[1st Edition] 9780128122877, 9780128122860

Table of contents :
Content:
Advances in BioenergyPage i
Front MatterPage iii
CopyrightPage iv
ContributorsPages vii-viii
Chapter One - Principles and Development of Lignocellulosic Biomass Pretreatment for BiofuelsOriginal Research ArticlePages 1-68Yi Zheng, Jian Shi, Maobing Tu, Yu-Shen Cheng
Chapter Two - Anaerobic Digestion ModellingOriginal Research ArticlePages 69-141Karthik R. Manchala, Yewei Sun, Dian Zhang, Zhi-Wu Wang
Chapter Three - Consolidated Bioprocessing Systems for Cellulosic Biofuel ProductionOriginal Research ArticlePages 143-182Ubaldo Ábrego, Zhu Chen, Caixia Wan
Chapter Four - Thermochemical Conversion of Plant Oils and Derivatives to LubricantsOriginal Research ArticlePages 183-231Robiah Yunus, Xiaolan Luo
Chapter Five - Development of Synthetic Microbial Platforms to Convert Lignocellulosic Biomass to BiofuelsOriginal Research ArticlePages 233-278Muhammad Aamer Mehmood, Ayesha Shahid, Liang Xiong, Niaz Ahmad, Chenguang Liu, Fengwu Bai, Xinqing Zhao

Citation preview

VOLUME TWO

ADVANCES IN BIOENERGY

VOLUME TWO

ADVANCES

IN

BIOENERGY

Edited by

YEBO LI Ohio State University, Wooster, OH, USA

XUMENG GE

Ohio State University, Wooster, OH, USA

Academic Press is an imprint of Elsevier 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States 125 London Wall, London EC2Y 5AS, United Kingdom The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom First edition 2017 Copyright Ó 2017 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: 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. ISBN: 978-0-12-812286-0 ISSN: 2468-0125 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Zoe Kruze Acquisition Editor: Alex White Editorial Project Manager: Helene Kabes Production Project Manager: Magesh Kumar Mahalingam Designer: Matthew Limbert Typeset by TNQ Books and Journals

CONTRIBUTORS  Ubaldo Abrego University of Missouri, Columbia, MO, United States Niaz Ahmad National Institute for Biotechnology & Genetic Engineering, Faisalabad, Pakistan Fengwu Bai Shanghai Jiao Tong University, Shanghai, China; Dalian University of Technology, Dalian, China Zhu Chen University of Missouri, Columbia, MO, United States Yu-Shen Cheng National Yunlin University of Science and Technology, Yunlin, Taiwan Chenguang Liu Shanghai Jiao Tong University, Shanghai, China Xiaolan Luo The Ohio State University/Ohio Agricultural Research and Development Center, Wooster, OH, United States Karthik R. Manchala Virginia Tech/Occoquan Laboratory, Manassas, VA, United States Muhammad Aamer Mehmood Shanghai Jiao Tong University, Shanghai, China; Government College University Faisalabad, Faisalabad, Pakistan Ayesha Shahid Government College University Faisalabad, Faisalabad, Pakistan Jian Shi University of Kentucky, Lexington, KY, United States Yewei Sun Virginia Tech/Occoquan Laboratory, Manassas, VA, United States Maobing Tu University of Cincinnati, Cincinnati, OH, United States Caixia Wan University of Missouri, Columbia, MO, United States Zhi-Wu Wang Virginia Tech/Occoquan Laboratory, Manassas, VA, United States

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viii Liang Xiong Dalian University of Technology, Dalian, China Robiah Yunus Universiti Putra Malaysia, Serdang, Malaysia Dian Zhang Virginia Tech/Occoquan Laboratory, Manassas, VA, United States Xinqing Zhao Shanghai Jiao Tong University, Shanghai, China Yi Zheng Clemson University, Anderson, SC, United States

Contributors

CHAPTER ONE

Principles and Development of Lignocellulosic Biomass Pretreatment for Biofuels Yi Zheng*, 1, Jian Shix, Maobing Tu{ and Yu-Shen Chengjj *Clemson University, Anderson, SC, United States x University of Kentucky, Lexington, KY, United States { University of Cincinnati, Cincinnati, OH, United States jj National Yunlin University of Science and Technology, Yunlin, Taiwan 1 Corresponding author: E-mail: [email protected]

Contents 1. Introduction 2. Physiochemical Features of Lignocellulosic Biomass 2.1 Cellulose 2.2 Hemicellulose 2.3 Lignin 3. Efficacy of Pretreatment 3.1 Crystallinity of Cellulose 3.2 Degree of Cellulose Polymerization 3.3 Accessible Surface Area 3.4 Lignin and Hemicellulose Shield 3.5 Hemicellulose Acetylation 3.6 Other Factors 4. Pretreatment 4.1 Physical Pretreatment 4.1.1 4.1.2 4.1.3 4.1.4 4.1.5 4.1.6

2 3 4 6 6 9 10 11 12 12 14 15 16 16

Mechanical Comminution Steam Explosion Liquid Hot Water Irradiation Pulsed Electric Field Pretreatment Extrusion

16 18 19 21 23 23

4.2 Chemical Pretreatment 4.2.1 4.2.2 4.2.3 4.2.4 4.2.5 4.2.6 4.2.7

24

Acid Pretreatment Ionic Liquid Pretreatment Alkaline Pretreatment Orgnosolv Pretreatment Catalysed Steam Explosion CO2 Explosion Wet Oxidation

Advances in Bioenergy, Volume 2 ISSN 2468-0125 http://dx.doi.org/10.1016/bs.aibe.2017.03.001

24 28 31 35 41 43 44 © 2017 Elsevier Inc. All rights reserved.

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4.3 Biological Pretreatment 4.4 Combined Pretreatment 5. Closing Remarks References

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Abstract Lignocellulosic biomass has been considered as the second generation feedstock for biorefinery to produce biofuels and bio-based products. Bioconversion is one of the major pathways involved in the development of biorefinery. However, it is significantly hindered by the structural and chemical complexity of biomass, which makes cellulosic biofuel economically unfit. Fermentable sugars of biomass carbohydrates such as cellulose and hemicellulose necessary for fermentation are trapped inside the crosslinking structure of the lignocellulose. Therefore, pretreatment of biomass is always required to convert lignocellulosic biomass from its native form, in which it is recalcitrant to biodegradation by enzymatic and microbial attacks, into a form amenable to biodegradation. The pretreatment itself is one of key cost contributors to the economics of biofuels while it also affects the cost efficiency of the downstream bioconversion processes. As a result, extensive research has been done on pretreatment. This chapter reviews currently existing pretreatment methods including physical, chemical and biological pretreatment with the focus on the principles, advantages/disadvantages, characteristics and recent development. In addition, we also cover the impact of biomass structural and compositional features on the pretreatment, the current status and challenges of pretreatment research and the future research targets.

1. INTRODUCTION The unsustainable and diminishing fossil fuel resources and their adverse impacts on the environment (e.g., greenhouse gas emission) make it critical to develop technologies and policies to enhance the uses and the production of renewable energy (Pragya et al., 2013). The development and implementation of biorefineries to produce biofuels and chemicals is one of the most feasible solutions to address the above concerns because biomass is produced through photosynthesis using sunlight and CO2 as energy and carbon source, respectively. Therefore, the consumption of biofuels and biomass-derived chemicals has no net carbon footprint. Lignocellulose, one of the most abundant biomass in biosphere, has showed as a promising feedstock and received extensive research for sustainable production of biofuels and bio-based products (Maurya et al., 2015; Sanchez and Cardona, 2008). In addition, developing alternative biofuels from renewable biomass has great potential to reduce US dependence on fossil

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oil while improving national energy security. The renewable fuel standard mandates that 36 billion gallons of biofuels should be produced annually by 2022, with 16 billion gallons coming from lignocellulosic biomass (RFA, 2016). However, due to the initial production shortfalls of cellulosic biofuels, the US Environmental Protection Agency (EPA) has substantially lowered the cellulosic biofuels mandate each year from 2010 to 2013. And the first cellulosic biofuel production was registered in 2012 at demonstration scale (Bracmort, 2015). To advance renewable biofuels production and to cater for a vital and achievable growth of the biofuels industry, EPA finalized the volume requirements (230 million gallons) for cellulosic biofuels in 2016 (Table 1). Despite significant progress has been made in biochemical conversion of biomass to biofuels, however, one of the major bottlenecks impeding production of economically viable biofuels from renewable biomass is the lack of cost-effective pretreatment processes for breaking down the refractory structure of plant cell walls for subsequent enzymatic hydrolysis and microbial fermentation. This chapter comprehensively reviews the field of lignocellulosic biomass pretreatment for biofuel production. It covers the area from early development which has partly already been reviewed before and provides an update on recent advances on certain types of pretreatment technologies such as organic solvents.

2. PHYSIOCHEMICAL FEATURES OF LIGNOCELLULOSIC BIOMASS Lignocellulosic biomass, including forestry residue, agricultural residue, yard waste, wood products, herbaceous crops, etc., is a renewable resource that stores energy from sunlight in its chemical bonds (McKendry, 2002). Table 1 The Environmental Protection Agency (EPA) proposed the final renewable fuel volumes in the United States Renewable fuel 2014 2015 2016 2017

Cellulosic biofuel (million gallons) Biomass-based diesel (billion gallons) Advanced biofuel (billion gallons) Renewable fuel (billion gallons)

33

123

230

N/A

1.63

1.73

1.90

2.00

2.67

2.88

3.61

N/A

16.28

16.93

18.11

N/A

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It is mainly composed of carbohydrates (cellulose and hemicellulose) and lignin (Fig. 1) as well as other minor contents, including pectins, extractives, glycosylated proteins and inorganic materials (Brandt et al., 2013). Typically, the cellulose, hemicellulose and lignin contents of biomass fall in the range of 30%e50%, 15%e35% and 10%e30%, respectively. Both cellulose and hemicellulose are carbohydrates that are polymers of sugars and can be hydrolysed into fermentable sugars which can be converted into fuels and chemicals. Lignin, an aromatic polymer forms a protective shield protecting cellulose and hemicellulose from degradation by enzymes and microbes. Lignin is also considered as glue to interlink cellulose and hemicellulose together leading to a strong cell wall structure.

2.1 Cellulose Cellulose is the most abundant organic compound on the planet and is one of the main constituents of lignocellulose cell wall. Unlike starch (amylose and amylopectin), cellulose molecules are neither coiled nor branched. It is a linear polysaccharide polymer of cellobiose (glucose disaccharides) even though the component monomer is glucose (Desvaux, 2005; Fengel and Wegener, 1985). Individual cellulose chains consist of several hundreds to ten thousands of recurring D-glucose units, linked by b-1,4 glycosidic linkages (Fig. 2). A number of hydroxylic groups are present on the inner and outer surfaces of cellulose forming both intra(in the same chain) and intermolecular (in vicinal chains) hydrogen bonds. Cellulose chains are interlinked by hydrogen bonds and van der Waals forces, resulting in microfibrils with high tensile strength

Figure 1 Composition and spatial arrangement of lignocellulosic biomass cell wall (Brandt et al., 2013).

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€fte, H., Gonneau, M., Vernhettes, Figure 2 Cellulose molecular structure. Adapted from Ho S., 2007. Biosynthesis of cellulose. In: Kamerling, J.P. (Ed.), Comprehensive Glycoscience: From Chemistry to Systems Biology, vol. 2. Elsevier Oxford. pp. 737e763.

(Ha et al., 1998). Cellulose molecules have different orientations throughout the structure. The linkage energy of hydrogen bond in water and cellulose is 15 and 28 kJ/mol, respectively which are much higher than that of van der Waals in water (only 0.15 kJ/mol) (Karimi et al., 2013). Therefore, the strength of cellulose mainly originates from the existence of hydrogen bond instead of van der Waals forces. In addition, the interchain hydrogen bonds also introduce order (crystalline) or disorder (amorphous) into cellulose structure, leading to crystalline and amorphous forms of cellulose (Atalla and VanderHart, 1984). Ding and Himmel (2006), Festucci-Buselli et al. (2007) and Sturcova et al. (2004) suggested cellulose consists of three regions including true crystal, subcrystalline (disordered structure in true crystal regions) and subscrystalline or noncrystalline regions. The crystallinity of cellulose is usually characterized by crystallinity index (CrI). It is widely accepted that increasing CrI leads to the decrease of chemical and biological hydrolysis of cellulose. However, contradictory results were reported that higher crystallinity of lignocellulose had higher digestibility (Akhtar et al., 2012; Kuila et al., 2011). One of the reasons could be the inaccuracy of crystallinity measurement method, e.g., lignin can contribute to the cellulose crystallinity using current method (Zheng et al., 2009a).

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2.2 Hemicellulose Hemicellulose is the second most abundant natural polymer carbohydrate after cellulose on the earth. In contrast to cellulose, hemicelluloses are branched, amorphous and random heterogeneous polysaccharides of pentoses (xylose and arabinose), hexoses (mannose, glucose, galactose and rhamnose) and acetylated sugars (Fig. 3). They differ in compositions among biomass. The dominant hemicellulose component in hardwood and agricultural residues/herbaceous crops is xylan with a small degree of acetylation and arabinose side groups while softwood hemicelluloses are composed mainly of glucomannan which is highly acetylated and contains galactose side groups. Xylan, the most common polysaccharide in hemicellulose consists of backbone chains of D-xylopyranose linked by b-1,4 linkage (Agbor et al., 2011). Hemicelluloses have lower molecular weight than cellulose. Short and branched lateral chains of hemicellulose are involved in formation of the network via hydrogen bond with cellulose microfibrils and interaction with lignin via covalent bonds, rendering an extremely rigid matrix of the celluloseehemicelluloseelignin. However, the amorphous and branched properties make hemicelluloses itself highly amenable to biological, thermal and chemical hydrolysis. Usually, hemicellulose in softwood is more difficult to be hydrolysed than that of hardwood due to the higher content of methyl glucuronic acid side groups in softwood (Teleman et al., 1995, 2002). A mixture of different types of monomeric sugars and acids can be presented in hemicellulose hydrolyses including xylose, mannose, glucose, galactose, arabinose, rhamnose and acetic acid. When thermochemical (e.g., acid and alkaline) hydrolysis is used, severity should be carefully controlled to avoid excessive hemicellulose degradation into furfural and hydroxymethyl furfural which are inhibitors for sugar fermentation (Cheng et al., 2010; Zheng et al., 2013).

2.3 Lignin Lignin is the second largest available organic compound in nature. It is an amorphous and highly complex aromatic hydrophobic heteropolymer which consists of phenyl propane units known as monolignols or lignin precursors, including p-coumaryl alcohol, sinapyl alcohol and coniferyl alcohol which are respective precursors of hydroxybenzaldehyde (H), syringyl (S) and guaiacyl (G), while containing hydroxyl, methoxyl and carbonyl functional groups (Stamatelatou et al., 2012; Zhang et al., 2003). These units

Principles and Development of Lignocellulosic Biomass Pretreatment

Figure 3 (A) Typical hexoses and pentoses found in hemicellulose (Brandt et al., 2013); (B) Example hemicellulose structure (Alonso et al., 2012).

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are connected by CeC and CeOeC bonds, as shown in Fig. 4. Lignin plays a cementing role for linkages (e.g., van der Waals, hydrogen bond and covalent bond) between cellulose and hemicellulose to form a 3-D structure of ligninepolysaccharide complex (LPC) in cell wall with high mechanical strength and makes plant resistant to pathogens and biodegradation by enzymes and microorganisms (Besombes and Mazeau, 2005; Brown and Chang, 2014). Lignin can be a physical barrier, has nonspecific and nonproductive adsorption of enzymes and is toxic to microorganisms. In general, lignin removal (delignification) can improve enzymatic hydrolysis. Pretreatment could melt lignin and disrupt lignin structure resulting in increase of surface area and accessibility of cellulose to enzymes. However, lignin recondensation during pretreatment (especially in cooling stage) can often happen leading to reduced pretreatment effectiveness and enzymatic hydrolysis of pretreated biomass. Such recondensation reduces the reactivity of lignin for its applications as well. Lignin structure alteration without substantial delignification during pretreatment can also make biomass more digestible. Although the major structural elements in lignin have been extensively studied, many aspects of lignin chemistry still remain unclear. Also, the structure and property change of lignin during pretreatment are also not clear. The lignin difference among different biomass is originated from not only content but also monomeric units and linkage types. Such differences

Figure 4 Macromolecular polymeric structure of lignin (Christopher et al., 2014).

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may cause significant differences in the susceptibility to pretreatment among lignocelluloses. Softwood can contain lignin content in the range of 25%e40% which is higher than that in hardwood (18%e25%) and herbaceous crops (10%e20%). Also, softwood usually has low syringyl to guaiacyl (S/G) ratio. Therefore, softwood is the most recalcitrant to pretreatment and biodegradation so that pretreatment on softwood is usually harsher than that on hardwood and herbaceous crops. The reason is softwood has less vessels than hardwood and herbaceous crops, thus renders greater heat and mass (e.g., chemicals and enzymes) transfer into biomass matrix during pretreatment and degradation (Karimi et al., 2013).

3. EFFICACY OF PRETREATMENT The purpose of pretreatment is to break down the naturally recalcitrant structure of lignocellulosic biomass which limits the bioconversion of cellulose and hemicellulose via enzymatic and/or microbial processes. The major structural features governing the enzymatic hydrolysis of cellulose can be classified as chemical and physical. The chemical features are composed of compositions and structure of cellulose, hemicellulose and lignin while the physical features include crystallinity of cellulose, degree of cellulose polymerization (DP), accessible surface area, lignin and hemicellulose shield and hemicellulose acetylation (Zheng et al., 2014; Zhu et al., 2008). Different pretreatment methods affect these features to a different degree. For example, dilute acid pretreatment can mainly remove hemicellulose and change accessible surface area, but has little effect on lignin while alkaline pretreatment can significantly reduce lignin content, but moderate and little effect on hemicellulose and cellulose, respectively. Although these two pretreatments alter different properties of biomass, both improve the enzymatic hydrolysis of pretreated biomass. Therefore, it is difficult, even impossible, to correlate lignocellulose structural and compositional properties to its biodegradability except for their relative contributions to the biodegradability, even though extensive research has been done on the pretreatment. When one specific property is changed during pretreatment, it is not possible to keep others intact for studying the effect of a single property parameter (Wyman, 1996). A desirable pretreatment technique is expected to reduce numerous barriers simultaneously to generate multiple effects (e.g., lignin reduction and cellulose crystallinity decrease). The effects of

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lignocellulosic properties on the enzymatic degradability are summarized and briefly discussed here.

3.1 Crystallinity of Cellulose In general, cellulose microfibrils consist of both amorphous and crystalline regions. Crystalline form is the major part in nature. Amorphous cellulose is more penetrable and accessible to enzymes and has higher enzyme binding capacity than crystalline counterpart so that it has higher hydrolysis rate. The crystallinity is characterized by the ratio of these two regions. The cellulose with high crystallinity usually has low enzymatic hydrolysis efficiency (Yoshida et al., 2008). One typical example is cotton fibre which is almost pure cellulose with high crystallinity and has low enzymatic hydrolysis yield even though there is no lignin or hemicellulose in it (Hall et al., 2010; Jeihanipour, 2011). The reduced cellulose crystallinity of loblolly pine by organosolv pretreatment rendered the substrate easily hydrolysable by cellulase while the crystallinity of cellulose increased and the relative proportion of amorphous cellulose decreased after enzymatic hydrolysis, indicating preferential hydrolysis of amorphous region by cellulase (Sannigrahi et al., 2010). In contrast, many reports show higher enzymatic digestibility of cellulose with higher crystallinity (Akhtar et al., 2012; Fan et al., 1980; Kim and Holtzapple, 2006; Kuila et al., 2011; Xiao et al., 2011). The calcium hydroxide pretreatment significantly increased enzymatic hydrolysis of corn stover, even though the crystallinity of cellulose increased from 43% to 60% (Kim and Holtzapple, 2006). It was concluded that oxidative lime delignification lowered lignin and acetyl groups of hemicellulose and increased the pore size to achieve high digestibility regardless of crystallinity. Despite increased crystallinity, ball milled cellulose still achieved high enzymatic hydrolysis yield due to particle size reduction. The contradictory results on the effect of cellulose crystallinity show that other factors may be more important than crystallinity under certain conditions, and the change of crystallinity is determined by both lignocellulosic feedstock and pretreatment method. Therefore, it could be concluded that cellulose crystallinity is an important factor affecting bioconversion of lignocelluloses, but not a sole factor in all cases. It is almost impossible to distinguish the contribution of crystallinity reduction from other factors to the improved enzymatic digestibility since pretreatment process modifies multiple instead of single factor, i.e., the effect of crystallinity should be evaluated together with other factors.

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3.2 Degree of Cellulose Polymerization The DP of cellulose has a significant effect on its enzymatic digestibility. The reduction of DP can improve enzymatic hydrolysis of cellulose because the number of cellulose chain ends accessible to cellulase and microorganisms increase (Martínez et al., 2007; Pan et al., 2008; Puri, 1984; Sinitsyn et al., 1989; Zhang and Lynd, 2004, 2005). Recent research in the biofuel area has been focused on understanding how the effects of the pretreatment impact the DP of cellulose and its subsequent influence on cellulose saccharification via enzymatic hydrolysis. As expected, pretreatment can cause the reduction of the DP of cellulose providing more reducing ends for the enzymatic hydrolysis, resulting in increased sugar yield, even though different pretreatment has different effect on the degree of DP change. Low pH pretreatments (i.e., dilute acid and SO2) usually lead to more cellulose depolymerization than the ammonia pretreatment [e.g., fibre explosion (AFEX) and ammonia recycled percolation (ARP)] (Kumar et al., 2009a). Dilute acid and SO2 pretreatments reduced the DP of corn stover cellulose by w60% (from w7200 to w2800), while AFEX and ARP reduced the DP by 6% and 38%, respectively (Kumar et al., 2009a). During the dilute acid pretreatment of spruce and pine mixture (50:50), increasing severity caused the decrease of the cellulose DP from 700 to 200 (Martínez et al., 2007). With the DP reduction from 400 to 200, cellulose saccharification increased from 5% to 40%. Carbon dioxide explosion (CDE) and alkaline explosion (AE) were more effective than ozone pretreatment on the reduction of cellulose DP. Puri (1984) compared ozone, CDE and AE pretreatment and reported that the DP of bagasse cellulose decreased from 925 to 800, 572 and 550 after ozone, CDE and AE pretreatments, respectively, leading to enzymatic hydrolysis yield increase from 28% for the untreated bagasse to 86%, 78% and 85% after ozone, CDE and AE pretreatment, respectively. Although the change of cellulose DP by pretreatment contributes to the improvement of enzymatic hydrolysis, it should be aware that additional changes in lignin and hemicellulose also occur and benefit high enzymatic hydrolysis. More research is needed to determine their relative importance. DP can be determined in terms of the number average (DPN), weight average DP (DPW) or viscosity average DP (DPV). DPN and DPW are measured by gel-permeation chromatography method, and DPV is measured viscosimetrically. Existing methods include steps of cellulose isolation and derivatization/dissolution which could significantly alter the native DP of cellulose. Therefore, future research needs to find a DP measurement

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technique that has minimal alteration of the native structure of cellulose and does not need a cellulose derivation step (Hallac and Ragauskas, 2011).

3.3 Accessible Surface Area Enzymatic hydrolysis of cellulose is a heterogeneous catalysis process which requires enzyme adsorption onto the cellulose surface before hydrolysis occurs. Thus, cellulose accessible surface area is among the main features for enzymatic hydrolysis (Sun and Cheng, 2002). Increasing the accessible area is one of the main results of pretreatment processes (Mosier et al., 2005a; Zheng, 2009). However, the accessible area is not considered as an independent factor affecting enzymatic hydrolysis because it is always correlated with the changes of other features such as lignin and/or hemicellulose removal and DP reduction (Rollin et al., 2011). There are two accessible surface areas including external and internal surface areas. The external surface area is related to the size and shape of the particles, whereas the internal area is related to the porosity and capillary structure of cellulose fibres (Taherzadeh and Karimi, 2008). Each pretreatment has a different effect on the external and internal areas. For example, size reduction (e.g., balling and grinding) increases external area, while chemical pretreatment (e.g., dilute acid and alkaline pretreatment) can significantly increase both surface areas. High accessible surface area results in high initial hydrolysis rate which is related to the hydrolysis of amorphous cellulose regions, but the rate decreases sharply in the latter stage of enzymatic hydrolysis despite increased surface area because the hydrolysis in the latter stage is on the crystalline cellulose (Fan et al., 1980). This means that the accessible surface area is not the main limiting factor for hydrolysis in the latter stage. It should be considered together with other features in the hydrolysis. Two techniques have been used to measure the accessible surface area including solute exclusion (SE) (Grethelin, 1985) and Simons’ stain (SS) (Behrendt and Blanchette, 1997; Chandra et al., 2008, 2009). The SE method is time-consuming, while SS method shows rapid prediction of the accessible surface area using adsorption of a small and a large molecule to indicate small and large pores of the fibres. The accessible surface area measured by the SS method was well related to enzymatic hydrolysis of lignocellulosic substrate (Esteghlalian et al., 2001).

3.4 Lignin and Hemicellulose Shield Lignin is considered as ‘glue’ to interlink with cellulose and hemicellulose via van der Waals, hydrogen bond and covalent bond and forms a 3-D

Principles and Development of Lignocellulosic Biomass Pretreatment

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structure of LPC rendering lignocellulose recalcitrant to biodegradation by enzymes and microorganisms. Plus, lignin itself is inert to biodegradation and also acts as a shield limiting the rate and extent of enzymatic hydrolysis of cellulose by preventing cellulose from being degraded by enzyme/ microorganisms and/or inhibiting the activities of enzyme/microorganisms (Chang and Holtzapple, 2000). Lignin composition, content and distribution play an important role in refractory properties of lignocellulosic materials (Li et al., 2012a). Softwoods are more recalcitrant than hardwoods and herbaceous crops, probably because they have higher guaiacyl lignin content, and guaiacyl lignin restricts fibre swelling and enzyme accessibility more than syringyl lignin (Ramos et al., 1992; Taherzadeh and Karimi, 2008). Therefore, S/G ratio has been used to reflect the digestibility of lignocellulose (Studer et al., 2011). Reduction of lignin content via delignification significantly increased enzymatic hydrolysis of pretreated biomass due to biomass swelling, lignin structure disruption and increase of accessible surface area by delignification. It was found that lignin structure modification is more important than the decrease of lignin content during delignification via alkaline pretreatment (Yan et al., 2015). With similar lignin content, lime pretreated corn stover achieved higher enzymatic hydrolysis than NaOH pretreated corn stover (Yan et al., 2015). Alkaline pretreatment can remove lignin, but also cause unwanted xylan losses (Martínez et al., 2015). Besides delignification technologies, lots of research has been conducted on genetic engineering to make lignin less recalcitrant and/or reduce lignin content in lignocellulosic biomass (Carmona et al., 2015; Fu et al., 2011; Poovaiah et al., 2014), but no success has been reported for large-scale biofuel production. In addition to lignin, hemicellulose forms another physical barrier blocking the access of enzymes and microorganisms to cellulose fibres. Hemicellulose is more degradable than lignin and can be hydrolysed under both acidic and basic conditions into its component sugars which are fermentable to produce biofuel and bio-based products. Hemicellulose removal is often correlated well with the increase of enzymatic hydrolysis of lignocellulosic biomass (Chandra et al., 2007; Ding et al., 2012; Rocha et al., 2013; Zheng et al., 2009b). Yang and Wyman (2004) found that the enzymatic digestibility of corn stover cellulose increased with the increase of hemicellulose removal by dilute H2SO4 pretreatment. Depending on the conditions, xylan removal by dilute acid pretreatment reached 70%e80% leading to more than 80% glucose yield from enzymatic hydrolysis of pretreated cellulose (Martínez et al., 2015). It was also found that excessive xylan removal under

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harsher pretreatment conditions resulted in decreased enzymatic digestibility of cellulose because lignin structure modification via recondensation on cellulose surface leads to more recalcitrant lignocellulosic material (Ding et al., 2012; Martínez et al., 2015). Lignin and hemicellulose removals often occur simultaneously during pretreatment so that it is difficult to distinguish individual effects from each other. They appear to be related to the increase of the accessible surface area. Lignin removal was found to be more important than hemicellulose removal, to improve enzymatic hydrolysis of lignocellulose and excessive hemicellulose removal should be avoided (Martínez et al., 2015).

3.5 Hemicellulose Acetylation Acetyl groups attach to hemicellulose and lignin matrix and the degree of hemicellulose acetylation is an important factor affecting cellulose and hemicellulose hydrolysis (Chang and Holtzapple, 2000). The presence of acetyl groups in pulp could inhibit cellulase productive binding by constraining cellulase accessibility to cellulose due to the increased diameter of cellulose and change in the enzyme hydrophobicity (Pan et al., 2006a)and also inhibit the formation of hydrogen bonds between cellulose and cellulase (Teixeira et al., 2000) so that enzymatic degradability of cellulose is decreased. Therefore, deacetylation was reported to improve enzymatic hydrolysis of lignocellulosic biomass with some differences reported in the degree of removal needed to be effective (Kim and Holtzapple, 2005, 2006; Kumar and Wyman, 2009a; Teixeira et al., 2000). Grohmann et al. (1989) showed that acetyl content removal appeared to become less important beyond 75%, while Kong et al. (1992) revealed continued improvements up to full removal of hemicellulose. Grohmann et al. (1986) showed that removing acetyl esters from aspen wood and wheat straw made them five to seven times more digestible. Kong et al. (1992) was able to use alkali metal hydroxide pretreatment to make pretreated aspen wood with different levels of acetyl groups while keeping all other compositional parameters constant. Thus they observed that increased deacetylation improved the yield of sugars from enzymatic hydrolysis of cellulose. In the combined alkaline and peracetic acid pretreatment, it was demonstrated that dilute alkaline was highly effective for deacetylation on hybrid poplar wood and sugarcane bagasse, which benefited the reduction of peracetic acid dosage while maintained high hydrolysis and ethanol fermentation yield (Teixeira et al., 2000). However, some researchers indicated that deacetylation had a minor effect on digestibility when lignin content of biomass is low and/or the

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crystallinity of cellulose is high. Chang and Holtzapple (2000) used similar pretreatment method to Kong et al. (1992) and found that the removal of acetyl bonds is less important than crystallinity reduction and/or lignin removal. Actually, removing acetyl groups also removes hemicellulose and usually alters the form of remaining lignin, making it almost impossible to study the effect of deacetylation alone. Organic solvent pretreatment [e.g., N-methylmorpholine-N-oxide (NMMO)] was found to be capable of selectively removing acetyl groups. NMMO pretreatment can remove over 88% of acetyl content of oak and spruce woods while the lignin and hemicellulose contents were not changed (Shafiei et al., 2010), which resulted in significant improvement in enzymatic hydrolysis. However, the effect of deacetylation was not examined with the consideration of other factors, e.g., crystallinity of cellulose and surface area. Deacetylation may indirectly affect cellulase effectiveness in enzymatic hydrolysis of lignocellulose, because the removal of acetyl and other substituents from xylan could increase xylan digestibility by xylanase and result in increased cellulose digestibility (Yang et al., 2011). Selig et al. (2009) reported that the removal of acetyl groups, either chemically or enzymatically, has little direct impact on the ability of cellulolytic enzymes to convert glucan present in the substrates, but has a significant impact on the ability of xylanolytic enzymes to access and hydrolyse xylan. Meanwhile, these researchers also showed that the removal of acetyl groups and xylan not only enhances both initial enzymatic conversion rates of xylan and glucan but also the overall extents of xylan and glucan conversion. Therefore, xylanase supplementation could be important to enhance cellulolytic enzyme hydrolysis of lignocellulosic biomass undergone deacetylation pretreatment such as alkali pretreatment.

3.6 Other Factors Other factors such as cell wall thickness and porosity were also found to affect biomass degradability. Tree bark impedes penetration of enzymes and the waxy barrier embracing grass cuticle; even milled, woody tissues and plant stems constraint liquid penetration by their nature (Alvira et al., 2010). The pretreatment which results in increased porosity of biomass can significantly improve the hydrolysis (Alvira et al., 2010). Studies indicated that the pore size of biomass in relation to the size of cellulase can significantly affect its enzymatic digestibility (Chandra et al., 2007). When the internal area of pores of biomass is much larger than the external area, cellulases have many possibilities to enter into the pore and get trapped in

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the pores (Zhang and Lynd, 2004). Drying was found to cause collapse in the pore structure of pretreated biomass and reduced the pore volume so that the degradability of biomass decreased (Grous et al., 1986). Therefore, dry storage of pretreated biomass may not be a good choice.

4. PRETREATMENT Owing to the recalcitrant nature (i.e., compositional and structural features described above) of lignocellulosic biomass to biodegradation, a number of pretreatment techniques have been studied to break down cell wall structure and make biomass more amenable to enzymatic and microbial attack. Pretreatment is considered one of key technical barriers and cost contributors for bioconversion of biomass, thus extensive research has been done to improve the techno-economic efficiency of pretreatment, which will eventually lead to cost-competitive biofuels and chemicals from biorefinery of biomass. Owing to the diverse nature of different biomass feedstocks and the specific working mechanisms of different pretreatment techniques, it is unlikely to develop a universal pretreatment for all biomass. Therefore, a variety of pretreatment methods have been examined during the last decades. In general, they can be classified into three categories based on their functioning mode and reagent used for pretreatment, including physical pretreatment, chemical pretreatment and biological pretreatment. Water is not considered as a chemical reagent in this chapter. Combined pretreatment is not considered as a distinct pretreatment category in this chapter, but will be briefly reviewed.

4.1 Physical Pretreatment 4.1.1 Mechanical Comminution Comminution is used to reduce particle size of biomass while increasing accessible surface area, reducing crystallinity and degree of polymerization of biomass, thus increases the biodegradability of biomass. Methods to do comminution include chipping, grinding and milling depending on the required final particle size. There are many different millings namely ball, hammer, knife, vibro, tow-roll, colloid, wet-disk and attrition millings. Harvesting and preconditioning reduce biomass size to 10e50 mm, chipping reduces the size to 10e30 mm while grinding and milling can reduce the particle size to 0.2e2 mm (Agbor et al., 2011). Chipping can reduce heat and mass transfer limitations, whereas grinding and milling are more

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effective to reduce particle size and alter the inherent ultrastructure of biomass (e.g., decrease in crystallinity and DP). Vibratory ball milling is more effective than ordinary ball milling to reduce cellulose crystallinity of spruce and aspen chips (Millet et al., 1976). Disk milling achieved higher enzymatic hydrolysis yield than hammer milling (Zhua et al., 2009). In addition, the moisture content of biomass should be considered in the selection of milling methods. For example, hammer and knife millings are suitable for dry biomass (moisture content, MC 15%), while ball and vibro milling can be used for both dry and wet biomass (Kratky and Jirout, 2011; Taherzadeh and Karimi, 2008). Size reduction has been used as a single pretreatment method to increase biodegradability of biomass for biogas, bioethanol and biohydrogen production (Delgenes et al., 2002). Biogas yield of agricultural residues (e.g., wheat straw and rice straw) increased with a decrease of particle size from 30 to 0.4 mm (Sharma et al., 1988). By reducing the size of bagasse and coconut fibres from 5 to 0.85 mm, the methane yield was increased by 30% (Kivaisi and Eliapenda, 1994). Hideno et al. (2009) used disk and ball millings to improve enzymatic hydrolysis of rice straw and found glucose and xylose yields increased from 78% and 41% to 89% and 54%, respectively. Ball milling did not have a significant effect on glucose and xylose yield of sugarcane bagasse and rice straw, whereas wet disk milling improved glucose and xylose yield of sugarcane bagasse and rice straw by 40% and 32%, respectively (Silva et al., 2010). However, size reduction does not always have a positive effect. Negligible biogas yield increase was found when particle size decreased from 0.4 to 0.088 mm (Sharma et al., 1988). Excessive size reduction caused a decrease in biogas production. De la Rubia et al. (2011) reported an achievement of higher biogas yield of 1.4e2.0 mm than 0.36e0.55 mm and 0.71e1.0 mm. The possible reason could be the variations in chemical compositions of the different size fractions. Small size particles may have high biodegradability which causes overproduction of volatile fatty acids during anaerobic digestion, inhibiting methane production (Izumi et al., 2010). An advantage of size reduction pretreatment is that it does not generate fermentation inhibitor, e.g., hydroxymethylfurfural (HMF) and furfural. However, high energy consumption is a big challenge for size reduction. For example, conventional mechanical comminution needed 70% more energy input than steam explosion to achieve the same size reduction (Holtzapple et al., 1989). Energy requirement depends on biomass

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characteristics, machine type and final particle size. Comminution of hardwoods usually requires more energy than that of agricultural residues (Cadoche and Lopez, 1989). Owing to the high energy cost, mechanical comminution as a single pretreatment technique is not economically feasible, especially at industrial scale. Another disadvantage of size reduction pretreatment is its inability to remove the lignin, a critical barrier to biodegradation of biomass. Except for being used as a pretreatment method, size reduction is usually a necessary step prior to chemical pretreatments. However, in explosive, organosolv and solvent pretreatments, particle size reduction may not be needed (Shafiei et al., 2013; Zhu and Pan, 2010). Size reduction can also be conducted after chemical pretreatment, which is more effective than prepretreatment size reduction. It can reduce energy consumption, reduce cost of solid/liquid separation, reduce energy cost for mixing during chemical pretreatment, increase high solid loading resulting in more concentrated hemicellulose sugars in pretreatment hydrolysates (Zhu et al., 2010; Zhua et al., 2009). However, such postsize reduction is not applicable to all pretreatments (Karimi et al., 2013). 4.1.2 Steam Explosion Steam explosion, also called autohydrolysis in this chapter means uncatalysed (without chemical addition, but steam) steam explosion. It is the most widely studied and used physical biomass pretreatment technique. During pretreatment, biomass particles are saturated with steam in a reactor under high pressure (0.7e4.8 MPa) and high temperature (160e260 C) for several seconds to a few minutes and rapidly depressurized (Zheng et al., 2014). The pretreatment leads to both mechanical and chemical effects which can reduce the fibre size of biomass and hemicellulose hydrolysis (autohydrolysis), respectively. At high temperature, water can act as an acid meanwhile acetic acid is also produced from acetyl groups of hemicellulose, which results in hydrolysis and solubilizing of hemicellulose. Steam explosion can also cause lignin redistribution and removal from biomass to certain extent (Pan et al., 2005a). Therefore, the cellulose remaining in the pretreated solid becomes more biodegradable by enzymes or microorganisms. Key parameters for steam explosion include residence time, temperature, particle size and moisture content. The advantages of steam explosion are low capital investment, low energy demand, adaptable to big particle size, no extra chemical addition, no recycling or environmental cost, low environmental impact, no excessive dilution of resulting sugars and high sugar

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recovery (Agbor et al., 2011; Avellar and Glasser, 1998; Li et al., 2010; Zheng et al., 2014). The disadvantages include inhibitor (furfural/HMF and phenolic compounds from sugar and lignin degradation, respectively) formation due to high temperature and acid condition, potential need of water washing for pretreated biomass (overall sugar yield may be decreased) and lignin condensation and precipitation reducing digestion of biomass. Steam explosion is effective for pretreatment of grassy biomass and hardwood, but less effective for softwood which may need acid catalysts (e.g., SO2 and H2SO4) and/or high severity (Sun and Cheng, 2002). It has been used to treat various biomass for ethanol and biogas production. Ruiz et al. (2006) used 10% solid of (dry basis) steam explosion treated olive tree wood (230 C for 5 min) and sunflower stalk (220 C for 5 min) to produce ethanol in simultaneous saccharification and fermentation (SSF) process and achieved 30 g/L and 21 g/L ethanol concentration. Floodplain meadow hay was pretreated by steam explosion at 200 C, and the highest glucose and ethanol yields of pretreated biomass reached 86% and 97%, respectively (Tutt et al., 2014). For spruce wood (softwood), steam explosion with high severity (T ¼ 235 C, P ¼ 31 bar and t ¼ 10 min, equivalent to severity ¼ 5.0) and high enzyme dosage (60 FPU/g cellulose) were used to achieve 90% enzymatic digestibility (Pielhop et al., 2016). Steam explosion was found to be effective for increasing methane yield of wheat straw by 20%e30%, compared with raw material (Bauer et al., 2009, 2010). Owing to the formation of inhibitory compounds, steam explosion was found to have no or negative effect on methane yield of paper tube residues at high temperature (e.g., 220 C) for 30 min (Teghammar et al., 2010). Steam explosion has been applied commercially to produce fibreboard in the Masonite process (Avella and Scoditti, 1998) and produce biogas or bioethanol (Bauer et al., 2010; Forgacs et al., 2012; Hooper and Li, 1996; Marousek, 2012; Schumacher et al., 2010). 4.1.3 Liquid Hot Water Liquid hot water (LHW) pretreatment uses water as a heating medium at high temperature (130e240 C) and high pressure (to maintain water in liquid phase) without the addition of chemicals (Akhtar et a., 2016; Alvira et al., 2010; Dien et al., 2006a; Negro et al., 2003; Rogalinski et al., 2008). Meanwhile, water can act as an acidic catalyst since it possesses acid properties at high temperature (Mosier et al., 2005a). During pretreatment, water can penetrate into biomass cell wall, leading to cellulose hydration,

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hemicellulose solubilization and slight removal of lignin. LHW pretreatment renders cellulose more accessible to biodegradation, and hemicellulose is recovered as oligosaccharides and monosaccharides. Compared to low pH pretreatment such as dilute acid and acid-catalysed steam explosion pretreatment, LHW pretreatment produces less inhibitors (Van Walsum et al., 1996). To further reduce the formation of inhibitors, pH can be controlled at 4e7 during LHW pretreatment by addition bases (e.g., NaOH and KOH), which is called pH-controlled LHW pretreatment (Kohlmann et al., 1995; Mosier et al., 2005a). According to the direction of the flow of the water and biomass into the reactor, LHW pretreatment can be performed in three types of reactor configurations such as cocurrent, counter current and flow through. Cocurrent LHW pretreatment has been used to pretreat corn fibre for ethanol production (Mosier et al., 2003; Weil et al., 1998). Yang and Wyman (2004) used flow through reactor for LHW pretreatment of corn stover and found it was more effective than batch for removing hemicellulose and lignin at the same severity, and less inhibitors were found because of instant removal of inhibitor and reduction of lignin recondensation reaction during pretreatment. LHW pretreatment has been extensively used to improve biomass degradability for ethanol production. Laser et al. (2002) compared LHW and steam explosion pretreatment to improve ethanol yield from sugarcane bagasse and found that LHW achieved better hemicellulose recovery (>80%) which was comparable to dilute acid pretreatment. Meanwhile ethanol yield was higher than 80% in SSF of LHW pretreated biomass. After LHW pretreatment under optimum conditions of 190 C for 15 min, enzymatic hydrolysis yield of corn stover reached 90% (Mosier et al., 2005b). However, it appears that LHW pretreatment did not perform as well on wood pretreatment as herbaceous biomass. Only 33% sugar yield was achieved from LHW pretreated hybrid poplar with the optimum conditions (200 C, 18 min and 20% solid loading) (Dai and McDonald, 2013). In addition, LHW pretreatment was also found to be effective to improve biogas yield from lignocellulosic biomass, including sunflower stalks, miscanthus, grass, microalgae and municipal solid waste (MSW) (Badshah et al., 2012; Gonzalez-Fernandez et al., 2013; Li et al., 2012b; Monlau et al., 2012; Qiao et al., 2011). Through LHW pretreatment at 200 C (pressure ¼ 1.55 MPa) for 10 min, the methane yield of wheat straw increased by 20% while the methane yield of rice straw increased by 222% under the same pretreatment conditions (Chandra et al., 2012). O-Thong et al. (2012) examined the effect of LHW pretreatment on the improvement of

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methane yield of oil palm empty fruit bunches (OPEFB) and reported methane yield of pretreated OPEFB increased by 29% at a temperature of 230 C for 15 min. The advantages of LHW pretreatment include no need of catalyst, low cost of the solvent (water only), no need of size reduction, no need of expensive material for reactor construction, high hemicellulose sugar recovery, reduction of the need for washing or neutralization of pretreated biomass and low inhibitory compound concentration in hydrolysates. The disadvantages of LHW pretreatment are high energy cost due to high temperature use, low concentration of solubilized hemicellulose sugar and downstream processing of a large amount of waste water. LHW pretreatment has been used in pulp industry, but only tested at lab scale for fuel ethanol production while the pH-controlled LHW pretreatment was considered for large-scale application and tested at the scale of 163 L/min for 20 min to pretreat corn fibre for fuel ethanol production, indicating its potential for large scale pretreatment of biomass for fuel ethanol production in the future (Mosier et al., 2005c). 4.1.4 Irradiation Irradiation processes have been used as pretreatment techniques to improve the digestibility of lignocelluloses. Irradiation includes gamma ray, ultrasound, microwave and electron beam. High energy irradiation can modify the structure of biomass to improve its biodegradability (Bak et al., 2009; Lawton et al., 1951). Gamma irradiation induces cellulose and lignin depolymerization and redistribution in the cell wall and makes irradiated materials more easily to be ground than the untreated samples (e.g., cellulose can be broken down to fragile fibres and low molecular weight oligosaccharides) to improve its biodegradability (Charlesby, 1955; de Lhoneux et al., 1984; Kumakura et al., 1986). It has been used to pretreat various lignocellulosic biomass, including wheat straw (Yang et al., 2008), spent corncob from mushroom cultivation (Tomoko et al., 2007), rice straw and sawdust (Begum et al., 1988; Bhatt et al., 1992), chaff (Kumakura et al., 1986), Douglas fir and yellow poplar (de Lhoneux et al., 1984) and bagasse (Kumakura and Kaetsu, 1983). For example, gamma irradiation was used to treat bagasse and glucose yield of bagasse on enzymatic and acid hydrolysis increased by two and four times, respectively after pretreatment with 100 MR irradiation (Kumakura and Kaetsu, 1983); Tomoko et al. (2007) used gamma irradiation of 500 kGy dose to pretreat the spent corncob from mushroom cultivation, and the saccharification yield and enzymatic

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hydrolysis rate of pretreated biomass increased by more than 100% and 80%, respectively. Gamma irradiation was also used to pretreat various sludge materials to improve biogas yield from anaerobic digestion. Ultrasound can disrupt cell wall structure, increase the specific surface area and reduce the degree of polymerization, resulting in increased biodegradability of biomass (Zheng et al., 2014). Few studies were conducted on ultrasound treatment of lignocellulosic biomass. Yachmenev et al. (2009) reported that the reaction rate and yield of enzymatic hydrolysis of corn stover and sugarcane bagasse were significantly increased in the presence of ultrasound. These researchers attributed the positive results to ultrasound’s benefits: enhancement of the transport of enzyme macromolecules toward the substrate’s surface and the collapse of cavitation bubbles which break up the substrate’s surface for enzymes’ attack. In addition, ultrasound has been extensively used to treat sludge and organic solid waste to achieve increased biogas yield and reduced residual sludge. It was shown to effectively improve biogas yield of pretreated waste activated sludge by 34% (Kim et al., 2003a). Ultrasound was also combined with ionic liquid treatment to further enhance enzymatic degradability (Ninomiya et al., 2012). Microwave is the most studied irradiation pretreatment technique. The major effect of microwave radiation is heating. It can rapidly heat a large volume of materials, thus reduces the treatment time and saves energy. It could be an effective alternative to conventional heating method (Eskicioglu et al., 2007). Microwave can be used alone for biomass pretreatment, but high temperature caused by microwave can result in the generation of inhibitors to downstream bioprocesses (Zheng et al., 2014). With microwave pretreatment, biogas yield of wheat straw was increased by 28%, while the treatment did not affect biogas production of switchgrass (Jackowiak et al., 2011a, b), which may be caused by the production of heat-induced refractory compounds during treatment. Therefore, in most cases, microwave was used in conjunction with other chemical pretreatments to reduce pretreatment severity and temperature, e.g., acid, alkaline and ionic liquid pretreatments (Chen et al., 2012; Cheng and Liu, 2010; Ha et al., 2011; Liu and Cheng, 2009; Rodrigues et al., 2011; Singh et al., 2011). Electron beam does not involve the use of high temperature, thus inhibitory substance can be avoided or minimized. It seems to induce cellulose degradation. It has been used to increase the enzymatic biodegradability of rice straw (Bak et al., 2009), softwood (Khan et al., 1986) and sawdust (Kumakura and Kaetsu, 1978). After electron beam treatment, glucose yield of rice straw was increased from 22% to 52% of theoretical

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maximum (Bak et al., 2009). Electron beam was also used to assist dilute acid pretreatment of rice straw to achieve up to 80% glucose yield of enzymatic hydrolysis in 24 h and glucose content reached 93% in hydrolysis products, meaning the major effect of electron beam is on cellulose modification (Lee et al., 2014). In addition, electron beam has been used to improve biogas production from sludge materials (Zheng et al., 2014). 4.1.5 Pulsed Electric Field Pretreatment Pulsed electrical field (PEF) is a relatively new pretreatment method for biofuel production from lignocellulosic biomass. During PEF, a very short burst (w100 ms) of high voltage strikes samples placed between two electrodes. High-strength external electric field induces critical electric potential across the cell membrane, leading to rapid electrical breakdown and local structural changes of the plant cell wall (Kumar et al., 2011). PEF can create damage in the plant tissue and hence facilitate the contact of acids or enzymes with cellulose which is then hydrolysed into its constituent sugars. PEF treatment has inherent advantages, including low energy and normal operation temperatures and pressure. Kumar et al. (2011) used PEF to treat southern pine chips and switchgrass and found PEF can increase porosity and decrease the crystallinity, which could enhance enzymatic hydrolysis of pretreated biomass. PEF has also been used to pretreat biosolid and sludge to improve biodegradability and biogas yield (Kopplow et al., 2004; Salerno et al., 2009). It was reported that PEF treatment increased the biological methane potential of pig manure and waste activated sludge by 80% and 100%, respectively after 25e30 days (Salerno et al., 2009). 4.1.6 Extrusion Extrusion treatment starts with the feeding of raw lignocellulosic biomass into one end of the extruder, and material is transported along the length of the barrel with a driving screw. The centre of an extruder is a compression zone, and the end of the barrel is an expansion/wearing zone. The whole process is somewhat similar to steam explosion, resulting in cell wall breakdown. During extrusion, materials undergo friction heat, mixing and vigorous shearing which causes defibrillation, fibrillation and depolymerization of cellulose, hemicellulose, lignin and/or protein to improve degradability of raw materials (Camire, 1998; Karunanithy and Muthukumarappan, 2010). Critical parameters influencing extrusion include temperature, pressure, material moisture content and screw speed (residence time) (Marousek, 2012; Yoo et al., 2011). The design of the screw

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can affect the extrusion efficacy, and additives such as starch and ethylene glycol can be used to aid extrusion process (Yoo et al., 2011). Enzyme has also been considered as an additive to enhance ethanol production from biomass (Alvira et al., 2010). Extrusion was used to improve the nutritional values and digestibility of maize as animal feed (Amornthewaphat and Attamangkune, 2008). After extrusion treatment, glucose yield of soybean hull by enzymatic hydrolysis reached 95% (132% higher than untreated material) which was higher than those of dilute acid and alkaline pretreated soybean hull (Yoo et al., 2011). Extrusion was conducted on rapeseed straw to increase its glucose and ethanol yield on enzymatic hydrolysis and SSF (Choi et al., 2013). These researchers found that the glucose yield was increased from 0.25 to 0.67 kg/kg biomass and ethanol yield was increased from 0.2 to 0.79 kg/kg biomass (corresponding to 2.23e16 g/L of ethanol). In addition, extrusion has been extensively used to improve biogas yield from anaerobic digestion (AD) of biomass. For example, five agricultural biomass materials, such as straw and grass, were treated by extrusion which resulted in 18%e70% methane yield during 28-day AD depending on biomass type (Hjorth et al., 2011). Extrusion also increased methane yield of maize, ryegrass and rice straw silage by 8%e13% compared to raw materials and the energy efficiency (energy output/input) was 8.0 (Simona et al., 2013). Extrusion has been used on industrial scale to improve AD of biomass for biogas production, especially in Europe (Br€ uckner et al., 2007; Maschinenbau Lehmann, 2016). The methane yield of grass and maize treated by extrusion was increased by 8%e27% after 30 days of digestion (Br€ uckner et al., 2007). When using extrusion as a pretreatment technique, we should be aware that extrusion process could generate some common pretreatment-related inhibitors such as furfural and phenolic compounds under certain conditions, especially high pressure and temperature, and these inhibitors decrease biogas production (Williams et al., 1997).

4.2 Chemical Pretreatment 4.2.1 Acid Pretreatment Hydrothermal (water-only) pretreatment is effective in releasing hemicellulose sugars and improving cellulose digestibility to glucose by cellulase enzymes (Mosier et al., 2005a). Often, a small amount of acid, such as sulphuric acid, is added to water as a catalyst (Wyman et al., 2009, 2011). The acid facilitates the hydrolysis of hemicellulose which normally relies on the release of acetic acid (autohydrolysis) during hydrothermal pretreatment (Galbe and Zacchi, 2007; Yang and Wyman, 2009). Mineral acids other

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than sulphuric acid, including hydrochloric (HCl), phosphoric (H3PO4), hydrofluoric acids (HF) and carboxylic acids such as formic, acetic, oxalic, maleic, lactic acids etc. were also evaluated (de Vasconcelos et al., 2013; Kootstra et al., 2009a, b; Scordia et al., 2011; Selke et al., 1982; Tsao et al., 1982; Zhang et al., 2013; Zhao et al., 2014a); however, the recovery and reuse of these more expensive acids were economically challenging (Tsao et al., 1982; Yang and Wyman, 2008). In practice, sulphuric acid and sometimes sulphur dioxide tend to be favoured if the technology is to be scaled, because of their low cost and low toxicity to the environment (Yang and Wyman, 2009). Compared with hydrothermal pretreatment, dilute acid pretreatment can process a wider range of biomass types and achieve higher monomeric sugar yields (Esteghlalian et al., 1997; Shi et al., 2011). However, the subsequent hydrolysate conditioning to remove inhibitors, the higher cost reaction vessels and the waste stream treatment can add extra cost to the overall process (Alvira et al., 2010; Humbird et al., 2011; Tao et al., 2011) (Fig. 5). Similar to hydrothermal pretreatment, dilute acid pretreatment hydrolyses most of the hemicellulose plus a small amount of the cellulose to make the remaining solids highly digestible by enzymes. Fig. 6 depicts that hydrolysis of a polysaccharide molecule (i.e., xylan) involves cleavage of the glycosidic bond by adding water molecules (H2O) that split into hydrogen cations (Hþ) and hydroxide anions (OH). Associated with this principle, the performance of dilute acid pretreatment correlates well with the combined severity factor to incorporate pH to the severity parameter

Figure 5 A typical process flow diagram for dilute sulphuric acid pretreatment.

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Figure 6 Hydrolysis of an b-1,4 bond in xylobiose to yield two xylose units.

as shown below (Chum et al., 1990; Lee and Jeffries, 2011). Thus, only three parameters can be varied to optimize sugar release: temperature (T,  C), pH and time (t, min).    T  100 CSF ¼ log10 t  exp  pH 14:75 Applicability of the combined severity factor have been studied extensively on many different types of biomass feedstocks for applications such as tracking the recovery of pentosans, extractable lignin and residual cellulose, all important parameters in describing pretreatment processes (Lloyd and Wyman, 2005; Schell et al., 2003; Shi et al., 2011; Tucker et al., 2003). The severity factor has proven to be a valuable indicator of pretreatment process performance. One can relate sugar yields in the liquid phase and cellulose digestibility in the solid residue to the severity factor for pretreatment. To explain the relationship between pretreatment severity and lignin reduction or xylan solubilization, linear or quadratic models based on severity factor have been employed (Avci et al., 2013; Silverstein et al., 2007). Furthermore, severity parameter allows correlation of data obtained from different reactors operated at different conditions and prediction of future pretreatment performance given the temperatureetime combination. Roughly, the pretreatment severity (reaction rate) will remain roughly constant if the reaction time is cut in half for every 10 C increase in temperature. Achieving high yields of glucose and hemicellulose sugars from both cellulose and hemicellulose in lignocellulosic biomass through the combined operations of pretreatment and enzymatic hydrolysis is essential for the commercial success of a biorefinery converting sugars to fuels or chemicals. However, the optimal pretreatment severity for xylose release is usually different from the pretreatment severity for the highest glucose yields during subsequent enzymatic hydrolysis of the pretreated cellulose (Fig. 7) (Shi et al., 2011). A two-step steam pretreatment configuration (step 1 at low severity to recover hemicellulose sugars and step 2 at higher severity to improve the enzymatic digestibility of the pretreated solids) has been proposed and shown that higher overall sugar yields than a one-step steam pretreatment process (Sannigrahi et al., 2008; S€ oderstr€ om et al., 2003, 2005). However, the

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Figure 7 Typical kinetic curves correlating pretreatment severity with sugar yields during dilute acid pretreatment and enzymatic hydrolysis (Shi et al., 2011).

high capital and energy costs along with an extra solid/liquid separation step between the two pretreatment steps hinder the commercial applicability of a two-step configuration (Wingren et al., 2004). In practice, compromise is generally needed to obtain the highest possible yields from the combined operations for overall sugar release from dilute acid pretreatment and enzymatic hydrolysis of the pretreated solids (Lloyd and Wyman, 2005; Shi et al., 2011). Further development is required to reduce the enzyme doses needed to realize high sugar yields during the coupled operations of dilute acid pretreatment and enzymatic hydrolysis to improve the economic viability. Recent studies have shown the importance of considering hemicellulose and cellulose sugar release during both pretreatment and enzymatic hydrolysis when identifying optimal pretreatment conditions to be applied and suggested not to focus solely on one step or one sugar (Galbe and Zacchi, 2007; Shi et al., 2011). In addition, feedstock type, harvesting, storage and preprocessing methods can have a significant effect on dilute acid pretreatment performance, with lignin compositions and mineral contents having potentially large effects (Dien et al., 2006b; Kenney et al., 2013; Kim et al., 2011; Lloyd and Wyman, 2004; Wolfrum and Sluiter, 2009). Numerous economic studies have pointed out that pretreatment is an expensive operation with pervasive impacts on the costs of other downstream steps (Aden and Foust, 2009; Alvira et al., 2010; Humbird et al., 2011). Thus, although dilute acid pretreatment appears to be one of the frontrunners currently, much more must be done to understand and advance this pretreatment technology to realize that low costs and high yields are

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essential to the production of commodity products, such as fuels and chemicals (Tao et al., 2011, 2012). 4.2.2 Ionic Liquid Pretreatment Ionic liquid (IL) is named to reflect the unique property of a group of molten salts with melting points below 100 C. A common IL consists of a large organic cation and a small organic or inorganic anion (Fig. 8). The near infinite possible combinations of cations and anions to form ILs provide opportunities to fine-tune their property and functionality, therefore ILs are often called ‘designer solvents’ (Plechkova and Seddon, 2007; Rogers and Voth, 2007). Furthermore, certain ILs are nonflammable and have excellent solvation properties while maintaining low vapour pressure at room temperature; they are considered as ‘green solvents’ in comparison to some volatile, flammable and toxic organic solvents (Earle and Seddon, 2000; Holbrey et al., 2003). Since the discovery in 1914 by Paul Walden, ILs have become one of the emerging research areas in chemistry and many other fields (Plechkova and Seddon, 2008; Angell et al., 2012). A variety of potential applications are being explored such as solvents for organic synthesis and extraction, fuel/solar cells, electrochemical capacitors, battery components, CO2 capture, biodiesel, catalysis and pharmaceuticals (Bates et al., 2002; Plechkova and Seddon, 2008; Armand et al., 2009; Angell et al., 2012). The application of ILs in biofuels area, especially as a pretreatment agent for lignocellulosic biomass was initiated with the pioneer work by Rogers et al. in 2002, where 1-butyl-3-methylimidazolium chloride (abbreviated as [C4C1Im][Cl]) was shown to readily solubilize microcrystalline cellulose (MCC) and the cellulose can be regenerated to a less crystalline form on

Figure 8 List of some of the common ionic liquids tested for biomass pretreatment.

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addition of an antisolvent such as water or ethanol (Swatloski et al., 2002). In subsequent reports, 1-ethyl-3-methylimidazolium acetate (abbreviated here as [C2C1Im][OAc]) was tested as a pretreatment agent for lignocellulosic biomass and proves to be highly effective at deconstructing lignocellulosic biomass, that leads to complete delignification, enhanced saccharification kinetics and near to 100% theoretical sugar yields (Li et al., 2011; Singh et al., 2009). During the solubilizationeregeneration process, the crystalline cellulose I structure in MCC or native plant biomass is transformed to cellulose II or amorphous cellulose, both forms are highly susceptible towards enzymatic hydrolysis (Cheng et al., 2011, 2012; Cruz et al., 2013). Lignin and hemicellulose removal is also considered contributing to the reduced biomass recalcitrance and high enzymatic accessibility during IL pretreatment (Li et al., 2011; Shi et al., 2014). To enable IL pretreatment as a biorefinery relevant technology, tremendous efforts have been carried out using [C2C1Im][OAc] as pretreatment medium (Fig. 9). These studies include testing the pretreatment efficiency on both single and mixed lignocellulosic feedstocks, including grasses, softwoods, hardwoods and MSWs (Li et al., 2013a; Shi et al., 2013a; Sun et al., 2015), the effectiveness at high solid loadings (Cruz et al., 2013; Li et al., 2013b), the scalability in pilot-scale reactors (Li et al., 2015) and the potential for extracting renewable aromatics from lignin (Varanasi et al., 2013).

Figure 9 A typical process configuration for Ionic liquid (IL) pretreatment using [C2C1Im][OAc] (Dutta et al., 2015).

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Despite the many technical merits compared with other pretreatments technologies, IL pretreatment has challenging economics. Based on numerous techno-economic analysis of IL-based biomass pretreatment process, the cost of the IL itself is one of the most significant factors determining the minimum ethanol selling price and thus the technoeconomics of a biorefinery using IL pretreatment as a platform technology (Klein-Marcuschamer et al., 2011; Konda et al., 2014). Synthesis of petroleum-based IL, such as [C2C1Im][OAc] requires either costly imidazole as a staring material or multistep synthesis routes. To reduce the cost of IL, both anion and cation should be made available from cheap and renewable resources. A new generation of ILs has been reported, such as triethylammonium hydrogen sulphate ([HNEt3][HSO4]) (Chen et al., 2014), aminoacids-based ILs made of amino-acid-based anions with bio-derived cations such as cholinium (Fukumoto et al., 2005) and renewable ILs made of materials derived from lignin and sugars (Socha et al., 2014). Many of these ILs have been shown to be highly effective at pretreating biomass, with overall sugar yield comparable to traditional ILs in biomass pretreatment (Fukumoto and Ohno, 2007; Hou et al., 2012; Liu et al., 2012; Sun et al., 2014). Biocompatible and biodegradable ILs are highly desirable for use in biomass pretreatment due to their intrinsic compatibility with both biorefinery downstream processes and ecosystems. Many imidazolium-based ILs are inhibitory to the commercial cellulases/hemicellulases and biofuels producing microbial strains, such as yeast and Escherichia coli, thus costly sugar separation out of the IL stream is required. ILs composed of bio-derived anions and cations have been shown to be more compatible with enzyme and microbes than petroleum derived ILs (Petkovic et al., 2012). Although the extraordinary potential of ILs in facilitating the fractionation and separation of biomass components in a biorefinery concept has been proven, the key to an economically viable and scalable IL-based biorefinery is the development of efficient and cost competitive separation processes to recover bioproducts and to recycle the IL. Until very low cost IL being developed, the economic feasibility of an IL-based pretreatment technology demands near complete recovery of the IL and by-products. To address this issue, a range of methods including extraction, evaporation, electrodialysis and pervaporation have been reported in the literature (Bélafi-Bak o et al., 2002; Gubicza et al., 2008; Shi et al., 2013b; Trinh et al., 2013; Yu et al., 2012). However, energy consumption, operating and capital costs have to be carefully analysed and compared with

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31

the alternative distillation/chromatography techniques under biorefinery scheme (Shill et al., 2011; Weerachanchai et al., 2014). Tremendous progresses have been made in the last few years to demonstrate the application of ILs as pretreatment media to fractionate cellulose, hemicellulose and lignin from a variety of biomass feedstocks and mixtures. It is proven that the IL pretreatment efficiency is equal or superior to the currently employed acid/alkali-based pretreatment methods for the production of second generation biofuels and chemicals. The selection of ILs, the processing conditions and recovery methods largely determine IL pretreatment chemistry, i.e., how the biomass being altered during the process, the characteristics of the cellulose and lignin streams and the pretreatment efficacy. As a rapidly evolving pretreatment technology, despite the enormous potential, there are certain challenges that have to be addressed to make IL pretreatment economically viable under the biorefinery context (Dutta et al., 2015). Some of the critical areas towards a biorefinery implementation are to (1) develop cheap, biocompatible and renewable ILs for biomass pretreatment applications; (2) understand the scale-up effects via larger scale demonstration under industrially relevant conditions; (3) explore cost/energy effective ways for IL recycle and product recovery and (4) identify the cost drivers by techno-economic and life cycle analysis and use them to guide the improvement of this technology. 4.2.3 Alkaline Pretreatment Many different kinds of alkali reagents such as calcium hydroxide, sodium hydroxide and ammonium hydroxide can be used to perform pretreatment of lignocellulosic biomass. Alkaline pretreatment has many favourable advantages in comparison to other pretreatment methods including relative mild reaction condition, less operation cost, reduced degradation of holocellulose and fewer inhibitor formation (Kim et al., 2016). The main mechanisms of alkaline pretreatment are the degradation of ester bonds, removal of acetylation and cleavage of ether bonds in the lignocellulosic cell wall matrix, which leads to the alteration of the structure of lignin, the reduction of the ligninehemicellulose complex, swelling of the fibrils and the partial decrystallization of cellulose (Cheng et al., 2010). The cleavage of ether bond appears to be the most important mechanism during alkali pretreatment of biomass, because the ether bonds are the most predominant bond in the lignin polymer and cell wall carbohydrate polymer. Under alkali condition, the ether bond of lignin is cleaved, and the aromatic rings are therefore separated (Fig. 10) (Tatsumi and Terashima, 1985). However,

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Figure 10 Cleavage of ether bond (b-O-4 linkage) by hydroxyl radicals. Reproduced from Tatsumi, K., Terashima, N., 1985. Oxidative degradation of lignin VII: cleavage of the b-O-4 linkage of ganiacylglycerol-beta-guaiacyl ether by hydroxyl radicals. Mokuzai Gakkaishi 31, 316e317.

the cleavage of ether bond by hydroxyl radical alone is a very slow reaction and therefore the alkali pretreatment is usually taken hours to days to achieve desired effects. The delignification rate and efficiency of alkali pretreatment might also depend on the type and content of lignin of the treated biomass. For example, dilute sodium hydroxide pretreatment was found to be effective for increasing enzymatic digestibility of herbaceous biomass with relatively low lignin contents but was observed to be less effective for woody biomass with lignin content (Kumar et al., 2009b). The other factors that can affect the efficiency of alkali pretreatment are the reaction temperature, pressure and solid loading of biomass. Elevated temperature and pressure could reduce the reaction time with increasing operation cost and risk as a trade-off. High temperatures and pressures are mostly applied with low alkali dosage ( > :


3

e

 (55)

2

ðpHpHUL Þ ðpHUL pHLL Þ

1

for

pH < pHUL

for

pH > pHUL

(56)

I ¼ 1þS1I =S

(57)

I ¼ 1þK1I =SI

(58)

KI, Inhibition parameter (g/L); S, Substrate concentration (g/L); SI, Inhibitor concentration (g/L); pHUL, pHLL, Upper and lower limits of pH where the growth rate is inhibited by 50%.

As shown in Fig. 4, the model concept of a typical single-tank anaerobic digester includes the inputs, reaction kinetics and output (Batstone et al., 2002b). The mass balance on a given substrate for a typical anaerobic digester as shown in Fig. 4 incorporating all the four steps of AD can be written as, dSliq qin $Sin qout $Sliq X ¼  þ rh;f ;g;G (59) dt V V

Input

Gas phase

B – Biomass X – Solid substrate K – Rate constant µmax – Specific growth rate Ks – Half saturation coeff. Y – Yield coefficient

Liquid phase

Output

B – Biomass S – Soluble substrate G – Gas

Reaction rate rx – Solid substrate rs – Soluble substance rb – Biomass rp – Product

Figure 4 Illustration of kinetics in an anaerobic digester.

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in which Sliq is soluble substrate concentration (g/L); P Sin is the initial concentration qin is inflow (L/d); qout is outflow (L/d); rh;f ;g;G is the sum of the rates of hydrolysis, fermentation (acidogenesis), growth and gas formation processes (g/L d) and V is volume of the digester (L). The differential equation system representing the mass balance of inorganic carbon (as CO2) incorporating growth kinetics, gas transfer and acidebase reactions of carbonate system is given by Eqs. (60) and (61), dSCO2 qin $Si;CO2 qout $Si;CO2 X ¼ þ þ rh;f ;g;G  rT þ rAB (60) dt Vliq Vliq dSHCO3  qout $SiHCO3   rAB ¼ dt Vliq

(61)

in which rT is the liquid/gas transfer rate (g/L d); rAB is the production rate of base from acid (CO2 from HCO3) (g/L d); Si;CO2 and Si;HCO3  are the initial concentrations of CO2 (g/L) and HCO3  , respectively. About 32 differential equations can be developed to determine the dynamic states of different components in AD using ADM1 (Gerber and Span, 2008). Some of the advantages of ADM1 provided for anaerobic digester design include increased model application for full-scale designs and process optimization, as well as the opportunity to implement AD research to industrial applications, which provides a good basis for other model development and validation studies (Batstone et al.). So far, ADM1 application to different types of substrates has been successful (Batstone et al., 2002b; Mendes et al., 2015). However, modelling the liquidesolid transformations like the precipitation and solubilization of ions is not considered in the ADM1 (Batstone et al., 2002b).

4. MODELLING MAJOR FACTORS AFFECTING ANAEROBIC DIGESTION Modelling a complex process such as AD requires a good understanding of factors associated with the system performance and process stability. Some of these factors include substrate composition, inhibitory compounds, form of microbial living (attached and suspended growth) and TS. The composition of the substrate affects the disintegration/hydrolysis and methane yield. Biodegradability of the substrate determines the methane production rate. Inhibition causes the reduction in reaction rate of the microorganisms due to unfavourable reactor conditions like low pH,

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accumulation of VFAs, hydrogen and ammonia. Accumulation of intermediate compounds formed during the AD process can also impede biological reaction. The form of living of microorganisms in a reactor, whether they are in suspension in the bulk liquid or attached to the substrate, makes a significant difference in the AD efficiency. As discussed earlier, the attached growth systems (biofilms) are more effective for hydrolysis enhancement and synergistic cooperation between AD microbial communities. Hence, specific models have also been established for biofilm-based AD processes. Moreover, the mode of AD reactor operation also plays an important role in the performance of an AD process. Different models have been developed for continuous, plug flow and batch reactor configurations. Last but not the least, the TS concentration in an AD process significant affects the AD effectiveness because at high TS concentration the diffusion limitation will become evident, contributing to the model complexity. A summary of mathematical models in the description of those factors is presented in this section.

4.1 Substrate Composition 4.1.1 Substrate The substrate in AD models is expressed as the chemical oxygen demand (COD) or total organic carbon (TOC). As described earlier, the composition of the substrate in terms of the carbohydrates, proteins and lipids will affect the intermediate product formation. For instance, the intermediate products generated due to AD of substrates with high protein content will result in high amino acids (through hydrolysis), acetate and hydrogen (through acidogenesis) (Batstone et al., 2002a). In contrast, substrates with high carbohydrates such as potato waste generate high VFAs through acidogenesis of simple sugars (Jacob and Banerjee, 2016). In AD process there is strong correlation between substrate composition and the microbial composition (Gavala et al., 1999). For instance, dairy waste has high percentage of acidogens, likewise piggery waste has high percentage of methanogens, and olive-mill waste will have high percentage of acetogens (Gavala et al., 1999). The maximum methane yield of a substrate depends on biodegradability of the substrate, therefore higher percentage of readily biodegradable fraction of the substrate yields high methane (Galí et al., 2009). To improve the methane production, wastes with high percentage of slowly biodegradable ingredients like lignocellulosic biomass are pretreated (Mata-Alvarez et al., 2014). Carbon to nitrogen (C/N) ratio is another indicator of the performance of AD process. Substrates (depending

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on biodegradability) with high C/N and poor buffering capacity may produce high VFAs. Substrates with low C/N and good buffering capacity may result in free ammonia inhibition to methanogenesis (Mata-Alvarez et al., 2014). Poggio et al. (2016) developed a model to determine the composition of carbohydrates, proteins and lipids in complex organic wastes such as food, green or kitchen waste. It was assumed that the COD of the substrate is equal to the theoretical CODth which is determined using the following stoichiometric equation (Eq. 62) assuming the substrate is fully oxidized to carbon dioxide and water,  

a b 3 a 3 Cn Ha Ob Nc þ n þ   c O2 /nCO2 þ  c H2 O þ cNH3 4 2 4 2 2 (62) From Eq. (63) the theoretical specific COD (g CODth/g VS) is estimated using the following expression,   n þ 4a  2b  34 c (63) CODth ¼ 32  Substrate molecular weight Molecular weight was determined using molecular formula assigned for the carbohydrate (C6H10O5), protein (C51H10O6), lipid [kitchen waste (C3.85H7.64NO2.17) and food waste (C3.95H7.74NO2.06)] to determine the COD and specific oxygen demand for these components. The biochemical fractions for the three components (carbohydrates, proteins and lipids) are determined using the COD, nitrogen and mass balance. The COD balance adopted in this model is expressed as, fc þ fp þ fl ¼ 1

(64)

in which fc, fp and fl are the fractions of carbohydrate, protein and lipid COD in the total COD, respectively. Once the nitrogen content is known, the protein fraction can be estimated by, fp ¼

gCODp gp gNsubstrate   gCODsubstrate gp gNp

(65)

in which gNsubstrate (gCODsubstrate)1 is the nitrogen content in the substrate (g/g); gCODp gp1 is the COD content in protein (gCOD/g); and gNp (gp)1 is the nitrogen content in protein (g/g).

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The mass balance on the three components is expressed as,  1      1 1 1 1 1 þ fp $ gCODp gp þ fl $ gCODl gl fc $ gCODc gc 
gCODsubstrate ¼1  gVSsubstrate

(66)

in which gCODsubstrate (gVSsubstrate)1 is specific COD of the substrate; gCODc gc1 ; gCODp gp1 and gCODl gl1 are the COD contents (gCOD/g) of carbohydrate, protein and lipid, respectively. Thereby, fp, and fl can be determined from Eqs. (64)e(66). The particulate fraction of the substrate includes the readily and slowly biodegradable fractions. Parametric estimation using ADM1 model structure was adopted to determine the readily biodegradable and slowly biodegradable fractions. Another similar biochemical method for determination of substrate composition was developed by Zaher et al. (2009a) based on the ADM1, using the mass balance of stoichiometric transformations of the carbohydrates, protein and lipids expressed in the form of a transformation matrix. 4.1.2 Hydrolysis Hydrolysis is usually regarded as the rate-limiting step of AD because others steps proceed relatively faster (Vavilin et al., 2008). Vavilin et al. (2008) considered the rate of hydrolysis to be dependent on the concentration of the biodegradable organic matter. Eq. (67) expresses the hydrolysis rate in terms of substrate concentration (Vavilin et al., 1996). 2

=

1

=

rh ¼ kh Sfb3 S

3

(67)

in which rh is the hydrolysis rate (g/L d); kh is the hydrolysis rate coefficient (d1); Sfb is the initial concentration (g/L) of biodegradable substrate; S is the current substrate concentration (g/L). The magnitude of hydrolytic coefficient is dependent on the substrate biodegradability, solubility, substrate concentration, temperature and mass transfer (Li et al., 2016). It was further determined that the hydrolysis rate coefficient is related to the particle size and density of the substrate (Sanders et al., 2000), and the density of biofilm covering substrate surface according to Eq. (68) (Vavilin et al., 1996), kh ¼ 6rmS

rB d ; rS d

(68)

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in which rmS is the maximum specific hydrolysis rate (d1); rB and rS are densities of biofilm and substrate (g/m3), respectively. d is the depth of biofilm layer on the substrate surface (m); and d is the diameter of the substrate particle (m). It was shown by Valentini et al. (1997) that the hydrolysis rate coefficient is exponentially related to the particle diameter as, d

kh ¼ ko e d0

(69)

in which kh is hydrolytic constant (d1), d is particle diameter, ko and do are constants. Assuming hydrolysis is the rate-limiting step, the cumulative methane production can be estimated with equation Eq. (70) using hydrolysis rate coefficient (Veeken and Hamelers, 1999),   CH4 ðtÞ ¼ CH4max 1  eðkh tÞ (70) in which CH4(t) represents the cumulative methane production at time t (in STP mL, i.e., mL at standard temperature of 0 C and standard pressure of 1 atm); CH4max represents the maximum methane yield of the substrate (STP mL), kh represents the hydrolysis rate constant (d1). Detailed derivation of Eq. (70) can be referred to the work by Veeken and Hamelers (1999).

4.1.3 Codigestion The concept of codigestion involves treating different types of wastes in the same reactors (Vavilin and Angelidaki, 2005). Advantages of codigestion include maintaining a balance of pH and C/N ratio (García-Gen et al., 2015). Use of ADM1 will provide a composition of complex organic matter in terms of carbohydrates, proteins and lipids (Zaher et al., 2009b). Benchmark Simulation Model No.2 was improvised for codigestion application to include features like addition of cosubstrates and inhibition of LCFAs (Arnell et al., 2016). This model applies ADM1 with additional input of biodegradable fraction (soluble and particulate fractions) of the substrate. The biodegradable fraction fD was determined using Eq. (71). fD ¼

YSM VS 350CODT

(71)

in which YSM is the ultimate methane potential (N m3 CH4 ton/VS), CODT is total COD (g/L), and VS is the volatile solids (g/L). The inhibition

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due to LCFAs modelled in ADM1 was expressed by Eq. (72), which is similar to Eq. (56), 8
2 > Sfa KI;fa;low > < 2:77259 KI;fa;high KI;fa;low Ifa ¼ e (72) for Sfa > KI;fa;low > > : 1 for Sfa  KI;fa;low in which Ifa is the inhibition function applied on maximum specific growth rate, Sfa is fatty acids concentration (g/L), and KI,fa,low (g/L) and KI,fa,high (g/L) are inhibition parameters. An ANN (artificial neural networks) type model was developed by Jacob and Banerjee (2016) to determine the methane yield for a codigestion application. The independent variables used in this model were substrate concentration, inoculum and cosubstrate. The model used Eq. (73) to optimize the model coefficients, YBM ¼ bo þ b1 S þ b2 Bi þ b3 C þ b11 S2 þ b22 B2i þ b33 C 2 þ b12 SBi þ b23 Bi C þ b13 SC (73) in which YBM is methane yield [L/(kg VS)], S is the substrate concentration (g/L), Bi is the inoculum concentration [% VS/(VS)], C is cosubstrate proportion (% TS, w/w), and bo, b1, . b13 are model coefficients.

4.1.4 Biogas Production Rate The biogas production rate is correlated to the hydraulic retention time of anaerobic digesters in continuous flow processes (Yu et al., 2013). Applying the steady-state conditions to the differential equation in Eq. (74) gives the production rate in terms of the substrate utilization (Karim et al., 2007), rCH4 ¼

Ysm $ðSo  SÞ HRT

(74)

in which rCH4 is the methane production rate (L/L d); HRT is the hydraulic retention time (d) defined as a ratio of reactor volume V (L) to the flow rate q (L/d); So and S are influent and effluent substrate concentrations (g/L); and Ysm is specific methane productivity (L/g). Nature of the substrate affects biogas production rate. High volatile fraction in the substrate correlates to higher biogas production. Volume of

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methane generated from AD of municipal sludge is dependent on the volatile solids reduction and can be approximated as (Appels et al., 2008), VCH4 ¼ 0:35  VSred

(75)

in which VCH4 is methane volume (L/d); VSred is the volatile solids destroyed in AD (g/d). Volatile solids destroyed can be expressed in terms of the substrate utilization and biomass generated as,
VSred ¼

E  BOD  1:42$

 YB=S $E$ðBODÞ 1 þ kd SRT

(76)

in which YB/S is a microbial growth yield coefficient (g/g); E is the efficiency of substrate utilization; BOD is ultimate biochemical oxygen demand of substrate (g/d); kd is an endogenous coefficient (d1); and SRT is solids retention time (d). Solids retention time is defined as the ratio of the biomass in the reactor to the biomass wastage rate.

4.2 Inhibition Inhibition will reduce microbial activity in an AD process. This could be due to specific compounds or reactor conditions (e.g., pH, weak acid/ base, product, cations) (Batstone et al., 2002a). During inhibition, the conditions in the reactor are toxic which limit the activity of enzymes, cell activity and diffusion of chemical substances in the organisms (Kythreotou et al., 2014). Inhibition is expressed using Eq. (77) to allow for easy substitution or addition of inhibition functions (Batstone et al., 2002a),
 mmax S rg ¼ $I1 $I2 .In B (77) KS þ S in which the first part of the equation is uninhibited Monod-type uptake and I1.In are the inhibition functions. An inhibition term (inhibition function) is multiplied by the Monod expression for specific growth rate as shown in Eq. (77) to estimate the inhibited growth rate (rg). The different types of inhibition used in modelling include, but not limited to the following: • Haldane approach for substrate inhibition. • Inhibition due to competition in substrate utilization. • Andrews approach for substrate inhibition.

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4.2.1 Haldane Approach Haldane derived the approach for enzyme inhibition at high concentration of substrate (Armstrong, 1930). Inhibition function in Eq. (77) is used to model inhibition from all intracellular processes with different parameters for acetogens, acidogens, hydrogen-utilizing methanogens and aceticlastic methanogens. Hydrogen inhibition of acetogens and free ammonia inhibition of aceticlastic methanogens can also be modelled using Eq. (54) (Batstone et al., 2002a) in which SI is the substrate concentration of inhibitor; KI is inhibition parameter (g/L). 4.2.2 Substrate Utilization Competition Although competition in utilization of the substrate does not has a direct impact on the microbial activity, it can be expressed as an inhibition function shown in Eq. (78) similar to Eq. (57) which shows the inhibition due to competitive uptake (Batstone et al., 2002a), I ¼

1  1 þ SSI

(78)

in which S and SI are the concentrations of the substrate and inhibitor (g/L). 4.2.3 Andrews Approach Andrews (1968) developed another approach for modelling inhibition by the substrate, instead of adding an inhibition function, an inhibition term was added in the denominator of the Monod’s growth rate expression as shown in Eq. (79), m¼

mmax ðSÞ   S þ KS þ S KSI

(79)

Hill and Barth (1977) modified Andrews growth rate inhibition by incorporating an additional term for other inhibitors, m¼

mmax S   S þ KS þ S KSI1I;1 þ KSI2I;2

(80)

in which SI1, SI2 are concentration of two different inhibitors (g/L); and KI1, KI2 are inhibition parameters (g/L). Other inhibition models considered are summarized in Table 3.

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Table 3 Summary of models incorporating growth rate inhibition Model Equations Notes

Webb (1963)

 IÞ m ¼ mmaxðSÞð1þbS=K

(81)

2

SþKS þKS

I

Yano et al. (1966)

m¼

mmax ðSÞ



KS þS 1þ

Grant (1967)

mmax m ¼ ðSþK IÞ

Aiba et al. (1968)

S e m ¼ ðKmmax S þSÞ

Hill et al. (1983)

kd ¼

Pn  S  i i¼1

KI;i

(83)

KS

kdmax KI 1þVFA

(82)

I

(84)

(85)

b represents the ‘allosteric’ effect of reaction rate Accounts for the influence of ‘n’ inhibitors on the specific growth rate Assumes a linear decrease in the growth rate due to inhibition at high substrate concentration Derived based on empirical correlation of substrate inhibition Microbial decay inhibition due to total VFA

kd, decay rate (d1); kd-max, maximum decay rate (d1); KI, inhibition parameter (g/L); VFA, concentration of total volatile fatty acids (g/L).

4.2.4 Inhibition due to Toxicity Toxicity affects specific targets on microbial cells and usually its effect is irreversible. Toxicity could be due to LCFA, detergents, aldehydes, nitrocompounds, cyanide, azide, antibiotics and electrophiles (Batstone et al., 2002a). From the modelling standpoint, toxicity affects biomass decay rate, while inhibition influences kinetic uptake and growth (maximum uptake, yield, half-saturation parameters) (Batstone et al., 2002a). LCFA inhibition was modelled using secondary noncompetitive inhibition as aceticlastic methanogens are affected by the LCFA (Zonta et al., 2013). Eq. (86) shows the inhibition function which is similar to the Haldane’s approach in Eq. (54), I¼

KI;LCFA ðKI:LCFA þ SLCFA Þ

(86)

in which KI,LCFA is the inhibitor parameter concentration (g/L); and SLCFA is LCFA concentration (g/L).

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4.2.5 Substrate Inhibition When a maximum specific growth rate is reached, a further increase of the substrate concentration may result in a decrease of the specific growth rate. According to the study by Gerber and Span (2008), this substrate inhibition effect can be attributed to a high osmotic pressure of the medium or a specific toxicity of the substrate. Substrate inhibition can also be due to intermediate products formed like VFAs, ammonia and hydrogen in AD process (Angelidaki et al., 1993; Hill et al., 1983; Palatsi et al., 2010). Increase in VFA concentration usually occurs when the rates of acetic acid and hydrogen consumption by the aceticlastic and hydrogenotrophic methanogens decrease. Hydrolysis rate is inhibited by the sum of VFAs (acetate, propionate and butyrate) on molar basis expressed in terms of acetate concentration generated in the acidogenesis step, the rate of this inhibition is expressed similar to the Haldane’s approach (Angelidaki et al., 1993),
 KI;VFA r h ¼ kh S P (87) VFA þ KI;VFA in which rh is the hydrolysis P rate (g/L d); kh is uninhibited hydrolysis first-order rate coefficient (d1); VFA is sum concentration of VFAs (acetate, propionate and butyrate) generated in the acidogenesis phase expressed in terms of acetate (g/L); KI,VFA is the inhibition parameter (g/L); and S is the substrate concentration (g/L). As VFAs accumulate, the pH of the reactor drops and in turn the methane production will be reduced. A study by Weedermann et al. (2015) presented a model of growth rate of acetoclastic methanogens, the inhibition due to pH and undisassociated acetic acid concentration is given by Eq. (88) in analogy to Andrews inhibition in Eq. (81), m¼

mmax $ðAcHÞ AcH þ KS;Ac þ ðAcHÞ KI;AcH

2

(88)

in which mmax is maximum specific growth rate (d1); AcH is acetic acid concentration (g/L); KS,Ac is half-saturation coefficient (g/L); and KI,AcH is inhibition parameter (g/L). In the same study, the growth of acetogenic bacteria and hydrogenotrophic methanogens inhibited by the hydrogen and acetate were given by Eqs. (89) and (90), respectively. mmax $ðBuHÞ  m¼ KS;Bu þ KI;H $H þ BuH

(89)

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Karthik R. Manchala et al.

mmax $ðHÞ  m¼ H þ KS;H þ KI;Ac $AcH

(90)

in which mmax is maximum specific growth rate (d1); BuH is butyric acid concentration (g/L); H is hydrogen concentration (g/L); KS,Bu is halfsaturation coefficient for butyric acid (g/L); KI,H is inhibition coefficient for hydrogen; AcH is acetic acid concentration (g/L); H is hydrogen concentration (g/L); KS,H is half-saturation coefficient (g/L); and KI,Ac is inhibition parameter for acetate.

4.2.6 Ammonia Inhibition Ammonia-bicarbonate system plays a crucial role in the stabilization of the pH in AD processes (Angelidaki and Ahring, 1993). However, when free ammonia accumulation in the reactor exceeds a certain limit, it will result in toxic conditions in the reactor and inhibit methanogenesis. As acetogenesis is inhibited, they are accumulated in the reactor, which contributes to the drop of pH. Angelidaki et al. (1999) modelled this scenario using livestock manure as substrate. The inhibition is due to the NH4 þ =NH3 equilibrium and its impact on the pH (hydrogen ion concentration). Eq. (91) shows the relation between the free ammonia and the hydrogen ion concentration. ðNH3 Þf ¼

ðNH3 ÞT þ 1 þ HKa

(91)

in which (NH3)f is free ammonia concentration (g/L), (NH3)T is total ammonia concentration (g/L), Hþ is the hydrogen ion concentration (mole), and Ka is the dissociation coefficient (mole). The free ammonia inhibition to the growth rate of the aceticlastic methanogenic organisms using Haldane’s approach is shown in Eq. (92). The pH function in Eq. (55) in Table 2 is also included as the growth rate is inhibited in a certain range of pH (upper and lower limits). 1 1 0 ! 0 ! mmax ðT ÞB @ 1 1 1 C A$B rg ¼ @ A$ KS;AcH $ KS;NH3 ðNH3 Þf 1 þ KLCFA 1 þ AcH 1 þ ðNH3 Þ 1 þ I;LCFA KI;NH3 T (92) !  0:5ðpH pH Þ  LL UL 1 þ 2$ 10 $ 1 þ 10ðpHpHUL Þ þ 10ðpHLL pHÞ

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in which rg is the growth rate (g/L d), KI;NH3 and KI,LCFA are inhibition parameters (g/L), mmax(T ) is temperature dependent maximum specific growth rate pHUL and pHLL are the lower and upper limits of pH where microorganism growth rates will be inhibited. The growth rate expressed in terms of the VFAs which inhibits acidogenesis and methanogenesis is given by Eq. (93) (Batstone et al., 2002a). Hill and Barth’s approach (Eq. 80) is used to develop this equation, mmax m¼ (93) KS NH3 1 þ AcH þ KAcH þ KI;NH I;AcH 3

in which m is specific growth rate, mmax is maximum specific growth rate; AcH is the undisassociated acetic acid concentration (g/L) that is also inhibitive, NH3 is the concentration of free ammonia as an inhibitor; KI,AcH is inhibition parameter (g/L). 4.2.7 Hydrogen as a Control Parameter Hydrogen is a key parameter to regulate the production of fatty acids from glucose (Gavala et al., 2003). The methane yield of AD process also depends on hydrogen as a portion of the methane generated by the hydrogen utilizing methanogens. Variation in the hydrogen partial pressure also affects the pH of the reactor and may result in the VFAs accumulation. Presence of dissolved hydrogen in anaerobic digester and its impact on microbial growth rate was studied by Mosey (1983). As hydrogen is the byproduct of the acetogenesis, when the gas phase concentration is high, the acidogenesis of glucose reaction moves towards the generation of propionate and butyrate rather than acetate. The model assumes that the relative availability of the reduced form (NADH) and oxidized form (NADþ) of nicotinamide adenine dinucleotide inside the microbial cell wall or the redox potential will affect the VFA generation (Batstone et al., 2002a) under the following conditions: 1. NADH/NADþ ratio (Eq. 94) inside the microbial cell is assumed constant irrespective of the reactor condition. 2. Free hydrogen can easily diffuse in and out of the cell wall ½NADH ¼ 1; 500pH2 ¼ 1:5  103 H ½NADþ 

(94)

in which pH2 is partial pressure of hydrogen, and H is concentration of hydrogen in digester gas (mg/L by volume).

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The substrate utilization rate is affected by the NADH/NADþ ratio. The rate of substrate consumption (glucose) can be expressed in terms of NADH/NADþ ratio as (Gavala et al., 2003), rgluc d½gluc ¼ dt 1 þ ½NADH þ

(95)

½NAD 

where rgluc ¼

kG $Bgluc $½gluc Km; gluc þ ½gluc

in which [gluc] is the concentration of glucose (mM), rgluc is the unregulated rate of the uptake of glucose (m moles/L d), kG is the maximum rate constant (ion moles/g d), Km,gluc is the Michaelis-type constant (mM), and Bgluc is the concentration of glucose fermenters (mg/L).

4.2.8 Influence of pH on Microbial Growth The pH of the digester is one of the key performance indicators. The growth rate of microorganism changes quite drastically with pH. The equilibrium of H2S/HS, NH3 =NH4 þ and CO2 =HCO3  =CO3 2 is dependent on the pH of the reactor. In most of the models, the pH is modelled based on the ionic equilibrium in the AD process. The reactor pH has a major influence on the microbial growth. pH is expressed as negative logarithm of hydrogen ion concentration in Eq. (96), pH ¼ log10 Hþ

(96)

The pH inhibition is a combination of disruption of homoeostasis and increased weak acids concentration. At low pH, inhibition occurs due to weak bases, on the other hand at high pH the transport limitations are observed affecting all organisms to some degree. Impact of pH on methanogenesis is of particular interest as methanogens can only use undissociated acetic acid (Gerber and Span, 2008). Several studies have shown that buffering the anaerobic digester to neutral resulted in improved methane generation (Fox et al., 1992; Kaseng et al., 1992; Lay et al., 1997). The kinetics of the substrate degradation is affected by inhibitor concentrations and ambient conditions (pH, ion equilibrium, gaseliquid equilibrium and temperature). To model the impact of pH inhibition on acetogenesis,

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the specific growth rate expressed in Eq. (97) was used (Angelidaki and Ahring, 1993), ! ! !   1 þ 2 100:5ðpHLL pHUL Þ mmax 1 m ¼ KS $ (97) $ 1 þ AcH 1 þ 10ðpHpHUL Þ þ 10ðpHLL pHÞ KI S þ1 in which m is specific growth rate (d1), S is either propionate or butyrate concentration (g/L), AcH is undisassociated acetic acid concentration (g/L), and pHUL, pHLL are the lower and upper limits of pH at which microorganism growth will be inhibited. Eqs. (98)e(101) were used by different researchers (Gerber and Span, 2008; Kythreotou et al., 2014; Moesche and Joerdening, 1999) to factor in the pH effect on the microbial growth rate, m ¼ KO þ K1 $pH þ K2 $pH2

(98)

mmax  mmax ðpHÞ ¼  K1 1 þ Hþ þ K2 $Hþ

(99)

m ¼ mmax $

KH K H þ Hþ

KOH m ¼ mmax $ KOH þ OH

(100) (101)

in which KH and KOH are half-saturation coefficients, Hþ and OH are the hydrogen and hydroxyl ion concentrations, and Ko, K1 and K2 are kinetic parameters. 4.2.9 Computational Fluid Dynamics Applications The biochemical and kinetic aspects of the AD have been studied extensively. In most of the cases for simplicity of modelling, it is assumed that the reactors are well mixed (L opez-Jiménez et al., 2015). However, in reality there are mixing issues resulting in decreased performance of the digesters. Traditionally, to determine the actual solids retention time, known concentration of tracers were added to the digester feed to correlate it with mass balance. An alternative approach is by using computational fluid dynamics (CFD) models which apply the fluid dynamics principles to solve flow problems. Conservation principles of mass (Eq. 102), momentum (Eq. 103) and energy (Eq. 104) are applied to determine the fluid mechanic

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characteristics. The advancement in the computers with respect to the computational power has facilitated the application of complex numerical methods for solving these flow problems. Following equations are essential for CFD modelling (L opez-Jiménez et al., 2015). Continuity or mass conservation equation (L opez-Jiménez et al., 2015): vr v ¼M þ V $Vr$! (102) vt v is velocity (m/s); M is mass within in which r is the fluid density (kg/m3); ! V$

the control volume (kg); V is the volume (m3); V is vector differential
 v v v operator vx þ vy þ vz .

Momentum equation: vÞ vðr$! g þ! v $! v Þ ¼ Vp þ Vs þ r$! þ Vr$ð! (103) F vt ! ! in which p is static pressure (Pa); g is acceleration due to gravity (m/s2); F is 3 the outer force (N/m ); and s is stress tensor (Pa). Energy equation: vðr$T Þ m$VT v $T Þ ¼ þf þ rVð! vt Pr

(104)

in which, T is temperature, m is dynamic viscosity, Pr is Prandtl number, and f is source heat flux (W/m2). CFD methods have been employed to study different aspects of the anaerobic digesters with draft tube aerators. For example, Karim et al. (2004) has applied the CFD methods to determine the mixing energy, velocity profiles and impact of gas flow rate on mixing. Research by Azargoshasb et al. (2015) developed a simulation based on three-dimensional CFD coupled with population balance equations of syntrophic (acetogenesis and methanogenesis) reactions in a continuous stirred reactor. The velocity vectors calculated by the model are shown in Fig. 5. The reaction rates for acidogenesis and methanogenesis were defined as, dSi ¼ k0 $Sin dt

(105)

in which species i represents acetic acid, butyric acid and propionic acid. Parameters n and k0 were determined using batch experiments operated at mesophilic (37  1 C) temperature conditions. Mixing law was used to calculate the density of the biogas from a mixture of hydrogen, methane and

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Figure 5 Velocity vectors on a plane at (A) z ¼ 0 and (B) y ¼ 4.5 cm. Adapted from the study by Azargoshasb, H., Mousavi, S.M., Amani, T., Jafari, A., Nosrati, M., 2015. Three-phase CFD simulation coupled with population balance equations of anaerobic syntrophic acidogenesis and methanogenesis reactions in a continuous stirred bioreactor. Journal of Industrial and Engineering Chemistry 27, 207e217.

carbon dioxide. Fig. 6 shows the molar concentration profiles for VFAs generated in the reactor.

4.3 Forms of Living Microorganisms grow either in the form of bioflocs in free suspension of reactor liquid medium or by forming a dense layer of biofilms attaching on the solid surface of the reactor medium. The mass transportation of substrates in the biofilms is driven by mass diffusion gradient. The interactions of the mass transport and substrate utilization processes were studied to understand the performance of biofilms. Mathematical models of three types of biofilms are presented in the following section. For further reading on biofilms, readers are referred to the ‘Mathematical modeling of Biofilms’ a report prepared by IWA Task group on Biofilm Modeling by Wanner et al., 2006. 4.3.1 Modelling Biofilm Biofilm will form two interfaces, i.e., one with the substratum (also known as carrier or surface on which biofilm is developed) and the other with the bulk liquid. The surface of the biofilm is considered as a boundary layer (Fig. 7). To model a biofilm, mass balance need to be constructed on the substrate concentration in biofilms, in the boundary layer and in the bulk liquid.

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Figure 6 The molar concentration profiles of (A) butyrate, (B) acetic acid, (C) propionate, (D) carbon dioxide, (E) hydrogen, (F) methane and (G) water during anaerobic digestion at steady-state condition on a plane at z ¼ 0. Adapted from the study by Azargoshasb, H., Mousavi, S.M., Amani, T., Jafari, A., Nosrati, M., 2015. Three-phase CFD simulation coupled with population balance equations of anaerobic syntrophic acidogenesis and methanogenesis reactions in a continuous stirred bioreactor. Journal of Industrial and Engineering Chemistry 27, 207e217.

x Bulk liquid

JL

Boundary layer

SB

JF

Biofilm

SF

Substratum

S

Figure 7 Schematic of biofilm, boundary layer, and substratum.

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The mass flux is a result of mass diffusion (substrate gradient) or convection (fluid velocity). The mass balance can be generally written using Eq. (106) (Wanner et al., 2006), vSF v $SF Þ þ VðDe $VSF Þ þ rsF ¼ Vð! vt

(106)

v is the in which SF is the concentration of substrate in the biofilm (g/L); ! fluid velocity vector (m/d); rsF is substrate utilization rate (g/L d); De is the diffusion coefficient (m2/d); and V is vector differential operator 
v þ v þ v . Depending on the model assumptions, Eq. (106) can be vx vy vz simplified to one- or two-dimension biofilm model. For instance, the mass transfer rate in one-dimension boundary layer is given by Eq. (107) (Wanner et al., 2006), JF ¼ De

dS De ðSB  SF Þ ¼ dx LF

(107)

in which SB is the concentration of the substrate in bulk fluid (g/L); SF is the concentration of the substrate in the biofilms (g/L); LF is the biofilm thickness (m); and JF is the mass flux in the boundary layer of the biofilms (g/m2 d). To solve the partial differential equation in Eq. (106), boundary conditions are required. Some of the typical boundary conditions applied are given as follows:
 vSF 1. Substrate flux is zero vx ¼ 0 at the surface of the inert substratum that supports the biofilm (x ¼ 0 in Fig. 7). 2. Substrate concentration is given at the surface of the biofilm (x ¼ LF in Fig. 7). 3. The diffusive flux at the surface of the biofilm is equal to the reaction rate within biofilms. 4.3.1.1 Modelling Biofilm on a Flat Surface

The dynamic accumulation of the substrate inside the biofilms is dependent on the diffusion through the biofilm and the substrate utilization rate. The mass balance equation (Eq. 108) (Morgenroth et al., 2000) used for modelling the diffusion and reaction in biofilm is based on the Fick’s law Eqs. (39) and (40), vSF De v2 SF ¼  rsF vt vx2

(108)

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in which rsF and SF are the substrate utilization rate (g/L d); and the substrate concentration (g/L) at a given depth (x) inside biofilms (m); De is the
 2 vSF diffusion coefficient in the biofilm (m /d). At steady state vt ¼ 0 , the partial differential equation (Eq. 108) can be used along with the boundary
 vSF conditions vx ¼ 0 at x ¼ 0; SF ¼ So at x ¼ LF to determine the substrate concentration as a function of biofilm thickness. 4.3.1.2 Modelling Biofilm on a Spherical Surface

The mass balance on the spherical substrate carrier illustrated in Fig. 8 is given by Eq. (109) (Odriozola et al., 2016).
2  vSF v SF 2vSF þ ¼ De  rsF vt vr 2 rvr

(109)

Boundary Conditions: For r ¼ RP ; SF ¼ SoF For r ¼ RS ;

dSF ¼0 dr

in which De is the diffusion coefficient (m2/d); SF is the substrate concentration in the biofilm (g/m3); SoF is the substrate concentration on the surface of the biofilm (g/L); r is the distance from the centre of the spherical biomass carrier (m); rsF is rate of substrate utilization and biofilm formation.

Biofilm RP

Lf

Carrier

RS

Figure 8 Schematic of biofilms on spherical carrier surfaces, in which Rp is the radius of the biofilm particle (m), RS is the radius of the biofilm carrier (m), and Lf is the biofilm thickness (m).

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4.3.1.3 Modelling Anaerobic Granules

Modelling biofilm in the form of anaerobic granules is a special case of modelling biofilm on spherical carrier with carrier radius equal to zero. The mass transfer occurs only in the radial direction of the granules. The mass balance inside the granule for a particular process is expressed in the same way as Eq. (109) (Odriozola et al., 2016). Boundary condition for anaerobic granules will be as follows: For r ¼ RP ; SF ¼ SoF For r ¼ 0;

dSF ¼0 dr

The reaction rate in the granule (rsF) is usually described with Monod equation (Eq. 19). For further reading on modelling anaerobic sludge granules, readers are referred to the work by Odriozola et al. (2016).

4.4 Total Solids TS concentration is an essential factor influencing the performance of AD process. The methane production rate was shown to decrease when the TS concentration is above 15% (Xu et al., 2014). At high TS, the internal mass diffusion limitation causes the hydrolytic products accumulation and in turn results in the inhibition of hydrolysis (Xu et al., 2014). The normal operating range for liquid anaerobic digestion (L-AD) is between 0.5% and 15% TS (Xu et al., 2014). If the TS exceeds 15%, it is usually considered as SS-AD (Bollon et al., 2011). Some of the wastes subjected to LS- and SSAD include the following: 1. low solids (0%e5% TS) e wasted activated sludge, food wastes, 2. medium solids (5%e15% TS) e dairy manure, swine manure, municipal sludge and 3. high solids (>15% TS) e organic fraction of municipal solid waste (OFMSW), agricultural wastes and pulp-paper sludge SS-AD is advantageous over L-AD in solid waste handling as it allows much higher loading in a smaller volume with less energy input and water addition (Karthikeyan and Visvanathan, 2013). Moreover, the compostlike digestate remaining after SS-AD is easier and less costly to transport (Li and Wang, 2011). The fibrous biomass that may cause floating and stratification problems in L-AD can also be easily handled by SS-AD (Xu et al., 2013). In spite of these advantages, SS-AD is also subjected to severe mass transfer limitations associated with the high TS concentration.

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Unlike L-AD in which a universally acceptable framework such as ADM1 has been well developed, there is not yet a consensus on SS-AD modelling due to the complexity in operational conditions, mass transfer mechanism, reaction kinetics and rate-limiting steps. Table 4 presents a general comparison of the differences in the characteristics associated with L-AD and SS-AD. In SS-AD due to high TS concentration, the overall microbial reaction rate is lower. This was attributed to the unfavourable conditions for acetoclastic methanogens due to VFA accumulation and drop in pH (Abbassi-Guendouz et al., 2013), limited mass transfer in the reactor (Liotta et al., 2014), lower hydrolysis rate constant (Xu et al., 2014) and higher half-saturation coefficient (Bollon et al., 2011). Three different categories of models are reviewed in this section which include models derived from theoretical principles, empirical approaches and statistical approaches.

Table 4 Comparison of solid- and liquid-state anaerobic digestion (Xu et al., 2015) L-AD SS-AD

Feedstock

Organic wastes like sewage Solid organic wastes like sludge, manure and food OFMSW, yard trimmings, wastes with smaller particle crop residues usually sizes and higher characterized with higher biodegradability. recalcitrance. Reactor design Effective mixing, shorter HRT Longer HRT, without mixing except leachate recirculation. Phases of mass Two phases (liquidegas) Three phases (solideliquid transfer egas) Rate of mass Reactor contents can be Mass transfer is severely limited. transfer assumed homogenous with Diffusion or convection via high mass transfer rate. leachate flow is assumed as the major forms of mass transfer. Rate-limiting Both hydrolysis and Hydrolysis is usually the ratestep methanogenesis can be the limiting step. rate-limiting step. Dispersion The inhibitors formed will be Dispersion is less due to limited of inhibitors dispersed easily. Shock or no mixing. loading will negatively affect the performance. HRT, hydraulic retention time; L-AD, liquid anaerobic digestion; OFMSW, organic fraction of municipal solid waste; SS-AD, solid-state anaerobic digestion.

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4.4.1 Theoretical Models Models developed based on the theoretical principles discussed in this section include two-particle model, reaction front model, distributed model, spatialetemporal model, modified ADM1 model and diffusion limitation model. A graphical illustration of the theoretical models is shown in Fig. 9. 4.4.1.1 Two-Particle Model

The two-particle model developed by Kalyuzhnyi et al. (2000) considers the SS-AD to be heterogeneous with two kinds of particles in one reactor, namely ‘seed’ and ‘waste’ based on the biodegradability and methanogenic activity. Seed is the inoculum with low biodegradability and high methanogenic activity, waste is the substrate with low methanogenic activity and high biodegradability. An illustration of this model is shown in Fig. 9. The model assumptions are as follows: • Seed particles and waste particles are adjacent to each other. ADM1 derived model

VFA

Biogas

Homogeneous substrate

Depleted zone

Inoculum

Methanogenic zone Buffer zone Acetogenic zone Solid substrate SS-AD reactor Diffusion

Inoculum

Substrate particle

Inoculum particle

Solid substrate VFAs Diffusion limitation model

“Two-particle” model

Figure 9 Theoretical solid-state anaerobic digestion models developed. Adapted from the study by Xu, F.Q., Li, Y.B., Wang, Z.W., 2015. Mathematical modeling of solid-state anaerobic digestion. Progress in Energy and Combustion Science 51, 49e66.

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• Diffusion of intermediate particles occurs from the waste to seed for biogas production. • Fick’s law is used to express diffusion rate. • No leachate flow will occur. The diffusion of intermediate products (VFAs) depends on the concentration gradients, size of seed and waste particles. The diffusion rate is expressed using Eq. (110). rD ¼

2De $ðPs  Pw Þ Ls2 þ Lw2

(110)

in which, rD (g/L d) is the diffusion rate; De (m2/d) is the diffusion coefficient; Ps and Pw (g/L) are the concentrations of the product in the seed and waste particles; Ls and Lw (m) are the diameters of seed and waste particles. The factors that influence the stability of SS-AD predicted by this model include the rate of solute transport and biodegradability of the waste. The diffusion limitations will limit the rate of VFAs entering ‘seed’ particles, which mitigates the inhibition of VFAs to methanogenesis. 4.4.1.2 Reaction Front Model

The reaction front model was developed to interpret the slow transport mechanism in the reactor. The reaction front as described by the model is a solid substrate composed of multiple layers of an acetogenesis zone, a buffer zone and a methanogenesis zone, followed by a depleted zone (Martin et al., 2003). There is a possibility of mass transfer of the solute limiting the methanogenesis process. The model assumptions include (Martin et al., 2003) the following: 1. The inoculum distribution is even at the start of the reaction. 2. Initial dimension of the inoculum particle is zero. 3. The thickness of the reaction front is constant. 4. Rate of reaction front advancement within the digester matrix is a constant. 5. The volume of the reaction fronts is related to the AD reaction rate. An illustration of the inoculum distribution in solids substrate is shown in Fig. 10. The initial distance between the ‘reaction fronts’ is 2R (m) and the radius of the ‘reaction front’ is r (m) at given time (t). G is the methane production (g/L) from the system. The fundamental concept of this model is the SS-AD reaction rate varies along with increase in the total surface area of the ‘reaction fronts’. The interaction between the reaction fronts is shown in Fig. 11. The surface

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Inoculum Input

2r

2R

Output G

R, r, F

Solid substrate

Figure 10 Reaction front model illustration. Adapted from the study by Martin, D.J., Potts, L.G.A., Heslop, V.A., 2003. Reaction mechanisms in solid-state anaerobic digestion: 1. The reaction front hypothesis. Process Safety and Environmental Protection 81, 171e179.

area of the reactor front (A), which is calculated as 4pr2, will decrease as it merges with six others. This process can be expressed as,   A ¼ 4pr 2  6p$ r 2  R2 (111) The findings of this model are that the distribution pattern of the reaction fronts would determine the reaction kinetics. To avoid VFA accumulation, model simulation suggested the thickness of the reaction front should be over 7 cm. Although the presence of the zones as described in the reactor front has never been experimentally proven, this model attempts to explain the mass diffusion mechanism within SS-AD reactor. A

r R

Figure 11 Illustration of interaction between reaction fronts. Adapted from the study by Martin, D.J., Potts, L.G.A., Heslop, V.A., 2003. Reaction mechanisms in solid-state anaerobic digestion: 1. The reaction front hypothesis. Process Safety and Environmental Protection 81, 171e179.

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4.4.1.3 Distributed Model

This is a simple one-dimensional model that involves the mass transfer due to diffusion and leachate flow in the SS-AD reactor (Vavilin et al., 2003). It is assumed that the solid matrix inside the reactor is homogenous in the horizontal direction. It is to be noted that the acidogenesis and acetogenesis steps are combined as one step based on the assumption that the fermentation process is faster to convert the hydrolytic products to acetate. The microorganisms and acetate are modelled to move in the vertical direction of the reactor. The illustration of the distributed model is shown in Fig. 12. The following equations Eqs. (112)e(114) represent the four steps considered in the AD. Acidogenesis and acetogenesis were considered as a single step, vX ¼ kh $X$f ðSÞ vt

(112)

in which X (g/L) is the concentration of solid substrate; kh (d1) is the firstorder hydrolysis rate constant; f(S) is the inhibition function of acetate to hydrolysis. Eqs. (113) and (114) take into consideration the mass diffusion, microorganism growth and substrate utilization with space and time, vS v2 S vS S$B ¼ DS $ 2 e qsa $ þ c$kh $X$f ðSÞ  rmax $ $gðSÞ vt vZ vZ KS þ S

(113)

vB v2 B vB S$B ¼ DB $ 2  qsa $a$ þ YB=S $rmax $gðSÞ  kd $B vt vZ vZ Ks þ S

(114)

in which S (g/L) and B (g/L) are the concentrations of the soluble substrate and microbes respectively; g (S) is the inhibition function of microbial growth; Z (m) represents the vertical coordinate of the reactor, with the Substrate inhibition

Product inhibition

Solid substrate

Intermediate products Hydrolysis/acidogenesis

CH4 Microbial cells (Bi)

Figure 12 Distributed model illustration. Adapted from study by Xu, F.Q., Li, Y.B., Wang, Z.W., 2015. Mathematical modeling of solid-state anaerobic digestion. Progress in Energy and Combustion Science 51, 49e66.

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115

boundary condition defined based on the total length of the effective volume L (0  Z  L); qsa (L/m2 d) is the volumetric liquid flow rate per unit surface area; c is the stoichiometric coefficient of different hydrolytic products to acetate; DS and DB (m2/d) are the diffusion coefficients of the soluble substrate (acetate in this research) and microbes, respectively; KS (g/L) is the half-saturation coefficient of acetate; a is the fraction of microbial cell mass transferred by liquid flow; YB/S (g/g) is the methanogen growth yield coefficient over acetate consumption; rmax (d1) is the maximum acetate utilization rate; kd (d1) is the microbial cell mass decay coefficient; and a is the fraction of the microbial cell mass transferred by liquid flow. The methane production (G) is related to the growth rate, substrate concentration and yield coefficient. The methane production is given by Eq. (115),   vG SB ¼ g$ 1  YB=S $rmax $ $gðSÞ (115) vt Ks þ S in which G (g/L) is the concentration of gaseous products; and g is the mass fraction of methane in biogas. This model considered the diffusion and leachate flow in one dimension (along the vertical axis) of the reactor which provides a good understanding with simplified approach. 4.4.1.4 SpatialeTemporal Model

The purpose of spatialetemporal model (Eberl, 2003) is to get a better understanding of heterogeneous mass distribution in SS-AD. The model concept is an extension of the distribution model to a special regime along with using the ‘reaction front’ mechanism. To reduce the complexity of the model, Monod kinetics was not used. The following model equations (Eqs. 116e119) are developed to determine the instantaneous microbial growth rate, substrate utilization and methane production, Xt ¼ De;X $DX  a1 $qc $VX  b1 $f ðSÞ$X

(116)

St ¼ De;s $DS  qc $VS þ k2 $X$f ðSÞ  b3 $gðSÞ$B

(117)

Bt ¼ De;B $DB  a2$ q$VB þ b4 $gðSÞ$B  b5 $B

(118)

Gt ¼ De;G $DG  qc $VG þ b6 $gðSÞ$B

(119)

in which X, S, B, and G (g/L) are the concentrations of solid substrate, soluble substrate, microbes and biogas, respectively; t is time; qc is the convective velocity of leachate flow (L/m2 d); Xt, St, Bt and Gt are functions

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of time; a1, a2, b1, b2, b3, b4 and b5 are model parameters; f(S) and g(S) are inhibition of acetate to hydrolysis and microbial growth, respectively; De,X, De,s, De,B and De,G are diffusion coefficients (m2/d). This model includes the diffusion and leachate flow for mass transfer with the reactor. Inhibition due to VFAs is also considered. 4.4.1.5 Modified ADM1

Abbassi-Guendouz et al. (2012) used the ADM1 to study the impact of SSAD on cardboard. TS of the substrate employed in this study ranges from 10% to 35%. The ADM1 was modified by calibrating the model using the methane generation data at TS concentration of 10%. It is assumed that the hydrolysis rate coefficient will decrease with the increase in TS concentration. It was shown that at high TS concentration, the mass transfer coefficient was reduced due to the pasty texture of the reactor content, and in turn reduced the release of gases (methane, carbon dioxide and hydrogen), thereby inhibited methanogenesis. 4.4.1.6 Diffusion Limitation Model

The purpose of diffusion limitation model (Xu et al., 2014) was to determine the impact of TS on lignocellulosic substrates. The underlying concept in this model is that AD is initiated when ‘pin-point microflora’ inoculated into the substrate. The hydrolytic products formed are diffused towards microflora due to substrate gradient. Therefore, the rate at which the substrate is utilized also depends on the mass diffusion. Fick’s law was used to predict the rate of diffusion, rd, through a layer of substrate. Eq. (120) represents substrate diffusion rate, rd ¼

De $A$ðS  S 0 Þ L$VSL

(120)

in which De is diffusion coefficient for the substrate (m2/d); A is the surface area of the microorganisms (m2); L is the thickness of the effective zone of enzymatic hydrolysis (m); VSL is the volume of the substrate layer around the microflora (m3); S 0 is the substrate concentration (g/L) inside the microflora and S is the concentration of the soluble substrate (g/L). The hydrolytic soluble substrate formed (glucose) during hydrolysis was assumed to inhibit the hydrolysis with an empirical expression for inhibition is given by, ri ¼ ki $S

(121)

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in which ri (g/L d) is the reduction of hydrolysis rate due to inhibition, and ki is the hydrolysis inhibition coefficient. 4.4.2 Empirical Models 4.4.2.1 Logistic Model

A logistic model was developed by Pommier et al. (2007) to predict the impact of water on the solid waste mechanization. The purpose of this study was to simplify the model so as to estimate the methane generation from the landfill. The landfill is considered as an SS-AD in this situation. The assumptions on which the logistic model is based on are as follows: 1. Methane generation is only due to the microorganisms. 2. Microorganisms growth is subject to the hydrolysis rate and substrate availability. 3. No inhibition is considered in an attempt to simply the model. The rate of the change of microbial concentration (B) is given by Eq. (122),  dB X0max  X (122) ¼ mmax $B 1  dt Xo in which mmax is the maximum specific growth rate (d1); X (gCOD/ginitial TS) is the solid substrate concentration; X0max (gCOD/g initial TS) is the initial value of X; X0 (gCOD/ginitial dry matter) is the microbial accessible solid substrate in X0max . It is assumed that the substrate saturated with water is available for methane production. The model assumes two parameters are linearly related to moisture content, namely microbial maximal specific growth rate mmax and the amount of accessible organic matter X0. The water content (u) was empirically correlated to mmax and X0 represented by the following expressions Eqs. (123) and (124): mmax ¼ s$mR max

(123)

Xo ¼ s$Xomax

(124)

in which s is a function such that if u < umin, s ¼ 0; if umin < u < uR, s ¼ (u  umin)/(uR  umin); if u > uR, s ¼ 1; and where uR is the water 1 holding capacity; mR max (d ) is the optimal maximum specific growth rate; and umin is the minimal water content required for the initiation of bioconversion. These equations suggest that if the actual water content u exceeds the water holding capacity uR, all the solid substrate S0 is accessible

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to microbes and reaches the optimal maximum specific growth rate mR max . While, below the minimal water content umin, no bioconversion is possible (S0 ¼ 0 and mmax ¼ 0). When u ranges between umin and uR, both S0 and mmax cannot reach their optimal values and are directly proportional to the water content. The model assumes that solids in the digester need to be in saturated condition for microbial activity to happen. This is a conservative assumption to simplify SS-AD model. Substrate used in this model consists of paper and cardboard with TS content ranging from about 6% to 80%. Model results showed that the water holding capacity (uR) of the substrate was at 34% water content and the minimal water content required for bioconversion (umin) was 60%. 4.4.2.2 General Kinetic Model

General kinetic model was implemented in a study by Fernandez et al. (2010) and Fdez-G€ uelfo et al. (2012) on SS-AD using OFMSW as substrate. The model is based on the biochemical rate law given by Eq. (125), k

B þ X/GBðG > 1Þ

(125)

in which k is the rate constant of the process; X (g/L) is the concentration of dissolved organic carbon in solid substrate; B (g/L) is the microbial concentration in dissolved organic carbon; and G is a stoichiometric constant. Based on the rate law, Eqs. (126) and (127) are derived, dB ¼ ðG  1Þ$k$X$B dt 

dX ¼ k$X$B dt

(126) (127)

With this concept, a general kinetic equation was derived as Eq. (128), dX ðh  XÞ$ðX  XNB Þ  ¼ mmax dt ðXo  XNB Þ

(128)

in which X0 (g DOC/L) is the initial solid substrate concentration (DOC stands for dissolved organic carbon); XNB (g DOC/L) is the concentration of nonbiodegradable substrate; and h (g DOC/L) is the maximum microbial cell mass concentration that can be reached. Expression for methane generation developed in this study is as shown in Eq. (129). Eq. (129) is a modified Gompertz equation (Xu et al., 2015), which is used for bioenergy

Anaerobic Digestion Modelling

119

production involving microbial growth (AD, fermentation and biohydrogen production). " # eðBtÞ  1

G ¼ YG=X (129) ð1=ðh  X0 ÞÞ þ eBt ðX0  XNB ÞÞ NB in which B ¼ mmax $XhX ; G (L/L) is the total methane produced; t is 0 XNB the time (day); X0 (g DOC/L) is the initial solid substrate concentration; XNB (g DOC/L) is the concentration of nonbiodegradable substrate; h (g DOC/L) is the maximum microbial cell mass concentration that can be reached, and YG/X (LCH4/g DOCconsumed) is the methane yield per unit substrate consumed.

4.4.3 Statistical Models The statistical models are helpful when the exact mechanistic explanation is not available. Statistical models may be relatively easy to use in case of SS-AD where the concepts of mass transfer are not completely studied. 4.4.3.1 Linear Regression

A study by Le Hyaric et al. (2012) has shown linear relationship of the specific methane activity to the moisture content during mesophilic AD of MSW. It was observed that the specific activity decreased significantly when the moisture content reduced from 82% to 65%. Liew et al. (2012) in a different study has shown linear relationship of the total methane yield to the lignin content. Although a good correlation (R2 ¼ 0.95) has been observed, application of the coefficients determined from this linear regression model to other studies may not be possible because the data used to develop the model are limited. However, if the experiments are well controlled with limited number of variables, simple linear regression will be effective to correlate the model prediction to experimental results. 4.4.3.2 Multiple Linear Regression

The study by Motte et al. (2013) developed a quadratic multiple linear regression model to determine the impact of TS content, inoculation ratio and particle size of lignocellulosic biomass on the SS-AD performance. The response variables used in the model were methane production, pH and VFAs. A three level BoxeBehnken experimental design was implemented in an SS-AD batch study using wheat straw as a model substrate.

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The response variable expressed in terms of the explanatory variable is given by Eq. (130), y ¼ a0 þ

k X

a m xi þ

i¼1

k X i¼1

amm x2i þ

k X

amn xi xj

(130)

i¼1

where y is a response variable standing for methane yield; VFA concentration stands for pH; xi and xj are explanatory variables, and a0, am, amm, and amn are model coefficients.

5. MODEL PROCEDURE The typical procedure adopted in model application for AD processes includes defining the objective followed by the model structure selection, conceptualization, calibration/sensitivity analysis, model validation and evaluation (Donoso-Bravo et al., 2011). Examples of the model objectives can be methane production prediction, rate of substrate degradation, optimum organic loading rate determination and suitable hydraulic retention time, etc. According to the study by Donoso-Bravo et al. (2011), the procedure for modelling AD includes the following steps as illustrated in Fig. 13. 1. Based on the model objectives and prior knowledge about the biological processes, a model structure or framework is selected to build the model concept. 2. The next step is model assumptions and formulation, which includes selection of appropriate equations for each model component based on the assumptions made. All possible parameters that will affect the AD processes are incorporated during this step. 3. Sensitivity analysis is performed to identify the key parameters showing more influence on the model output. Priority is given to those key parameters in the course of calibration for the parameter determination. Model selection is revisited depending on the outcome of the sensitivity analysis. 4. Depending on the objective and existing data availability, a new set of experimental data may be required to estimate the parameters. Data analysis is performed to compare model output against experimental data.

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Objective

Model selection

Experimental/Literature data

Model formulation

Calibration and sensitivity analysis

Experimental/Literature data

Objective function selection and parameter optimization

Direct validation

Comparison with experimental data Cross validation

Figure 13 Model procedure.

5. Model is validated using the experimental data used in step 4 or a new data set. Model parameter estimation and validation are important aspects in which lots of effort needs to be dedicated. Experimental studies are required to determine some, but not all, of specific parameter values (Donoso-Bravo et al., 2011). In this step, the model prediction is adjusted to fit the reality (experimental data). Typically, the parameter estimation is a major step in model calibration. Trial and error method to fit model output to the observed real experimental data is quite tedious. Therefore, computational model regression is helpful for determination of the parameter values. The interpretation of the model structure and the ability of the model to predict reality depend not only on how well the model is formulated but also how well it is calibrated.

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5.1 Model Concept and Structure Selection As recommended by Spriet (1985), an effective model structure selected needs to have the following four characteristics: 1. simple, 2. relevant, 3. include available parameters and 4. applicable to different conditions. In general, identifying the appropriate model structure will require a good understanding of (1) the experimental data obtained from experiment; (2) the level of detail the model prediction is intended for and (3) available methods to reduce the number of unknown parameters in the model development. The model structure in the case of AD process is characterized by a series of nonlinear differential equations with unknown parameters. Only some, but not all, of those unknown parameters can be determined through experimentation. Some of the model structures adopted programming languages such as C, and Matlab (Donoso-Bravo et al., 2011). The model development effort is an iterative step in which additional parameters may need to be deleted or refined to provide a simple model structure but still with mechanistic meanings.

5.2 Sensitivity Analysis Sensitivity analysis is an effort to determine which parameters play more influence on the model objectives. This analysis usually consists of identifiability analysis and uncertainty analysis. Identifiability is a procedure adopted to determine the unique model parameters from the available data to estimate the uncertainty of those parameter estimates. There are four types of sensitivity functions used to identify the unique model parameters (Reichert, 1998). 1. Absoluteeabsolute sensitivity function which is defined as the absolute change in variable y per unit change in parameter a, expressed as, vy (131) va 2. Relativeeabsolute sensitivity function which is defined as the relative change in variable y per unit change in parameter a, expressed as, da;a y;a ¼

dr;a y;a ¼

1 vy y va

(132)

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3. Absoluteerelative sensitivity function which is defined as the absolute change in variable y for a 100% change in parameter a, expressed as, vy (133) va 4. Relativeerelative sensitivity function which is defined as the relative change in variable y for a 100% change in parameter a, expressed as, da;r y;a ¼ a

dr;r y;a ¼

a vy y va

(134)

The absoluteerelative and relativeerelative sensitivity functions are commonly used as they are independent of the unit of the parameter. For most of the AD processes, the derivatives associated with the sensitivity functions are calculated using the finite difference approximation (DonosoBravo et al., 2011) expressed as, vy yðai þ Dai Þ  yðai Þ z vai Dai

(135)

in which Dai is 1% of the standard deviation sai of the parameter ai. The calculated sensitivity (change in variable caused by change in the parameter) can be correlated to the estimation of uncertainty of that parameter (Reichert, 1998). If the sensitivity is smaller for parameter a1 in comparison to parameter a2, the uncertainty of the estimate of parameter a1 is larger in comparison to a2.

5.3 Parameter Estimation Unknown parameters need to be estimated through model calibration. The collection of the experimental information is very crucial in this step of the modelling study. Increase in the number of processes and microbial pathways will add more parameters. Previous research work (Batstone and Keller, 2003; Derbal et al., 2009; Fedorovich et al., 2003; Galí et al., 2009; Lee et al., 2009; Ramirez et al., 2009) has successfully applied AD models to different type of wastewaters. In all these instances, the experimental data were able to match the model simulated data by calibrating specific model parameters. A typical model calibration procedure (Dewil et al., 2011) adopted includes the following: 1. Data collection from the anaerobic experiment. 2. Identification of key parameters from sensitivity analysis for model calibration.

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3. Use of literature suggested values as initial values of these parameters. 4. Adjustment of the selected parameters to fit model simulation with experiential data. As mentioned earlier AD processes involve multiple parallel reactions therefore an objective function is used to determine the deviation of the model value to the experimental data. The objective function is a model parameter vector, which defines the deviation between the model prediction and real experimental data (Donoso-Bravo et al., 2011). Sum of least squares (Eq. 136) is one of the objective functions commonly used in AD studies, where the standard deviation is assumed constant (Noykova and Gyllenberg, 2000), JðaÞ ¼ min

N  X

2 vexp ðiÞ  vsim ði; aÞ

(136)

i¼1

in which a is the parameter which is to be determined; vexp and vsim are the experimental and model (simulated) values, respectively; vexp(i) is the ith measurement; N is the number of measurements. This objective function was employed to perform sensitivity analysis on the parameters for simulating anaerobic mesophilic sludge digestion using ADM1 (Mendes et al., 2015). When the error (difference of the simulated and experimental values) does not have a constant standard deviation, weighting factors wt have to be used, then the objective function is defined using Eq. (137) (Donoso-Bravo et al., 2011; Palatsi et al., 2010), JðaÞ ¼ min

N X  2 wi vexp ðiÞ  vsim ði; aÞ

(137)

i¼1

If the measurements are in vector form the objective function can be expressed as Eq. (138), JðaÞ ¼ min

N  X    vexp ðiÞ  vsim ði; aÞ W vexp ðiÞ  vsim ði; aÞ

(138)

i¼1

in which W is an N  N weighting matrix. Weighting factor can be estimated as a difference between the maximum and minimum values (Palatsi et al.) or as deviation from the mean (Flotats et al., 2003). According to Donoso-Bravo et al. (2011), if the errors are

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unknown and relative in nature the following objective function Eq. (139) can be used,  N
X vexp ðiÞ  vsim ði; aÞ 2 JðaÞ ¼ min (139) vexp ðiÞ i¼1 Batstone et al. (2003) assumed that the logarithm of the error is relative and therefore objective function as shown in Eq. (140) was used, JðaÞ ¼ min

N   X  2 ln vexp ðiÞ  lnðvsim ði; aÞÞ

(140)

i¼1

After an appropriate objective function is selected, it is minimized using numerical methods along with the unknown parameter determination.

5.4 Data Collection The nature of the experimental data plays a crucial role in model formulation and calibration. As data collection is quite expensive, the complexity of the model and the number of parameters to be included in the model will drive the extent of data collection. There are two issues associated with the experimental data collection for AD modelling, one issue is that most of the systems are mixed culture and therefore the ‘culture history’ is dependent on the reactor operating condition (Donoso-Bravo et al., 2011); the other concern is that parameters of a Monod’s equation cannot be uniquely determined using batch experiments (Baltes et al., 1994), which is mostly commonly used reactor configuration for collecting experimental data.

5.5 Parameter Optimization According to the study by Donoso-Bravo et al. (2011), the model optimization is achieved by optimizing the objective function to determine the parameters for model prediction improvement. Parameter optimization is based on the trial and error method. This can be cumbersome; therefore, lots of effort has been invested in the development of optimization techniques. Numerical algorithms are used to determine the optimum values of parameters with the assistance of objective function. Two algorithms are available for parameter optimization, namely local and global methods (Donoso-Bravo et al., 2011). The local methods assume objective function to be a convex function (Fekih-Salem et al., 2012). An alternative method of optimization is Bayesian approach, which treats the model parameters as random variables.

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5.5.1 Local Methods Local optimization methods are common in AD modelling (Donoso-Bravo et al., 2011). The fundamental assumption of this method is convexity of objective function. For a convex function, value at the midpoint of two conditions is less than the mean of the two conditions. If the objective function is not convex, a random search of the initial parameter values called ‘multi-start strategy’ is implemented (Gyorgy and Kocsis, 2011). Some of the local methods are described in the following subsections. 5.5.1.1 Simple Unconstrained Optimization

Steepest descent method uses the first-order information to approach the gradient. A very large number of iterations are required to achieve convergence with this method. Another method used is the GausseNewton method, which is based on the minimization of sum of squares of errors. The GausseNewton method uses linear approximation of the errors using Taylor’s theorem. LevenbergeMarquardt method (Marquardt, 1963) combines both the steepest descent method and GausseNewton method. This is most commonly applied to AD models for treating livestock manual, distillery waste and animal wastes (Martin et al., 2002). The solution determined by this method depends on the initial and current values estimated during the subsequent iterations. 5.5.1.2 Nonlinear Constrained Optimization

Sequential quadratic programming is used as a numerical solution to the nonlinear optimization problems. In this method the objective function is approximated as a quadratic problem with constraints. If there are no constraints, this method reduces to Newton method (Donoso-Bravo et al., 2011). 5.5.1.3 Multiple Shooting

In this method, the objective function is evaluated repeatedly by solving the dynamic equations numerically. Another method employed is by setting up an optimization algorithm with model differential equations as constraints (Carbonell et al., 2016). 5.5.1.4 Direct Search Methods

The family of direct search methods includes pattern search methods, simplex methods and adaptive sets of search directions (Donoso-Bravo et al., 2011). Simplex methods were used for optimizing AD models. These

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methods come in handy where the numerical derivatives of the objective function are unknown (Lewis et al., 2000). The objective function is minimized by doing search in a multidimensional parameter space. A nondegenerate simplex [as combination of nþ1 points (vertices) in an N dimensional space] is first developed and then reflection is done by replacing a vertex to determine the minimum value. The simplex is not sensitive to the local minima in comparison to the other gradient-based methods (LevenbergeMarquardt, GausseNewton). The convergence to the optimum solution is slower and closer, and there are no means to determine the sensitivity of the solution. This method was used to estimate parameters on substrate and product inhibition in AD models (Moesche and Joerdening, 1999). 5.5.2 Global Methods In nonlinear parameter identification, problems exist when there are various local minimum present for the objective function. Global methods will provide solutions in these situations. Some of the global methods used in AD process are as follows: • simulated annealing, • genetic algorithms and • particle swarm optimization. Simulated annealing is a stochastic optimization procedure where the objective function is treated analogous to internal energy of the system and the undetermined parameters will be optimized in an imaginary system (Kirkpatrick, 1984). Generic algorithms are based on the theory of evolution, the solutions of which are developed by natural evolution techniques like inheritance, mutation, selection and crossover. This method is useful when sample space is large and difficult for analytical treatment. Particle swarm optimization is a stochastic optimization method inspired from social behaviour of animals (Bai et al., 2015). This method is similar to the genetic algorithms. Some of the advantages of this method include less number of assumptions and no differentiation of objective function is required. Bai et al. (2015) used this method for parameter estimation to model effective VFA generation from waste activated sludge. 5.5.3 Bayesian Approach Bayesian approach is a probabilistic approach used in the calibration of model parameters. These model parameters are considered as random variables with probability density function. A ‘joint posterior distribution’

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of parameters is used to define the ‘subjective beliefs’ during calibration (Omlin and Reichert, 1999). The probability function of a particular parameter is expressed using Bayes’ theorem as shown in Eq. (141), pðy=aÞ$pðaÞ pðyÞ

(141)

pðy=aÞ$pðaÞ fpðy=aÞ$pðaÞ a pðy=aÞ$pðaÞ$da

(142)

pða=yÞ ¼ pða=yÞ ¼ R

in which a is model parameters vector; y represents experimental data; p(a/y) is posterior probability function (posterior beliefs after having evaluated the model residuals); p(a) is prior probability distribution (modeller prior beliefs); p(y/a) is likelihood function of the observations; p(y) is probability of observations (expected value of the likelihood function over the parameter space). Readers are referred to the work by Donoso-Bravo et al. (2011) for more information on parametric optimization.

5.6 Model Validation The purpose of the model validation is to verify if the model developed is able to simulate the experimental data. The model validation step will improve the confidence with which it can predict the reality. There are two types of validation usually employed, one is direct validation and the other is cross-validation. 5.6.1 Direct Validation For direct validation, the experimental data used for parameter estimation are employed to validate the model. Residual analysis can be performed to determine the fit. Goodness of fit is expressed in terms of coefficients like R2 coefficient, estimation of variance or analysis of randomness. 5.6.2 Cross-Validation Cross-validation is required to confirm the validity of the complex models. A new data set, other than the one used for parameter estimation, needs to be used for the cross-validation of data. If a new data source is not available, two data sets are created from the available data source, and one is used for calibration and the other is used for cross-validation (Fig. 14). Crossvalidation of data will require modification to the initial conditions. Additional calibration is required when there are changes to substrate and microorganism population.

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Calibration

Data source -1 Data set -1

Data set -2 (different from the data used for calibration)

Direct validation (check for goodness of fit)

Cross validation

If a second data source not available

Data source -2

Figure 14 Cross-validation.

5.7 Model Assessment Model assessment is the last step employed in the AD modelling. Setting up the acceptable margin of error in the model prediction and the observed data is very important. Model assessment methods are dependent on the model complexity and data availability. Some of the methods used to assess how well the model predicted data are to that of the observed data are introduced below. 1. Average relative error (ARE) ARE is expressed as the ratio of difference between model prediction and experimental data, AREð%Þ ¼

vsim  vexp  100 vsim

(143)

in which vexp is the experimental value, and vsim is the model predicted value. 2. Relative root mean square error (RMSE) RMSE is an average of the square of the errors (difference of model and experimental values or observed values) (Jacob and Banerjee, 2016), vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP 2 un  u vsim ðiÞ  vexp ðiÞ t i RMSE ¼ (144) n in which n is the number of observations; vexp(i) and vsim(i) are the experimental value and model predicted value of the ith measurement, respectively.

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3. Model efficiency (ME) ME is a measurement of the deviation of the model to the observed data (Eq. 145) (Hosaini et al., 2009), n  n  2 P 2 P vexp  vexp  vsim  vexp

ME ¼

i

n  P

i

vexp  vexp

2

(145)

i

in which vexp is the experimental value; vsim is the model predicted value; vexp and vsim are the average of the experimental and model values. 4. Coefficient of residual mass (CRM) CRM coefficient compares the residual (difference between experimental and model values) to the experimental value (Eq. 146) (Hosaini et al., 2009). n P

CRM ¼

vexp ðiÞ 

i

n P

n P

vsim ðiÞ

i

(146)

vexp ðiÞ

i

5. Sum of residual squared error (SRSE) SRSE is another metric to measure the goodness of fit (Karim et al., 2007). A less value of SRSE metric indicates a better fit,  n
X vsim ðiÞ  vexp ðiÞ 2 SRSE ¼ (147) vexp ðiÞ i 6. Correlation coefficient Correlation Coefficient (r2) is a measure of the closeness of model fit to the observed data (Poggio et al., 2016). Unlike aforementioned methods where closer to zero is indicative of better-fit, r-squared value closer to one indicates a better fit, n  P

2 vsim ðiÞ  vexp ðiÞ

r 2 ¼ 1  iP n  2 vsim ðiÞ  vexp i

(148)

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in which r2 is correlation coefficient; vexp is the experimental value; vsim is the model predicted value; n is the number of observations; and vexp is the average of the experimental value.

6. MODELLING CHALLENGES Although the elements for a good model such as model objective, model assumptions and model validation should be included (Jakeman et al., 2006), there are challenges faced by AD modeler. In some situations, uncertainty analysis may be required to provide mechanistic reason to justify the model results. The typical challenges faced during the modelling of AD process include judicious selection of initial conditions, understanding the complexity of substrates, availability of data for model validation, model overfitting and the need for modelling species generally not accounted for in the typical models.

6.1 Initial Conditions One of the aspects, which is not given significant importance in model optimization, is the initial conditions of the variables (Donoso-Bravo et al., 2011). Many people think that the initial conditions can be determined based on the experimentation and existing literature. However, in many cases, this can be very challenging. For example, it is laborious and even impossible to experimentally determine the initial concentrations of various species of microorganisms in the seed sludge of AD. A common practice is to predict the steady state of an AD process from which the seed sludge was obtained, so that the seed sludge microbial constitutes can be estimated.

6.2 Complexity There has been significant progress on AD modelling. However, mechanisms in AD processes like disintegration and hydrolysis, are quite complex, and are usually simplified. For instance, most of the AD models assume that this process follows the first-order kinetics and the calibration is performed by estimating the rate constant (Dewil et al., 2011). According to Batstone et al. (2002b), modelling the hydrolysis through surface-based kinetics could provide more accurate results. Likewise, mass transfer limitation in SS-AD is another factor which adds complexity to the model development, for

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instance, the increase in TS content causes a steeper gradient of hydrolytic production which can inhibit hydrolysis in substrate layers (Xu et al., 2014). The loading rate of the substrate affects the accuracy of the model development. Research from Vavilin and Angelidaki (2005) on AD of MSW has shown that, at high organic loading rate, low mixing was found to increase the degradation as intensive mixing could disperse the ‘methanogenic centers’, and in turn impede the degradation. Modelling rate of waste degradation for complex substrate increases the model complexity, as the parameters like moisture content, particle size, waste density, leachate recirculation and neutralization need to be accounted for in addition to the common parameters which are associated with the AD process (Vavilin and Angelidaki, 2005). Although focussing on all the processes in the AD will result in large set of parameters to deal with, depending on the model objectives, adjusting the most important parameters while keeping the other parameters constant will simplify the modelling efforts.

6.3 Data Availability The accuracy and robustness of the AD models will improve if more independent data sets are available for model calibration and validation. One of the problems AD modellers are facing is the data availability. Although most of the reactor state variables are measurable, their determination takes significant time and efforts (Steyer et al., 2006). To overcome this data limitation, all unknowns are set as parameters and then experimental data are used to calibrate the model to give reasonable predictions of the remaining unknowns. Many online monitoring techniques can be employed to generate massive real-time data in terms of TOC, alkalinity, dissolved carbon dioxide/hydrogen, VFAs and nitrogen, phosphorus species, etc. (Dewil et al., 2011). The latest trend in AD modelling is the use of software sensors (Dewil et al., 2011), which is an indirect measurement of the components required for performing the model calibration and validation so that this can be applied to predict the new scenarios. Software sensors are software that is capable of predicting nonmeasured process variables based on a mathematical model. The model-based soft sensors used in the current AD modelling applications include the following: 1. Extended Kalman filters (Dochain and Perrier, 1998): In this method an initial estimate of the unknown parameters is made and correction is applied in subsequent steps based on the actual measurements. The

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accuracy of this method lies in the judicious selection of the initial estimate. 2. Extended Luenberger observers (Mendez-Acosta et al., 2010): This method is similar to the extended Kalman filter except that the uncertain terms associated with the influent composition of the substrate are determined by the extended Luenberger observer. 3. Adaptive observers (Bastin, 1990): In this method, the microorganism growth rate represents a time-varying parameter, which simplifies the model to improve convergence. 4. Asymptotic observers (Dochain and Perrier, 1998): This global method is an intermediate of the extended Kalman filter/extended Luenberger observer and adaptive observer. For more detailed information on the soft sensors, readers are referred to the review paper by Dewil et al. (2011). To improve the AD reactor performance, mathematical modelling of the heterogeneity of the reactor contents, mass transfer/diffusion limitations and operational parameters is required. Data sharing and coordination amongst the research groups will help solve many data availability issues (Xu et al., 2015).

6.4 Parameter Interpretation The process of the parameter calibration requires a lot of trial and error effort (Donoso-Bravo et al., 2011). One may find increasing the number of parameters will increase the freedom of the model fitness to the experimental data (Donoso-Bravo et al., 2011). However, it should be noted that increase in the number of parameters with limited data will result in overfitting where model validation will be challenged. Multiple iterations are required to find reasonable solutions to the problem. As a good practice, any parameters added to a model should have a solid mechanistic meaning. More experimental data are necessary to reasonably determine the parameter values in the course of parameter calibration.

6.5 Modelling Species Unaccounted for Although AD modelling does not consider all products or microbial species involved in the AD processes, it does not mean those unaccounted factors are unimportant. Take ADM1 for instance, it does not take into account the products like lactic acid and ethanol generated by the fermentation of glucose. However, literature has shown the presence of lactic acid during high loading conditions where the reactor pH is significantly low. Modelling these species needs to be considered depending on modelling objectives

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and model complexity. It has been pointed out that, even though ADM1 is the most comprehensive AD modelling framework developed so far, many processes are not included. These include the following: • alternative products to glucose (lactate and ethanol), • diffusion limitation, • sulphate reduction and sulfide inhibition, • weak acid and base inhibition to methanogenesis, • long chain fatty acid inhibition to methanogenesis, • acetate oxidation competition with aceticlastic methanogens, • homoacetogenesis competition with methanogens and • solids precipitation. The simultaneous sulphate and iron reduction process is associated with microbial interaction between acidogens, acetogens, sulphate-reducing bacteria and iron-reducing bacteria. Modelling this concept into a structured modelling framework has not been fully established. To study the impact of the structure of microbial species on AD performance and determine the microbial populations in AD, advance molecular techniques such as polymerase chain reaction (Karthikeyan et al., 2016), DNA sequencing, fluorescent in situ hybridization (Liu et al., 2012), DNA stable isotope probing, temperature and denaturing gradient gel electrophoresis have been applied (Mata-Alvarez et al., 2014). Depending on the objective of the model, the information determined from these techniques can be useful to quantify the change when there is a change in AD conditions (Mata-Alvarez et al., 2014).

7. CONCLUSIONS State-of-the-art models have been developed to describe methane yield improvement, reactor stability enhancement, operational issue identification and waste codigestion in AD systems. These mathematical models are capable of providing better understanding, design, optimization and prediction of the performance of AD systems. Theoretical, empirical and statistical approaches were taken to derive these AD models for describing substrates characteristics, rate-limiting conditions, process inhibition, operating conditions, methane potential and production rate, liquidegas interface mass transfer, as well as the application of computational fluid dynamics. An effective modelling procedure should be adopted in the course of model structure selection, parameter estimation and model

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validation. Software sensors can be used in the field of AD as an indirect measurement for model calibration and validation. Online monitoring is encouraged to generate real-time data to avoid the data limitation for AD model improvement.

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CHAPTER THREE

Consolidated Bioprocessing Systems for Cellulosic Biofuel Production a
Ubaldo Abrego , Zhu Chena and Caixia Wan1

University of Missouri, Columbia, MO, United States 1 Corresponding author: E-mail: [email protected]

Contents 1. Introduction 2. Cellulolytic/Hemicellulolytic Systems Involved in Consolidated Bioprocessing 2.1 Cellulases 2.2 Hemicellulases and Supplemental Enzymes 3. Wild-Type Single Microorganisms for Consolidated Bioprocessing 3.1 Bacteria 3.1.1 3.1.2 3.1.3 3.1.4

Saccharophagus degradans Caldicellulosiruptor Thermoanaerobacter Clostridia

148 150 151 151

3.2 Fungi 3.2.1 3.2.2 3.2.3 3.2.4 3.2.5

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Fusaria Trichoderma Neurospora crassa White Rot Fungi Other Fungi

152 152 153 153 154

4. Strategies for Designing Ideal Consolidated Bioprocessing-Enabling Microorganisms 4.1 Native Strategy 4.1.1 Native Strategy for Free Enzyme-Producing Strains 4.1.2 Native Strategy for Cellulosome-Forming Clostridia Strains

4.2 Recombinant Strategy 4.3 Microbial Consortia for Consolidated Bioprocessing 4.3.1 4.3.2 4.3.3 4.3.4

a

144 146 146 147 148 148

154 155 155 156

157 164

Natural Consortia for Consolidated Bioprocessing Artificial Consortia Synthetic Consortia Application of Ecology in Engineering a Consolidated Bioprocessing Consortium

164 164 166 167

These authors contributed equally to this work.

Advances in Bioenergy, Volume 2 ISSN 2468-0125 http://dx.doi.org/10.1016/bs.aibe.2017.01.002

© 2017 Elsevier Inc. All rights reserved.

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5. Metagenomics and Synthetic Biology for Consolidated Bioprocessing 5.1 Metagenomics 5.2 Engineering a Consolidated Bioprocessing Strain by Synthetic Biology 6. Concluding Remarks Acknowledgement References

169 169 170 171 172 172

Abstract Biofuel production from lignocellulosic biomass helps to mitigate climate change and alleviate our reliance on fossil fuels. Great efforts have been made to produce biofuels in an economical way. However, high cost is still a major limitation for industrial production of biofuels. In this context, consolidated bioprocessing (CBP) renders a possible strategy to lower the total cost by integrating enzyme production, sugar release, and biofuel fermentation into a single unit operation. A robust CBP system entails an efficient enzyme system to liberate sugars from lignocellulosic biomass and for conversion of both hexoses and pentoses into desired biofuels with satisfying yield and productivity. It is quite difficult for a single wild-type microorganism to perform several different tasks simultaneously without compromising conversion efficiency. Metabolically engineered microbes, microbial consortia, or combination of these two approaches may outperform a single wild-type CBP strain. Engineering a single strain or consortia with desired traits requires detailed knowledge of mechanisms of enzyme action, deep insights into the interaction between different enzymes and discovery of novel metabolic pathways and enzymes. This review covers enzyme systems involved in CBP, potential wild-type CBP strains, strategies for engineering a CBP strain or consortium, as well as major barriers for conducting CBP.

1. INTRODUCTION Plant biomass is the primary source for the production of biofuels, especially alcohol fuels, via biological conversion. Starch-based biofuels trigger the debate on its competition with food/feed supply, which is also the driving force for the intensive research and development of cellulosic biofuels. A variety of lignocellulosic biomass, such as agroforestry residues and dedicated energy crops, are nonedible, low cost, abundant plant materials and have been extensively explored for the production of cellulosic biofuels. However, it is intrinsically difficult to break down lignocellulosic biomass due to its recalcitrance. Cost-effective technologies are highly demanded for overcoming biomass recalcitrance and efficiently converting carbohydrates into fuel products. Pretreatment is a necessary step to reduce biomass recalcitrance and to facilitate the release of fermentable sugars from

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biomass feedstocks. Pretreated biomass undergoes enzymatic hydrolysis (saccharification) followed by microbial conversion of fermentable sugars to biofuels. Hydrolytic enzymes, such as cellulases and hemicellulases, are required for biomass saccharification. Biofuel production with high yield, productivity and titre is the criteria for fermentation using high-performance fuel-producing microorganisms. Enzymatic hydrolysis and fermentation are the key factors determining the economic viability of biofuel production. There are four major bioprocess configurations, as shown in Fig. 1, to convert polysaccharides to biofuels. Separate hydrolysis and fermentation (SHF) is a process in which enzymatic hydrolysis and fermentation are conducted at two separate stages, each at their own optimum temperatures. This strategy allows high saccharification efficiency since hydrolytic enzymes act at their optimum temperatures (typically 50 C). Simultaneous saccharification and fermentation (SSF) is a process in which enzymatic hydrolysis and fermentation is simultaneously carried out in one pot, which can overcome the inhibition to enzymatic hydrolysis due to glucose consumption by fermentation. The main drawback of SSF is the reduced saccharification rate since enzymes have a higher optimum temperature than fuel-producing microbes. Simultaneous saccharification and cofermentation (SSCF) further combines the conversion of both hexoses and pentoses in a single unit operation. Nevertheless, neither of the above three processes (SHF, SSF or SSCF) eliminates the addition of exogenous enzymes to the processes. In other words, enzymes are produced in a separate process and then externally added to SHF or SSF/SSCF processes. In contrast,

Figure 1 Different bioprocessing strategies based on biochemical conversion.


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consolidated bioprocessing (CBP) integrates hydrolytic enzyme production, biomass saccharification and fermentation in a single unit operation. CBP technology has the potential to lower the cost of biomass processing as it saves the operating and capital cost associated with enzyme production as featured in the other three process configurations. While the concept of CBP can be applied to any biomass-based chemicals or fuels, it is currently most oriented towards biofuel production. Industrial CBP has become a reality for starch-based ethanol production using an engineered yeast. CBP for cellulosic biofuels still faces the challenges for being implemented commercially. The most significant challenge is high efficiency of enzyme production without compromising the titres and yields of biofuels. The possible strategy is to enable microorganisms for CBP that possess both high hydrolytic activities and fuelproducing capability. This chapter has an emphasis on strategies for designing CBP-enabling microorganisms, including engineered single cultures and microbial consortia. It also reviews hydrolytic enzyme systems harboured in naturally occurring CBP strains and discusses the major barriers for implementing CBP technologies.

2. CELLULOLYTIC/HEMICELLULOLYTIC SYSTEMS INVOLVED IN CONSOLIDATED BIOPROCESSING Lignocellulosic biomass has heterogeneous, complex structure especially exemplified by hemicellulose and lignin. Hemicellulose comprises backbones such as xyloglucan, glucomannan and glucoronoxylan, depending on the types of plant species. Microorganisms with cellulolytic/hemicellulolytic activities secrete cellulases and hemicellulases for decomposing biomass (Parisutham et al., 2014). An overview of both enzyme systems and their expression/secretion by hydrolytic microorganisms is provided in the below two subsections.

2.1 Cellulases There are two cellulolytic enzyme systems produced by naturally occurring microorganisms. One is composed of free enzymes expressed and secreted mostly by aerobic microorganisms into culture media and the other one is constituted of mini-cellulosomes tethered onto cell wall of anaerobic microorganisms. Cellulases found in either system possess a catalytic domain (CD) and a carbohydrate-binding module (CBM). Both the CBM and CD

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are connected by a peptide with abundant threonine and serine residues (Kuhad et al., 2011; Zhang and Zhang, 2013). The complete hydrolysis of cellulose relies on synergistic action of three major free cellulases: (1) endoglucanases (EGs) (1,4-b-D-glucan 4-glucanohydrolases; EC 3.2.1.4); (2) exoglucanases, including cellodextrinases (1,4-b-D-glucan glucanohydrolases; EC 3.2.1.74), and cellobiohydrolases (CBHs) (1,4-b-D-glucan cellobiohydrolases) and (3) b-glucosidases (BGLs) (b-glucoside glucohydrolases; EC 3.2.1.21) (la Grange et al., 2010). EGs cleave 1,4-b-D-glucosidic bonds randomly at nonreducing ends, yielding cellobiose and cellooligosaccharides as hydrolysis products. Exoglucanases cleave both reducing and nonreducing ends of cellulose chains to release cellobiose and glucose as end products. BGLs hydrolyse cellobiose into glucose, which eliminates cellobiose inhibition (Percival Zhang et al., 2006). Mini-cellulosomes are the predominant enzyme systems found in anaerobic hydrolytic bacteria and considered the most efficient system for degrading cellulose (Bayer et al., 2008; Hyeon et al., 2013; Xu et al., 2013). Cohesin-containing scaffoldin(s) and dockerin-containing enzymes are the components that distinguish cellulosomes from free enzyme systems (Bayer et al., 2004). Scaffoldin is a protein including nine domains of cohesins and one domain for CBM, where cohesins interact with dockerins to affix hydrolytic enzymes (cellulases or similar) to cellulosome. Synergistic activities of different enzymes in cellulosome and tight, specific binding affinity with substrates enable efficient hydrolysis of cellulose.

2.2 Hemicellulases and Supplemental Enzymes Microorganisms also require hemicellulases and other supplemental enzymes to depolymerize highly heterogeneous, complex hemicellulose. In coniferous plants (e.g., softwood), mannan is the backbone of hemicellulose while deciduous plants, such as herbaceous plants and hardwood, comprise xylan as the backbone of hemicellulose. Depending on plant materials and their maturity, the backbone is linked with many side groups to a varying degree, further rendering the complexity and heterogeneity of hemicellulose. Xylose/mannose units in the backbone chains are generally linked to O-acetyl groups, L-arabino furanoside, or D-glucuronic acid residues, which may be further acetylated or methylated (Saha, 2003). Other minor components are D-galactose and L-arabinose linked to ferulic acid moieties. Therefore, hemicellulolytic microorganisms utilize a complex cocktail of enzymes to decompose hemicellulose, including endo-xylanases, b-xylosidase, endo-mannanases, endo-a-L-arabinanase,


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a-L-arabinofuranosidases, a-D-glucuronidases, a-galactosidases and many other supplemental enzymes (Shallom and Shoham, 2003). It was revealed that adding Fusarium verticillioides secretome to commercial cellulases enhanced the release of xylose and arabinose from wheat straw due to the presence of a broad range of hemicellulase and pectinase in the secretome (Ravalason et al., 2012). A proteomic analysis of the notable xylanase-producing strain, Thermomyces lanuginosus, found that multiple enzymes, including xylanase GH11 and b-xylosidase GH43, are involved in hemicellulose saccharification (Winger et al., 2014). The hydrolysis of wheat straw and wheat bran by Thermobacillus xylanilyticus revealed that this strain secreted four hemicellulases (two xylanases, one arabinosidase and one esterase), which worked synergistically to release simple sugars and phenolic acids (Rakotoarivonina et al., 2014).

3. WILD-TYPE SINGLE MICROORGANISMS FOR CONSOLIDATED BIOPROCESSING In nature, some fungi and bacteria have evolved sophisticated metabolic systems to decompose lignocellulosic biomass, and some valueadded products could be formed during lignocellulose degradation when suitable conditions are provided. Although up to date, none of these lignocellulose degraders can produce biofuels at satisfying levels, their potential in CBP could not be ignored. This section mainly discusses the naturally occurring fungi and bacteria that are considered potential candidates for CBP (Table 1).

3.1 Bacteria Many bacteria possess an array of hydrolytic activities and are capable of converting polysaccharides into valuable metabolites. The major genera with CBP capability are discussed later. Bacteria belonging to Clostridia have attracted extensive interest due to their ability to degrade lignocellulose and produce ethanol. Besides the notable Clostridia, some novel bacteria with unique features, such as high-biomass degradation efficiency and ability to grow at extremely thermophilic condition, were discovered to be potential CBP strains. 3.1.1 Saccharophagus degradans Saccharophagus degradans 2e40 is a marine bacterium capable of degrading a broad spectrum of substrates (e.g., agar, chitin, cellulose, fucoidan, xylan)

Table 1 Representative native consolidated bioprocessing-enabling microorganisms for biofuel production from lignocellulosic biomassa Microorganisms Substrates Pretreatment Biofuel productionb References

Xylan-extracted corncob residue Corn stover Bagasse

/

23 g/L ethanol

Lewis Liu et al. (2012)

AFEX Mild alkali

Jin et al. (2012a,b) Kundu et al. (1983)

Crystalline cellulose

N/A

7.0 g/L ethanol 1.09 g/L ethanol and 21% yield 2.66 g/L ethanol

Transgenic switchgrass

Diluted acid

0.33 g/g carbohydrate

Yee et al. (2012)

Filter paper

/

81% ethanol yield

Balusu et al. (2005)

Sugarcane bagasse cellulose Wheat straw

/

40e60 g/L ethanol and 70% yield 0.28 g ethanol/g straw

Maehara et al. (2013)

F. oxysporum F3

Brewer’s spent grain

Alkali

Fusarium verticillioides

Sugarcane bagasse

Alkali

Phlebia sp. MG 60

Sugarcane bagasse

/

Phlebia sp. MG 60

Unbleached hardwood kraft pulp (UHKP) Cellulose, barley straw, and hemp Xylan

/

Thermoanaerobacterium AK54 Thermoanaerobacterium saccharolyticum B6A-RI

Acid, alkali

0.11 g ethanol/g dry mass and 60% yield 4.6 g/L ethanol and 75% yield 0.11 g ethanol/g pretreated bagasse 8.4 g/L ethanol and a yield of 0.42 g/g UHKP 1.11 g/L ethanol

/

1.75 g/L ethanol

Christakopoulos et al. (1991) Xiros and Christakopoulos (2009) de Almeida et al. (2013) Khuong et al. (2014) Kamei et al. (2014) Sigurbjornsdottir and Orlygsson (2012) Lee et al. (1993)

This table is adapted from Salehi Jouzani, G., Taherzadeh, M.J., 2015. Advances in consolidated bioprocessing systems for bioethanol and butanol production from biomass: a comprehensive review. Biofuel Research Journal 2, 152e195. b Ethanol yield is reported as the percentage of the theoretical yield, unless stated otherwise.

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a

/

Dharmagadda et al. (2010)

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Clavispora NRRL Y-50464 Clostridium phytofermentans Clostridium thermocellum ATCC 27405 C. thermocellum ATCC 27405 C. thermocellum ATCC 27405 C. thermocellum ATCC 27405 Flammulina velutipes FV-1 Fusarium oxysporum

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(Ekborg et al., 2005). It was also reported that S. degradans 2e40 displays physiological evidence of ligninolytic activity (Weiner et al., 2008), but is unable to use lignin as the sole carbon source to support self-growth (Munoz et al., 2008). Genomic analysis revealed that S. degradans 2e40 secreted multiple GH5-, GH6-, GH9-containing b-1,4-endoglucanases (Taylor et al., 2006). A study on genome annotation, metabolic profiling and biochemical activities of the select genes indicated that this strain produced all the required enzymes to directly utilize xylan and arabinogalactan of marine and terrestrial origins (Hutcheson et al., 2011). Despite that S. degradans 2e40 has a very versatile hydrolytic enzyme system, it is unable to produce either ethanol or butanol from lignocellulosic biomass due to the lack of related metabolic pathways. Up until now, this strain is only used as a CBP strain for producing polyhydroxyalkanoate from cellulose under nitrogen limiting condition (Munoz et al., 2008). 3.1.2 Caldicellulosiruptor Caldicellulosiruptor is a genus of extremely thermophilic microbes with an optimum growth temperature around 80 C. It can simultaneously decompose lignin and carbohydrates in untreated biomass and also efficiently coutilize both C6 and C5 sugars. Due to such versatile metabolism, Caldicellulosiruptor has great potential for industrial conversion of biomass (Kataeva et al., 2013). Caldicellulosiruptor strains are able to produce hydrogen as one major metabolite by directly utilizing the polysaccharides in lignocellulosic complex. Thus, initial efforts have been made to use them as CBP strains for producing H2 from a variety of lignocellulosic biomass. For example, Caldicellulosiruptor saccharolyticus DSM 8903 was able to directly ferment switchgrass into H2 with an yield of 11.2 mmol H2/g switchgrass (Talluri et al., 2013). Despite having an array of active hydrolytic enzymes and capability of H2 production, most Caldicellulosiruptor strains lack the metabolic pathways for ethanol production. Thus, researchers have been seeking wild-type, ethanol-producing Caldicellulosiruptor or endowing the strains with the capability of ethanol production. Svetlitchnyi et al. isolated seven new Caldicellulosiruptor strains, which can produce ethanol when growing on pure cellulose as well as pretreated lignocellulosic biomass. These isolated strains produced up to 72 mM ethanol along with up to 97% degradation of holocellulose (cellulose and hemicellulose) in pretreated biomass (Svetlitchnyi et al., 2013).

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3.1.3 Thermoanaerobacter Thermoanaerobacter species possess an EmbdeneMeyerhofeParnas pathway with ethanol and lactate as major fermentation products (Taylor et al., 2009). Among various thermoanaerobacters, Thermoanaerobacter saccharolyticum has captured much attention as it can secrete endoxylanase and b-xylosidase to depolymerize xylooligosaccharides into xylose, although it does not harbour any cellulase secretion system (Schuster and Chinn, 2013). The researchers also found other Thermoanaerobacter strains with CBP capability. Thermoanaerobacterium thermosaccharolyticum M18 can directly utilize pure cellulose and xylan as well as raw lignocellulosic biomass (e.g., wheat straw, corn cob, corn stalk). Cao et al. (2014) reported that this strain degraded 56.07%e62.71% polysaccharides in untreated lignocellulosic biomass, yielding 3.23e3.48 mmol H2/g substrate. T. thermosaccharolyticum DD32 can convert cellulose into H2 with a yield of 12.08 mmol H2/g Avicel cellulose under the optimum conditions. The strain can also produce H2 directly from raw corn stover, reaching 6.38 mmol H2/g corn stalk and 44.29% lignocellulose degradation after 72 h of cultivation (Sheng et al., 2015). 3.1.4 Clostridia Clostridia is a notable class of bacteria for CBP due to its capability of utilizing various carbon sources, diverse metabolic pathways and high toxicity tolerance (Olson et al., 2012). For example, cellulolytic activity of cellulosome in Clostridium thermocellum could be promoted by formate and acetate and demonstrated higher tolerance to ethanol and higher thermostability than commercial fungal cellulase (produced by Trichoderma reesei) (Xu et al., 2010). C. thermocellum was reported for direct ethanol production from hemp, but its ethanol production profile was quite feedstock dependent (Agbor et al., 2014). Unlike C. thermocellum that uses a cellulosome system, Clostridium phytofermentans secretes free enzymes to degrade the major plant cell wall components at a relatively fast rate for ethanol production (Argyros et al., 2011). It was reported that C. phytofermentans DSM1183 was able to directly convert algal biomass (2%, w/v) into ethanol with a final concentration of 0.52 g/L (Fathima et al., 2016). When using AFEX-treated corn stover as the substrate, C. phytofermentans (ATCC 700394) produced 2.8 g/L ethanol along with the degradation of 76% glucan and 88.6% xylan ( Jin et al., 2011).

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3.2 Fungi Although many microorganisms are able to produce hydrolytic enzymes to break down lignocellulosic biomass, only fungi naturally produce cellulase with a titre that can meet the needs of industrial biofuel production (Xu et al., 2009). Moreover, some fungi with comprehensive hydrolytic enzyme systems were discovered with CBP capability. Monillia sp. is the first CBP-enabling fungus for cellulosic ethanol production (Gong et al., 1981). This subsection mainly discusses fungi belonging to the genera of Fusaria, Neurospora, Trichoderma, Phlebia and Aspergillus. These genera have been reported to directly ferment cellulose or lignocellulosic biomass into ethanol (Amore and Faraco, 2012). 3.2.1 Fusaria Fusaria has the enzymatic arsenal required for hydrolysing lignocellullosic biomass into fermentable sugars and further converting them into ethanol under anaerobic or microaerobic conditions. Fusaria is believed to be ideal for cellulase production at industrial scales as they produce cellulases with intriguing properties, such as high tolerance to ionic liquids (Amore and Faraco, 2012). Xu et al. (2015) reported that Fusarium oxysporum BN isolated from a chemically polluted environment produced cellulase with high tolerance to 1-ethyl-3-methylimidazolium phosphinate. When cultured under optimum fermentation conditions, the strain reached 64.2% of the theoretical ethanol yield (Xu et al., 2015). With 40 g/L pretreated sugarcane bagasse as a substrate, F. verticillioides produced 4.6 g/L ethanol (de Almeida et al., 2013). Another Fusaria strain, F. oxysporum F3, produced 14.5 g/L ethanol at 53.2% of the theoretical yield when it was cultured under nonaerated conditions (Christakopoulos et al., 1989). Although Fusaria strains demonstrate promising cellulosic ethanol production, their ethanol yield and productivity are still low compared to those obtained using pure sugars as carbon sources. This is partially due to the slow growth of Fusaria and unsatisfying hydrolytic enzyme activities. Some strategies have been proposed to improve hydrolytic activity specifically for certain feedstocks. For example, F. oxysporum inserted with genes encoding endo-b-1,4-xylanase produced approximately 60% more ethanol from corn cob than the wild-type strain, since corn cob is rich in xylan and requires high xylanase activities for hydrolysis (Anasontzis et al., 2011). 3.2.2 Trichoderma T. reesei is a well-known aerobic cellulase-producing strain, harbouring 7 genes encoding cellulase and 16 genes encoding hemicellulase (Martinez et al., 2008).

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Its cellulolytic machinery and mechanisms are well studied, and tools for gene manipulation are well developed (Schuster and Schmoll, 2010). In addition to complete hydrolytic enzyme systems, T. reesei also harbours metabolic pathways for ethanol production. Although T. reesei has been shown to be able to directly utilize pure cellulose for ethanol production (Stevenson and Weimer, 2002; Xu et al., 2009), its ethanol titre and productivity are far below those achieved by popular ethanol-producing yeasts or even other fungi. Therefore, the use of T. reesei for ethanol production is still at its early stage, and many obstacles need to be overcome. 3.2.3 Neurospora crassa The fungus Neurospora crassa can synthesize and secrete multiple enzymes for holocellulose degradation, making this strain a good candidate for biomass hydrolysis. Prior studies also showed its CBP capability, which was first revealed by the direct fermentation of pretreated wood (50 g/L) into ethanol (6 g/L) (Rao et al., 1985). About 90% ethanol yield was obtained by N. crassa when 2% alkaline-pretreated cellulose powder was used as the substrate (Deshpande et al., 1986). The same study also showed that nearly a theoretical ethanol yield was obtained from 1% Avicel when N. crassa was cultured after 4 days under the optimum conditions. Fermentation efficiency of this strain is significantly affected by inhibitory compounds in pretreated feedstocks, which is also commonly observed from typical ethanol-producing strains. Dogaris et al. (2012) reported that N. crassa only achieved 24.8% ethanol yield when pretreated, undetoxified sorghum bagasse was used for fermentation. 3.2.4 White Rot Fungi White rot fungi possess hydrolytic and ligninolytic activities for decomposing lignocellulosic biomass. Some white rot fungi also produce useful metabolites in addition to lignocellulose-degrading enzymes. Phlebia sp. MG-60 is a recently discovered CBP-enabling white rot fungi. This strain was reported to produce 8.4 and 4.2 g/L ethanol from 20 g/L unbleached hardwood kraft pulp and waste newspaper, respectively (Kamei et al., 2012b). In addition, Phlebia sp. MG-60 delignifies lignocellulosic biomass under aerobic conditions. By culturing the strain under aerobic conditions first and then switching to semiaerobic conditions, the strain was able to remove 40.7% of lignin in oak wood, and produced ethanol with 43.9% of the theoretical yield (Kamei et al., 2012a). Trametes hirsuta is another white rot fungus capable of utilizing various carbon sources for ethanol production. Using rice straw as the substrate,

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this strain produced ethanol with 3.0 g/L titre and 57.4% of the theoretical yield. Trametes versicolor IT-1 also shared some similarities with other CBPenabling white rot fungi. It can convert microcrystalline cellulose and wheat straw to ethanol with titres of 1.6 and 1.7 g/L, respectively (Kozhevnikova et al., 2016). Another Trametes species, T. versicolor KT9427, also produced ethanol with nearly theoretical yields from a variety of simple sugars. In addition, this strain exhibited favourable conversion of pure polysaccharides (e.g., cellulose, xylan, starch) and lignocellulosic biomass (e.g., wheat bran, rice straw) into ethanol (Okamoto et al., 2014). In addition to the genera of Phlebia and Trametes, Schizophyllum commune NBRC 4928 is another white rot fungus that possesses ligninolytic and hydrolytic activities as well as the capability of ethanol production (Horisawa et al., 2015). 3.2.5 Other Fungi In addition to the aforementioned fungi for cellulosic ethanol CBP, some recently isolated fungi also showed potential CBP capability for ethanol and other interesting metabolites. A unique oleaginous fungi, Cunninghamella echinulata FR3, can degrade the whole untreated plant cell wall for lipid production (Xie et al., 2015). Another study investigated the CBP capabilities of 19 Aspergillus strains and found that all of them can directly use cellulose for ethanol production but with limited capability (Skory et al., 1997).

4. STRATEGIES FOR DESIGNING IDEAL CONSOLIDATED BIOPROCESSING-ENABLING MICROORGANISMS Ideal CBP-enabling microorganisms should be capable of producing hydrolytic enzymes with high efficiency while simultaneously converting released fermentable sugars into alcohols. The specific traits of such microorganisms thus include suitably high production of hydrolytic enzyme cocktails, rapid saccharification of biomass, coutilization of multiple simple sugars (e.g., glucose, cellobiose, xylose) and tolerance to toxic compounds derived from pretreatment and fermentation end products. Finding or engineering a single strain or consortium with the above traits is necessary for efficient CBP. Up to now, wide type microorganisms discovered with CBP capability are far below the expectation for efficient alcohol production. A few strategies, such as native, recombinant and consortia, as

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discussed below, have been proposed to design ideal CBP-enabling microorganisms.

4.1 Native Strategy Native strategy often refers to modifying native cellulolytic strains, including free enzyme-producing strains and cellulosome-forming strains, to improve their product-related properties. Some cellulolytic microorganisms, such as Clostridum sp., Bacillus subtilis, and T. reesei, are able to produce ethanol or hydrogen as secondary metabolites but only in very small amounts. To improve their capability for producing important metabolites, some approaches, including isolation and characterization of new species, adaptive evolution and CBP optimization, have been used. Many examples have demonstrated the feasibility of manipulating some cellulolytic strains, such as Clostridia and Thermobifida, for the CBP application. 4.1.1 Native Strategy for Free Enzyme-Producing Strains Thermobifida fusca is a hydrolytic actinobacterium, producing multiple extracellular enzymes for the decomposition of lignocellulosic biomass, such as endocellulases, xylanases and mannanase. By harnessing its hydrolytic activities and introducing pathways for 1-propanol production, T. fusca produced 1-propanol from untreated lignocellulosic biomass under aerobic conditions. By inserting into T. fusca the genes encoding bifunctional butyraldehyde/alcohol dehydrogenase, the strain produced 0.48 g/L 1-propanol from switchgrass (Deng and Fong, 2011). Early efforts were also made to enable T. saccharolyticum to produce ethanol since it has outstanding xylanase activity and capability to use a wide spectrum of substrates. Elimination of the genes responsible for lactic acid production in T. saccharolyticum enhanced the acetic acid and ethanol yields (Desai et al., 2004). Depleting genes involved in organic acid formation in T. saccharolyticum led to ethanol as the dominant fermentation product with a maximum titre of 37 g/L (Desai et al., 2004). Caldicellulosiruptor bescii secretes many free cellulases, of which CelA plays an important role in the hydrolysis of lignocellulosic biomass as it has EG activity essential for breaking b-1,4 glycosidic bonds (Young et al., 2014). C. bescii with L-lactate dehydrogenase gene (ldh) deleted had reduced formation of lactic acid but more H2 production from cellobiose. When switchgrass was used as the sole carbon source, the engineered strain produced more than 55% H2 than the wild-type one (Cha et al., 2013). In another study, C. bescii was engineered by deleting the gene encoding

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lactate dehydrogenase and heterologously expressing a bifunctional acetaldehyde/alcohol dehydrogenase. The resultant engineered strain produced 12.8 mM ethanol directly from 2% (w/v) switchgrass, accounting for 70% of all the detected fermentation products (Chung et al., 2014). However, acetaldehyde/alcohol dehydrogenase introduced into C. bescii is only stable at temperatures below 65 C, which is much lower than the optimum temperature for its growth. To tackle this problem, C. bescii was inserted with the adhB and adhE genes from Thermoanaerobacter pseudethanolicus 39E and then proved to produce ethanol (0.4 mM) at 75 C from switchgrass (Chung et al., 2015). Besides cellulolytic enzymes, sugar transporters and enzymes involved in glycolysis also play a pivotal role in governing the efficiency of lignocellulose conversion into ethanol. Ali et al. (2013) found that Hxt gene in F. oxysporum is regulated by glucose concentration and in turn affects the uptake of both hexoses and pentoses. The overexpression of Hxt gene enhances F. oxysporum’s CBP capability and resulted in 39% increase in ethanol yield. The overproduction of phosphoglucomutase and transaldolase in F. oxysporum increased the specific activities of these two enzymes by 20% and 15%, respectively, under aerobic conditions (Anasontzis et al., 2014). In another study, an engineered F. oxysporum, FF11, with both phosphoglucomutase and transaldolase overproduced, showed significantly higher ethanol yield and lower acetic acid yield. More than 20 g/L ethanol was produced by this engineered strain after 139 h of fermentation under anaerobic conditions. In contrast, the wild-type one only produced 10 g/L ethanol with 0.080 g/L/h productivity (Anasontzis et al., 2016). 4.1.2 Native Strategy for Cellulosome-Forming Clostridia Strains C. thermocellum is a thermophilic, cellulosome-forming anaerobic bacterium and has the capability of producing ethanol directly from holocellulose. Researchers started to use it for fermenting cellulose into ethanol several decades ago. Date back to 1982, Zertuche and Zall used C. thermocellum to produce ethanol from cellulose, obtaining 9 g/L ethanol and 72.3% of the theoretical yield. Despite its long research history and unique features, wild-type C. thermocellum could not meet the industrial needs for producing fuels because a variety of other fermentation products are also produced with significant amounts during fermentation, including carbon dioxide (CO2), hydrogen (H2), lactate, formate and acetate (Akinosho et al., 2014). Thus, metabolic engineering is often applied to minimize the formation of undesired products and enhance the production of the most desired

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products. C. thermocellum with lactate dehydrogenase (ldh) and phosphotransacetylase (pta) genes knocked out could directly convert transgenic switchgrass pretreated by dilute acid to ethanol, which accounted for about 90% of the total metabolites, while the wild-type strain yielded acetic acid and ethanol at a ratio of about 2:1 (Yee et al., 2014). A similar study also showed that the ethanol yield was tripled by removing the pathways in C. thermocellum for the formation of acetate, lactate, formate and hydrogen (Papanek et al., 2015). Another barrier to the CBP application of C. thermocellum is its low tolerance to ethanol. Although the active synthesis of cellodextrin is proposed to be a mechanism for stress resistance to ethanol (Zhu et al., 2013), more understanding to the tolerance mechanism for C. thermocellum is needed to tackle this challenge and improve the applicability of C. thermocellum as a CBP strain. Clostridium cellulovorans is another anaerobic bacterium with the ability to selectively develop a cellulosome structure when growing on cellulose. Genomic analysis of C. thermocellum, C. cellulovorans and Clostridium cellulolyticum revealed that C. cellovorans is equipped with the most efficient cellulosome for degrading biomass (Tamaru et al., 2010). In addition, C. cellovorans can metabolize almost all the compounds derived from biomass, and it produced butyric acid as the main metabolite (Yang et al., 2015). Butyric acid can be readily converted to n-butanol, which is catalysed by alcohol/aldehyde dehydrogenase. In a recent work, C. cellovorans inserted with an aldehyde/alcohol dehydrogenase (adhE2) gene was able to produce 1.42 g/L of n-butanol and 1.60 g/L of ethanol directly from cellulose (Yang et al., 2015). C. cellulolyticum has similar properties to C. thermocellum and C. cellulovorans and is able to metabolize a wide range of carbon sources. However, C. cellulolyticum possesses a different fermentative capability and undergoes mixed acid fermentation. The typical metabolites include acetate, lactate, formate and ethanol, neither of which appears to be a dominant product. Engineered C. cellulolyticum, with the genes encoding lactate and malate dehydrogenase deleted, can produce ethanol as the dominant metabolite. The ethanol production by this mutant was 8.5 times that obtained with the wild-type one (Li et al., 2012) (Table 2).

4.2 Recombinant Strategy Recombinant strategy aims to endow fuel-producing microorganisms with hydrolytic activities (Linger and Darzins, 2013; Mazzoli, 2012). For solventogenic Clostridia species, none of the strains can utilize cellulose

References

Caldicellulosiruptor bescii

C. thermocellum

Sequential passaging

C. thermocellum

Deletion of ldh and phosphotransacetylase gene (pta) Disruption of six different chromosomal genes (cipA, hfat, hyd, ldh, pta and pyrF) Engineered ‘malate shunt’ pathway

Clostridium cellulolyticum

C. thermocellum

C. thermocellum

C. thermocellum

Insertion of pyruvate decarboxylase gene (pdc) and alcohol dehydrogenase gene (adh) from Zymomonas mobilis

Switchgrass

Ethanol accounted for 70% of fermentation product, and acetate production decreased by 38% compared to the wild type

Chung et al. (2014)

Switchgrass or cellulose

Ethanol concentration 8.5 times more than the wild type

Li et al. (2012)

Poplar hydrolysate Cellulose Avicel (92 g/L)

Improved ethanol production and substrate tolerance Improved ethanol tolerance (up to 80 g/L) Significant increase in ethanol production (38 g/L ethanol)

Linville et al. (2013) Williams et al. (2007) Argyros et al. (2011)

Cellobiose or Avicel

1.8 g/L ethanol titre and 56% yield

Mohr et al. (2013)

Avicel or cellobiose (5 g/L) Cellulose

Improved ethanol yield (3.25-fold higher than the wild type) 53% increase in ethanol and 150% increase in cellulose consumption

Deng et al. (2013)

Guedon et al. (2002)


Ubaldo Abrego et al.

C. thermocellum

Deletion of lactate dehydrogenase gene (ldh) and insertion of alcohol/ acetaldehyde dehydrogenase genes (adh/aldh) from Clostridium thermocellum Disrupt of ldh and L-malate dehydrogenase (mdh) genes Direct evolution

158

Table 2 Cellulolytic microorganisms engineered to be ethanologenic/solventogenic by native strategya Microorganisms Engineering approaches Substrates New traitsb

Overexpression of the sugar transporter (Hxt)

Straw, glucose or xylose

Inseration of endo-b-1,4-xylanase gene under control of the gpdA promoter Geobacillus Disruption of Ldh and thermoglucosidasius pyruvate formate lyase gene (pfl) as well as upregulated expression of pyruvate dehydrogenase Klebsiella oxytoca Insertion of pdc, adhB genes from Z. mobilis and endoglucanase genes from Erwinia chrysanthemi Thermoanaerobacterium Knockout of genes saccharolyticum involved in organic acid formation Trichoderma reesei Genome shuffling for CICC 40360 ethanol production and tolerance

Corn cob or wheat bran

F. oxysporum

Cellobiose or cellulose

39% increase in ethanol yield and significantly enhanced sugar transport capacity 60% more ethanol than the wild type and higher extracellular xylanase activities

Ali et al. (2013)

Enhanced ethanol production with higher yield and productivity, reaching 90% yield

Cripps et al. (2009)

Amorphous cellulose

Anasontzis et al. (2011)

Enhanced endoglucanase activity, cellulose conversion and ethanol production (58%e76% yield) Avicel (50 g/L) Ethanol (37 g/L) as the main fermentation product

Zhou and Ingram (2001)

Sugarcane bagasse (50 g/L)

Huang et al. (2014)

Fourfold increase in ethanol production (3 g/L) over the wide type

Consolidated Bioprocessing Systems

Fusarium oxysporum

Shaw et al. (2008)

a

This table is adapted from Salehi Jouzani, G., Taherzadeh, M.J., 2015. Advances in consolidated bioprocessing systems for bioethanol and butanol production from biomass: a comprehensive review. Biofuel Research Journal 2, 152e195. Ethanol yield is reported as the percentage of the theoretical yield, unless stated otherwise.

b

159

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directly as a substrate for ABE fermentation (Mazzoli, 2012). To engineer a solventogenic strain to be a CBP strain, free enzyme systems or cellulosome systems can be introduced to the strains. However, the secretion of multiple enzymes makes engineering a noncellulolytic microorganism very complicated. In terms of eukaryotic systems, yeasts are the forerunner because of their inherent capability of expressing heterologous proteins. Their ability to glycosylate proteins can also protect cellulases from degradation by proteases and help increase the affinity of cellulase for cellulose. Yeasts also have the ability to display mini-cellulosomes on cell surface (Lambertz et al., 2014). Saccharomyces cerevisiae, Escherichia coli and B. subtilis can be used as model strains for being engineered into a CBP strain by recombinant strategy. Basically, two approaches, namely enzyme secretion and surface display, are applied to endow a strain with cellulolytic activity. For the former one, enzymes are secreted extracellularly and act discretely on the substrate. This strategy is relatively simple and has been successfully applied to construct strains capable of utilizing cellulose directly to produce biofuels. By integrating genes encoding EG, cellobiohydrolase and b-glucosidase into its genome, an engineered S. cerevisiae strain can convert phosphoric acid swelling cellulose and pretreated rice straw into ethanol with titres of 7.6 and 7.5 g/L, respectively (Yamada et al., 2011a). By inserting cellulase expression and D-limonene synthesis pathways into E. coli (W€altermann et al., 2005), D-limonene, a fuel precursor, can be directly produced from the ionic liquidpretreated switchgrass in a one-pot process (Frederix et al., 2016). Although metabolic engineering enables the solventogenic strains to hydrolyse cellulose directly, it may be burdensome for a host strain to efficiently coexpress all the required enzymes at high levels. In addition, low cell densities with low expression of heterologous genes are often observed with recombinant CBP strains. The design of an efficient enzyme expression system for a host strain becomes another challenge for CBP. As enzyme secretion is dependent on signal peptides (SPs) (Inokuma et al., 2016), the efforts toward replacing native SPs with more efficient SPs were made to enhance enzyme secretion. Another useful strategy is diploidization that proved to improve the expression of heterologous genes (Liu et al., 2015). Compared with enzyme secretion system, surface-displayed cellulases can achieve better hydrolysis efficiency in many cases due to the improved stability since cellulases are immobilized on cell wall (Pack et al., 2002). In one study, S. cerevisiae surface-displaying EG and cellobiohydrolase I (CBHI) showed higher ethanol production than the one secreting free

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enzymes. In addition, after repeated batch fermentation for three cycles, the strain using surface display retained 60% of cellulose degradation efficiency of the first batch, 1.7 fold higher than that with a secretion system (Liu et al., 2015). However, hydrolysis efficiency may also be decreased by surface display due to hydrolysis inefficiency of processive enzymes (den Haan et al., 2015). Thus, enzyme characteristics and its reaction mechanism should be taken into account when selecting the optimum enzyme production system (Liu et al., 2015). It should be noted that the hydrolysis of cellulose into glucose depends on the synergy between different cellulolytic enzymes, i.e., EGs, exoglucanases and BGLs (Hasunuma and Kondo, 2012). It is hypothesized that via intramolecular and intermolecular proximity synergies, cellulose degradation can be enhanced by cellulosome systems probably due part to less competition to binding sites (Xu et al., 2013). Consequently, compared with a free enzyme system, cellulosome can be more efficient in cellulose degradation. A comparison study of wild-type C. thermocellum, C. bescii, and Caldicellulosiruptor obsidiansis indicated that C. thermocellum harbouring cellulosome only needs half of the time used by C. obsidiansis harbouring enzyme secretion system to hydrolyse 75% cellulose from dilute acidpretreated Populus (Yee et al., 2015). Thus, researchers have made efforts to assemble cellulosomes on cell surface. S. cerevisiae with surface display of a trifunctional mini-cellulosome directly converted cellulose to ethanol (Wen et al., 2010). In another study, S. cerevisiae produced 1.4 g/L ethanol from microcrystalline cellulose when it was engineered with surface display of cellulosomes with two miniscaffoldins (Fan et al., 2012). In addition to cellulases mentioned above, there is a new type of enzyme, called lytic polysaccharide mono-oxygenases (LPMOs), which work synergistically with cellulase (Vermaas et al., 2015). LPMOs primarily act on crystalline cellulose and have great potential to improve cellulose hydrolysis (Cannella et al., 2016). Incorporation of LPMOs from T. fusca into cellulosome increased cellulose hydrolysis by 1.7 folds over free enzymes with LPMOs and by 2.6 folds over free enzymes without LPMOs (Arfi et al., 2014). Liang et al. (2014) incorporated LPMOs and cellobiose dehydrogenases into the trifunctional mini-cellulosomes of S. cerevisiae via surface display, obtaining an ethanol titre of 2.7 g/L, which was 30% higher than that reported in their prior study on trifunctional mini-cellulosomes without LPMOs (Wen et al., 2010). However, LPMOs are oxygen dependent and exclusively found in aerobic microorganisms, which makes it difficult to be directly used under anaerobic conditions which are favoured by most fuel-producing microorganisms (Arfi et al., 2014) (Table 3).

Escherichia coli

Kluyveromyces marxianus K. marxianus

K. marxianus

Saccharomyces cerevisiae

References

Tethered

Cellulose

3.6 g/L ethanol and 95.4% yield

Ryu and Karim (2011)

Secretion

Cellobiose or CMC (100 g/L)

43.4 g/L ethanol

Hong et al. (2007)

Tethered

Cellulose (10 g/L)

4.24 g/L ethanol

Yanase et al. (2010)

Secretion

Avicel (10 g/L)

0.46e0.6 g/L ethanol Chang et al. (2013)

Secretion

PASC (10 g/L)

1.6 g/L ethanol

Yanase et al. (2010)

MiniPASC (10 g/L) cellulosome

1.8 g/L ethanol

Wen et al. (2010)


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S. cerevisiae

Expression of cellulase genes from Clostridium cellulyticum Expression of cellulase genes from Thermoascus aurantiacus Displaying Trichoderma reesei endoglucanase and Abdopus aculeatus b-glucosidase on cell surface Expression of cellulase genes from different fungi and a fungal cellodextrin transporter gene Expression of T. reesei EGII, CBHII, A. aculeatus BGL1 Expression of T. reesei EGII, CBHII, A. aculeatus BGLI, Clostridium thermocellum miniscaffolding

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Table 3 Ethanologenic/solventogenic microorganisms engineered to be cellulolytic by recombinant strategya Cellulase production system Substratesb Microorganisms Engineering approach Biofuel productionc

S. cerevisiae

S. cerevisiae

S. cerevisiae

S. cerevisiae

S. cerevisiae (K1-V1116) Saccharomyces pastorianus S. cerevisiae

Expression of T. reesei EGII, CBHII, A. aculeatus BGLI Expression of C. cellulyticum cellulase genes (celCCA, celCCE, cel_2454) Expression of T. reesei EGII, CBHII, A. aculeatus BGLI, A. oryzae A oelpl Expression of a cellodextrin transporter and an intracellular b-glucosidase from Neurospora crassa Coexpression of cellulases and a cellodextrin transporter Expression of T. reesei EG, CBH, BGL Expression of T. reesei EGI, CBHII, BGLI Expression of T. reesei EG, CBH, BGL

Tethered

PASC (20 g/L)

7.6 g/L ethanol

Yamada et al. (2011b)

MiniPASC (10 g/L) cellulosome

1.4 g/L ethanol

Fan et al. (2012)

Tethered

PASC (20 g/L)

3.4 g/L ethanol

Nakatani et al. (2013)

Secretion

Avicel (80 g/L)

27 g/L

Lee et al. (2013)

Tethered

PASC (10 g/L)

4.3 g/ethanol

Yamada et al. (2013)

Secretion

Pretreated corn stover (10%) PASC (25 g/L)

26 g/L ethanol and 63% yield 16.5 g/L ethanol

Hydrothermal pretreated rice straw

42.2 g/L ethanol and 86.3% yield

Khramtsov et al. (2011) Fitzpatrick et al. (2014) Matano et al. (2012, 2013)

Secretion Tethered

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S. cerevisiae

a

163

This table is adapted from Salehi Jouzani, G., Taherzadeh, M.J., 2015. Advances in consolidated bioprocessing systems for bioethanol and butanol production from biomass: a comprehensive review. Biofuel Research Journal 2, 152e195. PASC stands for phosphoric acid swollen cellulose. c Ethanol yield is reported as percentage of theoretical yield, unless stated otherwise. b

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4.3 Microbial Consortia for Consolidated Bioprocessing No single microorganism capable of effectively producing biofuel from lignocellulose has been isolated or developed. Consortia comprising both cellulolytic and ethanogenic/solventogenic microorganisms could be an attractive alternative to single culture approaches for CBP. A microbial consortium is defined as a mixed microbial population composed of two or more strains which interact with one another. Mixed strains perform the tasks which are difficult or impossible to be carried out by a single culture (Brenner et al., 2008). Consortia can be classified as natural, artificial and synthetic. Natural consortia are symbiotics occurring in nature as a result of evolution. Both artificial consortia and synthetic consortia are defined mixed culture systems while the latter one involves genetic modification to achieve specific interactions among mixed strains (Bernstein and Carlson, 2012) (Table 4). 4.3.1 Natural Consortia for Consolidated Bioprocessing Natural consortia are natural biomass utilization system that has evolved to hydrolyse and uptake a wide range of lignocellulosic biomass (Xie et al., 2014). It was reported that a natural consortium isolated from soil was able to produce 2.06 g/L ethanol directly from cellulose after the consortium was cultured at 55 C for 6 days (Du et al., 2015). Another work demonstrated that an isolated and stabilized natural consortium was able to hydrolyse 63.35% cellulose into glucose. This isolated consortium is not a CBP consortium but a hydrolytic one. When it was cocultured with Clostridium acetobutylicum ATCC824, the coculture system was able to convert filter paper (pure cellulose) into butanol with a titre of 3.73 g/L and a yield of 0.145 g/g glucose equivalent (Wang et al., 2015). The comparison experiment showed that the artificial consortium comprising individual effective strains identified in the above isolated hydrolytic consortium was only able to hydrolyse 21.77% cellulose into glucose. The findings revealed by this study suggested that natural consortia act in more delicate, complex ways than artificial ones due to many unknown factors governing the performance of a given consortium. 4.3.2 Artificial Consortia Artificial consortia do not act in an intricate way as a natural consortium does and is relatively easy to manipulate because of known characteristics of the strains and predictable interaction among the defined cultures (Zuroff and Curtis, 2012). The main challenge with artificial consortia is that

Cellulolytic microbes

Fuel-producing microbes

Clostridium cellulovorans 743B Clostridium phytofermentans

Substrates

Fuel productiona

References

Clostridium beijerinckii NCIMB 8052

Corn cob

Wen et al. (2014a)

Cellulose, filter paper

Clostridium thermocellum

Candida molischiana þ Saccharomyces cerevisiae cdt-1 Clostridium thermolacticum

11.8 g/L ABE, including 8.30 g/L butanol 22 g/L ethanol

C. thermocellum ATCC 27405

C. beijerinckii NCIMB 8052

C. cellulovorans 743B

C. beijerinckii NCIMB 8052 Clostridium saccharoperbutylacetonicum N-14 C. saccharoper -butylacetonicum N-14 C. beijerinckii NCIMB 8052 Cellulomonas uda

Corn cobs

3.81 g/L ethanol, 75% yield 19.9 g/L ABE, including 10.9 g/butanol 8.30 g/L butanol

Crystalline cellulose

7.9 g/L butanol

Nakayama et al. (2011)

Rice straw

5.5 g/L butanol

Kiyoshi et al. (2015)

Corn cobs

10.9 g/L butanol

Wen et al. (2014b)

Corn stover

73% ethanol yield

S. cerevisiae þ Scheffersomyces stipites

Dilute acid pretreated wheat straw

10 g/L ethanol and 67% yield

Speers and Reguera (2012) Brethauer and Studer (2014)

C. thermocellum C. thermocellum C. thermocellum ATCC 27405 Geobacter sulfurreducens Trichoderma reesei

Ethanol yield is reported as percentage of theoretical yield, unless stated otherwise.

Xu and Tschirner (2011) Wen et al. (2014b)

Wen et al. (2014a)

165

a

Microcrystalline cellulose Corn cob

Zuroff et al. (2013)

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Table 4 Examples of defined microbial consortia for consolidated bioprocessing for cellulosic biofuel production Consortia

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hydrolytic and fuel-producing microorganisms differ in optimal conditions. For instance, most productive cellulose-producing strains are obligate aerobic, including the genera of Trichoderma and Aspergillus, while most native fuel-producing microorganisms require anaerobic or microaerobic conditions for fuel production (Brethauer and Studer, 2014). Also, as aforementioned, optimum temperatures for hydrolytic bacteria are often much higher than that for fuel-producing microorganisms. Research efforts have been made to enable both anaerobic and aerobic strains to be cultured under the same fermentation conditions. Biofilms has been proposed for its favourable interface for coculturing aerobic and anaerobic microbes for CBP (Zuroff and Curtis, 2012). In the study conducted by Brethauer and Studer (2014), T. reesei and two yeast strains were sequentially cultured on a dense membrane, resulting in the formation of biofilms which were colonized by T. reesei at the bottom layer (next to the membrane) and by the yeast strains at the top layer (contacting liquid culture medium). Oxygen supplied from the bottom of membrane was consumed by the biofilm at the bottom layer to produce cellulases under aerobic condition. Free cellulases produced are transported to the liquid phase for hydrolysing the pretreatment slurry. The upper layer of the biofilm was free of oxygen, thus providing a favourable anaerobic environment for ethanol production by two yeast strains that worked together to ferment both hexoses and pentoses (Brethauer and Studer, 2014). Microaerated conditions also proved to be a feasible approach for culturing both aerobes and anaerobes, where lignocellulosic biomass can be directly converted into ethanol by a consortium. One study showed that by adjusting oxygen level in the medium C. phytofermentans and S. cerevisiae cdt-1 formed a stable consortium in which S. cerevisiae cdt-1 protected C. phytofermentans from oxygen exposure. C. phytofermentans in turn hydrolysed cellulose into glucose due to its cellulolytic activities. By adding EGs, this consortium can convert a-cellulose (100 g/L) into ethanol (22 g/L), while a single culture of either strain produced less than 10 g/L ethanol (Zuroff et al., 2013). Another study showed that the facultative anaerobe Caldibacillus debilis GB1 protected C. thermocellum from oxygen when cultured together, resulting in synergistic cellulose conversion under aerobic conditions (Wushke et al., 2015). 4.3.3 Synthetic Consortia Synthetic or semisynthetic consortia comprising genetically modified strains are used for achieving better CBP performance than natural consortia or

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artificial consortia (Argyros et al., 2011). As mentioned above, the synergistic interaction among different enzymes entails tuning the expression ratios of different enzymes in a system. In addition, the expression of different enzymes by a single strain would be very cumbersome. Thus, labour division by different engineered strains, instead of a single strain, is an alternative to tune the enzyme ratio in the system and enables more efficient hydrolysis of cellulose. A synthetic consortium was successfully constructed by coculturing four engineered strains for the functional presentation of mini-cellulosomes on the surface of yeast (Tsai et al., 2010). In this consortium one strain took a role of displaying the miniscaffoldin and the other three strains had the roles of expressing dockerin-tagged cellulases. By exploiting the interaction of separate cohesinedockerin pairs, the ratio of cellulase-secreting cells and miniscaffoldin-displaying cells can be tuned for optimum performance. After optimizing the cell ratio in the consortium, ethanol production was approximately doubled compared to a consortium with an equal ratio of cells although the ethanol titre was still low (1.87 g/L). Bearing this similar idea, Sujin Kim et al. (2013) constructed a consortium containing several types of cells which can express scaffoldin or cellulases fused with dockerin separately. This allowed the optimization of the enzyme ratios by controlling the cell ratio in fermentation, and 20% increase in ethanol production was achieved compared with the consortium with an equal amount of four strains (Kim et al., 2013). 4.3.4 Application of Ecology in Engineering a Consolidated Bioprocessing Consortium Although the application of consortia for CBP proves to be feasible by many studies, more insights into how microbial communities interacts with each other and results in higher-order properties are needed (Fredrickson, 2015). An artificial consortium might lose its stability after cultivation some time. For instance, artificial consortia are prone to be dominated by one species, and thus unable to perform multiple tasks intricately. The species in a consortium exchange signals or molecules directly or indirectly. Direct-contact interactions (also called contact-dependent interactions) are harnessed by microbial consortia/community for interchanging electrons (energy), DNA, plasmids, proteins and some small molecules. There are three exchange mechanisms of directecontact interactions: secretion systems (Type III, Type IV, Type V and Type VI), direct interspecies electron transfer (DIET) and intercellular tubes (Song et al., 2014). Secretion systems (SSs) are surface structures that are used to affect other

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cells’ physiology, including Type III (T3SS) that is a flagella injectosome to inject protein effectors into eukaryotic cells; Type IV (T4SS), a pili conjugation system for transferring plasmid DNA and proteins; Type V (T5SS), a contact-dependent growth inhibitor (CDI) inhibiting a neighbouring cell that accepts CDI as a membrane protein and Type VI (T6SS), a multistep system like bacteriophages T4 and P22 which transfer DNA by disrupting the membrane of host cells (Hayes et al., 2010). In DIET, a biological connection (pili) is established between cells and external electrons. In addition, external electron transfer can be mediated by abiotic conductive materials such as biochar, carbon cloth and activated carbon (Wang et al., 2014). Finally, nanotubes, which are made of the same materials of bacterial membranes, are used to exchange cytoplasmatic contents within a microbe or among species. The dimensions of nanotubes are approximately 1 mm in length and 30e130 nm in width. Quorum sensing (QS) is a mechanism used by both Gram-positive and Gram-negative bacteria to communicate among species using chemical signal molecules (Waters and Bassler, 2005). QS allows the cells to change their behaviours on a population-wide scale in response to the changes in the number and/or species in a community (Waters and Bassler, 2005). Acylmonoserine lactone (AHL) acts as an integral component of regulatory networks of Gram-negative bacteria, which facilitates the adaptation of individual bacteria to dynamic environment. Many Gram-negative bacteria, such as Pseudomonas, Rahnella, and Rhodobacter, exhibit AHL-mediated QS (Withers et al., 2001). Both the transcriptional regulator and AHL synthase are involved in AHL-mediated gene regulation (Loh et al., 2002). Once the transcriptional regulator is combined with AHL, it is able to recognize the specific promoter sequence, and stimulate gene expression (Loh et al., 2002). With a large enough bacterial population, AHL is accumulated to a level that can ensure its binding to the transcriptional regulator. The communication of Gram-positive bacteria depends on modified oligopeptides as signals and membrane-bound sensor histidine kinases as receptors. In addition to cooperative communication, QS also involves other interactions such as cues and chemical manipulations (Keller and Surette, 2006). Based on the above discussion, it is clear that constructing a stable and robust consortium is not only an engineering problem, but also an ecological one. By coupling ecology theory with engineering strategy, Minty et al. successfully constructed a stable T. reesei/E. coli consortium that produced isobutanol from AFEX-pretreated corn stover pretreated. Their study

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suggested that the stability and tunability of the consortium could be attributed to a cooperator-cheater dynamics within T. reesei/E. coli consortia (Minty et al., 2013). Linking the principles of microbial ecology to the design of microbial consortia is critical for the implementation of such communities in CBP.

5. METAGENOMICS AND SYNTHETIC BIOLOGY FOR CONSOLIDATED BIOPROCESSING 5.1 Metagenomics Pure culture is often the first step toward investigating microbial bioprocessing, but only about 1% of microorganisms are culturable under standard culture methods (Riesenfeld et al., 2004). Thus, developing culture-independent methods for exploring unknown microorganisms in nature is necessary, especially considering that natural ecosystems are reservoirs containing many important, yet to be discovered enzymes or metabolic capabilities (Simon and Daniel, 2011). Metagenomics, as a sequence-based approach, could bypass the dependence on culture and is a powerful technique for mining biological traits of interest. As discussed above, recombinant organisms derived from enzyme engineering would help achieve CBP. However, to date, enzyme engineering for biofuel production has shown little success, which could be possibly attributed to the fact that enzymes used as engineering templates were mainly derived from culturable microorganisms (Wen et al., 2009). In this context, metagenomics serves as a powerful tool for discovering abundant carbohydrate-active enzymes and/or even biofuel synthesis pathways. A metagenomic study identified 27,755 putative carbohydrate-active genes in rumen microbes colonizing on plant fibre, of which 57% genes expressed 90 candidate enzymes that can act on cellulosic substrates (Hess et al., 2011). Basically, metagenomics can be classified into function-based and sequencebased approaches, where the former is dependent on the functional screening of metagenomics library and the latter is based on sequence similarity between samples and sequences with known structures and functions (Santero et al., 2016; Simon and Daniel, 2011). Functional approach depends on the insertion of constructed metagenomics libraries into host bacteria and selection of biological activity of interest. Functional metagenomics have proven to be useful tools in screening biofuel-producing microorganisms more tolerant to toxic compounds

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(Ruegg et al., 2014; Sommer et al., 2010) and discovery of new biomassdepolymerizing enzymes with new properties (Hu et al., 2008; Lee et al., 2006). A novel thermoalkaliphilic hemicellulase with more than 90% enzyme activity remaining at pH 10 was discovered by using a functional metagenomics-based screening method from two wheat straw-degrading microbial consortia (Maruthamuthu et al., 2016). An EG with an optimum enzyme activity at pH of 6.8 and 30 C was isolated from rice straw via a metagenomic strategy (Meneses et al., 2016). The same strategy was adopted to isolated b-glucosidase from cow rumen microbes, which enhanced the sugar yields of pretreated corn stover by 20% when used with commercial T. reesei cellulase cocktail (Del Pozo et al., 2012). The advantages of functional metagenomics appear to guarantee biological activity and no requirement of prior knowledge of enzymes with desired properties (Li et al., 2009; Santero et al., 2016). Despite these advantages, several limitations hamper the application of functional metagenomics, including the inefficient expression of heterologous genes in hosts and the lack of screening procedures for selecting biological activities (Terr
on-Gonz
alez et al., 2014). Incompatibility between foreign genes and host expression systems hinders the efficient expression of genes from unknown origin. To date, E. coli is the most popular host for functional metagenomics. However, some foreign genes may not be expressed efficiently in E. coli. Many other hosts, such as Thermus thermophiles (Leis et al., 2015), Streptomyces lividans and Pseudomonas putida (Martinez et al., 2004), have been used as alternative hosts for heterologous gene expression. The inability of host to recognize promoters from the metagenome also prevents the hosts from expressing foreign genes. A recent study successfully enlarged the genomic space that can be functionally sampled in E. coli by expressing heterologous sigma factors in E. coli (Gaida et al., 2015). Sequence-based methods involve the identification of genes with already known genes, and only new variants with known functional classes of proteins can be identified (Simon and Daniel, 2011). Identifying complex biosynthetic pathways in addition to sequence-based methods could be greatly useful for engineering a strain with better performance (Santero et al., 2016).

5.2 Engineering a Consolidated Bioprocessing Strain by Synthetic Biology Ideal microorganisms for CBP should possess some key traits, including efficient enzyme systems for rapid depolymerization of biomass, rapid

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pathways for biomass-derived molecules transport, efficient conversion of multiple biomass-derived molecules, high tolerance to both inhibitors and products and production of desired fermentation products (Alper and Stephanopoulos, 2009; Salehi Jouzani and Taherzadeh, 2015). To this end, an intricate web of reaction pathways should be built by rational design. Synthetic biology, as an emerging technology aiming at integrating engineering principles with gene manipulation in microorganism, would make it possible to engineer a CBP strain with desired traits. Synthetic biology is a combination of different biological techniques involving metabolic engineering, design of gene networks and assemblage of artificial genomes in an engineering fashion approach (Kaznessis, 2007). This set of techniques is expected to help overcome difficulties associated to the different traits that nowadays are hindering strains’ performance. Synthetic biology enables the introduction of biosynthetic capacity into nonnative hosts. S. cerevisiae inserted with n-butanol biosynthetic pathway can produce n-butanol, but with a very low titre (2.5 mg/L) (Steen et al., 2008). A synthetic metabolic pathway assembled from different gene donors was introduced into a hyperthermophilic microbe for 1-butanol production, with a maximum titre of 70 mg/L (Keller et al., 2015). Although the engineered butanol-producing microbes gave very low titres compared with the native ones, they demonstrated that in vitro assemblage of various genes for performing sophisticated synthetic tasks is possible. More sophisticated modification in the synthesis pathways could lead to much higher butanol production by engineered microorganisms. For example, the insertion of modified clostridial 1-butanol pathway into E. coli along with the driving force provided by NADH and acetyl-CoA enabled high ethanol production with a titre of 30 g/L and a yield of 70%e88%, which was comparable to that obtained with native butanol-producing microbes (Shen et al., 2011). However, heterologous expression of genes in hosts may cause changes in fuel production capability, growth rates of engineered strains, stress responses, many other poorly understood aspects (d’Espaux et al., 2015). Therefore, a fundamental understanding of design principles and rules for synthetic biology is needed for engineering a CBP strain.

6. CONCLUDING REMARKS CBP is a promising platform to address the high cost and operation complexity of biofuel production. A robust CBP system entails an efficient

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enzyme system to liberate sugars from lignocellulosic biomass and the conversion of both hexoses and pentose into the desired biofuels with satisfying yields and productivities. In nature, some microorganisms have evolved to perform CBP to produce ethanol or other chemicals from lignocellulosic biomass when suitable conditions are provided. However, it is quite difficult for a single wild-type microorganism to perform multiple tasks simultaneously while still ensuring satisfactory efficiency. Engineered CBP strains, consortia or a combination of them would have a better performance than a wild-type strain. Up to date, there is still a challenge facing recombinant CBP strains for heterologously expressing hydrolytic enzymes in active form and sufficient quantities. Thus, native strategy is still preferred to engineer a CBP strain. In terms of consortia, the challenge is how to mimic actual behaviours of natural consortia. Deep insights into the interaction between enzymes, metabolic pathways and microorganisms are needed for constructing a robust and stable consortium. Metagenomics will enable the discovery of many untapped enzymes and metabolic pathways, some of which would find their applications in CBP.

ACKNOWLEDGEMENT This work was supported by University of Missouri Research Board.

REFERENCES Agbor, V., Zurzolo, F., Blunt, W., Dartiailh, C., Cicek, N., Sparling, R., Levin, D.B., 2014. Single-step fermentation of agricultural hemp residues for hydrogen and ethanol production. Biomass and Bioenergy 64, 62e69. Akinosho, H., Yee, K., Close, D., Ragauskas, A., 2014. The emergence of Clostridium thermocellum as a high utility candidate for consolidated bioprocessing applications. Frontiers in Chemistry 2. Ali, S.S., Nugent, B., Mullins, E., Doohan, F.M., 2013. Insights from the fungus Fusarium oxysporum point to high affinity glucose transporters as targets for enhancing ethanol production from lignocellulose. PLoS One 8, e54701. Alper, H., Stephanopoulos, G., 2009. Engineering for biofuels: exploiting innate microbial capacity or importing biosynthetic potential? Nature Reviews Microbiology 7, 715e723. Amore, A., Faraco, V., 2012. Potential of fungi as category I consolidated bioprocessing organisms for cellulosic ethanol production. Renewable and Sustainable Energy Reviews 16, 3286e3301. Anasontzis, G.E., Zerva, A., Stathopoulou, P.M., Haralampidis, K., Diallinas, G., Karagouni, A.D., Hatzinikolaou, D.G., 2011. Homologous overexpression of xylanase in Fusarium oxysporum increases ethanol productivity during consolidated bioprocessing (CBP) of lignocellulosics. Journal of Biotechnology 152, 16e23. Anasontzis, G.E., Kourtoglou, E., Mamma, D., Villas-Bo^as, S.G., Hatzinikolaou, D.G., Christakopoulos, P., 2014. Constitutive homologous expression of phosphoglucomutase and transaldolase increases the metabolic flux of Fusarium oxysporum. Microbial Cell Factories 13, 43.

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CHAPTER FOUR

Thermochemical Conversion of Plant Oils and Derivatives to Lubricants Robiah Yunus*, 1 and Xiaolan Luox *Universiti Putra Malaysia, Serdang, Malaysia x The Ohio State University/Ohio Agricultural Research and Development Center, Wooster, OH, United States 1 Corresponding author: E-mail: [email protected]

Contents 1. Plant Oils and Their Derivatives as Renewable Feedstocks 2. Overview of Biolubricants 2.1 Lubrication and Biolubricants 2.2 Market and Government Regulation 2.3 Lubricant Performance Requirement 3. Thermochemical Conversion to Biolubricants 3.1 Introduction 3.2 Esterification 3.3 Transesterification 3.4 Epoxidation 3.5 Hydrogenation 3.6 Oligomerization/Branching 4. Product Development and Quality Requirement of Biolubricants 4.1 Properties of Base Oils 4.2 Biolubricant Quality Requirement and Its Application 5. Conclusions Acknowledgements References

184 186 186 189 190 193 193 194 197 203 207 210 213 213 217 223 223 224

Abstract In the last few years, rising environmental concerns because of the increase in accidental spillage of lubricants have encouraged further development in plant oilebased lubricants. In some areas, tighter legislative controls have been introduced for the use of petroleum-based products. Biolubricants derived from plant oils offer promising solutions to these environmental issues. These bio-based products have been studied extensively and the differences in performances are mainly because of the different fatty acid composition of plant oils. Like most bio-based products, shortcomings of plant oils are attributable to their natural characteristics. Notably, Advances in Bioenergy, Volume 2 ISSN 2468-0125 http://dx.doi.org/10.1016/bs.aibe.2016.11.001

© 2017 Elsevier Inc. All rights reserved.

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the oils lack thermal and oxidative stabilities, thus requiring modification via thermochemical conversions to improve their properties. Five thermochemical methods have been explored in lubricant synthesis, namely esterification, transesterification, epoxidation, selective hydrogenation, and estolides formation. However, these thermochemical processes are complicated and usually require multiple lengthy steps such as epoxidation, followed by ring opening and finally acetylation or esterification of hydroxyl groups, and thus are economically not viable. Finally, the properties of the products obtained from these synthesis methods were characterized and compared. This chapter provides an overview of the development in the thermochemical conversion methods of plant oils to biolubricants, enabling an accurate evaluation of cost-effective technology, which is crucial for the future of the plant oilebased lubricants.

1. PLANT OILS AND THEIR DERIVATIVES AS RENEWABLE FEEDSTOCKS Plant oils are oils extracted from plants such as soybean, canola seeds, palm fruit, and jatropha. Some of these oils are edible such as canola oil, whereas others are non-edible like jatropha oil. On the other hand, plant oil derivatives are compounds that are derived chemically from the plant oils. Their names are usually have prefixes bio- to imply that the compounds are bio-based or from plant oil origin. The common plant oil derivatives include esters such as fatty acid methyl esters (biodiesel), alcohols such as bioethanol, acids such as levulinic acid, and ethers. Because of technical and processing requirements, plant oil derivatives are used as the feedstocks for the production of green chemicals. The fatty acid composition of plant oils determines their aptness as renewable feedstocks for various applications. Products derived from plants oils with highly saturated fatty acids may be more oxidatively stable but are not suitable in temperate countries because of their high pour points. Plant oils containing high percentages of polyunsaturated fatty acids (PUFAs) such as linoleic and linolenic acids are oxidatively and thermally unstable because of their double bonds. However, these double bonds are attractive sites for functionalization such as epoxidation. The use of epoxidized plant oils as feedstock is increasing for various green chemical synthesis. Currently, epoxidized soybean oil (ESO) is available commercially and is being used for polyurethane and biolubricant production. Fatty acid profile also affects the volatility and viscosity of the plant oils. Thus, the knowledge of the fatty acid profile of a plant oil is necessary before it is considered as a feedstock.

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Notwithstanding its inherent limitation, the use of plant oils as renewable feedstock to produce green chemicals is increasing compared with nonrenewable, non-biodegradable petroleum-based products. The availability of diversified biomass feedstocks (sugars, oils, protein and lignocellulose) combined with numerous biochemical, thermo, and chemical conversion technologies offer a wealth of products that are useful in many applications. Target applications include the polymer, lubricant, solvent, adhesive, herbicide, and pharmaceutical industries. Many of these industrial bio-based products have already penetrated the markets, but improved technologies promise new products that can compete with fossil-based products in both cost and performance. Among these markets, the biolubricant and adhesive markets represent an enormous potential for bio-based products. Despite the increased popularity of the bio-based or green products, the commercial production of these products is a challenge because the sustainability issue of these bio-based chemicals is still being disputed. The possibility of achieving environmental sustainability is still being questioned, considering the interrelated issues governing the environmental degradation, climate change, overconsumption and the pursuit of economic balance within these closed systems. To tackle these problems effectively, it requires reverting to fundamental principles that govern the technologies for bio-based chemical production. Are these technologies efficient, effective, safe and environmental friendly? Developing a technology that uses less energy to produce less waste and is safer to environment is a challenge. This is the paradigm shift of the early 21st century, innovating technologies for sustainable development. Ways of reducing negative impact on humans are also paramount which entails environmentally friendly engineering, environmental resources management and environmental protection. Although most bio-based products are derived from plant oils and derivatives, availability of efficient technology to produce these chemicals from lignocellulosic biomass and other wastes remains a challenge. Using biomass or wastes or by-products will certainly improve the economic viability of the bio-based products. However, bio-based chemicals derived from ethanol only can be produced on a commercial scale once the process of converting lignocellulosic biomass to ethanol is economically feasible. Furthermore, production of other (non-ethanol) bio-based chemicals from lignocellulosic biomass is even more difficult (Zhou et al., 2011). Another alternative would be to use waste/residual oils or by-products from oleochemical industry to produce these bio-based products. Among

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the potential candidates are palm fatty acid distillate, a by-product from palm oil refinery, and methyl ester, a by-product from vitamin E plants or biodiesel from waste cooking oil. Even though most methyl esters are produced from vegetable oils, the esters are used mainly as fuels in diesel engines. Fatty acids and methyl ester are among the important plant oil derivatives currently being used as starting materials to produce high-value plant oil derivatives. Comparing fatty acids and methyl ester as starting material, the fact that methyl ester has higher vapour pressure than fatty acids entails simpler processing technology. The unreacted methyl ester can be easily separated from the final product after synthesis, thus enhancing product purity. Considerable research efforts have been spent on the use of renewable fats and oils particularly plant oils as feedstocks in lubricant formulation for industrial applications (Erhan and Asadauskas, 2000; Fox and Stachowiak, 2007; Salimon et al., 2010; Srivastava and Sahai, 2013). However, the natural characteristics of fats and oils such as poor oxidative stability, weak corrosion resistance and low-temperature performance have been recognized as major impediments to their potential use as renewable feedstock (Campanella et al., 2010). Despite major development in technologies to overcome these problems, the real issues for bio-based chemical production are availability and sustainability. This chapter focuses on various thermal and chemical transformation methods used to derive the lubricating oils, followed by the recent developments in product formulation and quality requirement of biolubricants. Their properties, product advantages and the drawbacks over petroleum-based products are also discussed.

2. OVERVIEW OF BIOLUBRICANTS 2.1 Lubrication and Biolubricants Biolubricants are liquid lubricants derived from plant oils or their derivatives. The primary function of a lubricant is to keep friction, wear and heat from affecting the sliding surfaces by providing a layer of liquid between the surfaces. The lubricating fluids must have the capability to remove the heat developed from the friction and the abrasion of wear particles away from the load-carrying zone as soon as it is formed (Tang and Li, 2014). The specific heat capacity of plant oils is usually higher than that of most petroleum-based products; thus, the oils can absorb the heat and take it away quickly from the sliding surfaces. The surface

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temperature only increases marginally as a result of a friction. In addition, because of the high polarity index, plant oils are able to provide a welldeveloped lubrication layer that can effectively reduce wear and friction (Zulkifli et al., 2013). Lubricant is a functional fluid derived from a base fluid either from petroleum, fats and oils or from synthetic origins. Modern lubricants commonly comprise approximately 90% base oil and 10% additives. Petrochemical or mineral base oils account for 85%e90% of the total world lubricants, whereas synthetic and vegetable oilebased lubricants constitute 15% and 1% of the total world lubricants, respectively. Lubricants are one of the many hazardous contaminants of our environment, where almost 90% are mineral-based and almost 40% of the used oils are not regenerated and are released into the environment. Unlike fuel, which is converted to CO2 and H2O during the combustion process and generates tremendous amount of energy, lubricant remains as it is after being used in engines or machinery. Once the life of the oil has been exceeded, the lubricant must be changed because of the formation of wear debris. A single automotive oil change produces 4e5 L of used oil. If the lubricant is made from petroleum-based oil, the oil must be disposed off safely and in an environmentally friendly way. The best practice for the removal of used oil from a machine is to keep oil from ever being introduced to the environment. With the availability of efficient and effective re-refining technology, most of the used oils have recently been re-refined into reusable oil after removing the contaminants, adding fresh additives and passing through comprehensive performance tests. Every year about 2e4 million tons of lubricants enter the environment because of the deliberate and accidental losses via evaporation, leakages, illegal disposal and spills (Srivastava and Sahai, 2013). In Australia, it is reported that out of 500 million litres of lubricating oil sold each year, at least 250 million litres of used oil were generated (www. environment.gov.au/protection/used-oil-recycling/recycling-your-oil). Improper disposal of the used lubricants pollutes the environment. It is claimed that 1 L of oil is able to contaminate 1 million litres of water. According to Pawlak et al. (2010), out of 4.5 billion kilogrammes of oil consumed annually in United States, only 2.6 billion kilogrammes were collected back. The total loss to the environment is around 600 million kilogrammes. In the European Union although they used more oil at 5 billion kilogrammes per year, the total oil loss is only at 100 million kilogrammes per year.

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Biolubricant is an ester from renewable and bio-based resources. Recently, there is an increasing interest in the development of bio-based compounds suitable for use as lubricants and hydraulic fluids. This demand has been met to a certain degree by plant oils as they have the advantage of being natural, non-toxic, biodegradable, relatively less polluting, cost less than synthetics and derived from renewable feedstocks. Bio-based synthetic oils can also be synthesized from petroleum and vegetable oil feedstocks which are tailor-made for specific applications requirements. Many vegetable oilebased lubricants were formulated into various base fluids and chemical additives. However, many applications use plant oils in their unmodified form but the applications are limited. For broader applications, plant oils are converted to synthetic esters to overcome its low- and hightemperature performance limitations. The book by Bart et al. (2012) provides a comprehensive overview of scientific and technological developments in biolubricants. The main aim is to facilitate improvement in the development of cost-effective biolubricants, which is necessary for the sustainable future of the lubricant industry (Bart et al., 2012). The history of biolubricants dated back to 1400 BC, when Egyptians used olive oil and animal fats to facilitate the sliding of large stones, statues and building materials (Booser, 1993). However, five centuries later, petroleum-based oils had essentially displaced natural oils and fats as sources of lubricants because of the availability of petroleum at a lower cost and the utilization of natural oils for the production of food and pharmaceuticals. Alternatively, biolubricants can also be produced from animal fats but are commonly associated with solid lubricants or greases because of their highly saturated fatty acids content. In pursuit of environmentally friendly processes, various chemical and thermal conversion methods for the synthesis of biolubricants from plant oils are reviewed. The intention is to recognize the advantages and disadvantages of certain processing routes, their impact on the environment and the economic viability of the processes. Some processes are thermal, whereas others are mostly chemical conversion processes. These conversion processes are important in lubricant base oil development as the plant oils need to be converted to more stable compounds to take full advantage of their benefits and minimize their shortcomings. The future of biolubricants depends on the market potential, which could be accelerated by new government regulations on the use of bio-based products and public awareness of their impact to the environment.

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2.2 Market and Government Regulation Plant oils and their derivatives are the starting materials for bio-based products such as biofuels, biolubricants and bioadhesives. Although the demand for biofuels is very much driven by regulation, the demand for other bio-based chemicals is usually promoted by economic drivers and end users’ interest for green products. The term biolubricants refers to all lubricants that are nontoxic and biodegradable. Despite the negative impact of mineral-based lubricants on the environment, the global biolubricants market is still small. The market value for plant oilebased biolubricants is projected to reach $2422.07 million by 2020, at a compound annual growth rate (CAGR) of 6.27% from 2014 to 2019 (http://www.marketsandmarkets.com/Market-Reports/ biolubricants-market-17431466.html). The fastest growing market for biolubricants is in the North America, but in terms of value, Europe still holds the largest market demand. Both Europe and Northern America account for 85% of the global biolubricant market. Increasing regulatory actions on lubricants drive increasing demands for biolubricants over the coming decade. For example, the stringent regulations in North America include Vessel General Permit (VGP) and Bio Preferred program, especially in commercial transport. The US government has specified a minimum renewable content for a range of product categories. The US Air Force leads the way by supporting the use of plant-derived bio-based products as a fundamental and strategic approach to national security. Legislations related to emissions in the European Union, the United States and China would play a crucial role in promoting the use of biolubricants. Although the price of biolubricant base stocks used to be considerably higher than the petroleum base stock, the price differential has narrowed significantly in recent years. The increase in supply of high-performing, cost-competitive plant oilebased base oils in the context has driven the biolubricants market growth globally. This trend augurs well for the future. Although regulation and labels such as VGP in United States and European Ecolable (EEL) can be an incentive for growth, increasing customer interest in environmentally friendly products is driving the industry to develop more biolubricant products. The EEL label is well accepted in European countries. The requirement for the EEL label is that the renewable content must be between 45% and 70% and the components in the lubricant formulation that exceeds 0.1% must be evaluated for biodegradation.

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Despite these incentives given by various government agencies for green products, higher prices and original equipment manufacturers’ (OEMs) approvals still limit the growth of biolubricants. So far, these lubricants are successfully implemented only in environmentally sensitive areas, where pollution of oil and water are of particular concern such as in construction, mining, forestry, marine, government vehicles and agricultural industries. The biolubricant market has expanded gradually since 2010 because of the increased awareness on the harmful effect of mineral oilebased lubricants to human beings and the environment (Thinnes, 2011). The markets for biolubricants were valued at about US$2 billion in 2013 and estimated to increase to US$3 billion by 2020. In 2015, the global biolubricants market is projected to grow at a higher CAGR of 6.3% over the period 2014e2019. The main biolubricant products reported in the New Research Report at RnRMarketResearch.com are hydraulic oil, chain saw oil, spindle oil, metalworking fluids, industrial gear oils and greases (http://www.rnrmarketresearch.com/global-biolubricant-market2015-2019-market-report.html). However, the business remains tough, competitive and slow-gaining (Chang et al., 2015). The complex manufacturing process of biolubricants and the inability of agriculture to provide continuous supply of plant oil for large-scale production act as market deterrents. In recent years, the prices of biolubricants have slightly increased because of the increase in base stock price, which is affecting the growth of the market.

2.3 Lubricant Performance Requirement Plant oils can be used directly as lubricants in their natural forms but have some performance limitations. Most of the drawbacks are associated with the oxidative stability and low-temperature properties. Because almost 85%e90% of the finished lubricant comprise the base oil, the shortcomings of the plant oils will influence the lubricant performance. Plant oils are usually found in the seeds or fruits. They are predominantly triglycerides in which three similar or different fatty acid chains are attached to a glycerol backbone (Fig. 1). This differentiates them from petroleum-based lubricants, which consist of chains and rings of carbon and hydrogen. The lubricating performance of plant oilebased biolubricants depends significantly on the fatty acid compositions of plant oils. Lubricants derived from plants oils with highly saturated fatty acids may be more oxidatively stable but are not suitable in temperate

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O H 2C O HC O H 2C

O

O O

R R' R''

Figure 1 Chemical structure of plant oil triglycerides (R, R0 , R00 are fatty acid alkyl groups).

countries because of their high pour points. The fatty acid compositions of selected plant oils commonly used as biolubricants are shown in Table 1. Most plant oils contain a combination of saturated and unsaturated (with double bonds) fatty acids and others contain additional special functional groups such as hydroxyl groups (e.g., castor and lesquerella oils). Because plant oils contain ester groups, -OOR and carbonecarbon double bonds, these functional groups combine to give particularly good tribological properties apart from excellent biodegradability and low toxicity (Srivastava and Sahai, 2013). Nonetheless, because of the presence of oxygen and some percentage of unsaturated carbon, plant oils are reactive for the production of various bio-based chemicals and polymers. There are many reports on the use of certain types of plant oils in biolubricant formulation to satisfy certain performance requirement. The oils with high contents of PUFAs such as corn, soybean, canola and sunflower oils are not suitable for high-temperature and oxidation-prone applications. Thus, the oil must be firstly hydrogenated to convert some of the double bonds in linolenic and linoleic fractions into oleic fraction. Reeves et al. (2015) examined the influence of fatty acids on the tribological and thermal properties of avocado, canola (rapeseed), corn, olive, peanut, safflower, sesame and vegetable (soybean) oils as sustainable biolubricants and found that the viscosity, thermal stability, oxidative stability and the tribological performance of the lubricants are markedly affected by the fatty acid composition of the plant oils. The effect of fatty acid content on the oxidation stability and lowtemperature flow property of selected plant oils have also been investigated. The properties of regular soybean oil when blended with diluent and additives failed to match the performance of mineral oil (Erhan et al., 2006). However, when the oleic content of soybean oil was modified to mid-oleic or high-oleic soybean oils, the lubricity and physical properties were far superior over those of mineral oils. New lubricants from passion

Table 1 Fatty acid compositions (percentage) for common plant oilsa Saturated fatty acids Vegetable oil carbon atoms

Caproic Capric Caprilic Lauric Myristic Palmitic Stearic Arachidic Behenic Tetradecanoic Palmitoleic Oleic 6:0 8:0 10:0 12:0 14:0 16:0 18:0 20:0 22:0 24:0 16:1 18:1

Palm kernel oil (S) 0.1 Palm kernel oil 0.2 Palm kernel oil (O) 0.2 Palm oil (S) Palm oil Palm oil (O) Rapeseed oil Canola oil (LEAR) Cottonseed oil Corn oil Soybean oil Sunflower oil Linseed oil

2.4 3.3 4.3

3.2 3.4 3.7

55.2 48.2 42.55 0.7 0.1 0.2

0.1

19.9 16.2 12.4 1.5 1.0 1.0 0.1 0.1 0.7 0.1 0.1 0.1

8.1 8.4 8.4 55.8 44.4 39.8 3.8 4.1 21.6 10.4 10.6 7.0 5.4

3.3 2.5 2.5 4.8 4.1 4.4 1.2 1.8 2.55 2.0 4.0 4.5 3.5

LEAR, Low erucic, low glucosinoleat; O, Olein fraction; S, Stearin fraction. a Lawate et al. (1997).

Polyunsaturated fatty acids

Monounsaturated fatty acids

0.1 0.1 0.1 0.4 0.3 0.4 0.7 0.7 0.3 0.1 0.3 0.4 0.2

0.1 0.1 0.1 0.3 0.2 0.1 0.3 0.7 0.3

1.0 0.2 0.2

0.1

0.2 0.2 0.3 0.3 0.6 0.1 0.1

6.9 15.3 22.3 29.6 39.3 42.5 18.5 60.9 18.6 26.3 23.2 18.7 19.9

Gadeolic Erucic Linoleic Linolenic Iodine 20:1 22:1 18:2 18:3 value 0.8 2.3

0.1 0.1

6.6 1.0 0.3 0.1 0.3

41.1 0.7

7.2 10.0 11.2 14.5 21.0 54.4 59.0 53.7 67.5 17.9

0.1 0.4 0.2 11.0 8.8 0.7 0.3 7.6 0.8 51.2

6e10 14e19 25e31 45e50 50e55 56e60 100e115 100e115 100e120 118e128 123e139 125e140 170e190

Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

193

fruit and moringa oils were developed but required epoxidation of double bonds to obtain satisfactory tribological properties (Silva et al., 2015). Meadow foam oil contains over 96% unsaturated fatty acids with chain lengths of 20 carbons or more. It has exceptional oxidative stability because of its natural antioxidant content. On the other hand, the use of plant oils with high contents of saturated fatty acids, such as palm oil and coconut oil, is limited because of their high pour points (around 20 C). They are semi-solid at room temperature and only suitable for tropical countries (Yunus et al., 2004; Jayadas and Nair, 2006). Cuphea oil (Cermak et al., 2013) with 82% saturated fatty acids (68% C10:0) provides high pour point, excellent better wear resistance and high oxidation resistance property because of the low amount of unsaturated fatty acid fraction. Oxidation takes place at the double bonds; hence, oils with multiple double bonds such as canola oil (rapeseed) and soybean oil exhibit poorer oxidation stability than palm oil. Because oxidative degradation starts at a lower temperature than thermal degradation, oxidative stability is an important criterion when selecting plant oils as base oil for industrial lubricants. The presence of functional groups such as hydroxyl groups in the fatty acid chain of plant oils renders them suitable for applications in cold climates. Ricinoleic and lesquerolic acids are the hydroxyl fatty acids present in castor and lesquerella oils, respectively (Cermak et al., 2013). Castor oil with higher hydroxyl content worked well at 30 C compared with 24 C for lesquerella oil. The presence of hydroxyl groups also affects the oil viscosity. The viscosities of the hydroxy oils are significantly higher, reaching 136 cSt for lesquerella oil and 244 cSt for castor oil, at 40 C, respectively.

3. THERMOCHEMICAL CONVERSION TO BIOLUBRICANTS 3.1 Introduction Biolubricants derived from plant oils are renewable and completely biodegradable and have low ecotoxicity as compared with mineral oilebased lubricants. Nonetheless, there are many performance limitations associated with plant oils, e.g. thermal, oxidative and hydrolytic stabilities, and inadequate low-temperature fluidity because of their fatty acid compositions. These limitations are attributed to the double bonds present in the unsaturated fatty acids and the b-carbons located

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on the glycerol backbone of the plant oil triglycerides. The double bonds affect the oxidative stability of plant oils, whereas the b-carbons affect their thermal stability. Chemical modification is a means of improving tribological and physicochemical performance of plant oils or their derivatives so that their potential as lubricant base stocks is greater. Various thermochemical conversion methods have been applied to either change the backbone or eliminate the double bond but mainly focus on the carboxyl moiety of fatty acids; less attention has been paid to functionalization of the (unsaturated) fatty acid chains. Industrial applications of these conversion methods are frequently hampered by a lack of selective catalysts and high processing cost. Several studies have demonstrated the improvement in performance of plant oils through their structural modification via thermochemical conversion (Yunus et al., 2005; Hwang and Erhan, 2001; Hwang et al., 2003; Sulaiman et al., 2007; Lathi and Mattiasson, 2007; Sharma et al., 2008; Campanella et al., 2010). The most recent advancements in the synthesis of biolubricants from vegetable oils through chemical modification have been reviewed (McNutt and He, 2016). It was concluded that despite the improvement in the properties of plant oil derivatives achieved via thermochemical conversion, the cost-effectiveness of production methods is still far-fetched. Availability of cheaper feedstocks, higher performance catalysts and optimized reaction processes are necessary before the economic benefits of technologies will be able to attract supports from industry partners. In this chapter, five thermochemical conversion methods, namely, esterification, transesterification, epoxidation, hydrogenation and oligomerization/branching, have been identified as the most common methods used in biolubricant synthesis. For each method, the discussion will focus on feedstock and catalyst selection, process operating conditions and scaling up. At the end, the benefits of each conversion methods are also highlighted.

3.2 Esterification Esterification is a reaction between an acid and an alcohol to make an ester. The reaction is slow in the absence of a catalyst. Either acid or enzymatic catalysts can be used to accelerate the reaction rate. Esterification based on plant oils and their derivatives occurs between fatty acids and alcohol, which can be mono-alcohol or poly-alcohol (i.e., polyol). The reaction is commonly known as Fischer esterification (Fig. 2). The properties of the

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O R C OH

R' OH

O R C OR'

H2O

Figure 2 Esterification of fatty acids of plant oils.

esters can be tailored by the choice of the starting materials used in the esterification. The starting material for the synthesis of biolubricants via esterification is fatty acids either in pure form or in compound form. The compound form of fatty acids is obtained directly from the hydrolysis of plant oils (triglycerides) as shown in Fig. 3. Depending on the composition of fatty acids in triglycerides, the compound is a mixture of fatty acids with different carbon numbers and double-bond contents. Pure fatty acid can be produced by effective separation of the mixture. The other valuable product from the hydrolysis reaction is glycerol. The hydrolysis of plant oils takes place commercially at temperatures between 100 and 260 C, pressure of 100e7000 kPa, water to oil ratio of 0.4 to 1.5 and with or without a catalyst. Either an acid or base catalyst can promote the hydrolysis reaction. However, the use of a strong base such as NaOH usually causes the formation of soap, i.e., salts of fatty acids, reducing the yield of fatty acids. Solid catalysts with high catalytic activities, such as solid Fe-Zn double-metal cyanide (DMC) complexes have been investigated for the hydrolysis of plant oils. A complete conversion to fatty acids with selectivity greater than 73 wt% was obtained at temperatures as low as 463 K with 5 wt% of Fe-Zn DMC catalyst (Satyarthi et al., 2011). In addition, fatty acids can also be obtained by the hydrolysis of waste cooking oils. For example, a two-step process was successfully developed for the hydrolysis of waste cooking oil to fatty acids with the catalysis of Candida rugosa lipase (Chowdhury et al., 2013).

O H2C O HC O H2C

O

O

H2C OH

R R'

O R''

Triglyceride

3 H2O

HC OH H2C

O (R",R') R

OH

OH

Glycerol

Figure 3 Hydrolysis of plant oils.

Fatty acids

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The choice of fatty acids or alcohol for the synthesis of biolubricants via the esterification depends on the performance requirement of biolubricants. Viscosity and oxidative stability are two most important properties of biolubricants. The use of shorter chain mono-alcohols such as methanol produces methyl esters of fatty acids with lower viscosity than longer chain alcohols. Similarly, the use of shorter chain fatty acids produces lower viscosity ester than longer chain fatty acids (Knothe and Steidley, 2005). For example, methyl stearate has a kinematic viscosity at 40 C of 5.85 mm2/s as opposed to 7.59 mm2/s for butyl stearate. On the other hand, the kinematic viscosity for butyl palmitate is 6.49 mm2/s. In addition, the presence of the double bonds in fatty acid chains also helps to achieve esters with reduced viscosities. For instance, the viscosity of methyl oleate is 4.51 mm2/s compared with 5.85 mm2/s for methyl stearate. However, the double bond(s) in esters of linoleic and linolenic acids are susceptible to oxidation reaction, thus rendering them unsuitable as biolubricants. Because esterification reaction is highly reversible, the yield of the ester can be improved using Le Chatelier’s principle: either by removing one of the products or using excess reactants. The reaction is frequently driven to completion by the use of excess alcohol and continuous removal of water. Azeotroping agents such as toluene are sometimes used to aid the water removal process (Eychenne and Mouloungui, 1998). Both homogeneous inorganic acids and alkali may be used as esterification catalysts. However, alkali catalysts may cause saponification and acid catalysts affect the colour of the reaction products. Typical acid catalysts for industrial-scale esterification include H2SO4, HCl, H3PO4 or p-toluenesulfonic acid. To reduce the drawbacks associated with homogenous catalysts, heterogeneous catalysts are being developed for esterification of fatty acids for biolubricant production. A comprehensive review on the heterogeneous catalysts for esterification reaction is reported by Melero et al. (2009). The results showed that the esterification can proceed satisfactorily over these catalysts, and has emerged as an alternative to the mineral acid catalysts because of the easy recycling and regeneration. However, these catalysts require more severe reaction conditions than the conventional mineral acid catalysts (Su and Guo, 2014). Enzymatic catalysts have also been investigated for the esterification of oleic acid with various alcohols (Trivedi et al., 2015). However, oleic acid was mainly esterified with trimethylolpropane (TMP) using immobilised lipase B from Candida antarctica (Novozym 435) (Åkerman et al., 2011). TMP-trioleate exhibits suitable properties for use as hydraulic fluids, especially at extreme temperatures. Immobilised lipases from Candida rugosa

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Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

(Uosukainen et al., 1998), mucor or Rhizomucor miehei (Hayes and Kleiman, 1996) have also been used in the synthesis of diol and polyol esters. In most cases, the esterification was conducted in solvent-free media. Reactants such as the short-chain ester can act simultaneously as solvent and substrate (Hayes and Kleiman, 1996). Recently, enzymatic esterification was also performed using liquid (non-immobilized or free) lipase enzyme, without any additional organic solvent to synthesize higher esters in the range C12eC36 (Trivedi et al., 2015). They used different combinations of acids and alcohols, in a single-step reaction. The esters produced are biodegradable in nature and can be used as in additives, lubricating oils and hydraulic fluids. Solid acid catalysts have also been used in the synthesis of tri-esters of fatty acids through esterification of different acids with polyols. Commercial resins (Dowex 50W-X8, Amberlyst-15 and Purolite CT275DR) were tested for the esterification of castor oil-based fatty acid with 2-ethylhexanol for the production synthetic biolubricants (Saboya et al., 2015). Later WO3based catalysts supported on porous clay heterostructures with SieZr pillars were developed in the esterification of ricinoleic acid with 2-ethyl hexanol. The catalysts with a tungsten content of 20 wt% exhibited the highest conversion, reaching 91% after 24 h (Saboya et al., 2016). Bondioli (2004) proposed the use of metallic zinc and tin (II) oxides in the production of polyol esters. Zinc (II) chloride and stannous chloride have also been investigated for the esterification reactions of 10-undecenoic acid with TMP, neopentyl glycol (NPG) and pentaerythritol (PE) (Padmaja et al., 2012). The resulting polyol esters had a high yield of 92%e96% and showed lubricant properties similar to those of oleic acidebased polyol esters. Almost complete conversion and very high selectivity to triesters were obtained in the presence of a silica zirconia amorphous catalyst (Zaccheria et al., 2016). Although up to 10% w/w catalyst was required for the synthesis, the catalyst showed very high water resistance property and could be reused.

3.3 Transesterification Transesterification is a reaction of exchanging the R00 of an ester (RCOOR00 ) with the R0 of an alcohol (R0 OH) to produce a new ester (RCOOR0 ) and an alcohol (R00 OH) (Fig. 4). O R C OR'

R" OH

O R C OR"

Figure 4 Transesterification of plant oils.

R' OH

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The starting ester can be plant oils or their ester derivatives. Transesterification is a reversible reaction and can be catalyzed by either an acid (commonly HCl, H2SO4 and BF3) or alkaline catalyst (usually NaOCH3, KOH and NaOH) (Schuchardt et al., 1998) or enzyme. Strong acid catalysts donate a proton to the carbonyl group, whereas alkaline catalysts remove a proton from the alcohol, thus making it more receptive to an attack by an alkoxide ion. Like esterification, the choice of the starting ester and alcohol in transesterification reaction depend on the performance requirements of the lubricants. If the starting materials are plant oil triglycerides and short-chain alcohols such as methanol or ethanol, the resultant ester is biodiesel. On the other hand, if esters with larger alkoxy groups are required, plant oilebased methyl or ethyl esters are reacted with polyalcohol by heating and evaporating the smaller alcohol simultaneously to drive the reaction to equilibrium. The kinematic viscosity of the resultant ester increases with the chain length of either fatty acid ester or alcohol, but the chain length of fatty acid ester has a more significant impact on viscosity than alcohol. Branching structure of the alcohol moiety has little effect on viscosity, whereas the presence of hydroxyl groups increases the viscosity largely (Knothe and Steidley, 2005). The properties of esters produced via transesterification are similar to those of the ones from esterification (Chang et al., 2015), despite the difference in processes between these two reactions. The neutralization of fatty acids is usually not necessary for transesterification. However, the filtration and distillation steps are still needed in transesterification process to remove the soap generated with the alkaline catalysis and to separate the unreacted ester. The co-product from transesterification is alcohol, whereas the co-product generated in esterification is water. Removing the alcohol, especially loweboiling point alcohol is much easier than water because of its lower boiling point. Because of these advantages, more studies have been focused on transesterification than esterification for biolubricant production. Similarly, the use of excess reactants according to Le Chatelier’s principle can accelerate the reaction and improve the conversion, as the frequency of successful collisions increases, allowing for an increase in product yield. However, the use of excess fatty acids to drive the forward esterification reaction is not necessarily feasible because the neutralisation step is expensive. Using excess short-chain fatty acid esters such as methyl fatty acid esters in transesterification simplifies the purification steps as the

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Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

ester can be easily vapourized and reused (Abd Hamid et al., 2010; Hamid et al., 2012). Yunus and co-workers have investigated the transesterification reaction between palm oil methyl ester (POME) and TMP using alkali catalyst for the synthesis of environmentally acceptable lubricants (Yunus et al., 2003; Yunus et al., 2004a,b; Yunus et al., 2005). Using sodium methylate as catalyst (1.0% w/w) and under the refluxing condition at 110e120 C and reduced pressure of 2 kPa, a complete conversion of TMP to triester with a high yield of more than 90% could be achieved within 1 h. Earlier, a similar transesterification reaction involving rapeseed methyl esters with TMP was reported (Uosukainen et al., 1998). However, around 10 h at 120 C was required for a complete conversion of TMP to triester. Using alkaline catalyst in transesterification reaction involving plant derivatives has its inherent drawbacks. The presence of fatty acid impurities in the starting ester can initiate saponification reaction with the base catalyst (Fig. 5) (Chang et al., 2012). More fatty acids also can be formed from a reaction between the starting ester and moisture (water). As shown in Fig. 6, if the hydroxide ion is moisture, the carbonate will be fatty acids. Thus, the challenge of using a homogenous base catalyst for transesterification of esters is to make sure that the starting materials are anhydrous and free of fatty acids. Other approaches to avoid this issue are to reduce the amount of homogenous base catalyst without affecting the reaction rate or use a heterogeneous base catalyst, such as metal oxides. Heterogeneous calcium methoxide catalyst was used in transesterification of esters, where significant reduction in soap formation was observed (Masood et al., 2012). However, the reaction temperature was high up to 180 C and about 8 h was needed for a complete conversion (Chang et al., 2015). To

(X = Na, K, etc.) O R C OH

XOH

Free fatty acid

Base

O R C OX Soap

H2O Water

Figure 5 Saponification reaction of fatty acids with alkaline catalyst. O R C OR' Ester

HO Ion hydroxyde

O R C O

R' OH

Ion carbonate

Alcohol

Figure 6 Mechanism of the base hydrolysis of esters.

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accelerate the reaction rate with less amount of catalyst, other approaches such as sonication, pulsation and microwave technology were used to affect the chemical thermodynamic equilibrium of the reaction. The ability of a pulsed-loop reactor to enhance the kinetics of transesterification of jatropha oil with methanol was reported by Syam et al. (2012). A pilot-scale pulsed-loop reactor with vacuum unit was then proposed to synthesize biolubricant from TMP and POME (Hamid et al., 2016). The use of only 0.3% (w/w) of sodium methoxide could achieve 86% yield of TMP trimester (TMPTE) within 1 h. The amount of soap formed in the process was reported at 167 mg/g. The biolubricant from the TMPTE showed properties comparable with those of analogue obtained from a similar synthetic route while in a batch reactor (Yunus et al., 2004) and using a heterogeneous catalyst, namely calcium methoxide (Chang et al., 2015). The improvement was attributed to efficient vacuum and higher excess percentage of POME used in the reaction. As the molar ratio of POME: TMP increased from 3.9:1 to 10:1, the reaction time reduced from 45 to 20 min. Application of vacuum is vital in the transesterification. In absence of vacuum, an operating temperature of at least 200 C is commonly required to achieve a reasonable conversion to polyol esters (Basu et al., 1994; Eychenne and Mouloungui, 1998). The effect of vacuum on the rate of transesterification between POME and TMP at 130 C is shown in Table 2 (Yunus et al., 2002). Although the highest yield of TMPTE was obtained at 0.1 mbar, a high-energy input was required. A vacuum pressure between 10 and 50 mbar was found to be sufficient for the effective synthesis of palm oilebased TMP ester (Yunus et al., 2003). The kinetics study on transesterification of palm oilebased methyl esters with TMP highlighted the complexity of the transesterification reaction and the influence of vacuum on the reaction kinetics (Yunus et al., 2004). Studies on transesterification of other plant oilebased methyl esters with TMP using sodium methoxide as a catalyst under vacuum were conducted for the production of biolubricants. For example, 90.9% TMPTEs were obtained after 5 h reaction of canola oilebased methyl ester with TMP under a constant pressure of 1 mbar at 140 C using a molar ratio of ester to TMP of 5% and 1.5% sodium methoxide as a catalyst (Sripada et al., 2013). The synthesis of triesters from rapeseed oil methyl ester with TMP was also investigated under chemical and enzymatic catalysis (Uosukainen et al., 1998). Under a reduced pressure of around 2.0 kPa, an almost complete conversion for both chemically and enzymatically catalyzed

1.

47

53

120

C. rugasa lipase (40 wt%) Sodium methoxide (0.7 wt%)

2. 130

20

3. 150

5. 170

1013 Perchloric acid, sulphuric acid, (1 atm) p-toluenesulfonic acid, hydrochloric acid, nitric acid (2% wt%) 1013 Brønsted acidic ionic liquid (1 atm) 50 Calcium methoxide (0.3 wt%)

6. 140

1

7.

45

8

128

1013 Candida sp. Immobilized lipase (1 atm) (30 wt%) 2 Potassium hydroxide (1 wt%)

4. 100

9. 130

20

Sodium methoxide (0.9 wt%)

Sodium methoxide (1.5 wt%)

Sodium methoxide (0.3 wt%)

ROMEb:TMP 3.5:1 ROMEb:TMP 3.2:1 HOPOME: TMP 3.9:1 FAe: TMP 4:1

Transesterification (24 h) Transesterification (10 h) Transesterification (1 h) Esterification (3 h)

Oleic acid: TMP 3.6:1 HOPOME: TMP 6:1 COMEc: TMP 5:1 FA: TMP 8.4:1

Esterification (3 h) 93.3

Transesterification (8 h) Transesterification (5 h) Esterification (47 h) FAMEf: TMP 4: Transesterification 1 (1.5 h) Transesterification HOPOMEd: TMP 3.9:1 (1 h)

70

Uosukainen et al. (1998)

86 98 70

98 91. 95.5 85.7 80.6

Yunus et al. (2005) Arbain and Salimon (2011)

Li et al. (2012) Chang et al. (2012) Sripada et al. (2013) Tao et al. (2014) Wang et al. (2014) Hamid et al. (2016)

Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

Table 2 Synthesis of TMP esters from plant oil derivatives via esterification and transesterification routes Temperature Pressure TMPEa yield  No. ( C) (wt%) Reference (mbar) Catalyst loading Molar ratio Reaction

a

TMPE, trimethylolpropane ester. ROME, rapeseed oilebased methyl esters. c COME, canola oilebased methyl esters. d HOPOME, high-oleic palm oilebased methyl esters. e FA, fatty acids. f FAME, fatty acid methyl esters. b

201

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processes was obtained with a yield of about 98%. In enzymatic conversion, the reaction time of around 72 h was required at 42 C, whereas 10 h was enough for chemical conversion using sodium methoxide as a catalyst at 120 C. Transesterification of TMP with Jatropha oil has also been reported (Kamil et al., 2011; Mohd Ghazi et al., 2009). A high yield of more than 80% could be obtained at 120 C under 10-mbar pressure and 1 wt% catalyst. Cottonseed and soybean oilebased TMP esters were also produced via transesterification, where the oils were firstly converted to methyl esters then later synthesized with TMP (Dodos et al., 2010). Transesterification of canola oil (Brassica napus) with TMP using sodium methoxide as catalyst yielded 90.9% of TMPTEs after 5 h. Fatty acid methyl esters from waste cooking oil can also be used to produce TMP esters. Using potassium hydroxide as a catalyst, under a vacuum pressure of 200 Pa at 118 C for 1.5 h, 99.6% TFATE could be obtained (Wang et al., 2014). After removal of the unreacted FAME by distillation, 99.6% TFATE could be obtained. Table 2 compares the synthesis of TMP esters via various routes. Transesterification via chemical route seems to be faster and scalable. This is evident when the process was scaled up to 2 L capacity and able to achieve 95% yield of TMP ester using 0.3% w/w of sodium methoxide catalyst in only 1 h reaction. Other than TMP, other polyols such as PE and NPG have also been widely used for the synthesis of biolubricants from plant oils and their derivatives (Hashem et al., 2013; Aziz et al., 2014). Pentaerythryl ester can also be produced from POME, and more than 40% yield of tetraester was reported (Aziz et al., 2014). NPG has two hydroxyl groups. Gryglewicz et al. (2003) studied the possibility of using few natural fats, namely rapeseed oil, olive oil and lard, as starting material for the preparation of TMP esters and NPG. They also used fatty acid methyl esters obtained from these natural fats to react with TMP and NPG. The synthetic process used calcium methoxide as a catalyst and isooctane as a solvent. The reaction rate was very slow and only 85%e90% yield was reached after 20 h despite the continuous removal of methanol. Liao et al. (2015) synthesized high-viscosity biolubricants from NPG oligoesters based on adipic acid and rapeseed oil using a two-step process . The first step was the esterification of NPG with adipic acid and the second step was the esterification of oligomeric intermediates with rapeseed oil fatty acid. In both steps toluene was used as water entrainer, reaction temperature was maintained at 140e150 C and reaction time was 60 min. The range of viscosity was

Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

203

from 101.1 to 182.0 (mm2/s) at 40 C; viscosity index was over 200 and pour point was below 43 C (Liao et al., 2015). Thermochemical conversion of plant oils to biolubricants may take place via a two-step successive transesterification of vegetable oils. During the two-step transesterification, vegetable oil was firstly converted into the corresponding fatty acid methyl esters, which then reacted with a polyol for the production of polyol esters. Sunflower, linseed and jatropha oils were used in the synthesis of pentaerythryl esters (Hashem et al., 2013). The esters showed excellent thermal and oxidation stabilities and can be used as promising base oil for synthetic lubricants.

3.4 Epoxidation Soybean oil, canola oil and rapeseed oil are plants oils that possess certain excellent frictional properties suitable for lubricant base oil application. However, the oils contain a high degree of multiple or poly CeC unsaturation in the fatty acid chains, causing poor thermal and oxidative stability (Adhvaryu and Erhan, 2002). This property confines their use as lubricants to a modest range of temperature. On the contrary, the presence of PUFAs yields low pour point, which enables the oil to be used in temperate countries. The most common chemical conversion method in biolubricant preparation is epoxidation followed by ring-opening reaction via either acetylation or esterification. Epoxidation is a reaction of alkene group to form cyclic ether in which both carbon atoms of a double bond are bonded to the same oxygen atom. The products obtained from epoxidation are called epoxides or oxiranes. Many methods are available to prepare epoxide, with the most used being epoxidation with peracids (RCO3H). In lubricant synthesis, the removal of double bonds via epoxidation deteriorates the pour point, thus limiting the application of the lubricants (Cermak et al., 2013; Hwang and Erhan, 2006; Salimon et al., 2012; Salimon and Salih, 2010). Because of the high reactivity of the oxirane ring, various chemical modifications of epoxidized plant oils and their derivatives are possible to improve the low-temperature properties. One of the most commonly used is the ring-opening reaction, such as acetylation and esterification (Fig. 7) (Salih et al., 2012; Salimon et al., 2014; Salimon et al., 2011a,b). The effects of two different ring-opening approaches of epoxidized plant oils including soybean, sunflower and high-oleic sunflower oils (HOSOs) on properties of the products have been investigated

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OH O Formic acid, H2O2

OH O

O Heptanoic acid, PTSA

O O

OH

OH

O

2-ethylhexanol, H2SO4

O O

O

OH

O

Caproic/Octanoic/Capric/ Lauric/Myristic acids, H2SO4 O O

O

O

O O

n n = 0, 2, 4, 6, 8

Figure 7 Epoxidation, ring opening and esterification of plant oil derivatives (Salimon et al., 2012).

(Campanella et al., 2010). In the first method, glacial acetic acid was used for the ring-opening reaction, whereas the second method used a short-chain aliphatic alcohol (i.e., methanol and ethanol). The reaction with alcohol proceeded under mild conditions: 50 C and 60 min. Based on the viscosity alone, the products are all viable as lubricant base stocks.

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Most research on the epoxidation of plant oils for biolubricant production was focused on soybean oil because of the commercial availability of ESO (Adhvaryu and Erhan, 2002; Lathi and Mattiasson, 2007; Sharma et al., 2008; Hwang and Erhan, 2001). The epoxidation of soybean oil can also be conducted using hydrogen peroxide and Ti/SiO2 as catalysts in the presence of tert-butanol (Campanella et al., 2004). The ESO was later chemically modified to improve the pour point. The effect of alcohol structure on the ring-opening reaction of ESO has been studied using four types of Guerbet alcohols; C12-, C14-, C16- and C18 (Hwang and Erhan, 2006). By using excess alcohol, fully transesterified ring-opened products have been obtained in 20 h. Compared with ring-opened products of ESO which had a pour point of 18 C, the transesterified ring-opened product with excess C14 guerbet alcohol showed an improved pour point to 36 C. Acetylation of hydroxy groups of the transesterified ring-opened products further lowered pour points to 42 C without pour point depressant (PPD) and to 48 C with 1% of PPD. Later, similar studies were conducted on the ring opening of the ESO but using Amberlyst 15 catalyst (Lathi and Mattiasson, 2007). Three types of alcohols including isobutanol, iso-amyl alcohol and 2-ethyl hexanol were used. The ring-opened ESO with 2-ethylhexanol gave the low pour point value. However, the subsequent esterification with acetic anhydride did not improve the pour point as expected. Based on these reports, it is evident that introducing branched alcohol leads to marked improvement of pour point while increasing the chain length only affects the pour point of ring-opened products marginally. The effect of ring-opening approach using anhydrides of different chain length was explored in one-pot synthesis of ESO and various anhydrides (Sharma et al., 2008). After investigating the effects of seven different anhydrides, it was concluded that hexanoic anhydride gave the maximum epoxy ring opening. However, the synthesis requires large amount of solvent such as ethyl acetate for minimum polymerization because of disruption of the ester linkage. Besides soybean oil, epoxidation of other plant oils which contain high percentage of PUFAs have also been explored. The chemical conversion of four plant oils including castor oil, linseed oil, sunflower oil and jatropha oil into polyoleate esters via epoxidation, hydrolysis and esterification with excess oleic acid was reported (Hashem et al., 2013). The results showed that most esters were promising candidates as synthetic lubricants. Nonetheless, the ester produced from the direct esterification of castor oil with oleic acid showed poor thermal and oxidative stabilities. Epoxidation of the

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double bonds in canola oil followed by acetylation for ring opening using acetic anhydrate was also investigated (Sharma et al., 2015). These resulting products exhibited good low-temperature behaviour, friction and anti-wear properties, demonstrating great potential as alternative biolubricants to petroleum-based lubricants for automotive engine applications. Madankar et al. (2013) also investigated the ring-opening reaction of epoxidized canola oil using alcohols such as isobutanol, iso-amyl alcohol and 2-ethyl hexanol. The derivatives with higher chain alcohol showed more lubricity and better thermal stability. Plant oil derivatives such as fatty acids and fatty acid methyl esters can also be epoxidized to improve the oxidative stability of the finished lubricants. The epoxidation of oleic acid and ricinoleic acid has been reported with formic acid as a catalyst by Salimon and his coworkers (Salih et al., 2011; Salimon et al., 2011a,b). Three successive catalytic reactions of epoxidized oleic acid were conducted to produce triesters (Salimon et al., 2012), including (1) the ring opening of epoxidized oleic acid with heptanoic acid, (2) esterification with 2-ethylhexanol using sulphuric acid as a catalyst and (3) acetylation with fatty acids. Using epoxidized ricinoleic acid, the ring opening was done using the stearic acid, followed by the esterification with 2-ethylhexanol. The results showed that butyl 10,12-dihydroxy-9-behenoxystearate exhibited the most favourable lowtemperature performance with a pour point of 47 C (Salimon et al., 2011a,b). Similar to fatty acids, the epoxidation of fatty acid methyl esters may also take place following the same route, namely epoxidation, ring opening and esterification. Epoxidation of methyl ester of the oleic acid, followed by ring opening with acetic/capronic acid and subsequent esterification of hydroxyl group with lauric/palmitic chlorides to produce four triester derivatives of 9,10-dihydroxystearic acid has been described (Kleinov
a et al., 2008). Unlike plant oil fatty acids, the triester derivatives from plant oilederived methyl esters no longer contain hydroxyl group and hence exhibit superior lubricating properties. The epoxidation of trimethylolpropane esters (TMPTE) from palm oil has also been reported to improve the oxidative stability (Naidir et al., 2012a; Naidir et al., 2011). In situ epoxidation of double bonds in fatty acid chain of TMP esters was conducted using hydrogen peroxide and acetic acid as catalysts with the addition of a small amount of sulphuric acid. The kinetics study demonstrated that the maximum conversion of double bonds to oxirane groups was achieved in 1 h at the high-temperature region (70, 80, and 90 C), while more than 4 h was required to reach the maximum

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conversion at the low-temperature region (30, 50, and 60 C) (Naidir et al., 2012b). The results showed that more than 96% of the ethylenic unsaturation was converted into oxirane ring. The oxidative stability of epoxidized TMP ester improves compared with the original TMP ester at the expense of pour point.

3.5 Hydrogenation Partial hydrogenation of unsaturated fatty acid chains in plant oils and their derivatives is also used to improve their oxidation stability for lubricant base stock application. Plant oils in the natural form are easily oxidized, becoming thick and polymerizing to a plastic-like consistency (Fox and Stachowiak, 2007). By selective/partial hydrogenation, the poly-unsaturated fatty acids fraction such as linoleic (C18-2) and linolenic (C18-3) acids can be transformed into single unsaturated fatty acid, i.e., oleic acid (C18-1) without increasing the saturated part of the substance (C18-0) (Soni and Agarwal, 2014). This process is of great interest in the field of high-quality biolubricant production, as the elimination of double bonds improves the oxidative stability and low-temperature properties of lubricant base stocks (Aluyor and Ori-Jesu, 2008). Generally, the partially hydrogenated vegetable oils must contain a minimum of 80% of cis-oleic acid to meet the quality requirement of industrial lubricants. The hydrogenation process (Fig. 8) of plant oils typically takes place by flushing hydrogen gas into the reactor containing plant oils at pressure O H2C O HC O

linoleic O linoleic O oleic

H2C O

H2, Ni O H2C O HC O

stearic O oleic O oleic

H2C O

Figure 8 Hydrogenation of plant oils.

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between 69 and 413 kPa and at temperature of 150e225 C. Powdered nickel (Ni) is typically used as a catalyst (Shomchoam and Yoosuk, 2014). Because Ni catalyst has the tendency to leach into the oil, the toxicity of the oil because of the presence of traces of Ni is a concern. Thus, most of the reported work on the chemical conversion of plant oils via hydrogenation has been focused on catalyst selection. Noble metal catalysts are commonly used because of their high activity in small quantity and the possibility of reuse (Fern
andez et al., 2009; Nohair et al., 2005). The earlier work on hydrogenation of soybean oil was focused primarily on the production of hydrogenated products (shortening and margarine) as alternatives to their animal counterparts (lard and butter), as the latter became increasingly expensive and relatively scarce (www.soyinfocenter. com). To obtain base oils for lubricants from soybean oil via selective hydrogenation, a process was firstly carried out at 5-atm hydrogen, 500e 700 rpm and 170w200 C over catalysts (5% Pt/C and 23% Ni/SiO2), and then the reduced soybean oil was converted to soybean methyl ester (Lee et al., 2007). Supercritical CO2 and Pd/C catalyst were also used in the hydrogenation process of soybean oil to obtain lubricant base oil (Wang et al., 2011). The optimum conditions were determined as follows: catalyst level of 0.06% (w/w), reaction time of 45 min, reaction temperature of 50 C, CO2 pressure of 5.5 MPa and a stirring speed of 200 r/min. The partially hydrogenated soybean oil with varying degrees of unsaturation has been further modified to produce synthetic wax ester lubricants, which had potential as a replacement for sperm whale oil (Bell et al., 1977). Various silica-supported copper-based catalysts were evaluated in the liquid-phase soybean oil hydrogenation. The results showed that the types of support and the catalyst preparation method did not have a marked effect on catalytic activity. Copper exhibited unique properties for obtaining proper lubricants from soybean oil hydrogenation because of the high selectivity of hydrogenated unsaturated linolenic (C18:3) and linoleic (C18:2) fatty acids to unsaturated oleic acid (C18:1) (Trasarti et al., 2012). Sunflower oil contains more than 70% linoleic and linolenic acids. Reports on selective hydrogenation of sunflower oils were also focused on catalysis selection. The palladium and platinum supported on various substrates, such as ZrO2, TiO2, a-Al2O3, g-Al2O3, ZSM-5 and MCM22, on selective hydrogenation of sunflower oil were screened (Fern
andez et al., 2009; McArdle et al., 2011). Compared with Ni catalysts, Pd catalysts are more attractive because of their high activity and milder operation conditions. In addition, the g-aluminaesupported Pd catalyst, with a metal

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loading of 0.78 wt% and 60% dispersion, showed a specific activity higher than the other Pd catalysts (Fern
andez et al., 2009). A range of palladium and platinum catalysts supported on Al 2O3, ZrO2 and TiO2 were screened for the promotion of selectivity of cis C18:1 in hydrogenation of sunflower oil (McArdle et al., 2011). The performances of these catalysts were compared with a conventional Ni catalyst. Even though Pt catalyst was not as active as the Pd catalysts, Pt catalyst minimized formation of trans-fatty acids. The activity and selectivity of different noble metal catalysts on selective hydrogenation of sunflower oil ethyl ester was also investigated (Nohair et al., 2004, 2005). During the selective hydrogenation of sunflower oil ethyl esters at a temperature of 40 C in the presence of Pd, Pt and Ru catalysts, the palladium catalyst proved to be the most active, but the largest metallic particles tend to promote the C18:1 cisetrans isomerization. Through the modification of the palladium catalysts with copper or lead, or the addition of amines into the reaction medium, the selectivity in cis C18:1 ester could be improved and 95% conversion was reached after 13 min. In contrast, the Pt- and Ru-catalyzed hydrogenation processes needed longer durations and higher catalyst levels to obtain the same conversion. The order of the catalytic activity is as follows: Pd > Pt > Ru (Nohair et al., 2005). The selective hydrogenation of rapeseed oil and its methyl esters was conducted using different supported copper catalysts (Ravasio et al., 2002). With 8 wt% Cu/SiO2 catalyst, the C18:3 component can be eliminated and the C18:2 content was lowered from 22% to 5% without increasing the stearic C18:0 content. These hydrogenated oils with a C18:1 content up to 88% showed remarkable oxidation stability and maintained the pour point at 15 C. The partial hydrogenation of palm oil was also investigated with Pd/g-Al2O3 catalyst (Shomchoam and Yoosuk, 2014). The optimal reaction conditions were determined at 120 C, with pressure of 2 bar for 30 min and 0.02 wt% of 5% wt Pd/g-Al2O3. The oxidation stability of hydrogenated palm oil improved because of the decrease in the double bond and use of additives. However, the presence of additives would impair the biodegradability and increase the toxicity and cost (Cermak et al., 2013). Introducing branching on these double bonds via formation of estolides is known to improve the pour point of lubricant base stock. Typically, plant oils have pour points of 10 to 20 C (Erhan and Asadauskas, 2000), whereas estolides have pour points of 5 to 45 C (Isbell et al., 2001; Isbell, 2011) which are considered to meet most of lubricant applications.

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3.6 Oligomerization/Branching Oligomerization or branching of plant oils and their derivatives to form estolides has been developed for use in biolubricant area (Cermak and Isbell, 2001). The estolides have proved to negate poor cold-temperature properties and thermal oxidation stability associated with plant oilebased lubricants. By converting plant oils and derivatives to estolides, these properties will improve simultaneously and the need for expensive additive packages is reduced, as the use of too much additives will impair the biodegradability and increase the cost and toxicity of lubricants (Cermak et al., 2013). Typically, plant oils have pour points of 10 to 20 C (Erhan and Asadauskas, 2000), whereas estolides have pour points of 5 to 45 C (Isbell et al., 2001; Isbell, 2011) which is considered to meet most of lubricant applications. Estolides are natural and synthetic compounds that can be derived from plant oils with hydroxyl groups or from fatty acids by the condensation of fatty acids across the olefin of a second fatty acid (Isbell, 2011). The development of biolubricants based on estolides derivatives have been studied extensively by Isbell and coworkers (Cermak and Isbell, 2001, 2003b; Cermak et al., 2013; Erhan et al., 1995; Isbell et al., 1994; Isbell, 2011). Estolide formation of hydroxy fatty acids ends with a polymer containing a free hydroxy group on one end, which can be capped by fatty acid (Fig. 9). The synthesis can O OH Oleic acid

H2SO4 (n=0-3, 65%) HClO4 (n=0-10, 76%) p-Toluenesulfonic acid (n=0-3, 45%) Montmorillonite K-10 (n=0-1, 10-30%)

O O

O O

n

O OH

Estolides

Figure 9 Estolide formation from plant oil-derived fatty acid (Cermak and Isbell, 2003b).

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be conducted under high temperature and low pressure in the absence of a catalyst (Cermak and Isbell, 2002) or with strong inorganic acid catalysts (Cermak and Isbell, 2001, 2009; Isbell et al., 1994) or from lipase-catalyzed reactions (Hayes and Kleiman, 1995, 1996). The estolide structure is identified by the secondary ester linkage of one fatty acyl molecule to the alkyl backbone of another fatty acid fragment (Isbell et al., 1994; Isbell and Kleiman, 1996; Isbell et al., 1997). Estolides from castor and lesquerella oil can be derived from either the triglycerides or their free fatty acids using their hydroxyl moieties to establish the estolide bond (Cermak et al., 2013; Hayes and Kleiman, 1996; Isbell et al., 2006). Estolides synthesized from unsaturated fatty acids such as oleic acid have introduced a wide range of new estolide structures based on the position of the original olefin of the starting fatty acid (Cermak and Isbell, 2001). Most studies also suggested that the free hydroxyl group should be capped with a nonhydroxy fatty acid that is either saturated or branched to form an ester. Estolide esters from fatty acids usually had pour points (ranging from 54 to 6 C) lower than estolides from triglycerides (36 to 9 C). All these estolides were reported to have suitable viscosities and oxidative stability (Isbell et al., 2001, 2006; Isbell, 2011). García-Zapateiro et al. described the synthesis of estolides from fatty acids of HOSO (acid oils), containing more than 80% C18.1 (García-Zapateiro et al., 2010). The estolides displayed higher freezing temperatures and higher viscosities than the original fatty acid. Estolides from meadowfoam oil fatty acids and other monounsaturated fatty acids were also synthesized for potential lubricant application (Erhan et al., 1993). The role of water and other operating conditions in the synthesis of estolides was explored. It was evident that without water, a rapid degradation of the estolide took place and thus reduced the final yield. Cermak and coworkers have been developing various methods to synthesize estolides and estolide esters to gain a better understanding of the structural functions of estolides in fluids (Cermak and Isbell, 2009; Cermak et al., 2013; Cermak et al., 2006; Isbell et al., 2006). There are two different types of estolide esters: oleic-based estolide esters and saturated capped oleic-based estolide esters. For the saturated capped estolide, the top fatty layer of the estolides is hydrogenated to improve the oxidative stability. Both these estolides are later esterified with 2-ethylhexanol (2-EH) to provide extra branching and chain length responsible for the improved low-temperature properties. These estolide 2-EH esters were found to be far superior to other fully formulated petroleum and biolubricants

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(Cermak et al., 2006). The effect of branched and linear alcohols on physical properties of oleic acid estolides has also been investigated (Cermak et al., 2013). The branched esters out-performed the linear esters in terms of low-temperature properties. Estolides from the branched alcohols such as 2-hexyldecanol and 2-octyldodecanol showed the best pour points ranging from 24 to 39 C. Cermak et al. (2015) have recently synthesized new and different estolide esters from oleic acid and coconut oil fatty acids. They separated the dimer and trimer plus estolide esters from the estolides esters before exploring the effects of chain length and branching degree on the physical properties of the pure estolides. The estolide esters from 2-hexyldecanol and cocooleic dimer estolide showed lower pour point of 45 C and viscosity of 27.5e51.7 cSt at 40 C than the one with coco-oleic trimer plus estolide, which had pour point of 39 C and viscosity of 120.8e227.7 cSt at 40 C. Cermak et al. (2015) also investigated the potential of using pennycress (Thlaspi arvense L.)-based free acid to produce estolides, followed by the esterification with 2-EH. Effects of various capping fatty acids on physical properties of estolides esters were investigated. Kinematic viscosities of the free-acid estolides ranging from 494.4 cSt to 870.5 cSt at 40 C were higher than those of corresponding estolide 2-EH esters (116.3e245.75 cSt at 40 C). Nonetheless, the oxidative stabilities of all these estolides were not reported. All five thermochemical conversions that were discussed earlier are summarized and presented in Fig. 10. The simplest route would be either the alcoholysisetransesterification route or hydrolysiseesterification route. The first route involves the conversion of plant oils to alkyl ester, followed

Oligomerization

Fatty Acids Alkyl Ester

PLANT OILS

Epoxidation

Epoxidized Oil

Esterification Transesterification

Hydroxylation

Hydrogenated Transesterification Oil Estolides

Estolides Acid

Esterification

Esterification

Estolide Ester

Polyol Ester Polyol Ester Hydroxylated Esterification oil Transesterified Oil Estolide Ester

Figure 10 Thermochemical conversion of plant oils.

Epoxidized Ester

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213

by transesterification of alkyl ester with an alcohol to produce to a polyol ester. The other simpler route entails the conversion of plant oils to fatty acid via hydrolysis followed by esterification of fatty acid with a new alcohol. The type of alcohol used in the synthesis depends on the desired lubricating properties. Table 2 shows the conversion methods reported in the literature. The alcoholysisetransesterification route is faster and can take place at milder conditions. All other conversion routes are mostly suitable for plant oils that contain high percentage of double bonds, which need to be hydrogenated or epoxidized to improve the oxidation stability.

4. PRODUCT DEVELOPMENT AND QUALITY REQUIREMENT OF BIOLUBRICANTS 4.1 Properties of Base Oils Base oils constitute 80%e90% of the finished lubricants. Hence, the quality requirement of the finished biolubricants mostly determines which base oils are suitable for the intended applications. The properties of base oils depend on the structure of the fatty acids chain of the plant oils and alcohols (Gryglewicz et al., 2003). The base oils containing saturated linear fatty acids are highly resistant to oxidation and high temperature, but they have high pour points. In contrast, the base oils with PUFAs are susceptible to oxidation and thermal degradation but exhibit low pour points. An increase in the chain length of esters and the presence of hydrogen bonding shows a positive influence on the low-temperature properties of the esters (Salimon et al., 2011a,b). However, hydrogen bonding and long-chain esters are detrimental to the oxidation stability of these compounds. The conversion of double bonds from fatty acid esters improves the oxidative stability and increases the molecular weight and viscosity index of the products. Generally, the synthesized esters from plant oils and their derivatives show moderate thermal oxidative stability, which is the intrinsic properties of most synthetic oils. With the development of various chemical conversion methods introduced in Section 3, many plant oilebased base oils have been produced for biolubricant applications. Most of the base oils are produced by changing the glycerol backbone in the triglyceride structure of plant oils chemically via esterification or transesterification with an alcohol. This structural change improves the thermal stability, because the presence of hydrogen atoms in b position relative to the hydroxyl group in the glycerol molecule

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is disadvantageous for thermal stability of plant oils. The properties of the base oils from various polyol esters are summarized in Table 3. Most of the base oils synthesized from plant oils and their derivatives are TMP esters, which have a more stable backbone as compared with glycerol backbone in triglycerides of plant oils (Chang et al., 2015; Hamid et al., 2016; Sulaiman et al., 2007; Yunus et al., 2003). Replacing the b-H in glycerol with ethyl group in TMP improves the thermal and chemical stability of the compound. TMP esters are more popular among plant oilebased synthetic esters because their kinematic viscosity is similar to the plant oils and conforms to VG46 requirement (Yunus et al., 2004). The benchmark for TMP esters is TMP trioleate (TMPTO), which is currently available commercially, and its properties are shown in Table 3. Because TMPTO is derived from pure oleic acid, its kinematic viscosity is lower than that of palm oilebased TMP ester, which contains about 40% oleic acid fraction. The pour point of palm oilebased TMP lubricant is also above 5 C higher than the TMPTO’s pour point because of the saturated fatty acid fraction of palm oil. However, the pour point of higholeic palm oilebased TMP (TMPPOO) ester is much lower at 32 C, using high-oleic POME as starting ester (Yunus et al., 2005). Generally, TMP esters of palm oil have higher viscosity than analogous TMP esters from rapeseed oil, olive oil, canola oil and soybean oil. This trend is associated with the presence of PUFAs in the latter. Because rapeseed oil has the highest content of such acids, the products derived from rapeseed are least resistant to the action of oxygen and high temperature but possess lowest pour point. Additionally, the low-temperature property of canola oilebased TMP esters was outstanding and their derived lubricants worked well at 66 C. This was mainly ascribed to the high content of unsaturation and polyunsaturation in canola oil (B. napus)ebased TMP esters (Sripada et al., 2013). Nonetheless, the presence of polyunsaturated acid aggravates the oxidation stability. The oxidative induction time of canola oil (B. napus)ebased TMP esters was about three times lower (0.74 h) than that of the methyl oleateederived biolubricant (2.08 h). The properties of Jatropha curcas biolubricants are also reported by many researchers (Hashem et al., 2013; Kamil et al., 2011; Mohd Ghazi et al., 2009). However, the reported properties are markedly different depending on the preparation methods of biolubricants and the quality of feed stocks. The properties of esters derived from NPG and plant oils and their derivatives are also summarized in Table 3. NPG ester is basically a diester; thus, NPG esters of fatty acids have lower viscosity than analogous TMP

Kinematic viscosity at 40 C ASTM D445 46.3 (cSt) Viscosity index ASTM D445 189 Pour point (C) ASTM D97 36 Kinematic viscosity at 40 C (cSt) Viscosity index Pour point (C)

49.7 187 1

NPGOOi

48.4 196 32

NPGROi

42.6 183 6

ETCTOh

40.5 204 66

ETOEHh

36.00

35.34

72.78

PEEPOf

52.81

218.3 13.0

209.2 15.5

183 21

EROg

ESO2EHg

ETOGEh

ESFOg

176.8 -

Method

NPGPOi

ASTM D445

21

15.32

15.62

186

161.2

206.6

44.8

86.3

41.1

ASTM D445 ASTM D97

212 14

209.8 15.5

208.7 17.5

33

167 34

169 43

180 9

145 1

159 18

CO, canola oil; CTO, castor oil; E, epoxidized; 2-EH, 2-ethylhexanol; ET, estolides; JO, Jatropha oil; NPG, Neopentylglycol ester; OO, olive oil; PEE, pentaerythritol ester; PO, palm oil; POO, high-oleic palm oil; RO, rapeseed oil; SFO, sunflower oil; SO, soybean oil; TMP, trimethylolpropane; TO, Trioleate. a Chang et al. (2015). b Yunus et al. (2004b). c Mohd Ghazi et al. (2009). d Sripada et al. (2013). e Gryglewicz et al. (2003). f Aziz et al. (2014). g Wu et al. (2000). h Isbell (2011). i Raof et al. (2016).

Thermochemical Conversion of Plant Oils and Derivatives to Lubricants

Table 3 Basic properties of base oils from plant oilebased polyol esters Method TMPTOa TMPPOb TMPPOOb TMPJOc TMPCOd TMPOOe TMPROe PEEPOf

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esters. NPG esters derived from palm oil show higher viscosity and viscosity index. This is attributed to the high content of saturated fatty acids of palm oil. Therefore, palm oilebased NPG esters are viscous and resistant to the action of oxygen and high temperature. NPG esters contain a long linear hydrocarbon chain and exhibit higher viscosity index than the branched TMP esters (Gryglewicz et al., 2003), suggesting that their viscosity is very stable and changes very little with temperature. The pour points of NPG esters are also lower than those of TMP esters. This indicates the dependency of pour point on the chemical structure. Overall, the TMP esters exhibit better thermo-oxidative stability than NPG esters (Table 3). A highly branched polyol ester derived from PE and POME (i.e., palm oil-based PE ester, PEEPO) was reported by Aziz et al. (2014). Table 3 depicts the properties of the PEE that has surpassed the temperature resistance of the commercial PE tetraoleateand TMPTE properties (Yunus et al., 2005). The kinematic viscosity of PEEPO is relatively higher than TMPTE and more suitable for an oven chain application (Aziz et al., 2016). Besides polyol esters, epoxidized and estolide esters are typical base oils used in lubricant applications that require high viscosity and oxidatively stable oils such as gear oil, refrigeration oil and others. Epoxidation of the double bond improves the oxidative stability of the oils but impairs their low-temperature properties. The properties of selected esters of epoxidized plant oils and their derivatives are shown in Table 3. The lubricants from mono-, di- and triesters of 9,10-dihydroxyoctadecanoic acid exhibited excellent pour point and oxidative stability (Salih et al., 2011; Salimon et al., 2011a,b). The preparation of the triesters from oleic acid was carried out in four stages, namely epoxidation of oleic acid, oxirane ring opening, esterification of the carboxylic acid with an alcohol and acetylation of free hydroxyl group. The processes are too complicated, and would thus be costly for biolubricant production. The ESO followed by ring-opening reaction with 2-EH and then acetylation with acid anhydride has also improved the lubrication properties of soybean oil (Hwang et al., 2003). The pour point of the product was observed as low as 21 C. However, the products from ESO after the ring opening with higher molecular weight Guerbet alcohols (C12, C14, C16 and C18) followed by acetylation showed much lower pour point ranging from 27 to e 42 C (Hwang and Erhan, 2006). Moreover, the acetylated products had lower viscosities and higher viscosity indices than the intermediates before acetylation. The properties of estolides esters are mostly reported by Isbell and coworkers (Cermak et al., 2013, 2006; Erhan et al., 1993; Isbell, 2011).

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The compounds derived from lesquerolic and ricinoleic estolides, which were esterified with either oleic acid or branched 2-ethylhexanoic acid, have shown excellent pour points ranging from 36 to 54 C (Isbell, 2011). However, the oxidative stability of these estolides is generally poor because of the inherent unsaturation remaining in the triglyceride molecule even after the estolide is formed. Oleic estolide had a melting point of 31 C, which could be improved to 43 C upon esterification with a C18 Guerbet alcohol. The viscosity of estolide esters is generally higher than 150 cSt (Cermak and Isbell, 2001).

4.2 Biolubricant Quality Requirement and Its Application The quality requirements of lubricants and their technical specifications for intended applications define base oils and additives to be used in lubricants. The quality requirement for each application differs because of the technical specifications. OEM sets technical specifications for a given application in close cooperation with lubricant suppliers. The cooperation is important to ensure that both OEM and lubricant suppliers understand the performance requirement versus the inherent properties of the base oils from plant origin. The quality of the plant oils mostly depends on where the plants are grown. The amount of unsaturation in plant oils is climate dependent. For example, oil obtained from palm and coconut trees that grow in tropical regions contain higher level of saturated fatty acids, whereas plant oils such as soybean and rapeseed oils obtained from plants growing in colder climates contain more unsaturated fatty acids. Level of unsaturation affects the physical properties especially oxidative stability, whereas saturation level affects the low-temperature properties of the lubricants especially pour point. Thus, most of the unsaturated oils such as soybean, rapeseed (canola) and sunflower oils need to be chemically modified via thermochemical conversion methods to adjust their inherent properties to match the quality requirement of lubricants for the applications. Biolubricants are increasingly being accepted because of the green and sustainable image that is portrayed to industries. The biolubricants are used in hydraulics, engines, axles and gears oils, and their major applications include automotive oil, hydraulic oil, process oil, de-moulding oil, lubricating greases, chainsaw oils, compressor oils, turbine oils, industrial gear oils and metalworking oils. The automotive oils represent the largest sector of lubricant applications in 2013 despite a more stringent specification being imposed, including longer service life, lower fuel consumption, improved emission characteristics and stronger wear protection

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[http://energy.gov/sites/prod/files/2014/07/f17/fy2013_fuels_technologies. pdf ]. Biolubricants used in hydraulic equipments, especially in sensitive environments such as food processing plants, water/waste water processing and pharmaceutical processing, emphasize stringent toxicity and oxidative stability requirements. Biolubricants should be used more in agricultural equipment, including cranes, tractors and load carriers, to avoid toxicity in an environment where food is produced. The biolubricant development for any applications involves understanding the technical specifications followed by identifying the base oil and suitable conversion processes of plant oils and their derivatives at the lowest possible cost. In addition, finished products must have all the necessary additives to satisfy respective technical requirements, which differ from one application to another. The quality assessment of base oil and final product includes physicochemical and tribological tests, which are performed according to the accepted standard methods. The most common acceptable standard methods are ASTM methods and CEN methods proposed by the European Committee. The quality tests are obligatory to certify that the developed fluids fulfil the technical specifications for the selected applications. The most important physicochemical parameters required for biolubricants are the viscosity index at 40 C and 100 C, which can be measured according to ASTM D-445 and the pour point measured in terms of ASTM-D97. The viscosity is an ability to flow or the internal resistance of a fluid to flow. It is widely measured in centistoke (cSt). The viscosity of lubricating oils diminishes as the operation temperature rises. It is usually measured at a given temperature (e.g., 40 C). The viscosity of a lubricant affects the thickness of the oil layer between metal surfaces. The viscosity of an oil with a high viscosity index does not change significantly as it is heated up. This temperatureeviscosity relationship is the most critical and important consideration when selecting oils that will experience sudden change in temperatures. Applications such as gear and refrigerant oils require base oils with high viscosity and viscosity index while drilling fluids and transformers require low-viscosity base oils. The lower viscosity requirement necessitates the use of plant oils with shorter chain fatty acids as base oil such as coconut or palm kernel oil. Alternatively, chemical modification of plant oils with longer fatty acid chains such as soybean and palm oil by converting the glycerol moiety of the oil to simpler alcohol may also work. Conversely, high-viscosity base oils require the introduction of branching on the fatty acid chains or the conversion of the glycerol into complex alcohol such as PE, TMP and NPG.

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Pour point is another primary physical characteristic of lubricants that is affected by temperature. The pour point refers to the minimum temperature at which a lubricant continues to flow and it should be at least 10 C lower than the operating temperature. Below the pour point, the oil tends to thicken and ceases to flow freely. By maintaining the flowability of the oil, the oil is able to reach its intended lubrication position in a timely manner especially at the start-up. It also ensures that the oil continues to lubricate, protect and remove heat during normal operation. If the oil fails to reach the intended location because of inadequate lubrication flow, it could lead to excess friction, wear and heat in the system, resulting in equipment damage or failure. Pour point of a plant oil is related to the content of saturated fatty acid in the oil. Many authors have reported the poor low-temperature properties of plant oils (Erhan and Asadauskas, 2000; Quinchia et al., 2012; Yunus et al., 2005). The addition of additives often improves the pour points but some deficiencies usually remain. To enhance the low-temperature properties of plant oilebased lubricants, various measures have been considered such as winterization followed by filtration of either the starting materials or the final products (Gryglewicz et al., 2003). However, none of these measures improved the pour points of the products significantly. An attempt made by Yunus et al. (2005) to fractionate POME which was then reacted with TMP for lubricant production was proved to be successful as the pour point of palm oilebased TMP esters was lowered to 32 C as shown in Table 4. Almost all applications that use base oils from plant oils and their derivatives put emphasis on oxidatively stable oils. The oxidative stability Table 4 Pour points of different grades of high oleic palm oil based TMP esters Fatty acids (% w/w) PPOTE POTE1 POTE2 POTE3 POTE4 POTE5 POTE6

C12:0 C14:0 C16:0 C18:0 C18:1 C18:2 C18:3 Others Pour point for TMPb ester ( C)

0.3 0.9 31.8 4.0 47.6 14.4 0.3 0.7 1

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.8 5.8 7.3 8.7 9.8 10.1 6.0 6.2 6.2 6.1 6.2 5.8 66.2 68.1 67.5 66.4 66.0 64.7 18.3 18.6 17.9 17.3 18.0 17.7 0.7 0.8 0.7 0.8 0.7 0.7 0.9 0.4 0.4 0.8 1.2 0.9 32 37 36 29 11 9

POTE, High Oleic Palm Oil-based TMP ester; PPOTE, Palm Oil-based TMP ester; TMP, Trimethylolpropane.

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is associated to the reaction occurring at the double bonds of unsaturated fatty acids. Plants oils like rapeseed oil, soybean oil and sunflower oil contain high contents of linoleic and linolenic acids. Therefore, conversion methods need to be engaged to mitigate the oxidation stability of the oils, including epoxidation followed by ring opening, estolide formation and hydrogenation. Even after chemical modification, additives are still needed to be added to achieve biolubricants, which can meet the performance standards. For example, alpha-tocopherol (a-T), butylated hydroxyanisole (BHA) and tert-butylhydroquinone have been found to enhance the oxidative stability of TMP ester (TFATE) from waste cooking oil (Wang et al., 2014). A 3:1 mass ratio of a-T to BHA resulted in the greatest increase in the oxidative stability of TFATE, as indexed by induction period with a significant increase from 4.48 to 5.41 h. Currently, most of these modification methods mentioned above are complicated and costly, which may be unsuitable for scaling up for industrial applications (Cermak and Isbell, 2003a; Hwang and Erhan, 2001; Salimon et al., 2011a,b; Salimon et al., 2012). The acceptable tests for oxidative stability include RBOT (Rotary Bomb Oxidation Test), TOST (Turbine Oil Oxidation Stability Test) and DSC (Differential Scanning Calorimetry) tests (Cermak and Isbell, 2003a). The consequence of oxidation reaction is usually evaluated indirectly by monitoring the increase in viscosity and acidity of the lubricants. A comprehensive review on autoxidation of plant oils covering the basic mechanism, monitoring method and analysis of the oxidation products was reported (Fox and Stachowiak, 2007). It provides important information on the correlation between the oxidation products with lubrication performance. The environmental aspects of the biolubricants must be fully conformed to the acceptable biodegradability and toxicity tests. Any chemical modifications made to plant oils and additives added have an impact on the biodegradability and toxicity of products. There are different methods to determine the biodegradability and toxicity of lubricants and their components. For ready biodegradability, 301 A-F series which is the method developed by OECD (Organization for Economic Cooperation and Development, guidelines for testing of chemicals), ISO/TR 15462 and ASTM 5864, the BOD5/(ThOD or COD) ratio for each constituent substance in the product are available. According to the European Eco-label, a given substance is considered biodegradable when it gets 60% degradation in the OECD 301F manometric respirometry test, the OECD 306 test (biodegradation in seawater) or the OECD 310 test (head space test) or has a BOD5/COD or BOD5/ThOD ratio higher than 0.5. For aquatic

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toxicity, OECD 201, 202 and 203 or equivalent methods are acceptable. According to the EEL (group D), the requirement for nontoxic substance is having EC50/LC50/IC50 > 100 mg/L. More details on the environmental requirements of specific biolubricant products such as hydraulic fluids can be found in http://ec.europa.eu/environment/ecolabel/. As the main function of a lubricant is to reduce wear and friction, the basic tribological test is conducted with a Four-Ball Machine in terms of ASTM D 2783 to study the extreme pressure characteristics of lubricants under high unitary loads. Chemical modifications to plant oils and their derivatives affect the wear properties as the type of lubrication may change from boundary to hydrodynamic lubrication in the presence of different ester compounds. Although the laboratory tests mostly do not represent the real working conditions of the fluids, they do give some indication of the general wear and friction properties of materials and lubricants. The wear scar diameter formed on the ball reveals the ability of a fluid to resist wear and the coefficient of friction (COF) indicates the frictional behaviour of lubricants. The COF behaviour throughout the test duration shows stable friction. A sudden increase in friction indicates the occurrence of micro-welding because of the accelerated wear generation. Welding load is the load at which the balls connect to each other by effects of temperature and pressure. The most successful application for biolubricants is hydraulic oils; therefore, the development of hydraulic oils from plant oils is the focus in this chapter. The success of creating stable hydraulic oils with plant oils has led to the development of other lubricant products including metal working oils, coolants and greases (Padavich and Honary, 1995). Hydraulic fluids that have been identified as one of the major uses of biolubricants are commonly used in construction, forestry and agriculture and in other small industries (Honary and Richter, 2011). The development in bio-based hydraulic fluid from soybean oil was firstly reported in 1995 by Lou and Honary (1996) from Iowa State University and Glancey et al. from University of Delaware (SAE Technical Paper 981999, 1998). Later, the formulations of hydraulic fluids produced from plant oils including soybean oil (Padavich and Honary, 1995), rapeseed oil (Uosukainen et al., 1998), HOSO (Mendoza et al., 2011) and canola oil (Madankar et al., 2013; Sharma et al., 2015) were investigated. Soybean oilebased hydraulic fluids have been commercially available for many years. Environmental Lubricants Manufacturing is a company that has produced numerous soybean oilebased lubricants, including hydraulic fluids (SoyFluid Hydraulic All Season). The product offers stable viscosity through

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a wide operating temperature range with a viscosity index of 214, has a pour point of 36 C and a high flash point of 208 C for increased safety (Padavich and Honary, 1995). Hydraulic fluid from HOSO as the base fluid has also been reported (Mendoza et al., 2011). The use of additives has improved the performance at low temperatures and the resistance to oxidation of the HOSO lubricants. The field test of the HOSO lubricants was successful during the first 800 h and still continues successfully. Polyol esters are also preferred as hydraulic fluids because of their higher oxidative and thermal stabilities. Palm oilebased TMP esters and refined palm oil have been tested for the application as hydraulic fluids. The oxidative stability of the oil was evaluated by total acid number (TAN) and viscosity tests as shown in Fig. 11 (Alias et al., 2009). In general, base oil without an additive began to degrade after 200 h. When formulated with 1.0 additive package, the base oil was stable even after 800 h of operation. 9.0 TMP Ester TMP Ester + 0.5% A TMP Ester + 1.0% A TMP Ester + 1.5% A TMP Ester + 2.0% A RBD

8.0

TAN (mg KOH/ g)

7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

0

24

48

72

96 200 400 600 800 Time (hr)

100 90

Viscosity mm2/s

80

RBDPO 1.00%

70

2.00%

60

0.50% 1.50% TMP Ester

50 40 30 20 10 0

0

100

200

300

400

500

600

700

800

Time (hrs)

Figure 11 Effects of additives on the performance of hydraulic oil (Alias et al., 2009).

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The final TAN value of the formulated hydraulic oil was only 0.32 mg KOH/g as compared with 4.88 mg KOH/g for the oil without the addition of an additive. However, the kinematic viscosity of the oil with additive was almost the same as that of the neat oil and its viscosity index remained at 187. Polyol esters from rubber seed oil were found to possess good viscosity indices (205e222), satisfactory weld load behaviour and good copper corrosion. They have been well exploited for a number of hydraulic fluid formulations (Kamalakar et al., 2013). The biolubricants that were developed from epoxidized passion fruit and moringa oils displayed satisfactory tribological properties for potential application as hydraulic fluids (Silva et al., 2015). However, most of the commercial applications using these oils are still in the development phase with numerous R&D initiatives for the development of proprietary products and production technology. Over time, all the lubricant specifications will reflect the adaptation of oil qualities in the engine or hydraulic designs.

5. CONCLUSIONS Thermochemical conversion of various plant oils and their derivatives to biolubricants is discussed. Because of the inherent shortcomings such as poor oxidative and thermal stabilities, plant oils and their derivatives need to be chemically modified for the applications as lubricants or adhesives. Five chemical conversion methods were discussed and compared for the production of biolubricants from plant oils and their derivatives such as plant oilebased fatty acids, methyl esters and epoxidized oils. The selection of synthetic route and raw material are based on the performance requirements of the lubricants. Finally, the properties of the biolubricants obtained from these methods are evaluated against its intended applications. With these thermochemical conversions, the modified plant oils and their derivatives showed significantly improved properties, which meet or exceed the requirements of lubricants for applications as hydraulic oil, automotive oil, process oil, de-moulding oil, lubricating greases and other uses. However, many of the methods involve multiple steps and long processing time. Thus, it is difficult to scale up, hence decreasing the economic viability of these processes to some extent.

ACKNOWLEDGEMENTS The authors acknowledge the UPM and Ministry of Science and Technology, Malaysia for the financial support.

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CHAPTER FIVE

Development of Synthetic Microbial Platforms to Convert Lignocellulosic Biomass to Biofuels Muhammad Aamer Mehmood*, x, Ayesha Shahidx, Liang Xiong{, Niaz Ahmadjj, Chenguang Liu*, Fengwu Bai*, { and Xinqing Zhao*, 1 *Shanghai Jiao Tong University, Shanghai, China x Government College University Faisalabad, Faisalabad, Pakistan { Dalian University of Technology, Dalian, China jj National Institute for Biotechnology & Genetic Engineering, Faisalabad, Pakistan 1 Corresponding author: E-mail: [email protected]

Contents 1. Biomass to Biofuels: An Overview 1.1 Composition of Biomass and Scheme for Biofuels Production 1.2 Pretreatment 1.3 Saccharification 1.4 Fermentation 2. Designing Robust Microbial Strains to Produce Biofuels 2.1 Role of Synthetic Biology 2.2 Strain Development Techniques 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5

Classical Genetic Manipulation and Genome Editing Random Engineering Approaches Identification of Novel Targets for Strain Development Bioinformatics-Based Design of Metabolic Pathway Synthetic Microbes With Minimal Genomes

243 247 248 249 250

3. Yeast Strains for Robust Cellulosic Ethanol Production 3.1 Consolidated Bioprocessing 3.2 Development of Xylose Fermenting Yeast Strains 3.3 Development of Stress Tolerant Yeast Strains 3.4 Higher Alcohols-Producing Yeast Strains 4. Developing Bacterial Strains for Biofuels Production 4.1 Ethanol Production in Bacterial Hosts 4.2 Production of C3 and C6-Alcohols in Bacterial Hosts 5. Fine Tuning of Synthetic Microbial Factories 6. Conclusion and Future Prospects References

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251 251 254 256 256 257 258 262 265 266 266

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j

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Abstract The revolutionized economic growth, immense mobility and energy dependent lifestyles of the humankind have raised several new trends on the face of the earth. Exploration and development of renewable energy sources has become one of the leading research areas. Several attempts have been made to develop renewable energy sources, but high production cost of biomass-based biofuels is still a great challenge with issues, such as low yields, substantial feedback inhibition and limited stress tolerance. On the one hand, the inherent difficulty in conversion of lignocellulosic biomass into biofuels calls for development of robust biofuel-producing microbes. On the other hand, recent advances in genomics, transcriptomics, proteomics, metabolomics, bioinformatics and genome-editing tools have enabled us to develop deep understanding of microbial pathways followed by targeted engineering of the key genes involved. In this chapter, development of yeast strains of Saccharomyces cerevisiae and various bacteria for renewable energy production is summarized. Besides conventional metabolic engineering methods, we mainly focused on the employment of synthetic biologyebased genetic engineering strategies for the bioconversion of renewable feedstocks. The development of xylose assimilating and stress tolerant S. cerevisiae strains is discussed in particular. Moreover, the latest development of S. cerevisiae strains with CRISPR-Cas9 genome-editing tool is also summarized. This chapter is an effort to provide insights for further development of biofuels producers using advanced genome-editing technologies.

1. BIOMASS TO BIOFUELS: AN OVERVIEW It is believed that biofuels have potential to substitute fossil fuels, with additional benefits of mitigating global warming by reducing CO2 emission (Saqib et al., 2013; Skevas et al., 2014). At present, 10.0% of the global energy demand is being met through biofuels with an annual increasing rate of 2.5% (Edrisi and Abhilash, 2016). All over the world, governments have endowed heavy inputs, in terms of time and money, to accelerate research on production and commercialization of biofuels (Carriquiry et al., 2011; Mehmood et al., 2016). However, food-based biofuels may cause ecological and environmental problems along with food and landfor-food insecurity. Conventional biofuels produced from food crops directly contribute to deforestation and threaten biodiversity (Fargione et al., 2010; Pimentel et al., 2010). Lignocellulosic (LC) biomass is the most abundant biomass on Earth, which is produced at rate of 150170  109 tons per annum (Pauly and Keegstra, 2008) and is believed as a potential candidate for the production of bioalcohols (Unrean, 2016). Although it seems promising, development

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of biofuels has faced several challenges. There is no doubt that total calculated energy content of all the available biomass on Earth is inexplicably large, but unfortunately it is not easy to retrieve, and divert into the commercial ventures, because when it comes to the production of biofuels from biomass, problem is not to produce it, problem is produce it in cost-effective and energy efficient manner. Before we go into the synthetic biologyebased pathway design for the biological conversion of biomass to biofuels, we first present an overview of the whole process, and the challenges and prospects are further discussed.

1.1 Composition of Biomass and Scheme for Biofuels Production Commonly there are four major sources of LC biomass: (1) forest residues including foliage, woods and branches, (2) agricultural residues including sugarcane bagasse, wheat straw, rice straw, corn stover, cotton stalk, (3) energy crops including switchgrass, willow, poplar, oak etc. and (4) cellulosic components in food waste and municipal waste (Salehi Jouzani and Taherzadeh, 2015). LC biomass (Fig. 1 and Table 1) often comprised of cellulose microfibrils implanted in lignin, hemicellulose and pectin with an adjusted proportion during adaptation of each plant species in response to genetic and environmental factors (Nigam and Singh, 2011). Cellulose is a polysaccharide which is main component of LC feedstock. It consists of series of D-glucose linked by b-1,4-glycoside bonds. Hydrogen bonds link together cellulose fibrils to produce its crystalline structure. This structure of cellulose makes it insoluble in majority organic solvents as well as in water

Figure 1 General composition and structure of lignocellulosic compounds in biomass.

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Table 1 Biomass composition of various lignocellulosic feedstocks on dry mass basis Hemicellulose

Lignin

Biomass

Cellulose (%) Arabinan (%) Galactan (%) Xylan (%)

Acid-soluble Acid-insoluble (%) (%)

Soya stalk Cotton stalk Corn stover Rice straw Wheat straw Rye straw

34.5 14.4 38.3 31.1 30.2 30.9

e 14.4 2.7 3.6 2.8 e

e e 2.1 e 0.8 e

24.8 e 21.0 18.7 18.7 21.5

e e e e e 3.2

9.8 21.5 17.4 13.3 17 22.1

Sweet sorghum bagasse Sugarcane bagasse Switch grass Reed canary grass Miscanthus Oak Poplar

27.3

1.4

e

13.1

e

14.3

Nee’nigam et al. (2009) Nee’nigam et al. (2009) Li et al. (2010c) Chen et al. (2011a) Ballesteros et al. (2006) García-Cubero et al. (2009) Li et al. (2010a)

43.1

e

e

31.1

e

11.4

Martin et al. (2007)

39.5 24

2.1 e

2.6 e

20.3 36

4.0 e

17.8 e

Li et al. (2010b) Singh et al. (2014)

43 45.2 43.8

e e e

e e

24 20.3 14.8

e 3.3 e

e 21.0 29.1

Singh et al. (2014) Shafiei et al. (2010) Kumar et al. (2009)

References

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(Mood et al., 2013). Hemicellulose is a heterogeneous branched molecule that consists of pentose, hexose and organic acids (Gírio et al., 2010) and is the second major component of LC biomass. Unlike the cellulose, lower molecular weight as well as branched and amorphous structure of hemicellulose makes it easier to hydrolyze (Li et al., 2010b). Removal of hemicellulose from cellulose fibrils greatly enhances the digestibility of cellulose (Agbor et al., 2011). The third component lignin, however, is made up of complex molecules which are produced from phenyl-propanoid precursors (consist of guaiacyl, syringyl and p-hydroxy phenol) (Hutchison et al., 2016). Often, 50%e80% of total LC biomass consists of complex carbohydrates (mixture of pentose and hexose sugars). Lignin and cellulose together contributes towards recalcitrance nature of LC biomass making it a bottleneck challenge in the biological conversion of LC biomass to biofuels. Hence, LC biomass always requires pretreatments to open the structure and make it more accessible to enzymes (Salehi Jouzani and Taherzadeh, 2015). Despite of its difficult bioconversion, LC biomass has a number of advantages such as environmental sustainability, abundance, no direct competition with food and feed (Limayem and Ricke, 2012). The most obvious steps involved in the biomass conversion to biofuels are pretreatment, saccharification and fermentation (Fig. 2).

1.2 Pretreatment Pretreatment is the first step in the lignocellulose bioconversion, which facilitates to destroy the rigid structure of the feedstock and separating major

Figure 2 Systematic diagram of bioethanol production from the lignocellulosic (LC) biomass. LC biomass is subject to various pretreatments to open to compact structure of biomass / Pretreated biomass is subjected to enzyme hydrolysis which converts the cellulose and hemicellulose into simple sugars such as glucose, xylose etc., along with the release of small molecules which has inhibitory impact on the fermentation reaction / simple sugars released are utilized by various pathways of the genetically modified Saccharomyces cerevisiae strains to produce ethanol in fermenters / which is secreted out of the yeast cell.

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components cellulose, hemicelluloses and lignin from each other for more efficient enzymatic hydrolysis of the cellulose component (Nigam and Singh, 2011). It is reported that pretreatment contributes to 18%e20% of total production cost of bioalcohols from LC biomass (Yang and Wyman, 2008). Various pretreatment procedures have been grouped into four categories: biological, chemical, physical and physio-chemical (Rajendran and Taherzadeh, 2014). Each method has its own set of advantages but no single one is suitable for all biomasses (Salehi Jouzani and Taherzadeh, 2015). Development of cost-effective LC biomass pretreatment is major challenge in bioethanol technology (Singh et al., 2015). Pretreatments release several molecules from the cellulosic biomass which interfere with the fermentation process and cause inhibitory impact on the process. Therefore, a suitable pretreatment method should have following features: (1) high hydrogen bond disruption in cellulose, (2) breakdown of hemicellulose and lignin cross-linked matrix, (3) increasing the cellulose surface area for better enzymatic hydrolysis (Mood et al., 2013), (4) cost effectiveness, (5) low energy input and (6) high carbohydrate recovery rate with little or no lignin (Singh et al., 2014). The choice of the pretreatment method depends on the biomass itself, including (1) crystallinity index of cellulose, (2) hemicellulose covering of cellulose, (3) polymerization degree, (4) lignin content, (5) surface area and (6) acetyl content (Pan et al., 2006). Although the pretreatment benefits the following enzymatic hydrolysis, various toxic byproducts produced under harsh chemical and physical conditions hinder the viability of fermenting microbe to convert the sugar to biofuels. The inhibitors consist of three major groups: phenolic compounds, furan derivations and carboxylic acid (Thompson et al., 2016). Phenolic compounds released from lignin are complicated because of the heterogeneity of lignin, which cause membrane instability and induce reactive oxygen species damage (Larsson et al., 2000). Furan derivations such as furfural and 5-hydroxymethylfurfural (5-HMF) inhibit dehydrogenases, interfere membrane stability, consume reducing power and cause the disruption of DNA and mitochondria (Allen et al., 2010). Carboxylic acids are also released from cellulose and hemicellulose. They halt glycolytic enzymes; and aromatic amino acid import (Bauer et al., 2003) and uncouple the proton motive force and ATP reserves (Russell, 1992). Abundant research has been focused on inhibition mechanisms, but complex interactions of inhibitors from various feedstock and diverse pretreatments still result in synergistic effects that need to be analyzed case by case (Zhao et al., 2016).

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1.3 Saccharification The pretreated biomass is subjected to hydrolytic enzymes such as xylanases, cellulases and other carbohydrases (Khare et al., 2015) which hydrolyze the hemicellulose, cellulose and polymeric sugars into simple sugars. Some of these simple sugars can be converted into alcohols, which are often referred as fermentable sugars (Limayem and Ricke, 2012). Hydrolysis process is often categorized into two types (1) acid hydrolysis and (2) enzymatic hydrolysis. Dilute or concentrated acid may be used in acid hydrolysis. Higher reaction rate, sugar conversion efficiency and recovery rate are advantages of concentrated acid hydrolysis (Wijaya et al., 2014). However, high acid consumption (Moe et al., 2012), requirement of acid recovery and requirement of specialized reactors due to high corrosion and toxicity make this method economically unfavourable (Wijaya et al., 2014). These problems of acid hydrolysis moved the attention towards enzyme hydrolysis. Combination of cellulases (b-1,4-glucosidases, exo-b-1,4-glucanases, endo-b-1,4-glucanases) has been employed for breakdown of long chain glucose polymers to monomeric units from pretreated feedstock (Lamsal et al., 2010). Hemicellulose is hydrolyzed by xylanases or hemicellulases for the release of component sugars which then can be fermented through microbial catalyst (Khare et al., 2015). Some other enzymes, such as xyloglucanase, have been used for the degradation of secondary polysaccharides which cannot be converted into simple sugars by the activity of cellulases (Stickel et al., 2014). Enzymatic hydrolysis performed at high solid loadings is more economically feasible because of high sugar concentration at the completion of hydrolysis that can be converted into high ethanol concentrations resulting in reduced cost and energy demands for distillation (Modenbach and Nokes, 2013). Another saccharification method is termed as simultaneous saccharification and fermentation (SSF) in which fermentative microbes are used for simultaneous SSF of hemicellulose and cellulose (Mosier et al., 2005).

1.4 Fermentation Saccharomyces cerevisiae and some bacteria consume some selected sugars and convert them into CO2 and ethanol (Shah and Sen, 2011). Typically, S. cerevisiae is used during batch fermentation which converts usually hexoses (six carbon sugars, mainly glucose) into ethanol under controlled temperature and microaerobic (Fig. 3). In our recent work, flocculating Zymomonas mobilis was revealed to be a nice host for cellulosic ethanol production (Zhao et al., 2014). Genetically modified Z. mobilis has also

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Figure 3 An overview of ethanol producing pathway from cellulosic biomass.

been used for fermenting xylose which is the most prevalent five carbon sugar released by hemicellulose (Shah and Sen, 2011). During the pretreatment process of biomass, sugars and lignin are converted to degradation compounds which inhibit fermentation process (Talebnia et al., 2010). These inhibitory compounds often are yeast growth inhibitors that induce reduced productivity and yield of ethanol. Presence of these inhibitors in the hydrolysate is the major challenge in the bioethanol commercialization by the use of LC biomass (Pereira et al., 2014). Inhibitory compounds include aromatic and phenolic compounds, inorganic ions, furan aldehydes, aliphatic acids, bioalcohols and other fermentation products (J€ onsson et al., 2013). Pretreatment of lignin or sugar degradation produce aromatic/phenolic compounds(Annaluru et al. 2014) which interfere with the cell functionality

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by changing the lipid-to-protein ratio in cell membrane (J€ onsson et al., 2013) and also affect their ability as selective barriers, decrease growth of cell and sugar assimilation (Campos et al., 2009). It is reported that low molecular weight phenolic compounds are more inhibitory in nature (Behera et al., 2014). Furan aldehydes, such as 5-HMF, are produced by the pentose and hexose decomposition, respectively. These compounds inhibit yeast growth and decrease ethanol productivity and yield (J€ onsson et al., 2013). Formic acid, acetic acid and levulinic acid are called aliphatic acids and are produced by the breakdown of acetyl group which link sugar and hemicellulose backbone (Jovicevic et al., 2014) and they inhibit when their concentration exceeds 100 mM (J€ onsson et al., 2013). Similarly, chemicals used in pretreatment, hydrolysis conditions and process equipment cause the production of inorganic ions in LC hydrolysate which contribute to modify osmotic pressure resulting in inhibitory effect.

2. DESIGNING ROBUST MICROBIAL STRAINS TO PRODUCE BIOFUELS Developing high-yielding and robust microbial strains is required to address the current challenges in the microbial biofuel production. A variety of microbial strains have been developed through genome engineering and synthetic biology tools, which appeared to be fairly auspicious to improve yields of biofuel production including ethanol, butanol, biodiesel, terpenoids, syngas and hydrogen gas. Here wedescribe thegenome engineeringapproachesandtools at first, which are believed to derive the future of biofuel industry.

2.1 Role of Synthetic Biology Synthetic biology offers innovative approaches for a wide range of biotechnological applications ranging from sustainable bioenergy production through bioremediation and biopharmaceuticals. But the success of this technology depends on the availability of knowledge of genome sequences, metabolic engineering tools which have fortunately enabled us to engineer microbes for our desired purposes (Gomaa et al., 2016). Designing biofuelproducing cell factories with enhanced efficiency is now possible via designed engineering of biological systems in several steps (Fig. 4). Unlike high-value products (pharmaceuticals or enzymes), biofuels are cost sensitive and can only be competitive when their cost is comparable with the petroleum-based fuels. This goal can only be achieved through systematic design of robust strains which can efficiently convert renewable feedstocks into

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Figure 4 Schematic overview of synthetic bioengineering for strain development. (1) Identification and functional characterization of pathway genes through genomics, transcriptomics, metabolomics analyses to identify the key domains, active sites and the role of each gene in the target metabolic pathway via gene deletion of heterologous protein expression, and choosing a suitable host (Escherichia coli, Saccharomyces cerevisiae, Zymomona mobilis etc.) for the expression of metabolic pathway. (2) Synthetic pathway construction, which includes gene isolation, addition of suitable regulatory elements, codon optimization etc. (3) Choosing the suitable vectors or genome editing tools for the integration of designed pathway on the genome. (4) Analyses of the product and its impact on the synthesis of growth of the host for fine tuning of the host machinery for the enhance productivity and recovery of the biofuel. (5) Process optimization and scale for industry.

biofuels. Synthetic biology has capability to reduce the time required to make genetic modifications and enhance their reliability. The design of variation in many pathways can be more efficiently handled by assembly techniques rather than through synthesis of each variation. The assembly of smaller DNA fragments into large constructs has become of essential synthetic biology tool to design, construct and engineer metabolic pathways. Therefore, most of the work in synthetic biologyebased pathway construction involves intermediate joining of DNA fragments, encoding target proteins, using common restriction and ligation strategies. However, recent techniques employ standardized restriction enzyme assembly protocols such as BioBricks, Golden Gate methods, Gibson ligation (Ellis et al., 2011; Engler et al., 2014; Xu et al., 2013). There are techniques which are sequence independent, such as In-Fusion, SLIC and Gibson ligation which are becoming popular for combinatorial metabolic engineering, and in vivo DNA assembly in yeast (Ellis et al., 2011; Coussement et al., 2014; Chao et al., 2015). It is important to consider that how different techniques, for instance, ligation free techniques, can be utilized to design the required architecture of the metabolic pathway (Vroom and Wang, 2008). BioBrick methods (Weyman and Suzuki, 2016) have enabled rapid construction of

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pathways from existing genes in which each stage of assembly uses the enzymes which are identical to the previous stage. This cyclic nature of the method has enabled us to create large variation libraries in less time. Moreover, synthetic biology methods create reusable parts with predictable behaviour and can develop various regulatory expression systems (Mckeague et al., 2015; Williams et al., 2016), which subsequently offer a capability of fine-tuned expression to engineer metabolic pathways. Interestingly, synthetic biology has full potential to install plug-and-play systems with autoinduction switches in response to environmental changes; for instance, our designed microorganism can switch from a cellulose degradation mode to fuel production mode by sensing the surrounding environment (Berens and Suess, 2016). One of the challenges of metabolic engineering is the integration of multiple-DNA fragments into a host genome. Developing the regulatory networks of synthetic genes from scratch followed by in silico analyses are extremely encouraged in this regards. Synthetic biologists either modify existing pathways or design a completely new synthetic pathway. Designing several pathways and putting them together may lead to develop a completely synthetic organism with minimal genome having a minimal set of metabolic pathways (Gibson et al., 2008). However, transfer of one pathway from one organism to another is also a necessary move for higher productivity.

2.2 Strain Development Techniques To develop robust microbial strains for efficient biofuel production, we need to develop better understanding of cellular network of target strains in response to various environmental conditions and to identify essential and nonessential genes critical for cellular life and metabolism. Here we described the most common engineering strategies which have been employed to design biofuel-producing strains. Recent advances in synthetic biology have boosted the development of new tools which may be exploited to achieve set goals. Many of these techniques are readily adaptable to engineer the cellular metabolic networks of microorganisms for biofuels production. 2.2.1 Classical Genetic Manipulation and Genome Editing Overexpression and deletion of target genes are the most commonly used metabolic engineering approaches for strain development. To date, a wealth of gene disruption, deletion, replacement and integration systems have been

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developed for Escherichia coli and S. cerevisiae. Inactivation of target genes by simply replacing them through PCR (Datsenko and Wanner, 2000) has significantly facilitated us to generate specific mutants for the functional analysis of the genes. There are mobile-DNA elements distributed across the genome, which facilitate the recombination events to happen, such as transposition and horizontal gene transfer. These mobile elements are often named as jumping genes, which include insertion sequence (IS) elements, transposases, defective phages, integrases, and site-specific recombinases (Frost et al., 2005). As they are involved in auto-engineering of the genome, it is required to delete these elements to design a stable genome to avoid unwanted recombination. In addition, a Tn5-targeted Cre-IoxP excision system and Tn5-transposonebased high-throughput methods for systematic mutagenesis (Lee et al., 2014; Dohlemann et al., 2016) have enabled us to create deletion and insertion mutants without losing normal growth patterns. In particular, a powerful high-throughput technique, Multiplex Automated Genome Engineering (MAGE) (Wang et al., 2009) has potential to modify multiple loci at the same time in a single cell or across a population of cells using allelic replacement. This has allowed us to delete genes more extensively without risking the robustness. While homologous recombination is the basic rule for deletion or integration of specific fragments in the chromosome, S. cerevisiae has higher capacity for homologous recombination and so considered as a manageable organism when it comes to the deletion of target genes through homologous recombination. Taking the advantage of the Cre-loxP recombination system, deletion mutants of various yeast strains have been prepared by substituting the target genes with selectable markers (e.g., antibiotic) containing two repeated sequences (e.g., loxP) at the left and right arms of the marker gene (e.g., loxP-kanMX-loxP) (Gueldener et al., 2002). However, the homologous recombination-based techniques may cause unexpected/undesired deletion between the loxP sequences. A PCR-mediated seamless gene deletion technique permits recycling of URA3 selectable markers, which does not leave repeated sequences behind. This gene disruption technique is suitable for repeated use and is widely applicable to various yeast strains for gene disruption (Kondo et al., 2013). Moreover, genome reductions may improve metabolic efficiency and decrease the redundancy among microbial genes and regulatory circuits (Wohlbach et al., 2011). Therefore, a coherent design allows us to delete genes widely while keeping the robustness along, subsequently we can design biofuel-producing strains harbouring synthetic and/or engineered pathways.

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Unlike prokaryotic systems, one-promoter one-gene expression system exists in S. cerevisiae. To implement metabolic designs into S. cerevisiae, preparing sets of gene expression vectors to cover a wide variety of markers is essential (Ishii et al., 2009). It is also valuable to engineer multiple promoters into each vector. In addition, two proteins may be expressed together using dicistronic regulation system, which carry an internal ribosome entry site sequence (Joung and Sander, 2013). Yeast artificial chromosomes can also be used as a cloning system for DNA fragments larger than 100 kbp (Gibson, 2014) which has become a powerful tool for the bioengineering of yeast. The cocktail d-integration method is another unique technique for the chromosomal expression of multiple genes in S. cerevisiae (Kato et al., 2013). This approach has an advantage of single-step integration of multicopy gene expression cassettes, based on the integration of several copies of a particular gene cassette onto the d-sequences of the Ty-retrotransposon on the yeast chromosome (Shi et al., 2016a). Gap repair cloning technology is another way of gene integration into chromosomes, which is based on the indigenous ability of yeast to undergo efficient homologous recombination. In this technique, continuous assemblies of DNA fragments containing 25e30 bp of homologous sequences are integrated into the chromosome (Joska et al., 2014; Reddy et al., 2015). Gap repair cloning has previously been used for construction of libraries for two-hybrid systems (Haarer and Amberg, 2014), and it can be used to engineer proteins and to alter coenzyme specificities. Zinc-finger nucleases (ZFNs) (Gaj et al., 2013), transcription activatorelike effect or nucleases (TALENs) (Joung and Sander, 2013) and the RNA-guided CRISPR-Cas9 system (Doudna and Charpentier, 2014) have emerged as the popular genome-editing technologies in recent years. All these methods can generate double-strand breaks at specific genomic loci with the endonucleases. However, ZFN and TALEN-based targeting depends on customized DNA binding domain, while CRISPR-Cas9ebased cleavage is guided by small structured RNAs (tracrRNA and crRNA) (Jinek et al., 2012), making it highly specific, efficient, easier to design and the most suitable tool for genome editing in various hosts (Gaj et al., 2013; Ran et al., 2013). Chimeric single guide RNAs (sgRNAs) are designed for easier manipulation based on the structure of the original tracrRNA and crRNA (Cong et al., 2013; Jinek et al., 2012). The only requirement for Cas9 to recognize the target site is a Protospacer Adjacent Motif (PAM) sequence presenting directly at 30 of the 20-bp target sequence. Each Cas9 orthologue has a unique PAM sequence; for instance, the most commonly used Cas9 from Streptococcus pyogenes adopts an NGG

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PAM sequence, thus we can find a target sequence from an average 8-bp sequence in the genome. Besides, multiple gRNAs can be introduced into the host for multiple integrations or deletions (Jakoci unas et al., 2016). When the gRNAs sequence is flanked by self-cleaving hammer head and hepatitis delta virus ribozymes on the 5’- and 3’-ends, tandem gRNAs can be expressed in a single expressing plasmid to allow multiple knockouts and integrations (Gao and Zhao, 2014). Multiple sgRNA cassettes can also be integrated into a single expression plasmid to target multiple sites in S. cerevisiae (Ronda et al., 2015; Jessop-Fabre et al., 2016). It is also possible to obtain scarless constructs without any selection marker via this method (Jessop-Fabre et al., 2016). Beyond gene knockout and integration, the transcriptionally regulatory role of CRISPR-Cas9 system has also been investigated in other organisms by slightly modifying the gRNA-Cas9 structure (Doudna and Charpentier, 2014). CRISPRCas9 has been well developed and applied in the engineering industrial strains, including multiple gene knockout, integration and replacement of promoters (as reviewed by Jakoci unas et al., 2016) (Fig. 5).

Figure 5 An overview of the CRISPR-Cas9 based genome editing; (A) single plasmide based CRISPR-Cas9 system; (B) double plasmidebased CRISPR-Cas9 system; (C) diagram for recognition and cleavage of target sites by CRISPR-Cas9; (D) double stranded break mediated genome editing.

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During the past three years, the CRISPR-Cas9 system has been comprehensively optimized and employed for metabolic engineering of yeast strains. For example, multiple genes in b-carotenoids metabolic pathways were simultaneously integrated into the genome of S. cerevisiae strain (Ronda et al., 2015). The mevalonate level increased 41-folds when multiple genes in the metabolic pathway were simultaneous disrupted by CRISPR-Cas9 method (Jakoci unas et al., 2015). Recently, xylose-fermenting yeast strains were rapidly constructed with CRISPR-Cas9 by integrating the xyloseassimilating pathway into the genome of S. cerevisiae strains, two genes, PHO13 and ALD6, which hamper the assimilation of xylose, were disrupted at the same time, and no selection marker was integrated into the genome (Tsai et al., 2015). Cellobiose assimilating pathway was also introduced into S. cerevisiae strains (Ryan et al., 2014). Besides deletion or overexpression of certain genes, CRISPR-Cas9 was also employed for substitution of promoters; the promoter of TAL1 was replaced by a set of promoters with varied strengths for fine-tuned expression (Xu et al., 2016). In the future, more CRISPR-Cas9ebased genome-editing methods will emerge which will facilitate rapid strain development. 2.2.2 Random Engineering Approaches Along with the rational metabolic engineering methods, a set of random engineering approaches were also adopted for the development of microbial cell factories. Random mutagenesis and induced mutagenesis was applied to develop robust strains. An approach termed adaptive laboratory evolution was proved to be effective to develop S. cerevisiae strains with both improved inhibitor tolerance and xylose-assimilating abilities (also reviewed by Zhao et al., 2016).It is well known that the improvement of a single phenotype might involve a cluster of genes. Therefore, it is essential to explore the key genes which can control multiple genes. Interestingly, mutations of the TATA-binding protein improved cellular tolerance towards acetic acid (An et al., 2015). Moreover, artificial zinc finger proteins can be used to improve acetic acid tolerance (Ma et al., 2015). The performance of the indigenous proteins/enzymes is critically important for the success of bioengineering, which may be or may have to be modified as per requirement of the cellular reactions. For instance, protein engineering is often required to engineer the xylose utilization pathway in S. cerevisiae by changing the coenzyme specificity of either xylose reductase (XR) or xylitol dehydrogenase (XDH) to conquer the inherent coenzyme imbalance of heterologous XR-XDH pathway that form most species for

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efficient xylose assimilation by S. cerevisiae (reviewed by Kim et al., 2013). It is of importance to adopt protein engineering to change the coenzyme specificity (e.g., redox enzymes), activities (e.g., codon optimized enzymes) and substrate specificity (e.g., transporters) of specific enzymes. 2.2.3 Identification of Novel Targets for Strain Development Transcriptomics, proteomics, metabolomics and bioinformatics analyses together offer a comprehensive gene expression and global view of metabolisms which can be used to identify the genes responsible for various cellular activities including xylose fermentation, higher yield of alcohols and stress tolerance. Transcriptomics is the study of the complete set of RNA transcripts, the transcriptome, produced by a cell under specific environmental and growth conditions. The study involves high-throughput methods, such as microarray analysis and RNA-sequencing followed by computer aided analyses of molecular data. Comparative transcriptomics analyses can be used to identify the differentially expressed genes in discrete cell populations or in response to different environments provided. Therefore, comparative transcriptome analysis is a remarkable tool to reveal the specific genes which are responsive to specific conditions, which may lead us to select candidate genes for future manipulations. For instance, it was observed that deletion of PHO13 enhanced xylose assimilation in S. cerevisiae via expression of XR and xylose dehydrogenase (Van Vleet et al., 2008). To identify the underlying mechanism, a comparative expression profiling of PHO13 mutant and wild-type strain was performed. It was shown that key genes including ZWF1, SOL3 and ADH1 were upregulated in the PHO13 mutant and genes such as COX2 and CYC1 were down regulated (Fujitomi et al., 2012). Moreover, along with the overexpression of TAL1, the deletion of PHO13 improves ethanol production from LC hydrolysate in the presence of weak acids and furfural (Fujitomi et al., 2012; Wise et al., 2014). It was reported that the deletion of ALD6 (NADPþ-dependent aldehyde dehydrogenase gene) in the acetate biosynthesis pathway improved xylose fermentation (Lee et al., 2012; Vanholme et al., 2013). Moreover, 71% decrease in xylitol yield was observed in response to the deletion of FPS1 gene, and xylose fermentation was shown to be improved (Wei et al., 2013). Similarly, deletion of the YLR042c gene enhanced the specific xylose utilization rate and ethanol yield in strain TMB3057 (Parachin et al., 2010). Metabolomics is the methodical identification and quantification of the cellular metabolic products, the metabolome, of a single cell, a tissue, an organ or any biological fluid in response to any particular environment. Advanced

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spectroscopic techniques, including mass spectrometry and nuclear magnetic resonance, are most commonly used to profile the metabolome. Interestingly, comparative analyses of metabolome data also reveal the responsive pathways to various conditions, which may be targeted for future manipulations. For instance, a Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS) based metabolome profiling of yeast revealed that acetic acid addition may slow down the flux of the nonoxidative pentose phosphate pathway. In another study, comparative metabolome analyses of a wild-type and three deletion mutant strains was carried out, with higher intracellular glutathione concentration (Suzuki et al., 2011) which indicated that methionine synthesis activation may be used to enhance the intracellular glutathione concentration (Suzuki et al., 2011). Metabolomics has an advantage that it can be used to assess the posttranscriptional regulation of any target metabolic pathway by examining metabolic phenotypes (Yoshida et al., 2010). But unfortunately, the elucidation of the metabolome data is not easy when compared to the transcriptome data analyses, which itself is a tedious job. However, metabolome data cannot be used to unveil the true reasons that either the accumulation of intermediate molecule is due to its increased biosynthesis or decreased utilization. Hence, interpretation of metabolome data requires intensive literature and database search (Cherry et al., 2010). 2.2.4 Bioinformatics-Based Design of Metabolic Pathway In recent years, like any other field, computers have become an integral part of life sciences research too, which led towards the establishment of a new, but very important field called Bioinformatics. It is the application of computer to retrieve, store, analyze, process and predict the biological processes. Computerscanalsobeusedtodesignmetabolicpathwaysforbiofuelproduction. Bioinformatics-based tools can assist us to construct yeast metabolic model, to modify the metabolic model through simulations and to predict production of target molecules. However, a complete set of simulation comprising thermodynamics, reaction kinetics and stoichiometry is not easy to perform. A stoichiometry-based metabolic model is a set of metabolic reactions in the target organism, such as S. cerevisiae where a minimal model of central metabolism consists of only 50 reactions (Dobson et al., 2010; Matsuda et al., 2011) and the largest model constructed up to now contains 1412 reactions (Mo et al., 2009). In addition to stoichiometry-based simulation, Flux Balance Analysis (FBA) (Orth et al., 2010) had also been introduced in which an equilibrium condition is introduced as a constraint in the metabolic simulation and a balance between the synthesis and consumption of an

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intermediate molecule is studied. Although the FBA-based metabolic simulations undertake a prototypical metabolic condition and ignore regulation aspects such as gene expression and feedback mechanism, yet it can be used to evaluate the performance of a metabolic network (Shinfuku et al., 2009). The FBA-based simulations can be used to analyze the distribution of metabolic fluxes using a stoichiometric model without prior knowledge of concentrations and reaction kinetics of metabolic products. For instance, the impact of a gene deletion on the target metabolite synthesis can be simulated and evaluated using a modified stoichiometric model which lacks the corresponding reaction information. Previously, the in silico gene deletion strategy had been simulated to engineer indigenous metabolism in yeast using FBA, and it was shown that simultaneous deletion of five unrelated genes including ALT2, FDH1, FDH2, FUM1 and ZWF1 may increase formic acid secretion under aerobic environment (Kennedy et al., 2009) which was later demonstrated in the lab (Kennedy et al., 2009). Such studies endorsed that constraint-based simulation can help us to identify target genes for deletion and to design a metabolic pathway using synthetic biology techniques. The FBA-based simulation of yeast can also be used to elucidate and engineer the pathways for enhanced biosynthesis of sesquiterpenes, for analysis of the pentose utilization pathway (Ghosh et al., 2011), to cope stress tolerance and for enhanced production of short and long chain alcohols. 2.2.5 Synthetic Microbes With Minimal Genomes Deletion of alternative pathway genes, integration of desired pathway genes and engineering the indigenous proteins for the efficient balance of energy and the critical cellular reactions may help us design the synthetic microbes with minimal genomes. Reduced genomes would provide several associated benefits, such as higher electroporation efficiency, precise proliferation of recombinant plasmids, better adaptation to thermal and salt stress and utilization of unusual substrates (Trinh et al., 2008; Lee et al., 2014). Interestingly, selective sorting and deletion of aerobic or anaerobic reactions targeted to biomass and biofuel productions have shown the cells to have highest theoretical yields even with minimum metabolic functionality under anaerobic conditions (Lee et al., 2014). After deletion of the selective respiratory pathways, the remaining pathways showed nongrowth-associated conversion of sugars (C5 and C6) into ethanol. Catabolite repression was completely absent during anaerobic growth after the deletion of acetateproducing pathways with concurrent conversion of C5eC6 sugars into ethanol. It implies that removing the nonessential genes would be beneficial

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to achieve higher yields and for the economical production of desired molecules in synthetic microbes. Swift advances in gene synthesis have enabled us to assemble the complete microbial genomes (Gibson et al., 2008; Hutchison et al., 2016). Eukaryotic genomes are generally much larger and complex. The Chromosome III of S. cerevisiae was completely designed and synthesized recently, designated as synIII, which is approximately 14% smaller than its wild-type template and is fully functional with every gene tagged for easy removal (Annaluru et al., 2014). The bottom-up synthesis of yeast chromosome III represents part of the Sc2.0 Project, the synthesis of the other 15 chromosomes is ongoing. By introducing loxPsym sequences at sites downstream of nonessential genes, the SCRaMbLE system using an inducible Cre recombinase to shuffle-up regions of the genome was established in the synthetic genome, endowing the synthetic yeast a ‘hyperevolution’ capacity (Jovicevic et al., 2014, Annaluru et al., 2014). The landmark of the first synthesized designer eukaryote chromosome provides new perspectives on the future of synthetic biology and genome research.

3. YEAST STRAINS FOR ROBUST CELLULOSIC ETHANOL PRODUCTION Yeast strains are the leading industrial biocatalysts for the biological conversions of renewable feedstocks to biofuels (Table 2). However, the commercialization of cellulosic feedstock-based biofuels still has several technical issues to deal with. Here several strategies along with their challenges are discussed.

3.1 Consolidated Bioprocessing As discussed in the previous sections, LC biomass has recalcitrant nature which poses resistance to enzymatic hydrolysis. Moreover, presence of five carbon sugars is another technical issue (Lynd et al., 2005; Hasunuma and Kondo, 2012). Consolidated bioprocessing (CBP) is a promising strategy to overcome biomass recalcitrance by using cellulolytic microorganisms. Overall, the CBP technology is comprised of cellulase production, biomass hydrolysis and fermentation in single step. Cellulolytic microorganisms are being engineered to improve their hydrolysis capacity which can provide on-site low-cost enzymes. Among these, Clostridium thermocellum is being used for the production of ethanol through the CBP of plant biomass (Bayer et al., 2008). Genomic and proteomic analyses of several

Table 2 Metabolic pathway engineering in yeast for biofuel production Metabolic engineering Product description obtained Titre (g/L)

Xylose reductase (XR) and xylitol dehydrogenase (XDH) genes XR and XDH genes

XR (XYL1) and xylitol dehydrogenase (XYL2) from Pichia stipites Xylose utilization pathway from Scheffersomyces stipites into GLBRCY0 Addition of XR, XDH and b- glucosidase genes Overexpression of enzymes necessary for transhydrogenase like shunts and deletion of LPD1 gene Deletion of LPD1 gene and transhydrogenase like shunts activation Integration of butanol producing genes, coaA and adhE and deletion of ADH6 and GPD2 genes

Challenges/Limitations

References

Byproduct formation, toxic inhibition and difficulty in xylose utilization Biomass hydrolysate contain inhibitory compounds, reduced glucose utilization, cell growth and ethanol productivity under high salinity Inhibition due to toxic compounds

Erdei et al. (2013)

Ethanol

33

Ethanol

45

Ethanol

47

Ethanol

51.3

N/A

Ethanol

60

Iso-butanol

1.62

Laboratory strain and need to apply industrial strain for increased productivity and yield Reduced cell growth due to deletion of alcohol dehydrogenase and pyruvate decarboxylase genes

Iso-butanol

0.23

N/A

Kuroda and Ueda (2016)

n-butanol

0.13

Comparative low product yield specially by the presence of free Co-A

Schadeweg and Boles (2016)

Balan (2014); Casey et al. (2013) and Casey et al. (2010)

Moreno et al. (2013) and Nogué and Karhumaa (2015) Jin et al. (2013)

Balan (2014) and Ha et al. (2011)

Matsuda et al. (2013)

Development of synergistic pathway with overexpression of ADH and KDC enzymes

n-butanol

0.83 (Lab scale) 1.05 (Bioreactor)

High ethanol production as byproduct, instability in bioreactor performance, need to optimize dissolve oxygen requirement in process Low product yield due to glycerol production in large amount as side product

Shi et al. (2016b)

Deletion of GPD1 and GPD2 genes with addition of TER gene in place of ETFA, EFTB and BCD genes Novel pathway construction by expression of GOXB, MLS1, DAL7 and LEU2 genes Deletion of PDC1, PDC5, PDC6 genes with addition of MTH1T gene resulting in cofermentation of galactose and glucose molecules Blockage of degradation and activation ability of fatty acids with addition of optimized acetyl-CoA pathway and fatty acid synthase and overexpression of acetyl-CoA carboxylase hFAS mutation with overexpression of phosphorpantetheine transferase

1-butanol

0.014

1-butanol

0.092

Low yield, need to optimize pathway’s enzyme for better productivity

Branduardi et al. (2013) and Kuroda and Ueda (2016)

2,3-Butanediol

100

N/A

Lian et al. (2014)

FFAs, Fatty alcohols and Alkanes

10.4 (FFAs) 1.5 (Fatty alcohols) 0.0008 (Alkanes)

N/A

Zhou et al. (2016)

Short-chain Fatty Acid (SCFA)

0.11 (Total SCFAs) Improvements require for product selection and yield 0.08 (C8 FA)

Sakuragi et al. (2015)

Leber and Da Silva (2014)

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thermophilic microbes have shown significant number of hydrolytic enzymes which can be exploited in the future to further improve the cellulolytic potential of these microbes to bring robustness (Dam et al., 2011; Tolonen et al., 2011). Moreover, artificial enzymatic cellulosome complexes have been designed and evaluated for efficient degradation of crystalline cellulose, in vitro, ex vivo or in vivo (Vazana et al., 2012). Another approach of CBP technology is the expression of biomass degradation enzymes on the cell surface of yeast instead of using the cellulolytic microorganisms. Various amylolytic, cellulolytic and hemicellulolytic enzymes have been successfully expressed on the yeast cell surface by using the glycosyl phosphatidyl inositol, which enabled the S. cerevisiae to directly convert the biomass to biofuels or other bioproducts (Hara et al., 2012; Yoshida et al., 2011).

3.2 Development of Xylose Fermenting Yeast Strains Economic conversion of biomass to biofuels and chemicals requires efficient fermentation of all sugars present in cellulosic hydrolysates. After glucose, xylose is the most abundant sugar in the hydrolysates derived from LC biomass, which consists of up to 1/3 of the total sugar released from lignocellulose (Jin et al., 2004). However, the native strain of S. cerevisiae cannot ferment xylose into ethanol. Besides the introduction of the xylose-assimilating pathway into S. cerevisiae, numerous efforts have been made on the development of engineered S. cerevisiae strain to ferment xylose rapidly and efficiently, and genome-editing tools have been successfully employed to construct xyloseassimilating yeast, which have been reviewed previously (Jin et al., 2004). Although the XR/XDH pathway has intrinsic defect of cofactor imbalance, its thermodynamic advantage compared to the XI (xylose isomerase) pathway results in efficient xylose utilization and ethanol production (Karhumaa et al., 2007). Cofactor imbalance also acts as the main reason for xylitol accumulation in S. cerevisiae expressing XR-XDH pathway. Heterologous genes from different species with complementary cofactor specificity was also applied in the engineering of S. cerevisiae for efficient xylose fermentation, for example, csXR from Candida shehatae and ctXDH from Candida tropicalis was found to have the closest matched cofactor specificity among enzyme 20 XRs and 22 XDHs homologs tested (Du et al., 2012). Manipulating the cofactor levels by the overexpression and fine-tuning the expression of a water-forming NADH oxidase gene (noxE) from Lactococcus lactis resulted in reduced xylitol accumulation during xylose fermentation by engineered S. cerevisiae (Barrera et al., 2015; Li et al., 2012). Overexpression of all of the genes (RKI1, RPE1, TKL1,

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and TAL1) of the non-oxidative pentose phosphate pathway enhanced the growth of engineered yeast cells expressing either XI or XR-XDH pathway (Shen et al., 2012; Wu et al., 2012). As the metabolism of xylose been optimized by various strategies, engineered S. cerevisiae could convert xylose into ethanol much more efficiently, the bottleneck effect of xylose transport become more and more important (Young et al., 2012). Therefore it is of great importance to optimize the xylose transport in fast xylose-fermenting S. cerevisiae strains. Although expression of some of the individual transporters improved the xylose consumption ability of engineered S. cerevisiae; most original transporters showed either low efficiency or low specificity towards xylose. Hxt4p, Hxt5p, Hxt7p and Gal2p are important xylose-transporting proteins in S. cerevisiae cells (Hamacher et al., 2002), while Gal2p and Hxt7p showed higher specificity to xylose than any other HXTs (Young et al., 2012). GXS1 and GXF1 from C. intermedia, 2D01474 and XYLHP from D. hansenii and RGT2, XUT1 and XUT3 from S. stipitis demonstrated moderate transport efficiency and higher xylose preferences (Mehmood et al., 2013; Srirangan et al., 2014). Recently, directed evolution of the HXT11 resulted a mutant which reversed the transportation specificity from D-glucose into D-xylose, subsequently the mutant facilitated coconsumption of glucoseexylose in S. cerevisiae (Shin et al., 2015). Similarly, an N367A variant of Hxt36p can efficiently transport D-xylose subsequently enabling the cell to coconsume D-xylose and D-glucose (Nijland et al., 2015). By rational engineering of the conserved protein motifs, endogenous as well as heterogenous sugar transporters with reversed preference for glucose and xylose was obtained, and some of the variants showed negligible glucose inhibition (Srirangan et al., 2014; Farwick et al., 2014; Wang et al., 2015). Another variant, Gal2-N376F completely lost the ability to transport hexoses along with high xylose specificity (Farwick et al., 2014), while C. intermedia Gxs1 Phe38Ile39Met40, S. stipitis rgt2 Phe38Met40 and S. cerevisiae Hxt7 Ile39Met40Met340 were shown to be unable to grow on glucose but sustained growth on xylose (Young et al., 2014). Similarly, F432A and N360S mutations enhanced the D-xylose transport activities of Mgt05196p from M. guilliermondii, mutant N360 F specifically transported D-xylose without any glucose inhibition (Wang et al., 2015). Other than S. cerevisiae, the Scheffersomyces (Candida) shehatae, Pachysolen tannophilus, Scheffersomyces (Pichia) stipitis and Spathaspora passalidarum are native xylose-fermenting yeasts (Hazelwood et al., 2008). However, to achieve robustness for ethanol production from xylose, several improvements are required in future.

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3.3 Development of Stress Tolerant Yeast Strains Other than developing xylose-fermenting yeast strains, enhancing the stress tolerance of yeast is another key area. The biomass hydrolysate produced from pretreated biomass often contains various compounds, including formic acid, acetic acid and furfurals, which act as inhibitors during the fermentation reactions (Jonsson and Martin, 2016). To address the inhibitors issue, we need to uncover the underlying mechanism involved in adaptation to tolerate these inhibitors in yeast. So the functional genes involved in tolerance to various inhibitors may be elucidated using inhibitors-adapted S. cerevisiae strain with its parent by means of global transcript analyses. The latest research progress on development of stress tolerant yeast strains has been reviewed previously, where we emphasized the effect of cell flocculation and zinc supplementation on yeast stress tolerance (Zhao et al., 2016; Cheng et al., 2016a). We have been focussing on mechanisms of yeast stress tolerance and breeding of robust yeast strains for cellulosic ethanol production, and we have found that overexpression of histone H4 methyltransferase encoding gene SET5 and zinc finger protein PPR1 (Zhang et al., 2015) resulted in improved cell growth and ethanol fermentation in the presence of acetic acid stress. Furthermore, we found that absence of histone acetyltransferase Rtt109p (Cheng et al., 2016a), improved the tolerance of acetic acid of S. cerevisiae strains. In addition, we also revealed that deletion of the membrane transporter encoding gene QDR3 improved acetic acid tolerance (Ma et al., 2015). Most recently, overexpression of OLE1, which is responsible for fatty acid mechanisms, was reported to endow yeast strains with improved tolerance to multiple stressors (Nasutution et al., 2016). It was also revealed that overexpression of ARG4 or disruption of CAR1, which led to elevated intracellular arginine levels, enhances ethanol tolerance (Cheng et al., 2016b). From the studies mentioned earlier, it is clear that multiple genes can be manipulated to improve stress tolerance and biofuels production from cellulosic hydrolysates, but it should be emphasized that the genetic background of the different host strains should be considered, because even when the same gene is manipulated, improved stress tolerance can be only observed in specific yeast strains (our unpublished data). Therefore, it is also important to consider the host effect for the development of superior yeast cell factories using synthetic biology tools.

3.4 Higher Alcohols-Producing Yeast Strains Ethanol, though promising, yet has other technical problems in its storage and utilization as a sole fuel source due its hygroscopic nature. On the other

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hand, higher-chain alcohols are not hygroscopic and can provide energy densities equivalent to gasoline. For this reason, there is an increasing interest higher-chain (C3eC5) alcohols, such as 1-butanol and isobutanol (Connor and Liao, 2009; Weber et al., 2010). Isobutanol production has been attempted in S. cerevisiae using the Ehrlich pathway. The overexpression of genes responsible for valine biosynthesis increased isobutanol productivity by six-folds in S. cerevisiae (0.97 mg/g glucose) (Chen et al., 2011b). Moreover, isobutanol production in S. cerevisiae was further improved from 11 mg/L to 143 mg/L (6.6 mg/g glucose) via activation of valine biosynthesis by overexpressing ILV2, KIVD (from L. lactis) and ADH6, the PDC1gene which encodes a pyruvate decarboxylase (Kondo et al., 2012). In another study, KDC and ADH genes from different sources were overexpressed in yeast. The indigenous KDC, phenylpyruvate decarboxylase (ARO10), 2-ketoisocaproate decarboxylase (THI3) and 2KIV decarboxylase (KIVD) from L. lactis were overexpressed in S. cerevisiae to study their impact to enhance the butanol titre. Moreover, six genes including ADH1, ADH2, ADH5, ADH6, ADH7 and SFA1 were overexpressed in S. cerevisiae. Different KDCeADH combinations expressed and level of isobutanol was measured with additions of 8 g/L 2KIV, the isobutanol precursor. Interestingly, highest isobutanol (488 mg/L) was measured with KIVD-ADH6 combination. Furthermore, PDC1, which encodes an isozyme of the pyruvate dehydrogenase complex, was deleted to prevent acetaldehyde production. This strain was cultured in synthetic dextrose media supplemented with yeast nitrogen base and glucose and isobutanol titre was raised to 143 mg/L at a yield of 6.6 mg/g glucose (Kondo et al., 2012). However, the achieved titers indicate that engineering yeast for isobutanol production is still far from commercial requirements. So, it is suggested that novel approaches for the bioengineering of metabolic pathways are required to drastically improve production of C3eC5 alcohols by S. cerevisiae.

4. DEVELOPING BACTERIAL STRAINS FOR BIOFUELS PRODUCTION Due to various technical issues in the synthetic designs of yeast for biofuel production, several alternative strategies have come forward. One promising strategy is the use of nonnative and nonyeast hosts for the production of biofuels. Bacterial genomes are simpler and easy to modify when compared to yeast genome. Easy genetic modification is one of the various driving forces which can make the efforts fruitful in lesser time to alleviate

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negative impact in a synthetic strain. Because, altering a native pathway often play with growth and expression of several other genes which subsequently diminish cell fitness and create bottlenecks in the engineered system. A recurring problem is the pervasive occurrence of reversible reactions in the engineered metabolic pathways which slows down the production of key intermediate molecules and hence reduced the titre of our final product.

4.1 Ethanol Production in Bacterial Hosts Many microorganisms are being developed using synthetic biology tools for biofuel production (Table 3), but each microbial host has technical limitations and challenges to deal with to achieve the industrial robustness. Among these, yeast strains are the leading industrial biocatalysts for biofuel. However, genetically designed bacteria such as E. coli, Corynebacterium glutamicum, Z. mobilis, L. lactis and Bacillus subtilis have also been studied to address the industrial requirements. Previously, CBP technology has also been applied on genetically modified E. coli strains to produce biofuels from ionic liquid-pretreated switchgrass (Bokinsky et al., 2011). Macroalgae are being considered as feedstocks for biofuel production. A CBP-based microbial platform was developed for the bioconversion of macroalgae biomass to ethanol (Wargacki et al., 2012). A 36 kbp DNA fragment from Vibrio splendidus was installed into E. coli, with a capacity to encode enzymes required for alginate transport and metabolism. This fragment along with a preinstalled system for the extracellular degradation of alginate, generated a microbial platform with an installed capacity of simultaneous degradation, uptake and conversion of alginate to bioethanol directly from macroalgae biomass achieving w80% of the theoretical yield (Wargacki et al., 2012). Z. mobilis is a natural ethanol producer and encompasses many desirable features which make this microbe suitable for industrial biocatalysis. So, it has been used as a model system to study the various perspectives of substrate utilization and industrial robustness. Interestingly, Z. mobilis can utilize a broad range of carbon sources including biomass residues from industrial, agricultural and municipal wastes, so may be exploited for their bioconversion into valuable chemicals and biofuels (Yang et al., 2016b). Ethanol is the most established product produced by recombinant strains of Z. mobilis. In addition, its ethanol producing genes (PDC and ADH) have been expressed in various microbial hosts, including E. coli, to produce ethanol (Piriya et al., 2012). Moreover, its recombinant strain has demonstrated productive when used at pilot scale in combination with other commercial ethanol producers

Table 3 Metabolic pathway engineering of bacterial hosts for biofuel production Metabolic engineering Host description Product Titre (g/L)

Challenges/Limitations

References

Escherichia coliebased metabolic pathway engineering

LW06

KO11

Recombinant E. coli BuT-8

JCL260

TA76

CPC-PrOH3

LW02 strain with deletion of ADHE, ACKA, Frdabcd, and LDHA-FRT genes Alcohol dehydrogenase (ADHB) and pyruvate decarboxylase (PDC) from Zymomonas mobilis Overexpression of ATOB, HBD, CRT, TER, ADHE2 and FDH genes Butyrate conversion strain with deletion of undesired genes and addition of adhE2 and ATODA

Ethanol

>30

Ethanol

þ40

Low product production, Woodruff et al. (2013) efforts require for product tolerant traits Low product tolerance and Balan (2014) and Zhou instability of strain in et al. (2008) continuous system

1-butanol

30

Low titre

Shen et al. (2011)

n-butanol

6.2

Less efficiency due to toxic effect of product on strain, need to optimize the performance of coculture system 50 Byproduct formation, reduced cell densities and acetate accumulation 143 Lower isopropanol production rate resulting as hurdle in cost effectiveness 7 (1-propanol) Succinate accumulation 31 (Ethanol) due to limited NADH, presence of toxic metabolite

Saini et al. (2015)

Deletion of ADHE, FRDBC, Iso-butanol FNR, LDHA, PTA and PFLB genes Overexpression of THL, Isopropanol ATOAD, ADC, ADH

Activation of endogenous sleeping beauty mutase (Sbm) operon

1-propanol and Ethanol

Baez et al. (2011) and Lan and Liao (2013) Inokuma et al. (2010) and Zhang et al. (2011) Srirangan et al. (2014)

(Continued)

Table 3 Metabolic pathway engineering of bacterial hosts for biofuel productiondcont'd Metabolic engineering Host description Product Titre (g/L)

GAS3

Replacement of natural promoter with trc promoter, overexpression of FADD gene and introduction of ACR and CER1 genes in modified E. coli W3110 Modified GAS3 pTrcAtfA’TesA(L109P) and pTacAdhEmutFadD harbouring GAS3 strain Recombinant Engineering of 2 protein Escherichia coli lipoylation pathways Modified AL322 Introduction of Maqu_2220 gene in addition to modified FADD and TESA genes in E. coli ZF07/pZF15/ Addition of YBBO (aldehyde pKJ02 reductase) and AAR (acylACP reductase) and FADR genes with removal of PLSX gen in the native strain, modification of phospholipid and fatty acid synthesis pathways MGL2 Knockout the acyl-ACP thioesterases and competing genes (LDHA, PTA and ACKA) from other pathways

Hydrocarbons

0.58

Challenges/Limitations

References

Low strain activity under Choi and Lee (2013) aerobic conditions, need to enhance the activity of aldehyde decarbonylase

Short-chain Fatty 0.47 Acid Ethyl Ester (FAEE) Branched chain fatty 0.18 acid (BCFA) Fatty alcohols 1.72

Need to transfer the flux from C12 to C10 FAEE production N/A

Fatty alcohols

1.99

N/A

Fatma et al. (2016)

Fatty alcohols

6.33

N/A

Liu et al. (2016)

Choi and Lee (2013)

Bentley et al. (2016)

Low titre yields of product Liu et al. (2013) with toxic effect on producing cell

YJM33

Coexpression of MVAS and MVAE with OhyAEM (oleate hydratase) and OleTJE (fatty acid decarboxylase) for novel isoprene pathway

Isoprene

0.0022 (Lab scale) 0.62 (Fed-batch)

Low productivity, Yang et al. (2016a) economically unfeasible, need to enhance the pathway’s efficiency

Ethanol

þ42

Ethanol

136

Low cell performance Balan (2014) and Lau under toxic (acetic acid) et al. (2010) conditions N/A Wang et al. (2016)

Zymomonas mobilis and other microbial pathway engineering

Zymomonas mobilis AX101

Genetic engineering for glucose, xylose and arabinose fermentation Z. mobilis Integration of TESA, METB, TMY-FHPX YFDZ, AFTA, FAR, HSP and XYLA/XYLB/ TKTA/TALB genes Recombinant Overexpression of KIVD and Corynebacterium ADH3 genes with glutamicum (C12) inactivation of LDH and E1 subunit of ACEE genes Synechococcus elongatus Substitution of AdhE2 PCC 7942 (aldehyde dehydrogenase) with NAPH dependent YQHD (alcohol dehydrogenase) and BLDH (butyraldehyde dehydrogenase) Synechocystis sp. Deletion of PHB with PCC 6803 overexpression of PDC and adh2 genes under PRBC promoter S. elongatus Expression of KIVD/ALSSPCC 7942 ILVC-ILVD genes under PTRC/PLACO1 promoter

3-Methyl-1-butanol 0.49

N/A

Xiao et al. (2016)

1-butanol

0.0299

Malonyl-CoA synthesis which acts as limiting factor for synthesis of fatty acid

Lan and Liao (2012)

Ethanol

5.5

N/A

Lai and Lan (2015)

Iso-butyraldehyde

1.1

N/A

Lai and Lan (2015)

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including yeast. Furthermore, different metabolic pathways have been engineered in Z. mobilis to produce advanced biofuels or their intermediates. However, the low titre is a hindrance towards the commercialization of this technology in near future (Yang et al., 2016b). Although Z. mobilis can express cellulases and has potential to be an effective CBP strain, it still requires optimization of the metabolic pathways to meet the robustness required. Although, Z. mobilis has shown tolerance to ethanol and inhibitors yet synergistic action of several inhibitors can still have negative impact on its growth and ethanol productivity. L. lactis is famous for cheese production and has shown great potential for its use as cellular factory, owned to its higher glycolytic flux, metabolic potential to utilize various carbohydrates and ease of genetic manipulation (Kleerebezemab et al., 2000). Moreover, it can be successfully engineered to produce ethanol with heterologous expression of pyruvate decarboxylase and alcohol dehydrogenase with concomitant disruption of alternative product pathways (Solem et al., 2013). In an attempt to use L. lactis as an ethanol producing cell factory, its metabolic network was subjected to substantial rewiring. The ldh, pta, adhE genes were inactivated along with the heterologous expression of PDC and ADHB genes. The engineering resulted 41 g/L ethanol titre with 70% yield using a low-cost medium. This study revealed the potential of L. lactic cellular factory towards the bioconversion of dairy and corn milling industry waste into ethanol in a cost-effective manner (Liu et al., 2016). However, the fastidious nature and higher nutritional requirements make the L. lactis less attractive for industrial applications, where cost-effective production is the ultimate goal. However, use of cheaper fermentation media and the use of nutrient-rich waste substrates may help to circumvent this challenge which may allow us to use L. lactis as a cellular factory.

4.2 Production of C3 and C6-Alcohols in Bacterial Hosts Due to tremendous applications in plastics industry, in deicing and to prepare antifreeze fluids, and as additives in cosmetics, medicines, dyes, nutrition, liquid detergents and biofuels, annual consumption of 1,2-propanediol has reached to 1 billion pounds only in the United States (Saxena et al., 2010). Most of its demand is being met through petrochemical industry. However, this process produces toxic intermediates and side products which are not required under recent global scenario of cleaner production and consumption. So, more sustainable and environmental friendly routes are required to be found. Among these, fermentation-based conversion of renewable

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carbon sources to propane-diol has come forward. Various microorganisms including C. thermosaccharolyticum (Cameron and Cooney, 1986), S. cerevisiae (Jung and Lee, 2011), E. coli (Clomburg and Gonzalez, 2011), C. glutamicum (Niimi et al., 2011) and Synechococcus elongates (Li and Liao, 2013) have shown potential to produce 1,2-propanediol from renewable feedstocks. Among these, C. glutamicum have shown to be a better host for ethanol and butanol production, due to enhanced tolerance (Yamamoto et al., 2013) and higher isobutanol yields when compared to E. coli (Blombach et al., 2011). Moreover, C. glutamicum has shown higher tolerance ability to organic acids, furan and phenolic inhibitors present in lignocellulose hydrolysates (Sakai et al., 2007) which reflects the promising potential of C. glutamicum that is an alternative host for biofuel production. For sustainable production of biofuels using C. glutamicum as an alternative host, its substrate spectrum can be widened through metabolic engineering (Zahoor et al., 2012). For instance, deletion of the endogenous genes hdpA and ldh and the heterologous expression of mgsA, gldA and yqhD genes from E. coli, the C. glutamicum strain could produce 1,2-propanediol from glucose with a product yield of 0.343 mol/mol grown on a minimal salt medium (Siebert and Wendisch, 2015). Among various bacterial hosts, E. coli is the well-studied host for several purposes, and the situation is similar when it comes to the production of 1-butanol using E. coli as host. Interestingly, the production of 1-butanol from glucose by metabolically engineered E. coli has reached higher titers when compared to S. cerevisiae as a host (Atsumi et al., 2008a). The biological production of isobutanol in E. coli is achieved by the introduction of the Ehrlich pathway (Atsumi et al., 2008b), where robust synthesis of isobutanol is mediated by two genes including 2-keto acid decarboxylase (KDC) and alcohol dehydrogenase (ADH) using 2-ketoisovalerate as a substrate (Hazelwood et al., 2008). An isobutanol yield of 86% (22 g/L) was achieved by heterologous expression of KDC and ADH genes with concomitant deletion of competing pathways in E. coli. In addition to E. coli, bacterial hosts, such as C. glutamicum, Clostridium cellulolyticum and S. elongates have also been used for 1-butanol production (Atsumi et al., 2009; Higashide et al., 2012). The 1-butanol production pathway of Clostridium acetobutylicum was transferred into E. coli (Atsumi et al., 2008b) for the reason that Clostridium pathway can produce one 1-butanol molecule with the consumption of each glucose molecule and four NADH molecules. Using various engineering steps and with the deletion of several genes, the E. coli strain was shown to produce 1-butanol at the rate of 373 mg/L which was

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further increased to 552 mg/L when grown in Terrific Broth (TB)-enriched, glycerol-supplemented media. Understanding the chemical nature of enzymes involved in the 1-butanol production is very important to design a robust E. coli strains to produce 1-butanol. To get it done, an E. coli strain was double transformed with two compatible expression vectors, carrying phaA, phaB and adhE2 genes (Bond-Watts et al., 2011). The strain was shown to produce 1-butanol at the rate of 95 mg/L even after 6 days on glucose-supplemented TB-media. Upon further investigations, enzymes that generate stereo-specific products were found. Interestingly, replacing phaB with hbd (encoding hydroxybutyryl-CoA dehydrogenase from C. acetobutylicum), and crt with phaJ (encoding an R-specific enoyl-CoA hydratase from Aeromonas caviae), the butanol production was raised to 2.95 g/L. To enhance the availability and consumption of acetyl-CoA and NADH, aceEF-lpd (encoding the pyruvate dehydrogenase complex) was overexpressed, which subsequently provided two additional NADH molecules, and hence improved the production to a titre as high as 4.65 g/L. In another study, several genes from various genomes including atoB (E. coli), adhE2 (C. acetobutylicum), crt (C. acetobutylicum), hbd (C. acetobutylicum) and ter (T. denticola) were heterologously expressed in a special E. coli DadhEDldhADfrd strain (which cannot grow without an additional NADH-consuming pathway). The strain expressing the said genes could produce 1.8 g/L of butanoal under anaerobic conditions in glucosesupplemented TB-media. Later, the fdh gene encoding a formate dehydrogenase from Candida boidinii, was overexpressed to reduce excess pyruvate which caused the oxidation of formate into CO2 and NADH providing two additional NADH molecules required for butanol production. Another gene named pta, which encodes a phosphate acetyltransferase, was deleted to raise the intracellular acetyl-CoA, which dramatically raised the titre of butanol production to 15 g/L, only after 3 days achieving the 88% of the theoretical maximum yield (Shen et al., 2011). In another study, redox imbalance issue was addressed using a clostridial CoA-dependent synthetic pathway targeting three metabolite nodes including pyruvate, glucose-6-phosphate and acetyl-CoA. This engineering attempt was shown to exhibit the higher NADH level and butanol production titre of 6.1 g/L was obtained. It seems that production efficiency of fermentative products in microbes strongly depends on the intracellular redox balance and higher production of our desired products can be achieved by individual or coordinated modulation of these metabolite nodes (Saini et al., 2016). Other than E. coli, various bacterial hosts have been used for butanol production to see their potential,

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including C. acetobutylicum, Pseudomonas putida and B. subtilis (Nielsen et al., 2009). Interestingly, butanol pathway can be engineered to produce hexanol using E. coli as a host (Dekishima et al., 2011). Other than the modifying the butanol pathway, synthetic pathways can also be constructed to produce hexanol in E. coli. For instance, a synthetic pathway was constructed on two plasmids which were cotransformed into a modified E. coli DadhEDldhADfrdBC (Shen et al., 2011), and hexanol titre of 47 mg/L was achieved in 48 h. However, this titre is not comparable with butanol production in E. coli; therefore, further improvements are required to enhance the productivity and yield.

5. FINE TUNING OF SYNTHETIC MICROBIAL FACTORIES Once the metabolic pathways have been installed into industrial strains of yeast or bacteria, the last stage is the fine-tuning of the host to enhance the yield and productivity with reference to growth conditions and technicalities involved at the industry. Fine-tuning of the host metabolism, we need to understand that qualitative control of enzyme activities is based on the quantitative understanding of metabolic regulation. So to harness the maximum potential, slight upregulation and down regulation of enzyme activities are carefully studied on the biosynthesis of target molecules for each step of the reaction. The process is tedious and often assisted with modelling and simulation of the processes at first followed by the pilot scale optimization. For instance, a kinetic model, integrating the pentose phosphate and glycolysis pathways was constructed for Z. mobilis which revealed the TAL gene is very important to regulate the xylose fermentation (Altintas et al., 2006). Similarly, a xylose fermentation kinetic model was established (Parachin et al., 2011) using the experimentally determined kinetic parameters for the enzymes involved in xylose fermentation. The in silico analyses performed using this model were endorsed by the lab experiments to enhance xylose fermentation (Parachin et al., 2011). However, kinetic modelling is not sufficient and need detailed knowledge on the enzyme kinetics and their related parameters. For this reason, metabolic flux analysis, transcriptomics and metabolomics analyses are playing a critical role in synthetic bioengineering. Moreover, the pathway genes may be expressed under the regulation of various promoters, transcription factors and terminator sequences for enhanced expression of the target genes.

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The forced-evolution is being considered as a promising approach these days, because it has become possible to investigate that how evolution may lead the functional improvement. The prototype strains may be repeatedly subjected to subculturing under a particular selection pressure or stress to obtain a forcefully evolved strain with enhanced performance. Although the methodology is still infeasible, yet we need to focus on advancement of the key technologies for genome editing, promoter selection and de novo construction of pathways. Other than fine tuning of the synthetic microbial platform, modelling of bioprocess has been proven effective (Unrean, 2016) for achieving higher production, improved yield and productivity of desired product.

6. CONCLUSION AND FUTURE PROSPECTS Synthetic biology is a new hope for the construction of desired microbial cell factories using advanced techniques of analytical chemistry, biochemistry, bioinformatics and biotechnology. S. cerevisiae, E. coli, C. glutamicum, Z. mobilis and L. lactis have been the most desired host microorganisms to engineer for their versatile genetic capabilities, recycled fermentation, stress tolerance ability and suitability for biorefinery processes. However, the development of robust strains requires deletion and integration of genes along with a regulatory control over multiple genes. Fine-tuning of the installed pathway may require modification of promoters and/or deletions of several chromosomal genes. In near future, novel cellular factories may completely base on synthetic genomes with controlled regulation of the genes present on artificial chromosomes. The de novo synthesis of artificial chromosomes may be breakthrough in the future synthetic biologyebased engineering, where metabolic pathway engineering will no more require gene cloning and/or vector construction. Roles of suitable promoters, transcriptionally active elements and well-characterized terminator sequences cannot be ignored for the future synthetic designs of pathways. Though a long way to go, synthetic biology is the real hope of biotechnologists to construct robust microbial cell factories.

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