Optimization of the scale and the choice of pretreatment technology of a lignocellulosic ethanol plant (LEP) based on poplar.

Transcriptie

1 Faculty of Bioscience engineering Academic year Optimization of the scale and the choice of pretreatment technology of a lignocellulosic ethanol plant (LEP) based on poplar. Bral Andreas Promotor: Prof. dr. ir. Jeroen Buysse Tutor: Ludovico Balduccio Master thesis nominated to obtain the degree of Master in bioscience engineering: Chemistry and Bioprocess Technology

2

3 i Faculty of Bioscience engineering Academic year Optimization of the scale and the choice of pretreatment technology of a lignocellulosic ethanol plant (LEP) based on poplar. Bral Andreas Promotor: Prof. dr. ir. Jeroen Buysse Tutor: Ludovico Balduccio Master thesis nominated to obtain the degree of Master in bioscience engineering: Chemistry and Bioprocess Technology

4 ii Het is toegelaten vanwege de auteur en de promotor om deze scriptie te consulteren of te reproduceren voor persoonlijk doeleinden. Elke andere vorm van reproductie valt onder auteursrechterlijke bescherming, in het bijzonder met betrekking tot een verplichte bronvermelding, wanneer resultaten uit deze scriptie worden aangehaald. The consultation and partial reproduction for personal use of this thesis are authorized by the author and the promotor. Any other reproduction or use is subject to copyright protection, more specifically the source must be extensively specified when using results from this thesis. Ghent, June The promotor, The author, Prof. dr. ir. Jeroen Buysse Andreas Bral

5 iii SAMENVATTING Hoewel tweede generatie bio-ethanol een aanzienlijk potentieel bezit als hernieuwbare energiebron, zijn de toepassingen van deze technologie op grote schaal vandaag de dag beperkt. Dit komt onder meer door de onzekerheid in verband met de werking en de kost van de technologie op grote schaal, hetgeen potentiële investeerders weghoudt. Daarom kan het zinvol zijn om via (niet-)lineair programmeren een inzicht te krijgen in het volledige productieproces van biomassa tot ethanol op grote schaal. Optimale werkings- en designconfiguraties kunnen worden bepaald, belangrijke kosten worden geïdentificeerd, de rendabiliteit kan worden geschat en de gevoeligheid van het gehele systeem voor veranderingen in de economische of technische context kunnen worden in kaart gebracht. Deze thesis had als doel om de optimale pretreatment technologie, alsook de optimale werkingscapaciteit van een ethanol-producerend bedrijf met populieren als grondstof, te bepalen. De pretreatment van het hout, omvat een intensieve fysische en/of chemische behandeling, waarbij de recalcitrantie van de houtstuctuur wordt doorbroken, alvorens de omzetting tot ethanol kan worden voltrokken. Verschillende technologiëen zijn met het oog hierop ontwikkeld. Deze verschillen echter onderling op het vlak van uiteindelijk opbrengst, energie- en grondstofbehoeften en investeringskosten, waardoor het selecteren van de juiste technologie in een bepaalde context om delicate afwegingen vraagt. Hetzelfde geld voor de capaciteit van het productiesysteem. Aangezien het proces gebruik maakt van een in de ruimte verspreide grondstof, is er op het logistieke vlak een diseconomy of scale, hetgeen betekent dat de grondstof per eenheid duurder wordt, naarmate de schaal van het bedrijf toeneemt. Aan de productiekant is er een economy of scale, hetgeen betekent dat de productiekost per l geproduceerde ethanol afneemt, naarmate er meer wordt geproduceerd. In deze studie werd (niet-)lineair programmeren aangewend, om deze effecten tegenover elkaar af te wegen en te bepalen welke werkingscondities de optimale zijn. De belangrijkste resultaten van het onderzoek waren de volgende. Er werd geconcludeerd dat het systeem winstgevend kon zijn in het vooropgestelde basis scenario met een IRR van 12%. De minimale verkoopsprijs van ethanol waarbij het systeem winstgevend bleef, was 0,936 $ kg -1. Onder het basis scenario was oxidative lime de optimale keuze. Het verschil met SO 2 steam explosion was echter klein aangezien deze laatste technologie optimaal werd vanaf een verkoopsprijs van ethanol van 0,996 $ kg - 1, slechts 0,02 $ kg -1 hoger dan in het basis scenario. De optimale werkingscpaciteit was Mg d -1. Deze laatste waarde is groot ten opzichte van die van reeds bestaande bedrijven, die opereren rond 740 Mg d -1. Dit resultaat toont aan dat de huidige beperkingen van de capaciteit van tweede generatie ethanol producerende bedrijven eerder gerelateerd is met kapitaalsbeperkingen, en dat er vanuit zuiver economisch standpunt geen reden is om niet in een grotere capaciteit te investeren.

6 iv ABSTRACT Although second generation bio-ethanol has considerable potential to be used as a renewable resource, nowadays, the implementation of this technology into large scale facilities is limited. One possible reason for this is the uncertainty that exist about the performance and the costs of the technology at large scale, keeping potential investors away. Therefore, (non-)linear programming can be a useful tool to get an insight in the full production chain from biomass to ethanol on large scale. Optimal working and design conditions can be found, important cost contributing factors can be identified, profitability can be estimated and sensitivity of the system towards a changing economic or technical context can be unraveled. The goal of this thesis was to determine the optimal pretreatment technology, as well as the capacity of a single ethanol producing facility, using poplar as feedstock. Pretreatment of the wood involves the intensive physical and/or chemical treatment, in order to lift the recalcitrant structure of the wood, before it can be converted to ethanol. Several technologies have been developed for this purpose. However, these technologies mutually differ regarding the resulting ethanol yield, energy- and raw materials cost and capital investment, which demands a careful assessment of the selection of the optimal technology, under certain circumstances. The same is true for the capacity of the plant. Considering the fact that the process uses a feedstock which is distributed in space, there is a diseconomy of scale regarding the logistics, meaning that the feedstock per unit becomes more expensive, as capacity increases. On the other hand, regarding the production of ethanol, there is an economy of scale, resulting in a decreased per unit ethanol production cost, under increasing capacity. Using (non-)linear programming, these tendencies can be balanced, in order to determine the optimal working conditions. The results of the investigation can be summarized as follows. It was concluded that the production system could be profitable under the base case scenario, realizing an IRR of 12%. De minimal ethanol selling price for which the system retained its profitability, was 0,936 $ kg -1. Under this scenario, the oxidative lime was the optimal technological choice. However, the difference with SO 2 steam explosion was small since the latter technology became optimal at an ethanol selling price of 0,996 $ kg -1, which is only 0,02 $ kg -1 higher than in the base case scenario. The optimal capacity equaled about Mg d -1. This value is large compared to the scale of the existing companies, operating at around 740 Mg d - 1. This result shows that the current limitations of the capacity of second generation bio-ethanol producing facilities are probably related to constraints concerning capital availability, and that from an exclusively economic point of view, there is no reason why there shouldn t be invested in larger facilities.

7 v TABLE OF CONTENTS 1 Introduction Background Current status of the LBE economy Process overview Biomass production Poplar as feedstock for the bio-industry Biomass composition Biomass yield Conclusion Biomass transport Farmgate price Transport costs Conclusion Pretreatment Chemical methods Physico-chemical methods Remarks on pretreatment yield data Conclusion Simultaneous Saccharification and Co-Fermentation Process description Ethanol yield Ethanol titer Product recovery Heat and power recovery Materials and methods Optimization problem formulation Case study parameters Capital costs Fixed and variable operating costs Energy balance Results and discussion Energy requirements Preheating and distillation Evaporation... 31

8 vi Heat and power generation Energy balance Optimization model results Base case and genetic engineered scenario Sensitivity analysis Conclusion and future research Appendix: Energy balance calculation algorhythms Introduction Pretreatment and fermentation Downstream processing General remarks Preheating Distillation Evaporation Heat and power generation References... 54

9 1 1 INTRODUCTION During the past decades, numerous economic and environmental concerns have risen about the use of conventional fossil fuels in the economy, such as oil import dependency, depletion of the natural resources, fluctuating oil prices and greenhouse gas emissions. Increasing the use of bio-based fuels is a possible way to address to these problems. As additional advantages, biofuels can allegedly increase employment and stimulate agriculture and forestry by creating added-value for agricultural products [1, 2]. It is because of the above mentioned considerations, that many countries have recently set biofuel targets. For example, in 2007, the European Union (EU) expressed the desire to replace 10% of the total consumption of petrol and diesel in the transportation sector by biofuels [3]. Similarly, the US renewable Fuel Standards (RFS2) mandated the United States (US) in 2007 to blend a total of 136,2 billion liters of renewable fuel per year (BLY) in their transportation fuel [4]. Consequently, these policies have brought the EU share of biofuels in the transportation sector from 2,6 to 4,7%, respectively in 2007 and 2012, the latter value being equivalent to an absolute production of 10,4 BLY. On the other hand, in the US, the production of biofuels has increased from 20,5 to 55,6 BLY from 2007 to 2013 [5-7]. Bio-ethanol is a very promising renewable biofuel. The global production of bio-ethanol in 2012 was 88,6 BLY. The US and Brazil were the biggest producers, having respective shares 64% and 25% of the world production [8]. Nowadays, the bio-ethanol is almost exclusively produced through the so called first generation biofuels, meaning the ethanol is obtained by readily available sugars in feedstock such as corn, sweet sorghum, sugarcane, cassava or wheat [9]. More recently, due to the advantages those offer, a strong interest has risen to produce ethanol (also named second generation bio-ethanol or lignocellulosic bio-ethanol (LBE)) from alternative sources, such as agricultural waste (e.g. corn stover), cellulosic energy crops (e.g. switchgrass or hybrid poplar) and municipal waste. Firstly, lignocellulosic feedstock (LF) is generally much cheaper as low value agricultural products are used as input. Secondly, the substitution of gasoline with LBE reduces GHG emission by approximately 88%, as compared with a more modest reduction of 13% in the case of first generation ethanol [10]. Besides, first generation energy crops require cropland and consequently compete with the production of food and feed - giving rise to the food vs. fuel debate -, whereas LF can be grown on marginal lands, which are not suitable as cropland. Finally, the production needs of LF in terms of chemicals and energy are generally much lower [11-13]. Despite these numerous advantages, 2 nd generation bio-ethanol conversion technology has not yet achieved wide commercial application yet. In 2010 only 3 million gallons of LBE were produced in the US [14], which is still far beneath the mandated goal of 60,75 BLY by 2022 [4]. In fact, nowadays, several important barriers still hamper the widespread implementation of LBE. Some of these are related to the capital investment. A lignocellulosic ethanol plant (LEP) requires more expensive technology to be installed in comparison to plants producing first generation biofuels. Furthermore, few experience on large scale results in so called over-scaling the infrastructure, pushing

10 2 initial investment even higher. Finally, since the process is still in development phase, associated with high investment risks, fund institutions expect high returns on their loan [15]. On the other hand, the process uses a cheap, but in space very distributed feedstock. This requires careful planning of the full supply chain and the scale of a LEP since large scale plants benefit from the economy of scale on ethanol production level, while suffering at the same time from an increased feedstock cost, since the delivery radius is larger [16]. To summarize, many questions concerning the LBE industry are unanswered today and lead to a risk of investment that is too big for entrepreneurs to take. To facilitate the transition of technology from lab scale to industrial application, economic optimization models using linear programming (LP) can lead to helpful insights. In recent years, this area of investigation has received growing attention in scientific literature. In 2003, Tembo et al. used mixed integer mathematical programming (MILP) to optimize a gasification-fermentation process based in Oklahoma, optimizing the choice of the biomass type, timing of harvest, LEP size and LEP location [17]. Similarly, Dunnet et al. (2008), used MILP to optimize the scale, location, and logistical interconnectivity between decentralized parts of a poplar processing system within Europe [18]. Tursun et al. (2008), developed a model to define the optimal timing and location to build different plants as well as to organize an optimized energy crop plantation system in the state of Illinois [19]. On a larger scale, Marvin et al. (2011), defined the optimal distribution and scale of LEP s within a 9-state region in the Midwest of the US by linear programming [14]. Another study was conducted by Slade et al. (2009) focusing on the effect of different supply chain scenarios on the commercial feasibility of LBE [20]. Finally, Sammons et al. (2007), used linear programming optimization to determine the best product portfolio for a multiproduct biorefinery [21]. Although the body of literature in this domain is already quite large, it was noticed that very few optimization studies included technology as a decision variable in their study. Many possible configurations exist in the design of an LEP, which have specific features leading to both desirable and undesirable effects. One of those parts of the LEP technology that is realizable in different configurations, is the pretreatment section. Poplar wood is an inherently very recalcitrant feedstock, which needs to be pretreated to make it suitable for subsequent conversion. Finding low-cost and highyield methods to realize this pretreatment is a crucial condition for the lignocellulosic industry to flourish [15, 22]. As there are many types of pretreatment strategies available today, all having an important impact in the total capital costs, operational costs, and production yield, it is not necessarily clear which pretreatment method is most desirable when tailoring an LEP to a certain context, with a specific interplay of economic, geographic and agrarian conditions [23-29]. Additionally, most optimization studies mentioned above, which used linear programming as optimization tool, significantly simplified the underlying effects, which are generally not linear. In this study, non-linear equations, which have more resemblances to the actual processes, will be used in the optimization model. To summarize, this paper aims to explore the possibility of working out an NLP model that simultaneously optimizes the scale and the choice of technology of a hypothetical LEP based on poplar, anno 2014.

11 3 2 BACKGROUND 2.1 CURRENT STATUS OF THE LBE ECONOMY In 2010, the International Energy Agency (IEA) stated that in the future, biofuels should be capable of accounting for 26% of the total transport fuel consumption worldwide. Additionally, they declared that lignocellulosic biofuels will be capable of providing the major share of that part [30]. At that time, no LEP was operative at commercial scale. However, since the interest of energy companies in this sector is significant, the situation has changed. A recent report on the lignocellulosic industry of the Renewable Fuels Association (RFA) shows that in 2013, a limited number of commercial scale LEP s was operative or under construction [31]. Table 2-1 presents an overview of a selection of these projects. Only those plants of which the manufacturer claims that the technology is capable of handling woody residues like hybrid poplar, are included. Table 2-1 Overview of the planned and operative commercial LEP s in 2013, according to the Renewable Fuels Association [31] and European Biofuels Technology Platform (EBTB) [32]. Company Location Feedstock Scale Technology (Planned) Start-up year 10 6 L ethanol yr -1 Chemical hydrolysis BlueFire [33] Enzymatic Hydrolysis & Fermentation Mascoma [34] ZeaChem [35] Biogasol & Pacific [36] Beta Renewables [37] American Progress [38] Thermochemical treatment & Fermentation Lanzatech [39] Ineos [40] Fulton, MS, US Forest residues 71,92 Acid hydrolysis 2014 Kinross, MI, US Wood pulp and chips 75,71 Steam explosion 2015 Boardman, Poplar 94,64 Steam explosion 2015 OR, US (-) Poplar, straw 10,00 Dilute acid 2011 catalyzed steam explosion Crescentino, Various, 75,71 Steam explosion 2013 ITA including poplar Thomaston, Mixed 1,14 Organosolv 2013 GA, US hardwood Soperton, GA, US Vero Beach, FL, US Forest residues 15,14 Gasification + Fermentation Various, 30,28 Gasification + including poplar Fermentation

12 4 Some important tendencies can be derived from the numbers of the lignocellulosic industry gathered in Table 2-1. Firstly, it can be seen that most commercial LEP s are arising in the US. This might be related to the current US biofuel policy, vide supra, which incorporates a gradual phase-in of biofuels in the transportation sector and emphasizes the use of lignocellulosic biofuels by demanding that by 2022, 60,5 of the mandated 136,2 BLY of biofuels, should be second generation biofuels [41]. As stated above, a similar policy exists in the EU, however, without the clause that part of the biofuels should be of lignocellulosic origin [42]. Secondly, it can be seen that both enzymatic hydrolysis and thermochemical treatment are used as pre-fermentation steps. The figures show that in the case of enzymatic hydrolysis, the scales of the LEP s tend be higher. In general the scales of these plants are only moderately high. In the case of the BetaRenewables plant, the production of 75,71 million L of ethanol, corresponds to a biomass loading of approximately 740 Mg d -1, expressed as dry matter. For the other cases, the biomass loading was not made public, and calculating the value was not attempted since the value would rely on the composition of the feedstock and the yield of the particular process, data which were published by the respective companies neither. But we can assume that since for the largest plants, like those of ZeaChem and Mascoma, the ethanol production lies in the same order of magnitude, the biomass loading would be so as well. Generally speaking, the biomass loading of the current existing commercial LEP s is still lower than the scales assumed in most theoretical studies, which lie most often around 2000 Mg d -1 [25, 26, 43, 44]. Finally, the data show that for the plants based on enzymatic hydrolysis and fermentation, steam explosion pretreatment, with or without the addition of a dilute acid catalyst, is the method which is most frequently used nowadays. To resume, the current statistics concerning the lignocellulosic industry reveal the following tendencies: (1) most commercial facilities capable of converting poplar to ethanol are based or are at least planned to be based in the US, (2) currently the capacity of the plants using the enzymatic hydrolysis is around 740 Mg dry biomass per day and (3) the pretreatment technology of choice in these processed is steam explosion. 2.2 PROCESS OVERVIEW Figure 2-1 presents a schematic overview of the production steps involved in a wood-to-ethanol process. Firstly, biomass is produced in the area surrounding the LEP. Next, the biomass is transported to the production plant, where all the remaining processing steps are centralized. The design of the LEP is based on the work of Aden et al. (2002), splitting up the process in 5 elementary operations [43]. Chronologically, these operations include feedstock handling, pretreatment, simultaneous saccharification and co-fermentation (SSCF), and heat and power recovery via a boiler and generator system. Other process sections that are involved in the process, but are not shown in Figure 2-1, are wastewater treatment and utility providing equipment. The biomass feedstock enters the facility as dried wood chips. Feed handling refers to the piling, washing and sorting of these wood chips, and includes an additional size reduction step for the biggest particles. Pretreatment involves the physical or chemical treatment in order to overcome the natural recalcitrance of LF [45]. In the SSCF reactor, the biopolymers of interest, namely cellulose and hemicellulose, are hydrolyzed to their respective

13 5 monomers by adding cellulases and hemicellulases, and immediately fermented to ethanol by a fermenting organism, in casu Saccharomyces cerevisiae. Next, the ethanol is removed from the fermentation beer by a twofold distillation. The remaining products are then sent to the evaporator to reduce the moisture content. Finally, this rest fraction is combusted to generate steam. This steam is used for process heat first, after which any excess steam is used to drive the generator for the production of electricity. This electricity can be sold to the grid as a by-product of the process [43]. The remaining sections of this chapters will deal with the sequential discussion of these process steps in detail. Figure 2-1 General overview of the assumed design of the LEP Before continuing to this discussion, a short justification of the chosen design is appropriate, since obviously many other possible LEP design configurations could have been assumed as well. The National Renewable Energy Laboratory (NREL) of the US Department of Energy (USDOE) published several technical reviews regarding the design of LEP s, for example Wooley et al. (1999) with poplar as a feedstock, and an updated version of Aden et al. (2002) using corn stover as feedstock [43, 44]. These papers are seminal in this domain of research, since many techno-economic studies concerning LBE production which were published afterwards, used these same designs as a template [16, 23, 25]. In this study, the design of Aden et al. (2002) was preferred over the one by Wooley et al. (1999) although the latter used poplar as a feedstock and might therefore be at first sight the more evident choice. The general principles underlying these two designs are very similar, but some divergent aspects between these two descriptions make the most recent one more appropriate to use as a template in this study. These aspects will become clear in the following sections. Both descriptions give however enough details to comprehend that a different feedstock choice does not result in major differences in design parameters. Most of these processing parameters like scaling of reactors were equal or at least very similar in the corresponding unit operations. This consideration justifies the fact

14 6 that the design of Aden et al. (2002), however based on corn stover as a feedstock, was chosen as a representative design for an LEP based on poplar. 2.3 BIOMASS PRODUCTION Poplar as feedstock for the bio-industry For a number of reasons, poplar species are very well suited to serve as a feedstock for an economy based on biomass. Primarily, poplar trees have very favorable compositional characteristics (vide infra). Furthermore, poplar species are amongst the fastest-growing trees that are known. Moreover, investigations on hybridization within the genus Populus have led to a broad spectrum of hybrid poplar varieties, tailored to specific growing circumstances. Consequently, it is possible to grow poplar trees with reasonably high yields on many different types of land, including lands of low-quality. Additionally, the sequencing of the poplar genome has resulted in genetic engineering of the tree, optimizing its characteristics for use in the bio-industry [46, 47]. Finally, poplar trees are more resistant against diseases and the drought, than most of the other energy crops [48]. For these reasons poplar was chosen as feedstock in this case study Biomass composition Plant material consists of a complex assemblage of biochemical components. Cellulose, hemicellulose and lignin are the structural components in the plant cell wall forming a complex which renders the plant strength and protection [49]. On the other hand, all non-structural biochemical components which origin from inside the cell cytoplasm, like fatty acids, proteins, waxes, chlorofyll, nucleotides, are referred to as extractives. Inorganic elements finally, of which P, K, Na and Ca are the most important, constitute to the ashes fraction. Notwithstanding that the extractives could be a valuable side product for an LEP or a biorefinery in the future [50], we will not be focusing on these constituents here. Instead our attention will be focused on the structural components of biomass, cellulose, hemicellulose and lignin, together accounting for roughly 90 wt% of the dried biomass Cellulose Cellulose is a linear polymer consisting of β(1-4) linked glucopyransyl units as shown in Figure 2-2. These long chains are organized in parallel bundles, which are stabilized by intra and inter-molecular hydrogen bonds. These so called microfibrils render the molecule a rigid and water-insoluble crystal structure, and make it therefore an appropriate structural element of the plant cell wall [51]. Cellulose is constituted of the same building block as amylose and amylopectine, namely glucose molecules. By consequence, the widely abundant cellulose polymer has large potential to be an alternative source for this important molecule. The α(1,4) and α(1,6) linkages in amylose and amylopectine however, are far more digestible then the β(1-4) bonds in cellulose, making the depolymerization of cellulose to glucose on large scale practically impossible in the past. During the past decades however, intensive research to increase the yield and cost-effectiveness of cellulose degrading enzymes or cellulases, has cleared the path towards industrial application [52].

15 7 Figure 2-2 Molecular structure of cellulose [50] Hemicellulose Hemicellulose is a general term referring to low molecular-weight, branched polysaccharides consisting of a complex mixture of pentose sugars (xylose, arabinose) and hexose sugars (glucose, mannose and galactose). Their branched nature makes them ideally suited to stick the different microfibril units together and strengthen the cell wall structure [53]. Hemicellulose in poplar species consist mainly of O-acetylated 4-O-methyl-glucuronic acid xylan or glucuronoxylan, accounting for 80-82% of the total hemicellulose content, shown in Figure 2-3 [50, 54]. Xylanase enzymes which hydrolyze xylan to its respective monomers, namely xylose, are commercially available. Unfortunately, wild type fermenting organisms are not capable of fermenting pentoses like xylose into for example ethanol. Alternative uses of hemicellulose as such include the use as thickener, adhesive, emulsifier or stabilizer [50]. In hydrolyzed form, it can be transformed in to xylitol, with applications as a sugar alternative. Nowadays however, a cofermentation process is possible, since recombinant S. cerevisae yeast strains have been engineered to be capable of fermenting glucose as well as xylose into ethanol [28]. Figure 2-3 Molecular structure of the main type of hemicellulose in hardwoods [50] Lignin The third type of biopolymer associated in the cell wall complex is lignin. It differs somewhat from (hemi)cellulose since it is not a polysaccharide, but an amorphous, cross-linked polymer of three phenolic building blocks, namely p-coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, shown in Figure 2-4. Lignin is a very rigid structure and by itself very resistant to enzymatic hydrolysis, making it an important molecule to render secondary cell walls their biological and physical recalcitrance [49]. In the cell wall complex it is draped around the (hemi-)cellulose microfibrils and in that way, impeding hydrolyzing enzymes access to their polysaccharide substrate [55]. Moreover, it is the major structural molecule in secondary thickened cell walls. The presence of lignin is however not advantageous from an economic point of view since it is not transformable into ethanol on the one hand, and since it hampers the hydrolysis by absorbing the enzymes and blocking the access towards their substrate, on the other hand [56]. Lignin can be isolated from the rest of the biomass, for example by Organosolv

16 8 pretreatment [57, 58], making it a possibly valuable side product in an LEP since it can be used in adhesives, antioxidants or carbon fibres [59]. The method relies however on quite expensive chemicals, and this option is therefore not considered in this study. Lignin will instead be supposed of as an inert component that will be combusted after the process. Figure 2-4 Molecular structure of the building blocks of lignin. From the left to the right: p-coumaryl alcohol, coniferyl alcohol and sinapyl alcohol [50] Poplar compared to other LEP feedstock Several beneficial aspects related the use of poplar as feedstock for LBE production were already handled in chapter It was said that poplar has favorable compositional characteristics. In Table 2-2, these characteristics are compared to those of some other crops that are candidates for the use as feedstock in LBE production. Table 2-2 Compositional characteristics of hybrid poplar expressed in dry mass wt%, compared with the composition of other LBE feedstock. The respective varieties are Claudina DN-34, Saligna, Zea mays, Alamo, and John Stone. a [60]; b [54]. The different components may not sum up to 100% as a consequence of possible non-optimalities in the sequence of LAP (Laboratory Analytical Procedures) of the NREL [61]. Hybrid Poplar a Eucalyptus a Corn Stover a Switchgrass a Tall Fescue b Lignin 25,7 26,9 17,7 17,6 12,6 Glucan 41,5 48,0 34, ,8 Xylan 14,3 10,4 18,3 20,4 14,3 Arabinan 0,8 0,3 2,5 2,8 3,0 Mannan 1,9 1,2 0,4 0,3 0,4 Galactan 0,9 0,7 0,9 0,9 1,1 Extractives 4,2 4,2 7, ,4 Ash 1,8 1,2 10,2 5,8 10,9 It can be seen that compared to other hardwood species in this table, Eucalyptus, poplar has a lower cellulose content, but on the other hand also a slightly lower lignin and a higher hemicellulose content. Compared to herbaceous species like switchgrass or corn stover, poplar has a higher lignin content which is disadvantageous, but compensates this by having a considerably higher cellulose content. Generally speaking, the amount of cellulose and hemicellulose (and thus of potentially fermentable

17 9 sugars) is high as compared to herbaceous species. Lignin content however is higher than in herbaceous species, but slightly lower than in other hardwoods. These numbers show that poplar has a favorable composition for bio-ethanol production, at least from a mass balance point of view Genetic engineering of poplar From what is told above, it is clear that LF should preferably have a high cellulose and hemicellulose content, since these are the molecules of economic interest. On the other hand, a low lignin content is desirable, since it has less economic value and moreover, impedes the process by its recalcitrance. Therefore, it is not surprising that in recent years, genetic engineering has tried to tailor poplar species to application in the LBE production process. More specifically, lowering the lignin, and increasing the cellulose content, are the main targets of the current scientific developments in these domains [46, 62]. These goals follow from direct mass balance considerations or indirectly, from subtle influences that changing the biomass composition can have on the conversion process performance. As an example, feedstock with lower lignin content may require less severe pretreatment conditions which consequently, lowers the generation of fermentation inhibitors [63]. However economically favorable, changing the relative abundance of the cell wall polymers may come at a biological cost, since a lower lignin level could decrease the physical strength and the resistance to microbial attacks and can result in malformation and dwarfing [49, 64]. The beneficial effects on ethanol yield per unit of harvested biomass, would then obviously be counteracted by lower biomass yield on the field. It is therefore crucial that a good agronomic performance of the proposed transgenic species is proven. In 2002, Pilate et al. presented 2 large scale field trials in the UK and France to compare agronomic performance transgenic poplar with their wild type parents. After 4 years, the trees were composed of around 18% lignin, compared to 20% in the wild type, and showed no significant difference in growth parameters. Table 2-3 Comparison of the composition of the wood of transgenic species with their wild type (wt%, on a dry matter basis). All these species showed no significant difference in biomass yield in comparison to their wild types. a To specify the growing conditions used in the reviewed studies, G stands for Greenhouse Conditions, whereas F stands for Open Field Trial. Reference Variant Cellulose Hemicellulose Lignin Culture a % % % Min et al, 2014 [65] Wild type 43,5 15,2 22,0 G NMT1 44,2 16,4 23,2 G NMT3 50,0 17,3 14,3 G NMT4 47,7 15,2 19,6 G Wang et al., 2013 [46] Wild type 47,9 24,1 F, 5y B4CL28 51,5 22,2 F, 5y B4CL86 49,6 22,7 F, 5y BCOA264 51,8 21,6 F, 5y BCOA133 50,4 21,8 F, 5y Jung et al., 2013 [66] Wild type 41,0 29,1 23,5 G Ox8 45,0 29,4 23,2 G

18 10 Voelker et al., 2010 [62] Wild type 42,2 22 F, 2y ,5 F, 2y ,3 21 F, 2y ,9 19,5 F, 2y ,2 21,5 F, 2y Stout, 2011 [67] Wild type 22 F, 2y Pt(17,4) 17,4 F, 2y Ch(16,7) 16,7 F, 2y As4CL(19,2) 19,2 F, 2y Table 2-3 present an overview of other, more recent studies investigating growth characteristics of transgenic poplar trees. For all the species that are presented here, the obtained biomass yields were not significantly different from those of the wild type parent. The last column labeled Culture depicts the growing conditions used in the study, being a greenhouse environment (G) or a long term open field trial (F, growing duration). Uncontrolled conditions in open field, for example unpredictable climate conditions or diseases, have an influence on biomass growth that cannot be foreseen in controlled greenhouse experiments [64]. Therefore, results from greenhouse conditions are to be taken with caution. Both Wang et al. (2012) and Voelker et al. (2010) conducted open field trials [46, 62]. The cellulose content of their engineered species raised on average 6,0% and 6,9%, while the lignin content dropped by 8,6% and 6,5% respectively (both expressed on a relative basis). It is therefore concluded that it would be reasonable to assume that genetic engineering can realize a decrease of lignin and an increase of cellulose by 6% each, without suffering from reduced agricultural yields Biomass yield It is assumed that poplar trees are grown in so called Short Rotation Coppice (SRC) planting sites. This agricultural practice is in a mature state of experience, as it was introduced in the US in the sixties [68]. In SRC, fast growing woody species like poplar, willow or birch are grown in a high density setting, usually between trees per ha, before they are harvested, which happens typically after 3-6 years [69]. Coppicing means cutting the growing plant at its base after the first growing season, resulting in a number of sprouts emerging from the trunk base in the second year, which is supposed to raise the total biomass production [68, 70]. The biomass yield that can be expected from this production regime is a crucial parameter in the economic model since it influences other parameters such as the magnitude of the biomass collection area. In order to make a reasonable estimation of this parameter, a literature overview is presented of studies investigating the potential yield of poplar SRC plantations. This yielded quite divergent results ranging from 2,2 on the lower end, up to 40,2 Mg dry biomass ha -1 y -1 on the higher end [71, 72]. Biomass yields differ greatly from case to case, and results are difficult to compare as yield depends on many factors like planting density, fertilization and weed control, soil quality and

19 11 characteristics and the type of hybrid poplar used. What becomes clear while comparing these studies however, is that soil quality seems to be a dominant factor influencing the biomass yield. Paris et al. (2011) for example, studied poplar production of the same species in three different regions in Northern-Italy corresponding to low, medium and high soil quality, reporting a clear difference in average yields of respectively 14, 23 and 40 Mg ha -1 y -1 [71]. Generally speaking, the biomass yields which can be achieved under optimal conditions of climate, irrigation and fertilization are somewhat lower than the extreme value of 40, ranging between Mg ha -1 y -1 [73-76]. However, assuming the highest possible production yields would not be a very realistic estimation. A survey with Flemish farmers has shown that they wouldn t be willing to use their best agricultural soils for SRC of woody biomass for energy purposes [77]. In addition, Bergez et al. conducted an economic feasibility study in 1989, concluding that in France, growing short rotation energy crops on high quality agricultural land is not competitive with culturing traditional crops [78]. Thus, in this study it will be assumed that the poplar trees are produced on less productive, marginal lands. This assumption has some additional advantageous consequences for the LBE value chain that are worth to be mentioned here such as less competition with other (especially food and feed) crops, less expensive costs related to land use, and reconversion of lands that would otherwise be abandoned. Table 2-4 present an overview of possible poplar yields that can be expected from marginal lands. These yields are given as a broad interval, instead of the maximum reported yield, or record yield. Indeed, care must be taken in the use of these record yields since they are typically the result of a relatively small scale experiment (> 25m x 25m). A commercial plantation however, will cover a much larger area and consequently, soil heterogeneity comes into play as a parameter influencing the average biomass yield [79]. Table 2-4 Overview of best-growing poplar varieties under different non-optimal growing regimes. Yields are expressed on a dry matter basis. Reference Armstrong et al., 1999 [80] Laureynsens et al., 2003 [72] Bungart and Hütll, 2001 [81] Record yield species P.trichocarpa x P. Deltoides "Boelare" P. trichocarpa x P. Deltoides "Hazendans" Populus spp., variety Tacamahaca Country Yield Type of land Mg ha -1 yr -1 UK 8,1-13,6 former agricultural site Belgium 8,5-11,4 former clay pit Germany 9,2 former mining site Pearson et al., 2010 [82] P. deltoides x P. Nigra "OP367 " US 8,7-12 non-cropland arid climate Rosso et al., 2013 [83] P. alba "Villafrance" " " Italy 6,6-8,3 non-specified marginal land

20 Conclusion As a feedstock for an LEP, SRC poplar wood has interesting compositional characteristics. It has a high cellulose and hemicellulose content compared to herbaceous species, and a low lignin content in comparison to other hardwood species. The composition of poplar trees varies however largely, both between different varieties as within one variety. These differences originate from different agricultural practices, growth conditions, fertilizer use, harvesting time etc. [84]. Browsing the Biomass Feedstock and Composition Database from the USDOE, 7 different compositional characteristics of different samples of the same hybrid variety, Claudina DN-34, were found [54]. It was decided to use the average compositional characteristics of these 7 species, as baseline poplar composition in this study. Genetic engineering was assumed to realize an increase in cellulose and a decrease in lignin, both by 6%, as displayed in Table 2-5. Table 2-5 Compositional characteristics of the wild type polar species Claudina, DN-34, and the hypothetical composition of the genetically engineered version of this same species. a [60]. The different components may not sum up to 100% as a consequence of non-optimalities in the sequence of laboratory practices of the NREL [61]. Wild type Claudina, DN-34 a wt % Hypothetical transgenic type wt% Lignin 25,75 24,20 Glucan 41,51 44,00 Xylan 14,29 14,29 Arabinan 0,87 0,87 Mannan 1,91 1,91 Galactan 0,87 0,87 Extractives 4,24 4,24 Ash 1,79 1,79 Additional to the favorable composition, poplar can be grown on relative poor lands with conservation of reasonable yields. This feature is interesting since both economists and farmers are very skeptic about growing poplar for energy purposes on high yielding lands. A poplar yield between 8 and 11 Mg ha -1 yr -1 is considered as a reasonable estimation, considering the wide variety in both the individual poplar species and the soil quality on large scale plantations. Therefore, in this work, the base case poplar yield is assumed to be 9 ha -1 yr BIOMASS TRANSPORT Since an LEP uses a feedstock that is distributed in space, the logistics related to the process are an important consideration. These logistics show a diseconomy of scale, since the larger the capacity of the plant, the larger will be the feedstock costs per unit [16]. Accordingly, previous research has shown that the biomass related costs represent indeed a major part of the total production cost of ethanol, with an estimated share between 25% and 50%. [16, 44].

21 13 In an economic analysis, there are 2 principal methodologies to handle this cost. Most often, a bottomup approach is followed by summing up the sequence of the different processing steps, until the feedstock is ready to use in the plant [85-87]. However, some authors prefer to use a market price for biomass as a measure for the delivered cost in their economic analysis, arguing that this strategy is more conform to the rules of supply and demand, and by consequence gives more realistic projections on the project development [20]. However, considering the fact that nowadays, a functioning market for cellulosic biomass does not yet exist, it was decided to follow the bottom-up approach [88]. The delivered feedstock cost - which equals the cost of biomass when it reaches the LEP - is calculated as the sum of the farmgate price - equal to the costs of producing the biomass at farm level - and the transport costs Farmgate price The farmgate price compensates for different expenses related to the raising of poplar, such as the land cost including the land rent, planting cost including chemicals, fertilizers, equipment, maintenance and repair, fuels, interest, labor and seeding, harvest and collection cost, densification cost and distance fixed transport cost [85, 89]. Biomass densification covers the cost of chipping, bundling or pelletizing the wood to obtain a bulk material of higher density in order to make the transport and handling more efficient [90, 91]. The distance fixed transport cost includes loading at the farm and unloading and stacking at the facility. The choice of the biomass densification strategy is not self-evident and could be optimized in an extended NLP model, but this lies outside the scope of this work. We review here some common practices, after which one is selected on qualitative grounds. The harvested wood can be transported in unprocessed form. This saves investment and processing costs at the farm, resulting in a lower farmgate price. On the other hand, it increases the capital investment of the processing plant since an extra size reduction step needs to be implemented [89]. The latter could however be advantageous since size reduction cost can benefit from the economy of scale [92]. The principal disadvantages of unprocessed wood are its low density and high moisture content. By consequence, it raises transport costs since the capacity of the trucks is not used efficiently. The hauling cost of unprocessed wood in $ Mg -1 km -1 (vide infra) raises up to 0,22 [93], in comparison to a value of 0,09 calculated for wood chips [94]. Increasing the biomass bulk density can be done by chipping, bundling or pelletizing [86]. Wood pellets are made by drying, milling and extruding the wood in a cylindrical shape [95]. They are superior from an industrial point of view since they have the highest density lowest moisture content, and are most easy to handle at the LEP. Pellets are however much more expensive than bundles or chips. Sultana & Kumar (2011) estimated the cost of pelletizing to be 115,21 $ Mg -1, while this cost is in the range 13,23 to 26,46 $ Mg -1 for chipping [86, 95, 96]. Moreover, pellets can usually not be made on the plantation site itself, so an extra pellet plant must be available within a reasonable range. Chips are made by cutting the wood in small pieces, usually by mobile chippers available at the plantation site. Compared to the widespread used wood chips, bundling is a practice currently only

22 14 performed in Finland and Sweden. On most parameters wood chips perform slightly better: lower bulk density, lower hauling cost and a lower processing time [86]. Additionally, bundling suffers from the fact that an additional size reduction step needs to be implemented at plant level, although the same remark concerning possible economies of scale made in the discussion of unprocessed wood, could be made here. Bundles might have a logistical advantage since they can be stored much longer than chips (11 months vs. 2 months), which in turn reduces moisture content [97]. Considering all the previous remarks it is reasonable choice to assume wood chips as feedstock. An overview of the different processes, their related costs the resulting farmgate price, are shown in Table 2-6. Table 2-6 Summary of assumptions of the different components of fixed cost of feedstock. All values expressed as $ Mg -1. a These values are calculated by taking the arithmetic average of the outer values of the respective interval in the column Value. Land cost were calculated using a land rent price of 185 $ ha -1 y -1, and a biomass yield of 9 Mg ha -1 y -1. Cost Component Value Estimation $ Mg -1 $ Mg -1 Land cost - 20,55 Planting cost [98] 25,74-40,02 32,88 a Harvest and collection [98] 12,1-25,53 17,86 a Storage [76] 10,4 10,4 Chipping [95] 14,52-29,04 21,79 a Distance fixed transport cost [99] 4,23 4,23 Farmgate price 102, Transport costs The transport costs of biomass are modelled assuming that a circular area of land around the LEP, meets the biomass demand of the LEP. The first step consists of expressing the relation between the scale F of the LEP, expressed in dry Mg yr -1, and the radius R of the collection area surrounding the plant. Since we assume this area to be circular, the radius is directly proportional to the square root of the collection area, which in turn will be linearly related to the operational capacity S oper, and inversely related to assumed biomass yield Y biom, here expressed as dry Mg km -1 year -1. Finally this yields following equation [100, 101]: = 2-1 Obviously, assuming a circular area which is totally available as agricultural area wouldn t be a realistic scenario. That is why in equation 2-1, f lc stands for the fraction of the surrounding area which is actually farmland and f a is the fraction of this farmland which is dedicated to short rotation poplar production. It is assumed that f lc equals 75 and f a 10%, which are both realistic, yet conservative assumptions [85]. The next step is to calculate the average transportation cost c transp, expressed as $ Mg -1. It is assumed that this transport is done by trucks of a capacity of approximately 22,7 tonnes. A main factor affecting this cost is obviously the radius R. Furthermore the hauling cost C 1, depicting the cost of truck transport per unit weight and distance travelled, influences the cost, and is expressed as $ dry Mg -1 km -1. Finally

23 15 the transport cost of the biomass per dry matter, is obviously inversely related to moisture content m (%). The exact expression is given below: = 2 3(1 ) The factor 2/3 originates from the fact that in a circular area, the average travel distance from random point in the circle to the center mathematically described as the expected value of the radius equals 2/3*R. The factor f w is called the road-winding or tortuosity factor and corrects for the non-linear path trucks have to follow, and is assumed to be 1,27 [102]. Mathematically it equals the ratio between the actual travelled distance and the direct distance in line of sight. Mahmudi & Flynn (2006) calculated the hauling costs for chipped woody biomass to be 0,09 $ dry Mg -1 km -1 [94]. Compared to the value for wood pellets, equaling 0,07 $ dry Mg -1 km -1, this value is acceptably low [99]. Moisture content of wood chips is assumed to be 50% Conclusion Biomass transport is an important factor in the economic evaluation of an LEP, since the biomass delivered cost can account for 25% to 50% of the total ethanol production costs [16, 44]. This delivered costs is calculated by summing up the farmgate price and the transport costs. The farmgate price of the biomass equaled 102,20 $ Mg -1, and the relation between the LEP capacity and the transport costs was calculated by assuming a circular area of land around the LEP, of which the fraction of actual arable land available was assumed 75% and the percentage of this land used for poplar plantation 10% PRETREATMENT As stated earlier, the purpose of the pretreatment section of an LEP is to overcome the recalcitrance of the LF in order to make the glucan and xylan polymers more accessible [45]. The recalcitrance of the intact structure lies in the interplay of several specific features, being the crystallinity of cellulose, the protecting shield of inert lignin and the bundling effect of hemicellulose and the low available surface area, vide chapter [103]. Several pretreatment methods have been developed through the past years, and they can be classified as follows: chemical (e.g. dilute acids or bases), physical (e.g. steam explosion), biological (e.g. fungi treatment) and physico-chemical methods (e.g. example acidcatalyzed steam explosion or hot water treatment) [104]. They all bear in common that they use in some way or another rather stringent conditions, to overcome the biomass recalcitrance. This makes pretreatment a very expensive unit in the process, possibly the second-most expensive unit after feedstock cost [45]. However, not including a pretreatment step would be even more expensive since the detrimental effect this would have on the ethanol yield [23]. A schematic presentation of the process is given in Figure 2-5.

24 16 Figure 2-5 Schematic presentation of the lignocellulosic cell wall complex, and the goal of pretreatment [45]. Choosing between this possibilities is not evident since each of these methods have their merits but meanwhile suffer from specific drawbacks, which are described very thoroughly in a large collection of review articles published [45, 53, ]. These merits are most importantly represented by the final sugar yield after subsequent enzymatic hydrolysis, which equals the obtained amount of hydrolyzed sugar monomers expressed as their glucan equivalent, divided by the original amount of (hemi)cellulose in the biomass. Meanwhile, negative aspects of the pretreatment like the formation of fermentation inhibitors and sugar degradation, should be limited. Finally also the capital and working costs related to the operation of the technology, are an important aspect. In the next sections, the working principles of the most important pretreatment methods available today, are compared, as well as their effectiveness in the processing of poplar wood. The final choice of pretreatment technology will be the core output of this work Chemical methods Dilute acid pretreatment breaks the lignocellulosic structure by solubilizing a large part of the hemicellulose fraction, which eventually increases the digestibility of the remaining solids [107]. To achieve this, the biomass is sprayed with a dilute acid catalyst, typically sulfuric acid, at a loading of around 0,04-0,048 g acid g -1 biomass. This mixture is than heated to a temperature in the range of C, for a period ranging from seconds to minutes, depending on the conditions of the pretreatment [27, 28, 45, 53, 108]. Several recent studies stated that the effectiveness of a dilute acid pretreatment on poplar was moderate to high. Combined sugar yields, including yields of both xylose and glucose that are achieved after hydrolysis of the pretreatment solids, were reported between 46 and 82% [28, 29, 109]. This broad range of yields can be explained by different enzyme loadings used for the hydrolysis, ranging from 7,5 to 15 FPU g -1 cellulose. In general, benefits of the dilute acid pretreatment are the high yield, the fact that pretreatment yields xylose monomers directly, and the fact that acid digestion of biomass is a very familiar process to industry [53]. However, some important limitations exist such as the need for expensive corrosive-resistant equipment to withstand the acid conditions. Furthermore, acid pretreatment tends to result in a high rate of degradation products

25 17 formation which can act as fermentation inhibitors [110]. Finally, an overliming step (Ca(OH) 2) is needed to neutralize the acid, adjusting the ph to fermentation conditions. The resulting salts need to be disposed of, resulting in an extra cost. Oxidative Lime pretreatment uses a dilute alkaline chemical to pretreat the biomass. Several types of alkaline reactants, such as NaOH or KOH have been investigated [ ]. However in recent years, the use of lime (Ca(OH) 2) has gained increased attention due to its low cost, low toxicity and possibility for recovery [116, 117]. Another advantage is that formation of fermentation inhibitors is limited [53]. Amongst the disadvantages, high energy costs are important [23]. For biomass with high lignin content like poplar, the process is greatly enhanced by aeration and is therefore called oxidative lime pretreatment [117]. During lime pretreatment, biomass is treated with a water-lime solution while oxygen is purged into the reactor. At low temperatures (ca. 55 C) the process takes several weeks [118, 119], while at elevated temperatures (ca. 160 ) and pressures, the duration is shortened to several hours [28]. The main effect of lime pretreatment is the removal of lignin. Additionally, it removes various acetyl and uronic substitutions on the hemicellulose chain, making it more accessible for enzymatic attack [103]. Studies investigating the effectiveness of short term oxidative lime pretreatment on poplar reported combined sugar yields in the range of 77 to 91,3%, whereas in the case of long term a value of 76% was reported [28, 117, 119] Physico-chemical methods SO 2 catalyzed steam explosion is an extension of the uncatalyzed steam explosion, which is up until now the most regularly used pretreatment system for lignocellulosic materials [120]. In this method, biomass is treated with high pressure saturated steam ( C) for a few minutes (typically 5 ), after which the pressure is rapidly lowered to atmospheric level. This swift pressure drop causes explosive decompression of the materials, which results in lignin deformation and hemicellulose autohydrolysis [121]. In the acid-catalyzed version, wood is drained in SO 2 prior to steam explosion pretreatment for a period of 12 hours at room temperature [122], which results in a smaller residence time leading to less inhibitor formation and a better hydrolysis of hemicellulose [123]. Grous et al. (1986) investigated the effectiveness of steam pretreatment on poplar chips, and found a glucose yield of 90%, similar to the 88,4% glucose yield found by Cantarella et al. (2004) [124, 125]. In the case of SO 2 catalyzed steam explosion, a combined yield of 95,9% was reported [28]. This combined sugar yield is not only high, it is also achieved at a comparatively low energy cost. This is mainly due to the fact that relatively large wood chips can be used, compared to the other pretreatment categories which require a mechanical size reduction before pretreatment, which can account for roughly 30% of the energetic cost of pretreatment in poplar species [126]. Major limitations of the steam pretreatment is destruction of part of the xylan fraction and a relatively high formation of fermentation inhibitors [127] (Mackie et al., 1995). However, by decreasing the residence time and temperature, with the aid of an acid catalyst, these limitations can be overcome, as stated above. Adding an acid catalyst however comes at a cost, not only as an increased raw material cost obviously, but it also requires acid resistant equipment and neutralization of sugar degradation products [53].

26 18 In Liquid Hot Water pretreatment, water at temperatures above 200 flows through the biomass, while it is kept in its liquid state by applying high pressures [104, 128, 129]. Residence time is typically somewhere between 10 and 30 minutes [27]. Much like steam explosion, the essential feature of this type of pretreatment is the autohydrolysis of hemicellulose. This reaction is aided by the fact that water acts as an acid at these high temperatures [130]. Additionally to hemicellulose hydrolysis, water molecules can penetrate the cell wall and in this way cause swelling which disrupts the lignocellulosic matrix [53]. To minimize the formation of fermentation inhibitors, it is essential to keep the ph of the slurry in the reactor between 4 and 7 [45]. The reason for this effect is that under these circumstances, oligomeric rather than monomeric xylose molecules are formed, which results in lower monomeric sugar degradation. Very limited research has been done on the digestibility of liquid hot water pretreated poplar. Wyman et al., 2009, reported a very high xylose yield of 96%, while the glucose yield was only 56,7%. The combined sugar yield equaled 66,2% [28]. Better results were obtained by who found a cellulose yield between 80-90% after hydrolysis [131]. The use of liquid hot water pretreatment can be advantageous from a cost-saving point of view: there is no need for a catalyst, and consequently no need for expensive corrosion-resistant equipment. Moreover, less fermentation inhibitors are formed compared to steam explosion pretreatment. Amongst the disadvantages, high energy and water usage can be mentioned, as well as the fact that at this time, no commercial scale hot water pretreatment systems are in development [53, 104]. Ammonia Fiber Explosion pretreatment (AFEX) uses a principle quite similar to steam explosion, the difference being that anhydrous ammonia is used as a reactant rather than water (in the form of steam). In the first step, biomass is treated with liquid ammonia at high pressures and temperatures between 60 and 100 C. Subsequently, pressure is rapidly lowered to atmospheric level, which causes expansion of the ammonia, leading to a disruption of the biomass [104]. Total residence time is around 30 minutes [27]. Typically, a loading of 1 kg ammonia kg -1 dry biomass is applied [27, 28, 108, 132, 133]. Unlike other pretreatment methods, AFEX does not solubilize substantial amounts of either lignin, hemicellulose or cellulose, and by consequence, results in a single solid fraction. The principal effect AFEX has on biomass is the breakage of lignin-carbohydrate bonds [134] and decrystallization of cellulose [53]. The effectiveness of AFEX treatment is generally high on herbaceous biomass but rather poor on hardwood species. Hydrolysis yield was lower than 50% after AFEX treatment of aspen wood chips [120]. Similar results were obtained in research of Wyman et al. in 2009, who reported a modest combined sugar yield of 52,8% after enzymatic hydrolysis of AFEX pretreated poplar samples, despite of using considerably more harsh conditions (180 C) [28]. Remarkably, Balan et al. (2009) reported higher combined yield of 84,6% using the same harsh conditions [135]. This might be a consequence of the higher ammonia loading of 2 kg kg -1 dry biomass used. Overall, the most important benefits of the AFEX process include a very low amount of fermentation inhibitors that are formed and a lower energy consumption, as a consequence of the lower working temperature than in for example steam explosion, although the latter advantage does not longer apply when harsher conditions are used in treating hardwood species [53, 104]. Amongst the limitations, we can mention the costs associated

27 19 with ammonia recycling, but probably more importantly, the rather poor effectiveness on poplar wood [23, 28]. Ammonia Recycle Percolation (ARP) is another pretreatment method which utilizes ammonia. In this case, aqueous ammonia (5-15%) passes through the biomass, which is packed in the reactor. Working temperatures are around 180, percolation rate around 5 ml min -1, and residence time between 20 and 30 minutes [27, 28, 108, 136]. During ARP, removal of lignin is realized via a process called ammonolysis in the first stage. Later on, also part of the hemicellulose is solubilized. Removal levels of 75-85% and 50-60% respectively, were reported [136]. These values were obtained on corn stover, and much like AFEX, ARP is generally more effective on herbaceous biomass than on hardwoods species. Wyman et al. (2009) reported a combined sugar yield of 54,8% after hydrolysis of ARP pretreated poplar material [28]. ARP has the attractive feature that it is the only pretreatment removing both lignin and hemicellulose and thus leaves a solid residue very rich in glucan [136]. Moreover, as in AFEX, a relatively low amount of fermentation inhibitor is formed [137]. On the other hand, ARP suffers from high energy usage and liquid loadings, and a very limited effectiveness on hardwoods [28, 53] Remarks on pretreatment yield data Preceding paragraphs showed that a large body of literature exists on the effectiveness of pretreatment strategies on poplar wood. Comparison of the obtained results however is not selfevident since the methodologies used in these studies can differ considerably. There is for example a large variation in the actual set-up of the pretreatment method: different chemical loadings, temperatures or pressures are used. Additionally, different enzyme cocktails and enzyme loadings are utilized to hydrolyze the pretreated slurry, as well as a different hydrolysis time. As an example, Kim et al. (2009) digested liquid hot water pretreated poplar at enzyme loadings of 15 FPU g -1 cellulose for 120 h and 40 FPU g -1 cellulose for 72 h, and found sugar yields of 54% and 67% respectively [130]. Further, the interpretation of the results makes the meaning of the expression sugar yield ambiguous: sometimes only glucose accounted for instead of both xylose and glucose [131]. Additionally, authors are not always transparent in the way they calculate the yield. Sometimes it remains unclear whether a correction is made for the fact that the OH-group inserted between 2 glucose molecules during hydrolysis, does not originate from the wood but from water, and thus deflects the actual mass-based sugar yield [28]. Finally, the large variation that exist between different poplar species, gives rise to additional variation in results. These considerations give rise to the question which data for the pretreatment yield should be used when comparing the different technology options. Driven by these same considerations, the Biomass Refining Consortium for Applied Fundamentals and Innovation (CAFI) started a project in which different researchers compared different pretreatment techniques in a consistent way, initially focusing on corn stover, but in a second phase focusing on poplar as well. Since these researchers all used the same feedstock, the same enzyme cocktail at the same loading (15 FPU g -1 cellulose), the same analytical methods, the same pretreatment procedures and the same way of interpreting data, their data were less influenced by the interfering processes mentioned above, and, by consequence, unraveled more of the principal differences between

28 20 pretreatment technologies [28, 108]. That is the reason why the values reported in the summarizing article by Wyman et al. (2009), were used as a basis for the estimated pretreatment sugar yields in this study. The only exception was made for the long term oxidative lime case, whose sugar yield was taken from Sierra et al., 2009, and who also collaborated very closely to the CAFI project [119]. The reason for this decision is that capital cost used in this model account only for long term oxidative lime, while Wyman et al. (2009) investigated the yield of short term oxidative lime, having a different reactor design and different costs, accordingly [23, 28] Conclusion Several types of pretreatment methods are commercially available today and they all show particular qualities as well as drawbacks, which makes the pretreatment technology choice an interesting decision variable in a NLP optimization formulation. A large body on pretreatment yields data exists, but the large variation in laboratory practices and data interpretation makes it difficult to compare the sugar recovery data. However, a comparison of the effectiveness of pretreatment of poplar wood by the different technology options by a coordinated group of researchers, was presented by Wyman et al. (2009) and these data will be used as the data underlying the calculations made in this work. 2.6 SIMULTANEOUS SACCHARIFICATION AND CO-FERMENTATION Process description In SSCF, the saccharification reaction, which encloses the hydrolysis of cellulose and hemicellulose to their respective monomers, occurs simultaneously with the fermentation reaction. Originally, these processes were performed separately, in a so-called SHF or Separate Hydrolization and Fermentation. In this process however, the saccharification reaction suffers from feedback inhibition, which means that cellulases are inhibited by their product, glucose, which accumulates in the vessel [24]. However, if glucose is rapidly fermented into ethanol, as happens in SSCF, the inhibition mechanism is avoided, which leads to faster reaction rates, higher yields and higher ethanol titers [138]. On the other hand, SSCF suffers from the fact that temperature level is restricted by the tolerance of fermenting organism, which limits the rate of saccharification, since the latter is an enzymatic reaction benefitting from higher working temperatures [43]. In an attempt to unite the benefits of both technical configurations, Aden et al. (2002) decided to place a separate saccharification reactor train of 5 vessels, operating on higher temperatures (65 ), before the SSCF section, which is operated on lower temperatures (41 ). In this way, they profit from the enhanced saccharification rate at higher temperatures, without suffering from the inhibition effect of glucose since saccharification continues in the SSCF reactor train, albeit at a lower pace. In this way, the saccharification and cofermentation reaction takes only 3 days, instead of a residence time of 7 days in the design of Wooley et al. (1999), who did not implement such a system [44]. Additionally, the amount of fermentation vessels needed drops from 17 to 10, since lower residence time also means lower amount of reactors needed, assuming the throughput constant. Table 2-7 summarizes the reaction conditions in both the saccharification and SSCF reactors.

29 21 Table 2-7 Design parameters and operating conditions of the saccharification and SSCF reactor trains [43]. Saccharification SSCF Temperature 65 C 41 C Initial solids level 20% 20% Residence time 1,5 days 1,5 days Vessel volume 3596 m³ 3596 m³ Number of vessel 5 5 Number of trains 1 1 Cellulase loading 12 FPU g -1 cellulose 12 FPU g -1 cellulose Inoculum Level 10% Diammonium phosphate level 0,33 g L -1 Corn steep liquor level 0,25% To achieve an inoculum level of 10% at the beginning of the fermentation, S. cerevisiae needs to be grown in a seed train, which a series of fermentation vessels to gradually produce a higher amount of micro-organisms. This is done most efficiently by scaling the reactor dimensions up by a factor 10 each step. Cellulase enzymes that are used in the saccharification reaction can be bought externally, or produced on site. In the latter case, the cellulase producing Trichoderma reesei fungus, is grown on the hydrolyzate leaving the pretreatment stage [44]. Comparing different pretreatment methods on a consistent basis, would require cellulase yield data from Trichoderma reesei grown on the different types of hydrolyzate leaving the possible pretreatment technologies. To our knowledge, a study which consistently compares this aspect of the different pretreatment technologies is not available. Therefore, it was more convenient to assume that cellulose enzymes are bought from an external producer, rather than produced on site, as in Aden et al. (2002) [43]. Commercial available enzyme cocktails, consisting of a mixture of cellulases and hemicellulases are available in formulations of 60 FPU/ml [25, 26] Ethanol yield The ethanol yield (or productive yield) of a fermentation reaction is most commonly expressed as the percentage of the stoichiometric maximum amount of ethanol that can be obtained from the available sugar monomers. According to the chemical reactions displayed in equations 2-3 and 2-4, this amount equals 0,51 kg ethanol per kg sugars (xylose and glucose). 3! " # $! 5 ( "! $" + 5$ ( 2-3 * " ( $ * 2 ( "! $" + 2$ ( 2-4 A broad range of fermentation yields have been reported varying between 56,5 and 98,6% [108, ]. These and other studies show that the fermentation yield is affected by many parameters, such as type of biomass, the pretreatment, the type and initial concentration of the fermenting organism, the enzyme loading, reactor temperature, etc. The influence of pretreatment is of particular interest in this study and can be explained by the following mechanism. During pretreatment, by-products like

30 22 furfural, 5-hydroxymethylfurfural (HMF) and acetic acid are produced. The former two are results of degradation of monomeric pentoses and hexoses respectively, while the latter is liberated during hemicelluloses hydrolysis. These substances are known to act as inhibitors of the metabolism of yeast used in the ethanol fermentation reaction [142, 143]. The above considerations indicate that a comparative study of the different pretreatment technologies should include the possible effects of pretreatment on the fermentation yield. Preferably, this effect should be investigated in a consistent way, meaning that all other process parameters of the fermentation, should be equal. This research was conducted by Lu et al. in Moreover, since their work was realized within the framework of the CAFI research program, they used the same poplar species and the exact same pretreatment protocols as Wyman et al., 2009, which results in the fact that there is a clear concordance between the sugar yields from fermentation and the ethanol yields from fermentation [28, 108]. Both pretreatment sugar yields and fermentation ethanol yields are presented in Table 2-8. The sugar yields are reported as a percentage of the theoretically possible amount of sugars that can be obtained. Since hydrolysis involves the addition of on water molecule, this mass increase has to be accounted for as told above. In our case, since the glucan content of poplar equals 41,05 g per 100 g of wood, this means that the maximum recovery of glucose from glucan equals 46,12 g. Analogous, it was found that the maximum recovery of xylose from xylan equals 16,23 g, which together, results in a maximum of 62,36 g fermentable sugars per 100 g wood. Fermentations yields were obtained after 48h fermentation reactions on hydrolyzates of different pretreatment methods, using recombinant yeast strain, 424A(LNH-ST) of S. cerevisiae, able to coferment both glucose and xylose, and was developed by Dr. Nancy Ho [28, 108]. Table 2-8 Overview of hydrolysis and fermentation yields of the wild type poplar. a Sugar yield and Productive yield of fermentation according to Wyman et al., 2009; Lu et al., 2009 respectively [28, 108]. b Sugar yield and Productive yield of fermentation according to Sierra et al., 2009; Lu et al., 2009 respectively [108, 119]. Pretreatment Sugar yield Productive yield Ethanol yield +,-+./, ,-+./, h h.408 +,-+./, ,51,-+./, Dilute acid a 0,82 0,511 0,814 0,212 Hot water a 0,662 0,413 0,827 0,174 SO 2 steam 0,959 0,598 0,862 0,263 explosion a ARP a 0,528 0,329 0,986 0,166 AFEX a 0,545 0,340 0,886 0,154 Oxidative lime 0,76 0,474 1,000 0,242 (long term) b It is sometimes hypothesized that lowering the lignin content, by means genetic engineering might have a beneficial effect on biomass digestability during pretreatment [63]. However, while some

31 23 authors have experimentally found this effect, others did not [46, 62, 66]. Considering the present disagreement in scientific literature concerning this topic, it was supposed that sugar and fermentation yield data were constant, regardless the fact whether genetic engineered or wild type poplar was used in the process. Consequently, genetic engineering will be assumed to solely have a mass balance effect, and no impact on the principles of the process. Table 2-9 shows the calculated ethanol yields in case a genetic engineered feedstock was used. Table 2-9 Overview of hydrolyzation and fermentation yields of the genetic engineered poplar. a Sugar yield and Productive yield of fermentation according to Wyman et al., 2009; Lu et al., 2009 respectively [28, 108]. b Sugar yield and Productive yield of fermentation according to Sierra et al., 2009; Lu et al., 2009 respectively [108, 119]. Pretreatment Sugar yield Productive yield Ethanol yield +,-+./, ,-+./, h h.408 +,-+./, ,51,-+./, Dilute acid a 0,820 0,534 0,814 0,222 Hot water a 0,662 0,431 0,827 0,182 SO 2 steam 0,959 0,625 0,862 0,275 explosion a ARP a 0,528 0,344 0,986 0,173 AFEX a 0,545 0,355 0,886 0,160 Oxidative lime 0,760 0,495 1,000 0,252 (long term) b Ethanol titer A second important parameter which reflects the potential economic success of the fermentation process is the ethanol titer, referring to the ethanol concentration in the broth leaving fermentation. The importance of this parameter follows from the implications it has on the downstream processing sections. Product recovery based on distillation is a very energy intensive process of which the share can mount to 50% of the total energy consumption [144]. The energy requirements rely heavily on the ethanol titer, since the more dilute the product leaves the fermentation stage, the more energy it will take to purify the product up to 99% grade ethanol. Additionally, low ethanol titers will results in a higher water flow rate, which in turn will increase evaporation cost. So the higher the ethanol titer, the lower will be the resulting energy needs for subsequent distillation and evaporation [145]. Table 2-10 compares the ethanol titers of the economic analysis of a corn stover based LEP conducted by Eggeman & Elander (2005) and the ethanol titers as published by the technical report on poplar wood by Wyman et al., 2009 [23, 28]. It becomes clear that in most cases, the ethanol titers of the poplar based fermentation are lower than those based on corn stover. This suggests that energy costs of the downstream processing section, while be higher in the case of poplar. Table 2-10 Comparison of ethanol titers assumed in the economic analysis of a Eggeman & Elander, 2005 and obtained by the technical study of b Wyman et al., 2009 [23, 28]. Corn Stover a Poplar b

32 24 g l -1 g l -1 Dilute acid 50,7 26,4 Hot water 30,9 28,7 SO 2 steam explosion (-) 25,9 ARP 50,6 20,5 AFEX 46,2 35,5 Oxidative lime 31,6 39,9 2.7 PRODUCT RECOVERY The post-fermentation steps often are collectively referred to as downstream processing. This starts with a two-step distillation. The first column, also beer column, separates ethanol from the insoluble and dissolved solids as well as a part of the water. In the second or rectifying column, the ethanol is further dewatered until the mixture reaches its azeotropic composition. To end up eventually with 99,5 % pure ethanol, a vapor phase molecular sieve adsorption step is included. A water scrubber is used to extract ethanol from both the fermentation as well as the beer column vents, and the effluent containing ethanol and water is recycled to the beer column. The residues from the beer column containing dissolved and insoluble solids, are sent to a Pneumapress filter. The solid filter cake is directly sent to the combustor, and the nutrient containing liquid stream is partly recycled and partly sent to a triple effect evaporator. This provides relatively clean water that is sent to the process, and a concentrated syrup, which is mixed with the solid stream to be sent to the combustor. Alternatively, the dilute stream could also be sent to the wastewater treatment. Research has shown however, that evaporation is the more cost-effective choice that additionally gives a high purity water condensate, which is more appropriate for recycling to the process when compared to the effluent from wastewater treatment [43]. Wastewater from various sources in the process is treated in three steps, including a screening step to remove large particles, an anaerobic and an aerobic treatment, both removing organic matter from the water, producing respectively methane and biomass, which are both sent to the combustor. 2.8 HEAT AND POWER RECOVERY The side streams of the process are burned to produce heat and power. These side streams include the non-soluble solid fraction of the fermentation broth, the evaporator syrup, biogas from the anaerobic digestion and biomass from the aerobic wastewater treatment. The combined moisture content equals 52%, which is low enough for the combustion engine to retain a high combustion temperature and boiler efficiency consequently [146]. The advantages of this system include that the produced heat and electricity can (partially) cover the requirements of the plant, that the costs for solid waste disposal are reduced, and that any power generated in excess can be sold to the grid. The mixture is combusted in a Circulating Fluidized Bed Combustor [43].

33 25 3 MATERIALS AND METHODS 3.1 OPTIMIZATION PROBLEM FORMULATION The goal optimization problem was to choose the optimal technology at the optimal scale. The calculations were based on maximizing the net present value (NPV) of the project, which equals the sum of the discounted cashflows over the project s lifetime. It was assumed that capital investments were done at year 0, which is one year before the actual start of the operation of the LEP in year 1. Mathematically the calculation of the NPV can be written as follows: =>? / (1 + 6) B C D Where r i and c i equal the revenues and the costs at year i respectively, d equals the discount rate, assumed 7%, C invest equals the capital investment at year 0 and n equals the project lifetime of 20 years. The yearly cost c i break down in 4 different components being the biomass delivered costs c i,f, the variable operating costs c i,voc, the fixed operating costs c i,foc and the cost of capital c i,cc, resulting in: 3-1 =,E +,C +,E +, 3-2 The yearly biomass delivered cost c i,f can be calculated from the operational capacity S oper, the farmgate price c farm and the transport cost of the biomass by combining formulas 2-1 and 2-2, which results in:,e = E + 2 I F 1 H H L K 1000 K 3-3 G J With C h being the hauling cost, m the wood moisture content, f w the road tortuosity, f a the fraction of cropland available around the LEP, f lc the fraction of this land dedicated to the growing of poplar and Y biom the average biomass yield. For details about these parameters, the reader is referred to section The yearly variable operating costs c i,voc were calculated from the variable operating cost per Mg biomass processed VOC, and the operational capacity S oper, resulting in:,c =?$ 3-4 The yearly fixed operating costs c i,foc were calculated from the fixed operating cost per Mg biomass processed FOC, and the invested capacity S inv, resulting in:,e = C M$ 3-5

34 26 Finally, the yearly cost of capital was modelled by assuming a pay-back period P of the loan over 20 years, with an interest rate f of 7%, resulting in:, = N C (3 1) C O 3-6 > Revenues are to be made out of the sales of ethanol and electricity. The ethanol price p eth was assumed to be 0,976 $ kg -1 [147] and the electricity price p elec was assumed to be 0,12 $ kwh -1 [148]. The yearly revenues r i were calculated from the operational capacity S oper, the yields of ethanol, Y eth and electricity Y elec from the different processes, and the product prices. Mathematically, this is written as: / = F P F + P 3-7 Finally a scaling equation was needed to adjust the capital investment cost to any scale of investment, since only a capital investment was known at a fixed scale of 2000 Mg dry d -1. Therefore we used the following scaling equation, calculating the investment cost at a certain scale, when the investment C inv,ref at a particular reference scale S inv,ref is known. This methodology is very commonly used as an approximation for engineering purposes, and expresses the economy of scale related to the purchase of equipment [43, 44]: C = C,E N #,Q E O C,E CASE STUDY PARAMETERS Capital costs Capital costs of five LEP s based on dilute acid, hot water, AFEX, ARP and (long term) oxidative lime, were estimated by Eggeman & Elander (2005) [23]. This was realized by building models in which the respective pretreatment systems were inserted into a 2000 Mg d -1 processing LEP, designed identically to the work of Aden et al. (2002) [43]. Since the only difference with this case being that instead of poplar, corn stover was used as feedstock, these capital cost estimations were assumed to be representative to this case. This methodology can however only be justified, if an answer is given to the question as to whether capital costs estimates are interchangeable if only the type of feedstock differs. Several studies comparing the economic consequences of the use of different types of feedstock, indicate that capital investment costs dependency on the type of feedstock processed, is limited. Previous research has shown that project investment costs for a LEP s processing 2000 Mg d -1 of corn stover and poplar, equaled to 222 and 223 MM $, respectively [85]. Others compared capital investment costs for facilities processing straw eucalyptus, switchgrass and poplar and found that the values varied between 0,26 and 0,29 $ l -1 ethanol, compared to a total production cost of 0,76 $ L -1 [16]. These values indicate that capital cost are only limitedly dependent on the type of feedstock, and that it is by consequence an acceptable approximation to use the capital investment of values

35 27 calculated by Eggeman & Elander (2005). An overview of the investment costs is given in Table 3-1. Since SO 2 steam explosion technology was not included in the study of Eggeman & Elander, the capital cost estimate for this technology was taken from Kumar & Murthy (2011) [26]. However, the process they describe is uncatalyzed steam explosion, in which has no need for expensive equipment because of the non-corosiveness of the pretreatment, vide supra. Therefore, it is likely that this value is an underestimation of the actual capital investment involved in the SO 2 steam explosion process. Table 3-1 Overview of the capital costs of individual pretreatment areas, as well as the cost of the full lignocellulosic ethanol plant processing 2000 MT biomass/day, expressed in MM$. a Eggeman & Elander, b Kumar et al., 2011 [23, 26]. Pretreatment Complete facility MM$ MM$ Dilute acid a Hot water a SO 2 steam explosion b ARP a AFEX a Oxidative lime a Fixed and variable operating costs Definitions and remarks Variable operating costs include the costs of chemicals and enzymes, electricity and the amount of heat, if any, that is required more than the amount produced inside the LEP. The latter depends on the energy balance of the plant. This calculation is described elaborately in chapters 4 and 6. Fixed operating costs account for the costs related to labour, maintenance, insurance and overhead. As an estimation of the raw materials cost and fixed costs involved in operating the LEP, we used data calculated by Eggeman & Elander (2005) and Kumar & Murthy (2011), based on corn stover and tall fescue as feedstock respectively [23, 26]. Some important considerations have to made, prior to the comparative use of these data. Firstly, although both papers are based on the same facility design based on Aden et al. (2002), they differ significantly in scale: the modelled facilities are processing 2000 and 704,5 Mg d -1 of dry biomass respectively. This raises the question to which extent working costs are dependent on the scale of the facility. If the economy of scale is big, these costs would obviously be limitedly comparable. However, research has shown that the variation in working costs between plants of different size was negligible compared to the variation in for example capital investment or feedstock cost [16]. Furthermore, the costs have to be a relevant representation of the process underlying the technical yields that are assumed in this work. In other words, the operating conditions, and most importantly, the chemical loading, in the pretreatment and fermentation assumed in the economic studies must be relevant to the ones used to obtain technical data, such as ethanol yield. Table 3-2 presents a comparison of the most important parameters of the pretreatment including temperature, residence time, pressure and chemical loading, as used/assumed by the papers delivering technical data and papers delivering economic data [23, 26, 28, 119]. Since chemical loadings

36 28 are very similar, it was assumed that raw materials cost could be taken from Eggeman & Elander (2005) [23]. Table 3-2 Comparison of pretreatment conditions used by different authors investigating the technical parameters and economic aspects of different pretreatment systems. a Eggeman & Elander, 2005; b Wyman et al., 2009; c Kumar & Murthy, 2011; d Sierra et al., 2010 [23, 26, 28, 119]. Feedstock Temperature Pressure Residence time Chemical loading C atm min kg substance kg -1 dry biomass Dilute acid Corn stover a ,04 kg H 2SO 4 Poplar b 190 1,1 0,048 kg H 2SO 4 Hot water Corn stover a SO 2 steam explosion Poplar b Tall fescue c Poplar b % S0 2 ARP Corn stover a ,1859 kg NH 3 Poplar b ,5 3,667 kg NH 3 AFEX Corn stover a kg NH 3 Poplar b kg NH 3 Oxidative lime Corn stover a weeks 0,105 kg Ca(OH) 2 Poplar d weeks 0,11 kg Ca(OH) Level of fixed and variable operating costs The level of fixed and variable operating expenses are provided in Table 3-3. The values for SO 2 Steam Explosion originated from Kumar et al. (2011) whose study was based on a steam explosion pretreatment design without SO 2 catalysis, so an extra cost 3,45 $ Mg -1 was added based on an SO 2 price of 0,23 $ kg -1 and a SO 2 load of 3% [28, 122]. The enzyme cost was calculated bottom-up assuming a loading of 15 FPU g -1 cellulose. The enzyme cocktail was supposed to be delivered in a broth containing 60 FPU ml -1 at a cost of 0,57 $ kg -1 of broth [26]. The resulting enzyme costs per L ethanol produced varied somewhat between the different pretreatment systems ranging from 0,17 $ L -1 in the case of SO 2 catalyzed steam explosion, to 0,26$ L - 1 in the case of ARP, as a result of a difference in ethanol yield. Fixed costs were available from Eggeman & Elander (2005) for all technologies except for SO 2 steam explosion. In this case, the value was estimated by taking 3,6% of the annual investment cost. Variable operating costs were considered constant in this economic model, meaning that possible economies of scale were not accounted for

37 29 [16]. The electricity demand given in Table 3-3 is the gross electricity demand of the plant. In most cases, this demand was be met by the power recovery section. If not, the costs of the addititional amount of electricity purchased, were included in the variable operating costs. Table 3-3 Overview of the estimated raw material costs, enzyme cost and fixed costs of the studied LEP. a Based on Eggeman & Elander (2005), b Based on Kumer & Murthy (2011) [23, 26]. Fixed operating costs Variable operating costs Electricity demand $ Mg -1 $ Mg -1 kwh Mg -1 Dilute acid a 12,01 88,42 168,22 Hot water a 12,32 75,35 162,21 SO 2 steam explosion b 10,03 93,34 124,17 ARP a 13,73 74,56 142,99 AFEX a 12,17 77,58 209,08 Oxidative lime a 8,05 78,18 173, Energy balance To calculate the amount of excess electricity the technologies could produce, an energy and mass balance was worked out for the process, balancing the heat produced in the boiler with the heat needed for running the process. The steam is extracted from the turbine at high pressure (HP, 1317 kpa), low pressure (LP, 448 kpa) and very low pressure (VLP, 170 kpa). These fractions are used as heating source for the pretreatment section, the preheating and distillation section, and the evaporation section respectively. In addition to the low pressure steam extracted from the turbine, the evaporation section is heated by recuperating the energy available from condensing the overhead vapor stream of the rectification column. A detailed report of the calculations underlying this energy balance, are provided in chapter 6. If the amount steam produced was greater than the steam demand, the excess electricity was calculated using: = RS TUT S TT V W X Where Q elec is the electricity yield in kwh Mg -1, Q produced and Q needed are the total energy produced and energy needed in kj Mg -1, and ε generator the turbogenerator efficiency of 30% [26]. From Y elec, the electricity needs of the plant, as displayed in Error! Reference source not found., were subtracted in order to obtain the net amount of electricity that was available to be sold to the grid.

38 30 4 RESULTS AND DISCUSSION 4.1 ENERGY REQUIREMENTS Preheating and distillation Table 4-1 presents the energy requirements involved in the distillation process. The energy needs of the beer column are much higher than those of the rectifying column since the mass flow in the former is much bigger. Preheating requirements are high which is due to the large temperature difference between the fermentation temperature, equaling 41 C and the distillation feed bubble point, which equals 118,2 C. As expected, it can be seen that the required reflux rate in the beer column, increases when the ethanol titer decrease. This relation is shown graphically in Figure 4-1. The total energy requirements however, do not show a clear trend when plotted versus the ethanol titer. This is because energy requirements are not only governed by the ethanol titer, but also by the amount of liquor that needs to by distilled. The latter variable is inversely related to the ethanol titer but directly proportional to the ethanol yield as well. This explains why for instance the SO 2 steam explosion process has higher distillation energy requirements, than for example the ARP process, although it has a higher ethanol titer. Energy use (MJ kg -1 ) or RR (-) 5,00 ARP 4,50 4,00 SE DA 3,50 HW 3,00 AFEX 2,50 OL 2,00 SE OL 1,50 DA ARP HW AFEX 1,00 0,50 0, Ethanol titer (g l -1 ) Beer column reflux ratio Energy use Figure 4-1 Graphical representation of the beer column reflux ratio (RR) and the distillation energy needs, as a function of the ethanol titer after fermentation. SE=SO 2 steam explosion, DA= Dilute acid, HW=Hot water and OL=Oxidative lime.

39 31 Table 4-1 Overview of the calculated energy use in the preheating and distillation section. Energy demands are expressed as per kj per unit of dry wood processed by the plant. Ethanol titer Ethanol yield RR beer Q preheat Q dist, beer Q dist, rect Q dist, total g/l kg/kg wood (-) kj/kg wood kj/kg wood kj/kg wood kj/kg wood Dilute acid 26,5 0,212 3, ,15 999,88 172, ,80 Hot water 29,4 0,174 2, ,05 819,73 141,83 961,56 SO 2 steam 25,9 0,263 3, , ,38 214, ,54 explosion ARP 20,5 0,166 4, ,69 781,03 134,87 915,90 AFEX 35,5 0,154 2, ,74 722,59 125,10 847,68 Oxidative lime 39,9 0,242 2, , ,79 196, , Evaporation Evaporation energy requirements are given in Table 4-2. The differences in evaporation energy requirements between the different technologies follow the same trend as those found in the distillation energy requirements. It can be seen that technologies with high ethanol titers such as oxidative lime have lower energy needs than technologies with a similar ethanol productivity but a lower ethanol titer, such as steam explosion. Again, attention must be paid to the fact that energy requirements, expressed as kj/kg wood processed, can be delusive since technologies that have a low ethanol yield, will have lower energy cost due to this disadvantageous feature. Table 4-2 Overview of the LEP's evaporation section energy needs. Q evap, gross depicts the total energy demand of the evaporation process. Q evap, net is the required amount of energy which remains after subtracting the amount of energy that is recuperated from the distillation section Q recup, dist, from Q evap, gross m in MC m out m in-m out Q evap, gross Q recup, dist Q evap, net kg /kg wood % kg /kg wood kg /kg wood kj/kg wood kj/kg wood kj/kg wood Dilute acid 8,22 93,47 1,34 6, , , ,52 Hot water 6,18 91,48 1,32 4, ,51 847, ,47 SO 2 steam 10,29 94,77 1,35 8, , , ,79 Explosion ARP 8,40 93,14 1,44 6, ,64 805, ,16 AFEX 4,66 87,90 1,41 3, ,93 747, ,84 Oxidative lime 6,18 91,99 1,24 4, , , ,85 In Table 4-3, energy requirements of the preheating and distillation section are compared to those of the evaporation section. Now, values are expressed as MJ/kg ethanol produced, which eliminates the effect of the variable productivity, and thus allows to examine the effect of the ethanol titer exclusively. Additionally, the technologies are sorted according to increasing ethanol titer. It can be

40 32 seen that at low ethanol titers, evaporation energy needs are higher than distillation energy needs. However, at increasing ethanol titers, the difference becomes smaller and eventually, distillation costs become higher, suggesting that the evaporation process benefits comparatively more from increasing the ethanol titer than the distillation process. This effect is illustrated in Figure 4-2. Table 4-3 Comparison of evaporation ( Q evap) and the combined distillation and preheating (Q dist) energy requirements. Ethanol titer Q evap Q dist Q evap-q dist g/l MJ/kg ethanol MJ/kg ethanol MJ/kg ethanol ARP 20,5 29,47 23,58 5,89 SO 2 steam 25,9 23,10 19,84 3,26 explosion Dilute acid 26,5 21,65 19,52 2,13 Hot water 29,4 17,81 18,15-0,35 AFEX 35,5 11,97 16,04-4,07 Oxidative lime 39,9 11,77 14,89-3,12 Energy use in MJ kg -1 ethanol produced 30,00 28,00 26,00 ARP 24,00 22,00 20,00 SE DA 18,00 HW 16,00 14,00 AFEX OL 12,00 10, Ethanol titer g/l Qevap Qdist Figure 4-2 Graphical representation of the influence of the ethanol titer (originating from the different technologies) on the energy needs of evaporation Q evap and preheating and distillation Q dist. SE=SO 2 steam explosion, DA= Dilute acid, HW=Hot water and OL=Oxidative lime Heat and power generation The total energy available in the summed by-product streams, as well as the contribution of each of these streams and the energy which is finally available in the form of process steam, are displayed in Table 4-4. The solid fraction, containing lignin, unhydrolyzed (hemi)cellulose and yeast, is

41 33 contributing to over 81% of the recuperated energy, mainly due to the lignin residue which alone accounts already for around 50% of the total energy recuperated. The other fractions account for around 10%, 8% and 0,4%, respectively for the solutes, methane and WWT sludge fractions. The differences in final energy recuperation are mainly due to the differences in hydrolysis and fermentation yield. High yielding technologies have lower (hemi-)cellulose components in the solid fraction and derived components in the liquid fraction, resulting in less energy recuperated via combustion. Steam explosion, which is the highest yielding technology available in this study, recuperates only 291 kj/kg wood processed from unhydrolyzed (hemi)cellulose, while the same fraction in a low yielding technology like ARP generates 3234 kj/kg wood processed, which results in a clear difference in the amount of energy recuperated from the solid fraction. Table 4-4 Overview of the total energy recuperated from the combustion of the different by-product streams, and the contribution of these different components. Additionally, the energy captured in the form of process steam is also shown. Solid fraction Liquid fraction Methane WWT sludge Total energy potential Steam energy kj/kg wood kj/kg wood kj/kg wood kj/kg wood kj/kg wood kj/kg wood Dilute acid 9134, ,78 862,42 44, , ,10 Hot water 9847, ,37 862,42 44, , ,69 SO 2 steam explosion 8532, ,49 862,42 44, , ,52 ARP 10828, ,85 862,42 44, , ,44 AFEX 10530, ,95 862,42 44, , ,87 Oxidative lime 9203, ,85 862,42 44, , , Energy balance In Table 4-5, the final energy balance is presented. With exception of the SO 2steam explosion case, all LEP technologies are capable of meeting their process steam energy requirements by combusting the residual products. The SO 2 steam explosion case however, combines a large ethanol yield with a low titer, resulting in a large but dilute product stream making the downstream processing costs significantly higher than those of the other technologies. Since in this case all of the produced steam is used in the process, no steam is sent to the generator and excess electricity equals zero. Therefore, an utility cost of 29,91 $ Mg -1 wood reflects the sum of the amount of natural gas purchased to meet the steam demand and the amount of electricity purchased to meet the LEP s electricity demand. In the dilute acid case things are somewhat different. Although the energy balance is positive, electricity production, which equals 30 kwh Mg -1 wood, is not high enough to be able to meet the LEP s electricity needs, equaling 168 kwh Mg -1 wood. By consequence, excess electricity equals zero and some external electricity is purchased, expressed by an utility cost of 16,5 $ Mg -1 wood. In all other cases, some excess electricity production was realized and utility costs equaled zero.

42 34 Table 4-5 Overview of the final energy balance and electricity production of the LEP technologies. Energy needed Energy available Balance Excess electricity Utilities cost kj/kg wood kj/kg kj/kg wood kwh/ton wood $/ton wood wood Dilute acid 9025, ,10 364,09 0,00 16,55 Hot water 6926, , ,37 111,48 0,00 SO 2 steam 11503, , ,24 0,00 29,91 Explosion ARP 9002, , ,71 11,40 0,00 AFEX 4301, , ,61 330,14 0,00 Oxidative lime 6445, , ,21 85,49 0, OPTIMIZATION MODEL RESULTS Base case and genetic engineered scenario Table 4-6 presents the optimization results of the base case scenario and the scenario assuming a genetic engineered feedstock. Of all pretreatment alternatives, only oxidative lime and SO 2 steam explosion are profitable technology options. The optimal technology in both scenario s is, oxidative lime, which is not surprising since the oxidative lime technology combines a relative high ethanol yield with relative low working costs, vide Table 2-8 and Table 3-3. The base case generates an NPV of 703 MM $, equivalent to an IRR of 12,1%. Genetic engineering is capable of increasing these values to MM $ and 17%. The optimal capacities of the LEP equal and Mg d -1 for the two scenario s respectively. These capacities result in a biomass collection area having a radius of 207 and 269 km. Compared to other theoretical studies, these capacities are also comparatively large. LP programming model including LEP capacity as a decision variable, have reported optimal scales in the range of Mg d -1 [14, 17, 149]. However, these studies have placed upper bounds on the scale of operation, by a priori defining discrete levels of operational scale [14, 17], or by limiting the harvestable area around the LEP [149]. In an optimization study excluding any form of upper bound on capacity, the optimal capacity of single LEP was determined at Mg d -1, which is in the same order of magnitude as the results found in this work [18]. These results suggest that the economy of scale related to the ethanol production at plant level, outweighs the diseconomy of scale related to the biomass delivered cost. The capacities of the pioneer LEP s that are operated or planned today equal 750 Mg d -1 at the most, as described in chapter 2.1. It may be clear that the theoretical results found here, are of a different

43 35 order of magnitude. This raises the obvious question why the industry currently prefers to invest in smaller scaled projects, while from a theoretical point of view, there is no reason why the operational capacity shouldn t be planned bigger. This observation is related to the fact that the practical realization of projects of this scale is questionable under the current economic environment. It is unlikely for example, that enough funding can be raised to meet the high investment costs of around 1,4 billion $, given the fact that the technology is not yet proven at this scale. However, similarly large investment costs are not uncommon in the fuel production sector, such as the investment costs related to the traditional petroleum refineries [150, 151]. On other important factor is the possibility that social acceptance will hamper the implementation of an LEP of such a scale. Table 4-6 Overview of the optimal configurations of the LEP under the base case scenario and the genetic engineered scenario. Scenario Technology Capacity Collection Investment NPV IRR area radius cost Mg d -1 km MM $ MM $ % Base case scenario Genetic engineered scenario Oxidative lime Oxidative ime , The above considerations in mind, it was chosen to repeat the optimization procedure defining an upper limit to the invested capacity equal to 6000 Mg d -1, which is conform to previous LEP capacity estimations [43, 44]. The results of this calculation are shown in Table 4-7. Again oxidative lime and SO 2 steam explosion are the only profitable technology options, the former being the optimal technology choice, and again, genetic engineering can increases both the NPV and the IRR. A breakdown of the annual operation costs of the optimal design of the LEP is provided in Figure 4-3. Table 4-7 Overview of the optimal configurations of the LEP under the base case scenario and the genetic engineered scenario, when the LEP capacity was bounded to 6000 Mg d -1. Case Technology Capacity Collection Investment NPV IRR area radius cost Mg d -1 km MM $ MM $ % Base case scenario Genetic engineered scenario Oxidative lime Oxidative lime ,

44 36 3,59% 34,86% 45,57% Biomass farmgate cost Biomass transport cost Capital Cost Variable operating costs Fixed operating costs 9,84% 6,14% Figure 4-3 Breakdown of the annual costs of an oxidative lime based LEP processing 6000 Mg d -1 under the base case scenario. The annual capital costs were accounted for by multiplying the total investment cost with an annuity factor of 0,08925, which corresponds to a payment over 20 years at an interest rate of 7% Sensitivity analysis The exact value of some parameters in the model is subject to uncertainty. This uncertainty can have biological reasons (e.g. biomass yield variety), economic reasons (e.g. market price volatility), or can be due to the effects of scaling-up which are unknown since experience with the technology on large scale is limited (e.g. uncertainty in investment costs). A sensitivity analysis, in which the optimization is repeated while varying one specific parameter, ceteris paribus, can be helpful to understand the system under varying conditions Ethanol price Figure 4-4 presents the change in the NPV and the IRR of both oxidative lime and SO 2 steam explosion based LEP s under varying ethanol sale prices. This figure shows that both processes are very sensitive to the varying ethanol price. When the ethanol price is lowered to 0,936 $ kg -1, the NPV becomes smaller than 0, and equivalently, the IRR becomes lower than 7%. This means that when the ethanol price of the base case scenario is lowered with only 0,04 $ kg -1, the LEP would already be no longer profitable, regardless the choice of technology, or in other words, the minimal ethanol selling price (MESP) equals 0,936 $ kg -1. This observation has important consequences for project planning, considering the high variability in the market price of ethanol [152]. When on the other hand, ethanol prices rise, the NPV and the IRR increase linearly, since equation 3-7 expresses the linear relationship between revenues and product price. Interestingly, it can be seen that when the ethanol sale price rises above 1,096 $ kg -1, the NPV of an oxidative lime based design becomes lower than that of an SO 2 steam explosion design, indicating that from this point, the SO 2 steam explosion technology would be the optimal technology choice. Moreover, if the IRR would be used as a decision parameter rather than

45 37 the NPV, this effect takes place already at en ethanol sale price of around 0,996 $ kg -1, which is only 0,02 $ kg -1 above the base case assumption. These results show that the economic performances of these technologies are very similar. The fact that SO 2 steam explosion becomes more profitable at higher ethanol prices, is not unexpected. Table 2-8 and Table 3-3 show that SO 2 steam explosion has the higher ethanol yield, but suffers from higher working costs as well, which is the reason that oxidative lime was the optimal choice in the base case scenario. If the ethanol sale price is raised however, the revenues of the LEP will rise accordingly. Since the ethanol yield of steam explosion is higher than that of oxidative lime, an LEP based on SO 2 steam explosion will have a comparatively higher increase in revenues, as a consequence of an increased ethanol unit price, compared to an LEP based on steam explosion. Considered that cost functions remain unaltered, the exact same tendency will be expressed in the respective NPV curves. The fact that the change in optimal technology is noticed faster in the IRR curve than on the NPV curve, can be explained as follows. The IRR measures the amount of profit proportional to the size of the investment. Since the capital costs of the SO 2 steam explosion technology are lower than those of the oxidative lime technology, vide Table 3-1, and since it s profit expressed in absolute terms is already rising faster than that of oxidative lime under increasing ethanol sale prices, it becomes clear that the difference in the rate proportional profit increase, will be even bigger. NPV (MM $) IRR (%) 0 7 0,9 0,95 1 1,05 1,1 1,15 1, Ethanol price ($/kg) NPV Steam Explosion NPV Oxidative Lime IRR Steam explosion IRR Oxidative Lime Figure 4-4 Sensitivity of the NPV and the IRR of an LEP processing 6000 Mg d -1 based on both the oxidative lime and the SO 2 steam explosion technologies, towards the ethanol sale price Cropland availability It was hypothesized that the SO 2 steam explosion technology, having the higher ethanol yield and by consequence making more efficient use of the biomass, could become the optimal technology when biomass prices were increased. In Figure 4-5, the NPV s of SO 2 steam explosion and oxidative lime are investigated under decreasing values for cropland availability. The parameter expresses the fraction of

46 38 the land available around the LEP that is cropland, and is consequently a measure for biomass transport costs. Additionally, the optimal scale, without the upper limit of 6000 Mg d -1, is also shown. Figure 4-5 shows that the process remains profitable even when the cropland availability drops from 0,75 to 0,26, indicating that the process profitability is much less sensitive too decreasing cropland availability than to decreasing ethanol sale price. Moreover, oxidative lime remains the optimal technology, based on the value of the NPV. Finally, the optimal capacity varies largely, resulting in a value of 5207 Mg d -1 at a cropland availability of 0,26, compared to the value of Mg d -1, at the base vase value of 0,75. NPV (MM $) , , , , ,00 Capacity (Mg d -1 ) ,00 0 0,2 0,3 0,4 0,5 0,6 0,7 0, ,00 Cropland Availability (-) NPV Oxidative Lime NPV Steam Explosion Capacity Oxidative Lime Figure 4-5 Sensitivity of the NPV and the capacity of an SO 2 steam explosion and an oxidative lime based LEP, towards cropland availability Biomass yield Since the biomass yield is inherently an unpredictable parameter, the profitability of the process was investigated when the biomass yield fluctuated between 70% and 130% of the base case value. Figure 4-6 illustrates that even at the lowest biomass yield of 6,3 Mg ha -1 yr -1, the NPV remains positive (equal to 162 $), with an associated value of the IRR of 10,3%. Additionally, the optimal scale is not influenced by the biomass yield and remained equal to the upper limit, set at 6000 Mg d -1.

47 39 NPV (MM $) IRR (%) Percentage of base case biomass yield (%) NPV Oxidative Lime IRR Oxidative Lime Figure 4-6 Sensitivity of the NPV and IRR of an LEP processing 6000 Mg d -1 based on oxidative lime, towards the biomass yield Capital costs and working costs A sensitivity analysis was executed by varying the capital and working cost in a range of 85% to 115% of the base case scenario estimation. Figure 4-7 shows that the process becomes unprofitable when the capital and working cost increase to over 112% of the base case scenario estimation. When costs are equal or higher than the base case estimation, oxidative lime is the optimal choice of technology. However, when capital and working cost drop under 87%, the optimal pretreatment technology becomes SO 2 steam explosion. This effect that can be explained by the fact that in the base case, SO 2 steam explosion suffers from high working costs, vide Table 3-3, as a result of which this technology benefits in absolute numbers more from a decrease in percentage in these costs. The revenues remaining equal, meaning that the NPV will increase more rapidly in the case of SO 2 steam explosion, and will consequently become greater eventually. The fact that this switch in optimal technology happens at a lower decrease in costs if it is based on the IRR (at 98% of the original costs), is explained by the fact that this decision instrument expresses profit relative to the invested capital, the latter being lower in the case of SO 2 steam explosion.

48 NPV (MM $) IRR (%) Percent of base case capital and working costs (%) 6 NPV Oxidative Lime NPV Steam Explosion IRR Steam Explosion IRR Oxidative Lime Figure 4-7 Sensitivity of the NPV and the IRR of an LEP processing 6000 Mg d -1 based on both the oxidative lime and the SO 2 steam explosion technologies, towards the estimated capital investment and variable operating costs.

49 41 5 CONCLUSIONS AND FUTURE RESEARCH The outline of this work was to explore whether a poplar-to-ethanol production chain could be profitable, and to determine which decisions concerning the LEP s capacity and pretreatment technology could maximize the project s profit. Results have shown that under the assumed base case, the process could indeed be profitable, generating an IRR of around 12%. Additionally, it was found that the beneficial mass balance effect of genetic engineering of the feedstock, could increase the profitability to an IRR of 16%. A breakdown of the annual cost of the base case scenario showed that biomass farmgate price accounted for 45,5% of the costs, while biomass transport accounted for only 6%. On processing level, 35% of the cost could be traced back to variable operating costs, of which enzyme cost covered the major part, approximately 67%. Sensitivity analysis showed that the profitability was very sensitive to variations in ethanol price. Reducing the ethanol price with only 0,04 $ kg -1, resulted in an IRR lower than 7%, indicating that the process was no longer profitable. It was shown that amongst the current pretreatment technologies, oxidative lime and SO 2 steam explosion were the only technologies capable of completing the process in a profitable way. Under the base case scenario, oxidative lime was chosen as the optimal technology. Sensitivity analysis showed however that slight changes in ethanol price and capital and working costs estimates, could change the balance in favor of SO 2 steam explosion. Most notably, it was shown that this change in optimal technology happened when the ethanol price was increased with only 0,02 $ kg -1, an observation in line with the knowledge of SO 2 steam explosion having the higher working cost, but at the mean time also the higher ethanol yield of the two. The optimal scale for an LEP under the base case scenario was determined to be around Mg d - 1. This results is very big compared to the current standards. Although suffering from practical realization issues in the near future, this results nevertheless has theoretical value. It indicates that that the economy of scale related to the ethanol production capacity, outweighs the diseconomy of scale related to the biomass delivered cost. This means that, at least from a theoretical, economic point of view, there is no reason why an LEP could not be operated at scales comparable to those currently used in the traditional petroleum refining activities. Finally, some limitations of this research project are listed, giving rise to possible future research. Firstly, it is mentioned that most the technical data, covering for example the pretreatment sugar yields in the model, were gathered on laboratory scale. It is acknowledged that the relevance of these data for large scale operations is limited. However, since large scale comparison of different technologies is not conducted yet, there was no other option than to use laboratory scale data as indications. Secondly, it would be an obvious and interesting extension to the current model, to include technology choices at each node of the process, rather that only at the pretreatment node. Again, this methodology would require additional data, since the processes are interdependent, meaning that the yield at each node depends on the choices made at previous ones. Finally, it is mentioned that the

50 42 current capacity optimization formulation was principally a choice between a larger or a smaller centralized plant. The conclusion that LEP s should be built on relatively large scale, is to be understood within this narrow conception of capacity. An extension of this concept could be for example that the implementation of several smaller and decentralized LEP s was allowed, as an alternative to one centralized LEP.

51 43 6 APPENDIX: ENERGY BALANCE CALCULATION ALGORHYTHMS 6.1 INTRODUCTION This chapter covers the methodology that was used to estimate the energy requirements for the LEP. If possible, energy requirements reported by Eggeman & Elander (2005) were used, but in most cases, the assumed conditions were different and energy requirements had to calculated de novo. This was the case for all the process units of the downstream processing, which is caused by the fact that the ethanol titers were too divergent, vide chapter PRETREATMENT AND FERMENTATION Error! Reference source not found.2 provided a comparison between the pretreatment conditions used in Eggeman & Elander (2005) and Wyman et al. (2009) [23, 28]. Since the operating parameters that could influence the energy requirements, like temperature and residence time, were very similar, it was assumed that the energy requirements resulting from these conditions, as budgeted by Eggeman & Elander (2005) were representative to this LEP as well. The only case in which the operating conditions were divergent, was the AFEX case. In AFEX, the applied reaction temperature, and residence time are higher in the technical data gathered on poplar, then assumed in the economic estimations on corn stover. These working costs are thus not a proper reflection of the real working cost associated with the AFEX pretreatment. Unfortunately, in other technical papers similarly harsh operating conditions are applied when AFEX is used for pretreating hardwood, so no ethanol yield data under these softer reaction conditions could be found [135]. Since it was not within the scope of this work to make a mass and energy balances on other than downstream processing process units, it is acknowledged that pretreatment energy requirements of the AFEX technology are poorly budgeted. If the results of the NLP model would favor the AFEX as technology choice, this calculation should be done, in order to check if this result would be maintained under higher operating costs. However, considering the fairly low ethanol yield of AFEX, this scenario was supposed to be unlikely, and thus this calculation was not performed in first instance. 6.3 DOWNSTREAM PROCESSING General remarks Energy requirements for downstream processing are mainly influenced by 3 major operations, namely preheating, distillation and evaporation. During the calculations, the broth was conceived of as a binary ethanol-water mixture. There are several reasons for this simplifying assumption. Firstly, since the solution consists of biological components such as yeast cell mass and extractives originating from the wood, the exact chemical composition of the broth is unknown. Moreover, most components which are known to be present are not more volatile than water or ethanol and, by consequence, do not play an important role in the evaporation and distillation processes. Dissolved components could however give rise to a boiling point rise which would in turn increase energy requirement. However, using the compositional characteristics which were known the boiling point rise, determined using the Clausius-

52 44 Clapeyron equation, equaled only 0,3 C [28]. Therefore it was assumed that acceptable energy balances could be obtained, reducing the problem to a binary mixture of water and ethanol, where convenient Preheating Description and assumptions Before the fermentation broth is sent to the distillation column, it is first preheated in a separate heath exchange unit [43]. It is assumed that the feed enters at 41 C, which equals the temperature at which the fermentation process is operated, and leaves at 118,2 C, which is the boiling point of the broth at 2 atm, since this is the pressure inside the distillation column. This value was derived from binary system equilibrium data on the web [153]. The heat capacity of the feed was calculated by adding the average excess heat capacity of ethanol in water to the average heat capacity of water within the appropriate temperature interval [154, 155]. Following calculation algorhythm was used to calculate the heat requirements for preheating: the magnitude of the feed stream F, in moles kg -1 processed wood, was derived from the ethanol yield Y in g ethanol kg -1 processed wood, and the ethanol titer T in g l -1, by the following formula: M = Z [ \\ \\ F With ρ water the density of water in g l -1 and MM water and MM ethanol being the molar masses of water and ethanol respectively. Next, the heat requirements Q preheat in kj kg -1 wood were calculated according to the following formula: S F = M ([ U [ ) 6-2 With T in and T out respectively the incoming and outgoing temperature of the stillage in K, and C p in J mol -1 K Distillation General methodology As starting assumptions underlying the calculations, the post-fermentation streams corresponding to the different pretreatments, were, although different, distilled in the same column with the same diameter and height, and consequently, the same capital costs. The design of this tower is described by the technical report by Aden et al. (2002) and budgeted by Eggeman & Elander (2005) [23, 43]. Having ingoing streams with different ethanol concentrations, and outgoing concentrations that are fixed, operating costs should eventually reflect this difference in degree of separation, if capital costs are assumed equal. After all, distillation column design is essentially based on a trade-off between variable working costs, reflected in the reflux rate, and variable capital costs, reflected in the number of trays.

53 45 The reflux ratio is a measure of the amount of overhead vapor which is condensed and returned to the column at the top tray as a liquid stream. If the desired separation, expressed by the difference between in-and outgoing concentrations increases, the reflux ratio will rise accordingly, assuming a constant number of trays. A larger reflux ratio will in turn result in bigger flowrates inside the column and eventually a larger duty on the reboiler located on the bottom of the tower, where the liquid stream is to be (re)vaporized [156, 157]. This methodology has the advantage that capital cost data, as budgeted by Eggeman & Elander (2005) can keep their relevance to this case study, because the consequences of different entrance concentrations in the beer column, resulting from the different pretreatment technologies, will be reflected solely in a different reflux ratio, and consequently, a different operating energy demand, rather than different tower heights which would lead to different capital costs. When keeping the tower height constant and let the reflux ratio change, care must be taken however that the resulting working conditions remain optimal from an economic point of view. This economic optimum exists because above/below a certain threshold value for R, it would be more interesting to raise/decrease the amount of trays instead of increasing/decreasing the reflux rate further. As simple rule of thumb, the ratio between the reflux rate and the minimum reflux ratio, which equals the reflux ratio for the hypothetical case in which an infinitely large tower would be present, can be calculated. As long as this ratio remains between approximately 1,1 and 1,6, the design is optimal [158]. This rule of thumb was used to evaluate the results Assumptions and calculation algorhythm Distillation is realized in two distillation columns. Firstly, the fermentation broth is passed through the beer column with 32 actual trays at 48% efficiency removing the CO 2 from the broth and concentrating the ethanol-water mixture. The feed is introduced at tray 28. The overhead vapor with ethanol concentration x d, leaves as a mixture consisting of 87,7 wt% CO 2, 12 wt% ethanol and 4 wt% water. This stream is sent to a scrubber removing the CO 2, and is subsequently recycled to the beer column in order to minimize ethanol losses. The ethanol-water stream of interest is removed as a 39,4 wt% ethanol-water vapor-phase side stream at actual tray 8. This vapor stream is sent to a second distillation column, called rectifying column, where it is introduced at tray 44 from the top. This column has 69 trays at 57% efficiency, and has a double feed: the vapor-phase side stream from the beer column and a condensed stream, recycled from the subsequent molecular sieve unit operation, with a 72 wt% ethanol concentration introduced at tray 19 from the top. The overhead vapor from this second column leaves at a distillate concentration x d of 92,5 wt% ethanol. In both the columns, bottoms ethanol concentration x b was calculated from a total mass balance around the tower and were negligibly low. Operating pressure was set around 2 atm [43]. For both columns, a mass balance on in and out-going streams was made, based on the given compositions of the streams as well as the magnitude of the incoming stream. In this way, the magnitude of the overhead vapor, side-stream vapor and bottom liquid streams were calculated.

54 46 Next the, operating variables of each distillation column were determined by the classical graphical method, known as the McCab-Thiele Method. This method implies the assumption of constant molar overflow. This assumption is based on the fact that the difference between the latent heats of both components of the binary distillation is neglected, which results in the fact that liquid and vapor streams, passing through the trays of the tower, are constant as long as there is no extern mass flow present, such as a feed stream entering or a side stream leaving the tower [157]. The McCab-Thiele method starts by plotting the first diagonal (a 45 line through the origin), the equilibrium curve and the feed line on a single chart which has mole fraction ethanol in the liquid phase x on the x-axis and mole fraction vapor phase y on the y-axis. The equilibrium line gives the relation between mole fractions ethanol in the liquid phase and the vapor phase after vaporization at a given constant pressure, and were found in physico-chemical databases on the web [153]. The feed line is a function of feed conditions and can be written as: ] = ^ 1 ^_ _ E ^ with q (dimensionless) defined as: ^ = " C " E " C " 6-4 and H v the enthalpy of the feed at the dew point, H l the enthalpy of the feed at the boiling point, and H f the enthalpy of the feed at the entrance conditions [156]. Once this plot was constructed, slightly different calculation algorhythms were used for the beer and the rectifying column and they will be clarified next: Beer column 1. Using the McCab-Thiele plot, an operating line was drawn, passing through the point on the 45 -line defined by the overhead ethanol concentration on the one, and the pinch point, which is found by the intersection of the feed-line with the equilibrium curve on the other hand. This is shown in Figure 6-1. From the slope r of this straight line, the minimum reflux ratio R m was calculated using the following formula: / =

55 47 Figure 6-1 Presentation of the graphical determination of the minimum reflux ration R m, using the equilibrium line, operating line, feed composition F and distillate composition D [157]. 2. The minimum amount of trays, Nm, which equals the amount of trays of a hypothetical tower with infinitely large reflux ratio R, was found by counting the trays needed between x b and x d, using the first diagonal in Figure 6-1 as working line. 3. Since the actual number of trays N was given in the problem statement, and since Rm and Nm were determined graphically, it was possible to use the Erbar-Maddox correlation method to find the actual reflux ratio R. This empirical method relies on a plot on which values for R m/r on the y-axis are related to values of N m/n on the ordinate. The Erbar-Maddox correlation is given in Figure 6-2. Figure 6-2 Graphical representation of the Erbar-Maddox correlation [156].