the use of alternatives fuels; indeed, in the combustion of alternative jet fuels less molecular classes are involved, in comparison with fossil jet fuels (Zhang et al., 2016). Moreover, when alternative aviation fuel is used the thermal efficiency is superior, the CO, NOx, and SOx emissions are reduced and smaller soot particles are generated (Friedl, 2015; Zhang et al., 2016). An interesting result was presented by Corporan et al. (2012), reporting that it is possible to predict the particle emissions of the combustion of alternative fuels based on engine, engine setting, limited particle matter data, and fuel composition; this is an important functionality that can be used to improve the design of the conversion processes of the biomass in order to minimize particle emissions. However, the aromatic content has an important role in the density and neat heat combustion of biojet fuels; low aromatic content results in low density of the fuel but high net heat of combustion (Yang et al., 2019). Once that the renewable aviation fuel is tested in jet engines at ground level, then flight test must be performed. The flight test is the final testing step to demonstrate the use of a candidate jet fuel on a flying aircraft (Zhang et al., 2016). To date, renewable aviation fuel has been used in test and commercial flights all over the world. In 2007 the first air test was performed at Nevada by Green Flight International using canola oil with an aircraft Aerovodochody L29 Delfin, which was a military training aircraft. Nevertheless, the first test flight in a commercial aircraft (Boeing 747-400) was realized in 2008 by Virgin Atlantic; in this test flight, 20% of biojet fuel was used, which was produced from coconut and babassu oils. The first commercial flight was realized in 2011
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
by KLM, using 50% of biojet fuel produced from used cooking oil; the flight was from Amsterdam to Paris with an aircraft Boeing 737-800 (Gutie´rrez-Antonio et al., 2017). The gap between the first test and commercial flights was due to the time required for the certification of the fuels, along with the availability of the quantities of biofuel required for the tests. Between 2006 and 2013, 31 test flights were performed (Gutie´rrez-Antonio et al., 2017); from these flights, almost half employed renewable aviation fuel produced with the hydrotreating process of UOP Honeywell, while 35% used jet fuel generated with hydrotreated esters and fatty acids pathway from SkyNRG. There were other biojet fuel suppliers for the realization of these test flights; however, UOP Honeywell and SkyNRG were the main actors, even though Fischer Tropsch technology was also certified by the ASTM for biojet fuel production. According to International Air Transport Association, between 2011 and 2015, 22 airlines performed over 2500 commercial passenger flights with blends of up to 50% biojet fuel from feedstock including used cooking oil, jatropha, camelina, algae oils, and sugarcane (International Air Transport Association, 2018c,d). Moreover, in January 2016 a regular supply of renewable aviation fuel through the common hydrant system started at Oslo Airport, being Neste, SkyNRG, and Air BP the suppliers; recently a study reported that this action helps to reduce the greenhouse gases of the airlines by 10% 15% (Baxter et al., 2020). Later in March of the same year, United Airlines became the first company to introduce biojet fuel in its normal operation in its daily flights from Los Angeles Airport; the biofuel was provided by AltAir (International Air Transport Association, 2018c,d). From this point, the incorporation of biojet fuel in several airports began to increase; in December of 2018, more than 150,000 commercial flights have been performed using renewable aviation fuel. In addition, several airlines have concluded long-term offtake agreements with biofuel suppliers, most of which are reported as commercially competitive (International Air Transport Association, 2018c,d). According to the International Air Transport Association (2015), some of the successful airlines/biofuel’s producer symbiosis are the following ones. The aircraft of United Airlines uses renewable aviation fuel produced from AltAir; both companies signed in 2013 an agreement for the commercialization of biojet fuel produced from nonedible natural oils and agricultural wastes. On the other hand, Solena Fuels constructed a facility to produce biojet fuel for British Airways; the renewable aviation fuel will be produced from landfill waste. On the other hand, the airplanes of Air France will use renewable aviation
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fuel produced by Total from Amyris’s renewable farnesane. Finally, Fulcrum provides renewable aviation fuel to Cathay Pacific, which is obtained from the conversion of household garbage. In spite of these efforts, actually just few airlines companies use renewable aviation fuel in its commercial flights (Table 1.6). It is important to mention that the specific percentage of use of renewable aviation fuel is not provided. In spite of the interest of the airlines and biofuels producers, some countries have also contributed to promoting the use of renewable aviation fuel through mandatory policies. In the European Union there has been a great impulse to the use of biofuels for the different transportation types. From these initiatives, biodiesel and bioethanol are the most benefited biofuels; however, the use of biojet fuel as a strategy to decrease the environmental impact of the transport sector is just considered in Netherlands (Deane and Pye, 2018). Also, in the European Union, The Norwegian Government has mandated the use of renewable aviation fuel in mixtures of 0.5% with fossil jet fuel from 2020, being the target the increase of this percentage to 30% in 2030 (Baxter et al., 2020). In spite of these efforts, it is clear that the use of alternative aviation fuels in the European Union will be promoted when the new policy framework defined by the new Renewable Energy Directive (REDII) will be in place (Chiaramonti, 2019). In other countries, some efforts have been focused on the analysis of the scenarios for the implementation of biojet fuel supply chain. These studies suggest policies that could help to establish the biojet fuel supply chain in Canada (Mupondwa et al., 2016; Li et al., 2018), Turkey (Melikoglu, 2017), Sweden (Goding et al., 2018), China (Zhou et al., 2016; Liu et al., 2020), Brazil (Kamali et al., 2018;
Table 1.6 Airline companies that use renewable aviation fuel in commercial flights. Airline companies
Biofuel’s supplier
References
Air France AirBP KLM Lufthansa Scandinavian Airlines United Airlines Virgin Atlantic Virgin Australia
World Energy Neste Oil Neste Oil Neste Oil SkyNRG World Energy LanzaTech Gevo
Air France (2019) Neste (2019a) Neste (2019b) Neste (2019c) SkyNRG (2019) United (2020) Virgin (2018) Virgin Australia (2019)
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Mendes de Souza et al., 2018; Mendes et al., 2020), Me´xico (Domı´nguez-Garcı´a et al., 2017a; Domı´nguez-Garcı´a et al., 2017b), United States (Smith et al., 2017; Leila et al., 2018; Chao et al., 2019a; Chao et al., 2019b), and United Kingdom (Hudson et al., 2016). All the studies converge on the necessity to continue the research and development of new processes to produce biojet fuel and also the need to count with governmental regulations and policies to promote the use of this biofuel (Dodd et al., 2018). Finally, according to Scheelhaase et al. (2019), some potential policies for the impulse of the use of renewable aviation fuels include: • Policies directly raising the use of renewable aviation fuel, like mandatory use of blends of fossil and renewable jet fuels; • Policies lowering the net production costs and associated risks of renewable aviation fuel, such as subsidies for research and development or investment aids; • Policies improving the relative competitiveness of biojet fuels by increasing the cost for conventional fuels.
1.5
Summary
Biojet fuel is the feasible alternative for the sustainable development of the aviation sector. Considering the high complexity and the certifications involved in the operation of the airlines, biojet fuel must be a drop-in fuel with the same or superior properties than those of the fossil jet fuel. renewable aviation fuel must comply with the ASTM standards established for fossil jet fuel plus some additional ones related to the renewable nature of the fuel. At the moment, six pathways are certified for the production of biojet fuel and five more are into the approval process. However, the process of certification of new aviation fuels must be simplified in order to increase the use of renewable aviation fuel in the airports worldwide, since numerous test and commercial flights have proven the technical competitivity of biojet fuel in comparison with fossil jet fuel. Finally, the government participation through the implementation of policies is of vital importance to promote the use of renewable aviation fuel.
References Agosta, A., 2002. Development of a chemical surrogate for JP-8 aviation fuel using a pressurized flow reactor. Drexel University. Air France, 2019. Air France plans flights from San Francisco fueled with sustainable aviation fuel. ,https://corporate.airfrance.com/en/press-release/ air-france-plans-flights-san-francisco-fueled-sustainable-aviation-fuel. (accessed 21.04.20.).
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ASTM International, 2011a. ASTM D6379-11, Standard Test Method for Determination of Aromatic Hydrocarbon Types in Aviation Fuels and Petroleum Distillates—High Performance Liquid Chromatography Method with Refractive Index Detection. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2011b. ASTM D3363-05(2011)e2, Standard Test Method for Film Hardness by Pencil Test. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2012a. ASTM D5452-12, Standard Test Method for Particulate Contamination in Aviation Fuels by Laboratory Filtration. ,www. astm.org. (accessed 14.05.19.). ASTM International, 2012b. ASTM D6793-02(2012), Standard Test Method for Determination of Isothermal Secant and Tangent Bulk Modulus. ,www.astm. org. (accessed 14.05.19.). ASTM International, 2013a. ASTM D1405/D1405M-08(2013), Standard Test Method for Estimation of Net Heat of Combustion of Aviation Fuels. ,www. astm.org. (accessed 14.05.19.). ASTM International, 2013b. ASTM D1405/D1405M-08(2013), Standard Test Method for Estimation of Net Heat of Combustion of Aviation Fuels. ,www. astm.org. (accessed 19.05.19.). ASTM International, 2013c. ASTM D4066-13, Standard Classification System for Nylon Injection and Extrusion Materials (PA). ,www.astm.org. (accessed 19.05.19.). ASTM International, 2014a. ASTM D2276-06(2014), Standard Test Method for Particulate Contaminant in Aviation Fuel by Line Sampling. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2014b. ASTM D3120-08(2014), Standard Test Method for Trace Quantities of Sulfur in Light Liquid Petroleum Hydrocarbons by Oxidative Microcoulometry. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2014c. ASTM D3338/D3338M-09(2014)e2, Standard Test Method for Estimation of Net Heat of Combustion of Aviation Fuels. ,www. astm.org. (accessed 14.05.19.). ASTM International, 2014d. ASTM D4176-04(2014), Standard Test Method for Free Water and Particulate Contamination in Distillate Fuels (Visual Inspection Procedures). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2014e. ASTM D5001-10(2014), Standard Test Method for Measurement of Lubricity of Aviation Turbine Fuels by the Ball-on-Cylinder Lubricity Evaluator (BOCLE). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2014f. ASTM D5001-10(2014), Standard Test Method for Measurement of Lubricity of Aviation Turbine Fuels by the Ball-on-Cylinder Lubricity Evaluator (BOCLE). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2014g. ASTM D1331-14, Standard Test Methods for Surface and Interfacial Tension of Solutions of Paints, Solvents, Solutions of SurfaceActive Agents, and Related Materials. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015a. ASTM D156-15, Standard Test Method for Saybolt Color of Petroleum Products (Saybolt Chromometer Method). ,www.astm. org. (accessed 14.05.19.). ASTM International, 2015b. ASTM D323-15a, Standard Test Method for Vapor Pressure of Petroleum Products (Reid Method). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015c. ASTM D2624-15, Standard Test Methods for Electrical Conductivity of Aviation and Distillate Fuels. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015d. ASTM D3240-15, Standard Test Method for Undissolved Water in Aviation Turbine Fuels. ,www.astm.org. (accessed 14.05.19.).
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
ASTM International, 2015e. ASTM D4953-15, Standard Test Method for Vapor Pressure of Gasoline and Gasoline-Oxygenate Blends (Dry Method). ,www. astm.org. (accessed 14.05.19.). ASTM International, 2015f. ASTM D7153-15e1, Standard Test Method for Freezing Point of Aviation Fuels (Automatic Laser Method). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015g. ASTM D7154-15, Standard Test Method for Freezing Point of Aviation Fuels (Automatic Fiber Optical Method). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015h. ASTM D7524-10(2015), Standard Test Method for Determination of Static Dissipater Additives (SDA) in Aviation Turbine Fuel and Middle Distillate Fuels—High Performance Liquid Chromatograph (HPLC) Method. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2015i. ASTM D7974-15, Standard Test Method for Determination of Farnesane, Saturated Hydrocarbons, and Hexahydrofarnesol Content of Synthesized Iso-Paraffins (SIP) Fuel for Blending with Jet Fuel by Gas Chromatography. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2015j. ASTM B93/B93M-15, Standard Specification for Magnesium Alloys in Ingot Form for Sand Castings, Permanent Mold Castings, and Die Castings. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2015k. ASTM D924-15, Standard Test Method for Dissipation Factor (or Power Factor) and Relative Permittivity (Dielectric Constant) of Electrical Insulating Liquids. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2015l. ASTM D1414-15, Standard Test Methods for Rubber O-Rings. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2015m. ASTM D2240-15e1, Standard Test Method for Rubber Property—Durometer Hardness. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2015n. ASTM D5304-15, Standard Test Method for Assessing Middle Distillate Fuel Storage Stability by Oxygen Overpressure. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2015o. ASTM D6732-04(2015), Standard Test Method for Determination of Copper in Jet Fuels by Graphite Furnace Atomic Absorption Spectrometry. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2015p. ASTM E681-09(2015), Standard Test Method for Concentration Limits of Flammability of Chemicals (Vapors and Gases). ,www.astm.org. (accessed 19.05.19.). ASTM International, 2016a. ASTM D56-16a, Standard Test Method for Flash Point by Tag Closed Cup Tester. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2016b. ASTM D2622-16, Standard Test Method for Sulfur in Petroleum Products by Wavelength Dispersive X-ray Fluorescence Spectrometry. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2016c. ASTM D3227-16, Standard Test Method for (Thiol Mercaptan) Sulfur in Gasoline, Kerosine, Aviation Turbine, and Distillate Fuels (Potentiometric Method). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2016d. ASTM D3343-16, Standard Test Method for Estimation of Hydrogen Content of Aviation Fuels. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2016e. ASTM D3828-16a, Standard Test Methods for Flash Point by Small Scale Closed Cup Tester. ,www.astm.org. (accessed 16.05.19.).
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ASTM International, 2016f. ASTM D4294-16e1, Standard Test Method for Sulfur in Petroleum and Petroleum Products by Energy Dispersive X-ray Fluorescence Spectrometry. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016g. ASTM D4625-16e1, Standard Test Method for Middle Distillate Fuel Storage Stability at 43 C (110 F). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016h. ASTM D4737-10(2016), Standard Test Method for Calculated Cetane Index by Four Variable Equation. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016i. ASTM D5006-11(2016), Standard Test Method for Measurement of Fuel System Icing Inhibitors (Ether Type) in Aviation Fuels. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016j. ASTM D5453-16e1, Standard Test Method for Determination of Total Sulfur in Light Hydrocarbons, Spark Ignition Engine Fuel, Diesel Engine Fuel, and Engine Oil by Ultraviolet Fluorescence. ,www. astm.org. (accessed 16.05.19.). ASTM International, 2016k. ASTM D5972-16, Standard Test Method for Freezing Point of Aviation Fuels (Automatic Phase Transition Method). ,www.astm. org. (accessed 16.05.19.). ASTM International, 2016m. ASTM D7042-16e3, Standard Test Method for Dynamic Viscosity and Density of Liquids by Stabinger Viscometer (and the Calculation of Kinematic Viscosity). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016n. ASTM D7170-16, Standard Test Method for Determination of Derived Cetane Number (DCN) of Diesel Fuel Oils—Fixed Range Injection Period, Constant Volume Combustion Chamber Method. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016o. ASTM D7945-16, Standard Test Method for Determination of Dynamic Viscosity and Derived Kinematic Viscosity of Liquids by Constant Pressure Viscometer. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016p. ASTM D7959-16, Standard Test Method for Chloride Content Determination of Aviation Turbine Fuels using Chloride Test Strip. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016q. ASTM D8073-16, Standard Test Method for Determination of Water Separation Characteristics of Aviation Turbine Fuel by Small Scale Water Separation Instrument. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2016r. ASTM D5291-16, Standard Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2016s. ASTM D6304-16e1, Standard Test Method for Determination of Water in Petroleum Products, Lubricating Oils, and Additives by Coulometric Karl Fischer Titration. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2016t. ASTM D7111-16, Standard Test Method for Determination of Trace Elements in Middle Distillate Fuels by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). ,www.astm.org. (accessed 18.05.19.). ASTM International, 2016u. ASTM D412-16, Standard Test Methods for Vulcanized Rubber and Thermoplastic Elastomers—Tension. ,www.astm. org. (accessed 19.05.19.). ASTM International, 2016v. ASTM D471-16a, Standard Test Method for Rubber Property—Effect of Liquids. ,www.astm.org. (accessed 19.05.19.).
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
ASTM International, 2016w. ASTM D5291-16, Standard Test Methods for Instrumental Determination of Carbon, Hydrogen, and Nitrogen in Petroleum Products and Lubricants. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2016x. ASTM D5363-16, Standard Specification for Anaerobic Single-Component Adhesives (AN). ,www.astm.org. (accessed 19.05.19.). ASTM International, 2016y. ASTM D6304-16e1, Standard Test Method for Determination of Water in Petroleum Products, Lubricating Oils, and Additives by Coulometric Karl Fischer Titration. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2016z. ASTM D7171-16, Standard Test Method for Hydrogen Content of Middle Distillate Petroleum Products by LowResolution Pulsed Nuclear Magnetic Resonance Spectroscopy. ,www.astm. org. (accessed 19.05.19.). ASTM International, 2017a. ASTM D240-17, Standard Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter. ,www.astm. org. (accessed 14.05.19.). ASTM International, 2017b. ASTM D381-12(2017), Standard Test Method for Gum Content in Fuels by Jet Evaporation. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2017c. ASTM D1298-12b(2017), Standard Test Method for Density, Relative Density, or API Gravity of Crude Petroleum and Liquid Petroleum Products by Hydrometer Method. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2017d. ASTM D1840-07(2017), Standard Test Method for Naphthalene Hydrocarbons in Aviation Turbine Fuels by Ultraviolet Spectrophotometry. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2017e. ASTM D3242-11(2017), Standard Test Method for Acidity in Aviation Turbine Fuel. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017f. ASTM D3701-17, Standard Test Method for Hydrogen Content of Aviation Turbine Fuels by Low Resolution Nuclear Magnetic Resonance Spectrometry. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017g. ASTM D4529-17, Standard Test Method for Estimation of Net Heat of Combustion of Aviation Fuels. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017h. ASTM D4952-12(2017), Standard Test Method for Qualitative Analysis for Active Sulfur Species in Fuels and Solvents (Doctor Test). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017i. ASTM D6045-12(2017), Standard Test Method for Color of Petroleum Products by the Automatic Tristimulus Method. ,www. astm.org. (accessed 16.05.19.). ASTM International, 2017j. ASTM D7344-17a, Standard Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure (Mini Method). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017k. ASTM D7345-17, Standard Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure (Micro Distillation Method). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017m. ASTM D7668-17, Standard Test Method for Determination of Derived Cetane Number (DCN) of Diesel Fuel Oils— Ignition Delay and Combustion Delay Using a Constant Volume Combustion Chamber Method. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2017o. ASTM D7223-17, Standard Specification for Aviation Certification Turbine Fuel. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2017p. ASTM D4054-17, Standard Practice for Evaluation of New Aviation Turbine Fuels and Fuel Additives. ,www.astm.org. (accessed 18.05.19.).
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ASTM International, 2017q. ASTM D790-17, Standard Test Methods for Flexural Properties of Unreinforced and Reinforced Plastics and Electrical Insulating Materials. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2017r. ASTM D3359-17, Standard Test Methods for Rating Adhesion by Tape Test. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2017s. ASTM D4629-17, Standard Test Method for Trace Nitrogen in Liquid Hydrocarbons by Syringe/Inlet Oxidative Combustion and Chemiluminescence Detection. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2017t. ASTM E411-17a, Standard Test Method for Trace Quantities of Carbonyl Compounds with 2,4-Dinitrophenylhydrazine. ,www. astm.org. (accessed 19.05.19.). ASTM International, 2018a. ASTM D86-18, Standard Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure. ,www. astm.org. (accessed 14.05.19.). ASTM International, 2018b. ASTM D93-18, Standard Test Methods for Flash Point by Pensky-Martens Closed Cup Tester. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018c. ASTM D130-18, Standard Test Method for Corrosiveness to Copper from Petroleum Products by Copper Strip Test. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018d. ASTM D445-18, Standard Test Method for Kinematic Viscosity of Transparent and Opaque Liquids (and Calculation of Dynamic Viscosity). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018e. ASTM D613-18a, Standard Test Method for Cetane Number of Diesel Fuel Oil. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018f. ASTM D1266-18, Standard Test Method for Sulfur in Petroleum Products (Lamp Method). ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018g. ASTM D1319-18, Standard Test Method for Hydrocarbon Types in Liquid Petroleum Products by Fluorescent Indicator Adsorption. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018h. ASTM D1322-18, Standard Test Method for Smoke Point of Kerosene and Aviation Turbine Fuel. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2018j. ASTM D2887-18, Standard Test Method for Boiling Range Distribution of Petroleum Fractions by Gas Chromatography. ,www. astm.org. (accessed 14.05.19.). ASTM International, 2018k. ASTM D2892-18a, Standard Test Method for Distillation of Crude Petroleum (15-Theoretical Plate Column). ,www.astm. org. (accessed 14.05.19.). ASTM International, 2018l. ASTM D3948-14(2018), Standard Test Method for Determining Water Separation Characteristics of Aviation Turbine Fuels by Portable Separometer. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018m. ASTM D4052-18a, Standard Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018n. ASTM D4809-18, Standard Test Method for Heat of Combustion of Liquid Hydrocarbon Fuels by Bomb Calorimeter (Precision Method). ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018o. ASTM D6751-18, Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle Distillate Fuels. ,www.astm. org. (accessed 16.05.19.). ASTM International, 2018p. ASTM D6866-18, Standard Test Methods for Determining the Biobased Content of Solid, Liquid, and Gaseous Samples Using Radiocarbon Analysis. ,www.astm.org. (accessed 16.05.19.).
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
ASTM International, 2018q. ASTM D7224-14(2018), Standard Test Method for Determining Water Separation Characteristics of Kerosine-Type Aviation Turbine Fuels Containing Additives by Portable Separometer. ,www.astm. org. (accessed 16.05.19.). ASTM International, 2018r. ASTM D7797-18, Standard Test Method for Determination of the Fatty Acid Methyl Esters Content of Aviation Turbine Fuel Using Flow Analysis by Fourier Transform Infrared Spectroscopy—Rapid Screening Method. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018s. ASTM D7872-13(2018), Standard Test Method for Determining the Concentration of Pipeline Drag Reducer Additive in Aviation Turbine Fuels. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018t. ASTM D129-18, Standard Test Method for Sulfur in Petroleum Products (General High Pressure Decomposition Device Method). ,www.astm.org. (accessed 18.05.19.). ASTM International, 2018u. ASTM D2710-09(2018), Standard Test Method for Bromine Index of Petroleum Hydrocarbons by Electrometric Titration. ,www.astm.org. (accessed 18.05.19.). ASTM International, 2018v. ASTM D7359-18, Standard Test Method for Total Fluorine, Chlorine and Sulfur in Aromatic Hydrocarbons and Their Mixtures by Oxidative Pyrohydrolytic Combustion followed by Ion Chromatography Detection (Combustion Ion Chromatography-CIC). ,www.astm.org. (accessed 18.05.19.). ASTM International, 2018w. ASTM D6378-18a, Standard Test Method for Determination of Vapor Pressure (VPX) of Petroleum Products, Hydrocarbons, and Hydrocarbon-Oxygenate Mixtures (Triple Expansion Method). ,www.astm.org. (accessed 18.05.19.). ASTM International, 2018x. ASTM A240/A240M-18, Standard Specification for Chromium and Chromium-Nickel Stainless Steel Plate, Sheet, and Strip for Pressure Vessels and for General Applications. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2018y. ASTM B36/B36M-18, Standard Specification for Brass Plate, Sheet, Strip, and Rolled Bar. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2018z. ASTM D395-18, Standard Test Methods for Rubber Property—Compression Set. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2018aa. ASTM D3703-18, Standard Test Method for Hydroperoxide Number of Aviation Turbine Fuels, Gasoline and Diesel Fuels. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2018ab. ASTM D6378-18a, Standard Test Method for Determination of Vapor Pressure (VPX) of Petroleum Products, Hydrocarbons, and Hydrocarbon-Oxygenate Mixtures (Triple Expansion Method). ,www.astm.org. (accessed 19.05.19.). ASTM International, 2018ac. ASTM E1269-11(2018), Standard Test Method for Determining Specific Heat Capacity by Differential Scanning Calorimetry. ,www.astm.org. (accessed 19.05.19.). ASTM International, 2019a. ASTM D7566-19, Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons. ,www.astm.org. (accessed 12.05.19.). ASTM International, 2019b. ASTM D1655-19, Standard Specification for Aviation Turbine Fuels. ,www.astm.org. (accessed 14.05.19.). ASTM International, 2019c, ASTM D3241-19, Standard Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels. ,www.astm.org. (accessed 16.05.19.).
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ASTM International, 2019d. ASTM D5191-19, Standard Test Method for Vapor Pressure of Petroleum Products and Liquid Fuels (Mini Method). ,www. astm.org. (accessed 16.05.19.). ASTM International, 2019e. ASTM D2425-19, Standard Test Method for Hydrocarbon Types in Middle Distillates by Mass Spectrometry. ,www.astm. org. (accessed 18.05.19.). ASTM International, 2019f. ASTM D1002-10(2019), Standard Test Method for Apparent Shear Strength of Single-Lap-Joint Adhesively Bonded Metal Specimens by Tension Loading (Metal-to-Metal). ,www.astm.org. (accessed 18.05.19.). ASTM International, 2019g. ASTM D2425-19, Standard Test Method for Hydrocarbon Types in Middle Distillates by Mass Spectrometry. ,www.astm. org. (accessed 19.05.19.). ASTM International, 2016l. ASTM D6890-16e2, Standard Test Method for Determination of Ignition Delay and Derived Cetane Number (DCN) of Diesel Fuel Oils by Combustion in a Constant Volume Chamber. ,www.astm. org. (accessed 16.05.19.). ASTM International, 2017l. ASTM D7619-17, Standard Test Method for Sizing and Counting Particles in Light and Middle Distillate Fuels, by Automatic Particle Counter. ,www.astm.org. (accessed 16.05.19.). ASTM International, 2018i. ASTM D2386-18, Standard Test Method for Freezing Point of Aviation Fuels. ,www.astm.org. (accessed 14.05.19.). Baxter, G., Srisaeng, P., Wild, G., 2020. The use of aviation biofuels as an airport environmental sustainability measure: the case of Oslo Gardermoen Airport. Mag. Aviat. Dev. 8 (1), 6 17. Bernabei, M., Reda, R., Galiero, R., Bocchinfuso, G., 2003. Determination of total and polycyclic aromatic hydrocarbons in aviation jet fuel. J. Chromatogr. A 985, 197 203. Chiaramonti, D., 2019. Sustainable aviation fuels: the challenge of decarbonization. Energy Procedia 158, 1202 1207. Commercial Aviation Alternative Fuels Initiative, 2019. Fuel qualification. ,http:// www.caafi.org/focus_areas/fuel_qualification.html. (accessed 30.09.19.). Corporan, E., DeWitt, M.J., Klingshirn, C.D., Anneken, D., Streibich, R., Shafer, L., 2012. Comparisons of emissions characteristics of several turbine engines burning Fischer Tropsch and hydroprocessed esters and fatty acids alternative jet fuels. In: Proceedings of the ASME Turbo Expo. Copenhagen. GT2012-68656. Chao, H., Agusdinata, D.B., DeLaurentis, D.A., 2019a. The potential impacts of Emissions Trading Scheme and biofuel options to carbon emissions of U.S. airlines. Energy Policy 134, 110993. Chao, H., Agusdinata, D.B., DeLaurentis, D., Stechel, E.B., 2019b. Carbon offsetting and reduction scheme with sustainable aviation fuel options: fleet-level carbon emissions impacts for U.S. airlines. Transp. Res. D Transp. Environ. 75, 42 56. Chevron, 2007. Aviation fuels technical review. Chevron Products Company. ,https://www.chevron.com/-/media/chevron/operations/documents/ aviation-tech-review.pdf. (accessed 30.09.19.). Deane, J.P., Pye, S., 2018. Europe’s ambition for biofuels in aviation a strategic review of challenges and opportunities. Energy Strategy Rev. 20, 1 5. Dodd, T., Orlitzky, M., Nelson, T., 2018. What stalls a renewable energy industry? Industry outlook of the aviation biofuels industry in Australia, Germany, and the USA. Energy Policy 123, 92 103. Domı´nguez-Garcı´a, S., Gutie´rrez-Antonio, C., De Lira-Flores, J.A., Ponce-Ortega, J.M., El-Halwagi, M.M., 2017a. Strategic planning for the supply chain of aviation biofuel with consideration of hydrogen production. Ind. Eng. Chem. Res. 56 (46), 13812 13830.
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Domı´nguez-Garcı´a, S., Gutie´rrez-Antonio, C., De Lira-Flores, J.A., Ponce-Ortega, J.M., 2017b. Optimal planning for the supply chain of biofuels for aviation in Mexico. Clean Technol. Environ. Policy 19, 1387 1402. Friedl, R.R., 2015. Aviation meteorology aircraft emissions. In: North, G.R., Pyle, J., Zhang, F. (Eds.), Encyclopedia of Atmospheric Sciences, second ed. Academic Press, pp. 153 159. Goding, L., Andersson-Franko, M., Lagerkvist, C.J., 2018. Preferences for bio jet fuel in Sweden: the case of business travel from a city airport. Sustain. Energy Technol. Assess. 29, 60 69. Gutie´rrez-Antonio, C., Romero-Izquierdo, A.G., Go´mez-Castro, F.I., Herna´ndez, S., Briones-Ramı´rez, A., 2016. Simultaneous energy integration and intensification of the hydrotreating process to produce biojet fuel from Jatropha curcas. Chem. Eng. Process. Process Intensif. 110, 134 145. Gutie´rrez-Antonio, C., Go´mez-Castro, F.I., de Lira-Flores, J.A., Herna´ndez, S., 2017. A review on the production processes of renewable jet fuel. Renew. Sustain. Energy Rev. 79, 709 729. Holmgren, J., 2009. Biofuels: unlocking the potential. In: The International Conference on Biorefinery, Syracuse, New York. Hudson, L., Jefferson, A., Bauen, A., Nattrass, L., 2016. Chapter 14 sustainable aviation: a UK roadmap for sustainable aviation fuels. In: Chuck, C.J. (Ed.), Biofuels for Aviation. Academic Press, pp. 315 337. International Air Transport Association, 2009. A global approach to reducing aviation emissions first stop: carbon neutral growth from 2020. ,www.iata. org. (accessed 07.05.19.). International Air Transport Association, 2012. IATA guidance material for biojet fuel management. ,https://www.iata.org/publications/documents/guidancebiojet-management.pdf. (accessed 18.05.19.). International Air Transport Association, 2015. IATA sustainable aviation fuel roadmap. ,https://www.iata.org/contentassets/ d13875e9ed784f75bac90f000760e998/safr-1-2015.pdf. (accessed 18.04.20.). International Air Transport Association, 2018a. Economic performance of the airline industry end year report. ,https://www.iata.org/publications/ economics/reports/industry-econ-performance/iata-economic-performanceof-the-industry-end-year-2018-report.pdf. (accessed 06.05.19.). International Air Transport Association, 2018b. Annual review 2018. ,https://www. iata.org/publications/Documents/iata-annual-review-2018.pdf. (accessed 06.05.19.). International Air Transport Association, 2018c. Reducing aviation’s carbon footprint. ,https://www.iata.org/publications/tracker/june-2018/Pages/ corsia.aspx. (accessed 07.05.19). International Air Transport Association, 2018d. Sustainable aviation fuels fact sheet. ,https://www.iata.org/publications/tracker/june-2018/Pages/corsia. aspx. (accessed 07.05.19.). International Civil Aviation Organization, 2017. Status of technical certification of aviation alternative fuels. In: Conference on Aviation and alternative fuels. ,https://www.icao.int/Meetings/CAAF2/Documents/CAAF.2.WP.007.1.en. pdf. (accessed 18.05.19.). International Renewable Energy Agency (IRENA), 2017. Biofuels for aviation: technology brief. ,https://www.irena.org/documentdownloads/publications/ irena_biofuels_for_aviation_2017.pdf. (accessed 18.05.19.). Kamali, F.P., Borges, J.A.R., Osseweijer, P., Posada, J.A., 2018. Towards social sustainability: screening potential social and governance issues for biojet fuel supply chains in Brazil. Renew. Sustain. Energy Rev. 92, 50 61.
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Leila, M., Whalen, J., Bergthorson, J., 2018. Strategic spatial and temporal design of renewable diesel and biojet fuel supply chains: case study of California, USA. Energy 156, 181 195. Li, X., Mupondwa, E., Tabil, L., 2018. Technoeconomic analysis of biojet fuel production from camelina at commercial scale: case of Canadian Prairies. Bioresour. Technol. 249, 196 205. Liu, X., Hang, Y., Wang, Q., Zhou, D., 2020. Flying into the future: a scenariobased analysis of carbon emissions from China’s civil aviation. J. Air Transp. Manag. 85, 101793. Melikoglu, M., 2017. Modelling and forecasting the demand for jet fuel and biobased jet fuel in Turkey till 2023. Sustain. Energy Technol. Assess. 19, 17 23. Mendes de Souza, L., Mendes, P.A.S., Aranda, D.A.G., 2018. Assessing the current scenario of the Brazilian biojet market. Renew. Sustain. Energy Rev. 98, 426 438. Mendes de Souza, L., Mendes, P.A.S., Aranda, D.A.G., 2020. Oleaginous feedstocks for hydro-processed esters and fatty acids (HEFA) biojet production in southeastern Brazil: a multi-criteria decision analysis. Renew. Energy 149, 1339 1351. Mupondwa, E., Li, X., Tabil, L., Falk, K., Gugel, R., 2016. Technoeconomic analysis of camelina oil extraction as feedstock for biojet fuel in the Canadian Prairies. Biomass Bioenergy 95, 221 234. Neste, 2019c. Neste and Lufthansa collaborate and aim for a more sustainable aviation. ,https://www.neste.com/releases-and-news/aviation/neste-andlufthansa-collaborate-and-aim-more-sustainable-aviation. (accessed 21.04.20.). Neste, 2019b. Neste to supply KLM additional sustainable aviation fuel for flights out of Schiphol. ,https://www.neste.com/releases-and-news/aviation/ neste-supply-klm-additional-sustainable-aviation-fuel-flights-out-schiphol. (accessed 21.04.20.). Neste, 2019a. Neste and Air BP ready to deliver sustainable aviation fuel to Sweden. ,https://www.neste.com/releases-and-news/aviation/neste-andair-bp-ready-deliver-sustainable-aviation-fuel-sweden. (accessed 21.04.20.). Pavlenko, N., Kharina, A., 2018. Policy and environmental implications of using HEFA 1 for aviation, Working Paper 2018.06. The International Council of Clean Transportation. ,https://www.theicct.org/sites/default/files/ publications/Green-Diesel-Aviation_ICCT-Working-Paper_20180321_vF.pdf. (accessed 19.05.19.). Scheelhaase, J., Maertens, S., Grimme, W., 2019. Synthetic fuels in aviation current barriers and potential political measures. Transp. Res. Procedia 43, 21 30. SkyNRG, 2019. SAS & Norgewian use biofuel from SkyNRG. ,http://skynrg.com/ nordic/sas-norwegian-use-biofuel-from-skynrg/. (accessed 21.04.20.). Smith, P.M., Gaffney, M.J., Shi, W., Hoard, S., Ibarrola Armendariz, I., Mueller, D.W., 2017. Drivers and barriers to the adoption and diffusion of sustainable jet fuel (SJF) in the U.S. Pacific Northwest. J. Air Transp. Manag. 58, 113 124. United, 2020. United Airlines expands industry-leading commitment to biofuel, powering more flights with more biofuel than any other U.S. Carrier. ,https://hub.united.com/united-expands-commitment-biofuel-poweringflights-2637791857.html. (accessed 21.04.20.). Virgin, 2018. Virgin Atlantic announces world first in race to develop new sustainable aviation fuel. ,https://www.virgin.com/richard-branson/virginatlantic-announces-world-first-race-develop-new-sustainable-aviation-fuel. (accessed 21.04.20.).
Chapter 1 Biojet fuel: Driving the aviation sector to sustainability
Virgin Australia, 2019. Virgin Australia’s sustainable aviation fuel flies one million kilometres. ,https://newsroom.virginaustralia.com/release/virginaustralia%E2%80%99s-sustainable-aviation-fuel-flies-one-millionkilometres. (accessed 21.04.20.). Yang, J., Xin, Z., He, Q., Corscadden, K., Niu, H., 2019. An overview on performance characteristics of bio-jet fuels. Fuel 237, 916 936. Zhang, C., Hui, X., Lin, Y., Sung, C.J., 2016. Recent development in studies of alternative jet fuel combustion: progress, challenges, and opportunities. Renew. Sustain. Energy Rev. 54, 120 138. Zhou, W., Wang, T., Yu, Y., Chen, D., Zhu, B., 2016. Scenario analysis of CO2 emissions from China’s civil aviation industry through 2030. Appl. Energy 175, 100 108.
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Renewable feedstock and its conversion routes to biojet fuel 2.1
2
Introduction
Aviation grows at a vertiginous rate, which makes it the most dynamic industry in the transport sector. According to the International Air Transport Association (2019), in 2013 the consumption of fuel into aviation sector was 74 billion liters, while in 2018 this value reached 95 billion liters; this means an increase of 28% in just 5 years. At June of 2019, the consumption of fuel was reported as 97 billion liters (International Air Transport Association, 2019). Therefore, major amounts of aviation fuel are necessary to guarantee the economic development of the sector. However, this fuel must be renewable, in order to have a sustainable growth. Finally, from the technical point of view the renewable fuels must meet all the technical specifications of the fossil fuels, since it is not desirable to change the configuration of the aircraft due to the costs associated with its recertification. In brief, there is a growing demand of renewable aviation fuel that fulfills the ASTM standards, and it can be generated from renewable sources in sustainable production processes. The renewable aviation fuel can be produced from any type of biomass. According to Maity (2015), the biomass can be classified considering its chemical nature in triglyceride, lignocellulosic, sugar and starchy feedstock (Fig. 2.1). The triglyceride feedstock includes oil and fats that are edible, nonedible, and wastes. On the other hand, the lignocellulosic feedstock considers different biomasses such as wood, husks, straws, grasses, and kernels, among others. Moreover, sugarcane and sugar beet are classified as sugar feedstock, while corn and wheat are starchy feedstock. Based on the classification of the biomass, the conversion routes to produce biojet fuel are grouped, in analogy with the classification proposed for biorefineries by Maity (2015); thus, there are three types of processing routes to generate biojet fuel: triglyceride
Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00002-X © 2021 Elsevier B.V. All rights reserved.
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Figure 2.1 Triglyceride, lignocellulosic, and sugar feedstock.
conversion processes, lignocellulosic conversion processes, and sugar and starchy conversion processes. Each one of these conversion processes considers different technologies to generate biojet fuel. However, an interesting aspect of this classification is that raw materials of the different generations could be processed under the same type of technology. Thus, it could be possible to mix different raw materials of a variety of sources in the same processing facility; this alternative could help to guarantee the supply of the raw materials to the processing plants. In the literature, the transformation of these types of biomasses through different conversion pathways has been reported (Gutie´ rrez-Antonio et al., 2017). However, the technical feasibility of a production process to generate biojet fuel is not enough to use it commercially; the latter is only possible when the conversion pathway is certified. For renewable aviation fuel two types of certification are needed. The technical certification is related to the composition and properties required for the use of biojet fuel in airplanes, and the sustainability certification (International Civil Aviation Organization, 2017a) is related to compliance with legal, environmental, and social aspects in the whole supply chain for the production of the biofuel. Accordingly, in this chapter we present the different types of biomasses (Section 2.2) that can be used to produce biojet fuel. Moreover, in Section 2.3 a description of processing routes is given, including the certified pathways and those under the certification process.
2.2
Raw materials
As mentioned before, biomass can be classified according to its chemical nature. Thus, in this chapter we provide detailed information about triglyceride feedstock (Section 2.2.1), sugar and starchy feedstock (Section 2.2.2), and lignocellulosic feedstock (Section 2.2.3).
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
2.2.1
Triglyceride feedstock
The triglyceride feedstock consists of fatty acids, whose composition varies significantly depending on the source and geographical origin (Maity, 2015). The main types of fatty acids present in this type of feedstock are oleic, palmitic, stearic, linoleic, linolenic, and ricinoleic (Gui et al., 2008). In Fig. 2.2 the chemical structures of oleic, palmitic, and stearic acids are presented. In triglyceride feedstock vegetable oils, animal fats, microalgae oil, and waste oils are included. Fig. 2.3 presents the chemical structure of a typical triglyceride of vegetable oils, which is triolein. Vegetable oils are obtained from crops, which can be edible or nonedible; for instance, the oils obtained from canola, sunflower, and soybean are edibles, while those extracted from Jatropha, castor, and palm are non-edibles. Also, in this category, the oil extracted from algae, micro and macro, and insects, such as black soldier fly larvae, are considered. In Fig. 2.4 some representative second-generation vegetable oils and animal fats are shown. Table 2.1 shows the content, yield, and price of oils from different cultivated triglyceride feedstock; also, this table presents the time required for the source to reach its maturity, so it can be used for oil production. As can be observed in Table 2.1, these types of feedstock are obtained from the direct cultivation of crops and/or
Figure 2.2 Chemical structure for oleic, palmitic, and stearic acids.
Figure 2.3 Chemical structure of triolein.
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Figure 2.4 Examples of vegetable oils and animal fat.
Table 2.1 Oil content, yield, and price of oils from different cultivated triglyceride feedstock along with its productivity time (Gallagher, 2011; Atabani et al., 2012, 2013; Ruiz et al., 2013; ˇ ckova ˇ ´ et al., 2015; Wang, 2016; Dimian et al., 2019). Issariyakul and Dalai, 2014; Ci
Soybean Coconut Jatropha curcas Castor Microalgae (low oil content) Black soldier fly larvae (BSFL)
Oil content %
Oil yield (L/ha/year)
Productivity time
Oil price (USD/metric ton)
15 63 35 53 30 35
446 2 1892 1413 58,700 193,560,600
65 90 days 4.5 7 years 1 year 3 5 months 6 15 days 22 24 days
684 812 500 700 1706 1000a
20 65 40
40
a
Reference price for fish meal fabricated with BSFL, since the price of BSFL oil is not available in the literature.
microorganisms; due to this, its costs are usually elevated. Moreover, the yields and productivity times vary considerably. From the triglyceride feedstock, the oil extracted from BSFL is the one with the biggest potential of production per area unit; this value is even superior for microalgae with high content of oil (70%), whose productivity is 136,900 L/ha per year (Atabani et al., 2012). The idea of using BSFL for processing of organic waste was ˇ cˇ kova´ et al., 2015); however, in proposed almost 100 years ago (Ci the last years, its use as biological treatment has gained attention, and it is a very interesting source for triglyceride feedstock. In order to guarantee the economic feasibility of using this type of feedstock, it is suggested that the oil extraction is realized near to the places where biomass is produced. In this way, the transportation costs and, also, the associated carbon dioxide emissions are low.
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Moreover, there are also triglyceride feedstock that are residues; for instance, there are tallow oil, pork lard oil, poultry fat, and fish oil, along with waste oils from cooking and industrial processes. Unlike the cultivated biomasses presented in Table 2.1, the amount of residues generated depends on each country’s lifestyle and its consumption habits. For instance, the United States and China lead the generation of edible waste oil with 10 and 4.5 million tons per year, while Japan and Malaysia around 0.5 million tons per year (Gui et al., 2008). The amount of waste oils, for instance, is significative in some countries, and it can be used to produce renewable aviation fuel. However, this type of residue usually requires a chemical pretreatment, at least a filtration stage to remove the food solid residues, and usually a drying stage to eliminate water. Moreover, the main bottleneck is the recollection of the raw material, which is usually dispersed inside the cities; thus the costs associated with the recollection are elevated. Considering that the first articles where biojet fuel was mentioned were published in 2009 (Markets and Business, 2009; Marsh, 2009) it is clear that all the types of triglyceride feedstock have not been explored. An interesting opportunity area is the use of mixtures of triglyceride feedstock; this will help strengthen the supply chain for the production of renewable aviation fuel. For instance, a mixture of Jatropha curcas and waste cooking oils can be used to produce renewable aviation fuel (Gutie´rrez-Antonio et al., 2017); this also represents a design challenge, since the production facility must be flexible enough to deal with changes in the feedstock composition. In general, the obtention of triglyceride feedstock is expensive; however, its processing costs are lower than the raw material cost. The low-cost alternatives of this feedstock are the waste oil and fats; however, its processing costs are higher due to the necessity of pretreatments before its conversion to produce biojet fuel.
2.2.2
Sugar and starchy feedstock
The sugar and starchy feedstock consist of all biomasses with high contents of sugar/or starch (Maity, 2015); among sugars, sucrose, glucose, and fructose are the most important (Zabed et al., 2016). The chemical structure of the main monosaccharides in the sugar is presented in Fig. 2.5. Likewise, in Fig. 2.6 the chemical structure of the starch is presented. This type of biomass includes sugarcane, sweet sorghum, sugar beet, and sweet
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Figure 2.5 Chemical structure of monosaccharides from sugar.
Figure 2.6 Chemical structure of starch.
potato, among others; some examples of them are shown in Fig. 2.7, complementary to the sweet potato shown in Fig. 2.1. Almost all the sugar and starchy feedstock include edible crops. In some countries, such as Brazil and the United States, the use of this feedstock to produce biofuels is commonly accepted. However, in other countries the use of edible crops to produce biofuels is forbidden or allowed under some circumstances. For instance, in Mexico, the use of corn to generate biofuels is just allowed when there is a surplus in the national
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
39
production (Diario Oficial de la Federacio´n, 2019); in the last decade, 20% of the corn consumed in Mexico is imported, which indicates that this raw material is not promissory to produce renewable aviation fuels in this country (Food and Agricultural Organization of the United Nations, 2011). Therefore the use of this type of feedstock for the production of renewable aviation fuel must be verified in each country, considering the availability of the raw material and also the applicable legislation. Moreover, the use of edible crops to produce biofuels could affect the food safety; in order to overcome this dilemma, the study of Das (2017) suggests the use of another raw material such as microalgae and algae. Another important aspect is the productivity per hectare of this type of sugar and starchy feedstock, which is presented in Table 2.2. As can be observed in Table 2.2, the prices of this type of feedstock are minor than those for triglyceride feedstock; however, these materials have in general low content of sugars. Moreover, the yields and productivity times vary
Figure 2.7 Sugar and starchy feedstock.
Table 2.2 Sugar content, yield, and price of sugar and starchy biomasses along with its productivity time (Appiah-Nkansah et al., 2019; Barros-Rios et al., 2015; Lai et al., 2013; Paneque Ramirez, 2019; Food and Agricultural Organization of the United Nations, 2019a,b).
Sugar beet Sweet sorghum Sweet corn Sugarcane Sweet potato
Sugar content (%)
Crop yield (ton/ha/year)
Productivity time (months)
Crop price (USD/metric ton)
21.8 17 6.43 17.6 8.41
100 60 98 85 40
6 6 3 18 3 6
56.75 268.54 220.25 185.72 855.31
40
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
considerably. From the sugar and starchy feedstock, sugar beet and sweet corn are the ones with the biggest potential of production per area unit; also, the productivity of sugarcane is close to that high value. Due to this, the use of these raw materials to produce biofuels is of great interest. For this type of feedstock, the main challenge is the availability of an effective and efficient pretreatment, which allows extracting the sugar contained in them; nowadays, this challenge remains. Moreover, there are also sugar and starchy feedstock that are residues; for instance, there are residues from fruits and vegetables, as well as sugar residues from confectionery industries. Unlike the cultivated biomasses presented in Table 2.2, the amount of residues generated depends on each country and its consumption habits. Among food wastes, fruits and vegetables have the higher wastage rate, which is 45% worldwide, while in North American and Oceania zones the percentage increases to 53% (Food and Agricultural Organization of the United Nations, 2019a,b,c). The amount of sugar and starchy residues is significative in almost every country, and it can be used to produce renewable aviation fuel. However, this type of residue usually requires a pretreatment, in order to release the sugars contained in the feedstock. In addition, the main bottleneck is the recollection of the raw material, which is usually dispersed inside the cities; thus the costs associated with the recollection are elevated. Therefore it is clear that all the types of sugar and starchy feedstock have not been explored, especially those that are residues. Similar to the triglyceride feedstock, an interesting opportunity area is the use of mixtures of residues, where it can help strengthen the supply chain for the production of renewable aviation fuel. However, the use of mixtures of residues could complicate the processing, since there is not a pretreatment that works properly for all types of biomass. In general, the costs of obtention of sugar and starchy feedstock are intermediate, as well as the costs related to its processing. The low-cost alternatives of this type of feedstock are residues; however, its processing costs are higher due to the necessity of pretreatments and the recollection of these materials that are scattered in cities as well as cultivation areas. Considering that most of the sources of this type of feedstock are used for human feeding, the use of residues is one of the most promissory alternatives to produce biojet fuel and also solve the associated pollution problem.
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
2.2.3
Lignocellulosic feedstock
The lignocellulosic feedstock includes those materials composed mainly by cellulose, hemicellulose, and lignin; it also contains extractives, pectin, protein, and metal or ash in fractions (Marriott et al., 2016; Deshavath et al., 2019). In particular, cellulose and hemicellulose are polymeric carbohydrates that include different types of hexose and pentose sugars, which can be liberated during the hydrolysis (Deshavath et al., 2019); cellulose and hemicellulose are covered by lignin, which is a threedimensional methoxylated polyphenolic compound (Zhou et al., 2011; Deshavath et al., 2019). In Fig. 2.8 the chemical structures of cellulose, hemicellulose, and lignin are presented. It is worth mentioning that the lignin structure can show differences depending on the biomass type (Mahmood et al., 2018). In lignocellulosic feedstock are included agricultural and horticultural residues, forest residues, municipal solid waste, perennial grasses, bioenergy crops, aquatic plants, and paper and cotton wastes (Ramachandra et al., 2000; Ruane et al., 2010; Kurian et al., 2013); Fig. 2.9 shows examples of lignocellulosic feedstock. Agricultural residues include all the biomass that is generated in the species cultivation process. Thus, the major amount of this type of waste is generated in the cereal cultivations; some examples are wheat straw, rice straw and husks, barley straw, corn pots and leafs (Food and Agricultural Organization of the United Nations, 2018). On the other hand, forest residues include those generated from bamboo, oak, pinewood, and eucalyptus wood; while species such as switchgrass, elephant grass, and Citronella spent biomass are part of the bioenergy crops (Deshavath et al., 2019). Table 2.3 shows the worldwide availability and composition of different lignocellulosic feedstock; prices are not included since in most of the cases the biomass is free, at least initially, and the main contribution to the price relies on the transportation costs. As can be observed in Table 2.3, this type of feedstock is generated in considerable amounts. In spite of this, in most of the cases, these residues are geographically dispersed; this implies high costs associated with its collection, as well as a high environmental impact due to the same activity. Due to this, it is desirable to concentrate the biomass produced in a region, instead of trying to collect all the biomass generated in a country. From the lignocellulosic feedstock, municipal wastes have the biggest generation potential; nevertheless, the high variability in the composition of this type of feedstock makes difficult its efficient conversion. Considering the wide variety of
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Figure 2.8 Chemical structure: (A) cellulose, (B) hemicellulose, and (C) lignin.
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
43
Figure 2.9 Agricultural residues.
Table 2.3 The availability and composition of different lignocellulosic feedstock (Lamborn, 2009; Kurian et al., 2013; Deshavath et al., 2019; WB, 2020). Annual availability
Wheat straw Sugarcane bagasse Corn stover Rice straw Forest residues Municipal wastes Paper
1056 million tons 502 million tons 1413 million tons 1084 million tons 274 million tons 1.3 billion tons 0.34 billion tons
Composition (%) Cellulose
Hemicellulose
Lignin
40 46 37 35 38 37 81
28 27 27 18 4 8 9
16 14 14 7 16 15 2
49 48 42 49
lignocellulosic feedstocks it is clear that all the types of this biomass have not been explored. An interesting opportunity area is the use of mixtures of lignocellulosic feedstock; this will help strengthen the supply chain for the production of renewable aviation fuel. However, this also represents a big challenge in the design of the conversion processes. In general, the obtention of lignocellulosic feedstock is of low or null cost, since it is generated as a by-product of the cultivation processes; however, its processing costs are elevated due to the necessity of pretreatments before its conversion to produce biojet fuel. It is worth mentioning that in some cases, at the beginning the residues are given with no cost by those who generate them; however, as the time goes by its price begins to increase, since selling the residues has been detected as a business opportunity. Thus, this is an important issue if those who process the residue are not the same that generate them.
28 35 25 28
20 19 26 50
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
2.3
Production pathways
The synthetic aviation fuel can be produced from coal, natural gas, or biomass (Liu et al., 2013). Nevertheless, coal and natural gas are not renewable alternatives; therefore production processes where these raw materials are used are not covered in this book. Thus biojet fuel can be produced from lignocellulosic, triglyceride, sugar and starchy feedstock through many emerging technologies (Hari et al., 2015). There are several production pathways that allow converting the biomass, according to its chemical nature. Fig. 2.10 shows the main conversion routes to generate biojet fuel from biomass. Sugar and starchy feedstock can be converted to biojet fuel through biochemical processes, such as sugar to hydrocarbons and alcohol to jet (ATJ) (International Civil Aviation Organization, 2017b). The first route is called direct fermentation of sugars to hydrocarbons or synthesized isoparaffins produced from hydroprocessed fermented sugars (HFS-SIP); in this conversion pathway, modified microorganisms convert sugar into substances such as farnesene, synthetic isoparaffin (SIP), that can be converted into a product with as good characteristics as aviation fuel. On the other hand, in the ATJ process, as the name suggests, biojet fuel is obtained through the dehydration of alcohol and subsequent oligomerization and hydrogenation; it is worth mentioning that the alcohol production is not included as part of the process in the certified process, but this is generated through the conversion of sugar with microorganisms. Moreover, the lignocellulosic feedstock can be converted to biojet fuel through thermochemical pathways, which include pyrolysis followed by hydroprocessing, gasification followed by Fischer Tropsch, and hydrothermal upgrading (International Civil Aviation Organization, 2017b). The first process considers
Figure 2.10 Conversion pathways to produce renewable aviation fuel.
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
the conversion of lignocellulosic feedstock through pyrolysis, where bio-oil is obtained and later converted to biojet fuel with hydroprocessing technology. In the second process, the lignocellulosic biomass is gasified to obtain syngas, which is later converted to biojet fuel, and other renewable hydrocarbons, with Fischer Tropsch catalytic reaction followed by hydroprocessing. Finally, the hydrothermal liquefaction of biomass allows generating bio-oil, which is subsequently converted to biojet fuel through hydroprocessing. It is worth mentioning that bio-oil generated by pyrolysis and hydrothermal liquefaction differs between them in its chemical composition. In the hydroprocessing pathway, the triglyceride feedstock is transformed into biojet fuel through hydrodeoxygenation, hydroisomerization, and hydrocracking to produce biojet fuel (International Civil Aviation Organization, 2017b); depending on the process conditions, triglyceride type, and catalyst, biojet fuel could have aromatic compounds. Therefore a variant of the hydroprocessing technology is the hydroprocessing plus aromatics, where an additional processing line is considered to obtain aromatic compounds. In this way, the produced biojet fuel contains all the compounds included in the fossil jet fuel; this biofuel could be used without the necessity of mixtures with the fossil jet fuel, but this is not approved for use in commercial flights under ASTM standards. The conversion routes presented in Fig. 2.2 have different advantages and disadvantages. In general, triglyceride feedstock is expensive, but its processing costs are low, while lignocellulosic biomass is a low-cost feedstock with high processing costs due to the necessity of many conversion stages. In addition, biomasses with high contents of sugar and starchy are raw materials with intermediate costs (between triglyceride and lignocellulosic feedstock) and also intermediate processing costs (Gutie´rrez-Antonio et al., 2017). In this context, the best raw material is the one that is available in a proper amount to be processed, since for all types of feedstock there are compromises between its acquisition and processing costs; it is worth mentioning that raw materials costs and its transportation will affect the supply chain, which plays an important role in the financial viability of the production process of biojet fuel. In the literature, the study of these conversion pathways under different operating conditions, catalysts types, and raw materials have been reported (Liu et al., 2013; Wang, 2016; Gutie´rrez-Antonio et al., 2017; Va´squez et al., 2017; Wei et al., 2019; Zhao et al., 2019). Moreover, performance characteristics of biojet fuels have been reported, such as low-temperature fluidity, thermal oxidation
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
stability, combustion characteristics, and fuel volatility (Deane and Pye, 2018; Yang et al., 2019; Scheelhaase et al., 2019). In addition, the study of current policies and drivers for the establishment of the supply chain of the renewable aviation fuel has also been reported (Domı´nguez-Garcı´a et al., 2017a,b; Deane et al., 2017; Neuling and Kaltschmitt, 2018; Wei et al., 2019). However, the technical and economic feasibility are not enough to produce and commercialize renewable aviation fuel. Commercial aircraft are certified to operate on specified fuel, which must be evaluated under the process established in ASTM D4054 standard (ASTM International, 2017; Commercial Aviation Alternative Fuels Initiative, 2019); as a result of this evaluation the maximum amount of the new fuel allowed in the aircraft is defined, and then it is added to ASTM D7566 standard (ASTM International, 2019). In this way, a fuel is approved for use in all existing commercial aircraft (CAAFI, 2020). According to the International Civil Aviation Organization (2017a,b), the fuel quality is based on two key concepts: batches and traceability; the batches principle guarantees that a minimum volume is homogeneous, and traceability imposes a custody chain regarding the fuel specification. Thereby the quality of new alternative fuels is evaluated with the process established in ASTM D4054 standard (ASTM International, 2017), and it includes three phases according to Commercial Aviation Alternative Fuels Initiative (2019). During the first phase, a task force is integrated by the companies with similar transformation processes that are interested in getting the certification; in this phase, the specification and the fit-to-purpose properties are determined. The test’s results of the levels 1 and 2 are compiled in a report that is sent to the Original Equipment Manufacturers, which include companies as Safran, Boeing, Honeywell, Rolls-Royce, and Airbus. The manufacturers review this information to determine if the proposed renewable aviation fuel is fit for purpose for use on aircraft and engines, and, also, to identify the Phase 2 testing requirements (Commercial Aviation Alternative Fuels Initiative, 2019). In the Phase 2, more tests are realized oriented to evaluate the component/rig (Level 3) and the engine auxiliary power unit (Level 4). Again, all the results are integrated into a report that is sent to the Original Equipment Manufacturers to review and approval for use on aircraft and engines (Commercial Aviation Alternative Fuels Initiative, 2019). After all the tests and revisions, during the Phase 3 the US Federal Aviation Administration review the approval of the Original Equipment Manufacturers and initial balloting occurs at the
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
subcommittee level (Commercial Aviation Alternative Fuels Initiative, 2019). After that, ASTM gives a period a time to receive additional comments, which can be addressed or solved through additional tests. Later, when all comments are attended there is a final balloting at the committee level and ASTM adds the new fuel to the D7566 standard as a new annex (Commercial Aviation Alternative Fuels Initiative, 2019). The certification process of a new fuel usually takes between 3 and 5 years, since it is a multistage and multifactor process and requires up to 890,000 L of blended jet fuel to be completed (Pavlenko and Kharina, 2018). Considering the operation conditions of airplanes and the high number of casualties in accident cases, it is clear that a careful evaluation process of new alternative fuels must be conducted. However, this process must be streamlined as much as possible in order to allow further diversification of conversion processes and feedstocks for the production of aviation alternative fuels (International Civil Aviation Organization, 2017a,b). Nowadays, there are six pathways that are approved for the production of biojet fuel and its use in commercial aviation, and there are others under evaluation for approval by ASTM; these processes will be presented in Sections 2.3.1 and 2.3.2, respectively.
2.3.1
Certified pathways
The first certified process for biojet fuel production was Fischer Tropsch synthetic paraffinic kerosene (FT-SPK), and it was approved in 2009. The feedstocks that can be used for this process include natural gas, coal, and biomass (International Civil Aviation Organization, 2017a,b). In this process, the feedstock is gasified to obtain syngas, which contains carbon monoxide and hydrogen; the syngas is transformed with Fischer Tropsch catalytic reaction and later converted to biojet fuel, and other renewable hydrocarbons, through hydroprocessing. The renewable aviation fuel produced through FT-SPK can be used in mixtures with fossil jet fuel with compositions up to 50% in volume, according to ASTM D7566 standard (ASTM International, 2019). The companies that have commercialization proposals for FT-SPK technology are Fulcrum Bioenergy, Red Rock Biofuels, SG Preston, Kaidi, Sasol, Shell, and Syntroleum (Commercial Aviation Alternative Fuels Initiative, 2019). In the scientific literature the main raw materials studied for this type of process include octacosane, hexatriacontane, subbituminous coal and blends of coal and various biomass sources such as
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
olive pits, switchgrass, and dried distillers’ grain (Gutie´rrez-Antonio et al., 2017). In 2011 it was approved the hydroprocessed esters and fatty acids synthetic paraffinic kerosene (HEFA-SPK) process. The feedstocks that can be used for this process include vegetable oils, bio-oils from pyrolysis, waste oils, and animal fats (International Civil Aviation Organization, 2017a,b). In this process, the triglyceride feedstock is converted to renewable hydrocarbons through the reactions of hydrodeoxygenation, hydroisomerization, and hydrocracking; as products light gases, naphtha, biojet fuel, and green diesel are obtained and purified through distillation. The renewable aviation fuel produced through HEFA-SPK can be used in mixtures with fossil jet fuel with compositions up to 50% in volume, according to ASTM D7566 standard (ASTM International, 2019). The companies that have commercialization proposals for this process are AltAir Fuels, Honeywell UOP, Neste Oil, Dynamic Fuels, and EERC (Commercial Aviation Alternative Fuels Initiative, 2019). In the scientific literature, the main raw materials studied are oils from soybean, J. curcas, castor, along with corn stalks biooil, cooking oil, and vacuum gas oil of vegetable oil mixtures (Gutie´rrez-Antonio et al., 2017). The HFS-SIP, also known as direct sugar to hydrocarbons, was the third approved process, in 2014, for biojet fuel production. The feedstocks that can be used for this process include sugars and all the biomass used for sugar production (International Civil Aviation Organization, 2017a,b). In this process, the feedstock is converted to farnesene, a C15 hydrocarbon, with the use of modified yeasts; later, farnesene is hydrotreated in order to generate a biofuel that can be used until 10% in volume in mixtures with fossil jet fuel, according to ASTM D7566 standard (ASTM International, 2019). The companies that have commercialization proposals for this process are Amyris and Total (Commercial Aviation Alternative Fuels Initiative, 2019). In 2015 the Fischer Tropsch synthetic paraffinic kerosene with aromatics (FT-SPK/A) process was approved. The feedstocks that can be used for this process are the same for FT-SPK technology, and they include natural gas, coal, and biomass (International Civil Aviation Organization, 2017a,b). In this process, the feedstock is gasified to obtain syngas that contains carbon monoxide and hydrogen. The syngas is transformed with Fischer Tropsch catalytic reaction followed by the alkylation of light aromatics (primarily benzene) to create a hydrocarbon blend that includes aromatic compounds; these compounds are required to ensure elastomer seal swell in aircraft components to prevent fuel leaks (Commercial
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Aviation Alternative Fuels Initiative, 2019). The renewable aviation fuel produced through FT-SPK/A process can be used in mixtures with fossil jet fuel until 50% in volume according to ASTM D7566 standard (ASTM International, 2019). For this process, Sasol is the only company that has a commercialization proposal (Commercial Aviation Alternative Fuels Initiative, 2019). The alcohol-to-jet synthetic paraffinic kerosene (ATJ-SPK) process was approved in 2016. This process uses as feedstock all the biomass that is used for starch and sugar production, and cellulosic biomass for alcohol production (Commercial Aviation Alternative Fuels Initiative, 2019). In this process, the dehydration of isobutanol or ethanol followed by oligomerization and hydrogenation are performed to produce a hydrocarbon jet fuel (Commercial Aviation Alternative Fuels Initiative, 2019). The produced biofuel can be used until 30% in volume in mixtures with fossil jet fuel, according to ASTM D7566 standard (ASTM International, 2019). The companies that have commercialization proposals for this technology are Gevo, Cobalt, Honeywell UOP, Lanzatech, Swedish Biofuels, and Byogy (Commercial Aviation Alternative Fuels Initiative, 2019). Recently, in 2018 it was approved the coprocessing of renewable lipids (plant and animal fats) with crude oil-derived middle distillates in petroleum refineries (Commercial Aviation Alternative Fuels Initiative, 2019). The produced biofuel can be used in mixtures with fossil aviation fuel up to 5% (Thion, 2019). According to Thion (2019), among the certified technologies FT-SPK, FT-SPK/A, and ATJ-SPK processes are at demonstration level, while HEFA-SPK and HFS-SIP are at the commercialization stages. At the moment, the biojet fuel produced for any of the approved pathways is not competitive in the market (Thion, 2019); therefore the efforts must be focused on the use of lowcost raw materials and the development of efficient conversion processes.
2.3.2
Advances in the certification of new pathways
The research and development of new processes for the production of renewable aviation fuel are highly dynamic. By January of 2020, eight new pathways are in different phases of evaluation, with the objective of getting the approval of ASTM (Commercial Aviation Alternative Fuels Initiative, 2019).
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In the first phase, the active force tasks of the processes under evaluation are the following ones (Commercial Aviation Alternative Fuels Initiative, 2019): • high freeze point hydroprocessed esters and fatty acids synthetic kerosene (HFP HEFA-SK) impulsed by Boeing; • hydrodeoxygenation synthetic aromatic kerosene (HDOSAK) lead by Virent; • alcohol-to-jet synthetic kerosene with aromatics (ATJ-SKA) promoted by Byogy and Swedish Biofuels; • integrated hydropyrolysis and hydroconversion (IH2) presented by Shell; and • hydroprocessed Esters and Fatty Acids Synthetic Paraffinic Kerosene (HEFA-SPK) by IHI. In the first phase there is another inactive task force lead by KiOR with the pathway Hydrotreated depolymerized cellulosic jet (HDCJ). On the other hand, in the Phase 2 there is one active task force lead by ARA for the catalytic hydrothermolysis synthetic kerosene (CH-SK) process, while the hydrodeoxygenation synthetic kerosene (HDO-SK) process promoted by Virent is actually inactive (Commercial Aviation Alternative Fuels Initiative, 2019). From the eight processes under evaluation, three of them employ sugar and cellulosic feedstocks (HDO-SK, HDO-SAK, ATJSKA), two of them use plant and animal fats, oils, and greases (CHSK, HFP HEFA-SK), one of them converts hydrocarbon-rich algae oil (HEFA-SPK), and another one considers forest residues (HDCJ), while the IH2 can process multiple feedstock (Commercial Aviation Alternative Fuels Initiative, 2019).
2.4
Summary
Biojet fuel can be produced from all types of biomass, for example, triglyceride, lignocellulosic, sugar, and starchy. There are three main processing pathways for conversion of the biomass, which include chemical, biochemical, and thermochemical processes. Considering these conversion pathways, several raw materials have been studied, and this research field has had a fast growth. However, the production processes of biojet fuel must be approved by ASTM for its use in commercial flights. The certification process takes between 3 and 5 years, and almost 900,000 L of renewable aviation fuel are required for the tests during the certification process. Therefore in order to increase the supply of renewable aviation fuel it is necessary to
Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
continue researching in the development of new production processes and in parallel to expedite the certification process.
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Chapter 2 Renewable feedstock and its conversion routes to biojet fuel
Mahmood, Z., Yameen, M., Jahangeer, M., et al., 2018. Lignin isolation from the spruce wood. Lignin as Natural Antioxidant Capacity. Intech, pp. 182 205. Maity, S.K., 2015. Opportunities, recent trends and challenges of integrated biorefinery: part I. Renew. Sustain. Energy Rev. 43, 1427 1445. Markets and Business, 2009. Biokerosene takes off in aviation sector. Focus Catal. 1, 2. Marriott, P.E., Go´mez, L.D., McQueen-Mason, S.J., 2016. Unlocking the potential of lignocellulosic biomass through plant science. New Phytol. 209, 1366 1381. Marsh, G., 2009. Small wonders: biomass from algae. Renew. Energy Focus 9 (7), 74 78. Neuling, U., Kaltschmitt, M., 2018. Techno-economic and environmental analysis of aviation biofuels. Fuel Process. Technol. 171, 54 69. Paneque Ramirez, G., 2019. Cultivation harvesting and storage of sweet potato products. Food and Agriculture Organization. ,http://www.fao.org/3/ T0554E/T0554E14.htm. (accessed 06.01.20.). Pavlenko, N., Kharina, A., 2018. Policy and environmental implications of using HEFA 1 for aviation. Working Paper 2018.06. The International Council of Clean Transportation. ,https://www.theicct.org/sites/default/files/ publications/Green-Diesel-Aviation_ICCT-Working-Paper_20180321_vF.pdf. (accessed 19.05.19.). Ramachandra, T.V., Joshi, N.V., Subramanian, D.K., 2000. Present and prospective role of bioenergy in regional energy system. Renew. Sustain. Energy Rev. 4, 375 430. Ruane, J., Sonnino, A., Agostini, A., 2010. Bioenergy and the potential contribution of agricultural biotechnologies in developing countries. Biomass Bioenergy 34, 1427 1439. Ruiz C.J.A., Medina G.G., Gonza´lez A.I.J., Flores L.H.E., Ramı´rez O.G., Ortiz T.C., et al., 2013. Requerimientos agroecolo´gicos de cultivos. Libro Te´cnico Nu´m. 3. Segunda Edicio´n. Instituto Nacional de Investigaciones Forestales Agrı´colas y Pecuarias, Me´xico, 564 pp. Scheelhaase, J., Maertens, S., Grimme, W., 2019. Synthetic fuels in aviation current barriers and potential political measures. Transp. Res. Procedia 43, 21 30. Thion, S., 2019. Flying green sustainable aviation fuels. Offsetting the environmental impact of a fast growing industry. ,https://www.irena.org/-/ media/Files/IRENA/Agency/Events/2019/May/biojet-EUBCE/5-Sthepane-Thion. pdf?la 5 en&hash 5 D11D36850F29EA282D63ECDF9B122CDFEAA7AE26. (accessed 18.01.20.). Va´squez, M.C., Silva, E.E., Castillo, E.F., 2017. Hydrotreatment of vegetable oils: a review of the technologies and its developments for jet biofuel production. Biomass Bioenergy 105, 197 206. Wang, W.-C., 2016. Techno-economic analysis of a bio-refinery process for producing hydro-processed renewable jet fuel from Jatropha. Renew. Energy 95, 63 73. Wei, H., Liu, W., Chen, X., Yang, Q., Li, J., Chen, H., 2019. Renewable bio-jet fuel production for aviation: a review. Fuel 254, 115599. World Bank, 2020. Trends in solid waste management. ,http://datatopics. worldbank.org/what-a-waste/trends_in_solid_waste_management.html. (accessed 12.01.20.). Yang, J., Xin, Z., He, Q.S., Corscadden, K., Niu, H., 2019. An overview on performance characteristics of bio-jet fuels. Fuel 237, 916 936.
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Production processes for the conversion of triglyceride feedstock 3.1
3
Introduction
As presented in Chapter 2, Renewable Feedstock and Its Conversion Routes to Biojet Fuel, the triglyceride feedstock consists of fatty acids, whose composition varies significantly depending on the source and the geographical origin (Maity, 2015). The triglyceride feedstock includes vegetable oils, animal fats, microalgae oil, and waste oils; also, bio-oils obtained from processes such as pyrolysis and hydrothermal liquefaction are considered in this type of feedstock. In addition, the lipids extracted from black soldier fly larvae are classified in this type of raw material. In Fig. 3.1, a representative scheme of triglyceride feedstocks from different sources is presented; those materials can be used to produce biojet fuel through hydroprocessing. The hydroprocessing of triglyceride feedstock allows generating renewable aviation fuel along with other renewable hydrocarbons. In this process, the triglyceride feedstock is converted with hydrogen to produce renewable hydrocarbons; usually the conversion process is carried out at high pressures and temperatures. Later, the produced renewable hydrocarbons, which include light gases, naphtha, green diesel, and biojet fuel, are separated by distillation columns. In 2008 it was submitted the first patent document for a hydrotreating process to produce biojet fuel, which was granted in 2011 to UOP Honeywell company (McCall et al., 2011); in the same year, this process was certified by ASTM for the production of biojet fuel that can be used for commercial flights. An important characteristic of this process is its similarity with petro-refineries; therefore the facilities of petro-refineries can be revamped to produce biojet fuel. In addition, almost half of the demonstration flights have been performed with biojet fuel generated with hydroprocessing technology, being UOP Honeywell Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00003-1 © 2021 Elsevier B.V. All rights reserved.
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Chapter 3 Production processes for the conversion of triglyceride feedstock
Figure 3.1 Triglyceride feedstocks from renewable sources.
and SkyNRG the main actors (Gutie´rrez-Antonio et al., 2017). Nowadays this technology is ready for commercialization. This chapter is focused on the production of biojet fuel from triglyceride feedstock. First, the description of the chemical transformations occurring to the triglyceride feedstock is presented. After, the technologies required to obtain pure final products are mentioned; in addition, the combustion studies reported for the renewable aviation fuel produced with this process are described. Finally, an example of the simulation and assessment of a process for the production of biojet fuel from triglyceride feedstock is presented and discussed.
3.2
Conversion processes of the triglyceride feedstock
The hydroprocessing involves a broad variety of hydrogenation catalytic processes, in which sulfur, oxygen, and nitrogen
Chapter 3 Production processes for the conversion of triglyceride feedstock
57
heteroatoms are removed from the triglyceride feedstock; this removal is realized through a series of hydrogenolysis and hydrogenation steps, which generates long-chain hydrocarbons (Romero-Izquierdo et al., 2018). The reactions included in the hydroprocessing are carried out in the presence of hydrogen. The first reaction is the hydrogenation, which is the saturation of the double bonds in a triglyceride molecule by the catalytic addition of hydrogen at certain temperature and pressure condition, causing the breaking of the glycerol compound, releasing propane and free fatty acid chains. After hydrogenation, three reactions can occur simultaneously, hydrodeoxygenation (HDO), hydrodecarboxylation (DCOx), and hydrodecarbonylation (DCO); wherein the carboxylic acid group from the free fatty acids is removed to form straight chains alkanes. In the HDO route a hydrocarbon is produced, with the same number of carbon atoms as the fatty acid chain and two moles of water. Then, in the DCOx a hydrocarbon with one less atom than the fatty acid chain and a mole of CO2 is obtained. Finally, the DCO produces a hydrocarbon with one carbon less and one mole of CO and water (Va´squez et al., 2017). A simplified representation of this complex mechanism is presented in Fig. 3.2. In the conventional hydrotreating process, the conversion of the triglyceride feedstock is performed in two main stages: hydrodeoxygenation and hydroisomerization/hydrocracking. The hydrodeoxygenation reaction is very important, since the total removal of oxygen from the triglyceride feedstock is required to reach the freeze point. Besides, the biomass contains almost 167 times the oxygen content (in ppmw) of petroleum. Therefore, in the hydrotreating of biomass the removal of oxygen from the triglyceride or fatty acid molecule, that is, deoxygenation, is the most important reaction. As product of this reaction long-chain hydrocarbons are obtained, along with H2O, CO2, and/or CO. The renewable hydrocarbons that are
Figure 3.2 Conversion of a triglyceride molecule under hydrotreatment. Source: Extracted from Va´squez, M.C., Silva, E. E., Castillo, E.F., 2017. Hydrotreatment of vegetable oils: a review of the technologies and its developments for jet biofuel production. Biomass Bioenergy 105, 197206.
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Chapter 3 Production processes for the conversion of triglyceride feedstock
produced are more similar to a diesel; thus it is necessary to cut and branch the chains to generate hydrocarbons in the boiling point range of C8C16, corresponding to jet fuel. Due to this, a second reactive stage is required to perform the hydroisomerization and hydrocracking of renewable hydrocarbons produced in the first reactive stage. From the second reactive stage, the following products can be obtained: light gases, naphtha, biojet fuel, and green diesel. In general, the common operational conditions for the hydrotreating process include high pressure, greater than 20 bar, and temperature range of 300 C350 C; these values depend on the feedstock and catalytic system used (RomeroIzquierdo et al., 2018). Moreover, a high H2 NPT ml/oil ml ratio is required, around 1500 in order to avoid the generation of coke in the catalyst. Regarding the catalysts, some of them are used in fossil hydroprocessing, such as Pd/C or CoMo/γ-Al2O3, and they have a good performance in the conversion of renewable triglyceride feedstock. Fig. 3.3 shows the overall hydrotreating process from triglyceride feedstocks, which involves two reactive stages and a separation stage. The hydrotreating process is a multicomponent and multireaction system; therefore it is complicated to identify the specific reaction pathway followed. Due to this, most of the hydrotreating studies report conversion and/or selectivities, while just a few works present kinetic models; from these last
Figure 3.3 Overall hydrotreating process.
Chapter 3 Production processes for the conversion of triglyceride feedstock
studies, only a few present kinetic models oriented to biojet fuel (Romero-Izquierdo et al., 2018). Moreover, the scarce kinetic models reported are lumped due to the complexity of the reactive system and the high number of products. It is important to mention that experimental studies that focus on the hydrotreating of mixtures of triglyceride feedstock are missing in the literature. From the reactive zone of the hydrotreating process, four products can be obtained: light gases, naphtha, biojet fuel, and green diesel. Due to the thermodynamic characteristics, these products can be separated through distillation. The design of the distillation sequence must guarantee the minimum energy consumption to allow a competitive price for the obtained fuels. Due to this, from all the possible sequences for the separation of these four products, the schemes with more potential are those where the light gases are separated in the first distillation column since the use of refrigerant is minimized. In spite of all renewable fuels, obtained from the distillation sequence, can be commercialized, all the research efforts have been focused on the increase of the yield to biojet fuel; as reference, in the first patent for the hydroprocessing technology by UOP Honeywell, the maximum yield to biojet fuel was reported by McCall et al. (2011) as 36%. Next, information about the features of coproducts generated in the hydrotreating process is presented. The light gases generated in the hydrotreating process consist of hydrocarbons from C1 to C4; thus, methane, ethane, and LPG (propane and butane) are included. The light gases can be used as fuel in boilers, cooking devices, heating appliances, power plants, and some vehicles with internal combustion engines. Naphtha, from fossil origin, consists of hydrocarbons with a boiling temperature from 30 C to 200 C; indeed, it can be classified in light (30 C90 C, C5C6) and heavy naphtha (90 C200 C, C6C12). In the hydrotreating process, the naphtha produced consists of hydrocarbons from C5 to C7, since biojet fuel includes hydrocarbons from C8 to C16; therefore the naphtha produced consists of light naphtha and some of heavy naphtha. This product can be used as fuel and solvent, and also, it is an important resource used to produce several petrochemical products (Lyu et al., 2014). According to Hua et al. (2018), the pyrolysis of naphtha is the major source of ethylene and another olefins feedstock. On the other hand, green diesel consists of renewable hydrocarbons from C17 to C21. Unlike biodiesel, green diesel can be
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Chapter 3 Production processes for the conversion of triglyceride feedstock
used as a replacement for fossil diesel, since it is composed of hydrocarbons. Green diesel can be used as fuel in internal combustion engines, boilers and power plants. Moreover, this fuel can also be used as raw material for the production of biojet fuel through the hydrocracking process.
3.3
Conventional processes: state of the art
In this subsection the state of the art of the conventional hydrotreating processes is presented. A conventional process consists of a series of unit operations, where the conditioning, reaction, and/or separation are performed one at a time in a different vessel; thus no additional strategies to reuse energy or make synergy between unit operations are proposed (QuirozPe´rez et al., 2019). It is worth to mention that it is desirable to reduce both operation and capital costs in the processes, which can be obtained through the application of process intensification tools; the use of the aforementioned tools for the production of biojet fuel will be discussed in Chapter 6, Process Intensification and Integration in the Production of Biojet Fuel. From all the processes to produce biojet fuel, the hydroprocessing is the most studied without any doubt. As mentioned before, the hydrotreating process was developed with base on the technology developed for petro-refineries. Therefore several catalysts were already available to perform tests with vegetable oils and animal fats. In 2007 Holmgren presented the vision of UOP Honeywell to produce renewable aviation fuels from second- and thirdgeneration raw materials in the International Symposium on Biofuels (Holmgren, 2017). However, until 2009the first scientific articles of the hydroprocesing technology were reported (Markets and Bussiness, 2009; Marsh, 2009). At the date, a search in Elsevier’s database (Sciencedirect, 2020) reveals that more than 200 articles have been published related with the production of renewable aviation fuel through hydroprocessing technology. From the total, more than 35 are review articles, which denote the growing interest of this topic in the scientific community. In particular, several reviews focused on the production process of renewable aviation fuel (Liu et al., 2013; Hari et al., 2015; Wang and Tao, 2016; Gutie´rrez-Antonio et al., 2017; Cruz Neves et al., 2019; Wei et al., 2019; Perkins et al., 2019), and some of them specifically in the hydroprocessing technology (Va´squez et al., 2017; Romero-Izquierdo et al., 2018; Why et al., 2019). From these works, it was identified that several
Chapter 3 Production processes for the conversion of triglyceride feedstock
triglyceride feedstocks have been considered for the production of biojet fuel in both experimental and simulation studies; these materials include oil from Jatropha curcas, castor, micro-algae, soybean, camelina, carinata, coconut, babassu, inedible corn oil, peanut, sunflower, macauba palm, cotton, palm (refined and crude), tung along with chicken fat, vacuum gas oil of vegetable oil mixtures, waste cooking oil, waste vegetable oils, animal fats, microbial oils, and bio-oil from cornstalk and Douglas fir pellets (Gutie´rrez-Antonio et al., 2017, 2018; Zhao et al., 2019; Moreno-Go´mez et al., 2020; Dujjanutat and Kaewkannetra, 2020; Mendes de Souza et al., 2020). An important finding is that most of the works reported the experimental or modeling studies in two steps: hydrodeoxygenation followed by hydroisomerization and hydrocracking (Gutie´rrez-Antonio et al., 2017). However, some works reported the one-step hydroprocessing, where all the reactions involved in the hydroprocessing are carried out in one vessel (Verma et al., 2011; Liu et al., 2015; Hanafi et al., 2016). Respect to the catalysts, there have been used those based on Ni, Mo, Co, Pd, Ro, Pt, and bimetallic sulfide catalysts, such as Ni-Co-Fe, Mo-WU, NiMoS2, Ni-W/ SiO2, CoMoS2, and NiWS2 supported on Al2O3 (Anand et al., 2016; Gutie´rrez-Antonio et al., 2017; Zhao et al., 2019; Wei et al., 2019). Recently, the use of a new catalyst SDBS-Pt/SAPO-11 was proposed for the conversion of Jatropha oil (Li et al., 2019); SDBS is sodium dodecyl benzene sulfonate. At the moment, the major yield to biojet fuel (77%) have been reported for the conversion of microalgae oil with 5% NiO, 18% MoO3/H-ZSM-5 (micromesoporous)-HSASC (Verma et al., 2011); it is worth to mention that this high yield is reached with the one-step hydroprocessing. Typically, pressures for hydrotreating of vegetable oil range from 20 to 110 bar, and the temperatures comprise from 250 C to 430 C (Romero-Izquierdo et al., 2018; Zhao et al., 2019; Wei et al., 2019). Finally, respect to the hydrogen flow, the studies report from 800 to 2500 H2 NPTmL/oil mL (Gutie´rrez-Antonio et al., 2017). In the same way, the technological development has a vertiginous rate of growth. The total number of patents related with the production of renewable aviation fuel increased from 2 in 2008 to 60 in just 7 years; by January 2020, a search in the PATENTSCOPE database of the World Intellectual Property Organization generates 480 documents, when the words renewable aviation fuel, biojet fuel, biokerosene, and synthetic paraffinic kerosene are used (World Intellectual Property Organization, 2020). The increase in the number of patents is consistent with the increase in the task forces and companies
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behind them, issue discussed in Chapter 2, Renewable Feedstock and Its Conversion Routes to Biojet Fuel. From the 500 patent documents identified previously, in 240 the hydroprocessing technology is considered. This result evidences that the hydroprocessing technology is still the predominant one, in spite of other processes are under research and development. The raw materials reported in the document patents cover vegetable oils, animal fats, algae biomass, feedstock with high amounts of free fatty acids, feedstock that contains triglycerides, free fatty acids and/or fatty acid alkyl esters, bio-oil, and even biodiesel. Some of the reported catalysts employed Pd, Pt, Ni, Mo, and Co supported on alumina, gamma-alumina, zeolitic molecular sieve, or silicoaluminophosphate (Gutie´rrez-Antonio et al., 2017). The operating conditions (pressure and temperature) are similar to those reported in open literature. The major yield to biojet fuel (80%) has been reported for the conversion of biological feedstock with nanocoated palladium as a catalyst supported by activated carbon at 69 bar and temperatures of 250 C360 C (Parimi and Nguyen, 2012, 2013). In general, the hydrotreating pathway has been studied from both catalysis and separation points of view; however, most of the works are experimental, which is understandable since the study of the involved reactions is relatively recent, especially for renewable raw materials (Gutie´rrez-Antonio et al., 2017). Regarding the raw materials, the efforts must be focused on the use of third-generation biomass, which can be cultivated in wastewaters, and residues; these materials will allow a significant decrease in the operating costs and also the price of biojet fuel. Moreover, a decreasing in the environmental and hydric impacts of the production of renewable aviation fuel can be achieved. Respect to the catalyst, there have been significant advances from the 36% conversion reported in 2008, when the patent document by UOP was submitted, to the 70% reported in 2011 by Verma et al. (2011); another important aspect to remark is that the high conversion reported by Verma et al. (2011) was reached in a single vessel with a multifunctional reactor. Finally, the use of mixtures of triglyceride feedstock, as the one presented in the case study, represents a viable alternative to strengthen the supply chain for the production of the renewable aviation fuel. In brief, the technological and scientific advances in the hydroprocessing of triglyceride feedstock have made it the most mature technology for the production of renewable aviation fuel. Another crucial aspect for the certification of the biojet fuel is, without any doubt, the combustion tests of the fuels
Chapter 3 Production processes for the conversion of triglyceride feedstock
generated with this technology, a topic that will be discussed in the next section.
3.4
Combustion tests for biojet fuel from triglyceride feedstock
The combustion tests are oriented to evaluate the performance of the renewable aviation fuel inside the engine (lean blowout, atomization, ignition, and altitude relight), and the combustion products (emissions, smoke, and carbon deposit) (Zhang et al., 2016). The performance of each of the alternative jet fuel can exhibit its own, unique behavior during combustion due to its properties (Zhang et al., 2016), that, at the same time, are strongly affected by the chemical composition of the fuel, which depends on the processing route and renewable feedstock (Vozka and Kilaz, 2018, 2020). Therefore it is necessary to perform combustion tests of these alternative fuels in jet engines. Among all alternative aviation fuels, biojet fuel obtained from hydroprocessing has been widely studied from the combustion point of view. At the beginning of the past decade, Corporan et al. (2010) determined the emissions of the combustion of JP8 military fuel and a mixture of 50% of JP8 military fuel with biojet fuel produced with the hydroprocessing of beef tallow. They found that there were not detected anomalies in the operation on the engine. Moreover, a reduction of 30% in carbon monoxide and 50% of sulfur oxides was observed, while the nitrogen oxides kept unchanged. Also, there was a decreasing of the particle matter emissions, due to the lower aromatic content. Later, Corporan et al. (2012) studied the emissions of the combustion of renewable aviation fuel produced from FischerTropsch and hydroprocessing pathways. Their study confirmed the findings reported in their previous work of 2010 regarding the emissions. In addition, they concluded that it is possible to model the particle number emissions from turbine engines operated on alternative fuels based on engine, engine setting, limited particulate matter (PM) data, and fuel composition (Corporan et al., 2012). Two years later, Altaher et al. (2014) analyzed the emissions of unburned hydrocarbons and carbonyl compounds of Jet A1, and four alternative jet fuels, which were produced by gas-toliquid and hydroprocessing routes. The results showed that formaldehyde and aromatic emissions were lower in the alternative fuels in comparison with Jet A1, while no differences
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Chapter 3 Production processes for the conversion of triglyceride feedstock
were observed in acetaldehydes and acrolein emissions between all the analyzed jet fuels. Buffi et al. (2017) performed a study for the characterization of the emissions of biojet fuel derived from the hydroprocessing of used cooking oil and its blends with Jet A1. The results showed that the hydroprocessed jet fuel can reduce emissions and lead to a more compacted and homogenous heat release zone, which also allows the decreasing of soot and nitrogen oxides. It was until 2019 when the first study of evaluation of the effect of the raw material used to produce the renewable aviation fuel was presented. Sundararaj et al. (2019) reported the combustion and emissions characteristics of blends of Jet A1 with biojet fuel derived from the hydroprocessing of Camelina and J. curcas oils. For biojet fuel derived from Camelina, they found that by increasing the concentration of that fuel, the net heat of combustion increases, which elevated the combustion temperature and NOx emissions; however, at these conditions the soot concentration decrease. On the other hand, for Jatropha-derived biojet fuel, as the concentration of the biojet fuel increased, the carbon dioxide emissions augment but nitrogen oxides are reduced. Recently, the autoignition characteristics of four conventional aviation jet fuels (Jet-A POSF-4658, Jet-A POSF-10325, JP5 POSF-10289, JP-8 POSF6169), five alternative jet fuels, Syntroleum S-8 (S8), shell synthetic paraffinic kerosene (Shell SPK), Sasol isoparaffinic kerosene (Sasol IPK), hydroprocessed renewable jet (HRJ8), alcohol to jet (ATJ), and 50/50 vol.% blends of JP-8/alternative jet fuels were explored (Kang et al., 2019). The results indicated S8, Shell SPK, and HRJ8 provide stronger low-temperature ignition, while Sasol IPK and ATJ exhibit weaker low-temperature ignition characteristics, when compared to conventional jet fuels. This result confirmed the dependence of the performance of the biojet fuel of its production process and raw material
3.5 3.5.1
Case of study: hydroprocessing of a mixture of vegetable oils Problem statement
In Mexico, the production of vegetable oil from nonedible energetic crops is an advantageous strategy to produce renewable fuels. In particular, Chiapas state, located at southwest of
Chapter 3 Production processes for the conversion of triglyceride feedstock
Mexico, has a rich biodiversity, extensive and fertile lands; moreover, during the last decades this state has participated in several projects for the developing of sustainable biofuels (Reza et al., 2010; Gonza´lez, 2012; Riegelhaupt and Odenthal, 2015; Red Tema´tica de Bioenergı´a, 2016; Garcı´a-Bustamante and Massera-Cerutti, 2016). In Chiapas, two energetic crops have gained interest in the production of renewable fuels: palm oil and J. curcas, mainly due to their high oil content and smoothly cultivation conditions. The production of palm oil in Chiapas represents the 79% of total production in Mexico (Wiedersatz, 2012); the oil yield through agroindustry techniques is 2.3 L per 10 kg of palm oil fruit (Sandoval-Garcı´a et al., 2016). In 2014 into Soconusco region 32,798 Ha of palm oil were cultured, covering 16 municipalities in the state with an average fruit yield of 20 ton fruit per Ha (Sa´nchez et al., 2014; Castellanos Navarrete, 2018). If we consider the palm oil density of 898 kg/m3 at 40 C (Unipalma, 2020), the availability of palm oil could be up to 135,459.88 ton oil. Regarding to J. curcas fruit production in Mexico, the Chiapas state has 230,273 Ha with optimum potential lands to sow J. curcas, locating this state in the fourth position to produce this energetic crop. In the Soconusco region the average yield per Ha per year is 5 ton seeds with roughly 55 wt.% of oil (Zamarripa Colmenero et al., 2009). This yield is reached after 5 years of sowing and until 50 years, which is the estimated lifetime of the plant (Inurreta Aguirre et al., 2013). In this case of study, it is considered the processing of a mixture of 80 wt.% J. curcas oil and 20 wt.% palm oil, from Soconusco region in Chiapas, to produce biojet fuel as main product, through the hydrotreatment process. The feed stream of the process consists of 500 kg/h palm oil and 2000 kg/h of J. curcas oil. The block diagram to represent the hydrotreatment of a vegetable oils mixture is presented in Fig. 3.4.
3.5.2
Modeling of the hydrotreating of the mixture of oils
Currently in the literature there are few kinetic studies to describe the hydrotreating of a mixture of vegetable oils (Sebos et al., 2009; Becker et al., 2015; Sonthalia and Kumar, 2019), and particularly the experimental data for the mixture proposed in this case of study (20 wt.% palm oil and 80 wt.% Jatropha oil) have not been reported. Thus, in order to model the mixture of
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Chapter 3 Production processes for the conversion of triglyceride feedstock
Figure 3.4 Block diagram of hydrotreating process (Gutie´rrez-Antonio et al., 2016).
Table 3.1 Lipidic profile of palm oil (Ve´lez Manco, 2014). Fatty acid
Formula
% w/w
Oleic Palmitic Linoleic Stearic Myristic Traces
18:1 16:0 18:2 18:0 14:0 20:0; 12:0; 18:3; 20:1; 16:1; 17:0; 24:0; 22:0; 15:0
38.9 34.6 13.1 8.1 2.2 B3.1
palm and Jatropha oils, we assume that the hydrotreating is carried out simultaneously in two separate equipment; in this way, we can employ kinetic data already reported in the literature for the separated hydroprocessing of each oil. However, for both reactors the temperature, pressure, H2/oil volumetric ratio, and geometric relations will be the same. Later, the output streams will be mixed to the next reactor. Next, the hydroprocessing of J. curcas and palm oils is presented. In 2014 Ve´lez Manco (2014) studied the hydrotreatment of palm oil in an experimental continuous trickle bed reactor using NiMo/γ-Al2O3 (Haldor Topsoe TK-561 BRIM) catalyst. The lipidic profile of the palm oil is presented in Table 3.1, from where it can be observed that almost 75% of composition corresponds to oleic and palmitic acid. In the study reported by Ve´lez Manco (2014), the experimental reactor made of stainless steel was a vertical tube of 31 cm length and roughly 1 cm of internal diameter, while the total active bed height was 5 cm. The hydrogen flow inserted during
Chapter 3 Production processes for the conversion of triglyceride feedstock
67
the operation was a molar NPT ratio H2/oil equal to 20. The hydrogen pressure established for the operation was 60 bar, with a maximum temperature of 365 C and a minimum value of 335 C. Table 3.2 presents the composition of the outlet stream of the reactor, at a temperature of 365 C. According to his results, Ve´lez Manco (2014) proposed a set of reactions to model this process, adjusting the experimental information to a first-order kinetic model and determining the kinetic parameters. In Table 3.3 the reactions proposed, and the kinetic data are presented. The modeling of palm oil considers only its majority components, normalizing the compositions for the first four components: 41.08 wt.% triolein, 35.54 wt.% tripalmitin, 13.83 wt.% trilinolein, 8.55 wt.% tristearin; in spite of this, there is no modification in the kinetic parameters experimentally determined as well in the chemical pathway established for each triglyceride. However, in order to consider the formation of each component according to Table 3.2, an extended model for the set of reactions proposed by Ve´lez Manco (2014). The extended set of reactions and its kinetic data are presented in Tables 3.4 and 3.5, respectively. It is worth to mention that the percentage of free fatty acids into the oil is less than 1%; thus we assume that the palm oil is formed only by triglycerides. The hydrotreatment of Jatropha oil is modeled based on a kinetic study where Co-Mo/Al2O3 was used as catalyst, and the operation conditions are varied (Anand and Sinha, 2012). The experimental
Table 3.2 Composition of the outlet stream of the hydrotreating reactor (Ve´lez Manco, 2014). Compound
% w/w
C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20
0:1 6 0:01 0:1 6 0:01 0:18 6 0:02 0:24 6 0:04 0:42 6 0:06 0:68 6 0:013 16:26 6 2:54 25:55 6 5:36 22:68 6 3:39 32:34 6 6:67 0:37 6 0:05 0:33 6 0:06
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Chapter 3 Production processes for the conversion of triglyceride feedstock
Table 3.3 Proposed set reactions for hydrotreatment of palm oil and kinetic data (Ve´lez Manco, 2014). Reaction no.
Reaction
1
C57 H98 O6 1 6H2 -C57 H110 O6 Trilinolein (LLL) 1 H2-tristearin (SSS) C57 H104 O6 1 3H2 -C57 H110 O6 Triolein (OOO) 1 H2-tristearin (SSS) C51 H98 O6 1 12H2 -3C16 H34 1 C3 H8 1 6H2 O Tripalmitin (PPP) 1 H2-hydrocarbons C57 H110 O6 1 12H2 -3C18 H38 1 C3 H8 1 6H2 O Tristearin (SSS) 1 H2-hydrocarbons C51 H98 O6 1 3H2 -3C15 H32 1 C3 H8 1 3CO2 Tripalmitin (PPP) 1 H2-hydrocarbons C57 H110 O6 1 3H2 -3C17 H36 1 C3 H8 1 3CO2 Tristearin (SSS) 1 H2-hydrocarbons
2 3 4 5 6
Kinetic expression
Kinetic parameters
r 01 5 k1 CLLL
9:4 3 1011 149.9
r 02 5 k2 COOO
4:4 3 1013 162.5
r 03 5 k3 CPPP
3:8 3 1010 147.4
r 04 5 k4 CSSS
5:6 3 1010 149.1
r 05 5 k5 CPPP
1:4 3 1010 150.6
r 06 5 k5 CSSS
Ao (1/s)
8:2 3 1011
Ea (kJ/ mol)
169.2
Note: ki 5 Aoi e2Ea =RT .
Table 3.4 Extended reactions set for the hydrotreatment of palm oil. Triglycerides
Reactions
Triolein
C57 H104 O6 1 6:2H2 -C18 H38 1 C17 H36 1 C16 H34 1 0:2C15 H32 1 CO 1 2CO2 1 H2 O C57 H104 O6 1 6:4H2 -C19 H40 1 C14 H30 1 C13 H28 1 0:4C20 H42 1 CO 1 2CO2 1 H2 O C57 H104 O6 1 8:33H2 -C12 H26 1 C11 H24 1 C10 H22 1 2:33C9 H20 1 CO 1 2CO2 1 H2 O C51 H98 O6 1 3:44H2 -C18 H38 1 C17 H36 1 1:44C9 H20 1 CO 1 2CO2 1 H2 O C51 H98 O6 1 3:7H2 -C16 H34 1 C15 H32 1 1:7C10 H22 1 CO 1 2CO2 1 H2 O C51 H98 O6 1 4:0833H2 -3:0833C12 H26 1 C11 H24 1 CO 1 2CO2 1 H2 O C51 H98 O6 1 3:1H2 -C14 H30 1 C13 H28 1 C19 H40 1 0:1C20 H42 1 CO 1 2CO2 1 H2 O C57 H98 O6 1 9:2H2 -C18 H38 1 C17 H36 1 C16 H34 1 0:2C15 H32 1 CO 1 2CO2 1 H2 O C57 H98 O6 1 9:4H2 -C14 H30 1 C13 H28 1 C19 H40 1 0:4C20 H42 1 CO 1 2CO2 1 H2 O C57 H98 O6 1 11:33H2 -C12 H26 1 C11 H24 1 C10 H22 1 2:33C9 H20 1 CO 1 2CO2 1 H2 O C57 H110 O6 1 3:2H2 -C18 H38 1 C17 H36 1 C16 H34 1 0:2C15 H32 1 CO 1 2CO2 1 H2 O C57 H110 O6 1 4:3636H2 -C14 H30 1 C13 H28 1 C12 H26 1 1:3636C11 H24 1 CO 1 2CO2 1 H2 O C57 H110 O6 1 3:6H2 -C19 H40 1 C20 H42 1 0:6C10 H22 1 C9 H20 1 CO 1 2CO2 1 H2 O
Tripalmitin
Trilinolein
Tristearin
Chapter 3 Production processes for the conversion of triglyceride feedstock
69
Table 3.5 Kinetic data for reactions sets extended. Triglycerides
Kinetic expression
Kinetic parameters
r 01 5 k1 COOO r 02 5 k2 CPPP r 03 5 k3 CLLL r 03 5 k4 CSSS
Triolein Tripalmitin Trilinolein Tristearin
Ao (1/s)
Ea (kJ/mol)
8:2 3 1011 3:8 3 1010 9:4 3 1011 5:6 3 1010
169.2 147.4 149.9 149.1
Table 3.6 Lipidic profile of Jatropha oil (Anand and Sinha, 2012). Fatty acid
Formula
% w/w
Oleic (OOO) Linoleic (LLL) Palmitic (PPP) Stearic (SSS)
18:1 18:2 16:0 18:0
45.4 27.2 19.5 7.9
reactor made of stainless steel with a 1.3 cm internal diameter and 30 cm of length was used to identify the reaction pathway for hydrotreating of Jatropha oil, evaluating its kinetic parameters. The composition of Jatropha oil is presented in Table 3.6. In the study of Anand and Sinha (2012), temperature is varied from 320 C until 360 C, while the pressure range was from 20 to 80 bar; also, 5002000 NL/L were used as H2/oil volumetric ratio. According to the results, the maximum conversion of Jatropha oil achieved was 99% using 360 C, 80 bar, and 1500 NL H2/L oil. Likewise, the product distribution determined by chromatographic techniques indicates that the range of hydrocarbons formed was C5 until longer chains thanC21. The validated reaction pathway is presented in Fig. 3.5; moreover, its kinetic parameters arranged as a lumped kinetic model of pseudo-first order are presented in Table 3.7. To model the conversion of Jatropha oil during the hydrotreatment, it is necessary to follow the reaction pathway presented in Fig. 3.3 for each triglyceride and propose the reactions set that describe it. It is important to note that the free fatty acid’s behavior was not described in the reaction pathway, due to its low composition.
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Chapter 3 Production processes for the conversion of triglyceride feedstock
Figure 3.5 Reaction pathway for hydrotreatment of Jatropha oil (Anand and Sinha, 2012).
Table 3.7 Kinetic parameters to describe the hydrotreating of Jatropha oil (Anand and Sinha, 2012). Kinetic expression r 012tgi 5 k1 Ctgi r 022tgi 5 k2 Ctgi r 032tgi 5 k3 Ctgi r 042tgi 5 k4 Ctgi r 052tgi 5 k5 Ctgi r 062tgi 5 k6 Ctgi
k (1/h)
Ao (1/h)
1 2 19 2 0.1 0.1
62:62 3 10 4:2 3 1010 13:73 3 104 N/D 14:58 3 104
Ea (kJ/mol) 5
83 127 47 N/D 47
Notes: tgi refers to each triglyceride in the oil. ki 5 Aoi e2Ea =RT . N/D, No data.
The reactions set proposed to describe the conversion pathway showed in Fig. 3.3 is presented in Table 3.8, indicating each kinetic constant according to Table 3.7. It is important to mention that the set of reactions to describe the hydrotreating zones must be constructed based on the reaction pathway and product distribution from the experimental procedure. Thus each designer can propose a different set of reactions taking into account the experimental data. Likewise, each chemical reaction must be well balanced. Summarizing, the processing starts with the mixture of 500 kg/h palm oil and 2000 kg/h Jatropha oil in liquid phase at 1 bar, according to the information presented in Section 3.5.1. Both oils are fed separately to the first reactive zone, which is
Chapter 3 Production processes for the conversion of triglyceride feedstock
71
Table 3.8 Reactions set proposed to describe the hydrotreating of Jatropha oil. Triolein k1
C57 H104 O6 1 9:375H2 - C5 H12 1 C6 H14 1 C7 H16 1 4:375C8 H18 1 CO2 1 H2 O 1 3CO
k2
C57 H104 O6 1 6:64286H2 - C9 H20 1 0:5C10 H22 1 C11 H24 1 0:5C12 H26 1 C13 H28 1 0:642857C14 H30 1 CO2 1 H2 O 1 3CO
k3
C57 H104 O6 1 5:1875H2 - C15 H40 1 0:1875C16 H34 1 C17 H36 1 C18 H38 1 CO2 1 H2 O 1 3CO
k4
C57 H104 O6 1 4:63158H2 - 0:631579C19 H40 1 C20 H42 1 C21 H44 1 CO2 1 H2 O 1 3CO
k1
k2
k3
k4
Trilinolein k1
C57 H98 O6 1 12:375H2 - C5 H12 1 C6 H14 1 C7 H16 1 4:375C8 H18 1 CO2 1 H2 O 1 3CO
k2
C57 H98 O6 1 9:64286H2 - C9 H20 1 0:5C10 H22 1 C11 H24 1 0:5C12 H26 1 C13 H28 1 0:642857C14 H30 1 CO2 1 H2 O 1 3CO
k3
C57 H98 O6 1 8:1875H2 - C15 H40 1 0:1875C16 H34 1 C17 H36 1 C18 H38 1 CO2 1 H2 O 1 3CO
k4
C57 H98 O6 1 7:63158H2 - 0:631579C19 H40 1 C20 H42 1 C21 H44 1 CO2 1 H2 O 1 3CO
k1
k2
k3
k4
Tripalmitin k1
C51 H98 O6 1 5:625H2 - C5 H12 1 C6 H14 1 C7 H16 1 3:625C8 H18 1 CO2 1 H2 O 1 3CO
k2
C51 H98 O6 1 3:21429H2 - C9 H20 1 0:5C10 H22 1 C11 H24 1 0:5C12 H26 1 C13 H28 1 0:214286C14 H30 1 CO2 1 H2 O 1 3CO
k3
C51 H98 O6 1 1:875H2 - C15 H40 1 0:375C16 H34 1 C17 H36 1 0:5C18 H38 1 CO2 1 H2 O 1 3CO
k4
C51 H98 O6 1 1:31579H2 - 0:315789C19 H40 1 C20 H42 1 C21 H44 1 CO2 1 H2 O 1 3CO
k1
k2
k3
k4
Tristearin k1
C57 H110 O6 1 6:375H2 - C5 H12 1 C6 H14 1 C7 H16 1 4:375C8 H18 1 CO2 1 H2 O 1 3CO
k2
C51 H98 O6 1 3:64286H2 - C9 H20 1 0:5C10 H22 1 C11 H24 1 0:5C12 H26 1 C13 H28 1 0:642857C14 H30 1 CO2 1 H2 O 1 3CO
k3
C57 H98 O6 1 2:1875H2 - C15 H40 1 0:1875C16 H34 1 C17 H36 1 C18 H38 1 CO2 1 H2 O 1 3CO
k4
C57 H98 O6 1 1:63158H2 - 0:631579C19 H40 1 C20 H42 1 C21 H44 1 CO2 1 H2 O 1 3CO
k5
C18 H38 1 0:4444H2 - C14 H30 1 0:4444C9 H20
k1
k2
k3
k4
Heavy to middle k5
C17 H36 1 0:4H2 - C13 H28 1 0:4C10 H22 k5
C16 H34 1 0:3636H2 - C12 H26 1 0:3636C11 H24 k5
C15 H32 1 0:5454H2 - C9 H20 1 0:5454C11 H24 k5
Middle to light k6
C13 H28 1 H2 - C7 H16 1 C6 H14 k6
C12 H26 1 1:1666H2 - C5 H12 1 1:1666C6 H14 k6
C11 H24 1 0:8H2 - C7 H16 1 0:8C5 H12 k6
C10 H22 1 0:2857H2 - C8 H18 1 0:2857C7 H16 k6
72
Chapter 3 Production processes for the conversion of triglyceride feedstock
formed by two simultaneous reactors, one for each vegetable oil. We consider 350 C and 50 bar as the operation conditions to the hydrotreating zone, since the kinetic data for palm oil and Jatropha oil cover these operation conditions. Likewise, the hydrogen flow to each reactor is calculated based on the same H2/oil volumetric ratio of 1500 NL/L for each oil, which corresponds to 176.19 kg/h of hydrogen for palm oil and 712.66 kg/h for Jatropha oil; these amounts were calculated taking into account the density of hydrogen at 0 C and 1 bar (Engineering Software, 2019). It is important to mention that the hydrogen and oils must be conditioned before it is fed to the first reactive zone until 350 C and 50 bar. Thus a pump, a compressor, and two heat exchangers are chosen as conditioning equipment. The commercial hydrogen is sold at a pressure of 10 bar (Dagdougui et al., 2018). After the first reactive stage, the output streams are joined to enter to the second reactive zone. The hydrocracking and hydroisomerization of long-chain hydrocarbons are described with the model molecules studied by Calemma et al. (2000). In their study, a reaction pathway was proposed to describe the hydrocracking and hydroisomerization of n-C16H34 (n-C16), n-C28H48 (n-C28), and C36H74 (n-C36) long-chain paraffins. The operation conditions were 345 C380 C and 80 bar, using Pt/Al2O3-Al2O3 as catalyst. The reaction network validated by the experimentation is presented in Fig. 3.6. Likewise, the experimental kinetic data for n-C16 and n-C28 are presented in Table 3.9. For this case of study, we assume the hydrocracking reaction is taking place by the (AE) and (AD) routes, while the hydroisomerization is performed through (AB) route; both reactions are carried out at 360 C and 50 bar. The kinetic data for the routes correspond to n-C16 and n-C28. Likewise, to describe the behavior of these reaction pathways a set of reactions was proposed, based on the product distribution after hydrotreating reactor. The reactions set and the kinetic constant for each reaction are presented in Table 3.10. Similar to the hydrotreating reactors, the set of reactions depends on the product distribution and reaction pathway experimentally obtained; also, this proposal can be modified according to the designer criteria. At the end of reactive stages, the separation of each biofuel is performed by defining the appropriate splits. Based on the product distribution from the reactive stages, as well as the fuels specification provided by the Energy Government
Chapter 3 Production processes for the conversion of triglyceride feedstock
73
Figure 3.6 Reaction network for the hydrocracking— hydroisomerization of nparaffins (Calemma et al., 2000).
Table 3.9 Rate constant for reaction network at 360˚C. n-Paraffins
Kinetic expression
k (1/s)
n-C16
r 012C16 5 k12C16 CC16 r 042C16 5 k42C16 CC16 r 022C16 5 k22C16 CC16 r 012C28 5 k12C28 CC28 r 042C28 5 k42C28 CC28
8 3 1027 1:615 3 1027 1:8 3 1025 1:2983 3 1026 1:615 3 1026
n-C28 Note: ki 5 Aoi e2Ea =RT .
Institution in Mexico (Secretarı´a de Energı´a) (SENER, 2019), three products must be separated: 1. Light fuels defined as naphtha: iso-C5, C5, iso-C6, C6, iso-C7, C7, and traces of C1, C2, C3, and C4. 2. Biojet fuel: iso-C8, iso-C9, C8, C9, C10, C11, iso-C12, C12, C13, iso-C16, C14, C15, and C16. 3. Green diesel: C17, C18, C19, C20, and C21. For the separation of each fuel, a distillation scheme is commonly used. Thus a multicomponent distillation train with three products must be designed. For this case of study, the design of a direct distillation train with two columns will be realized. The block diagram for the hydrotreatment of the mixture of vegetable oils (palm oil 1 Jatropha oil) defined in Section 3.5.1 is presented in Fig. 3.7. Each zone in this process is identified by colored dotted line rectangles: red line to conditioning zone, green line to hydrotreating zone, orange to hydrocrackinghydroisomerization zone, and blue line to separation zone.
Table 3.10 Reactions set proposed to hydrocracking—hydroisomerization. Hydrocracking
k1-C28
C21 H44 1 H2 - C11 H24 1 C10 H22 k1 2 C28
C20 H42 1 H2 - C16 H34 1 C4 H10 k1 2 C28
C19 H40 1 H2 - C12 H26 1 C7 H16 k1 2 C28
k1-C16
C18 H38 1 0:6923H2 - C9 H20 1 0:6923C13 H28 k1 2 C16
C17 H36 1 0:6428H2 - C8 H18 1 0:6428C14 H30 k1 2 C16
k4-C28
C20 H42 1 H2 - C12 H26 1 C8 H18 k4 2 C28
C19 H40 1 1:2H2 - 1:2C10 H22 1 C7 H16 k4 2 C28
C20 H42 1 1:0714H2 - 1:0714C14 H30 1 C5 H12 k4 2 C28
C19 H40 1 1:8H2 - 1:8C10 H22 1 CH4 k4 2 C28
C20 H42 1 2:125H2 - 2:125C8 H18 1 C3 H8 k4 2 C28
k4-C16
C10 H42 1 1:6H2 - 1:6C5 H12 1 C2 H6 k4 2 C16
C8 H18 1 1:25H2 - 1:25C4 H10 1 C3 H8 k4 2 C16
Hydroisomerization
k2-C16
C5 H12 - isoC5 H12 k2 2 C16
C6 H14 - isoC6 H14 k2 2 C16
C7 H16 - isoC7 H16 k2 2 C16
C8 H18 - isoC8 H18 k2 2 C16
C9 H20 - isoC9 H20 k2 2 C16
C10 H22 - isoC10 H22 k2 2 C16
C12 H26 - isoC12 H26 k2 2 C16
C16 H34 - isoC16 H34 k2 2 C16
Figure 3.7 Block diagram of hydrotreatment process to the mixture of vegetable oils (palm oil 1 Jatropha oil).
Chapter 3 Production processes for the conversion of triglyceride feedstock
3.5.3
Simulation of the hydrotreating process
According to the modeling of vegetable oils mixture and the conceptual design presented in Section 3.5.2, the simulation of hydrotreatment process in Aspen Plus V.10.0 is presented in this section. The simulation starts with the definition of each component involved in the process, which includes triglycerides, hydrogen, water, carbon monoxide, carbon dioxide, and the hydrocarbons. After selecting all components involved in the process, the next step is choosing the appropriate thermodynamic method to model it; in this case the PengRobinson method is selected (Carlson, 1996). The flowsheet starts with the oil streams, based on the normalized composition of Table 3.2, for the palm oil, and Table 3.6, for the Jatropha oil, along with the information defined in Section 3.5.1. In the case of hydrogen stream, the entrance conditions are described in Section 3.5.3. In this simulation, the total flow of hydrogen required for both oils is included in the same stream. Once the oil and hydrogen streams are defined, the equipment to perform its conditioning must be designed. In the case of oil streams, it is necessary a pump to elevate the pressure until 50 bar for each stream (Pump module). The pump for palm oil stream is called PUMP-PO and for Jatropha oil, PUMP JC. Regarding the hydrogen stream, a compressor to elevate the pressure condition must be selected (module Compr, model Compressor, Type Isentropic); the hydrogen compressor is called COMP-H2. In order to complete the conditioning zone, the output streams from PUMP-PO, PUMP-JC, and COMP-H2 must be adjusted to satisfy the 350 C condition. Thus a heat exchanger is chosen for each output streams (Heater module); the Heater modules for palm oil, Jatropha oil, and hydrogen are called EX-PO, EX-JC, and EX-H2, respectively. The hydrotreating reactor will be modeled as two simultaneous reactors at the same operation conditions (RPlug module). Thus R-HDO-PO and R-HDO-JC for palm oil and Jatropha oil are inserted. The output stream from EX-PO is mixed with the hydrogen stream, called H2-PO, required for palm oil hydrotreating; the flow rate of this stream was calculated in Section 3.5.2. This mixture of streams is carried out in a MIXER module, MIX-1. In the case of Jatropha oil, the output stream from EX-JC is joined with the H2-JC stream in a Mixer module called MIX-2, according to the hydrogen required for Jatropha oil hydrotreating. The output streams from MIX-1 and MIX-2 are fed to hydrotreating reactors. In Fig. 3.8, the flowsheet constructed for the simulation is shown.
75
Figure 3.8 Flowsheet of the hydrotreating process.
Chapter 3 Production processes for the conversion of triglyceride feedstock
77
Inside each RPlug module, a set of reactions is created based on the Power-law type, and considering the kinetic expressions defined in Sections 3.5.2 and 3.5.3 (first-order reactions). The reactors operates at constant temperature (350 C). The output streams from R-HDO-PO and R-HDO-JC enter a Mixer module (MIX-3). The output from MIX-3 is introduced to Sep module, called SEP-1, where CO, CO2, and water (COCO2W1 stream) are separated from the hydrocarbon’s mixture. The remaining stream from SEP-1 is fed to the hydrocrackinghydroisomerization reactor, called R-HCRIS, which is modeled with a RPlug module, at constant temperature (360 C). The output stream from RHCRIS reactor is fed to SEP-2, which is a Sep module used to remove the remaining hydrogen (Split fraction of hydrogen equal to 1), called H2-OUT. The second output stream from SEP-2 contains the hydrocarbons, which must be separated in a distillation train. According to the simulation results, this stream must be conditioned from 360 C and 50 bar to 1 bar and 30 C before it is fed to the distillation train. With this purpose, a Pump module (TURBI-1) is inserted along with a Heater module (EX-4). The output stream from EX-4 is called HYDROCAR. The product distribution at the end of hydrotreating and hydrocracking-hydroisomerization reactors are presented in Tables 3.11 and 3.12, respectively. From Table 3.11, it can be observed that the triglyceride conversion is almost 100%. Based on the product distribution, 76.8 wt.% of Table 3.11 Product distribution from hydrotreating reactor. Components
Flow (kg/h)
Components
Flow (kg/h)
Triolein Trilinolein Tripalmitin Triestearin H2 C5 C6 C7 C8 C9 C10 C11 Water Total
0.00 0.00 0.0038 0.0039 857.15 7.33 9.45 12.23 37.42 86.79 47.27 63.89 52.05
C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 CO CO2
67.75 81.88 74.78 380.77 127.80 454.05 434.86 81.63 81.74 67.10 210.03 152.90
3388.97
78
Chapter 3 Production processes for the conversion of triglyceride feedstock
Table 3.12 Product distribution from hydrocracking-hydroisomerization reactor. Components
Flow (kg/h)
Components
Flow (kg/h)
C13 Triolein Trilinolein Tripalmitin Tristearin H2 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 Total
81.93 0.0000 0.0000 0.0038 0.0039 857.15 0.0021 0.0004 0.0062 0.0070 7.31 9.40 12.19 37.32 86.42 47.11 63.91 67.46
C14 C15 C16 C17 C18 C19 C20 C21 iso-C5 iso-C6 iso-C7 iso-C8 iso-C9 iso-C10 iso-C12 iso-C16
74.86 380.77 127.20 453.95 434.77 81.53 81.61 67.08 0.0359 0.0463 0.0599 0.1833 0.4249 0.2315 0.3316 0.6255
2973.99
the total hydrocarbons obtained corresponds to the paraffins group (C15C21). Moreover, only 3.1 wt.% of total hydrocarbons corresponds to light gases (C3C5). These results are consistent with the experimental data presented by Anand and Sinha (2012) and Ve´lez Manco (2014). Regarding to the hydrocracking-hydroisomerization reactor, it is observed from Table 3.12 that the number of isomers obtained is low, the mass ratio of iso-alkanes/n-alkanes is roughly 0.001. Likewise, the light hydrocarbons C1C4 are 0.001 wt.% of total hydrocarbons obtained. These results are due to the low values of kinetics constants reported in Calemma et al. (2000). The HYDROCAR stream is separated into three products (naphtha, biojet fuel, and green diesel) through distillation. The design procedure to the distillation train starts with the shortcut methods, which are based on the Winn, Underwood, and Gilliland Equations, modeled in Aspen Plus through the DSTWU module. For this, we assume 10 psia of pressure drop along the column, and a reflux ratio 1.33 times the minimum reflux ratio. The recoveries of the key components are specified as 99%; BK10 was the thermodynamic method used. The design data of the distillation train are presented in Table 3.13.
Chapter 3 Production processes for the conversion of triglyceride feedstock
79
Table 3.13 Results from COL-1 and COL-2. Condenser results from COL-1
Condenser results from COL-2
Temperature ( C) Heat duty (cal/s) Distillate rate (kg/h) Reflux ratio
56.96 1142.81 28.97 0.5500
Reboiler results from COL-1
Temperature ( C) Heat duty (cal/s) Bottoms rate (kg/h)
3.5.4
Temperature ( C) Heat duty (cal/s) Distillate rate (kg/h) Reflux ratio
197.65 62,440.63 972.16 1.45
Condenser results from COL-2 256.32 81,372.42 2087.86
Temperature ( C) Heat duty (cal/s) Bottoms rate (kg/h)
Economic assessment
The economic assessment for the hydrotreatment process is carried out through the calculation of total annual cost (TAC), shown in Eq. (3.1). The TAC is defined as the sum of annual operating cost (COP) and annual capital cost (CCA). Utilities cost, such as steam, cooling water, hydrogen, electricity consumption from pumps and compressors, and the raw material cost (vegetable oils) are included in the COP. On the other hand, the CCA involves the process equipment cost, calculated by the equations presented in Turton et al. (2008), and updated by the Chemical Engineering Process Cost Index. In this evaluation, the costs associated with the transportation of raw material and hydrogen, as well as the catalysts cost used, were not considered. TAC USD=year 5 annual operating cost ðCOP Þ 1 ð3:1Þ annual capital cost ðCAC Þ To calculate the COP, the palm oil, Jatropha oil, hydrogen, and electricity prices are presented in Table 3.14; the Eqs. (3.2)(3.5) defined each cost. Likewise, it is considered that the process has a continuous operation of 8500 h per year. Regarding the saturated vapor, the price is referred to exchanged energy, defined as 1 USD/GJ, whilst the price of cooling water is 14.8 USD/ 1000 m3. These values are obtained from Turton et al. (2008). The COP is calculated by the Eq. (3.6). kg USD h 3 PH 2 3 8500 ð3:2Þ CH2 5 mH2 h kg year kg USD h 3 Ppalm oil 3 8500 ð3:3Þ Cpalm oil 5 mpalm oil h kg year
339.93 70,562.31 1115.70
80
Chapter 3 Production processes for the conversion of triglyceride feedstock
Table 3.14 Prices in the market of items involved in COP . Item
Price in the market
P (USD/kg)
Reference
Hydrogen Jatropha oil Palm oil Electricity
1.80 USD/kg 1.50 USD/L 730 USD/ton 1.47 MX/kWh
13.99 4.477 0.73 0.075a
Hydrogen Energy Systems (2019) Aumkiipure (2017) COMEXPALMA (2018) CFE (2019)
Notes: Density of Jatropha oil 5 0.91 g/mL (Lizarde et al., 2015). 1 USD 5 19.63 MX (Banco de Me´xico, 2019). a USD/kWh.
Table 3.15 Capacity parameters (A). Equipment
Capacity parameter (A)
Units
Pumps Heat exchangers Process vessels
Power Area Length or height
kW m2 m
CJatrophaoil 5 mJatrophaoil
kg USD h 3 PJatrophaoil 3 8500 h kg year ð3:4Þ
Celect 5 kW req 3 Pelect
USD h 3 8500 kWh year
COP 5 CH2 1 Cpalm oil 1 CJatropha oil 1 Celect 1 Cwat 1 Csteam
ð3:5Þ ð3:6Þ
On the other hand, CAC is obtained through calculations of the equipment cost used in the process. This methodology starts with the base cost (CP) determination. Eq. (3.7) describes the CP; the K parameters involved in the Eq. (3.7) are related with the equipment type, its operation, and its physical features. Likewise, A is a parameter related to the capacity or size of the equipment. The definitions of A parameters are described in Table 3.15. The K parameters are referred to carbon steel as construction material. Also, in the case of vessels, the maximum pressure is 400 barg. log10 Cp 5 K1 1 K2 log10 A 1 K3 ðlog10 AÞ2
ð3:7Þ
Chapter 3 Production processes for the conversion of triglyceride feedstock
Regarding the distillation columns, the determination cost of these equipment includes the sum of shell, plates, reboiler, and condenser. The base cost of each plate into the column is calculated by the Eq. (3.8), wherein D is the diameter of the column. Cp 5 235 1 19:80D 1 75:07D2
ð3:8Þ
The base cost can be corrected by the Eq. (3.9), if the pressure of the equipment is different from the one established by Guthrie or the equipment must be constructed using a material different from carbon steel. Thus the base cost is modified to 0 obtain the bare cost module CBM . 0 0 5 Cp FBM 5 Cp B1 1 B2 FM Fp CBM ð3:9Þ 0 is the bare cost module factor, Bi depends on the Where FBM equipment type, FM depends on type of material, and Fp is the correction due to pressure. Regarding the plates of a distillation 0 column, the bare cost module CBM is calculated with Eq. (3.10). 0 5 Cp NFBM Fq CBM
ð3:10Þ
Where N is the number of stages of the column, FBM is a factor dependent on plates material, and Fq is a quantity factor, which is a function of N. In the case of compressors, the bare cost 0 module CBM is calculated with Eq. (3.11). 0 5 Cp FBM CBM
ð3:11Þ
The equation to consider the pressure correction is presented in Eq. (3.12). The variable P is the manometric pressure of the equipment (barg). log10 Fp 5 C1 1 C2 log10 P 1 C3 ðlog10 PÞ2
ð3:12Þ
In the case of pumps, the pressure correction is calculated with Eq. (3.13). Fp 5 C1 1 C2 log10 P 1 C3 ðlog10 PÞ2
ð3:13Þ
To adjust the pressure of the vessels, the pressure correction is calculated with Eq. (3.14) when 3:7 , P , 400barg. Fp 5 0:5146 1 0:6838log10 P 1 0:2970ðlog10 PÞ2 1 0:0235ðlog10 PÞ6 1 0:0020ðlog10 PÞ8 ð3:14Þ Also, If 20.5 , P , 3.7 barg, P , 2 0.5 barg, Fp 5 1.25.
Fp 5 1.0,
whilst
for
81
82
Chapter 3 Production processes for the conversion of triglyceride feedstock
The previous equations and the factors involved were proposed in 1996; thus these values must be updated, considering the changes in economic conditions. This variation is represented by the most recent index, which was reported in April 2019 (Chemical Engineering, 2019), and whose value is 613.35, whilst in 1996 the value was 382. Eq. (3.15) presents the update of equipment cost (Cmdaj). I2019 0 Cmdaj 5 CBM ð3:15Þ I1996 Additional costs must be included, which are called A1 and A2. The first one is 18% of Cmdaj; this parameter includes contingencies and other fees associated with the installation. A2 is 61% of Cmdaj, and it involves costs related with machinery, equipment, and maintenance. Finally, the capital cost is annualized on the recovery of capital investment, defined for this project as intermediate processing size. Thus the CAC is presented in Eq. (3.16). CAC USD=year 5
n P Cmdaji 1 A1i 1 A2i
i51
RCI
ð3:16Þ
In Table 3.16 the summary of costs for the hydrotreatment process is presented. From this table it can be observed that 98.32% of total cost is due to the raw material cost, which considers palm oil, Jatropha oil, and hydrogen; from all the raw materials, Jatropha oil has the major cost. The equipment cost represents only 1.3% of total cost. From all the costs, the one
Table 3.16 Summary of the costs for hydrotreatment process. Operating cost (USD/year) Heating utilities Cooling utilities cost cost
Palm oil
81,110.69
3,102,500.00 28,021,978.02 70,008.15
6530.63
Jatropha oil
Electricity Total operating cost 44,881,541.33
Annual capital cost (USD/year) Equipment cost A1
A2
Total annual cost (TAC)
1,694,761.07
1,033,804.25
606,724.46
305,056.99
TAC (USD/year)
$45,488,265.79
Chapter 3 Production processes for the conversion of triglyceride feedstock
with the lowest contribution is corresponding to the cooling water utility required by the process.
3.5.5
Estimation of price of biojet fuel
The minimum selling price can be obtained through the estimation of minimum utilities for the production process (Jime´nez-Gutie´rrez, 2003). The minimum selling price can be calculated with Eq. (3.17), wherein V is the additional profit regarding to the minimum profit established in the project, P is the net income, I is the investment required, and imin is a minimum value established by the company to measure the project profitability. Usually imin is defined as 0.2, according to Jime´nez-Gutie´rrez (2003). V 5 P 2 imin I 5 0
ð3:17Þ
Then, P can be calculated with the gross income (R), equipment’s depreciation, and payment of taxes, according to the Eq. (3.18). It is important to mention that equipment’s depreciation is represented by “e” and “d” factors, whilst, “t” is the tax rate. In this case study, e 5 d 5 0.08 (Pinkerton et al., 2016), and t 5 0.28 (Sadhukhan et al., 2008). P 5 R 2 eI 2 t ðR 2 dI Þ
ð3:18Þ
The gross income (R) is defined as the annual sales (S) minus the annual operating cost (C). The annual sales are referred to the main product obtained in the process, which is biojet fuel in this case. Also, the annual operating cost involves the items defined by Eq. (3.6) minus the price of naphtha and green diesel obtained in the process. The gross income (R) and operating cost (C) are shown in Eqs. (3.19) and (3.20). According to the current market, the price of naphtha is 0.465 USD/kg (Aljazira Capital, 2019), whilst the price of diesel is 0.9553 USD/kg (U.S. Energy Information Administration, 2019); the density values for naphtha (Density of Petroleum naphtha, 2019) and diesel (The Engineering Toolbox, 2019) are 0.74 and 0.849 kg/L, respectively. R5S2C C 5 CH2 1 Cpalm oil 1 CJatropha oil 1 Celect 1 Cwat 1 Csteam 2 SPnapht 1 SPgreendies
ð3:19Þ ð3:20Þ
On the other hand, the total investment of the project is the annualized total equipment cost plus the operating cost, which
83
84
Chapter 3 Production processes for the conversion of triglyceride feedstock
Table 3.17 Minimum price of biojet fuel. I (USD/year)
Pn i51 Cmdaji 1,694,761.07 P Operating cost (5% of ni51 Cmdaji ) 151,681.12 Total 1,846,442.19 S (USD/year) Q (kg/year) Minimum price of biojet fuel (USD/kg) 4.43
P (USD/year)
C (USD/year)
imin 0.2 P 5 imin 3 I Total
Naphtha (kg/h) 246,273.90 Green diesel (kg/h) 9,483,521.90 Total 35,975,742.90
369,288.44 36,636,358.89 8,263,366.55
P is equivalent to 5% of equipment cost ( ni51 Cmdaji ). This last value was extracted from Wang (2016). Finally, in order to obtain the unit price of biojet fuel, the annual sales (S) must be divided by the annual production volume of biojet fuel (Q), according to the Eq. (3.21). Price biojet 5
S Q
ð3:21Þ
In Table 3.17 is presented the overview of the variables involved in the determination of biojet fuel minimum price. According to IATA (2019), the market price of kerosene fuel (jet fuel A1) is 0.4849 USD/L or 0.6380 USD/kg, considering 0.8075 kg/L the density of kerosene (The Engineering Toolbox, 2019). The price obtained in the case study is roughly 6.9 times the price in the market, mainly due to the high operation cost determined for the process. However, this price could be improved with the application of energy integration and process intensification strategies on the process.
3.5.6
Environmental assessment: CO2 emissions
The determination of CO2 emissions for the hydrotreatment process considers those related with the generation of steam and the use of electricity. Regarding the emissions involved in the steam generation, the mass of the fuel used in the boiler must be calculated; usually, the fuel considered is natural gas. The mass fuel is obtained with the Eq. (3.22), where msteam is the steam mass required, provided by the simulation; hv is the saturated steam enthalpy,
Chapter 3 Production processes for the conversion of triglyceride feedstock
˜ ´ıa which can be consulted on saturated steam tables (Compan Especialista en Vapor, 2019); hAA is the enthalpy of water fed to boiler (25 C, 1 atm), which is 104.878 kJ/kg; Cpfuel is the calorific capacity of the natural gas, which is 65.5738 kJ/kg; and η is the boiler efficiency, defined as 80%. mfuel 5
msteam ðhv 2 hAA Þ Cpfuel η
ð3:22Þ
An emission factor of natural gas boilers to produce steam has been determined by Oficina Catalana del Cambio Clima´tico (2011). This factor is 2.15 kg CO2/Nm3 (Fgn ) for natural gas. Thus the mass of natural gas calculated by Eq. (3.23), must be converted in a volumetric value, according to the Eq. (3.22), where Vfuel is the natural gas volume in Nm3, R is the ideal gases constant, T is 0 C, PM is the molecular weight of natural gas, and P is 1 atm. Vfuel 5
mfuel 3 R 3 T PM 3 P
ð3:23Þ
Finally, the carbon dioxide emissions due to the generated steam are obtained from Eq. (3.24). kg CO2 5 Fgn 3 Vfuel h
ð3:24Þ
On the other hand, the CO2 emissions due to electricity required by the process are calculated by an emission factor, associated with the electricity demand from the productive activities for a country. In Mexico, the factor is 0.166 kg CO2/ kWh (MITECO, 2011). The emissions due to electricity are obtained with the Eq. (3.25). kg CO2 5 Felec 3 Power consumption ðkWhÞ helec
ð3:25Þ
In Table 3.18 the CO2 emissions related by the steam and electricity used in the process are presented.
Table 3.18 CO2 emissions due to steam and electricity generation. Emissions due to steam generation (Mkg CO2/year)
Emissions due to electricity (Mkg Total CO2/year) emissions (Mkg CO2/ year)
26,440.25
1.387
26,441.64
85
86
Chapter 3 Production processes for the conversion of triglyceride feedstock
The sum of CO2 emissions due to steam and electricity is 26.442 Mton CO2/year. The emissions of CO2 could be reduced with the application of energy integration and process intensification techniques.
3.6
Conclusion
Triglyceride feedstock can be converted to biojet fuel and other hydrocarbon fuels through the hydroprocessing technology. The hydrotreating is the most mature and studied process, which also has certification from ASTM. Additionally, most of the processing equipment is the same used for petrochemical processes; thus existing equipment could be used to process triglyceride materials. More than half of the test flights have been realized with biojet fuel produced with hydroprocessing technology. Moreover, the combustion studies show advantages of the use of hydroprocessed jet fuel in comparison with fossil jet fuel. From the case study, it can be clearly seen that the price of raw material has a great impact on the total annual cost. This has driven research and technological developments for the processing of other raw materials with lower cost.
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Chapter 3 Production processes for the conversion of triglyceride feedstock
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4
Introduction
Biomass containing sugar and starch can be used as a raw material to produce biojet fuel. Among the crops which can be used as a source of such components are sugarcane, corn, sorghum, wheat, rye, and cassava (Siegmeier et al., 2019). Corn is a widely produced source, with an approximate global production of 1.04 billion metric tons during the period 201617, with the United States and China being the main producers (Mohanty and Swain, 2019). In the case of wheat, a global production of 755 million metric tons has been reported, with the European Union and China as the main producers (Mohanty and Swain, 2019). In the case of sugarcane, a global production of around 1.8 billion tons has been reported by 2012, with Brazil being the major producer, contributing in 39% to the global production (Silalertruksa and Gheewala, 2018). Sugar and starchy biomass have been widely used for bioethanol production, with the United States and Brazil being the main producers; these countries contributed with 50% and 28.8%, respectively, of the global bioethanol production in 2016 (Mohanty and Swain, 2019). Since most of the sugar and starchy sources are part of the human diet, concerns about its use as raw materials for biofuels production have been manifested. There are reports predicting that the production of biofuels from these sources would cause a rising in the food prices (Chakravory et al., 2009), although other predictions indicate that such rises would be caused by other factors (Timilsina and Shrestha, 2011). To reduce such potential issues, governments have developed proper politics. As an example, in Brazil the cultivation of sugarcane for bioethanol production is promoted on marginal land and degraded pastures (de Oliveira Bordonal et al., 2018). In the case of Mexico, the use of sources as corn Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00004-3 © 2021 Elsevier B.V. All rights reserved.
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and sugarcane is only allowed when there is a surplus in the production of such crops (Diario Oficial, 2008). For the European Union, in 2015 the European Commission established a 7% upper limit for the usage of food-based biofuels in trans´ jo et al., 2017). portation by 2020 (Arau Through additional treatment, bioethanol and other alcohols can be transformed into biojet fuel; thus sugar and starchy biomass are sources for biojet fuel and other hydrocarbon-based fuels. This production approach has been reported as having high potential in countries with a well-developed infrastructure for sugarcane production, as Brazil; although there is still the need for financial and regulatory support to promote the production of biojet fuel from raw materials as sugarcane (Zanatta Martini et al., 2018). On the other hand, pathways to directly transform sugar and starchy biomass into jet fuels have been developed, avoiding the production of bioethanol as intermediate product. In this chapter, both processing routes will be analyzed, that is, direct conversion of biomass to hydrocarbons and the production of renewable jet fuel and other products from biomass-derived ethanol.
4.2
Conversion of sugars to biojet fuel
Raw materials with high content of sugars can be processed to obtain biojet fuel. Two main strategies have been reported for this conversion: the alcohol-to-jet (ATJ) pathway and the sugar-to-jet (STJ) pathway. In Fig. 4.1 is presented the conversion of sugar and starchy feedstocks into available sugars, which are the internal raw material to produce biojet fuel at downstream processing. Then, Fig. 4.2 shows an overall diagram of downstream processing to produce biojet fuel from the available sugars, while Fig. 4.3 presents the overall diagram of the catalytic route to produce biojet fuel from sugars. Each kind of process presented in Figs. 4.2 and 4.3 will be described in detail in the following subsections.
4.2.1
Alcohol-to-jet pathway
The ATJ process includes fermentation of the sugars to an alcohol, followed by dehydration, oligomerization, and hydrogenation. These steps are known as the alcohol-to-jet process and will be discussed in this section. In the case of fermentation, different microorganisms, as bacteria, yeast, or fungi, can transform sugars to ethanol under anaerobic conditions
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Figure 4.1 Pretreatment of sugar and starchy feedstocks.
Figure 4.2 Downstream processing from sugars released.
Figure 4.3 Catalytic route to produce biojet fuel from sugars.
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(Hamelinck et al., 2005). Selecting the proper microorganism is one of the main issues in the fermentation step, since each kind of microorganism can successfully transform a given kind of sugar produced in the hydrolysis step; due to this, the use of a set of different microorganisms has been explored in the last years. Moreover, different microorganisms may lead to a variation on ethanol yield; Saccharomyces cerevisiae and Zymomonas mobilis have been reported among the best yeast and bacteria, respectively, for ethanol production using hexoses as a raw material (Talebnia et al., 2010). The development of modified microorganisms through genetic engineering is one of the paths to enhance the fermentation process, allowing cofermentation of two or more sugars simultaneously. As examples, Kim et al. (2010) proposed the use of modified strains of Escherichia coli, S. cerevisiae, and Z. mobilis, among other microbes, to ferment glucose and xylose. Rodrussamee et al. (2011) found that the yeast Kluyveromyces marxianus can transform glucose, mannose, galactose, xylose, and arabinose. Moreover, Feng et al. (2017) reported that the use of immobilized Candida shehatae and S. cerevisiae allows the simultaneous fermentation of pentose and hexose, while Papapetridis et al. (2018) studied the use of mutated S. cerevisiae strains to ferment glucose and xylose. Recently, Rech et al. (2019) proposed the use of Spathaspora hagerdaliae for the cofermentation of hexoses and pentoses. The development of new microorganisms is still an area of opportunity since it is required to find organisms which are economically feasible for its use at industrial scale, able to obtain high yields of ethanol, and with low potential for inhibition. Another alternative to enhance the ethanol production step implies performing the saccharification and fermentation in a single equipment, which improves ethanol yield in comparison with performing the hydrolysis and fermentation in separated vessels (Sarkar et al., 2012). Nevertheless, one of the main challenges for this approach is concealing the best operating conditions for hydrolysis and fermentation. Another strategy to increase the ethanol production is the separation of the alcohol in situ, with the use of a membrane; this modifies the reaction equilibrium toward an increase in the production of ethanol (Baeyens et al., 2015). The main limitation of this strategy is the ease of membrane plugging due to the suspended solids present in the fermentation or hydrolysis/fermentation processes. Once the ethanol is obtained, additional steps are required to transform it into hydrocarbons in the required range. The first
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
step is dehydration. This stage implies the treatment of ethanol in an acid catalyst to produce ethylene and water: catalyst
C2 H5 OH - C2 H4 1 H2 O
ð4:1Þ
According to Phung and Busca (2015a), high temperatures promote the ethanol dehydration to ethylene. Moreover, they mention that zeolites, alumina, and silica-alumina catalysts allow a dehydration mechanism from ethanol to diethyl ether and diethyl ether to ethylene in a range between 180 C and 300 C. Nevertheless, Phung and Busca (2015b) reported that zeolites and γ-alumina catalysts are more active than silica alumina. In other work, Phung et al. (2015) studied the synthesis of ethylene with a yield of 99.9% using H-FER zeolite and faujasites at 300 C. Rodrigues de Oliveira et al. (2018) found that ZSM-5 zeolites allow selectivity to ethylene close to 90% at 220 C. On the other hand, Austin et al. (2018) proposed the use of nanocatalysts to perform the dehydration reaction, mentioning that such arrangements allow avoiding the formation of diethyl ether. Once the ethylene has been produced, the oligomerization takes place. In the oligomerization step, several molecules of ethylene are converted to long-chain linear olefins, as shown in Eq. (4.2) catalyst
nC2 H4 - C2n H4n
ð4:2Þ
Andrei et al. (2015) reported the use of a Ni_AlSBA-15 mesoporous catalyst for the oligomerization of ethylene to produce C4C10 olefins. The optimal operation conditions are reported as 150 C and 3.0 MPa for a continuous device. The authors also mention obtaining 175 g of oligomers per gram of catalyst. Later, Rozhko et al. (2017) presented the development of covalent organic frameworks to support nickel as a catalyst for ethylene oligomerization, obtaining high selectivity mainly to C4C8 olefins, for a temperature of 50 C and a pressure of 15 bar. In the same year, Liu et al. (2017) developed a chromium catalyst in a metal-organic framework, namely, MIL-100(Cr), reaching a selectivity of 99% to C6, C8, and C10 olefins. In the production of biojet fuel, it is desired to produce chain lengths in a range between C8 and C16. Tomov et al. (2017) reported the use of chromium-based complexes with bis(benzimidazolemethyl)amine ligands, which allows generating proportions up to 10% for olefins with eight or more carbon atoms. Timken et al. (2018) proposed an oligomerization step with an ionic liquid catalysis, mentioning that hydrocarbons in the range of C10C55 are obtained.
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Once the olefins are generated, it is necessary to saturate the hydrocarbon molecules. This is done through hydrogenation, aiming to produce alkanes in the same carbon range of the alkenes leaving the oligomerization reactor. According to Doyle et al. (2003), low-pressure hydrogenation of alkenes occurs on supported palladium nanoparticles, more than on single-crystal palladium surfaces. On the other hand, Spielmann et al. (2008) found that hydrogenation of alkenes may take place by using organocalcium catalysts, at 20 C and 20 bar H2. Chirik (2015) mentioned that more than using precious metals for the development of hydrogenation catalysts, efforts must be focused on the use of transition metals, as iron or cobalt, due to environmental and economic aspects. Gieshoff et al. (2017) proposed the use of iron nanocluster catalysts for the hydrogenation of alkenes under conditions of 20 C and 14 bar. After hydrogenation, a set of saturated hydrocarbons is produced. The hydrocarbons must be then separated to obtain the desired fractions. The purification step will be discussed in Section 4.3.
4.2.2
Sugar-to-jet pathways
There are two conversion strategies to directly transform sugars into hydrocarbons: biological and catalytic. The biological route has fermentation as main step, potentially followed by hydrotreating and/or hydrocracking/hydroisomerization, while the catalytic route involves hydrogenation, reforming, either condensation or dehydration followed by oligomerization, hydrotreating, and hydrocracking/hydroisomerization (Wang et al., 2016). For the biological process, the first step implies the biological conversion, either aerobic or anaerobic, of the sugars. In this step, depending on the employed microorganism, several products can be generated, as pentadecane, farnesene, fatty esters, and fatty acids, among others. Rude and Schirmer (2009) mentioned monoterpenes as potential components for jet fuel. On the other hand, Peralta-Yahya and Keasling (2010) reported pinene, sabinene, and terpinene, along with fatty acid and isoprenoid-based biofuels, as potential replacements for jet fuel. Ladygina et al. (2006) described the generation of hydrocarbons in a glucose-containing medium using several microorganisms, including S. cerevisiae and Pseudomonas fluorescens, among others. Liu and Khosla (2010) presented the biological pathways for the production of several glucose and xylose derivatives, as pinene, farnesol, and farnesene, among others, using
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
E. coli. Farnesene (C15H24) is obtained through the mevalonate pathway, following the general reactions (Blanch, 2012): C6 H12 O6 1 6H2 O-6CO2 1 H2
ð4:3Þ
C6 H12 O6 1 4:8H2 -0:4C15 H24 1 6H2 O
ð4:4Þ
Once the farnesene is obtained, it is hydrogenated to farnesane, which then goes through hydrocracking and hydroisomerization to produce the biojet fuel (Wang et al., 2016). Another biological alternative implies the conversion of the sugars into triglycerides or fatty acids by using microalgae as a fermentation organism (Westfall and Gardner, 2011). Babau et al. (2013) reported the conversion of glucose and xylose to oleic acid and palmitic acid, using Rhodotorula glutinis. If such pathway is followed, the next steps imply hydrotreatment and hydrocracking/hydroisomerization stages, as described in Chapter 3: Production processes for the conversion of triglyceride feedstock. Another alternative implies the use of modified E. coli to directly produce hydrocarbons. Such an approach has been reported by Schirmer et al. (2010), obtaining alkanes and alkenes in the range C13C17. To produce hydrocarbons in the boiling point range of biojet fuel, such alkanes and alkenes must go to a hydrocracking stage, with hydroisomerization if necessary. In the catalytic route, the first step is the hydrogenation of the sugars into polyhydric alcohols (Wang et al., 2016). Hydrogenation of glucose into sorbitol can be represented as catalyst
C6 H12 O6 1 2H2 - C6 H14 O6
ð4:5Þ
Hydrogenation of sugars has been widely studied and several catalysts have been proposed to perform it. Chao and Huibers (1982) described the hydrogenation of glucose in solution with a nickel catalyst, with pH between 7 and 13, temperatures in the range of 130 C180 C and pressures between 500 and 2000 psig, with hydrogen/feed ratios between 1000 and 5000; they reported that the conversion to sorbitol is at least 98 wt.%. Degelmann et al. (1999) studied the hydrogenation of sugars and sugar mixtures using a shell catalyst based on Raney metals, as nickel, cobalt, copper, iron, or zinc. Elliott et al. (2003) presented the hydrogenation of sugars derived from sources as corn. The reaction occurs in the aqueous phase at temperatures below 120 C in pressure preferably ranging from 250 to 1900 psig, using ruthenium-based catalysts. Liaw et al. (2010) proposed an amorphous nanocatalyst of CoNiB and a polyvinylpyrrolidone-stabilized CoNiB catalyst to hydrogenate a
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Chapter 4 Production processes for the conversion of sugar and starchy feedstock
fructose/glucose mixture. According to their results, the proposed catalysts resulted much more active than Raney nickel, NiB, and CoB catalysts. Sifontes et al. (2010) reported the hydrogenation of sugars in a pressurized slurry reactor with a Ru/C catalyst; they pointed out that for a continuous operation such configuration would allow better results than a fixed-bed reactor. Sifontes Herrera et al. (2011) studied the hydrogenation of sugars such as D-galactose and L-arabinose, among others, on a Ru/C catalyst, with its operating conditions ranging from 90 C to 130 C and 4060 bar; as result, a 100% conversion of the sugars is obtained. A detailed review of the different catalysts used for the hydrogenation of glucose and fructose can be found in the work of Ahmed and Hameed (2019); in that review, it is indicated that Ru-based catalysts have the best results in terms of conversion and yield, also having high stability with potential to be reused in several cycles. In the second stage of the catalytic approach, the polyhydric alcohols are treated in an aqueous phase reforming system, where several reactions may occur: reforming to hydrogen, dehydrogenation, deoxygenation, hydrogenolysis, and cyclization (Wang et al., 2016). According to Duarte et al. (2016), the selectivity to liquid alkanes in the aqueous phase reforming can be increased by promoting the C-O hydrogenolysis reactions and preserving the CC bonds, which will be strongly influenced by the kind of catalyst employed. Davda and Dumesic (2004) reported the aqueous phase reforming of glucose to produce H2 using Pt/ Al2O3 as a catalyst; they mentioned that alternate reaction pathways may lead to the formation of alkanes and other products such as organic acids, aldehydes, and carbonaceous deposits. Huber et al. (2004) studied the production of alkanes by aqueous phase reforming with several catalysts. According to their data, the highest yield to alkanes is obtained with Pt/SiAl and Pd/SiAl catalysts, using a feed of hydrogen and operating at pressures between 29.3 and 58.2 bar, with temperatures ranging from 225 C to 265 C. Kirilin et al. (2012) presented a study about the production of hydrogen by aqueous phase reforming of xylitol and sorbitol, using a Pt/Al2O3 catalysis. Among their results, it is mentioned that selectivity to alkanes is promoted at low values for space velocity, reaching a maximum for selectivity close to 30%. Additionally, it is mentioned that the alkanes produced from xylitol are mainly in the range C3C5. Zhang et al. (2012) proposed the use of Ni/HZSM-5 catalysts to promote the formation of hydrocarbons in the gasoline range. According to their results, the maximum yield to hydrocarbons in the boiling point range of gasoline is 47.6%, with the reaction
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
taking place at 240 C and 4 MPa, with high selectivity to isohexane. Qiu et al. (2019) described a Ru-MoO3-x/C catalyst as a promoter of the hydrogenolysis of sorbitol, reporting the highest yield of 87.3% to C5C6 hydrocarbons at 280 C and 4 MPa, with sorbitol conversion higher than 99.9%. Other products that can be generated in the aqueous phase reforming are oxygenates; for instance, alcohols, ketones, aldehydes, furans, diols, triols, and organic acids (Davis et al., 2015). These compounds can be upgraded by condensation reactions. The condensation reaction allows obtaining alkanes, isoalkanes, and aromatics from oxygenates (Wang et al., 2016). Condensation reactions may occur together with ketonization reactions. In general terms, by condensation/ketonization reactions, the small molecules can form larger molecules, in the presence of acid or basic catalysts (Chheda et al., 2007), while oxygen molecules are released from the oxygenates (Wang et al., 2017). According to Bloomel and Cortright (2008), condensation with basic catalysts promotes the formation of hydrocarbons in the boiling point range of jet fuel and diesel. Besides hydrocarbons, condensation may also produce alcohols and ketones (Cortright and Blommel, 2015). Davis et al. (2015) mentioned that basic catalysts can be poisoned by organic acids, thus recommending the use of acid catalysts. According to their data, approximately 50% of the carbon in the stream from the aqueous phase reforming can be converted into C8C14 hydrocarbons, using a catalyst based on a precious metal with a mixed oxide support (Blommel et al., 2012; Davis et al., 2015). Gayubo et al. (2004a,b) proposed the use of a HZSM-5 zeolite catalyst to condensate alcohols, aldehydes, ketones, and acids. Nevertheless, the produced hydrocarbons are mainly in the boiling point range of gasoline. Kunkes et al. (2008) presented a production system where oxygenates are treated with hydrogen in a CuMg10Al7Ox catalyst, at 5 bar and 300 C. They reported obtaining a 40% of conversion of the carbon in the feed stream to C8C12 species, although the catalyst was deactivated due ¨ rbu¨z et al. (2010) proposed to the presence of organic acids. Gu a two-bed system to transform oxygenates into hydrocarbons, using a CeZrOx bed to perform ketonization, and a Pd/ZrO2 bed to perform condensation/hydrogenation. Nesterenko et al. (2012) studied the condensation of ethanol in a basic catalyst to produce superior alcohols and olefins, mentioning that the presence of the condensation products in the olefins stream aids to increase the yield to C10 and higher hydrocarbons in a subsequent oligomerization step. Zapata et al. (2012) proposed a catalyst composed of basic oxide nanoparticles/carbon nanotubes, obtaining products in the range C8C13.
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Another pathway to treat the oxygenates implies dehydration and hydrogenationdehydration reactions to obtain alkanes and alkenes. Hydrogenation of ketones and aldehydes produces secondary and primary alcohols. Such reactions can be performed in a separated way, using acid or base catalysts for dehydration and metal-based catalysts for hydrogenation (Chheda et al., 2007). On the other hand, both reactions can be performed in a single equipment with the proper catalyst. West et al. (2009) proposed the use of a Pt catalyst supported in Nbbased solid acids to dehydrate/hydrogenate sorbitol at 54 bar and 257 C. According to their results, low space velocities allow increasing the yield to alkanes. Gu¨rbu¨z and Dumesic (2013) reported that coupled dehydration and hydrogenation can be performed with a bifunctional acid/metal catalyst, obtaining ¨ rbu¨z et al. (2010) alkanes. The dual-bed system proposed by Gu has as final step a dehydration/hydrogenation reactor, where ketones are converted into alkanes in a Pt/SiO2-Al2O3 catalyst, obtaining in the final product 53% of alkanes in the range of diesel. Alkenes generated after the dehydration/hydrogenation can be further oligomerized (Wang et al., 2016), followed by isomerization/cracking if necessary.
4.3
Technologies on separation zone
Once the desired products are obtained, it is necessary to separate them with the required purity; this is the main goal of the separation zone. In the case of the ATJ process, three sections can be noticed: ethanol production, ethanol dehydration, and ethylene oligomerization. Each section requires their own purification equipment. In the ethanol production, most of the removal of liquid and gaseous byproducts can be performed by distillation, until an azeotropic composition for the ethanol/water mixture (around 96 wt.%) is reached. If higher purities were required, technologies as extractive distillation or pervaporation could be required (Conde-Mejı´a et al., 2016). Nevertheless, it has been reported that the mixture of ethanol and water can be used as a feedstock for catalytic dehydration (Pearson, 1983). Once the ethanol is dehydrated and ethylene is obtained, it can go through a set of purification operations: quenching, scrubbing, and drying. In the quenching step, most of the water is separated, together with the remaining ethanol. In the scrubbing tower, traces of CO2 are removed by washing with sodium hydroxide. Finally, if highpurity ethylene is required, drying may take place by molecular sieves (Mohsenzadeh et al., 2017). In the oligomerization step, the main products are hydrocarbon chains. Thus, to obtain the
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
desired cuts, heat-based separation processes, as distillation, can be used. The main product in the ATJ process is biojet fuel, which can be used as aviation fuel blended with fossil jet fuel. Additionally, naphtha and green diesel are generated as coproducts. Naphtha can be used as a solvent or further treated to produce gasoline. Green diesel can be used as a substitute of fossil diesel. Since it is composed of hydrocarbons, there is no need to blend it with fossil diesel, as it occurs with biodiesel. Additional uses of these products were described previously in Chapter 3: Production processes for the conversion of triglyceride feedstock. In the case of the biological approaches for the conversion of sugars to biojet fuel, if the sugars are first converted into triglycerides/fatty acids, the following hydrotreating and hydrocracking/isomerization steps are equal to those reported in Chapter 3: Production processes for the conversion of triglyceride feedstock. Thus the separation alternatives are also the same. A similar situation is observed if direct conversion to alkanes occurs; following the hydrocracking/hydroisomerization stage, the resulting fractions can be purified through distillation. In the case of the conversion of sugars to farnesene, the stream leaving the reactor can be separated into two phases by de-emulsification and liquidliquid centrifugation to recover farnesene, which then enters to a flash equipment (Amyris Inc., 2013). After hydrogenation of farnesene, the resulting stream goes to a hydrocracking/ hydroisomerization section, whose separation schemes have been described in Chapter 3: Production processes for the conversion of triglyceride feedstock. In the case of the catalytic process, most of the required separations are needed with the recovery of the different hydrocarbon fractions, thus distillation can be used, as in the case of the hydrotreating process.
4.4
Conventional processes: state of the art
In this section, reported processes for the production of biojet fuel from sugar containing biomass will be presented. First, cases of commercial production of biojet fuel through the ATJ route are presented. The company Gevo Inc. produces isobutanol by fermentation of the sugars contained in grain crops, sugarcane, and sugar beets. Then, the isobutanol is transformed into renewable gasoline, biojet fuel, and other products, by dehydration and oligomerization reactions (Green Car Congress, 2010). The jet fuel produced in this process meets the ASTM specifications (Gevo, 2019). On the other hand, the company Byogy Renewables Inc. produces biojet fuel from the ethanol derived
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from sugarcane. According to the company, their product can directly be used in the jet engine, without blending with fossil jet fuel (Green Air, 2014). Additional to the commercial processes, information about the production pathways can also be found in the scientific literature. Harvey and Quintana (2012) presented a process to convert branched olefins, derived from the fermentation of biomass into jet fuel. Narula et al. (2012) studied a process to obtain hydrocarbons from a biomass-derived alcohol, using a catalyst consisting of a metal supported in zeolites. Pansare et al. (2013) proposed a process for the transformation of alcohols, derived from biomass, into hydrocarbons, through a hydrocondensation followed by a hydrodeoxygenation. The product includes hydrocarbons in the range of jet fuel and diesel. For hydrocondensation, a Pt/Pd catalyst supported in silica-alumina is described. Peters and Taylor (2015) reported a process for the fermentation of cellulose-containing biomass to produce isobutanol, then dehydrating, oligomerizing, and hydrogenating to obtain alkanes in the range C12C16. The dehydration takes place using several catalysts, as sulfonic acid resins and α-alumina, among others. On the other hand, oligomerization is performed with sulfonic acid resins, acidic zeolites, or solid phosphoric acid. Brooks et al. (2016) summarize the conversion pathways of ethanol to biojet fuel in four categories: 1. With ethylene as intermediate, through dehydration, oligomerization, and hydrotreating stages; 2. With propylene as intermediate, where ethanol is first converted into propylene, followed by oligomerization and hydrotreating steps; 3. With a higher alcohol as intermediate, which may imply ethanol dehydrogenation, self-aldolization, dehydration, hydrogenation, and dehydration of the formed higher alcohol, along with the oligomerization and hydrotreating; 4. With a carbonyl intermediate, where ethanol is dehydrated, followed by hydroformylation and further dehydration, oligomerization, and hydrotreating. Tao et al. (2017) studied the production of biojet fuel by the ATJ process from corn grain, comparing it with the production using corn stover as a raw material. A minimum jet fuel selling price of 4.20 USD per gallon is reported for the biojet fuel from corn grain, which is lower than the selling price estimated for the biojet fuel from corn stover (6.14 USD per gallon). Geleynse et al. (2018) evaluated different ATJ-based pathways from a technoeconomic point of view. Among their results, they
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
indicated that alcohol production contributes with approximately 80% to the total production cost. Additionally, they mentioned that the use of higher alcohols instead of ethanol may help to reduce costs in the ATJ process. In the case of STJ pathways, some industrial-level processes have been reported. The company Amyris started running a process, developed by Total, to produce a drop-in jet fuel which accomplishes with the ASTM standard (Total, 2014). In the Amyris process, sugars are aerobically fermented with yeast to obtain farnesene (Wang et al., 2016), with further distillation and hydrogenation (Westfall and Gardner, 2011). Santos et al. (2018) described a similar process where sugars from sugarcane are fermented to farnesene, which is then hydrocracked, with the fractions further separated. The company LS9 also had a one-step fermentation process to transform sugars into biojet fuel (Wei et al., 2019), from which alkanes in the range of C8C20 are generated, which can be further treated to produce biojet fuel (Wesoff, 2010). Nevertheless, the company shifted their production to other products (Mawhood et al., 2016). In the case of the conversion of sugars into biojet fuel through catalytic STJ approaches, Bloomel and Cortright (2008) reported the patented technology BioForming, which is owned by the company Virent Energy Systems, Inc. The process’s core is the aqueous phase reforming technology owned by Virent Energy Systems, Inc., which makes use of a proprietary heterogeneous catalyst. The process can handle different raw materials. If sugar-based materials are used, they go first to a hydrogenation stage, then to the aqueous phase reforming. If the desired product is biojet fuel, the following steps are condensation and hydrodeoxygenation.
4.5
Combustion tests for biojet fuel from sugar and starchy feedstocks
In this subsection, combustion tests developed for biojet fuels derived from sugar/starchy biomass are presented. In terms of the engine performance, Won et al. (2016) studied the combustion behavior for several biojet fuels, including a Gevo ATJ jet fuel, and blends of those alternative fuels with JP-8. They performed three kinds of tests: global oxidative species profiles, diffusion flame extinction, and premixed flame initiation. Three main parameters are analyzed: the cetane number, which measures the autoignition propensity of distillate range fuels; the smoke point, which is related to the kind of hydrocarbons
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composing the fuels, where a high smoke point indicates that the fuels tend to produce little smoke (ASTM, 2019); and the H/C ratio, which is an indicator related to the burning velocity of the fuel. According to the results presented by Won et al. (2016), the biojet fuel produced through the ATJ process had the lowest values for cetane number, in comparison with fossil-derived jet fuels and other renewable jet fuels. On the other hand, the ATJ biojet fuel showed higher smoke point than the fossil jet fuels, but similar or lower smoke point than most of the other biojet fuels, with a H/C ratio higher than that of fossil jet fuels. Additionally, it is reported that the ATJ jet fuel had lower extinction strain rates. Richter et al. (2017) studied the laminar burning velocity and ignition delay time of the Gevo ATJ biojet fuel. The highest values for laminar burning velocity imply that the fuel will combust faster (Cracknell et al., 2013). On the other hand, for ignition delay time, which is defined as the time interval between a starting reference point and the onset of ignition, low values are desired (Zhang et al., 2016). According to the results reported by Richter et al. (2017), both combustion properties showed slightly lower values for the renewable jet fuel. In the case of emissions due to the use of biojet fuel, Zschocke et al. (2012) studied this topic considering jet fuel obtained by direct fermentation of sugars (i.e., farnesane), using a CFM56-5C4 engine. According to their results, CO emissions are almost the same than those for keronese, while farnesene showed slightly lower emissions of NOx than kerosene. BraunUnkhoff et al. (2017) mentioned that, in general terms, biojet fuels show lower soot particle emissions than fossil jet fuel, but emissions of CO, CO2, and NOx are similar for both kinds of aviation fuels. They indicated that the amount and type of aromatics have a strong influence on the soot particle emissions. This is proved for the particular case of ATJ biojet fuel by Schripp et al. (2019), who studied the soot particle emissions in a CFM56-5C4 engine. According to their results, the use of the renewable jet fuel, which has no aromatics, allowed reducing 70% the particle emissions in comparison with kerosene.
4.6 4.6.1
Case of study: conversion of sugar and starchy feedstocks Problem statement
In Mexico, the production of sweet sorghum has presented an annual average growth of 3.74% since 2016; according to
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
SAGARPA (2017), this growth rate will keep until 2030, achieving a potential production of 8.37 millions of tons in that year. The main use of sweet sorghum is an ingredient to elaborate animal feed, which represents 95.71% of the total production. This crop has high levels of sugar and starchy; thus it is considered as a feedstock to produce biojet fuel. It is worth to mention that the sorghum is native to India, but due to climatological conditions of Mexico, its growth is almost constant along the country. In Fig. 4.4, a typical sorghum cane from the Guanajuato state (Mexico) is presented. The production of sweet sorghum in Mexico has been classified by regions in accordance with climatological seasons: 24 regions in spring-summer (SS) and 20 regions in fall-winter (FW) (SAGARPA, 2017). For this case of study, we have considered the production of sorghum in the region that includes the states of Aguascalientes, Guanajuato, Jalisco, Michoaca´n, and Zacatecas. The production of sorghum from this region is 5.06 ton/ha during SS, while it is 3.13 ton/ha during FW. The land extension available for this crop in the selected region is 3,426,562 ha for SS and 175,078 ha for FW. An average of 744,081.5 ton/year or 87,539 kg/h (considering 8500 h/year as the operation time) is the potential raw material to produce biojet fuel; this data is estimated based on the annual growth rate for 2019. The production of biojet fuel from sweet sorghum will be obtained in two stages. In the first one, the production of
Figure 4.4 Components of sorghum cane.
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bioethanol by acid hydrolysis (AH) and fermentation occurs, while in the second one, the conversion of bioethanol by the ATJ process to produce biojet fuel takes place.
4.6.2
Modeling of sugar and starchy feedstocks
The composition of sweet sorghum (whole crop) is reported by Sipos et al. (2009); this composition is taken in this study with an adjustment to consider only the lignocellulosic and carbohydrate composition, dismissing the ash content. Regarding the carbohydrate profile, a typical composition of sweet sorghum is reported by Va´zquez-Garea (2017). Thus the composition considered for this case of study is presented in Table 4.1. Information about the physical and chemical properties of sorghum grain and stem can be found in Garcı´a et al. (2019). The carbohydrate components of sorghum are included in the Aspen Plus database V.10.0; however, the lignocellulosic components must be added by the user. In Table 4.2, the chemical formula of sorghum compounds is presented. It is important to mention that all the components shown in Table 4.1 must be specified as solids in Aspen Plus. Thus, for each component of the carbohydrates profile, it is necessary to add the “Solid heat capacity (DHSFRM),” which is taken as 2486,800,000 J/kmol (Chavelas and Garcı´a-Herna´ndez, 2009).
4.6.3
Production process: conceptual design
In Fig. 4.5, the block diagram of the processing of sorghum (whole crop) is presented, where sorghum was previously grinded to improve the performance of the reactive stages. Table 4.1 Composition of sweet sorghum (Sipos et al., 2009; Va´zquez-Garea, 2017). Compounds
wt.%
Cellulose Hemicellulose Lignin Carbohydrates
27.56 15.37 14.11 42.93
Carbohydrates profile
Galactan Mannan Arabinan
9.41 3.62 29.90
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
109
Table 4.2 Chemical formula of sorghum components. Lignocellulosic profile Cellulose
Hemicellulose
Lignin
C6 H10 O5
C5 H8 O4
C7:3 H13:9 O1:3
Carbohydrates profile Galactan
Mannan
Arabinan
C6 H10 O5
C6 H10 O5
C5 H8 O4
Figure 4.5 Block diagram to produce biojet fuel from the conversion of grinded sorghum.
According to Fig. 4.5, the biojet fuel production is carried out in two stages. In the first one, the ethanol is obtained, while in the second one, the ethanol conversion through the ATJ process to produce biojet fuel takes place. The ethanol production starts with the pretreatment of grinded sorghum by diluted sulfuric acid (DA, H2SO4 0.49 wt.%) at 160 C and atmospheric pressure; this is one of the pretreatment methods commonly used for lignocellulosic material (Conde-Mejı´a et al., 2012). Next, the pretreated material is fed to AH stage at 220 C to extract the fermentable sugars, which are converted into ethanol and other subproducts into fermentation reactor, operated at 34 C. The production of ethanol from lignocellulosic feedstock has been reported by Conde-Mejı´a et al. (2012, 2013) taking into account only the lignocellulosic compound, while the ethanol
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Chapter 4 Production processes for the conversion of sugar and starchy feedstock
production using the same reactive stages, but including the carbohydrates profile to describe the lignocellulosic raw material, has been presented by Dimian and Bildea (2008). The reaction set for each reactive stage is shown in Table 4.3, along with the conversion data for each reaction.
Table 4.3 Reactions for the ethanol production (Dimian and Bildea, 2008; Conde-Mejı´a et al., 2012, 2013). Reaction
Conversion
Reference reactant
0.065 0.007 0.75 0.15 0.75 0.15 0.75 0.15 0.75 0.10
Cellulose Cellulose Hemicellulose Hemicellulose Galactose Galactose Mannan Mannan Arabinan Arabinan
0.012 0.8 1.0 0.99 0.8
Cellulose Cellulose Cellobiose Hemicellulose Xylose
0.99 0.92 0.002 0.008 0.022 0.013 0.85 0.029 0.009 0.024 0.014
Urea Glucose Glucose Glucose Glucose Glucose Xylose Xylose Xylose Xylose Xylose
Diluted acid, pretreatment
C6 H10 O5 1 H2 O-C6 H12 O6 C6 H10 O5 1 0:5H2 O-0:5C12 H22 O11 C5 H8 O4 1 H2 O-C5 H10 O5 C5 H8 O4 -C5 H4 O2 1 2H2 O C6 H10 O5 1 H2 O-C6 H12 O6 C6 H10 O5 -C6 H6 O3 1 2H2 O C6 H10 O5 1 H2 O-C6 H12 O6 C6 H10 O5 -C6 H6 O3 1 2H2 O C5 H8 O4 1 H2 O-C5 H10 O5 C5 H8 O4 -C5 H4 O2 1 H2 O Acid hydrolysis
C6 H10 O5 1 0:5H2 O-0:5C12 H22 O11 C6 H10 O5 1 H2 O-C6 H12 O6 C12 H22 O11 1 H2 O-2C6 H12 O6 C5 H8 O4 1 H2 O-C5 H10 O5 C5 H10 O5 -C5 H4 O2 1 3H2 O Fermentation
CH4 N2 O 1 H2 O-2NH3 1 CO2 C6 H12 O6 -2C2 H6 O 1 2CO2 C6 H12 O6 1 2H2 O-2C3 H8 O3 1 O2 C6 H12 O6 1 2CO2 -2C4 H6 O4 1 O2 C6 H12 O6 -3C2 H4 O2 C6 H12 O6 -2C3 H6 O3 3C5 H10 O5 -5C2 H6 O 1 5CO2 3C5 H10 O5 1 5H2 O-5C3 H8 O3 1 2:5O2 3C5 H10 O5 1 5CO2 -5C4 H6 O4 1 2:5O2 2C5 H10 O5 -5C2 H4 O2 3C5 H10 O5 -5C3 H6 O3
Notes: Furfural (C5 H4 O2 ); cellobiosa (disaccharide, C12 H22 O11 ); ethanol (C2 H6 O); urea (CH4 N2 O); glycerol (C3 H8 O3 ); succinic acid (C4 H6 O4 ); lactic acid (C3 H6 O3 ); acetic acid (C2 H4 O2 ).
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
111
Additional reactions that can occur during the fermentation stage are shown in Table 4.4, in accordance with Dimian and Bildea (2008). It is worth to mention that the lignin is not solubilized in any reactive stage. The stream leaving the fermentation reactor is separated in a distillation train of three columns to recover the ethanol. In the first column, the CO2 and other light gases are removed, and a side stream enriched with ethanol is extracted. This last stream is fed to the second column, in which the ethanol is purified to a concentration close to the azeotropic point (96 wt.% ethanol). Finally, in the third column, the azeotropic point is overcome using an entrainer, reaching 99 wt.% of ethanol recovery at distillate stream. The recovered ethanol is fed to the ATJ process. This process involves three stages: ethanol dehydration, ethylene oligomerization, and alkenes hydrogenation. The first stage was reported by Lundin (2011) at 450 C and 11.4 bar of pressure, using injected steam (1:1 alcohol:steam mass ratio). The conversion data and reactions to describe this reactive stage are presented in Table 4.5. The separation of the obtained ethylene is carried out in a distillation column, reaching up 99% ethylene recovery.
Table 4.4 Additional reactions (Dimian and Bildea, 2008). Reaction
Conversion
Reference reactant
C2 H4 O2 1 NH3 -C2 H7 NO2 H2 SO4 1 2NH3 -N2 H8 SO4 C5 H12 O6 -2C3 H6 O3 3C5 H10 O5 -5C3 H6 O3
1 1 1 1
Acetic acid Sulfuric acid Glucose Xylose
Notes: Ammonium acetate (C2 H7 NO2 ); ammonium sulfate (N2 H8 SO4 ).
Table 4.5 Ethanol dehydration reactions (Lundin, 2011). Reaction
Conversion
Reference reactant
C2 H6 O-C2 H4 1 H2 O 2C2 H6 O-ðC2 H5 Þ2 O 1 H2 O C2 H6 O-C2 H4 O 1 H2
0.988 0.00052 0.002
Ethanol
Notes: Ethylene (C2 H4 ); Diehylether [ðC2 H5 Þ2 O]; Acetaldehyde C2 H4 O.
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Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Table 4.6 Oligomerization reactions and conversion data (Heveling et al., 1998). Reaction
Conversion
Reference reactant
2C2 H4 -C4 H8 2:5C2 H4 -C5 H10 3C2 H4 -C6 H12 3:5C2 H4 -C7 H14 4C2 H4 -C8 H16 4:5C2 H4 -C9 H18 5C2 H4 -C10 H20 5:5C2 H4 -C11 H22 6C2 H4 -C12 H24 7C2 H4 -C14 H28 7:5C2 H4 -C15 H30 8C2 H4 -C16 H32 8:5C2 H4 -C17 H34 9C2 H4 -C18 H36 9:5C2 H4 -C19 H38 10C2 H4 -C20 H40
0.3185 0.0015 0.2355 0.0015 0.1801 0.0015 0.0425 0.00150 0.04254 0.04254 0.00150 0.04254 0.00150 0.04254 0.00150 0.04254
Ethylene
The purified ethylene is fed to the second reactive stage, called oligomerization. In this reactive stage, the ethylene is converted to olefins, from C4 to C20, at 120 C and 35 bar, according to Heveling et al. (1998). The product distribution and the reaction set used to describe the oligomerization are presented in Table 4.6. Regarding hydrogenation stage, the reaction set, together with the conversion data used to describe it, is presented in Table 4.7. This process was reported at 100 C and 15 bar by Andrei et al. (2015). The hydrogen amount used for hydrogenation is 10 wt.% of the olefins stream. Likewise, the olefins conversion is assumed as 99%. To separate the biojet fuel, two distillation columns are required. In the first one, naphtha (C4C7) cut is recovered at the top of the column, while in the second one, biojet fuel (C8C16) is separated at the distillate stream and the green diesel (C17C20) at the bottom. The distillation columns are designed considering recoveries of 99% in the key components.
4.6.4
Simulation of reactive and separation zones
The simulation of the process to produce biojet fuel from sweet sorghum, presented in Fig. 4.3 and described in
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
113
Table 4.7 Hydrogenation reactions and conversion data (Andrei et al., 2015). Reaction
Conversion
Reference reactant
C4 H8 1 H2 -C4 H10 C5 H10 1 H2 -C5 H12 C6 H12 1 H2 -C6 H14 C7 H14 1 H2 -C7 H16 C8 H16 1 H2 -C8 H18 C9 H18 1 H2 -C9 H20 C10 H20 1 H2 -C10 H22 C11 H22 1 H2 -C11 H24 C12 H24 1 H2 -C12 H26 C14 H28 1 H2 -C14 H30 C15 H30 1 H2 -C15 H32 C16 H32 1 H2 -C16 H34 C17 H34 1 H2 -C17 H36 C18 H36 1 H2 -C18 H38 C19 H38 1 H2 -C19 H40 C20 H40 1 H2 -C20 H42
0.99
n-Butene n-Pentene n-Hexene n-Heptene n-Octene n-Nonene n-Decene n-Undecene n-Dodecene n-Tetradecene n-Pentadecene n-Hexadecene n-Heptadecene n-Octadecene n-Nonadecene n-Eicosene
Section 4.5.3, is carried out by using Aspen Plus V.10.0. The simulation starts with definition of each component involved in the process. In Table 4.8, the selected components are presented. It is worth to mention that the lignocellulosic components are not included in the Aspen Plus database for V.10.0. On the other hand, in accordance with Carlson (1996) and Conde-Mejı´a et al. (2013), the thermodynamic method chosen for the ethanol production (including ethanol purification) is NRTL, while the whole ATJ process is modeled with PengRobinson (Carlson, 1996). Regarding the distillation train for biojet fuel separation, BK10 has been chosen as thermodynamic method (Carlson, 1996; Gutie´rrez-Antonio et al., 2016). The flowsheet starts with the definition of the sorghum stream (SORGHUM), based on the composition shown in Table 4.1. The sorghum stream is fed to the pretreatment reactor (PRETREAT), along with the diluted sulfuric acid stream (H2SO4DI1) and saturated steam (STEA-160) at 160 C. The reactor is simulated by RStoic module. The operation conditions are 160 C, atmospheric pressure 0.49 wt.% sulfuric acid, ´ vila Rodrigues and and steam:biomass ratio of 1:4 (de A Guirardello, 2008; Conde-Mejı´a et al., 2012). Fig. 4.6 shows the flow diagram of the process.
114
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Table 4.8 List of components involved in the production of biojet fuel from sweet sorghum. Component
Chemical formula from Aspen Plus V.10.0
Stage of the process
Cellulose (solid) Hemicellulose (solid) Lignin (solid) Arabinan Galactan Mannan Galactose Mannose Arabinose Cellobiose Glucose Xylose Urea Hidroximetilfurfural (HMF) Furfural Succinic acid Glycerol Acetic acid Lactic acid Ammonium acetate Ammonium sulfate Water Sulfuric acid Ethanol Carbon dioxide Oxygen Ammonia Ethylene Diethyl ether Acetaldehyde Butene Pentene Hexene Heptene Octene Nonene Decene Undecene
C6 H10 O5 C5 H8 O4 C7:3 H13:9 O1:3 C5 H8 O4 2 D1 C1:2 H2 O 2 N1 C3 HO3 2 N2 C6 H12 O6 2 N2 C6 H12 O6 2 N5 C5 H10 O5 2 D1 C12 H22 O11 2 D1 C6 H12 O6 C5 H10 O5 2 D2 CH4 N2 O C6 H6 O3 2 N5 C5 H4 O2 C4 H6 O4 2 2 C3 H8 O3 C2 H4 O2 2 1 C3 H6 O3 2 D1 C2 H7 NO2 ðNH4 ÞSO4 H2 O H2 SO4 C2 H6 O 2 2 CO2 O2 NH3 C2 H4 C4 H10 O 2 5 C2 H4 O 2 1 C4 H8 2 1 C5 H10 2 2 C6 H12 2 3 C7 H14 2 7 C8 H16 2 16 C9 H18 2 3 C10 H20 2 5 C11 H22 2 2
Raw material
Diluted acid Acid hydrolysis Fermentation
Ethanol dehydration
Oligomerization
(Continued )
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Table 4.8 (Continued) Component
Chemical formula from Aspen Plus V.10.0
Dodecene Tetradecene Pentadecene Hexadecene Heptadecene Octadecene Nonadecene Eicosene Butane Pentane Hexane Heptane Octane Nonane Decane Undecane Dodecane Tetradecane Pentadecane Hexadecane Heptadecane Octadecane Nonadecane Eicosane Hydrogen
C12 H24 2 2 C14 H28 2 2 C15 H30 2 2 C16 H32 2 2 C17 H34 2 D1 C18 H36 2 1 C19 H38 2 D1 C20 H40 2 D1 C4 H10 2 1 C5 H12 2 1 C6 H14 2 1 C7 H16 2 1 C8 H18 2 1 C9 H20 2 1 C10 H22 2 1 C11 H24 C12 H26 C14 H30 C15 H32 C16 H34 C17 H36 C18 H38 C19 H40 C20 H42 H2
The output stream from PRETREAT reactor is conditioned at 220 C, before the AH reactor (AC-HYDRO), using a heater (EX-1). The AC-HYDRO reactor operates at 220 C through the RStoic module. The nonconverted biomass is removed by a Filter module (FILT-1). Next, the liquid stream is fed to cooler equipment (EX-2) to reduce its temperature until 34 C, which is the operation temperature of the fermentation reactor (FERMENTA) where is added a stream of urea (UREA). The mass ratio of fed: urea is 442.5:1, according to Conde-Mejı´a et al. (2012). It is worth to mention that for the reactive stages defined before, the reaction set and the conversion data for each one were
Stage of the process
Hydrogenation
115
Figure 4.6 Flow diagram for the complete process.
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
presented in Table 4.3. The output stream from the fermentation reactor is inserted to FILT-2, where the solid components are removed. Next, the liquid stream is fed to a separation train, which has three distillation columns to separate the ethanol. In order to simulate each distillation column, the module RadFrac is necessary. The first column (STRIPP-C) is designed with 33 stages without condenser, and the feed stream is located in stage 1. The side stream rich in ethanol has a flow of 39,000 kg/h, and it is extracted on stage 4. The operation pressure is 1 atm, with a column pressure drop of 0.68 atm; the recovery of ethanol is 99 wt.%. On the other hand, the ETOH-RICH stream is fed at stage 22 of the second column, called RECTIFIC-C. This column has 30 stages and a Partial-Vapor-Liquid condenser, with a reflux ratio of 3.3. The column pressure is 1 atm, reaching 95 wt.% of recovery for the ethanol. In the third column called RECOVERY, a glycerol stream is added to overcome the azeotropic point. This column is designed with 15 stages, reflux ratio of 0.3, and total condenser, which allows reaching 99 wt.% of ethanol recovery. The rich ethanol stream from the second column is inserted at stage 12, while the glycerol column at stage 2. The ethanol:glycerol mass ratio is 1:1. The operating pressure of the distillation column is 1 atm. The ETOH-2 stream contains the ethanol available for the ATJ process. This stream is inserted to a Pump module (PUMP-1) and a Heater module (EX-3) to increase the pressure until 11.4 bar and its temperature up to 450 C, respectively. The adjusted stream is fed at the dehydration reactor (1S-ATJ, module RStoic), operated at 450 C, 11.4 bar, along with a stream of saturated steam at 11.4 bar (alcohol:vapor mass ratio of 1:1). The reaction set and conversion data are presented in Table 4.5. A turbine is collocated for diminish the pressure of the output stream from the 1S-ATJ reactor until 3 bar. This is done through the module Compr (TURBI-1, model isentropic). The resulting stream is rich in ethylene, which must be purified. The ethylene recovery is carried out by a distillation column with Partial-Vapor-Liquid condenser, called B4 (module RadFrac); this distillation column has 14 stages, a reflux ratio of 4, and an operating pressure of 3 atm. The ethylene stream (S14) is fed at stage 2. The traces of components in the ETHYLE-1 stream are removed by the equipment FLASH-1 (module Flash2), which operates at 2103 C and 1 bar. The pressure of the purified ethylene (ETHYLE-2) is increased until 35 bar through compressor, called COMP-1 (module Compr, model isentropic). Next, this stream is heated at 120 C using EX-4 (module, Heater). These conditions are suitable for the second reactor.
117
118
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
The second reactive stage from the ATJ process is the ethylene oligomerization. This reactor is modeled by the RStoic module called 2S-ATJ. The operation conditions for this stage are 120 C and 35 bar, while its reaction set was presented in Table 4.6. The output stream from 2S-ATJ reactor is inserted at the hydrogenation stage. This reactor is modeled by RStoic module, called 3S-ATJ. In the hydrogenation stage, a hydrogen stream is conditioned at 15 bar and 100 C, using the modules Compr (COMP-2, model isentropic) and Heater (EX-5). The 3S-ATJ reactor is operated at 100 C and 15 bar, according to the reaction set presented in Table 4.7. The hydrogen excess in the output stream from 3S-ATJ (called S15) is removed by Sep module (SEP-1). Also, the pressure is decreased until 1 bar, using a module Pump as turbine (TURBI-2). This hydrocarbon stream is fed to a distillation train, which separates the biojet fuel. The distillation train includes two distillation columns, modeled by RadFrac module: COL-1 and COL-2. In the first one, the naphtha cut is obtained at the top of the column with a recovery of 99 wt.%. This column is designed with 42 stages, a reflux ratio of 0.23, 1 atm as operating pressure, and a total condenser. The S25 stream is located at stage 23. Finally, the distillation column COL-2 is designed with 87 stages, a reflux ratio of 0.4, total condenser, feed stream located at stage 44, and 1 atm as operating pressure. At the top of the column, the biojet fuel is obtained with 99 wt.% recovery, while at the bottom, the green diesel is purified with 99% recovery.
4.6.5
Economic assessment
The calculation procedure and the prices of steam, cooling water, hydrogen, and electricity were described in Chapter 3: Production processes for the conversion of triglyceride feedstock. The prices of sorghum, urea, glycerol, and sulfuric acid required in this case of study are presented in Table 4.9. It is Table 4.9 Market price of supplies required for the process. Item
Price in the market
References
Sorghum Urea Glycerol Sulfuric acid
0.63 USD/kg 0.31 USD/kg 1 USD/kg 15.52 USD/L
Mercado Libre (2019) Index Mundi (2020) Alibaba (2020) Restauro Online (2019)
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
119
Table 4.10 Total annual cost of sweet sorghum processing to produce biojet fuel. Operating cost (USD/year) Heating utilities Cooling cost utilities cost
Additional supplies
Raw material
105,448,035.20
237,609,251.96
467,444,154.19 731,478.86
7,293,779.69
Electricity (sale)
818,526,699.89
Annual capital cost (USD/year) Equipment cost A1
A2
Total annual cost (USD/year)
14,943,836.60
9,115,740.32
5,349,893.50
2,689,890.59
TAC (USD/year)
822,413,635.67
worth to mention that the Mexican sorghum price considered in this case of study is the price at the animal feed market. Likewise, the sulfuric acid price is for a solution with 98 wt.%, which is then diluted. Table 4.10 presents the total annual cost (TAC), including an overview of all the factors involved in its calculation. Regarding the utilities cost, the raw material has the higher cost regarding equipment, steam, water, and other supplies. On the other hand, the electricity consumption is 43.76% of the generated electricity by turbines; thus the remaining amount can be sold. According to Table 4.10, the operating cost represents 99.52% of TAC. This high contribution is due to the raw material price and additional supplies required by the process, which represent 56.85% and 28.89% of TAC, respectively. In the case of heating services, the steam price represents 12.82% of TAC, a value almost 14 times higher regarding cooling water. It is worth to mention that this processing approach involves three separation stages, and a raw material in competition with the animal feed market. The TAC could be reduced by using a raw material with inferior quality to that required for the animal feed market and/ or through the implementation of energy saving strategies.
4.6.6
Total operating cost (USD/year)
Estimation of price of biojet fuel
The procedure for estimation of minimum price of biojet fuel was presented in Chapter 3: Production processes for the conversion of triglyceride feedstock. For this case of study,
120
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Table 4.11 Minimum price of biojet fuel from sweet sorghum processing. I (USD/year)
P (USD/year)
C (USD/year)
n P
imin
Naphtha (kg/h)
0.2 P 5 imin I Total
4966.78 Green diesel (kg/h) 771.10 Total 802,977,061.13
i51
Cmdaji
14,943,836.60 n P Operating cost (5% of Cmdaji ) i51 1,337,473.38 Total 16,281,309.97 S (USD/year) Q (kg/year)
3,256,261.99 808,802,152.03 26,406,848.12 Minimum price of biojet fuel (USD/kg)
30.63
Table 4.11 presents an overview of the data involved in the calculation of biojet fuel minimum price. The reference price of kerosene fuel is 0.6380 USD/kg, according to the data provided in Chapter 3: Production processes for the conversion of triglyceride feedstock. Thus, based on the estimation presented in Table 4.11, the minimum price for biojet fuel is 48 times the reference price for fossil jet fuel. This value could be improved with the use of a raw material with lower price and through the application of energy saving strategies to the process.
4.6.7
Environmental assessment: CO2 emissions
According to the methodology described in Chapter 3: Production processes for the conversion of triglyceride feedstock, the estimation of CO2 emissions due to steam and electricity consumption is presented in Table 4.12. The steam required by the ethanol recovery distillation train contributes with almost 50% of total CO2 emissions due to steam generation; however, the emissions of CO2 due to steam requirements could be reduced with energy integration and process intensification techniques, which are described in Chapter 6. It is important to mention that the emissions by electricity consumption are zero, due to the electricity excess obtained in the process.
Chapter 4 Production processes for the conversion of sugar and starchy feedstock
Table 4.12 Total CO2 emissions due to steam and electricity generation.
4.7
Emissions by steam generation Emissions by electricity (Mton CO2/year) generation (Mton CO2/year)
Total CO2 emissions (Mton CO2/year)
4542.64
4542.64
0.0
Conclusions
Sugar and starchy biomass, as corn, wheat, and sugarcane, can be used to produce biojet fuel, either through the fermentation of biomass and the conversion of the obtained ethanol into hydrocarbons in the desired boiling point range; or by the direct conversion of sugars into hydrocarbons and other products. The technologies for fermentation of sugars are widely known, and the technologies for the conversion of alcohols into hydrocarbons have also been studied in detail since decades ago. On the other hand, the development of catalysts for the direct conversion of sugars into fuels has also been studied. In this topic, it is necessary to synthetize new catalysts to increase the yield to biojet fuel since it is still an area of opportunity. From the presented case study, it is clear that the cost of a raw material plays an important role in the total production cost of biojet fuel from sugar and starchy biomass. Heating costs have also an important contribution, which makes necessary the implementation of advanced technologies, as process intensification and integration, to reduce the total energy requirements.
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Production processes from lignocellulosic feedstock 5.1
5
Introduction
Lignocellulosic biomass consists of materials with high content of cellulose, hemicellulose, and lignin; such biomass is usually available as waste from agricultural activities. Each year, various tons of lignocellulosic residues are produced, leading to the necessity of their disposal. Direct burning of the residues may have a serious environmental impact; thus other alternatives must be explored. Among those alternatives, the conversion of the agroindustrial wastes into fuels is an attractive path in economic terms, giving a second use to the residues and contributing to the transition from a fossil-based economy to a circular economy. There are several crops which produce plenty residues to obtain biojet fuel, or other biofuels, in an adequate scale to, at least, partially satisfy the energy demand for the transport sector. The kind of crops depends on the location of the land and the climatologic characteristics of the region. As example, for Mexico in 2006, a production of 75.73 million tons of agricultural residues has been reported, with 79% of residues corresponding to primary crops, such as corn, sorghum, sugarcane, among others (Saval, 2012). In Brazil, the sugarcane industry has as by-products 200 million tons of bagasse and 220 million tons of stray per year, where sugarcane bagasse has a content of cellulose between 40% and 50% (Nicode`me et al., 2018). According to Tye et al. (2016), there is a worldwide production of agricultural residues of 2839.5 million tons per year, derived from crops as barley, corn, oat, rice, sorghum, wheat, sugarcane, and oil palm. These residues have cellulose composition ranging from 14.7% to 56%, while the composition of lignin is between 5.5% and 53.6%, depending on the biomass source (Tye et al., 2016). Anwar et al. (2014) also mentioned newspapers (40%55% of cellulose), grasses (25%40% of cellulose), banana waste (13.2% of cellulose), and sponge gourd fibers (66.59% of cellulose) as lignocellulosic materials.
Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00005-5 © 2021 Elsevier B.V. All rights reserved.
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Agroindustrial residues, together with other lignocellulosic materials, are produced in high proportions, representing a renewable source of biomass with potential to be converted into biofuels. Therefore this chapter is focused in the production of biojet fuel from lignocellulosic raw materials. First, the available pretreatment technologies for lignocellulosic materials are described. Then, a description of the chemical transformations occurring to the cellulose and hemicellulose is presented. After, the technologies required to obtain pure final products are mentioned, along with the reported results on the combustion tests applied to the biojet fuel derived from this kind of feedstock. Finally, an example of the simulation and assessment of a process for the production of biojet fuel from lignocellulosic biomass is presented and discussed.
5.2
Pretreatment technologies
One of the processing routes to obtain biojet fuel from lignocellulosic biomass implies the initial conversion of the raw material into alcohols; then, the alcohols are treated to obtain biojet fuel. Nevertheless, in the case of lignocellulosic materials, the conversion to alcohols is not direct, since the access to the cellulose and hemicellulose is avoided by the lignin. Thus the lignin must be first eliminated; this is the main objective of the pretreatment. Fig. 5.1 presents the pretreatment effect on the lignocellulosic material. Additionally, the pretreatment increases the proportion of amorphous cellulose, which makes easier the further processing steps (Sarkar et al., 2012). Several technologies have been proposed for the pretreatment of lignocellulosic materials. According to Sarkar et al. (2012), pretreatments can be classified into four categories: physical, physicochemical, chemical, and biological. In the following subsections, the pretreatments will be described, following the classification presented by Sarkar et al. (2012).
5.2.1
Physical pretreatments
The physical pretreatments are based on the use of a source of energy to make easier the access to the cellulose. The mechanical size reduction can be mentioned as a first type of physical pretreatment, where the biomass is treated in a mill to obtain smaller particles with a given size distribution. This reduction
Chapter 5 Production processes from lignocellulosic feedstock
Lignin Pretreatment Cellulose
Hemicellulose
Figure 5.1 Effect of pretreatment on lignocellulosic material.
is usually a first step in the pretreatment stage, since the particularization of the biomass enhances mass and heat transfer in the following pretreatment steps, which can be of any of the four categories. According to Cadoche and Lo´pez (1989), a size particle in the range of 36 mm avoids the use of excessive amount of energy in the mill. Nevertheless, the required particle size would depend on the operation following the size reduction. Ballesteros et al. (2002) report that for a steam explosion pretreatment, particle sizes in the range of 812 mm enhance the cellulose recoveries. Nevertheless, for the pretreatment of corn stover through the AFEX technology, Chundawat et al. (2008) used a particle size smaller than 0.1 mm. A second type of physical pretreatment is the pyrolysis, where the biomass is treated at high temperatures to obtain a set of by-products, which can later be treated to generate alcohols. Nevertheless, by controlling the pyrolysis conditions, biooil can be produced, which is then hydrotreated to directly obtain biojet fuel. The most important variables in the pyrolysis are the temperature, pressure, and residence time. According to Jenkins et al. (2016), the operation in a temperature range between 800 C and 1000 C with vapor residence time lower than 0.5 s and particle size lower than 3 mm maximizes the
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yield to bio-oil to 75 wt.%; this operation is also known as flash pyrolysis, and the biochar yields are around 12 wt.%. Moreover, the selection of the reactor configuration and its proper design also have an effect on the bio-oil yield and purity (Thangalazhy-Gopakumar and Adhikari, 2016). Other physical pretreatment implies the use of microwaves to heat the biomass, breaking the lignin and allowing the release of cellulose; these operations enhance the potential to extract sugars in a further step. Hu and Wen (2008) reported that for switchgrass the microwave heating of the biomass soaked in alkali allows obtaining a high yield of sugars, around 90% of the maximum potential sugars. Intanakul et al. (2003) also found an increase in the production of sugars with the microwave pretreatment for rice straw and sugarcane bagasse.
5.2.2
Physicochemical pretreatments
Among the physicochemical pretreatments, four can be mentioned: steam explosion, liquid hot water method, ammonia fiber explosion, and CO2 explosion. In the pretreatment by steam explosion, high-pressure saturated steam enters in contact with the biomass, under conditions in the range of 2050 bar and 160 C290 C (Sarkar et al., 2012). In this process, the hemicellulose is hydrolyzed, and the lignin structure is altered, although not completely destroyed (Anwar et al., 2014); this operation finishes with a sudden reduction in pressure (Conde-Mejı´a et al., 2012). In the case of the liquid hot water approach, the biomass enters in contact with pressurized water, at temperature conditions in the range of 170 C230 C (Sarkar et al., 2012). This method allows completely removing of the hemicellulose, while leaving 65%40% of the lignin unremoved (Mosier et al., 2005). In the ammonia fiber explosion pretreatment, the biomass enters in contact with high-pressure concentrated liquid ammonia. Once the lignin and hemicellulose are exposed, the pressure is reduced (Kumar et al., 2009), similarly to the final step in the steam explosion process. The process takes place under temperatures in the range of 60 C100 C (Sarkar et al., 2012); its main drawbacks are the cost of ammonia (Holtzapple et al., 1992) and its difficulty to treat materials with high content of lignin (Anwar et al., 2014). Other explosion-based pretreatment is the CO2 explosion, which is similar to steam explosion and ammonia fiber explosion. It has been reported that this pretreatment has lower yields than the other two explosion processes, but it has lower
Chapter 5 Production processes from lignocellulosic feedstock
costs and avoids the formation of inhibitory by-products that are generated in the steam explosion (Hamelinck et al., 2005; Prasad et al., 2007). For this kind of pretreatment, Gu et al. (2013) studied operation pressures in the range of 75275 bar, with temperatures between 40 C and 210 C; the pretreatment conditions depend on the kind of biomass. Gu et al. (2013) also reported the use of ionic liquids as solvents for lignocellulosic biomass. Among the advantages of such approach is that the operation can take place at low pressure and relatively low temperature. Depending on the used ionic liquid, it can be selective to dissolve lignin, hemicellulose, or cellulose, or the three components simultaneously. Nevertheless, ionic liquids are still expensive, and they may have a high environmental impact since they are non-biodegradable. Moreover, they may act as inhibitors for further treatments (Guragain et al., 2011; Haykir et al., 2013).
5.2.3
Chemical pretreatments
In this classification, four pretreatments can be mentioned: acid pretreatment, alkaline pretreatment, wet oxidation, and organosolv pretreatment. In the case of the acid pretreatment, the biomass interacts with either concentrated or diluted acids, in a temperature range of 130 C210 C (Sarkar et al., 2012). Although sulfuric acid is the most used in this kind of pretreatment, the use of hydrochloric acid, acetic acid, and nitric acid has also been reported (Cardona et al., 2010). The acid tends to hydrolyze the hemicellulose but produces a set of inhibitors (Sarkar et al., 2012). The process may occur in a temperature range of 160 C220 C (Mosier et al., 2005). Due to the use of acids, the process may be hazardous and toxic; in addition, the equipment may be corroded (Anwar et al., 2014). To avoid the corrosion, Lloyd and Wyman (2005) proposed the use of very diluted acids. In the case of the alkaline pretreatment, sodium hydroxide, potassium hydroxide, calcium hydroxide, or ammonium can be used to digest the lignin, allowing further treatment for cellulose and hemicellulose (Sarkar et al., 2012). This pretreatment can take place under ambient conditions, although the process can take hours or even days (Mosier et al., 2005). When the temperature is increased, the residence time can be reduced (Chang et al., 1998). On the other hand, wet oxidation is a method where the biomass is treated with water in an oxidant atmosphere, provided by air or oxygen. This treatment is useful to break the association of lignin with cellulose, while dissolving hemicellulose
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(Mosier et al., 2005). The process takes place at temperatures above 120 C (Sarkar et al., 2012). Martı´n et al. (2007) reported that the best temperature for the pretreatment of sugarcane bagasse is 195 C, obtaining a high solubilization of hemicelluloses. Nevertheless, it has been found that the method is expensive (Mosier et al., 2005; Conde-Mejı´a et al., 2012). The organosolv pretreatment implies the use of a mixture consisting of an organic solvent and water to simultaneously hydrolyzing and delignifying the biomass (Conde-Mejı´a et al., 2012). Some of the most used solvents can be inhibitors in the following treatments; thus they must be recovered, which represents a high contribution to the energy requirements of the pretreatment step. Moreover, the use of solvents may increase the inherent risk of the process due to their inflammability (Conde-Mejı´a et al., 2012).
5.2.4
Biological pretreatments
The use of microorganisms to release the cellulose is another strategy to treat the lignocellulosic materials. Some of the microorganisms which can perform this task are the white rot fungi, the brown fungi, and the soft rot fungi (Sarkar et al., 2012; Anwar et al., 2014). This kind of pretreatment can be used as substitute of the chemical pretreatments or as a complement to them (Iqbal et al., 2013). The use of such biotechnological approach is advantageous, since it reduces considerably the environmental impact and the risk of the pretreatment. Nevertheless, it still faces challenges, as its very slow rate of hydrolysis (Conde-Mejı´a et al., 2012). Moreover, as occurs with many of the microorganism-based processes, the dimensions of the equipment required for such treatment are big, and the growth conditions must be carefully controlled. Additionally, the microorganisms not only solubilize the lignin but also the hemicellulose and the cellulose (Anwar et al., 2014).
5.3
Conversion processes of the lignocellulosic feedstock
Once the lignin, and in some cases the hemicellulose, has been broken and transformed, the remaining materials must go through further processing to produce biojet fuel. One of the pathways implies first obtaining ethanol, and then convert it into biojet fuel and other by-products in the process known as alcohol-to-jet. A second alternative is related to the direct fermentation of sugars, through the sugar-to-jet approach. A third
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135
pathway implies the thermochemical treatment of the biomass, by gasifying it and then treating the effluent in a FischerTropsch (FT) process, followed by hydrotreating and isomerization/cracking steps. All the routes will be discussed in the next subsections. Fig. 5.2 schematically presents both pathways to produce biojet fuel.
Lignocellulosic material
Mechanical pretreatment (size reduction)
Gasification
Hydrolysis (acid/basic/enzymatic)
Recovery and purification (ethanol/isobutanol)
ATJ pathway
Biological pretreatment (fermentation stage)
Fischer–Tropsch reaction
Hydrotreating
Separation stage
Biojet fuel
Figure 5.2 Biojet fuel production from lignocellulosic material.
Isomerization/cracking
Separation stage
Biojet fuel
ATJ process
Other hydrocarbon fuels
Fischer – Tropsch pathwa y
Chemical pretreatment
Other hydrocarbon fuels
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5.3.1
Alcohol-to-jet process
Once the lignocellulosic biomass has been pretreated and the lignin has been removed, two additional steps are required to produce the ethanol: hydrolysis and fermentation. In the hydrolysis stage, the biomass is treated with water in an enzymatic medium to release the sugars contained in the cellulose and hemicellulose, mainly glucose, xylose, arabinose, galactose, and mannose (Sarkar et al., 2012). The most common enzymes used in the hydrolysis step are endoglucanases, exoglucanase or cellobiohydrolase, and β-glucosidase (Prasad et al., 2007). Glucoronidase, acetylesterase, xylanase, β-glucosidase, among others are used to transform the hemicellulose (Duff and Murray, 1996). It has been reported that enzymatic hydrolysis works better at temperatures in the range 40 C50 C, with pH between 4 and 5 (Neves et al., 2007). Another important parameter is the substrate concentration; increasing such concentration causes an increase in the yield and the reaction rate (Cheung and Anderson, 1997). Nevertheless, a high concentration may cause inhibition, reducing the reaction rate (Prasad et al., 2007). It is important to mention that the enzymes represent one of the highest contributions to the cost of the alcohol production (Prasad et al., 2007). Once the sugars are available, they are fermented to produce ethanol. Then, the ethanol is dehydrated, oligomerized, and hydrogenated, as described in Chapter 4, Production Processes for the Conversion of Sugar and Starchy Feedstock. The produced hydrocarbons are then separated to obtain the desired fractions, which has already been discussed in Chapter 4, Production Processes for the Conversion of Sugar and Starchy Feedstock.
5.3.2
Sugar-to-jet process
Like the alcohol-to-jet approach, in the sugar-to-jet pathway for lignocellulosic biomass, the lignin must be first retired through a proper pretreatment. Then, hydrolysis must take place to extract the sugars. Once the sugars have been released, the jet fuel can be obtained by one of the routes described in Chapter 4, Production Processes for the Conversion of Sugar and Starchy Feedstock.
5.3.3
Thermochemical route
In the thermochemical route, two main processing alternatives can be mentioned: the gasification/FT path and the
Chapter 5 Production processes from lignocellulosic feedstock
pyrolysis/hydrotreatment path. Both processing methods are described in this section. In the gasification/FT path, gasification occurs first. In gasification, the raw material is heated at high temperatures, above 700 C. Gasification is performed in a low-oxygen atmosphere to avoid combustion (Nicode`me et al., 2018). The main product of the gasification is a mixture of gases known as syngas, which is mainly composed by carbon monoxide and hydrogen; but it also may contain small quantities of carbon dioxide, methane, and water (Molino et al., 2016). Besides syngas, a liquid fraction, known as tar, and a solid fraction, or char, are obtained. In gasification, five consecutive steps can be identified: dehydration (around 100 C), pyrolysis (between 250 C and 700 C), and, as temperature raises, oxidation, reduction, and additional reactions (Nicode`me et al., 2018). Once the syngas has been produced, it enters to the FT reactor. In the FT synthesis, the carbon monoxide from the syngas reacts with hydrogen, generating both linear long-chain and branched hydrocarbons; nevertheless, most of the yield corresponds to linear hydrocarbons. The general reaction can be stated as follows (Demirbas, 2009): m nCO 1 n 1 ð5:1Þ H2 -Cn Hm 1 nH2 O 2 where n is the average length of the hydrocarbon chain, which will strongly depend on the operating conditions. According to Nicode`me et al. (2018), to produce gasoline and light olefins, temperatures from 330 C to 350 C are required; while temperatures in the range 220 C250 C promote the production of hydrocarbons in the diesel range. Iron and cobalt-based catalysts are among the most used for FT synthesis. Several developments have been reported to enhance the selectivity and yield of the reaction. Wang et al. (2016b) studied the use of Fe2O3 nanoparticles inside TiO2 nanotubes, claiming that the confinement can be a useful strategy for the adjustment of the product distribution of long-chain hydrocarbons. Li et al. (2016) proposed a Co/ZrO2-SiO2 bimodal catalyst for the FT synthesis; their results indicated that adding 1-olefins as additives enhances the selectivity to hydrocarbons in the boiling point range of biojet fuel. Savost’yanov et al. (2017) reported the development of a Co-SiO2 catalyst, mentioning that this catalyst is stable under variable operational conditions; also, they indicated that their catalyst is recommendable for its use in industrial production. Rytter et al. (2019) proposed the use of a catalyst of α-alumina impregnated with cobalt and rhenium.
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Depending on the distribution of the products obtained in the FT synthesis, it could be necessary to perform additional steps, as hydrocracking and hydroisomerization, to produce the highest yield to the jet fuel fraction, with the required proportion of branched hydrocarbons. Such chemical transformations have already been described in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. The second thermochemical route to produce biojet fuel involves a pyrolysis step, followed by hydrotreatment, hydrocracking, and/or hydroisomerization. As in gasification, pyrolysis implies the thermal treatment of biomass, but in an atmosphere with low concentration of oxygen, or even free of oxygen. The main product of the pyrolysis of biomass is known as bio-oil. Pyrolysis may take place under temperatures around 500 C, with low residence time, generating high yields of the liquid product (bio-oil). This kind of treatment is known as fast pyrolysis (Leibbrandt et al., 2011). On the other hand, to produce a high yield of char, vacuum pyrolysis can be used, operating at temperatures around 300 C, under large negative pressure conditions (Cai et al., 2016; Nicode`me et al., 2018). Since the liquid fraction (bio-oil) is the one to be converted into hydrocarbons, in the production of biojet fuel the flash pyrolysis is preferred. In fast pyrolysis, a water phase can also be obtained (Zhang et al., 2005). The oil phase can then be upgraded through hydrotreatment, which has been discussed in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. For the hydrotreatment of bio-oil, some reported catalysts are CoMo/γ-Al2O3 and NiMo/γ-Al2O3 (Senol et al., 2005). In terms of the composition of bio-oil, hydrocracking may also take place with CoMoS/Al2O3 or HZSM-5 catalysts (Zheng et al., 2015). The production of hydrocarbons in the boiling point range of biojet fuel through the alkylation of aromatics from bio-oil has been reported, using an ionic liquid to promote the reaction (Wang et al., 2015).
5.3.4
Lignin as a source for aromatics
Wang et al. (2020) describes the treatment of lignin as a strategy to make use of this fraction of lignocellulosic biomass to produce aromatics and cycloalkanes; these compounds can be blended with the hydrocarbons obtained by the treatment of cellulose and hemicellulose. The conversion of lignin includes steps as depolymerization to generate monomers and dimers, followed by coupling and hydrodeoxygenation of the monomers and
Chapter 5 Production processes from lignocellulosic feedstock
hydrodeoxygenation of the dimers. Chio et al. (2019) described several methods for lignin depolymerization, including thermochemical methods, chemical/catalyst-based methods, and biological methods. Pyrolysis is mentioned as one of the most studied methods for lignin depolymerization, requiring temperatures up to 800 C. Among the chemical/catalyst-based methods, acid catalysis, basic catalysis, metallic catalysis, ionic liquid assisted catalysis, catalysis assisted by subcritical or supercritical fluids, and oxidative depolymerization can be mentioned. For acid catalysis, the use of hydrochloric acid (Hewson and Hibbert, 1943) and diluted sulfuric acid (Mahmood et al., 2015) has been reported. In the case of basic catalysis, Evans et al. (1999) proposed the use of strong bases as NaOH and KOH, since they transformed more lignin than weaker bases. In the case of metallic catalysis, Song et al. (2013) reported the use of heterogeneous nickel-based catalysts, such as Ni/C, Ni/SBA-15, and Ni/Al2O3, to obtain monomeric phenols from native birch wood lignin. The use of ionic liquid helps to stabilize the intermediates during the depolymerization reaction (Chio et al., 2019), avoiding the oxidation of aldehyde products and increasing their yields (Liu et al., 2013). According to Chio et al. (2019), oxidation is among the most used methods for lignin depolymerization, with hydrogen peroxide and potassium permanganate as the most used oxidants. In the case of the use of subcritical and supercritical fluids, Wahyudiono and Goto (2008) studied the degradation of lignin in water at critical conditions and conditions near to the critical state. They reported that as reaction time is increased, the amount of lower molecular weight fractions is also enhanced. Yong and Matsumura (2013) studied the depolymerization of lignin in subcritical water (300 C370 C) and supercritical water (390 C450 C). According to their results, under subcritical conditions the reaction rate is higher, but supercritical conditions allowed a higher degree of depolymerization. For biological depolymerization, several organisms have been studied, which include bacteria, fungi, and enzymes. Unfortunately, such approach has low efficiency on the conversion of lignin to monomers, which limits the industrial application of these methods. However, this can be seen as an area of opportunity for the development of modified microorganisms, which can perform the depolymerization task with higher efficiency. As a following step, lignin monomers can be coupled into dimers and then hydrodeoxygenated, while the dimers are directly hydrodeoxygenated. Wang et al. (2020) remark that special care must be taken with coupling reactions to avoid the formation of oversize molecules; this can be controlled through the proper
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selection of catalyst. Yan et al. (2010) proposed the use of nanoparticle catalysts with ionic liquids to perform the hydrodeoxygenation of phenols. They reported that a catalyst based on Rh/Ru nanoparticles in combination with the ionic liquid 1-butyl-3methylimidazolium bis(trifluoromethylsulfonyl)imide allowed reaching conversion of 99%, with a selectivity of 99% to cyclohexane, at 130 C and 40 MPa H2. Zhang et al. (2005) presented the use of a Ru/HZSM-5 catalyst to hydrodeoxygenate phenolic compounds. According to their results, conversions of 99.9% are obtained, with selectivity of at least 95% to cyclohexane for temperatures higher than 120 C. Chen et al. (2015) proposed the use of a Pd/C catalyst for the coupling reaction between phenols and amines, generating as products secondary and tertiary cyclohexylamine derivatives. In the same year, Yao et al. (2015) reported the use of a Ru/H-Beta catalyst for the hydrodeoxygenation of phenolic compounds. They found that at 140 C and 4 MPa H2, conversion up to 80.5% can be obtained, with a yield of 68.3% to cyclohexane. Liu et al. (2016) studied the synthesis of magnetic nitrogen-doped carbon supported cobalt nitride catalysts to perform the hydrodeoxygenation of phenols. They determined that at 200 C and 2 MPa H2, a yield of 99.1% to propylcyclohexane can be reached if HZSM-5 is added to the reaction system. Wang et al. (2016a,b) reported the use of ZnCl2 and Ru/C as cocatalyst to transform lignin into phenolic products by depolymerization, followed by coupling and hydrodeoxygenation reactions to produce more stable cyclic hydrocarbons. On the other hand, direct hydrodeoxygenation of anisole has been studied by Wang et al. (2017), where conversions higher than 99 wt.% with NiMo/Al2O3 and Ru/ Al2O3 catalysts are observed. According to their results, most of the products obtained with the NiMo/Al2O3 catalyst are phenol, alkylphenols, benzene, and alkylbenzenes. On the other hand, the Ru/Al2O3 catalyst promotes the formation of cycloparaffins.
5.4
Technologies on the separation zone
In the case of the gasification/FT approach, the generated products are mainly hydrocarbons; thus distillation can be used as purification technology to separate the desired fractions. To remove the CO2 in the syngas before entering the FT reactor, some reported operations are the absorption with amines (Ai et al., 2005), membranes (Franz and Scherer, 2010), or the Rectisol washing approach (Sun and Smith, 2013). Additionally, to recover the hydrogen from the gas fraction of the hydrocracking step, the use of pressure swing absorption has been proposed (Diederichs et al., 2016).
Chapter 5 Production processes from lignocellulosic feedstock
For the pyrolysis-based process, the products are hydrocarbons; therefore distillation is the predominant purification technology to obtain the desired fractions. In the case of the sugar-to-jet pathway, after hydrolysis it is necessary to purify and concentrate the hydrolysate to satisfy the needs of the following steps (Wei et al., 2019). To achieve this, Davis et al. (2015) recommended the use of a microvapor recompression evaporator, microfiltration, and ion exchange filtration. After obtaining the hydrocarbon fractions, further purification is required to obtain the biojet cut. This final refining can be performed through distillation. The main product in the analyzed approaches is biojet fuel, which can be used as aviation fuel blended with fossil jet fuel. Additionally, naphtha and green diesel are generated as coproducts. On the other hand, naphtha can be used as solvent, or further treated to produce gasoline. Finally, green diesel can be used as substitute of fossil diesel; since it is composed by hydrocarbons, there is no need to blend it with fossil diesel, as occurs with biodiesel.
5.5
Conventional processes: state of the art
In this section, a revision of the state of the art related to conventional processes where lignocellulosic biomass is converted to biojet fuel is presented. It is worth to mention that the term “conventional processes” is employed to designate those processes which make use of traditional processing technologies, with no intensified equipment. Rusek and Ziulkowski (2009) presented a process to obtain mesitylene and isopentane, by fermentation of biomass feedstock, producing either ethanol and then acetic acid, or directly acetic acid. The acetic acid is then converted to acetone, and the acetone is transformed to the final products, which are mentioned as potential replacement for aviation fuel. Rusek et al. (2012) reported a process to generate a replacement for jet fuel consisting on mesitylene and alkanes, such as octadecane, octane, nonane, or tetradecane. Wright et al. (2012) presented a process proposal for the oligomerization of butene using bis(cyclopentadienyl) zirconium dichloride as catalyst. Peters and Taylor (2013) proposed a process for the production of renewable jet fuel by fermentation of biomass to isobutanol, dehydration, and oligomerization of the alcohol, and subsequent hydrogenation. The authors mentioned that the production of renewable jet fuel either by separated reaction steps, or by pairs of reaction sets taking place at the same reaction zone.
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For the sugar-to-jet approach, Amyris Inc. (2013) reported the production of farnesane from wood chip and wheat straw in a pilot plant. According to their results, the wheat straw reduced the fermentation yield and productivity, while the use of wood chip showed similar yields to those obtained by fermentation of sorghum syrup or dextrose. Lane (2012) described the CASE (cold acid solvent extraction) process, from Virdia, which uses pine wood as raw material to produce jet fuel (Wang et al., 2016a,b); however, there is only little information about this approach. Baral et al. (2019) proposed a processing route to transform sorghum biomass to jet fuel. The process includes the conditioning of biomass by milling, followed by a pretreatment with an ionic liquid to allow dissolving the lignin fraction. The obtained cellulose and hemicellulose are then hydrolyzed with Escherichia coli to generate limonene, linalool, bisabolene, among others. Those products are then hydrogenated. It is mentioned that some of the obtained molecules require additional treatment, as dehydration, hydrogenation, dimerization, and isomerization. In the case of thermochemical approaches, Cheiky and Malyala (2011) and Cheiky et al. (2016) reported a process where biomass is gasified in a number of stations, and the volatile components passes through a set of catalysts, intended to produce renewable fuels, including jet fuel and diesel. Dayton et al. (2013) presented a process involving the catalytic pyrolysis of lignocellulosic biomass, using metallic oxides as catalysts, followed by deoxygenation of the bio-oil. Cheiky et al. (2013) studied a process where biomass is pyrolyzed into hydrocarbons and biochar; the hydrocarbons are upgraded into renewable fuels, and the biochar is processed to obtain dimethyl ether.
5.6
Combustion tests for biojet fuel from lignocellulosic feedstock
In this section, combustion tests developed for biojet fuels derived from lignocellulosic biomass are presented. Biojet fuels obtained from lignocellulosic feedstock by the alcoholto-jet and sugar-to-jet pathways are similar to those produced from sugar/starchy feedstock, whose combustion tests have been already discussed in Chapter 4, Production Processes for the Conversion of Sugar and Starchy Feedstock. Thus the focus of this section is the tests for biojet fuels
Chapter 5 Production processes from lignocellulosic feedstock
obtained by thermochemical pathways. Unfortunately, to the knowledge of the authors, there are no works dealing with combustion tests of biojet fuel obtained from lignocellulosic biomass. Nevertheless, Edwards et al. (2010) mentioned that the biomass-based jet fuels obtained from a FT process are not expected to differ from the FT jet fuels produced from coal or natural gas, since the characteristic of the original feedstock is destroyed in the gasification step. Thus some studies related to the FT jet fuel performance will be mentioned here, even if the SPKs are not generated from lignocellulosic biomass. Hui et al. (2012) studied the combustion characteristics of a variety of FT jet fuels, comparing them with jet fuels obtained by hydrotreating and with conventional Jet A. According to their results, most of the FT fuels showed lower ignition delay times than Jet A, except the IPK from Sasol, which has large amounts of iso-paraffins and cycloparaffins. In the case of the derived cetane number, the FT fuels, except the Sasol IPK, showed higher values than Jet A. This implies that the other FT jet fuels are more reactive than Jet A, and much more reactive than IPK. Won et al. (2014) studied the extinction limits of diffusion flames and the critical flame initiation radius of two FT jet fuels and two hydrotreating-based jet fuels, comparing their performance with the conventional JP-8 jet fuel. It is mentioned that the Sasol IPK showed the larger critical flame initiation radius, implying that the ignition of that jet fuel would be more difficult than that of the other jet fuels. On the other hand, the Shell SPK showed the best performance among the studied fuels. Thus the absence of n-paraffins in the Sasol IPK affects its combustion performance. In terms of emissions, Lobo et al. (2011) presented a study of the particulate matter (PM) released when burning jet fuels in a gas turbine engine. Particularly, an FT jet fuel, a 50-50 mixture of FT jet fuel with Jet A-1, and mixtures of fatty acid methyl esters with Jet A are studied and compared with Jet A-1. According to the results, all the alternative jet fuels showed lower emissions of PM than the fossil jet fuel. Nevertheless, the FT jet fuel has higher emissions than the other analyzed fuels. The 50-50 mixture of FT jet fuel and Jet A-1 showed the second lower value for PM. Khandelwal et al. (2013) studied the greenhouse gas emissions of three jet fuels: Jet A-1, FT jet fuel and a mixture of 50% FT jet fuel and 50% Jet A-1, used in a gas turbine engine. Their results show that there is only small difference in the emissions of CO2, CO, NOx, NO, and NO2 for the three fuels.
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5.7 5.7.1
Case study: conversion of lignocellulosic waste Problem statement
The sustainable production of jet fuel using lignocellulosic wastes from edible and nonedible crops has become into a strategic route. Some processes have been developed to transform wastes in biofuels, electricity, or high-value chemicals (Ge et al., 2017; Mahmood et al., 2019; Raud et al., 2019). However, in Mexico the use of energy from agricultural wastes is a strong opportunity area. Currently, the use of wastes from the main cultures as corn, sorghum, wheat, and barley is oriented to land protection (roughly 50%), animal feed (approximately 27%), and burning without any benefit (approximately 20%) (Garcı´a Bustamante and Masera Cerutti, 2016). In this context, is important to recognize its energetic potential and orientate it toward the production of biofuels, electricity, and other products. Regarding the cultivation of energetic crops as palm oil and Jatropha curcas, the harvesting stages and oil extraction are important sources of lignocellulosic wastes. In the case of palm oil, after removing the palm oil fruit from racemes, three lignocellulosic biomasses are obtained: racemes empty (called tusa 53.2 wt.%), fiber (32.3 wt.%), and shell without kernel (14.6 wt.%) (Osorio Florez, 2013); Fig. 5.3 shows each one of these components. According to Del Hierro-Santacruz (1993), per 1000 kg of fruit raceme, 600 kg of palm oil fruit, and 400 kg of solid wastes
Kernel Palm oil shell
Fiber
Palm oil racemes empty racemes (TUSA)
Figure 5.3 Components of palm oil fruit.
Palm oil fruit
Chapter 5 Production processes from lignocellulosic feedstock
are obtained. Also, it is important to mention that shell covers kernel, which is rich in oil. On the other hand, the J. curcas fruit is formed by shell (37.5 wt.%) and seed (62.5 wt.%). The seed is composed by 42 wt.% of husk and 58 wt.% of kernel (Singh et al., 2008). Kernel contains the J. curcas oil, while shell and husk are lignocellulosic residues. Fig. 5.4 presents the components of the J. curcas fruit. In this case of study, the shell from palm oil and J. curcas fruit will be considered as the raw material to produce biojet fuel and other products as green diesel and methanol. The flow rate of palm oil shell considered corresponds to the one obtained by the processing of 2000 kg/h of palm oil fruit, which was used as calculation basis in the case of study presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Using a material balance, according to the palm oil racemes composition described before, the flow of palm oil shell is 195 kg/h. In the same way, the flow rate of J. curcas shell is 5811 kg/h, which can be calculated from the inlet flow rate of 2000 kg/h of J. curcas oil, assuming 35 wt.% of oil into kernel and 100% of yield in the oil extraction stage. These lignocellulosic wastes will be processed through gasification to obtain syngas, which will be transformed in a mixture of higher alcohols as methanol, ethanol, propanol, and isobutanol, oriented selectively to isobutanol. Then, reactive stages as dehydration, oligomerization, and hydrogenation will be modeled to produce biojet fuel and green diesel.
5.7.2
Modeling of lignocellulosic waste
The modeling of lignocellulosic wastes is based on the experimental analysis of their chemical composition and physicochemical properties. The elemental composition, moisture level, volatile material, ash content, calorific values, and lignocellulosic profile are the main features to describe any lignocellulosic biomass. For this case of study, we assume that the
Shell
Seeds
Husk
Kernel
Figure 5.4 Components of Jatropha curcas fruit.
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Table 5.1 Lignocellulosic composition of palm oil shell (Van Dam, 2016) and J. curcas shell (Singh et al., 2008). Palm oil shell
Jatropha curcas shell
Compounds
wt.%
Compounds
wt.%
Cellulose Hemicellulose Lignin
22.08 24.10 53.82
Cellulose Hemicellulose Lignin
61 18 21
wastes are moisture-free and can be described only by their lignocellulosic profile. Table 5.1 presents the lignocellulosic composition of palm oil shell and J. curcas shell. For a more detailed description about the physicochemical properties of palm oil shell see Van Dam (2016) and Sechage Cortes et al. (2017), and for J. curcas shell, please refer to Singh et al. (2008) and Betancur-Prisco et al. (2014). Since the composition of each waste is established according to their lignocellulosic profile, it is important to have information about the physical properties of all the components involved in the modeling of biomass, with special focus on the lignocellulosic components. This information can be found in the work of Wooley and Putsche (1996), who described the chemical structure of cellulose, hemicellulose, and lignin as well as properties such molecular weight, solid heat of formation, solid heat capacity, and solid molar volume.
5.7.3
Production process: conceptual design
A block diagram for the transformation of the shell (lignocellulosic wastes) from J. curcas and palm oil into biojet fuel is shown in Fig. 5.5, taking as basis the works of Atsonios et al. (2013, 2015). According to Fig. 5.5, the process starts with the gasification of the solid biomass to generate syngas. We assume that the waste biomass is dried by solar action and crushed before entering to the gasification reactor. Based on the nature of the biomass fed, products such as CO2, H2S, NH3, HCl, among others, can be obtained; thus a cleaning stage of syngas to remove them is necessary. Into a gasifier four stages can be defined: drying, pyrolysis, oxidation, and reduction, as can be seen in Fig. 5.6 (Sharma,
Chapter 5 Production processes from lignocellulosic feedstock
Methanol
147
Methanol
Lignocellulosic biomass MAS
Condensation
Gasification
Isobutanol
Rectisol
Dehydration
Oligomerization
Biojet fuel
Green diesel
Hydrogenation
Figure 5.5 Block diagram of the conversion of lignocellulosic residues to biojet fuel.
Fuel Fuel Syngas
Air
Drying
Drying
Pyrolysis
Pyrolysis
Oxidation
Air
Reduction
Oxidation
Syngas Downdraft
Reduction
Air
Air
Updraft
Figure 5.6 Definition of zones inside two typical gasifiers (Sharma, 2008; Gagliano et al., 2017).
2008; Gagliano et al., 2017). To describe the behavior of biomass inside a gasifier, some complex kinetics models have been proposed (Safarian et al., 2019). However, a simple first-order Arrhenius-based kinetic model has been reported to describe the biomass gasification oriented to produce hydrogen through the CO2 capture (Inayat et al., 2010). Table 5.2 describes the reactions proposed to define the gasification chemistry and the kinetic parameters for each one. As can be seen in Table 5.2, the biomass is represented by carbon element, describing only the oxidation and reduction stages as the gasification process; therefore to complete the model of palm oil shell and J. curcas shell, a pyrolysis process will be
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Table 5.2 Kinetic model for the gasification of biomass (Inayat et al., 2010). Arrhenius parameters (mol/m3 s)
No. reaction
Reaction
1
Gasification
C 1 H2 O-CO 1 H2
2
Methanation
C 1 2H2 -CH4
3
Boudouard
C 1 CO2 -2CO
4
Methane reforming
CH4 1 H2 O-CO 1 3H2
5
Water gas shift
CO 1 H2 O2CO2 1 H2
6
Carbonation
CO2 1 CaO-CaCO3
ri 5 2 ki CA CB
2 3 105 exp 26000 T 8 4:40exp 2 1:62T 3 10 0:12exp 2 17;921 T 3 3 105 exp 2 15;000 T 106 exp 26370 T Kw 5 520exp 27230 2 44:5T 10:20exp T
A; B:reactants
included to produce the biochar. Thus the gasification process will be modeled as two reactive stages: (1) pyrolysis and (2) gasification of char from pyrolysis. However, a pyrolysis process using both shells has not been reported; due to this, the pyrolysis of each residue will be modeled separately, but at the same operating conditions, this assumption allows to consider that both pyrolysis processes are performed in one equipment. Due to this, in the cost analysis, a single equipment with the total volume required to treat both biomasses will be considered. The pyrolysis of J. curcas shell was studied by Manurung et al. (2009) in a temperature range of 400 C500 C, particle size ,2 mm, and atmospheric pressure. The product distribution resulting at the end of the operation was 50 wt.% bio-oil, roughly 20 wt.% of biogases, and approximately 30 wt.% of biochar. The composition of bio-oil was not determined, but the ultimate analysis of biochar was performed (Table 5.3); based on these data, an empirical formula can be constructed to represent the biochar. The components distribution in the biogases is 36.5 wt.% CO, 51.9 wt.% CO2, and 8.9 wt.% CH4 (Manurung et al., 2009) Regarding the bio-oil composition, the two major components reported by Murata et al. (2012) will be taken to model it: 6-octadecanoic acid and hexadecenoic acid. The reactions set to describe the pyrolysis process is displayed in Table 5.4, which are based on those reported for the pyrolysis of the lignocellulosic compounds of biomass (Anca-Couce and Scharler, 2017). On the other hand, the fast pyrolysis of palm oil shell was reported by Huang et al. (2019) at 100 C500 C using air,
Chapter 5 Production processes from lignocellulosic feedstock
149
Table 5.3 Ultimate analysis of biochar and its empirical formula (Manurung et al., 2009) from Jatropha curcas shell. Elements
%C
%H
%O
%N
wt.% Normalized composition (negligible %N)
43.18
6.13
44.67
1.57
Elements
%C
%H
%O
%N
wt.% Empirical formula: C1:2 H2 O
45.95
6.52
47.53
0
Table 5.4 Reaction set of Jatropha curcas shell pyrolysis. Lignocellulosic profile
Reaction
Cellulose
C6 H10 O5 1 O2 -5CO 1 CO2 1 5H2 6C6 H10 O5 1 3H2 -C18 H34 O2 1 C16 H32 O2 1 2CO2 1 11O2 C6 H10 O5 -5C1:2 H2 O C5 H8 O4 1 32 O2 -CO 1 3CO2 1 CH4 1 2H2 8C5 H8 O4 1 H2 -C18 H34 O2 1 C16 H32 O2 1 6CO2 1 8O2 C5 H8 O4 1 6H2 1 7CO2 -10C1:2 H2 O 1 4O2 C7:3 H13:9 O1:3 1 3O2 -5:3CO 1 CO2 1 CH4 1 4:95H2 340C7:3 H13:9 O1:3 1 46H2 -73C18 H34 O2 1 73C16 H32 O2 1 75O2 60C7:3 H13:9 O1:3 1 143:5O2 -365C1:2 H2 O 1 52H2
Hemicellulose
Lignin
wherein the product distribution was 50 wt.% bio-oil, roughly 35 wt.% biochar, and 16 wt.% biogases; the normalized composition of biogases was 20.3 wt.% CO, 75.15 wt.% CO2, 4.47 wt.% CH4, and 0.07 wt.% H2. Regarding to bio-oil composition, the major compounds of bio-oil from palm oil shell at 650 C are phenol and acetic acid (Chang et al., 2016). Finally, an ultimate analysis of biochar from palm oil solid wastes is provided by Liew et al. (2018) (Table 5.5). The set of reactions used to describe the pyrolysis of palm oil shell is presented in Table 5.6. Thus the pyrolysis process of palm oil shell and J. curcas shell operates at 550 C, 1 bar, and an air:biomass mass ratio of 1:1 for both pyrolysis reactors, using the reactions set shown in Tables 5.4 and 5.6.
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Chapter 5 Production processes from lignocellulosic feedstock
Table 5.5 Ultimate analysis of biochar and its empirical formula (Liew et al., 2018) from palm oil shell. Elements
%C
%H
%O
%N
wt.% Normalized composition (negligible % N)
64
5
30
1
Elements
%C
%H
%O
%N
wt.% Empirical formula: C3 HO3
64.65
5.05
30.3
0
Table 5.6 Reaction set of palm oil shell pyrolysis (Anca-Couce and Scharler, 2017). Lignocellulosic profile
Reaction
Cellulose
C6 H10 O5 1 O2 -5CO 1 CO2 1 5H2 2C6 H10 O5 -C6 H6 O 1 3C2 H4 O2 1 H2 1 1:5O2 C6 H10 O5 1 0:5O2 -2C3 HO3 C5 H8 O4 1 32 O2 -CO 1 3CO2 1 CH4 1 2H2 2C5 H8 O4 -C6 H6 O 1 2C2 H4 O2 1 H2 1 1:5O2 6C5 H8 O4 1 3H2 -10C3 HO3 1 19H2 C7:3 H13:9 O1:3 1 3O2 -5:3CO 1 CO2 1 CH4 1 4:95H2 2C7:3 H13:9 O1:3 1 3:5O2 -C6 H6 O 1 C2 H4 O2 1 6:6CO 1 8:9H2 30C7:3 H13:9 O1:3 1 90O2 -73C3 HO3 1 172H2
Hemicellulose
Lignin
Regarding the gasification reactor, Table 5.7 exhibits the reactions set for biochar from both pyrolysis processes. The operating conditions for this reactor are 676.85 C, air as gasifying agent (air:biochar mass ratio of 1:1), and 1 bar. The kinetic parameters to describe each reactor are presented in Table 5.2, without carbonation reaction. The output streams from both pyrolysis reactors are the raw material to gasification reactor; additional coproducts as NH3 or H2S are not considered, thus the CO2 is the only compound that must be removed from the syngas stream after the gasification. The syngas is cleaned by removing the CO2 using the Rectisol process described by Atsonios et al. (2013); the Rectisol process is modeled as an absorption column with 10 stages,
Chapter 5 Production processes from lignocellulosic feedstock
151
which allows capturing until 99 wt.% of CO2 in a syngas mixture. The cleaned syngas is converted to a mixture of alcohols, through a catalytic reactor called mixed alcohol synthesis (MAS); this reactive system involves a complex chemical mechanism to produce a mixture of low carbon chain alcohols (C2C4), methanol, hydrocarbons, and CO2, according to the process conditions and catalyst type. The general and simple reaction to describe this process is proposed by Atsonios et al. (2015): nCO 1 2nH2 -Cn H2n11 OH 1 ðn 2 1ÞH2 O
ð5:2Þ
In this case of study, the catalyst Cs-doped Cu/Zn described by Kulawska and Skrzypek (2001) is used to model the conversion of syngas into long-chain alcohols. The operation conditions are 320 C and 90 bar, reaching a hydrogen conversion of 70.9%. The proposed reactions set to describe the behavior of this system are shown in Table 5.8. The catalytic action produces high levels of methanol; thus the output stream from MAS reactor is fed into a condensation
Table 5.7 Set of gasification reactions. No. reaction
Reaction
1
Gasification
2
Methanation
3
Boudouard
4 5
Methane reforming Water gas shift
C1:2 H2 O 1 0:2H2 OðgÞ -1:2CO 1 1:2H2 C3 HO3 1 H2 OðgÞ -3CO 1 1:5H2 1 0:5O2 C1:2 H2 O 1 14H2 -12CH4 1 5O2 C3 HO3 1 5:5H2 -3CH4 1 1:5O2 C1:2 H2 O 1 CO2 -2:2CO 1 2H2 1 0:4O2 C3 HO3 1 CO2 -4CO 1 0:5H2 1 0:5O2 CH4 1 H2 O-CO 1 3H2 CO 1 H2 O2CO2 1 H2
Table 5.8 Set of mixed alcohol synthesis (MAS reactor). No. reaction
Reaction
% Conversion of H2
1 2 3
CO 1 2H2 -CH3 OH 2CO 1 4H2 -CH3 CH2 OH 1 H2 O 3CO 1 6H2 -CH3 CH2 CH2 OH 1 2H2 O
51.66 13.64 5.6
152
Chapter 5 Production processes from lignocellulosic feedstock
reactor to obtain isobutanol as main compound. In the condensation reactor, the dimerization of alcohols (mainly methanol) with liberation of water to generate branched alcohols as isobutanol (Guerbet reaction) is carried out (AOCS, 2019). This process was studied by Carlini et al. (2003) using Cu/MeONa [Cu-1955P] as catalyst at 200 C and 30 atm, reaching 1.6% of propanol selectivity and 98.4% of isobutanol selectivity; moreover, 71.2% conversion of ethanol to propanol and 82% conversion of propanol to isobutanol have been reported (Carlini et al., 2002). The proposed reactions set to describe the condensation system are presented in Table 5.9. The products from the condensation reactor are separated by a distillation column, to obtain methanol at the top of the column and isobutanol, while at the bottom of the column traces of ethanol, propanol and water are obtained. The bottom stream is fed to a dehydration reactor to convert the isobutanol into isobutene and 1-butene. Additional dehydration reactions take place due to the presence of methanol, ethanol, and propanol traces. The dehydration of isobutanol was studied by Taylor et al. (2010) using γ-Al2O3 as catalyst; the operation conditions of 285 C and 1 bar allows to reach 99.8% of isobutanol conversion, and selectivity up to 95% and 5% to isobutene and 1-butene, respectively. The proposed reactions set to describe the dehydration after condensation unit are shown in Table 5.10. Table 5.9 Set of reactions to describe the condensation system. No. reaction
Reaction
% Conversion
1 2
CH3 OH 1 CH3 CH2 OH-C3 H7 OH 1 H2 O CH3 OH 1 CH3 CH2 CH2 OH-C4 H9 OH 1 H2 O
Ethanol 5 71.2 Propanol 5 82.0
Table 5.10 Reactions and conversions for dehydration stage. No. reaction
Reaction
% Conversion (reference component)
1 2 3 4 5 6
2CH3 OH-CH3 OCH3 1 H2 O CH3 OH-C2 H4 1 H2 O CH3 CH2 CH2 OH-C3 H6 1 H2 O C4 H9 OH-C4 H8 ðisobuteneÞ 1 H2 O C4 H9 OH-C4 H8 ð1 2 buteneÞ 1 H2 O CH3 OCH3 -C2 H4 1 H2 O
100 (methanol) 100 (ethanol) 100 (propanol) 95 (isobutanol) 5 (isobutanol) 100 (dimethyl ether)
Chapter 5 Production processes from lignocellulosic feedstock
153
The output stream from the dehydration reactor enters to the oligomerization reactor. The isobutene oligomerization was studied by Alca´ntara et al. (2000) with Amberlyst-15 as catalyst at 100 C and 1.1 bar, reaching a complete conversion of isobutene. The selectivity to 1-octene, 1-dodecene, and 1-hexadecene was 20%, 75%, and 5%, respectively. Since the oligomerization procedure generates a group of alkenes as products, it was assumed that the selectivity information could describe the next alkenes ranges: (C8C11) 20%, (C12C15) 75%, and (C16) 5%. In this context, the reactions set proposed to describe the oligomerization reactor are presented in Table 5.11. After the oligomerization reactor, the alkenes must be converted into paraffins through hydrogenation reactions using Pd/zeolite as catalyst at 250 C and 30 bar (Peters and Taylor, 2011; Atsonios et al., 2015); the reached conversion of alkenes was 100%. The reactions set to describe this final step are shown in Table 5.12. Finally, the separation of the biojet fuel from the output stream of the hydrogenation reactor is carried out by a distillation column; wherein naphtha (C4C7) is obtained at the top, and biojet fuel (C8C16) is recovered at the bottom.
5.7.4
Simulation of the overall process
In this section, the procedure to perform the simulation of the J. curcas and palm oil shell processing into biojet fuel Table 5.11 Reactions and conversions for oligomerization stage. No. reaction
Reaction
% Conversion (reference component)
1 2 3 4 5 6 7 8 9 10 11 12 13
2C2 H4 -C4 H8 2:5C2 H4 -C5 H10 2C3 H6 -C6 H12 3:5C3 H6 -C7 H14 2C4 H8 -C8 H16 4:5C4 H8 -C9 H18 10C4 H8 -4C10 H20 11C4 H8 -4C11 H22 3C4 H8 -C12 H24 13C4 H8 -4C13 H26 14C4 H8 -4C14 H28 15C4 H8 -4C15 H30 4C4 H8 -C16 H32
50 (ethylene) 50 (ethylene) 50 (propylene) 50 (propylene) 5 (isobutene) 5 (isobutene) 5 (isobutene) 5 (isobutene) 18.75 (isobutene) 18.75 (isobutene) 18.75 (isobutene) 18.75 (isobutene) 5 (isobutene)
154
Chapter 5 Production processes from lignocellulosic feedstock
Table 5.12 Proposed reaction set by hydrogenation stage. No. reaction
Reaction
% Conversion (reference component)
1 2 3 4 5 6 7 8 9 10 11 12 13
C4 H8 1 H2 -C4 H10 C5 H10 1 H2 -C5 H12 C6 H12 1 H2 -C6 H14 C7 H14 1 H2 -C7 H16 C8 H16 1 H2 -C8 H18 C9 H18 1 H2 -C9 H20 C10 H20 1 H2 -C10 H22 C11 H22 1 H2 -C11 H24 C12 H24 1 H2 -C12 H26 C13 H26 1 H2 -C13 H28 C14 H28 1 H2 -C14 H30 C15 H30 1 H2 -C15 H32 C16 H32 1 H2 -C16 H34
100 100 100 100 100 100 100 100 100 100 100 100 100
(n-butene) (n-pentene) (n-hexene) (n-heptene) (n-octene) (n-nonene) (n-decene) (n-undecene) (n-dodecene) (n-tridecene) (n-tetradecene) (n-pentadecene) (n-hexadecene)
is described, following the conceptual design proposed in Section 5.7.3. The simulation is carried out in the process simulator Aspen Plus V10.0. The simulation starts with the definition of each component involved in the process, which includes cellulose, hemicellulose, lignin, n-hexadecanoic acid, acid-6octadecenoic, phenol, acetic acid, carbon monoxide, carbon dioxide, methane, hydrogen biochar, oxygen, nitrogen, methanol, ethanol, propanol, isobutanol, and hydrocarbons. As was mentioned in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, if the components are not included in the Aspen Plus V10.0 database, it is necessary adding them. Once all components are loaded, the user must choose the appropriate thermodynamic method to model the process. According to Carlson (1996) and Atsonios et al. (2013), the pyrolysis, gasification, alcohol synthesis, condensation, dehydration, and oligomerization reactions are modeled with the NRTL solution model; the thermodynamic behavior of the Rectisol process is described with the RK-SOAVE method (Atsonios et al., 2013), the hydrogenation reaction is modeled with Peng-Robinson, while BK10 is used as thermodynamic model for the distillation column where the separation of biojet is performed (Atsonios et al., 2013). The flowsheet starts with the definition of the J. curcas shell (SHELL-JC) and palm oil shell (SHELL-PA) streams, according to Table 5.1. It is worth to mention that the lignocellulosic
Chapter 5 Production processes from lignocellulosic feedstock
components must be included as the CISOLID stream. To complete the feed to each pyrolysis reactor, the air stream is mixed with each shell stream using a mass biomass:air ratio of 1:1. The air mass composition assumed in this process is 1 wt.% CO2, 1 wt.% H2, 21 wt.% O2, and 77 wt.% N2. Fig. 5.7 presents the process diagram for the conversion of the lignocellulosic biomass. Each pyrolysis reactor (PYR-JC for J. curcas shell pyrolysis; PYR-PA for Palm oil shell) is operated at 550 C and 1 bar, and they are modeled through the RSTOIC module. The set of reactions for each reactor is shown in Tables 5.4 and 5.6 for J. curcas shell and palm oil shell pyrolysis, respectively. The output streams from both pyrolysis reactors enter in a MIX-module (MIXED-3). The output stream from MIXED-3, called PRODPYR, together with a stream of saturated steam at 200 C (STEAM-GA) enters to the gasification reactor (R-GASIFI) with a biomass: steam mass ratio of 1:1. The R-GASIFI is modeled with the RBatch module, which operates at 676.85 C and 1 bar. The reaction set was defined according to Table 5.7, and the batch cycle time defined is 1 h, which is the default value. The results from R-GASIFI are exhibited in Fig. 5.8. Approximately after 5 min (0.09 h), the composition shows no variations; thus the trends are shown before that time. As can be seen in Fig. 5.8, the operation time could be reduced, since composition shows not variation from about 0.1 h. Also, it is observed the formation of hydrogen and carbon monoxide in the gasifier. After the gasification reactor, a FILTER module (FILT-1) can be implemented to eliminate solid traces. Next, the bio-oil (noncondensable gases) is condensed by a FLASH module (FLASH-1) at 30 C and 1 bar, where up to 99 wt. % of the bio-oil components are removed. Thus the GAS-NONC stream has CO, CO2, WATER (gas), and air traces (O2 and N2). Then, the CO2 is removed in an absorption column using methanol as solvent; this process, known as Rectisol, is modeled through the Extract module (RECTISOL) with 10 stages, choosing as key components methanol, water, and CO2. The methanol (265 C) is inserted at stage 1 with a methanol:syngas (CO-H2) mass ratio of 1:1. As result, up to 99 wt.% of CO2 is removed by the methanol. The CO-H2 stream is fed into SEP-0 to remove the air traces. The cleaned syngas is inserted to a compressor module of isentropic type (COMP-1), which is operated at 90 bar; this equipment is collocated to increase the pressure of syngas. Then, the temperature is decreased until 320 C into a Heater (EX-2). The S23 stream from EX-2 is inserted into R-MAS reactor, which is modeled as RStoic module operated at
155
Figure 5.7 Flow diagram of the conversion process.
Figure 5.8 Composition versus reaction time at the gasification reactor.
158
Chapter 5 Production processes from lignocellulosic feedstock
320 C and 90 bar. The reaction set for this reactor is presented in Table 5.8. The output stream from R-MAS is fed to SEP-1 (Sep module) to remove the CO-H2 traces. Then, to condition the S31 stream before enters to R-CONDEN reactor, a turbine (TURBI-1, Comp module) at 30 bar and a Heater module (EX-3) at 200 C are implemented. The R-CONDEN reactor is modeled as a RStoic module at 200 C and 30 bar. The output stream from R-CONDEN consists of a mixture of alcohols, where methanol is the majority component with 80.79 wt.% followed by isobutanol with 10.25 wt.%. Since the next reactive stages use isobutanol as raw material, the methanol must be removed from the resulting mixture. Thus a RadFrac distillation column (COL-REC1) is collocated. The design of this distillation column has 70 stages (Atsonios et al., 2015), allowing to achieve a methanol recovery of 99% at the top of the column; the operating specification of the column was distillate rate of 1433.52 kg/h, feed stream located at stage 35 and a reflux ratio of 2.7. At the bottom of the distillation column (ISOBU-RE), 99% of isobutanol is recovered with traces of methanol, ethanol, propanol, and water. The ISOBU-RE is conditioned before entering to the dehydration reactor, using a Heater module (EX-4), which increases the temperature to 285 C. This temperature level is defined as the operating temperature of the R-DESHID reactor modeled through the RStoic module. The output stream from R-DESHID has a high amount of water; to remove this excess, a Flash module (FLASH-2) is implemented. The stream from FLASH-2, with a lower amount of water, is inserted to Sep module (SEP-3) to remove its traces. Then, the free-water stream is fed to a Heater module (EX-5) to adjust the temperature to 100 C before R-OLIGOM. The oligomerization reactor is modeled with a RStoic module (R-OLIGOM), due to the absence of kinetic parameters to describe its behavior. The R-OLIGOM operates at 100 C and 1.1 bar, according to the set of reactions and degree of conversion presented previously. The output stream from R-OLIGOM contains hydrocarbons from C5 to C16 and must be conditioned before entering the hydrogenation reactor. For this, a Heater module (EX-6), a Comp module (COMP-2), and a second Heater module (EX-7) are connected consecutively to adjust the temperature and pressure values. The EX-6 vaporizes the stream until 210 C, while the COMP2 increases its pressure until 30 bar; as a result, its temperature increases until 315 C. This high temperature level must be decreased to 250 C (by EX-7), which is the temperature required
Chapter 5 Production processes from lignocellulosic feedstock
at the hydrogenation reactor. In the hydrogenation reactor, a hydrogen stream (H2) is fed to the process in a isobutanol: hydrogen mass ratio of 1:1 at 10 bar. The H2 increases its temperature and pressure levels through a Comp module (COMP-3) and Heater module (EX-8) connected consecutively; the goals for temperature and pressure are 250 C and 30 bar. Finally, the conditioned isobutanol and hydrogen streams are mixed (MIX-5) and fed to the hydrogenation reactor; this equipment is modeled with a RStoic module (R-HYDROG) operating at 250 C and 30 bar. At the end of the reaction, the excess of hydrogen is removed by a Sep module (SEP-2). The output stream of the hydrogenation reactor allows generated hydrocarbons from C4 to C16. The hydrocarbons stream after R-HYDROG must be separated to obtain the naphtha and biojet fuel in a distillation column. Before the distillation column, the pressure of the hydrocarbons stream must be decreased at 1 bar through a Pump module (TURBI-2). The design of the distillation column is obtained with a DSTWU module, considering recoveries of 99%; the resulting designs are simulated in RadFrac module (COL-1). The naphtha and biojet fuel flows are 56.10 and 125.44 kg/h, respectively.
5.7.5
Economic assessment
The economic evaluation of the process described in this case study is performed through the calculation of the total annual cost (TAC). The calculation procedure was presented in the Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, along with the prices of steam, cooling water, hydrogen, and electricity; these values are used in the case of study of the present chapter. Moreover, the equations to obtain the equipment cost were also presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. It is worth to mention that the shells employed as raw material in this process are assumed as wastes; thus the raw material cost involves only the hydrogen used as reactant and the methanol employed as solvent. The summary of the items involved in the calculation of the TAC is shown in Table 5.13. From Table 5.13, it can be observed that the higher cost for the operating costs is that of the raw material, due to the need of hydrogen for the operation of the reactor. Moreover, methanol price represents 17% of the total raw material cost; this low cost is due to the assumption of the methanol being reused
159
160
Chapter 5 Production processes from lignocellulosic feedstock
Table 5.13 Total annual cost of lignocellulosic waste processing. Operating cost (USD/year) Heating utilities cost
Cooling utilities cost
Raw material cost
Electricity cost
Total operating cost (USD/year)
49,644.3693
255,627.90
4,095,372.34
1,183,956.70
6,241,281.08
Annual capital cost (USD/year) Equipment cost
A1
A2
Total annual cost (USD/year)
4,156,201.08
748,116.19
2,535,282.66
1,486,678.08
TAC (USD/year) 7,727,959.16
after the elimination of CO2. Also, the making up of the methanol stream as solvent is done by the methanol obtained in COLREC1. On the other hand, the cooling utility defined for EX-1 was taken from Aspen Plus as a refrigerant at extremely low temperature: 2102 C as inlet temperature and 2103 C as outlet temperature. The energy cost for this service defined by the simulator was 8.45 3 1026 USD/kJ. Finally, it is worth to mention that the electricity cost was calculated as the electricity purchased, according to the difference between the required electricity (compressors) and the electricity provided by the turbines in the process. So, the operating cost represents 80.76% of TAC.
5.7.6
Estimation of price of biojet fuel
The estimation of the minimum price of the biojet fuel obtained in this case study was done according to the procedure described in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, taking as reference the same market price of kerosene fuel of 0.4849 USD/L (0.6380 USD/kg). Table 5.14 presents the minimum biojet fuel price along with the main variables involved in the calculation. The price obtained in this scheme is 11.23 times the price of fossil jet fuel in the market, due to high operating cost required for the process. As mentioned in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock and
Chapter 5 Production processes from lignocellulosic feedstock
161
Table 5.14 Minimum price of biojet fuel from lignocellulosic waste processing. I (USD/year)
P (USD/year)
C (USD/year)
n P
imin
Naphtha (kg/year)
4,156,201.08 Operating cost (5% of n P Cmdaji )
0.2 P 5 imin I
476,901.02
371,980.00 Total 4,528,181.08 S (USD/year) Q (kg/year) Minimum price of biojet fuel (USD/kg) 7.16
Total
i51
Cmdaji
i51
905,636.22 7,639,604.67 1,066,277.82
Chapter 4, Production Processes for the Conversion of Sugar and Starchy Feedstock, this price could be reduced by the application of intensification and energy integration techniques. Also, particularly for this process, the conversion degree of mixed alcohol synthesis must be improved.
5.7.7
Environmental assessment: CO2 emissions
The CO2 emissions of the process presented in this case study were calculated according to the assumptions and procedure described in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Table 5.15 presents the emissions calculations for the process. The sum of CO2 emissions due to steam and electricity is 13.02 Mton CO2/year. The emissions of CO2 could be reduced with energy integration and process intensification techniques. Finally, Table 5.16 presents a summary of the results obtained for the three production scenarios developed in Chapters 35. From this table, it can be observed that the high price of biojet fuel from sorghum is due the cost of raw material used for its processing. On the other hand, the high quantity of CO2 released is attributed to the number of process equipment required, regarding to the other cases study. It is clear that, according to the results, the use of triglyceride feedstock as raw materials may allow the lowest selling price for biojet fuel, since
Total 6,019,522.10
162
Chapter 5 Production processes from lignocellulosic feedstock
Table 5.15 CO2 emissions due to steam and electricity generation. Emissions by steam generation (MkgCO2/year)
Emissions by electricity (MkgCO2/year)
Total CO2 emissions (MkgCO2/year)
13,024.30
3.95
13,028.25
Table 5.16 Product prices and CO2 emissions for the production of biojet fuel with different raw materials. Triglyceride feedstock Emissions Minimum price (ton CO2/kg of biojet fuel biojet fuel) (USD/kg)
Sugar and starchy feedstock Minimum Emissions price (ton CO2/kg of biojet fuel biojet fuel) (USD/kg)
Lignocellulosic feedstock Minimum Emissions price (ton CO2/kg of biojet fuel biojet fuel) (USD/kg)
4.43
30.63
7.16
3.2
172.02
12.22
the conversion of triglycerides through hydrotreatment requires fewer processing steps than the ATJ process. In the case of the conversion of lignocellulosic feedstock, additional processing steps are required, although the cost of raw material is lower than the cost of the sugar/starchy feedstock.
5.8
Conclusion
Transforming agricultural residues in fuels and other products represents a way to give a better use to such lignocellulosic materials, contributing to the greening of the aviation sector. Several advances on the processing routes have been developed on the last years. The transformation of the biomass into alcohols and further conversion to biojet fuel is a proper alternative, although several processing stages are required. On the other hand, the conversion through thermochemical processes requires high temperatures, but liquid and solid by-products can be obtained, which may be further processed to enhance the economy of the process. An important opportunity area is the realization of combustion tests for the biojet fuel generated for this kind of process. From the presented case study, it can be seen that there are still areas of opportunity to reduce the cost of biojet fuel, mainly in terms of the operating costs.
Chapter 5 Production processes from lignocellulosic feedstock
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Zheng, X., Chang, J., Fu, Y., 2015. One-pot catalytic hydrocracking of diesel distillate and residual oil fractions obtained from bio-oil to gasoline-range hydrocarbon fuel. Fuel 157, 107114.
Further reading Conde-Mejı´a, C., Jime´nez-Gutie´rrez, A., Go´mez-Castro, F.I., 2016. Purification of bioethanol from a fermentation process: alternatives for dehydration. Comput. Aided Chem. Eng. 38, 373378. Mohsenzadeh, A., Zamani, A., Taherzadeh, M.J., 2017. Bioethylene production from ethanol: a review and techno-economical evaluation. ChemBioEng Rev. 4, 7591. Pearson, D.E., 1983. Process for catalytic dehydration of ethanol vapor to ethylene. US Patent 4423270.
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6
Introduction
As any other industrial process, the production of biofuels requires the use of external energy to accomplish several heating tasks. Nevertheless, only a fraction of the energy is used for such purpose, and the other fraction is lost into the surroundings. This is particularly true for operations with low thermodynamic efficiency, for example, distillation, with the consequence of requiring higher energy inputs to fulfill the separation tasks. Such energy requirements are usually fulfilled with steam, which is obtained by burning fossil fuels. This implies that, the more energy is required in the process, more fuel is used, and more carbon dioxide is released. Thus even if the raw material employed to produce biofuels is renewable, the process itself could have a high environmental load in terms of emissions of the greenhouse gas. This leads to the need of more energyefficient processes, in order to obtain more sustainable production schemes. Process intensification and process integration are tools that can be helpful to achieve such goal. Additionally, process intensification may lead to economic savings, aiding to turn the biofuels production more profitable. In this chapter, a description of process intensification and process integration is presented, together with a review on the application of such tools on the production of biojet fuel. An example of the use of such tools in a hydrotreating process is presented in detail, and the impact of intensification and integration over the economic and environmental indexes of the process is assessed.
6.2
Process intensification
Process intensification is a branch of Chemical Engineering related to the development of production technologies to allow Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00006-7 © 2021 Elsevier B.V. All rights reserved.
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substantial enhancements in the processes. Several definitions of process intensification have been reported; here, some of them will be presented. Ramshaw (1995) established one of the first definitions of process intensification, as “a strategy for making dramatical reductions in the size of a chemical plant so as to reach a given production objective.” Stankiewicz and Moulijn (2000) defined the concept of process intensification as “the development of novel apparatuses and techniques that, compared to those commonly used today, are expected to bring dramatic improvements in manufacturing and processing, substantially decreasing equipment-size/production-capacity ratio, energy consumption, or waste production, and ultimately resulting in cheaper, sustainable technologies.” Later, Stankiewicz and Moulijn (2002) mentioned that the target of process intensification is “to make a quantum leap in process and plant efficiency.” Charpentier (2005) described process intensification as “complex technologies that replace large, expensive, energy-intensive equipment or processes with smaller, less costly, more efficient plants, or plants that combine multiple operations into a single apparatus or into fewer devices.” Lutze et al. (2010) mentioned that “process intensification can be achieved by adding/enhancing phenomena in a process through the integration of operations, functions, phenomena, or alternatively through the targeted enhancement of phenomena in an operation.” Several common ideas can be found in the previously mentioned and in other definitions. First of all, it is the high innovation expected from an intensified system, compared with the existing equipment. Second, the intensified system must allow achieving noticeable enhancements in the process. Third, process intensification must tend to reduce the size of the equipment, so the space required for a given process is each time smaller. Fourth, an intensified system must allow either performing multiple operations in a single equipment or enhancing the performance of a given operation. With these considerations in mind, examples of intensified systems can be mentioned; some of them are schematically presented in Fig. 6.1. The chemical reactors are key equipment since they perform the conversion of raw materials to products (Li et al., 2018). In conventional reactors, mass and heat transfer are limited, especially for heterogeneous reactions; due to this, reactants in excess and devices to create turbulence are needed to guarantee the contact between reactants along with a better heat transfer (Quiroz-Pe´rez et al., 2019a). Therefore the miniaturization of such devices has been presented as a strategy with high
Chapter 6 Process intensification and integration in the production of biojet fuel
Figure 6.1 Examples of intensified technologies.
potential for the enhancement of the conversion and selectivity; in addition, it can be expected reductions in the processing costs, an increasing in the safety of the process, among others (DeWitt, 1999). To pursue this objective, several devices have been proposed to perform the reactions, for example, the microreactors. As the name suggested, microreactors are reactors with channel sizes in the order of micrometers, at which diffusion is the dominant mixing mechanism (Reay et al., 2008). Different kinds of microreactors have been proposed; as examples, the T-shaped microreactors (Tonomura et al., 2009) and the monolithic microreactors (Nagaki et al., 2016) can be mentioned. Another proposal for enhancing the performance of
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chemical reactions implies using waves with different frequencies, as high-frequency waves in sonoreactors (Gonze et al., 1998) or microwaves (Cablewski et al., 1994). In sonoreactors the application of ultrasound enhances the rates of chemical reactions and eliminates or reduces the need of catalysts (Reay et al., 2008). Due to ultrasound frequencies, the molecules of the reactants vibrate faster, which increase the rate of the chemical reaction; since the interaction between reactants is guaranteed, there is no necessity for feeding one of them in excess. Therefore a reduction in the operating costs, due to the decrease in the reactant quantities and to avoiding the later separation of the excess reactant, can be expected. Another interesting intensified equipment is the spinning disk reactor, in which the heat, mass, and momentum transfer is increased since the reactants are present in very thin films on a rotating surface (Reay et al., 2008). These reactors are very compact in comparison to conventional reactors; however, its production rates are the same because its residence times are small in comparison with the conventional reactors. In the case of heat exchange devices, several efforts have been presented in the development and design of compact heat exchangers, such as plate-and-fin exchangers (Najafi et al., ˜ ez et al., 2010), 2011), plate-and-frame exchangers (Pico´n-Nu´n among others. Compact heat exchangers have area densities in the order of 20010,000 m2/m3 with hydraulic diameters of less than 5 mm; despite their small size, the operation temperature range covers from 250 C to 900 C (Reay et al., 2008). In the case of unit operations, the thermally coupled distillation systems are examples of equipment where heat transfer is enhanced. A thermally coupled distillation systems consist of distillation columns that are linked between them through liquid and/or vapor interconnection flows. The interconnection flows allow supplying the liquid and/or vapor requirements into the distillation columns, and, at the same time, eliminating a condenser or reboiler, respectively (Briones-Ramı´rez and Gutie´rrez-Antonio, 2009). Among the most known thermally coupled systems, the dividing wall column (Dejanovic et al., 2010) and the indirect and direct thermally coupled sequences ¨ nnebier and Pantelides, 1999) can be mentioned. In general, (Du a thermally coupled distillation system can have reductions in the energy consumption between 30% and 50%, with respect to the equivalent conventional system. All the intensified equipment mentioned before allows to perform one task (e.g., heating/cooling/reaction/separation) with a significant increase in the heat, mass, and momentum
Chapter 6 Process intensification and integration in the production of biojet fuel
transfer. Moreover, there are other types of intensified equipment where two or more unit operations are performed in the same equipment. In this case, the original operations must be feasible at the same operating conditions, in order to share a thermodynamic window where they may simultaneously occur. One of the systems that allow performing reaction and separation in the same equipment is the reactive distillation (Malone and Doherty, 2000). In this equipment, the removal of the products alters the reaction equilibrium toward the formation of more product, which promotes increasing the conversion and/or selectivity to the desired product. On the other hand, the extractive distillation (Lei et al., 2003) allows the purification of mixtures where it is necessary to use a solvent to separate one of the components, due to the high nonideality of the mixture. Additional efforts for the combination of thermal coupling and simultaneous operations have been reported, for example, the reactive thermally coupled distillation systems (Mueller and Kenig, 2007; Miranda-Galindo et al., 2011) and the extractive dividing wall column (Bravo-Bravo et al., 2010). On the other hand, the heat exchanger reactor constitutes a remarkable example of the synergy between heat exchangers and reactors. A heat exchanger reactor is basically a compact heat exchanger where a reaction takes place. In this kind of equipment, the candidate reactions for heat exchanger reactors must be fast, produce, or absorb heat and form byproducts, such as nitration, hydrogenations, and polymerizations (Reay et al., 2008). All the previously mentioned technologies accomplish with at least one of the criteria to be considered intensified systems. In the case of the processes focused on the production of biojet fuel, only few applications of process intensification have been reported. As seen in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, the hydroprocessing of vegetable oils requires two reaction steps: hydrodeoxygenation/hydrodecarboxylation as first step, and hydrocracking/ hydroisomerization as second step. Nevertheless, efforts have been done to synthesize bifunctional catalysts, which allow performing both sets of reactions with a single catalyst. Verma et al. (2011) reported the conversion of a modified Chlorella micro-algae to produce renewable aviation fuel in a single-step process, where hydrodeoxygenation, hydroisomerization, and hydrocracking reactions are carried out in the same vessel. The used catalyst is 5% NiO, 18% MoO3/H-ZSM-5 (micromesoporous)-HSASC. The major yield to biojet fuel (78.5%) was obtained at 410 C and 50 bar.
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Liu et al. (2015) reported the use of a Ni/MCM-41-APTESUSY catalyst, obtaining yields around 80.3% for hydrocarbons in the range of biojet fuel, at 300 C and 3 MPa. Sousa et al. (2018) proposed the use of a thermally activated HBeta zeolite catalyst to simultaneously perform the deoxygenation, cracking, and isomerization of vegetable oils. A yield to hydrocarbons between 80% and 96% is reported, at 350 C and 10 bar. Such bifunctional catalysts may represent important reductions in the cost of an industrial-scale production process, since only a single equipment would be required to obtain the linear and branched hydrocarbons, and, in some cases, also aromatics. Moreover, by reducing the number of equipment, the safety of the process can also be enhanced. Other cases where process intensification has been applied to the production of biojet fuel are focused on the purification stage, where the different hydrocarbon fractions are separated by distillation. Gutie´rrez-Antonio et al. (2015) analyzed the use of various thermally coupled schemes to separate the light hydrocarbons, biojet fuel, and green diesel fractions. They reported that the best configuration for the intensified sequences is the thermally coupled direct sequence, with energy savings around 16% in comparison with the optimal design of the conventional direct sequence. For the analyzed mixture, the dividing wall column gave a bad performance in energetic terms. The dynamic performance of such intensified sequences was studied by Acosta-Solo´rzano et al. (2016), finding that the dividing wall column was the system with better control properties, but high heat duty. Nevertheless, it is mentioned that the thermally coupled direct sequence showed acceptable values for the dynamic properties under analysis, with the lowest heat duty among the studied sequences. The use of reactive distillation to perform the hydrocracking/hydroisomerization reactions, while simultaneously separating the hydrocarbons into two fractions, has been reported by Gutie´rrez-Antonio et al. (2018a). The hydrocarbons stream is the effluent of a reactor where Jatropha curcas oil is hydrotreated. According to the results, the use of a dividing wall column in the hydrotreating process may reduce equipment costs up to 25% and utilities costs up to 57%, in comparison with the conventional distillation train. Nevertheless, it is mentioned that most of the total annual cost (TAC) is given by the raw material. A process for the production of biojet fuel from microalgae oil has been proposed by Gutie´rrez-Antonio et al. (2018b), using a single reactor with a bifunctional catalyst and thermally coupled distillation sequences in the separation stage, namely the thermally coupled direct sequence and the thermally coupled
Chapter 6 Process intensification and integration in the production of biojet fuel
indirect sequence. According to the reported results, for such separation processes, the conventional direct sequence has the lowest costs, with equipment costs 39% lower than the thermally coupled direct sequence, and utilities costs 4% lower. Nevertheless, the thermally coupled direct sequence allows reducing the carbon dioxide emissions by 34%, in comparison with the conventional direct sequence. The design of intensified equipment can be realized using different strategies such as graphical methods, shortcut methods, finite element, and optimization strategies. For the design of microreactors, computational fluid dynamics (CFD) is a powerful tool, and its value has been proved in different applications where reaction yield and heat and mass transfer must be enhanced (Quiroz-Pe´rez et al., 2019b). On the other hand, for the design of sonoreactors, the use of shortcut methods (Son, 2017), CFD (Niazi et al., 2014; Khodaei et al., 2018; Rahimi et al., 2019; Quiroz-Pe´rez et al., 2019a), and computer-aided tools (Da¨hnke et al., 1999) has been reported. Petlyuk (2004) proposed graphical methodologies for the design of thermally coupled, azeotropic distillation, and extractive distillation columns. For reactive distillation columns, Doherty and its research group led the efforts in the use of graphical, shortcut methods, and computer-aided tools (Doherty and Fariss, 1985; Barbosa and Doherty, 1988a,b; Doherty and Buzad, 1994; Buzad and Doherty, 1994, 1995; Huss et al., 1999; Gadewar et al., 2004). The use of stochastic optimization strategies has also been reported in the literature for the design of thermally coupled distillation columns (Briones-Ramı´rez and Gutie´rrez-Antonio, 2009), extractive conventional and thermally coupled distillation (Bravo-Bravo et al., 2010), reactive conventional and thermally coupled distillation (Miranda-Galindo et al., 2011), hybrid distillation/melt crystallization process (BravoBravo et al., 2013), thermally coupled distillation sequences with nonequilibrium model (Go´mez-Castro et al., 2015), and azeotropic thermally coupled distillation columns (Gutie´rrez-Antonio et al., 2014). It is important to mention that adequate heat and mass transfer must be ensured, especially when intensified equipment are considered. To reach this objective, CFD is a powerful tool. CFD consists of the use of powerful computers and numeric methods to solve the transport equations in fluid systems (May-Va´zquez et al., 2017). The analysis of fluid flows, especially the reactive ones, is key to guaranteeing and improving the adequate performance of equipment, in terms of energy efficiency, product yields, or even carbon dioxide
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emissions (Quiroz-Pe´rez et al., 2019b). In spite of all these advantages, to the authors’ knowledge there is no reported work on the literature about the use of CFD for the analysis of intensified systems in the production of renewable aviation fuel.
6.3
Process integration
Process integration, and particularly energy integration, is a helpful approach to make a better use of the energy available in a given process, or even in a set of processes, to partially fulfill the heating and cooling requirements using the process streams, instead of utilities. Fig. 6.2 shows a simple example of process integration. There are two process streams: H1 needs to be cooled, thus it is called “hot stream”; while C1 requires heating, and it is called “cold stream.” H1 is at a temperature Thi, and it must be taken to a temperature Tho. On the other hand, C1 needs to be heated from Tci to Tco. To achieve the desired temperature levels, H1 is cooled down using cooling water, while C1 is heated with steam, as shown in Fig. 6.2A. An alternative of integrated scheme is shown in Fig. 6.2B. If H1 and C1 coincide in a range of temperatures, H1 may transfer a part of its heat content to C1. To achieve such goal, both streams enter into a heat exchanger HE1. C1 reaches a given temperature Tc,ex and H1 is cooled down to Th,ex. If Th,ex . Tho, additional cooling is required, which can be given by cooling water. Similarly, if Tc,ex , Tco, additional heating must be provided by steam. Nevertheless, the heating and cooling requirements will be lower than those of the nonintegrated case, since the energy content of the process stream has been used. Of course, for a feasible integration, Thi must be higher than Tc,ex and Th,ex must be higher than Tci. All the previous concepts are the basis for energy integration. Nevertheless, when there are several cold and hot streams, a systematic method is necessary to choose the best cold stream-hot stream combinations for integration. One of the most known strategies for energy integration is the pinch point methodology (Linnhoff and Hindmarsh, 1983). However, it is important to mention that the pinch analysis was later extended to the optimization of mass integration networks by El Halwagi and Manousiouthakis (1989), along with other derived works that employ optimization strategies, mainly with mathematical programming techniques (Ahmetovi´c et al., 2015; Zhang et al., 2018; Klemesˇ et al., 2018).
Chapter 6 Process intensification and integration in the production of biojet fuel
179
Figure 6.2 Fulfillment of energy requirements for two process streams (A) using utilities, (B) by energy integration.
The pinch point methodology is based on the determination of the pinch temperature for the set of cold and hot streams under analysis. To understand the concept of pinch point, the composite curves shown in Fig. 6.3 can be useful. Composite curves are graphical representation of the temperature of all the streams in the process, and the energy content of each stream, represented as a change on the enthalpy. In Fig. 6.3A, both composite curves are overlapped in a given range of enthalpy; in this region, energy integration may occur. The rest of the hot composite curve represents the cooling requirements that must be fulfilled by utilities, for example, cooling water. Similarly, the nonoverlapped region of the cold curve means the heating requirements that must be fulfilled by utilities, for example, steam. It can be observed that there is a point where both curves have the minimal vertical distance between them, which is the ΔTmin. Since the x-axis represents
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Chapter 6 Process intensification and integration in the production of biojet fuel
Figure 6.3 Composite curves: (A) low energy integration and (B) high energy integration.
enthalpy intervals, the hot composite curve can be displaced to the right, as shown in Fig. 6.3B. By doing this, the region with potential of energy integration is greater, and the external heating and cooling requirements are reduced. On the other hand, the magnitude of ΔTmin is reduced. This value cannot be reduced without limit, since there will be a point from which the second law of thermodynamics will be violated. So, this limiting temperature is the pinch point, which represents the minimum feasible heating and cooling requirements for the studied streams. In the pinch point methodology, the pinch temperature is usually identified through a heat cascade diagram, which represents the heat balances for the whole set of streams, for each interval of temperature. Those intervals are defined in terms of the initial and final temperatures of each stream. In the initial calculation, some negative values of heat can be obtained for some temperature intervals. The most negative heat value is then used as the minimum external heating requirement, and a second cascade diagram is made. A value of heat equal to zero must be obtained on a given interval; the corresponding temperature represents the pinch point. Once the pinch point and the minimal utility requirements have been determined, the streams network is divided in two: a zone to the left of the pinch point, and a zone to the right of the pinch point. Then, exchanges between the streams are proposed, avoiding
Chapter 6 Process intensification and integration in the production of biojet fuel
exchanges crossing the pinch point and taking care of not having temperature crossing in the exchangers. Moreover, the use of heating utilities is only valid in the region to the right of the pinch point; the use of cooling utilities is only acceptable in the region to the left of the pinch point. On the last years, energy integration has been proposed as an excellent tool to reduce utilities costs in the biojet fuel production processes. Moreover, since the use of steam can be reduced, the environmental impact is simultaneously reduced. Gutie´rrezAntonio et al. (2016a) presented a proposal for the energy integration of a hydrotreating process, using J. curcas as raw material. According to the results, the integrated process with a direct distillation sequence allows savings in utilities costs up to 26% in comparison with the nonintegrated schemes. Nevertheless, when both utilities and equipment costs are considered, the best scenario is a nonintegrated scheme, with savings in TAC up to 6% in comparison with the other analyzed cases. This is due to the additional equipment required to integrate the process. In a second approach, Gutie´rrez-Antonio et al. (2016b) presented the energy integration of a hydrotreating process with intensified systems in the purification zone; once more, the raw material was J. curcas. According to the reported results, the best case in terms of utilities costs is the integrated process with a conventional direct distillation sequence, with savings of up to 32% in comparison with the other process proposals. In terms of the combined utilities plus equipment costs, the best alternative is the nonintegrated process with a conventional direct distillation sequence, with savings up to 13%. Nevertheless, if the emissions of carbon dioxide are considered, the integrated process with a conventional direct distillation sequence shows savings up to 90%. Villegas-Herrera et al. (2018) proposed the energy integration between the hydrotreating process and a one-step process for the production of biodiesel with supercritical ethanol. As preliminary results, it is reported that the integration between both processes may allow savings around 60% in heating requirements, by making use of the heat released by the hydrotreating reactor. Moreover, the cooling requirements could be reduced up to 95%.
6.4
Techno-economic analysis of alternatives
As it can be deducted from the information provided in Sections 6.2 and 6.3, neither process intensification nor process integration is applicable for all the processing routes. When
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proposing an intensification/integration alternative, it is necessary to evaluate if such proposal effectively enhances the process. Once the process has been properly designed, it must be evaluated and compared with the conventional processing route. This can be done through several criteria such as reaction yield, energy requirements, TAC, selling price of the products, profit, environmental impact, safety of the process, controllability, among others. When proposing intensification or integration in a given process, two of the initial criteria to take as feasible the new system are the energy requirements (or the utilities costs) and the TAC (which includes the equipment costs). This is because, if the intensified system has higher energy requirements or higher TAC, it does not represent any enhancement for the traditional processing scheme. On the other hand, if the integrated system reduces slightly the utilities costs but greatly increases the equipment costs, the integration could not be desirable for the studied process. The environmental impact is directly related with the heating requirements, and it can be an additional criterion to compare two or more systems. As described in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, the equipment costs, either for a conventional or an intensified/integrated process, can be computed through the Guthrie method (Turton et al., 2012). In the case of the costs of utilities, they can be obtained from the total requirements of each utility and the unitary costs in the region of interest. Environmental impact can be assessed in several ways, but one of the most direct is the calculation of the carbon dioxide emissions, through methodologies as that proposed by Gadalla et al. (2005). For a more detailed analysis of the environmental impact, a full life cycle analysis could be performed, but this kind of analysis is out of the scope of an initial screening. Finally, the selling price of the products can be obtained through the TAC and the internal rate of return, as reported by Gutie´rrez-Antonio et al. (2016b).
6.5
Application of process intensification to a hydrotreating process
6.5.1
Conceptual design of the intensified process
A systematic methodology for applying process intensification on multicomponent distillation trains with zeotropic or azeotropic mixtures was presented by Guang-Rong and Errico (2012). The main objective in the construction of intensified
Chapter 6 Process intensification and integration in the production of biojet fuel
sequences is keeping the structural simplicity, but with less columns and heat exchangers, regarding to conventional columns. This methodology is carried out using the algorithm presented in Fig. 6.4, wherein the detailed explanation of each step is presented. It is important to mention that the design procedure of each column presented in the methodology is similar to the procedure for conventional columns design. According to the case of study developed in Chapter 3, Production Processes for the Conversion of Triglyceride
Figure 6.4 Procedure for intensified distillation columns (Guang-Rong and Errico, 2012).
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Feedstock, a distillation train with two columns was designed to separate biojet fuel, naphtha, and green diesel. The application of the systematic procedure presented in Fig. 6.4 is carried out for this separation scheme.
6.5.2
Simulation of the intensified process
In order to obtain the intensified process, Figs. 6.5 and 6.6 present the simulation results from Aspen Plus, for Step 1 and 2. These designs are obtained through the shortcut method in DSTWU module, and, then, through the rigorous simulation by
Figure 6.5 Results from simple column 1 (SC-1).
Figure 6.6 Results from simple column 2 (SC-2).
Chapter 6 Process intensification and integration in the production of biojet fuel
RadFrac module, as explained in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Based on the results indicated in Figs. 6.5 and 6.6, lower total energy consumption is presented by SC-1. Thus the original thermally coupled column (OTC) is designed from this scheme. In Fig. 6.7 the single OTC design is presented. The number of stages for each column in the OTC sequence is the same as the equivalent column in the sequence SC1. In accordance with Fig. 6.7, the energy savings by condenser and reboiler of OTC design are 19.97% and 7.28%, respectively, regarding to SC-1. The next step is designing the thermodynamically equivalent structures (TES), which is presented in Fig. 6.8. The number of stages in the column TES-1 is given by the number of stages in the column OTC-1, plus the number of stages in the stripping section of the column OTC-2. Consequently, the number of stages in the column TES-2 is equal to the number of stages in the rectifying section of OTC-2. The energy savings by TES design are 23.23% and 9.74% for condenser and reboiler, respectively. It is important to mention that all configurations presented before were designed with 99% recovery in each established cut. The next step is identifying the TES structures that fulfill the criteria presented. In this case, the TES design has not a side transport column and the side column (TES-2) has not reboiler; thus this design does not fulfill any criteria to generate an intensified sequence column (ISC) configuration. In this
Figure 6.7 Results from OTC design.
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Figure 6.8 Results from TES design.
context, Steps 6, 7, and 8 for the design of ISC are not possible. The design presented in Fig. 6.8 is collocated in simulation of the case of study developed in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, due to its energy savings.
6.6
Application of process integration to a hydrotreating process
The pinch point methodology (Linnhoff and Hindmarsh, 1983) is applied to the process presented in case of study presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, taking into account the results from the intensification procedure presented in Section 6.5. The flowsheet of this process is presented in Fig. 6.9.
6.6.1
Conceptual design of the energy integration
To apply the energy integration on the selected process through the pinch point methodology, the problem table algorithm is applied. The algorithm involves six steps for developing the energy integration network, based on simulation data from case study presented in Chapter 3, Production
Figure 6.9 Flowsheet of case of study presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, with TES.
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Chapter 6 Process intensification and integration in the production of biojet fuel
Processes for the Conversion of Triglyceride Feedstock. Next, each step is presented.
6.6.1.1 Step 1. Data input and ΔT definition The streams data, for the case of study developed in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, are presented in Table 6.1, considering the TES design. The minimum ΔT defined for this case is 10 C.
6.6.1.2 Step 2. Adjustment of temperatures This step is presented in Table 6.2. The order of temperatures is from the highest to the lowest value. Table 6.1 Input data for case of study of Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Stream
Tin (˚C)
Tout (˚C)
Hot/cold
wCp (kW/˚C)
ID stream
EX-PO EX-H2 EX-JC EX-4 COND-TES1 REB-TES1 COND-TES2
619.169 2 204.61 622.049 356.204 78.8421 336.692 252.129
350 350 350 30 56.48 339.93 197.58
Hot Cold Hot Hot Hot Cold Hot
0.42 3.63 1.68 1.63 0.27 177.21 3.63
H1 C1 H2 H3 H4 C2 H5
Exothermic Exothermic Endothermic
73.35 202.57 51.89
kW kW kW
Reactors
R-HDO-PO R-HDO-JC R-HCRIS
Table 6.2 Adjustment of temperatures. Stream 1. Hot 2. Cold
Tin Tout Tin Tout
T original
T adjusted
Order
x x1 y y1
x-ΔTmin x1-ΔTmin y y1
T1 T3 T2 T4
Chapter 6 Process intensification and integration in the production of biojet fuel
189
Figure 6.10 Values of temperature intervals.
Table 6.3 Values of ΔHi. ΔHi (kW) ΔH1 ΔH2 ΔH3 ΔH4
6.6.1.3
ΔH5 ΔH6 ΔH7 ΔH8
4.85 546.84 2 5.80 0.63
0.13 2 580.67 2 189.85 88.65
Step 3. Temperature intervals
The definition of each interval is realized according to Fig. 6.10.
6.6.1.4
Step 4. Heat balance per interval
To calculate the value of each interval, Eq. (6.1) is used. In Table 6.3 all the temperature intervals for this case of study are presented. " # X X wi Cpi 2 wi Cpi ðTi 2 Ti11 Þ ð6:1Þ ΔHi 5 hot streams
6.6.1.5
cold streams
Step 5. Heat cascade
In this step the minimum values for heating and cooling services are obtained. The heat excess in each interval is transferred to next interval, building an ideal thermal machine, through the Eq. (6.2). In Fig. 6.11 the heat cascade for this case of study is presented.
ΔH9 ΔH10 ΔH11 ΔH12
2 238.39 2 75.18 2 53.17 2 817.28
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Chapter 6 Process intensification and integration in the production of biojet fuel
Figure 6.11 Heat cascade.
Qi11 5 Qi 1 ΔHi
ð6:2Þ
According to Fig. 6.11, the pinch point temperature is 2194.61 C and 2204.61 C for hot and cold temperatures, respectively. Likewise, the minimum heating utilities are 1319.5 kW, without cooling utilities (zero kW). With these data, in the next step is developing the heat exchanger network.
6.6.1.6 Step 6. Energy integration network In Fig. 6.12 the energy integration network is presented; this network is obtained through energy balances between the streams that are candidate for integration. Such balances allow establishing the temperature changes that can be allowed for a given integration. It is important to mention that the network was only constructed above the pinch point. The energy requirement of the heaters, which make use of steam to reach the objective temperatures, is equal to the minimum energy requirements predicted by the heat cascade. Thus the proposed network accomplishes with the prediction of the pinch point methodology, achieving the minimal use of utilities. This network is simulated in Aspen Plus. On the other hand, it is important to mention that this design was realized considering the minimum equipment for heat exchangers, which is computed as 7. This value is obtained from Eq. (6.3).
Chapter 6 Process intensification and integration in the production of biojet fuel
Figure 6.12 Energy integration network.
Minimum number of heat exchanger 5 #hot streams ð5Þ 1 #cold streams ð2Þ 1 utilities ð1Þ 2 1
6.6.2
ð6:3Þ
Simulation of the hydrotreating process with energy integration
The simulation of the energy network is presented in Fig. 6.13. The blue and red colors mean cold and hot streams, whilst green color is assigned to streams ready to be integrated into the process equipment. The details about the simulation were presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Also, the design of the intensified distillation train was presented in Section 6.5.
191
Figure 6.13 Flowsheet of the integrated process.
Chapter 6 Process intensification and integration in the production of biojet fuel
193
Table 6.4 Summary of the costs for hydrotreatment process with intensified train and energy integration. Operating cost (USD/year) Heating utilities cost
Cooling utilities cost
Palm oil
Jatropha oil
Electricity Total operating cost (USD/year)
39,106.20
62.90
3,102,500.00 28,021,978.02 70,008.15
44,833,069.11
Annual capital cost (USD/year) Equipment cost
A1
A2
Total annual cost (USD/year)
631,677.08
317,603.00
1,076,321.28
631,677.08
TAC (USD/year)
45,464,746.19
6.6.3
Economic assessment
The calculation procedure and the factors involved are equal to those presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. In Table 6.4 the TAC for this case of study is presented. The economic savings of cooling and heating services for this case of study are 55.31%, regarding to these services costs in the case of study 3. It is important to mention that the cooling and heating services contributed with 0.19% of TAC in the case of study developed in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock; whilst in this case of study, with 0.09% regarding TAC. The saving seems small due to the greater contribution to the TAC is the raw material; however, considering only the utility costs, the reduction obtained when making the intensification and integration is considerable. Regarding to equipment cost, this cost increased 4.11% in contrast to the hydrotreating process presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. The total TAC saving is 0.1%, since the main factor contributing with this value is the raw material cost, which represents 98% of TAC in both case studies, the one presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, and the one discussed in this chapter. If this factor diminishes its value, the economic savings achieved by the methodologies presented will be remarkable.
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Chapter 6 Process intensification and integration in the production of biojet fuel
Table 6.5 Minimum price of biojet fuel. I (USD/year)
P (USD/year)
C (USD/year)
n P
imin
Naphtha (kg/h)
0.2 P 5 imin 3 I Total
246,273.90 Green diesel (kg/h) 9,483,521.90 Total 35,938,283.30
i51
Cmdaji
1,764,461.11 n P Operating cost (5% of Cmdaji ) i51 157,919.27 Total 1,922,380.38 S (USD/year) Q (kg/year)
6.6.4
384,476.08 36,626,068.28 8,263,366.54 Minimum price of biojet fuel (USD/kg) 4.43
Estimation of price of biojet fuel
The procedure for the estimation of minimum selling price was extensively presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. The Table 6.5 presents the minimum price of biojet fuel for this case of study. It is important to mention that the end product flows (naphtha, biojet fuel, and green diesel) are the same that in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, due to only the separation scheme was changed, but the reactive zones were conserved without changes. The minimum price of biojet fuel for the process presented in the case of study of Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, is 4.43 USD/kg. As can be seen in Table 6.5, there is not a significant difference between the prices. These results are due to the main factor affecting the TAC is the price of raw material, which is the same in both cases. However, it is important to emphasize the high economic and energy savings obtained through the application of these methodologies.
6.6.5
Environmental assessment: CO2 emissions
The procedure for the counting of CO2 emissions was presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock. Table 6.6 presents the summary of emissions due to steam and electricity requirements into the process
Chapter 6 Process intensification and integration in the production of biojet fuel
195
Table 6.6 CO2 emissions due to steam and electricity requirements. Emissions by steam generation (Mkg CO2/ year)
Emissions by electricity generation (Mkg CO2/year)
Total CO2 emissions Ton CO2/kg (Mkg CO2/year) biojet fuel
2130.92
1.38
2132.31
presented in this case study. It is important to mention that the emissions by electricity are the same as those for the case study of Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, because this requirement was not modified. If the results of Table 6.6 are compared with the results from the case study of Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, the CO2 emissions due to steam generation are 92% lower when process intensification and energy integration is applied; likewise, the ton CO2 per kg of biojet fuel for the process developed in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock, is 12.4 times higher than the enhanced process presented in this chapter. These results show the positive effect of the application of process intensification and energy integration.
6.7
Conclusion
The design of environmentally sustainable and economically viable processes requires special strategies to achieve the competitiveness of the obtained products in the market. In this context, process intensification is a good alternative to diminish the size, number of equipment, and reduce the utilities necessary for the suitable development of processes. Likewise, energy integration has become a good approach to minimize the utilities consumption in a new process or retrofitting an existing one. This energetic advantage allows the reduction of processing costs, improving its competitiveness. Additionally, important savings on CO2 emissions can be obtained, reducing the environmental impact associated with the process. These conclusions have been demonstrated in the case study presented in this chapter, which represents an enhancement for the process evaluated in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock.
0.25
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Chapter 6 Process intensification and integration in the production of biojet fuel
References Acosta-Solo´rzano, A.D.A., Guerrero-Farfa´n, O., Ramı´rez-Ma´rquez, C., Go´mezCastro, F.I., Segovia-Herna´ndez, J.G., Herna´ndez, S., et al., 2016. Controllability analysis of distillation sequences for the separation of bio-jet fuel and green diesel fractions. Chem. Eng. Technol. 39 (12), 22732283. Ahmetovi´c, E., Ibri´c, N., Kravanja, Z., Grossmann, I.E., 2015. Water and energy integration: a comprehensive literature review of non-isothermal water network synthesis. Comput. Chem. Eng. 82, 144171. Barbosa, D., Doherty, M.F., 1988a. Design and minimum-reflux calculations for single-feed multicomponent reactive distillation columns. Chem. Eng. Sci. 43 (7), 15231537. Barbosa, D., Doherty, M.F., 1988b. Design and minimum-reflux calculations for double-feed multicomponent reactive distillation columns. Chem. Eng. Sci. 43 (9), 23772389. Bravo-Bravo, C., Segovia-Herna´ndez, J.G., Gutie´rrez-Antonio, C., Dura´n, A.L., Bonilla-Petriciolet, A., Briones-Ramı´rez, A., 2010. Extractive dividing wall column: design and optimization. Ind. Eng. Chem. Res. 49 (8), 36723688. Bravo-Bravo, C., Segovia-Herna´ndez, J.G., Herna´ndez, S., Go´mez-Castro, F.I., Gutie´rrez-Antonio, C., Briones-Ramı´rez, A., 2013. Hybrid distillation/melt crystallization process using thermally coupled arrangements: optimization with evolutive algorithms. Chem. Eng. Process. Process Intensif. 67, 2538. Briones-Ramı´rez, A., Gutie´rrez-Antonio, C., 2009. Pareto front of Petlyuk sequences using a multiobjective genetic algorithm with constraints. Comput. Chem. Eng. 33 (2), 454464. Buzad, G., Doherty, M.F., 1994. Design of three-component kinetically controlled reactive distillation columns using fixed-points methods. Chem. Eng. Sci. 49 (12), 19471963. Buzad, G., Doherty, M.F., 1995. New tools for the design of kinetically controlled reactive distillation columns for ternary mixtures. Comput. Chem. Eng. 19 (4), 395408. Cablewski, T., Faux, A.F., Strauss, C.R., 1994. Development and application of a continuous microwave reactor for organic synthesis. J. Org. Chem. 59 (12), 34083412. Charpentier, J.-C., 2005. Process intensification by miniaturization. Chem. Eng. Technol. 28 (3), 255258. Da¨hnke, S., Swamy, K.M., Keil, F.J., 1999. Modeling of three-dimensional pressure fields in sonochemical reactors with an inhomogeneous density distribution of cavitation bubbles: comparison of theoretical and experimental results. Ultrason. Sonochem. 6 (12), 3141. Dejanovic, Lj, Matijasevic, Lj, Olujic, Z., 2010. Dividing wall column a breakthrough towards sustainable distilling. Chem. Eng. Process. Process Intensif. 49 (6), 559580. DeWitt, S.H., 1999. Micro reactors for chemical synthesis. Curr. Opin. Chem. Biol. 3 (3), 350356. Doherty, M.F., Fariss, K.E., 1985. Distillation in the presence of non-equilibrium reversible reactions. Chem. Eng. Sci. 40 (3), 532534. Doherty, M.F., Buzad, G., 1994. New tools for the design of kinetically controlled reactive distillation columns. Comput. Chem. Eng. 18 (Suppl. 1), S1S13. ¨ nnebier, G., Pantelides, C.C., 1999. Optimal design of thermally coupled Du distillation columns. Ind. Eng. Chem. Res. 38 (1), 162176. El Halwagi, M.M., Manousiouthakis, V., 1989. Synthesis of mass exchange networks. AIChE J. 35, 12331244.
Chapter 6 Process intensification and integration in the production of biojet fuel
Gadalla, M.A., Olujic, Z., Jansens, P.J., Jobson, M., Smith, R., 2005. Reducing CO2 emissions and energy consumption of heat-integrated distillation systems. Environ. Sci. Technol. 39 (17), 68606870. Gadewar, S.B., Tao, L., Malone, M.F., Doherty, M.F., 2004. Process alternatives for coupling reaction and distillation. Chem. Eng. Res. Des. 82 (2), 140147. Go´mez-Castro, F.I., Segovia-Herna´ndez, J.G., Herna´ndez, S., Gutie´rrez-Antonio, S., Briones-Ramı´rez, A., Gamin˜o-Arroyo, Z., 2015. Design of non-equilibrium stage separation systems by a stochastic optimization approach for a class of mixtures. Chem. Eng. Process. Process Intensif. 88, 5869. Gonze, E., Gonthier, Y., Boldo, P., Bernis, A., 1998. Standing waves in a high frequency sonoreactor: visualization and effects. Chem. Eng. Sci. 53 (3), 523532. Guang-Rong, E., Errico, M., 2012. Synthesis of intensified simple column configurations for multicomponent distillation. Chem. Eng. Process. Process Intensif. 62 (1), 117. Gutie´rrez-Antonio, C., Ojeda-Gasca, A., Bonilla-Petriciolet, A., SegoviaHerna´ndez, J.G., Briones-Ramı´rez, A., 2014. Effect of using adjusted parameters, local and global optimums, for phase equilibrium prediction on the synthesis of azeotropic distillation columns. Ind. Eng. Chem. Res. 53 (4), 14891502. Gutie´rrez-Antonio, C., Go´mez-Castro, F.I., Herna´ndez, S., Briones-Ramı´rez, C., 2015. Intensification of a hydrotreating process to produce biojet fuel using thermally coupled distillation. Chem. Eng. Process. Process Intensif. 88, 2936. Gutie´rrez-Antonio, C., Romero-Izquierdo, A.G., Go´mez-Castro, F.I., Herna´ndez, S., 2016a. Energy integration of a hydrotreatment process for sustainable biojet fuel production. Ind. Eng. Chem. Res. 55 (29), 81658175. Gutie´rrez-Antonio, C., Romero-Izquierdo, A.G., Go´mez-Castro, F.I., Herna´ndez, S., Briones-Ramı´rez, A., 2016b. Simultaneous energy integration and intensification of the hydrotreating process to produce biojet fuel from Jatropha curcas. Chem. Eng. Process. Process Intensif. 110, 134145. Gutie´rrez-Antonio, C., Soria Ornelas, M.L., Go´mez-Castro, F.I., Herna´ndez, S., 2018a. Intensification of the hydrotreating process to produce renewable aviation fuel through reactive distillation. Chem. Eng. Process. Process Intensif. 124, 122130. Gutie´rrez-Antonio, C., Go´mez-De la Cruz, A., Romero-Izquierdo, A.G., Go´mezCastro, F.I., Herna´ndez, S., 2018b. Modeling, simulation and intensification of hydroprocessing of micro-algae oil to produce renewable aviation fuel. Clean Technol. Environ. Policy 20 (7), 15891598. Huss, R.S., Chen, F., Malone, M.F., Doherty, M.F., 1999. Computer-aided tools for the design of reactive distillation systems. Comput. Chem. Eng. 23 (Suppl.), S955S962. Klemeˇs, J.J., Varbanov, P.S., Walmsley, T.G., Jia, X., 2018. New directions in the implementation of pinch methodology (PM). Renew. Sustain. Energy Rev. 98, 439468. Khodaei, B., Rahimi, M., Sobati, M.A., Shahhosseini, S., Jalali, M.R., 2018. Effect of operating pressure on the performance of ultrasound-assisted oxidative desulfurization (UAOD) using a horn type sonicator: experimental investigation and CFD simulation. Chem. Eng. Process. Process Intensif. 132, 7588. Lei, Z., Li, C., Chen, B., 2003. Extractive distillation: a review. Sep. Purif. Rev. 32 (2), 121213. Li, J., Huang, W., Ge, W., 2018. Multilevel and multiscale PSE: challenges and opportunities at mesoscales. Comput. Aided Chem. Eng. 44, 1119.
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Linnhoff, B., Hindmarsh, E., 1983. The pinch design method for heat exchanger networks. Chem. Eng. Sci. 38 (5), 745763. Liu, S., Zhu, Q., Guan, Q., He, L., Li, W., 2015. Bio-aviation fuel production from hydroprocessing castor oil promoted by the nickel-based bifunctional catalysts. Bioresour. Technol. 183, 93100. Lutze, P., Gani, R., Woodley, J.M., 2010. Process intensification: a perspective on process synthesis. Chem. Eng. Process. Process Intensif. 49 (6), 547558. Malone, M.F., Doherty, M.F., 2000. Reactive distillation. Ind. Eng. Chem. Res. 39 (11), 39533957. ´ ngeles, M.A., Go´mez-Castro, F.I., Uribe May-Va´zquez, M.M., Rodrı´guez-A Ramı´rez, A.R., 2017. Hydrodynamic feasibility of the production of biodiesel fuel in a highpressure reactive distillation column. Chem. Eng. Process. Process Intensif. 112, 3137. Miranda-Galindo, E.Y., Segovia-Herna´ndez, J.G., Herna´ndez, S., Gutie´rrezAntonio, C., Briones-Ramı´rez, A., 2011. Reactive thermally coupled distillation sequences: Pareto Front. Ind. Eng. Chem. Res. 50 (2), 926938. Mueller, I., Kenig, E.Y., 2007. Reactive distillation in a dividing wall column: rate-based modeling and simulation. Ind. Eng. Chem. Res. 46 (11), 37093719. Nagaki, A., Hirose, K., Tonomura, O., Taniguchi, S., Taga, T., Hasebe, S., et al., 2016. Design of a numbering-up system of monolithic microreactors and its application to synthesis of a key intermediate of Valsartan. Org. Process. Res. Dev. 20 (3), 687691. Najafi, H., Najafi, B., Hoseinpoori, P., 2011. Energy and cost optimization of a plate and fin heat exchanger using genetic algorithm. Appl. Therm. Eng. 31 (10), 18391847. Niazi, S., Hashemabadi, S.H., Razi, M.M., 2014. CFD simulation of acoustic cavitation in a crude oil upgrading sonoreactor and prediction of collapse temperature and pressure of a cavitation bubble. Chem. Eng. Res. Des. 92 (1), 166173. Petlyuk, F.B., 2004. Distillation Theory and Its Application to Optimal Design of Separation Units. Cambridge University Press. ˜ ez, M., Polley, T.G., Jantes-Jaramillo, D., 2010. Alternative design Pico´n-Nu´n approach for plate and frame heat exchangers using parameter plots. Heat Transf. Eng. 31 (9), 742749. Quiroz-Pe´rez, E., de Lira-Flores, J.A., Gutie´rrez-Antonio, C., 2019a. Microreactors: design methodologies, technology evolution and applications to biofuels production. Process Intensification Design Methodologies. De Gruyter, pp. 125143, Chapter 5. Quiroz-Pe´rez, E., Gutie´rrez-Antonio, C., Va´zquez-Roma´n, R., 2019b. Modelling of production processes for liquid biofuels through CFD: a review of conventional and intensified technologies. Chem. Eng. Process. Process Intensif. 143, 107629. Ramshaw, C., 1995. The incentive for process intensification. In: Ramshaw, C. (Ed.), Proceedings of the 1st International Conference on Process Intensification for the Chemical Industry, 68 December 1995, Antwerp, Belgium. BHR Group Limited, London, pp. 14. Rahimi, M., Shahhosseini, S., Movahedirad, S., 2019. Hydrodynamic and mass transfer investigation of oxidative desulfurization of a model fuel using an ultrasound horn reactor. Ultrason. Sonochem. 52, 7787. Reay, D., Ramshaw, C., Harvey, A., 2008. Chapter 5 reactors. Process Intensification Engineering for Efficiency, Sustainability and Flexibility. Butterworth-Heinemann, pp. 103186.
Chapter 6 Process intensification and integration in the production of biojet fuel
Son, Y., 2017. Simple design strategy for bath-type high-frequency sonoreactors. Chem. Eng. J. 328, 654664. Sousa, F.P., Silva, L.N., de Rezende, D.B., de Oliveira, L.C.A., 2018. Simultaneous deoxygenation, cracking and isomerization of palm kernel oil and palm olein over beta zeolite to produce biogasoline, green diesel and biojet fuel. Fuel 223, 149156. Stankiewicz, A.J., Moulijn, J.A., 2000. Process intensification: transforming chemical engineering. Chem. Eng. Prog. 2234. Stankiewicz, A.J., Moulijn, J.A., 2002. Process intensification. Ind. Eng. Chem. Res. 41 (8), 19201924. Tonomura, O., Kubota, M., Kano, M., Hasebe, S., 2009. Design of T-shaped microreactors by reduced order approach. Comput. Aided Chem. Eng. 27, 891896. Turton, R., Bailie, R.C., Whiting, W.B., Shaeiwitz, J.A., Bhattacharyya, D., 2012. Analysis, Synthesis and Design of Chemical Processes, fourth ed. Prentice Hall, Michigan (Appendix A). Villegas-Herrera, L.A., Go´mez-Castro, F.I., Romero-Izquierdo, A.G., Gutie´rrezAntonio, C., Herna´ndez, S., 2018. Feasibility of energy integration for highpressure biofuels production processes. Comput. Aided Chem. Eng. 43, 15231528. Zhang, B.J., Tang, Q.Q., Zhao, Y., Chen, Y.Q., Chen, Q.L., Floudas, C.A., 2018. Multi-level energy integration between units, plants and sites for natural gas industrial parks. Renew. Sustain. Energy Rev. 88, 115.
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Supply chain for the production of biojet fuel 7.1
7
Introduction
The development of the biojet fuel industry, as it occurs for any other biofuel, has several aspects to be included in the planning of the production and distribution. Even if the production process has been properly designed by using modern technologies and methodologies, if the whole supply chain is not correctly planned, the biofuel production may not be economically feasible, or environmentally friendly. Thus, the detailed study of the supply chain, and its optimization through efficient techniques, is mandatory to choose the best distribution alternatives, together with the best location of the facilities. The systematic design and optimization of the supply chains for biofuels production have been appointed as a strategy to accelerate the transition to the large-scale and production of such renewable fuels (Yue et al., 2014). This chapter is focused on the supply chain for biojet fuel. First, the elements involved in a supply chain analysis for biojet fuel are presented, and some insights about the generation of the required information are discussed. Then, standards for product certification considering the supply chain are described. The methodology for modeling and optimization of the biojet fuel supply chain is presented, and a case study for Mexico is developed. Finally, the importance of the life cycle analysis (LCA) in the production of biojet fuel and its relationship with the supply chain studies is discussed.
7.2
Elements of the supply chain to produce biojet fuel
There are several elements involved in the supply chain for the production of a biofuel, or various biofuels and bioproducts. Those elements can be classified as: • Raw materials • Transportation of raw material to the facilities Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00007-9 © 2021 Elsevier B.V. All rights reserved.
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• Number and location of facilities • Transportation of the product to the market In the case of raw materials, it is important to know what kind of materials can be used to produce the biofuels. It is also necessary to know the available amounts of the raw material, the zones where it can be obtained, and its unitary cost. Moreover, if there is information about changes in availability of the raw materials along the seasons, it can be quite useful for planning purposes. If the biofuel will be produced from lignocellulosic biomass, it is important to know how much residue can be obtained per hectare of crop, and the geographic regions where such crop can be produced. In the case of biofuels obtained from edible and nonedible vegetable oils, it is necessary to know the zones where the crops or plants can be produced, and the quantity of seeds that can be used to obtain the oils. In the case of microalgae oil, it is necessary to identify the geographic regions with potential for the installment of ponds, mainly in terms of climate and water availability. If waste oils are used, it is necessary to determine the potential for producing and collecting used oils in different zones. In any case, the availability of raw materials does not remain constant, since it may change under different climate conditions, or different consumption patterns, in the case of waste oils. Thus, historic data about changes in the availability of the raw materials can be useful for a better planning on the production of biofuels from a variety of biomass sources. Once the potential raw materials, their location, and availability are determined, the next required information is about how the materials will be transported to the facility (or facilities) where they will be processed into biofuels and/or bioproducts. Thus, it is necessary to know the distances between the biomass sources and the potential locations of the facilities where the biomass will be converted. Additionally, it is required to have information about the kind of transportation device required, commonly trucks or, in some cases, train. Necessary information about the transportation device includes capacity and fuel consumption per kilometer, and the cost due to the transportation of a fully loaded truck/wagon. Moreover, since the supply chain is expected to be not only economic but also having low environmental impact, it is desirable to know how much greenhouse gases are released per kilometer traveled. Another important aspect to consider in the supply chain is the location of the facilities. In the case of bioprocesses and biorefineries, it is recommended to establish a distributed network, due to the variations in raw materials and markets from region
Chapter 7 Supply chain for the production of biojet fuel
to region (Santiban˜ez-Aguilar et al., 2012). Thus potential locations of the facilities must be defined. Depending on the detailed level of the available data, the facilities are assumed as located in a given municipality, state, or region of the country. Some information can be useful to eliminate alternatives from a first insight, reducing the complexity of the problem. Since most of the bioprocesses require high quantities of water, regions with water scarcity can be removed from the set of feasible locations. On the other hand, zones with poor industrial infrastructure can also be nonappropriated for the purposes of the study. Another kind of information associated with the biomass processing is the technology employed to obtain the biofuels. The detailed design of each processing alternative is usually not performed in a supply chain analysis, but there must be knowledge about the mean yields and purities that each technology can achieve, their annual cost per unit of biofuel produced, and the utility requirements. Moreover, it is necessary to have information about the costs for utilities, and even the cost of the industrial land for each potential location. For a more detailed analysis, the emissions of greenhouse gases and other pollutants per unit of produced biofuel could also be necessary.
7.3
Data generation
All the data required to determine a proper supply chain for the production of biojet fuel can be obtained from different sources. Information about the availability of crops is usually reported by government agencies, as the Department of Energy in the United States, or the Service of Agrifood and Fisheries Information (Servicio de Informacio´n Agroalimentaria y Pesquera, SIAP) and the Ministry of Agriculture, Livestock, Rural Development, Fisheries and Food (Secretarı´a de Agricultura, Ganaderı´a, Desarrollo Rural, Pesca y Alimentacio´n, SAGARPA) in Mexico. Moreover, scientific papers and reviews can be found with reports on the availability of sources of vegetable oils (e.g., Atabani et al., 2012) or lignocellulosic biomass (e.g., Valdez-Vazquez et al., 2010; Tauro et al., 2018; Deshavath et al., 2019). For some regions, reports about the availability of waste cooking oil can also be found (e.g., SheinbaumPardo et al., 2013; Khan et al., 2019). For the transportation of raw materials to the facilities and transportation of products from facilities to the final user, one of the main data required for the supply chain is the distance between the source and the destination. This information can be easily acquired from different applications, such as Google Maps. Other related data are the cost of transportation of raw
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materials per kilometer and per ton of raw material, and, if necessary, in terms of the objectives of the study, the emissions associated with the transportation of biomass. Such information can be found reported in literature for a number of cases, for example, Bo¨rjesson (1996), Forsberg (2000), Kumar and Sokhansanj (2007), and Me´ndez-Va´zquez et al. (2017). For the case of Me´xico, an interesting tool, that is in process of development in other countries, is the Geo-Space Platform (Plataforma Geo-espacial del Cluster de BCS, 2020); in this tool the available amount of biomass in a region specified by the user along with its energetic potential and distances from the main processing centers and/or final users is available. In order to determine the number and location of the facilities, it is necessary to establish the potential locations of the facilities. The number of potential locations can be high at a first sight if, for example, the production of biofuels in a whole country is considered. Nevertheless, the number of potential locations can be reduced through the use of geographic information systems, which can discard infeasible locations, in terms of factors such as accessibility to biomass, transportation network, and accessibility to water bodies (Zhang et al., 2016). In the case of the information related to the production process, estimations for the yields from biomass to the desired product, together with approximated production costs, can be found in literature. The works of Wang et al. (2016), Gutie´rrez-Antonio et al. (2017), Pereira et al. (2017), and Va´squez et al. (2017) are examples of reports where this information can be found for the production of biojet fuel from a diversity of sources.
7.4
Standards for product certification
Any product, process, or service offered to a given market would have more chances to be successful if it has been certified by a recognized instance. Certification makes the product, service, or process trustworthy, since it ensures a certain level of quality. In the case of biofuels, one of the most recognized certifying instances is the roundtable on sustainable biomaterials (RSB). RSB can certify, among others, drop-in biofuels, as it is the case of biojet fuel, or even a complete supply chain for biofuels. The RSB standard for certification takes into account 12 principles (RSB, 2020a): 1. Legality This principle implies that the development of the biofuel production system accomplishes all the applicable laws and regulations.
Chapter 7 Supply chain for the production of biojet fuel
2. Planning, monitoring, and continuous improvement This principle requires that the project having a defined strategy to continuously identify and mitigate environmental and social risks. 3. Greenhouse gas emissions This principle involves that the production scheme allows significant reductions in greenhouse gas emissions, in comparison with the production of fossil fuels. 4. Human and labor rights This principle implies that the project promotes proper conditions for the workers involved in the whole production scheme, avoiding violations to human or labor rights. 5. Rural and social development This principle implicates that the project contributes to social and economic development, particularly for local, rural, and indigenous communities. 6. Local food security This principle entails that the project helps to improve food security. 7. Conservation This principle implies that the project avoids damages to biodiversity and ecosystems. 8. Soil This principle is referred to the development of strategies to maintain soil health, or to reverse soil degradation. 9. Water This principle implies that the project respects the rights for water-use of the people on the locality, promoting the maintenance or enhancement of the quality and quantity of surface and groundwater. 10. Air This principle requires the project to keep air pollution along the whole supply chain at minimum. 11. Use of technology, inputs, and management of waste This principle calls for the development of strategies to maximize the efficiency of all the procedures, while simultaneously minimizing the potential damages to environment and to society. 12. Land rights This principle implicates that the project to respect traditional land rights of indigenous and local communities. An important aspect of this standard is the fact that it involves all the actors that participate in the supply chain: generators of biomass, producers of biomass, producers of biofuels, and distributors of biofuels. So, the certification recognizes that
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a biofuel is produced sustainably. The main documentation related with the global standard, which applies for production in any country, is summarized in Table 7.1. The standard for advanced fuels is referred to biofuels generated from production residues, which is the case of solid wastes and cooking oil. Thus, if biojet fuel produced from those raw materials must accomplish this standard for its certification. The RSB certification also requires ensuring that the biofuel production scheme has low impact due to the land use changes. This is proved by demonstrating of accomplishing three categories: yield increase, unused or degraded land, and waste and residues. In the case of yield increase, producers must demonstrate cultivating additional biomass to replace that used to produce biofuels. In the case of the category of unused or degraded land, it must be proved that the biomass was produced on marginal land with null or little preliminary use for agriculture. For the waste and residues category, it must be proved that the biomass used for the production accomplished the RSB standard for wastes and residues. • As reported by RSB (2020c), 11% of their certified products correspond to biojet fuel. Additionally, only two commercial biojet fuel producers are certified, AltAir and SkyNRG. AltAir received its certification by 2018, producing biojet fuel, green diesel, and gasoline from waste feedstocks (RSB, 2018). According to the reported data, their production allowed reductions in greenhouse gases by more than 60%, in comparison with their fossil counterpart, which is higher than the minimum reduction required by RSB, that is, 50%. On the other hand, SkyNRG
Table 7.1 Documentation related to the RSB global standard (RSB, 2020b). Code
Document
RSB-STD-01-001 RSB-STD-30-001 RSB-PRO-60-001 RSB-STD-20-001 RSB-STD-04-002 RSB-STD-01-003-01 RSB-PRO-50-001 RSB-STD-01-010
Principles and criteria Standard for participating operators Procedure for risk management Standard for traceability Methodology for displacement effects GHG calculation methodology Procedure on communication and claims Standard for advanced fuels
Chapter 7 Supply chain for the production of biojet fuel
has the RSB certification of biojet fuel produced from used cooking oil and other feedstocks, which is valid until 2020 (RSB, 2020c).
7.5
Modeling and optimization of the supply chain
The decision of the best supply chain for a given biofuel can be taken aided by mathematical modeling and optimization tools. The development of a proper model to achieve this purpose implies relating the interactions between the different elements of the supply chain, which have been mentioned in Section 7.2. The model must allow deciding between choosing or not a given raw material source, transporting the raw material to a given facility, and so on. To help the modeler to visualize all the potential interactions, a superstructure must be developed. An example of a superstructure for the supply chain of a biofuel is shown in Fig. 7.1, taking into account the basic elements. In Fig. 7.1, a given supplier can produce raw material 1, raw material 2, and so on, or may produce only some of the raw materials. The raw materials are processed in one or more facilities. The facilities transform the raw materials into different products, which are then translated to the markets. It is clear that, for a given case, some of the interactions between the elements on the superstructure may not exist, or other elements can be included, as hubs to collect the raw materials, then sending them to the facilities.
Supplier 1
Raw material 1
Facility 1
Product 1
Market 1
Supplier 2
Raw material 2
Facility 2
Product 2
Market 2
Supplier m
Raw material n
Facility o
Product p
Market q
Figure 7.1 General superstructure for the supply chain of a biofuel.
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7.5.1
The generalized disjunctive programming representation
Once the superstructure has been developed, it can be represented through a generalized disjunctive programming (GDP) model. This kind of representation has been developed by Tu¨rkay and Grossmann (1996) for the optimal selection of processing routes, also applying it for the design of thermally coupled distillation columns (Yeomans and Grossmann, 2000) and reactive distillation columns (Jackson and Grossmann, 2001). In the GDP models, the elements in the superstructure and their interactions are represented through logic-based constraints and logic propositions. Thus the existence of a given element and its interaction with other elements in the superstructure can be true or false. Fig. 7.2 shows a superstructure for a simple process, where two kinds of reactors can be selected to perform a given transformation of raw materials into products; then, two possible separation processes can be used. In the case of the reactor, a logic proposition PR would indicate that the CSTR configuration will be selected for this process (PR is true), while the logic proposition PP would indicate that the PFR configuration is chosen (PP is true). Similarly, PD and PE could represent the selection of the distillation column and the extraction column, respectively. The relationships between the
CSTR reactor
Distillation column
PFR reactor
Extraction column
Figure 7.2 Simple process structure for decision-making.
Chapter 7 Supply chain for the production of biojet fuel
elements in the superstructure can be stated in terms of logical propositions, as shown in Table 7.2. To exemplify, the logical proposition PR indicated that the continuous stirred-tank reactor (CSTR) configuration is not selected. On the other hand, the proposition PRXPP implies that PR and PP are simultaneously true, and both CSTR and plug flow reactor (PFR) configurations are selected. The proposition PR3PP would imply that PR can be true, or PP can be true, or both propositions can be true, that is, there is the possibility to choose between the CSTR equipment or the PFR equipment, or even choosing both equipment. If the PR3PP proposition is used, it implies that only a single equipment can be selected. The logical proposition PR.PD is an implication, meaning that when the CSTR system is selected, then the distillation column is also selected. Nevertheless, if the CSTR system is not chosen, there is still a possibility for the distillation column to be selected, in combination with the PFR configuration. On the other hand, the double implication PC3PD means that the two equipment are dependent on each other. If the CSTR system is selected, the distillation column is also selected. If the CSTR configuration is not chosen, the distillation column is not selected. In a similar way, logical propositions can be used to represent the superstructures for supply chains, modeling the interactions between the elements on the superstructure. Several works have been reported for the modeling and optimization ˜ ezof supply chains for bioethanol and biodiesel (Santiban Aguilar et al., 2014), bio-hydrogen (El-Halwagi et al., 2013), pellets from residual biomass (Me´ndez-Va´zquez et al., 2017), the production of renewable energy from different sources (Martı´n and Grossmann, 2018), among others. Respect to biojet fuel, the determination of the optimal supply chain for its
Table 7.2 Logic operators, logic propositions, and binary equivalents. Symbol
Logical operator
Logical proposition
Binary equivalent
⌐ X
NOT AND
⌐Pj Pi3Pj
3 = . 3
OR EXCLUSIVE OR IF THEN IFF THEN
Pi3Pj Pi3Pj Pi.Pj Pi3Pj
1 2 yj yi $ 1 yj $ 1 yi 1 yj $ 1 yi 1 yj 5 1 yi # yj yi 5 yj
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production has been reported in the literature (Domı´nguezGarcı´a et al., 2017a; Domı´nguez-Garcı´a et al., 2017b; Neuling and Kaltschmitt, 2018). Implications are commonly used to represent the interactions between the components of the superstructure; sometimes, it is possible to formulate more complex implications, which must be then converted into constraints in terms of binary variables. To do this, it is necessary to modify the implications’ statement to derive a more convenient formulation, known as the conjunctive normal form. To obtain that form, the following steps are followed: 1. Transform all the implications into disjunctions, using ⌐Pi3Pj as equivalent for Pi.Pj. 2. Apply the DeMorgan’s theorem: Pi XPj 5 Pi 3Pj ð7:1Þ
Pi 3Pj 5 Pi XPj
ð7:2Þ
3. Distribute the OR operators over the AND operators. The objective of the Step 3 is to obtain a set of disjunctions united by AND operators, as exemplified in Eq. (7.3): ð7:3Þ Pi 3Pj XðPk 3Pl 3Pm ÞX? The conjunctive normal form is useful to obtain the binary equivalence of the implications, since, as shown in Table 7.2, each logical proposition related by an AND operator will result in a binary-based constraint. Thus the composed proposition Pi 3Pj from Eq. (7.3) will be represented as a separate constraint yi 1 yj $ 1, and so on. To show the procedure, the scheme shown in Fig. 7.2 will be analyzed. Either the CSTR or the PFR can be selected for the processing of the raw materials. In any case, when a given kind of reactor is selected, a separation process will follow, either the distillation column or the liquidliquid extraction column. This can be represented in terms of an implication: ðPR 3PP Þ.ðPD 3PE Þ
ð7:4Þ
The implication is transformed into a disjunction: ðPR 3PP Þ3ðPD 3PE Þ
ð7:5Þ
Applying the DeMorgan’s theorem to the disjunction on the left: ðPR XPP Þ3ðPD 3PE Þ
ð7:6Þ
Chapter 7 Supply chain for the production of biojet fuel
Distributing the OR operators: ðPR 3PD 3PE ÞXðPP 3PD 3PE Þ
ð7:7Þ
Each composed proposal will result in a binary-based constraint. The disjunction at the left side of the AND operator is equivalent to: 1 2 yR 1 yD 1 yE $ 1
ð7:8Þ
yR 2 yD 2 yE # 0
ð7:9Þ
or Eq. (7.9) implies that if the CSTR is selected (i.e., yR 5 1), either the distillation column or the extraction column must be selected to satisfy the constraint. On the other hand, if the CSTR is not selected (i.e., yR 5 0), any of the separation systems can still be selected. Similarly, the disjunction at the right side of the AND operator in Eq. (7.7) is equivalent to: 1 2 yP 1 yD 1 yE $ 1
ð7:10Þ
yP 2 yD 2 yE # 0
ð7:11Þ
or
which can be interpreted similarly to Eq. (7.9). To indicate exclusivity, that is, that if the CSTR reactor is selected, the PFR cannot be simultaneously selected, or vice versa, the following constraint must be added: yR 1 yP 5 1
ð7:12Þ
For the distillation and extraction columns a similar constraint can be added: yD 1 yE 5 1
ð7:13Þ
Once the interactions between the elements composing the superstructure have been established, it is necessary to develop the model for each element. Moreover, it is mandatory to determine the objective function for the optimization problem. A general representation of the GDP statement is given by Eq. (7.14). X minz 5 ci 1 f ðxÞ i
s.t. g ðxÞ # 0
211
212
Chapter 7 Supply chain for the production of biojet fuel
2
3 Yik viAD 4 hik ðxÞ # 0 5 ci 5 γ ik
ð7:14Þ
kAK ΩðY Þ 5 TRUE xARn Y AfTrue; False g ci $ 0 The objective function is given by a general nonlinear function f ðxÞ and the sum of fixed costs ci. There can exist K disjunction constraints, and each of these constraints can be related to I logical propositions. When a given logical proposition Yik is true, the set of constraints hik ðxÞ # 0 is also true, and ci 5 γ ik . On the other hand, the constraints g ðxÞ # 0 depend only on the continuous variables x and are not affected by the logical propositions Yik . On this last set of constraints, those who relate the elements in the superstructure must also be included. Once the GDP model has been developed, it can be converted into an mixed-integer linear programming or mixedinteger non linear programming model through relaxation techniques, which are presented in the next subsection.
7.5.2
Relaxation of a generalized disjunctive programming model
Although the GDP representation allows developing logical models in a simple way, to solve the model it is better to have a discrete equivalent, in terms of binary variables yik instead of the logical variables Yik . Two approaches can be used to obtain this equivalent: The Big-M approach and the convex hull approach. These approaches will be described next.
7.5.2.1 The Big-M approach Consider the disjunction shown as Eq. (7.15): Yi viAD hi ðxÞ # 0
ð7:15Þ
Chapter 7 Supply chain for the production of biojet fuel
where hi ðxÞ is a nonlinear function. The disjunction (Eq. 7.15) can be relaxed through the Big-M approach as: ð7:16Þ hi ðxÞ # Mi 1 2 yi For the relaxation, the term on the right of the inequality is simply replaced by Mi 1 2 yi . The parameter Mi is known as the Big-M, and it takes a value big enough to surpass the region where hi ðxÞ is defined. Thus if Yi is true, it implies that yi 5 1, and the relaxed constraint remains as hi ðxÞ # 0, satisfying the condition given by the logical proposal. If Yi is false, yi 5 0, and the constraint is hi ðxÞ # Mi . Thus if Mi is big enough, it defines a pseudo-infinite feasible region, where the solution will be defined by the other logical propositions. Notice that the constraint in Eq. (7.15) is an inequality. If there is an equality in the proposition, as follows: Yi viAD ð7:17Þ fi ð x Þ 5 0 Before its relaxation, the inequality must be converted to an equivalent set of inequalities: 2 3 Yi ð7:18Þ viAD 4 fi ðxÞ # 0 5 2fi ðxÞ $ 0 This way, two constraints in terms of the binary variable yi are obtained: fi ðxÞ # Mi 1 2 yi ð7:19Þ fi ðxÞ $ 2Mi 1 2 yi
ð7:20Þ
In this case, if yi 5 1, the constraints are fi ðxÞ # 0 and fi ðxÞ $ 0. The only way to accomplish both constraints is if fi ðxÞ 5 0, which is the original equality constraint. Otherwise, the constraints are given by fi ðxÞ # Mi and fi ðxÞ $ Mi , which, once more, if Mi is big enough, originates a pseudo-infinite feasible region, implying that the solution will be given by the other logical propositions.
7.5.2.2
The convex hull approach
In the convex hull approach, each variable xj in the vector of variables x is represented as the sum of a set of variables ν ji , known as disaggregated variables: X xj 5 ν ji ð7:21Þ i
213
214
Chapter 7 Supply chain for the production of biojet fuel
Each variable ν ji will be related to a logical proposition Yi . The convex hull equivalent of the disjunction given by Eq. (7.15) is: νi yi hi #0 ð7:22Þ yi ν ji $ 0
ð7:23Þ
up
ν ji # ν ji yi
ð7:24Þ up ν ji
where ν i is the vector of disaggregated variables, and is the upper limit for the variable ν ji , which can be fixed in terms of the upper limit for the corresponding variable xj . It can be seen that, if yi 5 0, ν ji is set as zero, and the value of xj will be given by the other disaggregated variables. If the constraint in the disjunction is an equality, as in Eq. (7.17), convex hull can be applied directly, obtaining: νi yi fi 50 ð7:25Þ yi Of course, Eqs. (7.23) and (7.24) must be added to Eq. (7.25) to complete the convex hull equivalent of Eq. (7.17). Both, Big-M and convex hull approaches, can be used to relax the disjunctions in a GDP model. The resulting constraints, containing the binary variables, are added to the constraints that are not dependent on the disjunctions. In general terms, the Big-M approach is relatively easier to apply than convex hull. Nevertheless, the reliability of the results will strongly depend on the values chosen for Mi. On the other hand, the convex hull approach will generate additional variables and constraints. Nevertheless, in the case of nonlinear constraints inside the disjunctions, convex hull is better than Big-M, and it is highly recommended in those cases.
7.6
Case study: optimization of the biojet fuel supply chain in Mexico
In this section we present a case of study related to the determination of the optimal supply chain for the production of biojet fuel in Mexico. The localization problem of a biojet fuel factory in Mexico using mathematical optimization (GDP) is based in the case of study presented in Chapter 3, Production Processes for the Conversion of Triglyceride Feedstock; wherein the raw
Chapter 7 Supply chain for the production of biojet fuel
215
material flow is 21,250,000.00 kg/year (palm oil 1 Jatropha curcas oil) and the TAC is 14,363,787.77 USD/year, without the raw material cost. The biojet fuel produced, according to the simulation results, is 8,263,366.55 kg/year. For this case of study, seven producers of J. curcas and palm oil have been considered to supply these raw materials, which are distributed along the country. Also, according to the higher growth indexes registered in Mexico, three zones have been selected as alternatives to establish the biojet fuel factory. Regarding end consumers of biojet fuel, 12 airports alongside the country have been selected to receive the biofuel. The selection of the best zone for establishing the biojet fuel factory is carried out by the TAC minimization. This economic factor includes the equipment cost, the operating cost, which involves the raw material cost, the steam and cooling water cost, and the electricity; and the cost related with the raw material transportation (production zone to possible location plant), the biojet fuel transportation (possible location plant to airports) and the land cost, required for each considered zone. Table 7.3 presents the overview of equipment and operating cost, whilst in Tables 7.4 and 7.5 it is shown the raw material available from each producer and the biojet fuel required for each selected airport, respectively, taking into account mixtures of fossil and renewable jet fuel of 50 wt.%; this percentage is the maximum volume of biojet fuel that can be used according to standard ASTM D7566 (2019). Finally, in Table 7.6 the transportation costs
Table 7.3 Overview of operating and capital cost, excluding raw material cost. Operating cost (USD/year) Heating utilities cost
Cooling utilities cost
Electricity cost
Total operating cost (USD/year)
81,110.68
6530.63
70,008.15
13,757,063.30
Annual capital cost (USD/year) Equipment cost
A1
A2
Total annual cost (USD/year)
1,694,761.07
305,056.99
1,033,804.25
606,724.46
Total operating cost (USD/year) 1 total annual cost (USD/year) 14,363,787.77
216
Chapter 7 Supply chain for the production of biojet fuel
Table 7.4 Producers and oil capacity. Producer
Total oil capacity (kg/year)
1 2 3 4 5 6 7
4,342,000.00 6,353,800.00 3,945,760.00 4,605,460.00 1,115,282.00 3,480,100.00 5,643,160.00
Table 7.5 Airport and biojet fuel requirements. Airport
Biojet fuel requirement (kg/year)
1 2 3 4 5 6 7 8 9 10 11 12
105,343.8 49,769.00 63,872.6 2,682,465 2,149,702 2,055,753 161,451.5 242,538.7 691,865.00 549,813.00 11,981,764.6 3,813,398.00
are shown: producer to factory and factory to airport. In Fig. 7.3 the problem statement is presented. The minimization of total annual cost (TAC) is presented in Eq. (7.26): minTAC 5
3 X j51
CPLTj 1
7 X 3 X i51 j51
CPij 1
3 X 12 X
CDjk
ð7:26Þ
j51 k51
where CPLTj USD 5 total cost of the factory “j” year CPij USD year 5 transport cost between raw material producer “i” and factory “j”
Chapter 7 Supply chain for the production of biojet fuel
217
Table 7.6 Transportation cost. Transportation cost (USD/kg) Producer to factory
Transportation cost (USD/kg) Factory to airport
Producer
Factory 1
Factory 2
Factory 3
Airport
Factory 1
Factory 2
Factory 3
1 2 3
11.34 6.93 20.58
23.11 41.62 15.01
26.27 13.20 38.80
4
20.91
30.44
5.68
5
12.27
21.88
15.47
6
32.09
5.80
17.15
7
5.178
19.06
27.62
1 2 3 4 5 6 7 8 9 10 11 12
34.43 19.42 7.13 14.70 51.83 4.23 33.88 13.48 30.22 4.25 2.1 2.901
36.50 43.23 17.08 6.1 45.15 1.92 34.97 23.45 39.81 31.31 21.84 14.49
11.16 4.61 3.85 16.44 44.93 9.79 26.98 6.59 4.93 27.52 13.84 36.95
Producer
Location
Figure 7.3 Problem statement of the localization of a biojet fuel factory.
Airport
218
Chapter 7 Supply chain for the production of biojet fuel
CDjk USD year 5 transport cost between biojet fuel factory “j” and airport “k” TAC USD year 5 total annual cost of localization function j 5 index for possible factories: 1, 2, 3 (see Fig. 7.1) i 5 index for raw material producers: 1, 2,. . .,7 (see Fig. 7.3) k 5index for airports: 1, 2,. . .,12 (see Fig. 7.3) USD Mij kg 5 raw material from “i” to “j” Mresi USD 5 raw material remaining in production place kg (not required) kg 5 total raw material to “j” (for each processing MPTEj year plant is 21,250,000.00 kg/year) USD CTj year 5 land cost in each zone “j” (zone 1 5 285,010.00 USD/year; zone 2 5 300,846.00 USD/year; zone 3 5 369,460.00 USD/year) CPFj USD year 5 equipment cost 1 operating cost (excluding the raw material cost, 14,363,787.77 USD per year) 5 raw material cost (palm oil 1 J. curcas oil, CM USD kg 2.38 USD per kg) Yj 5 binary variable, existence of the plant “j” kg Bjk year 5 biojet fuel from “j” to “k” kg BTj year 5 biojet fuel obtained in (8,263,366.55 kg/year) E 5 step size (0.001) Subject to:
factory
Y1 1 Y2 1 Y3 $ 1 Y1 :Y1 3 CPLT1 5 CPF1 1 MPTE1 CM 1 CT1 CPLT1 5 0 Y2 :Y2 3 CPLT2 5 CPF2 1 MPTE2 CM 1 CT2 CPLT2 5 0
“j”
ð7:27Þ
Y3 :Y3 3 CPLT3 5 CPF3 1 MPTE3 CM 1 CT3 CPLT3 5 0
ð7:28Þ ð7:29Þ
ð7:30Þ
The constraints for possible location zones are as follows: CP11 5 M11 CR11
ð7:31Þ
CP12 5 M12 CR12
ð7:32Þ
CP13 5 M13 CR13
ð7:33Þ
Chapter 7 Supply chain for the production of biojet fuel
M11 1 M12 1 M13 # 4342000
ð7:34Þ
Mres1 5 4342000 2 ðM11 1 M12 1 M13 Þ
ð7:35Þ
CP21 5 M21 CR21
ð7:36Þ
CP22 5 M22 CR22
ð7:37Þ
CP23 5 M23 CR23
ð7:38Þ
M21 1 M22 1 M23 # 6353800
ð7:39Þ
Mres2 5 6353800 2 ðM21 1 M12 1 M23 Þ
ð7:40Þ
CP31 5 M31 CR31
ð7:41Þ
CP32 5 M32 CR32
ð7:42Þ
CP33 5 M33 CR33
ð7:43Þ
M31 1 M32 1 M33 # 3945760
ð7:44Þ
Mres3 5 3945760 2 ðM31 1 M32 1 M33 Þ
ð7:45Þ
CP41 5 M41 CR41
ð7:46Þ
CP42 5 M42 CR42
ð7:47Þ
CP43 5 M43 CR43
ð7:48Þ
M41 1 M42 1 M43 # 4605460
ð7:49Þ
Mres4 5 4605460 2 ðM41 1 M42 1 M43 Þ
ð7:50Þ
CP51 5 M51 CR51
ð7:51Þ
CP52 5 M52 CR52
ð7:52Þ
CP53 5 M53 CR53
ð7:53Þ
M51 1 M52 1 M53 # 1115282
ð7:54Þ
219
220
Chapter 7 Supply chain for the production of biojet fuel
Mres5 5 1115282 2 ðM51 1 M52 1 M53 Þ
ð7:55Þ
CP61 5 M61 CR61
ð7:56Þ
CP62 5 M62 CR62
ð7:57Þ
CP63 5 M63 CR63
ð7:58Þ
M61 1 M62 1 M63 # 3480100
ð7:59Þ
Mres6 5 3480100 2 ðM61 1 M62 1 M63 Þ
ð7:60Þ
CP71 5 M71 CR71
ð7:61Þ
CP72 5 M72 CR72
ð7:62Þ
CP73 5 M73 CR73
ð7:63Þ
M71 1 M72 1 M73 # 5643160
ð7:64Þ
Mres7 5 5643160 2 ðM71 1 M72 1 M73 Þ
ð7:65Þ
7 X
Mi1 5 Y1 MPTE1
ð7:66Þ
Mi2 5 Y2 MPTE2
ð7:67Þ
Mi3 5 Y3 MPTE3
ð7:68Þ
i51 7 X i51 7 X i51
The constraints for routes of raw material producers to factory are the next ones: CD11 5 B11 CRD11
ð7:69Þ
CD12 5 B12 CRD12
ð7:70Þ
CD13 5 B13 CRD13
ð7:71Þ
Chapter 7 Supply chain for the production of biojet fuel
CD14 5 B14 CRD14
ð7:72Þ
CD15 5 B15 CRD15
ð7:73Þ
CD16 5 B16 CRD16
ð7:74Þ
CD17 5 B17 CRD17
ð7:75Þ
CD18 5 B18 CRD18
ð7:76Þ
CD19 5 B19 CRD19
ð7:77Þ
CD110 5 B110 CRD110
ð7:78Þ
CD111 5 B111 CRD111
ð7:79Þ
CD112 5 B112 CRD112
ð7:80Þ
12 X
B1K 5 8; 263; 366:55 Y1
ð7:81Þ
CD21 5 B21 CRD21
ð7:82Þ
CD22 5 B22 CRD22
ð7:83Þ
CD23 5 B23 CRD23
ð7:84Þ
CD24 5 B24 CRD24
ð7:85Þ
CD25 5 B25 CRD25
ð7:86Þ
CD26 5 B26 CRD26
ð7:87Þ
CD27 5 B27 CRD27
ð7:88Þ
CD28 5 B28 CRD28
ð7:89Þ
k51
221
222
Chapter 7 Supply chain for the production of biojet fuel
CD29 5 B29 CRD29
ð7:90Þ
CD210 5 B210 CRD210
ð7:91Þ
CD211 5 B211 CRD211
ð7:92Þ
CD212 5 B212 CRD212
ð7:93Þ
12 X
B2K 5 8; 263; 366:55 Y2
ð7:94Þ
CD31 5 B31 CRD31
ð7:95Þ
CD32 5 B32 CRD32
ð7:96Þ
CD33 5 B33 CRD33
ð7:97Þ
CD34 5 B34 CRD34
ð7:98Þ
CD35 5 B35 CRD35
ð7:99Þ
CD36 5 B36 CRD36
ð7:100Þ
CD37 5 B37 CRD37
ð7:101Þ
CD38 5 B38 CRD38
ð7:102Þ
CD39 5 B39 CRD39
ð7:103Þ
CD310 5 B310 CRD310
ð7:104Þ
CD311 5 B311 CRD311
ð7:105Þ
CD312 5 B312 CRD312
ð7:106Þ
k51
12 X k51
B3K 5 8; 263; 366:55 Y3
ð7:107Þ
Chapter 7 Supply chain for the production of biojet fuel
The constraints for distribution routes from factory to airports are as follows: 3 X
Bj1 # 105; 343:8
ð7:108Þ
j51 3 X
Bj2 # 49; 769
ð7:109Þ
Bj3 # 63; 872:6
ð7:110Þ
Bj4 # 2; 682; 465
ð7:111Þ
Bj5 # 2; 149; 702
ð7:112Þ
Bj6 # 2; 055; 753
ð7:113Þ
Bj7 # 161; 451:5
ð7:114Þ
Bj8 # 242; 538:7
ð7:115Þ
j51 3 X j51 3 X j51 3 X j51 3 X j51 3 X j51 3 X j51 3 X
Bj9 # 691; 865
ð7:116Þ
Bj10 # 549; 813
ð7:117Þ
Bj11 # 1; 198; 764:6
ð7:118Þ
j51 3 X j51 3 X j51
223
224
Chapter 7 Supply chain for the production of biojet fuel
3 X
Bj12 # 3; 813; 398
ð7:119Þ
j51
In order to solve this optimization problem, the convex hull strategy is used to relax the following constraints: ðCPLT11 Þ 5 ðY1 1 E ÞðCPF1 1 MPTE1 CM 1 CT1 Þ; CPLT12 5 0 ð7:120Þ
CPLT1 5 CPLT11 1 CPLT12 ðCPLT21 Þ 5 ðY2 1 E ÞðCPF2 1 MPTE2 CM 1 CT2 Þ; CPLT22 5 0 ð7:121Þ
CPLT2 5 CPLT21 1 CPLT22 ðCPLT31 Þ 5 ðY3 1 E ÞðCPF3 1 MPTE3 CM 1 CT3 Þ; CPLT32 5 0 ð7:122Þ
CPLT3 5 CPLT31 1 CPLT32 The remaining constraints are not relaxed because they are not logical constraints. It is worth to mention that the constraint about possible location zones implies that more than one factory could exist. This problem is solved in GAMS software; the GAMS code is presented in Appendix A. According to the results, the minimum value for TAC (Eq. 7.26) is 312,840,000.00 USD per year, which corresponds to location at zone 1. Also, the producers selected to provide raw material to factory are presented in Table 7.7, whilst in Table 7.8 the amount of biojet fuel for the selected airports is displayed. The solver used for this problem was DICOPT. The results obtained are presented in Fig. 7.4.
Table 7.7 Results: producers chosen and the selected capacity. Producer
Total oil capacity (kg/year)
1 2 3 5 7
4,342,000.00 6,353,800.00 3,795,800.00 1,115,300.00 5,643,200.00
Chapter 7 Supply chain for the production of biojet fuel
Table 7.8 Airport and biojet fuel requirement. Airport
Biojet fuel requirement (kg/year)
3 4 6 8 10 11 12
63,872.6.00 339,230.00 2,055,800.00 242,540.00 549,810.00 1,198,800.00 3,813,400.00
Producer
Location
Airport
Figure 7.4 Results of localization, biojet fuel factory.
It can be seen that from all the initial alternatives considered in the superstructure presented in Fig. 7.3, the best solution consists of using just one facility (from three available) to process the biomass generated by five producers (from seven available); this allows to satisfy the demand of biojet fuel to be used in the airports in mixtures of 50% in volume. It is worth to mention that the code included in Appendix A can be used to implement other study cases, modifying the parameters and involved constraints.
225
226
Chapter 7 Supply chain for the production of biojet fuel
7.7
Importance of the life cycle analysis
The production of any biofuel is expected to be sustainable, showing low environmental impact in the whole supply chain. Unfortunately, this is not necessarily true. To ensure the sustainability of the production scheme of the biofuels, it is desirable to perform additional studies to determine the environmental impact of a given production route; detecting the stages with higher environmental loads, thus identifying areas of opportunity to obtain a more sustainable production scheme. This can be performed through the LCA, which is a tool helpful for the measurement of the environmental impact related to a product through all its life cycle. The environmental impact can be defined as an alteration in the quality of the environment due to human activity (Garmendia Salvador et al., 2005). Such impact must be measurable, through environmental and impact indicators. An environmental indicator is a factor that indicates the current status of an ecosystem, while an impact indicator is an environmental indicator used to assess the changes in the environmental quality of an ecosystem due to a given action. There are two kinds of elements that are involved in an LCA: inputs and outputs. As inputs, resources, raw materials, parts, products, transportation, and energy can be mentioned. The outputs are usually emissions, either to the atmosphere, soil, and/or water, along with residues and by-products. In a complete LCA, all the stages in the production of the studied product must be taken into account to assess the total impact: the impact caused by the acquisition of the raw materials, the impact due to the production process, the distribution of the product, its use and its final disposal (ISO 14040:2006, 2020). The association between the inputs/outputs and the production stages is shown in Fig. 7.5. Although the final stage in the production chain is the disposition of the product, it is desirable to promote recycling and reusing materials as much as possible, following the principles of circular economy. In the previous paragraph, the term “complete life cycle analysis” has been mentioned. The scope of the LCA can vary in terms of the objectives of the analysis, the interests of the instance performing the analysis, or even the available information. The complete LCA involves the study of all the inputs/outputs for all the stages in the production of the product, from the extraction of the raw materials to its final disposition. Such analysis is known as “cradle to grave.” It is also possible to perform the analysis involving the extraction of the raw materials until the product is placed in the market. In other words, the impact due to the use
Chapter 7 Supply chain for the production of biojet fuel
Inputs
Energy
Raw materials
Materials and components Production
Distribution and selling Use Recycling
Final disposal
Outputs
Residues
Emissions
Figure 7.5 Inputs, outputs, and production stages involved in a life cycle analysis.
and final disposition of the product is not included. This kind of analysis is known as “cradle to gate.” Finally, an analysis can be performed by only including the inputs/outputs related to the production process, that is, the impact due to the extraction and transportation of raw materials, and those associated with the use and final disposition of the product are not included. Such analysis is known as “gate to gate.” Independently of the scope of the study, an LCA consists of four stages, whose interaction can be observed in Fig. 7.6: 1. Definition of objectives. 2. Inventory: obtaining information about all the inputs and outputs for all the processes involved in the production system. 3. Evaluation of impacts: implies transforming the inventory into indicators.
227
228
Chapter 7 Supply chain for the production of biojet fuel
Definition of objectives
Inventory
Figure 7.6 Stages of a life cycle analysis.
Interpretation
Evaluation of impacts
4. Interpretation of results. The inventory stage is usually one of the most timedemanding steps in an LCA, since it requires collecting information about all the inputs and outputs defined as important in the first stage of the analysis. Once the inventory is completed, the information is converted into indicators, which are classified into impact categories. There are several methodologies to evaluate impacts, among which the Eco-Indicator 99 methodology can be mentioned (Goedkoop and Spriensma, 2001); this methodology takes into account several potential impacts associated with the production of biofuels. In this methodology, environmental impacts are grouped into three categories: damages to resources, damages to human health, and damages to ecosystem quality. In the Eco-Indicator 99 methodology, the inventory involves how much resources are required, use of land, and the quantity of pollutants released. Once the inventory has been completed, the impacts are evaluated through damage models, obtaining punctuations for each category. The individual punctuations are then normalized, weighted, and summed to obtain the final value of the ecoindicator, which is measured in milli-points (mPt); 1 Pt represents a thousandth of the environmental load of an average European inhabitant. The damages to resources are measured in terms of the surplus energy, which represents the additional effort required by future generations to extract the remaining minerals and/or hydrocarbons. This category quantifies how much nonrenewable resources are used to produce a given product, and how will this affect the obtention of the same resource by future generations.
Chapter 7 Supply chain for the production of biojet fuel
The damages to human health are measured in units known as DALYs (disability-adjusted life years), implying either the reduction of the life expectation or the number of years living with a disability, both caused by exposition to a pollutant. This category includes damages to the respiratory system, cancer, effects of climate change and depletion of the ozone layer on the human health, and effects due to radiation by ionization. The damages to ecosystem quality are measured in terms of the percent of species, which will potentially disappear as a consequence of a given human activity. This category includes the effect of ecotoxicity, acidification, and eutrophication. Additionally, the effects due to land use and land use changes are included, considering the effect of the action on the current use of the land (local effect) and the effect of the action on the surroundings (regional effect). Since the three categories are measured with different units, the impact of each category must be normalized. Additionally, different levels of importance are given for each category through weighting factors, whose value will depend on the cultural perspective, that is, the ideology of the decision-makers. Three cultural perspectives can be mentioned: • Egalitarian, which is a long-term perspective. It gives greater importance to the damage to the ecosystem quality. • Individualist, which is a short-term perspective. It gives greater importance to the damage to the ecosystem quality. • Hierarchist, which is a mid-term perspective. It gives equal importance to the damage to the ecosystem quality and the human health. The most common perspective is the hierarchist for both, the damage models and the weighting factors, since it is a more balanced perspective. In summary, the discussed methodology implies the calculation of the eco-indicator, as follows: EI99 5 ðDR ωR Þ 1 ðDHH ωHH Þ 1 ðDE ωE Þ P DR 5
i Ri IR;i
DHH 5 P DE 5
k pk IE;k
1
ð7:124Þ
nR
P
P
j pj IHH;j
nE
ð7:125Þ
nHH
l Aocc;l tocc;l IE;l
1
ð7:123Þ
P
m Acha;m IE;m
ð7:126Þ
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Chapter 7 Supply chain for the production of biojet fuel
where EI99 is the Eco-Indicator 99, DR, DHH, and DE are the damages to resources, human health, and ecosystems, respectively. ωR , ωHH , and ωE are the weighting factors for resources, human health, and ecosystems, respectively. Ri is the total quantity of the resource i (e.g., petroleum, aluminum, zinc), IR,i is the unitary impact due to the extraction of the resource i, and nR is the normalization factor for resources. pj represents the total mass released of the pollutant j affecting human health, IR,i is the unitary impact caused by releasing the pollutant j, and nHH is the normalization factor for human health. Here, it is important to recall that a given pollutant can be released to air, water, and/or soil, causing different negative effects to human health, and all the potential effects must be included. pk represents the total mass released of the pollutant k affecting the ecosystem quality, IE,k is the unitary impact caused by releasing the pollutant k, Aocc,l is the area of the land to be occupied, tocc,l represents the occupation years, IE,l is the unitary impact due to the occupation of the land with area Aocc,l, Acha,m is the area of the land whose use will be changed, IE,m is the unitary impact due to the change in the use of the land with the area Acha,m, and nE is the normalization factor for ecosystem. It is important to mention that the eco-indicator is more useful to compare alternatives for the production of a given product, since the best alternative in terms of environmental impact is the one with the lowest value of the eco-indicator. Also, eco-indicator can be useful to identify the step in the whole production procedure with the highest contribution to the environmental load. A more detailed description of the methodology, together with the unitary impacts, the normalization factors, and the weighting factors, can be found in the work of Goedkoop and Spriensma (2001). If the production chain of biojet fuel is analyzed, several impacts can be detected. Transportation of raw materials to the production facilities, and the production process itself, causes emissions of particles, carbon dioxide, and other greenhouse gases, which may affect human health by causing either global warming or respiratory affections. A similar effect occurs when the biojet fuel is burned. Depending on the raw material used to produce the biojet fuel, changes in land use to promote the farming of a given crop, or changes in land use to construct the production facility, would have an impact on ecosystem quality. Additionally, the use of fossil fuels for the transportation of the raw materials, or to produce the energy in the production process, together with the use of minerals to produce the materials
Chapter 7 Supply chain for the production of biojet fuel
for the construction of the processing plant, would have an impact on the resources. Nevertheless, the production of biojet fuel has a benefit in terms of CO2 captured by the crops from which raw materials as oils or lignocellulosic residues are obtained. From this simplified analysis, it is clear that, even if an optimal supply chain has been obtained in terms of minimum cost or maximum profit, the potential environmental impact must be measured and actions must be taken if highimpact stages are detected; to ensure that the production of the biofuel has indeed less environmental load than the production and use of the conventional energy sources. Some works have been developed dealing with the LCA for the production of biojet fuel. Agusdinata et al. (2011) reported that the production and use of biojet fuel in 5050 blends is not enough to reach the greenhouse gases (GHG) reduction target by 2050. They recommend the development of additives and enhancement in the fuel efficiency of the aircraft fleet. Fortier et al. (2014) performed an LCA for the treatment of microalgae through hydrothermal liquefaction to produce biojet fuel. They proposed two locations for the production process: a wastewater treatment plant and a refinery. According to the reported results, most of the equivalent emissions of carbon dioxide are due to the transportation of biomass and waste nutrients; thus the lowest environmental load is obtained by locating the production process in the treatment plant. Li and Mupondwa (2014) reported an LCA for biodiesel and biojet fuel from camelina oil, including the impacts associated with global warming, human health, ecosystem quality, and use of energy resources. Based on the reported results, the highest environmental load is due to the activities related with the obtention of the camelina oil. Lokesh et al. (2015) reported a cradle-to-grave LCA for the production of biojet fuel from different raw materials, using the ALCEmB model with focus on the greenhouse gas emissions. Their results indicated that reductions from 58% to 70% are obtained in the life cycle by producing synthetic paraffinic kerosenes with camelina, microalgae, and J. curcas oils, in comparison with the reference fuel Jet-A1; the use of camelina oil to produce biojet fuel represented the highest reduction in greenhouse gases emissions. Budsberg et al. (2016) performed a cradle-to-grave LCA for the conversion of poplar biomass into biojet fuel; additionally, hydrogen is obtained. The analysis is based on the assessment of the equivalent carbon dioxide emissions and the use of fossil resources. According to the reported results, the production of hydrogen has the highest contribution to the emissions of greenhouse gases and the usage of fossil fuels. de Jong et al. (2017) reported an LCA for the production of biojet fuel with different technologies, mentioning that the
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FischerTropsch processing route represents the highest reduction in greenhouse gas emissions in comparison with fossil jet fuel. Hydrothermal liquefaction and the alcohol-to-jet process also showed important reductions in greenhouse gases. Cultivation of the feedstock is indicated as one of the main factors contributing to the environmental impact, together with hydrogen production. Michailos (2017) presented a life cycle assessment for the production of biojet fuel from sugarcane bagasse, through an alcohol-to-jet approach. The author reports that the conversion of sugarcane bagasse into jet fuel allows a reduction of 47% in greenhouse gas emissions, in comparison with fossil jet fuel. Pierobon et al. (2018) performed a cradle-to-grave LCA for the production of biojet fuel from residual woody biomass. An important result from this study is that the use of the residual biomass to produce biofuels significantly reduces the impacts in categories as respiratory effects, carcinogenics, and ecotoxicity, since the direct burning of the residues is avoided. In general terms, most of the LCA reported for the production of biofuels are focused on the emissions of greenhouse gases, coinciding in the fact that the whole life cycle of biojet fuel shows lower emissions than that of fossil jet fuel. Additionally, one of the main contributions to environmental impact in biojet fuel production is given by the cultivation of triglyceride-containing feedstocks. The use of lignocellulosic residues has been detected as an area of opportunity to further enhance the environmental evaluation in the life cycle, avoiding the disposal of those residuals by highly pollutant means, as direct burning. The production of the hydrogen required in the production paths has also been detected as a great contributor to the environmental load; thus the production of renewable hydrogen has been proposed. An additional factor that is important in the LCA of biofuels is the allocation of the products and by-products, as has been indicated by Ubando et al. (2019).
7.8
Conclusion
When developing a biofuel production scheme, it is important not only taking into account the production process itself, but also all the supply chain involved, from the production of the raw materials to the final use of the biofuel. This would allow a global vision of the potential steps where the cost can be elevated. A more detailed analysis, through tools as the LCA, would also allow to establish the steps in the supply chain where environmental impact is high. A strategy to determine the best supply chain in the biojet fuel production is the development of logic-based mathematical models, with an appropriate objective
Chapter 7 Supply chain for the production of biojet fuel
function; this tool will help to find the best configuration of the elements required in the supply chain to minimize one or two objective functions of interest. To make the production of biojet fuel sustainable, the objective function must include not only economic aspects, but also environmental ones.
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Chapter 7 Supply chain for the production of biojet fuel
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Appendix A $Title Case of study 7 Supply chain for the production of biojet fuel set i productors/1*7/; set j factory/1*3/; set k airport/1*12/; parameter CR(i, j)/1.1 11.34, 2.1 6.93, 3.1 20.58, 4.1 20.91, 5.1 12.27, 6.1 32.09, 7.1 5.178, 1.2 23.11, 2.2 41.62, 3.2 15.01, 4.2 30.44, 5.2 21.88, 6.2 5.80, 7.2 19.06, 1.3 26.27, 2.3 13.20, 3.3 38.80, 4.3 5.68, 5.3 15.47, 6.3 17.15, 7.3 27.62/; parameter MPTE(j)/1 21250000, 2 21250000, 3 21250000/; parameter CT(j)/1 285012, 2 300846, 3 369460/; parameter CPF(j)/1 14363787.77, 2 14363787.77, 3 14363787.77/;
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parameter CM/2.38/; parameter CRD(j, k)/1.1 34.43, 1.2 19.42, 1.3 7.13, 1.4 14.707, 1.5 51.836, 1.6 4.23, 1.7 33.886, 1.8 13.48, 1.9 30.22, 1.10 4.25, 1.11 2.1, 1.12 2.901, 2.1 36.505, 2.2 43.23, 2.3 17.08, 2.4 6.1, 2.5 45.15, 2.6 1.92, 2.7 34.97, 2.8 23.45, 2.9 39.81, 2.10 31.31, 2.11 21.84, 2.12 14.49,3.1 11.16, 3.2 4.61, 3.3 3.85, 3.4 16.44, 3.5 44.93, 3.6 9.79, 3.7 26.98, 3.8 6.59, 3.9 4.936, 3.10 27.52, 3.11 13.84, 3.12 36.95/; parameter BT(j)/1 8263366.55, 2 8263366.55, 3 8263366.55/; parameter E/0.01/; binary variables Y(j); positive variables CP(i, j), M(i, j), CPLT(j), CD(j, k), B(j, k), Mres(i); variable TAC; equations OBJ, IMP1, IMP2, IMP3, IMP4, IMP5, IMP6, IMP7, IMP8, IMP9, IMP10, IMP11, IMP12, IMP13, IMP14, IMP15, IMP16, IMP17, IMP18, IMP19, IMP20, IMP21, IMP22, IMP23, IMP24, IMP25, IMP26, IMP27, IMP28, IMP29, IMP30, IMP31, IMP32, IMP33, IMP34, IMP35, IMP36, IMP37, IMP38, IMP39, IMP40, IMP41, IMP42, IMP43, IMP44, IMP45, IMP46, IMP47, IMP48, IMP49, IMP50, IMP51, IMP52, IMP53, IMP54, IMP55, IMP56, IMP57, IMP58, IMP59, IMP60, IMP61, IMP62, IMP63, IMP64, IMP65, IMP66, IMP67, IMP68, IMP69, IMP70, IMP71, IMP72, IMP73, IMP74, IMP75, IMP76, IMP77, IMP78, IMP79, IMP80, IMP81, Res1, Res2, Res3, Res4, Res5, Res6, Res7, Res8, Res9, Res10, Res11, Res12; *Constraints for possible location zones IMP1.. CP(‘1’,‘1’) 5 e 5 (CR(‘1’,‘1’)*M(‘1’,‘1’)); IMP2.. CP(‘1’,‘2’) 5 e 5 (CR(‘1’,‘2’)*M(‘1’,‘2’)); IMP3.. CP(‘1’,‘3’) 5 e 5 (CR(‘1’,‘3’)*M(‘1’,‘3’)); IMP4.. M(‘1’,‘1’) 1 M(‘1’,‘2’) 1 M(‘1’,‘3’) 5 l 5 4342000; IMP5.. Mres(‘1’) 5 e 5 4342000-(M(‘1’,‘1’) 1 M(‘1’,‘2’) 1 M(‘1’,‘3’)); IMP6.. CP(‘2’,‘1’) 5 e 5 (CR(‘2’,‘1’)*M(‘2’,‘1’)); IMP7.. CP(‘2’,‘2’) 5 e 5 (CR(‘2’,‘2’)*M(‘2’,‘2’)); IMP8.. CP(‘2’,‘3’) 5 e 5 (CR(‘2’,‘3’)*M(‘2’,‘3’)); IMP9.. M(‘2’,‘1’) 1 M(‘2’,‘2’) 1 M(‘2’,‘3’) 5 l 5 6353800; IMP10.. Mres(‘2’) 5 e 5 6353800-(M(‘2’,‘1’) 1 M(‘2’,‘2’) 1 M(‘2’,‘3’)); IMP11.. CP(‘3’,‘1’) 5 e 5 (CR(‘3’,‘1’)*M(‘3’,‘1’)); IMP12.. CP(‘3’,‘2’) 5 e 5 (CR(‘3’,‘2’)*M(‘3’,‘2’)); IMP13.. CP(‘3’,‘3’) 5 e 5 (CR(‘3’,‘3’)*M(‘3’,‘3’)); IMP14.. M(‘3’,‘1’) 1 M(‘3’,‘2’) 1 M(‘3’,‘3’) 5 l 5 3945760; IMP15.. Mres(‘3’) 5 e 5 3945760-(M(‘3’,‘1’) 1 M(‘3’,‘2’) 1 M(‘3’,‘3’)); IMP16.. CP(‘4’,‘1’) 5 e 5 (CR(‘4’,‘1’)*M(‘4’,‘1’)); IMP17.. CP(‘4’,‘2’) 5 e 5 (CR(‘4’,‘2’)*M(‘4’,‘2’));
Chapter 7 Supply chain for the production of biojet fuel
IMP18.. CP(‘4’,‘3’) 5 e 5 (CR(‘4’,‘3’)*M(‘4’,‘3’)); IMP19.. M(‘4’,‘1’) 1 M(‘4’,‘2’) 1 M(‘4’,‘3’) 5 l 5 4605460; IMP20.. Mres(‘4’) 5 e 5 4605460-(M(‘4’,‘1’) 1 M(‘4’,‘2’) 1 M(‘4’,‘3’)); IMP21.. CP(‘5’,‘1’) 5 e 5 (CR(‘5’,‘1’)*M(‘5’,‘1’)); IMP22.. CP(‘5’,‘2’) 5 e 5 (CR(‘5’,‘2’)*M(‘5’,‘2’)); IMP23.. CP(‘5’,‘3’) 5 e 5 (CR(‘5’,‘3’)*M(‘5’,‘3’)); IMP24.. M(‘5’,‘1’) 1 M(‘5’,‘2’) 1 M(‘5’,‘3’) 5 l 5 1115282; IMP25.. Mres(‘5’) 5 e 5 1115282-(M(‘5’,‘1’) 1 M(‘5’,‘2’) 1 M(‘5’,‘3’)); IMP26.. CP(‘6’,‘1’) 5 e 5 (CR(‘6’,‘1’)*M(‘6’,‘1’)); IMP27.. CP(‘6’,‘2’) 5 e 5 (CR(‘6’,‘2’)*M(‘6’,‘2’)); IMP28.. CP(‘6’,‘3’) 5 e 5 (CR(‘6’,‘3’)*M(‘6’,‘3’)); IMP29.. M(‘6’,‘1’) 1 M(‘6’,‘2’) 1 M(‘6’,‘3’) 5 l 5 3480100; IMP30.. Mres(‘6’) 5 e 5 3480100-(M(‘6’,‘1’) 1 M(‘6’,‘2’) 1 M(‘6’,‘3’)); IMP31.. CP(‘7’,‘1’) 5 e 5 (CR(‘7’,‘1’)*M(‘7’,‘1’)); IMP32.. CP(‘7’,‘2’) 5 e 5 (CR(‘7’,‘2’)*M(‘7’,‘2’)); IMP33.. CP(‘7’,‘3’) 5 e 5 (CR(‘7’,‘3’)*M(‘7’,‘3’)); IMP34.. M(‘7’,‘1’) 1 M(‘7’,‘2’) 1 M(‘7’,‘3’) 5 l 5 5643160; IMP35.. Mres(‘7’) 5 e 5 5643160-(M(‘7’,‘1’) 1 M(‘7’,‘2’) 1 M(‘7’,‘3’)); IMP36.. sum(i, M(i,‘1’)) 5 e 5 MPTE(‘1’)*Y(‘1’); IMP37.. sum(i, M(i,‘2’)) 5 e 5 MPTE(‘2’)*Y(‘2’); IMP38.. sum(i, M(i,‘3’)) 5 e 5 MPTE(‘3’)*Y(‘3’); *Constraints for factory location IMP39.. Y(‘1’) 1 Y(‘2’) 1 Y(‘3’) 5 g 5 1; IMP40.. CPLT(‘1’) 5 e 5 (Y(‘1’))*(CPF(‘1’) 1 MPTE(‘1’)*CM 1 CT(‘1’)); IMP41.. CPLT(‘2’) 5 e 5 (Y(‘2’))*(CPF(‘1’) 1 MPTE(‘2’)*CM 1 CT(‘2’)); IMP42.. CPLT(‘3’) 5 e 5 (Y(‘3’))*(CPF(‘1’) 1 MPTE(‘3’)*CM 1 CT(‘3’)); *Constraints for routes: raw material productors to factory IMP43.. CD(‘1’,‘1’) 5 e 5 (CRD(‘1’,‘1’)*B(‘1’,‘1’)); IMP44.. CD(‘1’,‘2’) 5 e 5 (CRD(‘1’,‘2’)*B(‘1’,‘2’)); IMP45.. CD(‘1’,‘3’) 5 e 5 (CRD(‘1’,‘3’)*B(‘1’,‘3’)); IMP46.. CD(‘1’,‘4’) 5 e 5 (CRD(‘1’,‘4’)*B(‘1’,‘4’)); IMP47.. CD(‘1’,‘5’) 5 e 5 (CRD(‘1’,‘5’)*B(‘1’,‘5’)); IMP48.. CD(‘1’,‘6’) 5 e 5 (CRD(‘1’,‘6’)*B(‘1’,‘6’)); IMP49.. CD(‘1’,‘7’) 5 e 5 (CRD(‘1’,‘7’)*B(‘1’,‘7’)); IMP50.. CD(‘1’,‘8’) 5 e 5 (CRD(‘1’,‘8’)*B(‘1’,‘8’)); IMP51.. CD(‘1’,‘9’) 5 e 5 (CRD(‘1’,‘9’)*B(‘1’,‘9’)); IMP52.. CD(‘1’,‘10’) 5 e 5 (CRD(‘1’,‘10’)*B(‘1’,‘10’)); IMP53.. CD(‘1’,‘11’) 5 e 5 (CRD(‘1’,‘11’)*B(‘1’,‘11’)); IMP54.. CD(‘1’,‘12’) 5 e 5 (CRD(‘1’,‘12’)*B(‘1’,‘12’)); IMP55.. sum(k, B(‘1’,k)) 5 e 5 8263366.55*Y(‘1’); IMP56.. CD(‘2’,‘1’) 5 e 5 (CRD(‘2’,‘1’)*B(‘2’,‘1’)); IMP57.. CD(‘2’,‘2’) 5 e 5 (CRD(‘2’,‘2’)*B(‘2’,‘2’));
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IMP58.. CD(‘2’,‘3’) 5 e 5 (CRD(‘2’,‘3’)*B(‘2’,‘3’)); IMP59.. CD(‘2’,‘4’) 5 e 5 (CRD(‘2’,‘4’)*B(‘2’,‘4’)); IMP60.. CD(‘2’,‘5’) 5 e 5 (CRD(‘2’,‘5’)*B(‘2’,‘5’)); IMP61.. CD(‘2’,‘6’) 5 e 5 (CRD(‘2’,‘6’)*B(‘2’,‘6’)); IMP62.. CD(‘2’,‘7’) 5 e 5 (CRD(‘2’,‘7’)*B(‘2’,‘7’)); IMP63.. CD(‘2’,‘8’) 5 e 5 (CRD(‘2’,‘8’)*B(‘2’,‘8’)); IMP64.. CD(‘2’,‘9’) 5 e 5 (CRD(‘2’,‘9’)*B(‘2’,‘9’)); IMP65.. CD(‘2’,‘10’) 5 e 5 (CRD(‘2’,‘10’)*B(‘2’,‘10’)); IMP66.. CD(‘2’,‘11’) 5 e 5 (CRD(‘2’,‘11’)*B(‘2’,‘11’)); IMP67.. CD(‘2’,‘12’) 5 e 5 (CRD(‘2’,‘12’)*B(‘2’,‘12’)); IMP68.. sum(k, B(‘2’,k)) 5 e 5 8263366.55*Y(‘2’); IMP69.. CD(‘3’,‘1’) 5 e 5 (CRD(‘3’,‘1’)*B(‘3’,‘1’)); IMP70.. CD(‘3’,‘2’) 5 e 5 (CRD(‘3’,‘2’)*B(‘3’,‘2’)); IMP71.. CD(‘3’,‘3’) 5 e 5 (CRD(‘3’,‘3’)*B(‘3’,‘3’)); IMP72.. CD(‘3’,‘4’) 5 e 5 (CRD(‘3’,‘4’)*B(‘3’,‘4’)); IMP73.. CD(‘3’,‘5’) 5 e 5 (CRD(‘3’,‘5’)*B(‘3’,‘5’)); IMP74.. CD(‘3’,‘6’) 5 e 5 (CRD(‘3’,‘6’)*B(‘3’,‘6’)); IMP75.. CD(‘3’,‘7’) 5 e 5 (CRD(‘3’,‘7’)*B(‘3’,‘7’)); IMP76.. CD(‘3’,‘8’) 5 e 5 (CRD(‘3’,‘8’)*B(‘3’,‘8’)); IMP77.. CD(‘3’,‘9’) 5 e 5 (CRD(‘3’,‘9’)*B(‘3’,‘9’)); IMP78.. CD(‘3’,‘10’) 5 e 5 (CRD(‘3’,‘10’)*B(‘3’,‘10’)); IMP79.. CD(‘3’,‘11’) 5 e 5 (CRD(‘3’,‘11’)*B(‘3’,‘11’)); IMP80.. CD(‘3’,‘12’) 5 e 5 (CRD(‘3’,‘12’)*B(‘3’,‘12’)); IMP81.. sum(k, B(‘3’,k)) 5 e 5 8263366.55*Y(‘3’); *Constraints for distribution routes: factory to airports Res1.. sum(j,B(j,‘1’)) 5 l 5 105343.8; Res2.. sum(j,B(j,‘2’)) 5 l 5 49769; Res3.. sum(j,B(j,‘3’)) 5 l 5 63872.6; Res4.. sum(j,B(j,‘4’)) 5 l 5 2682465; Res5.. sum(j,B(j,‘5’)) 5 l 5 2149702; Res6.. sum(j,B(j,‘6’)) 5 l 5 2055753; Res7.. sum(j,B(j,‘7’)) 5 l 5 161451.5; Res8.. sum(j,B(j,‘8’)) 5 l 5 242538.7; Res9.. sum(j,B(j,‘9’)) 5 l 5 691865; Res10.. sum(j,B(j,‘10’)) 5 l 5 549813; Res11.. sum(j,B(j,‘11’)) 5 l 5 1198764.6; Res12.. sum(j,B(j,‘12’)) 5 l 5 3813398; *Objective function OBJ.. TAC 5 e 5 sum(j, CPLT(j)) 1 sum(i, CP(i,‘1’)) 1 sum(i, CP (i,‘2’)) 1 sum(i, CP(i,‘3’)) 1 sum(k, CD(‘1’,k)) 1 sum(k, CD(‘2’, k)) 1 sum(k, CD(‘3’,k)); *Binary variables initialization Y.l(‘1’) 5 1; Y.l(‘2’) 5 1; Y.l(‘3’) 5 1;
Chapter 7 Supply chain for the production of biojet fuel
B.l(‘1’,‘1’) 5 13517.27167; B.l(‘1’,‘2’) 5 13517.27167; B.l(‘1’,‘3’) 5 13517.27167; B.l(‘1’,‘4’) 5 13517.27167; B.l(‘1’,‘5’) 5 13517.27167; B.l(‘1’,‘6’) 5 13517.27167; B.l(‘1’,‘7’) 5 13517.27167; B.l(‘1’,‘8’) 5 13517.27167; B.l(‘1’,‘9’) 5 13517.27167; B.l(‘1’,‘10’) 5 13517.27167; B.l(‘1’,‘11’) 5 13517.27167; B.l(‘1’,‘12’) 5 13517.27167; B.l(‘2’,‘1’) 5 13517.27167; B.l(‘2’,‘2’) 5 13517.27167; B.l(‘2’,‘3’) 5 13517.27167; B.l(‘2’,‘4’) 5 13517.27167; B.l(‘2’,‘5’) 5 13517.27167; B.l(‘2’,‘6’) 5 13517.27167; B.l(‘2’,‘7’) 5 13517.27167; B.l(‘2’,‘8’) 5 13517.27167; B.l(‘2’,‘9’) 5 13517.27167; B.l(‘2’,‘10’) 5 13517.27167; B.l(‘2’,‘11’) 5 13517.27167; B.l(‘2’,‘12’) 5 13517.27167; B.l(‘3’,‘1’) 5 13517.27167; B.l(‘3’,‘2’) 5 13517.27167; B.l(‘3’,‘3’) 5 13517.27167; B.l(‘3’,‘4’) 5 13517.27167; B.l(‘3’,‘5’) 5 13517.27167; B.l(‘3’,‘6’) 5 13517.27167; B.l(‘3’,‘7’) 5 13517.27167; B.l(‘3’,‘8’) 5 13517.27167; B.l(‘3’,‘9’) 5 13517.27167; B.l(‘3’,‘10’) 5 13517.27167; B.l(‘3’,‘11’) 5 13517.27167; B.l(‘3’,‘12’) 5 13517.27167; M.l(‘1’,‘1’) 5 125142.8571; M.l(‘2’,‘1’) 5 125142.8571; M.l(‘3’,‘1’) 5 125142.8571; M.l(‘4’,‘1’) 5 125142.8571; M.l(‘5’,‘1’) 5 125142.8571; M.l(‘6’,‘1’) 5 125142.8571; M.l(‘7’,‘1’) 5 125142.8571; M.l(‘1’,‘2’) 5 125142.8571; M.l(‘2’,‘2’) 5 125142.8571;
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M.l(‘3’,‘2’) 5 125142.8571; M.l(‘4’,‘2’) 5 125142.8571; M.l(‘5’,‘2’) 5 125142.8571; M.l(‘6’,‘2’) 5 125142.8571; M.l(‘7’,‘2’) 5 125142.8571; M.l(‘1’,‘3’) 5 125142.8571; M.l(‘2’,‘3’) 5 125142.8571; M.l(‘3’,‘3’) 5 125142.8571; M.l(‘4’,‘3’) 5 125142.8571; M.l(‘5’,‘3’) 5 125142.8571; M.l(‘6’,‘3’) 5 125142.8571; M.l(‘7’,‘3’) 5 125142.8571; model ProyFinal/all/; solve ProyFinal using minlp minimizing TAC;
The future trends in the production of biojet fuel 8.1
8
Introduction
Biojet fuel has been identified as the most promissory alternative for the sustainable growth of the aviation sector. The main advantage of biojet fuel is its renewable nature; additionally, it is expected that the use of biojet fuel in the aviation sector will allow, at least, partial fuel independence (Gutie´rrez-Antonio et al., 2017). As described in the previous chapters, biojet fuel can be produced from all types of biomass through different conversion processes. In particular, the study of the conversion processes and related topics has gained the attention of the scientific and technological community in the last years; in this context, several raw materials and technologies have been analyzed. In spite of all the advances in the development of production processes, the use of biojet fuel in commercial flights is only possible if the production process that generated it is certified by ASTM. At the moment, six conversion pathways have been certified for the production of biojet fuel, and others are under evaluation. In addition, strategies such as process intensification and energy integration have been applied with the objective of decreasing the external energy requirements and/or capital costs of the biojet production processes, simultaneously reducing the environmental impact associated with emissions of greenhouse gases in the process. Other advantages of applying process intensification are related to the reduction in the required space, enhancement of the safety on the process, and, in some cases, a better controllability. Moreover, according to the International Air Transport Association, between 2011 and 2015, 22 airlines performed over 2500 commercial passenger flights with blends of up to 50% biojet fuel from feedstocks including used cooking oil, Jatropha, camelina, or algae oils, and sugarcane (IATA, 2018). Also, some studies have been reported regarding the determination of the optimal supply chain for the production of biojet fuel.
Production Processes of Renewable Aviation Fuel. DOI: https://doi.org/10.1016/B978-0-12-819719-6.00008-0 © 2021 Elsevier B.V. All rights reserved.
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Based on all the advances in the study of production processes of biojet fuel, in this chapter the identified opportunity areas related to the use of raw materials, production processes, and the supply chain are presented. Finally, a brief review of the biojet fuel projects worldwide is discussed, mentioning their main features.
8.2
Opportunity areas for raw materials
Several types of raw materials have been studied for the production of biojet fuel, such as vegetable oils, waste oils, animal fats, microalgae oil, biomasses with high contents of sugar and starchy, as well as lignocellulosic biomasses. Nevertheless, it is important to clarify that not all the existent materials in each category have been studied; therefore there are still many raw materials that can be analyzed. Considering all types of feedstocks, the residues represent raw materials of great relevance, since their costs are low and they are generated in great volume; in this way, a contamination problem can be transformed into an energetic alternative. Finally, the use of carbon dioxide for the production of biojet fuel has not been reported in the literature. Considering the chemical stability of carbon dioxide, photocatalysis is a promissory technology for its conversion into hydrocarbons in the boiling point range of jet fuel. Next, some raw materials still not explored for the production of biojet fuel are presented according to its chemical nature.
8.2.1
Triglyceride feedstock
Among all raw materials, microalgae are a promissory one since they have high biomass productivities in a short time interval; besides, microalgae do not require fertile lands to grow and consume carbon dioxide as part of its growth, contributing to the reduction of the carbon footprint of the whole life cycle. The main disadvantage of microalgae relies on the high costs required for its cultivation and fractionation (oil and carbohydrate fractions). Thus, the use of microalgae for the treatment of sewage water can help to reduce its production costs, since the employed water is residual and allows to generate other products in the process. Another important area of opportunity is the use of native microalgae; this alternative has not been reported in the literature, and it can be a robust option that allows decreasing the production costs.
Chapter 8 The future trends in the production of biojet fuel
Regarding the waste oils, those generated in the cooking processes have been studied in literature as raw materials for biojet fuel production. However, the residual oils generated in the industries (from production machinery for instance) remains as an opportunity area, which can be feasible for the production of biojet fuel with an adequate pretreatment.
8.2.2
Sugar and starchy feedstock
The residues of the confectionery industry (sugar and starchy feedstock) are also of interest for the production of biojet fuel. A main advantage is that most of these residues are already simple carbohydrates; therefore the number of pretreatments required could be low and the operating conditions relatively soft. On the other hand, according to the Food and Agriculture Organization of the United Nations, roughly one-third of the food produced in the world for human consumption, approximately 1.3 billion tons every year, gets lost or wasted (FAO, 2019). Among food wastes, fruits and vegetables have the higher wastage rate, which is of 45% worldwide, while in North American and Oceania zone, where Mexico is included, the percentage increases to 53% (FAO, 2019). In particular, fruits and vegetable wastes can be used to produce biofuels, and it is the type of residue with more potential for valorization according to Ki Lin et al. (2013). Fruits and vegetables are interesting raw materials for the production of biojet fuel, due to its high contents of sugar or starchy; also, they usually are located in specific places, therefore its recollection could be less expensive than other residues. An important aspect of this raw material is that usually its composition is variable, since the residues include different types of fruits and vegetables; this represents a challenge in the pretreatment of these residues, requiring the development of technologies with the potential to treat a wide range of components.
8.2.3
Lignocellulosic feedstock
In the lignocellulosic feedstock all the residues from agriculture, processing of crops, pruning, and forestry are included. Also, the energetic crops such as grass are considered in this type of feedstock. For the production of biojet fuel, this type of feedstock is the less explored, and at the same time is the most abundant and available. For this type of feedstock, the emphasis must be put in a better use of the residues, maximizing its exploitation for their conversion into useful products.
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8.3
Opportunity areas for processing routes
The production of biojet fuel can be performed with chemical, biochemical, and thermochemical processes, which were defined in the previous chapters. In general, all the conversion pathways must be flexible for the processing of different raw materials, or even mixtures of them; it would be desirable to achieve this goal just by adjusting the operating conditions but in the same infrastructure. Another opportunity area is the processing of raw materials to produce renewable aviation fuel under a biorefinery scheme; this processing scheme allows generating fuels, energy, and also value-added products, whose diversity enables increasing the rentability of the process. It must be emphasized the application of energy integration and/ or process intensification tools to the production processes of biojet fuel; these tools allow reaching a decrease in the energy consumption of the process, and as a consequence on its environmental impact; moreover, the economy of the process is enhanced, along with its safety. According to the type of process, the potential improvement areas are described next.
8.3.1
Chemical pathways
In the chemical pathways, the technical feasibility to produce biojet fuel has been proved. In order to enhance these processes, it is important to achieve higher selectivities to biojet fuel at softer operating conditions; therefore the development of better catalysts regarding product conversion and selectivity, as well as economically more competitive is required. Additionally, reducing the hydrogen requirements is important since it has a high contribution to the total annual cost, and its transportation represents an additional challenge. Some recent developments point to the development of processing routes using nitrogen instead of hydrogen (Almeida Scaldaferri and Duarte Pasa, 2019). Another important field is the application of process intensification tools that allow an increase in the yield, decreasing the operating conditions, or making thermodynamic synergy due to the combination of unit operations. In this context, an important task is the development of multifunctional catalysts, which allows performing all the hydrotreating reactions in the same vessel, and at softer operating conditions. Also, the use of reactive distillation is an interesting intensification alternative, which has been reported only for the hydroprocessing of Jatropha curcas oil (Gutie´rrez-Antonio et al., 2018).
Chapter 8 The future trends in the production of biojet fuel
Moreover, the application of extractive reaction, proposed for biodiesel production, remains as an opportunity area for the production of renewable aviation fuel. Even though the use of thermally coupled distillation columns has been reported in the literature (Gutie´rrez-Antonio et al., 2015; Gutie´rrez-Antonio et al., 2016), such distillation systems are still not used in industrial applications for those processes. Moreover, the use of reactive thermally coupled distillation remains as an opportunity area.
8.3.2
Biochemical pathways
The production of biojet fuel through biochemical pathways has several opportunity areas. Regarding the alcohol-to-jet (ATJ) process, the main improvement must be made in the production of the alcohol, which is an intermediate product. To achieve this, a set of microorganisms must be tested in order to increase the conversion of the sugar feedstock; also, the genetic modification of the microorganisms is required so they can digest any type of sugars, and not just certain types of them. Moreover, the development of robust membranes for the in situ remotion of bioethanol must be tackled; this could increase the production of bioethanol without significantly elevating the capital costs associated with the replacement of the membranes due to plugging. Another opportunity area relies on the extraction of sugar and simultaneous reaction to produce bioethanol. Once the alcohol is produced, a series of chemical reactions must take place to generate biojet fuel; in this context, the development of multifunctional catalysts is desirable. On the other hand, the biological pretreatment has gained attention recently. In particular, the cultivation of black soldier fly is a promissory strategy for the conversion of organic residues, such as fruits and vegetables. The main advantage of this pretreatment is the high conversion rate (at least 80%); moreover, during the conversion of the residues the larva of black soldier fly is generated, which usually consists of proteins, carbohydrates, and lipids. Then, the larvae of black soldier fly can be processed to generate biojet fuel, and better yields and lower processing costs can be expected, in comparison with traditional processing.
8.3.3
Thermochemical pathways
In the case of thermochemical pathways, one of the main drawbacks to the overall efficiency is the low conversion of the
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pretreatments. Therefore the development of efficient and ad hoc processes is mandatory; in this context, the use of renewable energies, such as solar, or the use of sonotrodes, an intensified equipment, could be promissory to reduce the costs associated to the pretreatments. Another proposal is the use of lignocellulosic residues to generate fuel pellets; these pellets can be burned to generate electricity and/or thermal energy. In addition, combustion gases are generated, from which carbon dioxide can be extracted and converted through biojet fuel with photocatalysis. Considering that other products are generated, this pathway could be more feasible from the economic point of view.
8.4
Opportunity areas for supply chain
Regarding the supply chain for the production of biojet fuel, two main opportunities are detected. The first one is the development of local or regional production systems that allows decreasing the associated costs for the transportation of biomasses and biofuels; in general, studies have shown that long transportation distances eliminate the environmental benefit and make biofuel production economically infeasible. When studies for the supply chain of biojet fuel are developed, it is important to consider the water footprint and the social impact of the production scheme, along with the economics and the environmental impact. Another important aspect to be considered is the pursuit of the Roundtable on Sustainable Biomaterials certification; as mentioned in Chapter 7, Supply Chain for the Production of Biojet Fuel, this certification involves the complete supply chain and it is given for a product. Besides, the biojet fuel price must be competitive, so it could be possible to access to fiscal incentives if the produced biojet fuel is certified as sustainable.
8.5
Worldwide industrial projects to produce biojet fuel
In the last two decades, the number of projects of research and development of technologies to produce biojet fuel has increased, mainly due to the goals of sustainability established by the aviation authorities (Wang et al., 2016). Those objectives pointed out the gradual increase in the blending ratio of biojet fuel-fossil jet fuel until 25% by 2020, 30% by 2030, and 50% by
Chapter 8 The future trends in the production of biojet fuel
2040 (Air Transportation Action Group, 2011). Furthermore, according to some forecasts, 35% 100% of the global biojet fuel demand could be covered by biojet fuel in 2050 (European Commission, 2011). The high biojet fuel demand is derived from the goals related to the reduction of carbon dioxide emissions by the aviation sector until 50% by 2050, regarding to 2005 levels (IATA, 2015). Thus the investment in the development of research centers and processing plants to produce biojet fuel is climbing, and this trend is expected to continue through the next years. According to Abdelraheem-Elhaj and Lang (2014), in 2014, nine projects to produce biojet fuel were developed worldwide, which are presented next. It is important to mention that some projects are currently under development, and some of them have been concluded. • ITAKA Project (United States) In this project, the industrial facility has a production capacity of 4000 tons/year of biojet fuel from camelina oil as primary feedstock, using the hydroprocessed esters and fatty acids (HEFA) process; the project is headed by Neste Oil in agreement with KLM. The main objective of this project was linked to the supply chain and the biojet fuel demand, establishing good relationships between feedstock producers, biofuels producers, distributors, and airlines. The starting date of the project was November 1st, 2012, and the ending date, October 31st, 2016 (Cordis, 2012). • Petrixo Oil & Gas (United Arab Emirates, Fujairah) Petrixo Oil & Gas started a new plant costing $800 million, using the Honeywell UOP technology to produce biojet fuel and green diesel. The processing capacity is 500,000 tons/year of renewable feedstocks (UOP Honeywell, 2014). • Algae.Tec (Australia) Algae.Tec and Lufthansa invested into the construction of a large-scale facility to produce biojet fuel from algae cultures. The algae oil shall be certified by the ISCC Standard (US Renewable Energy Directive) (Algae.Tec Limited, 2012). • Dynamic Fuels (United States) The facility owned by Dynamic Fuels, company of Syntroleum Corporation and Tyson Foods, started on November 8th, 2010. This plant is located at Geismar, Louisiana, United States. The current production is 2500 barrels of biojet fuel per day, and also it is considered as the largest commercial renewable synthetic diesel fuel plant in North America. The feedstocks used in the facility involve animal by-products such as pork lard, greases, beef tallow, and chicken fat (Chemicals Technology, 2020).
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• Byogy Renewables (United States) Byogy Renewables produces biojet fuel through the ATJ process from any ethanol source. This facility is located at San Jose´ California and Qatar Airways is an investor. The operation of this facility is active (Byogy Renewables, 2020). • Solazyme (United States) The Solazyme’s unit called Solajet produces biojet fuel from algae oil. This production is considered as the world’s first microbially derived biojet fuel, which meets the ASTM D1655 specifications. The production capacity in 2015 was 500,000 metric tons of biojet fuel. This company had an agreement with United Airlines to supply 75 million liters of biojet fuel by the year 2014. Nowadays, Solazyme is still in the biojet fuel market, and the company division dedicated to the biojet fuel production is Solajet (Solazyme, 2020). • AltAir (United States) The company AltAir has been the only that has a facility that produces biojet fuel for commercial use since 2016. The raw materials are waste feedstocks converted in biojet fuel and green diesel through the UOP Honeywell technology. According to its estimations, the renewable products obtained in this facility, including the biojet fuel, are capable to reducing greenhouse gas emissions by more than 60%, regarding to their fossil counterparts This facility is located at Paramount, California. In 2013 AltAir signed an agreement with United Airlines to provide 18.75 million liters each year for 3 years. Nowadays, this company is still producing biojet fuel (RSB, 2020). • GreenSky London Project The main objective of the GreenSky London Project was the construction of a processing plant to convert the municipal wastes from London into biojet fuel; the conversion is performed using a combination of plasma gasification and Fischer Tropsch technology. This initiative was led by Solena and British Airways. The production capacity was 50,000 tons of biojet fuel from 500,000 tons of waste. This project started in 2016 and closed in 2016 (ICAO Environment, 2016). • Amyris Plant (Brazil) The facility of Amyris was opened at Campinas, Brazil in 2010 as a demonstrative large-scale plant, with a capacity of 10,000 gallons per year. The raw material used was sugarcane, and the biojet fuel produced in this plant was used in demonstration and testing flights. The biofuel obtained was supplied to GOL airline and US Air Force. There is another Amyris facility with the same functional principle in Brotas, Brazil (MIT Technology Review, 2009).
Chapter 8 The future trends in the production of biojet fuel
According to the International Renewable Energy Agency (IRENA Report, 2017), in 2017 there were about 100 initiatives to produce biojet fuel; pointing out that in the period 2009 15, the new ventures increased by 10 20 new projects. Among these new initiatives, the use of novel raw materials gained interest. For instance, the project developed by the Sustainable Bioenergy Research Consortium (SBRC) has as main objective the creation of a new alternative agriculture to produce biofuels, such as biojet fuel, in the United Arab Emirates (Etihad, 2020). This initiative is supported by Etihad Airways, airline that had a commercial flight from Abu Dhabi to Amsterdam operated with a blend of fossil jet fuel and biojet fuel derived from Salicornial plants grown in saltwater. On the other hand, another innovative project is the initiative to produce biojet fuel from the waste streams of potato crops; this process, proved in a lab, is scaled and achieved a viable production chain (Wageningen, 2020). This project is led by Wageningen Food & Biobased Research, and it started on May 1, 2017 to conclude on April 30, 2020. Based on the report presented by Radich (2015), an overview of the biojet fuel production facilities in North and South America is presented in Fig. 8.1, including those in planning stage. In the case of Europe and Asia, the facilities to produce biojet fuel are shown in Fig. 8.2. Another important initiative, which has derived in other novel and viable projects, was proposed by KLM, called SkyNRG. This initiative was created in 2010 to supply sustainable jet fuel to KLM and other aircraft operators, mainly produced from used cooking oil, through HEFA process. According to International Civil Aviation Organization (2016), this company supplies most of the biojet fuel flights worldwide. Some of the new projects where SkyNRG is involved have been created to enhance the production and supply chain of biojet fuel; these are presented next (Meijerink, 2020). 1. RENJET 3, 2019 Project to promote the renewable jet fuel supply chain and flight operations. 2. Bio4A, 2018 22 Project developed to scale up the production (precommercial production), and the market of biojet fuel from residual lipids in the United States. 3. FlexJET, 2018 21 The main objective of the project is providing technical and economic validation to produce biojet fuel from a diverse range of organic wastes feedstocks; also, the construction of a precommercial scale plant is included.
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Prairie–Horizon/WB services 3 mm gal/year HEFA Phillipsburg, Kansas
Green energy products/WB services 3 mm gal/year HEFA Sedwick, Kansas
Red rock biofuels 12 mm gal/year Fischer–Tropsch Lakeview, Oregon
SG preston 120 mm gal/year HEFA South point, Ohio
Fulcrum bioenergy 11 mm gal/year Fischer–Tropsch Mccarran, Nevada
East kansas agri–Energy/WB services 3 mm gal/year HEFA Garnett, Kansas Altair Fuels 30 mm gal/year HEFA Biojet fuel los angeles, California REG Synthetic Fuels 68 mm gal/year HEFA Geismar, California
Diamond Green Diesel 162 mm gal/year HEFA Norco, California
Amyris 13 mm gal/year SIP diesel, Jet fuel Brotas, Brazil
Figure 8.1 Facilities to produce biojet fuel in North and South America. Source: Extracted from Radich, T., 2015. The flight paths for biojet fuel. ,https://www.eia.gov/workingpapers/pdf/flightpaths_biojetffuel.pdf. (accessed 29.04.20.).
4. REWOFUEL, 2018 21 The goal of the project is the production of aviation biofuels from residual softwood. 5. Waste to Wing, South Africa, 2018 21 The main goal of the project is validating the feasibility of using wastes as raw material to produce biojet fuel in South Africa.
Preem 271 mm gal/year HEFA Gothenbugu, Sweden
Neste Oil UPM 129 mm gal/year 32 mm gal/year HEFA HEFA Porvoo, Finland Lappeenranta, Finland
Solena 17 mm gal/year Fischer–Tropsch Thurrock, Essex, UK Neste Oil 271 mm gal/year HEFA Rotterdam, The Netherlands 169 mm gal/year HEFA La mede, France ENI 155 mm gal/year HEFA Venice, Italy Petrixo 288 mm gal/year HEFA Fujairah, United Arab Emirates
Neste Oil 271 mm gal/year HEFA Singapore
Figure 8.2 Facilities to produce biojet fuel in Europe and Asia. Source: Extracted from Radich, T., 2015. The flight paths for biojet fuel. ,https://www.eia.gov/ workingpapers/pdf/flightpaths_biojetffuel.pdf. (accessed 29.04.20.).
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6. Assessment of likely Technology Maturation Pathways, Canada, 2016 18 The project looks to establish a wider knowledge of biooil from wood residues to produce biojet fuel. 7. Flying on forest residues in Sma˚land, Sweden, 2018 20 The objective of the project is to demonstrate the feasibility of biojet fuel local production, identifying the most promising supply chain. 8. Swedish Biofuels The objective of the project is the design, construction, and operation of a demonstration facility to produce biojet fuel from woody biomass, municipal waste, bioethanol, or syngas, in Sweden. It is clear that the research and development, as well as the investment to improve the overall factors and bottlenecks, in the production of biojet fuel are important to achieve the environmental and economic competitiveness of biojet fuel. Likewise, the government and airlines support are key pieces to motivate the creation of new projects, which contribute to the proper development of this important biofuel.
8.6
Summary
Biojet fuel is the only alternative for the sustainable development of the aviation sector. Until now, several production processes have been developed; some of them are certified and others are under evaluation. Nevertheless, the main challenge is producing biojet fuel with minimum environmental impact, a competitive price, and economically profitable. Still, there are many potential raw materials as well as many opportunities for the improvement of existing conversion pathways, or even the development of new ones. Therefore, without any doubt, the development of production processes with high thermodynamic efficiency and low environmental impact is one of the main strategies to drive the aviation sector to sustainability. Furthermore, the production of biojet fuel at industrial scale requires the involvement of investors along with government policies, which allows to establish the supply chain of aviation biofuel.
References Abdelraheem-Elhaj, H.F., Lang, A., 2014. The worldwide production of biojet fuels the current developments regarding technologies and feedstocks, and innovative new R&D developments. Technical Report SD-ARC-WBA-001/2014.
Chapter 8 The future trends in the production of biojet fuel
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Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.
A Acid hydrolysis (AH), 107 108 Acid pretreatment, lignocellulosic feedstock, 133 Agricultural residues, 41, 43f, 129 Alcohol-to-jet (ATJ) process, 64, 118, 231 232, 245 lignocellulosic feedstock, 134 136 sugar and starchy feedstock, 94 98, 104 106 Alcohol-to-jet synthetic kerosene with aromatics (ATJ-SKA), 17, 50 Alcohol-to-jet synthetic paraffinic kerosene (ATJSPK) process, 17, 49 Algae.Tec (Australia), 247 Alkaline pretreatment, lignocellulosic feedstock, 133 AltAir (United States), 248 Amorphous cellulose, 130 Amyris Plant (Brazil), 248 Amyris process, 105 Arrhenius-based kinetic model, 146 147 ASTM standards, 6 17, 45 ASTM D1655 standard, 6, 7t, 11t ASTM D4054 standard, 11 13, 45 46 ASTM D5054 standard, 14t ASTM D7223 standard, 11, 12t ASTM D7566 standard, 6 11, 11t, 45 49
ATJ-SKA. See Alcohol-to-jet synthetic kerosene with aromatics (ATJ-SKA) ATJ-SPK. See Alcohol-to-jet synthetic paraffinic kerosene (ATJ-SPK) process
B Big-M approach, 212 214 Biochemical pathways, 245 Biodiesel, 20, 181, 209 210 Bioethanol, 20, 209 210, 245. See also Sugar and starchy feedstock BioForming, 105 Biogases, 148 149 Bio-hydrogen, 209 210 Biojet fuel, aviation sector to sustainability ASTM standards, 6 17 basic concepts, 3 6, 4t, 5f, 6f combustion and flight tests, 17 21, 20t motivation, 1 3 four-pillar strategy, 1 2, 2f fuel consumption and number of travelers, growth of, 2f Biojet fuel, future trends in production, 241 242 energy integration, 241 in Europe and Asia, 251f in North and South America, 250f opportunity areas for processing routes, 244 246 biochemical pathways, 245
chemical pathways, 244 245 thermochemical pathways, 245 246 opportunity areas for raw materials, 242 243 lignocellulosic feedstock, 243 sugar and starchy feedstock, 243 triglyceride feedstock, 242 243 opportunity areas for supply chain, 246 process intensification, 241 worldwide industrial projects, 246 252 Algae.Tec (Australia), 247 AltAir (United States), 248 Amyris Plant (Brazil), 248 Byogy Renewables (United States), 248 Dynamic Fuels (United States), 247 GreenSky London Project, 248 ITAKA Project (United States), 247 Petrixo Oil & Gas (United Arab Emirates, Fujairah), 247 Solazyme (United States), 248 Biological pretreatment, lignocellulosic feedstock, 134 Bio-oils, 17, 44 45, 55, 138, 148, 155 158 Biorefineries, 33 34, 202 203 Byogy Renewables (United States), 248
255
256
Index
C Capital cost, 82 Carbonation reaction, 150 Carbon dioxide (CO2) emissions, 1 2, 5 lignocellulosic feedstock, 161 162, 162t process integration, 194 195, 195t sugar and starchy feedstock, 120, 121t Catalytic approach, 100 Catalytic hydrothermolysis synthetic kerosene (CHSK) process, 50 Cellulose, 41 chemical structure, 42f Certification process, 47 CFD. See Computational fluid dynamics (CFD) Char, 137 Chemical Engineering Process Cost Index, 79 Chemical pathways, 244 245 Chemical pretreatment, lignocellulosic feedstock, 133 134 CO emissions, 106 Cold acid solvent extraction (CASE) process, 142 Cold stream, 178, 179f, 191 Compact heat exchangers, 172 175 Complete life cycle analysis, 226 227 Composite curves, process integration, 180f Compressor module of isentropic type, 155 158 Computational fluid dynamics (CFD), 177 178 Condensation reaction, 101 Condensation system, 151 152, 152t Conjunctive normal form, 210 Conventional direct distillation sequence, 181
Convex hull approach, 213 214, 224 Corn, 93, 99 100, 104 105 Cradle to gate LCA, 226 227 Cradle to grave LCA, 226 227, 231 232
D Data generation, for supply chain, 203 204 Dehydration mechanism, 96 97, 103 104 Dehydration reactor, lignocellulosic feedstock, 152 153 reactions and conversions for, 152t DeMorgan’s theorem, 210 211 Depolymerization reaction, 138 139 Direct sugar to hydrocarbons, 13 17, 48 Disability-adjusted life years (DALYs), 229 Disaggregated variables, 213 214 Dual-bed system, 102 Dynamic Fuels (United States), 247
E Eco-Indicator 99 methodology, 228 230 Edible and nonedible vegetable oils, 202 Energy integration, 178 181, 179f, 241 adjustment of temperatures, 188, 188t conceptual design, 186 191 data input and ΔT definition, 188 energy integration network, 190 191, 191f heat balance per interval, 189, 189t heat cascade, 189 190, 190f network, 190 191, 191f
simulation of, 191 192, 192f temperature intervals, 189, 189f Enthalpy, 84 85, 179 180 Environmental impact, 181 182, 226 Environmental indicator, 226 Escherichia coli, 94 96 Ethanol dehydration reactions, 111t Ethylene, 97 Exchanged energy, 79 80
F Farnesene (C15H24), 44, 48, 98 99, 103 Fast pyrolysis, 138 of palm oil shell, 148 149 Fatty acids, 35 Fermentation, 94 96, 98, 107 108, 111 Fischer Tropsch catalytic reaction, 44 45 Fischer Tropsch (F T) process, 134 138, 142 143, 231 232, 248 Fischer Tropsch synthetic paraffinic kerosene (FTSPK), 47 48 Fischer Tropsch synthetic paraffinic kerosene with aromatics (FT-SPK/A) process, 48 49 Fit-for-purpose properties, 11 13 Flash pyrolysis, 131 132, 138 FlexJET, 2018 21, 249 Food and Agriculture Organization, 243 Fossil fuels, 230 231
G Gasification, 147 148 kinetic model for, 148t reaction set of, 151t Generalized disjunctive programming (GDP) model, 208 212, 209t
Index
Big-M approach, 212 214 conjunctive normal form, 210 convex hull approach, 213 214, 224 relaxation of, 212 214 simple process structure for decision-making, 208f Genetic engineering, 94 96 Geo-Space Platform, 203 204 Gevo ATJ jet fuel, 105 106 Green diesel, 59 60, 73, 102 103, 141 Greenhouse gas emissions, 171, 205, 231 232, 248 lifecycle, 5f GreenSky London Project, 248 Gross income, 83 Guthrie method, 182
H Heat balance per interval, 189, 189t Heat cascade, 189 190, 190f Heat exchange devices, 174 HEFA Plus (Green Diesel), 17 HEFA-SPK. See Hydroprocessed esters and fatty acids synthetic paraffinic kerosene (HEFA-SPK) process Hemicellulose, 41, 132 chemical structure, 42f Heterogeneous reactions, 172 174 Hexadecenoic acid, 148 High freeze point hydroprocessed esters and fatty acids synthetic kerosene (HFP HEFA-SK), 50 Hot stream, 178, 179f, 191 Hydrocarbons, 96 99, 140 141, 176 177 Hydrochloric acid, 138 139 Hydrocondensation, 103 104 Hydrocracking reaction, 60 61, 72, 137 138, 140 141 of n-paraffins, 73f product distribution from, 78t
reactions set proposed to, 74t sugar and starchy feedstock, 99 Hydrodecarbonylation (DCO), 57 Hydrodecarboxylation (DCOx), 57 Hydrodeoxygenation (HDO), 60 61, 138 140 sugar and starchy feedstock, 103 104 triglyceride feedstock, 57 Hydrodeoxygenation synthetic aromatic kerosene (HDO-SAK), 50 Hydrodeoxygenation synthetic kerosene (HDO-SK) process, 50 Hydrogen, 231 232 Hydrogenation dehydration reactions, 102 Hydrogenation reactions, 57 proposed reaction set, 154t sugar and starchy feedstock, 99 100, 112, 113t triglyceride feedstock, 57 Hydrogenation reactor, 153 Hydroisomerization reaction, 60 61, 72, 99, 137 138 of n-paraffins, 73f product distribution from, 78t reactions set proposed to, 74t Hydrolysis alcohol-to-jet process, 136 sugar and starchy feedstock, 94 96 Hydroprocessed esters and fatty acids (HEFA) process, 247 Hydroprocessed esters and fatty acids synthetic paraffinic kerosene (HEFA-SPK) process, 48, 50 Hydroprocessed fermented sugars synthetic isoparaffin (HFS-SIP), 44 Hydroprocessed renewable jet (HRJ8), 64 Hydroprocessing technology, 44 45, 55, 57, 59 lignocellulosic feedstock, 44 45
257
of mixture of vegetable oils, triglyceride feedstock, 64 86 kinetic data for reactions sets extended, 69t lipidic profile of Jatropha oil, 69t lipidic profile of palm oil, 66t modeling of hydrotreating, 65 74 problem statement, 64 65 rate constant for reaction network, 73t Hydrothermal liquefaction, 55, 231 232 Hydrotreated depolymerized cellulosic jet (HDCJ), 50 Hydrotreatment process J. curcas, 176 177, 181 process integration, 186 195, 187f adjustment of temperatures, 188, 188t CO2 emissions, 194 195, 195t conceptual design, 186 191 costs for, 193t data input and ΔT definition, 188 economic assessment, 193 energy integration network, 190 191, 191f estimation of price of biojet fuel, 194, 194t heat balance per interval, 189, 189t heat cascade, 189 190, 190f simulation of, 191 192, 192f temperature intervals, 189, 189f process intensification, 182 186 conceptual design, 182 184, 183f costs for, 193t
258
Index
Hydrotreatment process (Continued) simulation, 184 186, 184f, 185f, 186f triglyceride feedstock, 57 59, 57f, 58f, 66f, 74f CO2 emissions, 84 86, 85t composition of outlet stream, 67t economic assessment, 79 83, 80t, 82t estimation of price of biojet fuel, 83 84, 84t flowsheet of, 76f green diesel, 59 60 kinetic parameters, Jatropha oil, 70t
Jet A1, 3 4, 63 64, 231 232 JP-5 fuels, 3 4 JP-8 fuels, 3 4 light gases, 59 naphtha, 59 operational conditions for, 58 palm oil and kinetic data, proposed set reactions for, 68t palm oil, extended reactions set for, 68t reaction pathway for, Jatropha oil, 70f reactions set, Jatropha oil, 71t simulation of, 75 78, 76f, 77t, 78t, 79t
I
Ketonization reactions, 101
Ignition delay time, 105 106 Integrated hydropyrolysis and hydroconversion (IH2), 50 International Civil Aviation Organization (ICAO), 46 International Renewable Energy Agency (IRENA), 249 Isobutanol, 103 104 Isobutene oligomerization, 153 Isopentane, 141 ITAKA Project (United States), 247
J Jatropha curcas fruit, components of, 145, 145f Jatropha curcas shell biochar and its empirical formula from, 149t lignocellulosic feedstock, composition of, 146t pyrolysis of, 148 149 reaction set of, 149t Jatropha oil kinetic parameters, 70t lipidic profile of, 69t reaction pathway for, 70f reactions set, 71t Jet A, 3 4
K L Life cycle analysis (LCA), 226 232 complete life cycle analysis, 226 227 cradle to gate, 226 227 cradle to grave, 226 227, 231 232 egalitarian, 229 environmental impact, 226 hierarchist, 229 individualist, 229 inputs, 226, 227f outputs, 226, 227f production stages, 227f stages of, 227 228, 228f Light fuels, naphtha, 73 Lignin chemical structure of, 42f source for aromatics, 138 140 Lignocellulosic feedstock, 34f, 109 110, 202, 231 232, 243 biojet fuel production from, 135f combustion tests for biojet fuel from, 142 143 conventional processes, 141 142
conversion processes of, 134 140 alcohol-to-jet process, 136 lignin as, source for aromatics, 138 140 sugar-to-jet process, 136 thermochemical route, 136 138 lignocellulosic waste, conversion of, 144 162 CO2 emissions, 161 162, 162t composition vs. reaction time at gasification reactor, 157f condensation system, 151 152, 152t dehydration stage, reactions and conversions for, 152t economic assessment, 159 160 estimation of price of biojet fuel, 160 161, 161t hydrogenation stage, proposed reaction set by, 154t J. curcas shell, composition, 146t kinetic model for gasification, 148t modeling of, 145 146 oligomerization reactor, reactions and conversions for, 153t palm oil shell, composition, 146t problem statement, 144 145 production process, 146 153, 147f simulation of overall process, 153 159, 156f pretreatment technologies, 130 134, 131f amorphous cellulose, 130 biological, 134 chemical, 133 134 physical, 130 132
Index
physicochemical, 132 133 raw materials agricultural residues, 43f availability and composition of, 43t cellulose, chemical structure of, 42f hemicellulose, chemical structure of, 42f lignin, chemical structure of, 42f separation zone, technologies on, 140 141 thermochemical pathways, 44 45
M Mesitylene, 141 Methanol, 151 152 Microalgae, 99, 242 Microreactors, 172 174 Mixed alcohol synthesis (MAS), 150 151, 151t Mixed-integer linear programming (MILP) model, 212 Mixed-integer non linear programming (MINLP) model, 212 Modified Chlorella micro-algae, 175 Monosaccharides, chemical structure of, 38f Municipal wastes, 41 43
N Nanoparticle catalysts, 139 140 Naphtha, 59, 73, 83, 102 103, 141 NRTL solution model, 153 154
O 6-Octadecanoic acid, 148 Olefins, 97 98, 103 104, 112 Oleic acids, chemical structure for, 35f Oligomerization reactor, 153 reactions and conversions for, 153t
Oligomerization, sugar and starchy feedstock, 97, 103 104, 112t Organosolv pretreatment, lignocellulosic feedstock, 134
P Palladium nanoparticles, 98 Palmitic acids, chemical structure for, 35f Palm oil fruit, components of, 144f lipidic profile of, 66t kinetic data, proposed set reactions for, 68t extended reactions set for, 68t palm oil shell, composition, 146t Palm oil shell biochar and its empirical formula from, 150t fast pyrolysis of, 148 149 lignocellulosic feedstock, composition of, 146t pyrolysis of, 149, 150t Partial-Vapor-Liquid condenser, 117 Particulate matter (PM), 143 Peng Robinson method, 75 Petrixo Oil & Gas (United Arab Emirates, Fujairah), 247 Photocatalysis, 242 Physical pretreatment, lignocellulosic feedstock, 130 132 Physicochemical pretreatment, lignocellulosic feedstock, 132 133 Pinch point methodology, 179 181, 186 Plasma gasification, 248 Plate-and-fin exchangers, 174 Plate-and-frame exchangers, 174 Process integration, 171, 178 181 cold stream, 178, 179f, 191 composite curves, 180f
259
energy integration, 179 181 hot stream, 178, 179f, 191 hydrotreating process, 186 195, 187f adjustment of temperatures, 188, 188t CO2 emissions, 194 195, 195t conceptual design, 186 191 costs for, 193t data input and ΔT definition, 188 economic assessment, 193 energy integration network, 190 191, 191f estimation of price of biojet fuel, 194, 194t heat balance per interval, 189, 189t heat cascade, 189 190, 190f simulation of, 191 192, 192f temperature intervals, 189, 189f techno-economic analysis, 181 182 Process intensification, 171, 244 245 compact heat exchangers, 172 175 computational fluid dynamics, 177 178 definitions, 171 172 energy savings, 176 177 examples of, 173f graphical methodologies, 177 hydrotreating process, 182 186 CO2 emissions, 194 195, 195t conceptual design, 182 184, 183f costs for, 193t economic assessment, 193 simulation, 184 186, 184f, 185f, 186f microreactors, 172 174
260
Index
Process intensification (Continued) sonoreactors, 172 174, 177 spinning disk reactor, 172 174 techno-economic analysis, 181 182 thermally coupled direct sequence, 176 177 thermally coupled distillation systems, 174 thermally coupled indirect sequence, 176 177 ultrasound frequencies, 172 174 Pyrolysis, 44 45, 55, 131 132, 138 139, 147 148 of J. curcas shell, 148 149, 149t of palm oil shell, 149, 150t
R Raw materials, 34 43 lignocellulosic feedstock, 41 43 agricultural residues, 43f availability and composition of, 43t cellulose, chemical structure of, 42f hemicellulose, chemical structure of, 42f lignin, chemical structure of, 42f sugar and starchy feedstock, 37 40, 39f, 39t triglyceride feedstock, 35 37 animal fat, 36f oil content, yield and price of oils, 36t oleic acids, chemical structure for, 35f palmitic acids, chemical structure for, 35f stearic acids, chemical structure for, 35f triolein, chemical structure for, 35f vegetable oils, 35, 36f
Recertification process, 3 Rectisol process, 150 151, 155 158 Rectisol washing approach, 140 141 Renewable feedstock, 33 34 lignocellulosic feedstock, 34f production pathways, 44 50, 44f advances in certification of new pathways, 49 50 certified pathways, 47 49 raw materials, 34 43 lignocellulosic feedstock, 41 43 sugar and starchy feedstock, 37 40, 39f, 39t triglyceride feedstock, 35 37 sugar feedstock, 33 34, 34f sustainability certification, 33 34 technical certification, 33 34 triglyceride feedstock, 33 34, 34f Renewable hydrocarbons, 55 RENJET 3, 2019, 249 REWOFUEL, 2018 21, 250 Rhodotorula glutinis, 99 Roundtable on sustainable biomaterials (RSB), 204 207 air, 205 conservation, 205 documentation, 206t greenhouse gas emissions, 205 human and labor rights, 205 land rights, 205 legality, 204 local food security, 205 planning, monitoring, and continuous improvement, 205 rural and social development, 205 soil, 205 technology use, inputs and waste management, 205
for wastes and residues, 206 207 water, 205 RSB. See Roundtable on sustainable biomaterials (RSB)
S Saccharification, 94 96 Saccharomyces cerevisiae, 94 96 Sasol isoparaffinic kerosene (Sasol IPK), 64 Shell synthetic paraffinic kerosene (Shell SPK), 64 SkyNRG, 19, 249 252 Sodium dodecyl benzene sulfonate (SDBS), 60 61 Solajet, 248 Solazyme (United States), 248 Solena Fuels, 19 20 Sonoreactors, 172 174, 177 Spinning disk reactor, 172 174 Starchy, chemical structure of, 38f Stearic acids, chemical structure for, 35f Sugar and starchy feedstock, 33 34, 34f, 37 40, 39f, 39t, 243 biochemical processes, 44 for bioethanol production, 93 94 catalytic route, 95f combustion tests for biojet fuel, 105 106 conventional processes, 103 105 conversion of sugars to biojet fuel, 94 102 additional reactions, 111t alcohol-to-jet (ATJ) pathway, 94 98, 104 106 CO2 emissions, 120, 121t complete process, 116f conceptual design, 108 112 economic assessment, 118 119, 118t
Index
estimation of price of biojet fuel, 119 120, 120t ethanol dehydration reactions, 111t grinded sorghum, biojet fuel from, 109f hydrogenation reactions and conversion data, 113t modeling of, 108 oligomerization reactions and conversion data, 112t problem statement, 106 108 reactions for ethanol production, 110t simulation of reactive and separation zones, 112 118 sugar-to-jet (STJ) pathway, 94, 98 102, 105 downstream processing, 95f pretreatment of, 95f technologies on separation zone, 102 103 Sugarcane, 94, 105 Sugar-to-jet (STJ) pathway lignocellulosic feedstock, 134 136, 141 142 sugar and starchy feedstock, 94, 98 102, 105 Supply chain for biojet fuel production, 201 data generation, 203 204 elements of, 201 203 life cycle analysis, 226 232 complete life cycle analysis, 226 227 cradle to gate, 226 227 cradle to grave, 226 227, 231 232 egalitarian, 229 environmental impact, 226 hierarchist, 229 individualist, 229 inputs, 226, 227f outputs, 226, 227f production stages, 227f
stages of, 227 228, 228f modeling, 207 214 generalized disjunctive programming model, 208 212 general superstructure for, 207f optimization, in Mexico, 214 225 airport and biojet fuel requirements, 216t, 225t operating and capital cost, 215t problem statement of localization of biojet fuel factory, 217f producers and oil capacity, 216t producers chosen and selected capacity, 224t results of localization, 225f transportation cost, 217t standards for product certification, 204 207 Sustainability certification, 33 34 Sustainable Bioenergy Research Consortium (SBRC), 249 Swedish Biofuels, 252 Sweet sorghum, 106 107 components of, 107f, 114t carbohydrate, 108 chemical formula, 109t composition of, 108, 108t minimum price of biojet fuel from, 120t total annual cost of, 119t Syngas, 44 45, 48 49, 137 138, 140 141, 146, 150 151 Synthetic iso-paraffin (SIP), 6 11, 13 17, 44 Synthetic paraffinic kerosene (SPK), 4, 6 11 Synthetic paraffinic kerosene plus aromatics (SPK/A), 6 11 Syntroleum S-8 (S8), 64
261
T Tar, 137 Technical certification, 33 34 Thermally coupled direct sequence, 176 177 Thermally coupled distillation systems, 174 Thermally coupled indirect sequence, 176 177 Thermochemical approaches, lignocellulosic feedstock, 136 138, 142 F T synthesis, 137 138 gasification, 137 pyrolysis, 138 Thermochemical pathways, 245 246 Thermodynamic method, 113, 153 154 Total annual cost (TAC), 176 177, 216 of lignocellulosic waste processing, 159, 160t process integration, 181 182, 193 process intensification, 181 182 sugar and starchy feedstock, 119 of sweet sorghum processing, 119t Transportation device, 202 Triglyceride feedstock, 33 34, 34f, 45, 187f, 242 243 combustion tests for biojet fuel, 63 64 conventional process, 60 63 conversion processes of, 56 60 hydrodecarbonylation, 57 hydrodecarboxylation, 57 hydrodeoxygenation, 57 hydrogenation, 57 hydroprocessing, mixture of vegetable oils, 64 86 kinetic data for reactions sets extended, 69t lipidic profile of Jatropha oil, 69t
262
Index
Triglyceride feedstock (Continued) lipidic profile of palm oil, 66t modeling of hydrotreating, 65 74 problem statement, 64 65 rate constant for reaction network, 73t hydrotreatment process, 57 59, 57f, 58f, 66f, 74f CO2 emissions, 84 86, 85t composition of outlet stream, 67t economic assessment, 79 83, 80t, 82t estimation of price of biojet fuel, 83 84, 84t flowsheet of, 76f green diesel, 59 60 kinetic parameters, Jatropha oil, 70t light gases, 59 naphtha, 59
operational conditions for, 58 palm oil and kinetic data, proposed set reactions for, 68t palm oil, extended reactions set for, 68t reaction pathway for, Jatropha oil, 70f reactions set, Jatropha oil, 71t simulation of, 75 78, 76f, 77t, 78t, 79t input data for case of study, 188t raw materials, 35 37 animal fat, 36f oil content, yield and price of oils, 36t oleic acids, chemical structure for, 35f palmitic acids, chemical structure for, 35f stearic acids, chemical structure for, 35f
triolein, chemical structure for, 35f vegetable oils, 35, 36f from renewable sources, 55, 56f Triolein, chemical structure for, 35f T-shaped microreactors, 172 174 Typical gasifiers, 146 147, 147f
U UOP Honeywell technology, 19, 55 56, 60 61, 248
V Vegetable oils, 35
W Wet oxidation, 133 134
Z Zymomonas mobilis, 94 96