A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY. The Faculty of Graduate Studies (Forestry)

Transcription

1 DESIGN AND SCHEDULING OF AGRICULTURAL BIOMASS SUPPLY CHAIN FOR A CELLULOSIC ETHANOL PLANT by Mahmood Ebadian B.Sc., Amirkabir University of Technology, 2003 M.Sc., University of Tehran, 2006 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate Studies (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2013 Mahmood Ebadian, 2013

2 Abstract The overall objective of this dissertation is to design and schedule a highly constrained agricultural biomass supply chain to meet the daily biomass demand of a commercial-sized cellulosic ethanol plant at the minimum delivery cost possible. To this end, an integrated simulation/optimization model is developed. The developed simulation model plans and schedules a flow of multi-biomass in the supply chain to meet the daily demand subject to the dynamics and stochasticity of the supply chain. The developed optimization model is used to meet the annual demand at the minimum delivery cost by prescribing the design of the supply chain. The design includes the selection of farms, the location of storage sites, and the assignment of the farms to the storage sites. It also determines the flow of biomass between farms, storage sites and the plant. The integration of the models is made via an iterative procedure. In this procedure, the design is used in the simulation model to manage the flow of biomass in the supply chain. On the other hand, the outputs of the simulation model are used as the inputs of the optimization model to adjust the design. The iterative procedure continues until no improvement can be made in the design. The integrated model is applied to a proposed ethanol plant in Prince Albert, Saskatchewan. The numbers of selected farms and the established storage sites in the integrated model are reduced by 6% and 10%, respectively, compared to the optimization model. Compared to the simulation model, the integrated model leads to the reduction in number of farms (15%), number of storage sites (57%), amount of purchased biomass from farmers (7%), harvested area (13%), supply radius (13%), number of maximum trucks (2 trucks), supply costs (6-12%), energy input (19%), and emitted CO 2 (12%). The results of the sensitivity analysis reveal that the most influential parameter on the design is biomass yield. In addition, bale bulk density and in-field and road transportation operations have the highest impacts on the total supply cost compared to other input parameters. ii

3 Preface This dissertation provides a clear explanation of the research problem, objectives to tackle this problem, critical review of literature, a case study, data gathering and evaluation, development and application of decision support models to the case study, and the analysis of the obtained results. The academic and industry experts in Canada and the US were consulted during the course of this study. All of the stages of this study were conducted by the author, Mahmood Ebadian under the supervision of his academic advisers Dr. Taraneh Sowlati and Dr. Shahab Sokhansanj. They advised him in the process of defining the research topic, data gathering, model development and validation and the manuscript publication. They are co-authors on all the published manuscripts. This dissertation includes two peer-reviewed manuscripts: A version of Chapter 4 was published. Ebadian, M., Sowlati, T., Sokhansanj, S., Stumborg, M., Townley-Smith, L., A new simulation model for multi-agricultural biomass logistics system in bioenergy production. Biosystems Engineering, 110(3), A version of Chapter 5 was published. Ebadian, M., Sowlati, T., Sokhansanj, S., Townley- Smith, L., Stumborg, M., Modeling and analyzing storage systems in agricultural biomass supply chain for cellulosic ethanol production. Applied Energy, 102, A version of Chapter 5 will be submitted. Development of an integrated tactical and operational planning model for supply of feedstock to a commercial-scale bioethanol plant A version of Chapter 2 will be submitted. Literature review on modeling and analyzing the agricultural biomass supply chain. iii

4 Table of Contents Abstract... ii Preface... iii List of Tables... vii List of Figures... viii Acknowledgements... x Dedication... xi Chapter 1. Introduction Motivation Problem description Research objectives and contributions Research objectives Research contributions Case study Organization of the dissertation Chapter 2. Literature review Synopsis Static modeling in the biomass supply chain Dynamic modeling in the biomass supply chain Optimization modeling in the biomass supply chain Combined modeling methods in the biomass supply chain Discussion and conclusions Chapter 3. Case study Synopsis Ethanol plant data Supply area data Farm data Crop data Weather data iv

5 3.7 Harvest schedule data Structure of the supply chain in the case study Equipment data Chapter 4. Development of a new simulation model Synopsis Framework of the developed simulation model Modules in the developed simulation model Assumptions Verification of the simulation model Outputs of the simulation model for the case study Flow of biomass in the supply chain Daily delivery scheduling Supply costs Energy input and the associated emitted CO Sensitivity analysis on farmer participation rate Validation of the simulation model Discussion and conclusions Chapter 5. Development of an integrated simulation /optimization model Synopsis Structure of the optimization model Integrated simulation/optimization model Comparison of the integrated model with the simulation and optimization models Number of farms and storage sites Supply costs Energy input and the associated emitted CO Sensitivity analysis Discussion and conclusions Chapter 6. Conclusions, strengths, limitations and future research Conclusions Strengths and limitations of the study Future research v

6 References Appendices Appendix A Appendix B Appendix C vi

7 List of Tables Table 1-1: Canadian ethanol plants (Canadian Renewable Fuels Association, 2010)... 3 Table 1-2: Crop type, number of farms and cultivated area for each crop in Prince Albert Table 2-1: Literature review on the static modeling of biomass supply chain Table 2-2: Literature review on the dynamic modeling of biomass supply chain Table 3-1: Characteristics of the proposed cellulosic ethanol plant Table 3-2: Farm size range in Saskatchewan (Saskatchewan Agriculture and Food, 2006) Table 3-3: Specifications of equipment pieces (Hess et al., 2009, Sokhansanj et al. (2008), Sokhansanj and Turhollow (2002)) Table 3-4: Specifications of equipment pieces (Hess et al., 2009, Sokhansanj et al. (2008), Sokhansanj and Turhollow (2002)) Table 3-5: Cost data of the machines and equipment (Turhollow and Sokhansanj, 2007) Table 4-1: Construction cost and average dry matter loss of different storage regime (Brummer et al., 2000) Table 4-2: Annual recovered biomass and dry matter loss (DML) in the supply chain Table 4-3: Distribution of contracted farms within 160-km radius Table 4-4: Number of required trucks for three different weeks Table 4-5: Average supply costs Table 4-6: Impact of farmer participation rate on the transportation system Table 4-7: Comparison of logistics costs ($ t -1 ) in different studies Table 4-8: Comparison of energy input (MJt -1 ) in different studies Table 4-9: Comparison of emitted CO 2 (kg t -1 ) in different studies Table 5-1: Input parameters of the optimization model estimated by the simulation model Table 5-2: Total delivery cost ($/t) in different iterations of the integrated model Table 5-3: Comparison of the supply area Table 5-4: Average number of created items and the computational time Table 5-5: Supply costs ($/t) Table 5-6: Energy input and emitted CO Table 5-7: Impact of farmer participation rate on the outputs of the integrated model vii

8 List of Figures Figure 1-1: World ethanol production, (Brown, 2010)... 1 Figure 1-2: Sequence of operations in agricultural biomass supply chain... 5 Figure 1-3: Distribution of produced wheat straw on Canadian Prairies (by permission from Agriculture and Agri-Food Canada)... 6 Figure 1-4: Sources of complexity and uncertainty in an agricultural biomass supply chain Figure 3-1: Location of the cellulosic ethanol plant, Prince Albert, Saskatchewan (by permission from Iogen Corp.) Figure 3-2: 160-km supply area considered for the proposed cellulosic ethanol plant Figure 3-3: Crop districts and rural municipalities in the province of Saskatchewan Figure 3-4: Distribution of wheat grain (spring wheat, winter wheat and durum) yield in three rural municipalities inside the 160-km supply radius Figure 3-5: Weekly harvest progress for different wheat crops (Saskatchewan Ministry of Agriculture, 2011) Figure 3-6: Monthly harvest percent for different wheat crops Figure 3-7: The modeled wheat straw supply chain Figure 4-1: Schematic of the simulated agricultural supply chain Figure 4-2: Structure of the simulation model Figure 4-3: Delay logic flowchart in the simulation model Figure 4-4: Stack configuration in a closed storage site according to the fire codes Figure 4-5: Contribution of logistics operations to the total DML in the supply chain Figure 4-6: Daily delivered biomass to the ethanol plant in a week Figure 4-7: Daily inventory of the at-plant storage site Figure 4-8: Number of daily truckloads delivered to the ethanol plant in a year Figure 4-9: Annual delivered truckloads to the ethanol plant from different distance ranges Figure 4-10: Daily biomass inventory level of a roadside storage with 540 t capacity Figure 4-11: Range of moisture content of biomass in the supply chain Figure 4-12: The components of the total supply cost Figure 4-13: Histogram of the total supply cost Figure 4-14: Energy input of different operations in the supply chain Figure 4-15: Emitted CO 2 in different operations in the supply chain Figure 4-16: Daily delivered biomass to the ethanol plant during the harvest season (25% participation rate) Figure 4-17: Number of annual truckloads delivered to the ethanol plant from different distance ranges (50% participation rate) Figure 5-1: Mathematical symbols representing the annual flow of biomass in supply chain Figure 5-2: General structure of the integrated simulation/optimization model Figure 5-3: Flowchart of the integrated simulation/optimization model Figure 5-4: Total delivery cost in different iterations of the integrated model viii

9 Figure 5-5: daily biomass delivery Figure 5-6: Average and maximum hauling distance Figure 5-7: Total supply cost ($/t) in the integrated model Figure 5-8: Sensitivity of the supply design to the input parameters Figure 5-9: Sensitivity of the total supply cost to the input parameters ix

10 Acknowledgements The preparation of this thesis would not have been possible without the support, hard work and endless efforts of a number of individuals and institutions. First and foremost, I offer my enduring gratitude to my supervisors, Dr. Taraneh Sowlati and Dr. Shahab Sokhansanj, for offering direction, technical advice and constructive criticism. Their encouragement, guidance and support, from concept to completion, enabled me to develop an understanding of the subject. I would also like to express my gratitude to another member of my supervisory committee, Dr. Paul McFarlane for his extremely helpful ideas and suggestions for improving the work presented in this dissertation. I am indeed grateful to Mr. Mark Stumborg and Dr. Lawrence Townley-Smith from Agricultural and Agri-Food Canada (AAFC) for providing relevant information and technical support for this thesis. I also extend my appreciation to Ms. Tamara Rounce from AAFC and Mr. Glenn Payne from the Saskatchewan Ministry of Agriculture for providing the data on the Prince Albert region. I express my deepest appreciation to all of the institutions that support me financially. This study is funded in part through the University of British Columbia s Graduate Fellowship, the Natural Sciences and Engineering Research Council of Canada, Agriculture and Agri-Food and the BC Ministry of Forest, Lands and Natural Resource Operations. The Oak Ridge National laboratory is acknowledged for providing data and helping in the validation of the developed models. Last but not least, I am thankful to all graduate students in the Industrial Engineering Research Group (IERG) and the Biomass and Bioenergy Research Group (BBRG) for their support and encouragement throughout my program. x

11 Dedication To my family for their unconditional and unceasing love and support. xi

12 Million gallons Chapter 1. Introduction 1.1 Motivation The production of biofuels has increased because they are an environmentally attractive and technologically feasible replacement for conventional fuels (Judd et al., 2010). The primary reasons for such a rapid growth are: (1) reductions in greenhouse gas (GHG) emissions, (2) production of a new income stream for farmers and economic growth in rural communities, and (3) enhancement of energy security by diversifying energy sources and utilizing local sources (Klein and LeRoy, 2007). Figure 1-1 indicates the fast development of the ethanol production market in the world over the last three decades. 25,000 20,000 15,000 10,000 5, Year Figure 1-1: World ethanol production, (Brown, 2010) Despite the rapid growth of the biofuel market on a global scale, the development of the biofuel industry in Canada has been far slower than in other countries. In 2005, ethanol production in Canada was less than 2% of that in the United States and less than several other countries such as South Africa and Ukraine (Klein and LeRoy, 2007). To boost this emerging industry, federal and provincial governments have put several measures in place including incentive programs, research assistance, and consumption mandates. The federal government in Canada has created the Renewable Fuels Strategy (RFS) to increase their share of biofuels in the 1

13 national transportation fuel basket. This strategy aligns with the federal commitment to reduce Canada s total GHG emissions to 83% of 2005 levels by The RFS mandates a blend of 5% ethanol in gasoline and 2% biodiesel in the distillate pool. In addition to the federal government, several provinces have developed mandates on the consumption of biofuels (Canadian Renewable Fuels Association, 2010). For instance, the provinces of British Columbia and Saskatchewan approved 5% and 7.5% ethanol-blended gasoline at gas stations, respectively. The production capacity of the Canadian ethanol plants in operation is over 1.7 billion liters (Canadian Renewable Fuels Association, 2010). In contrast, the total federal and provincial renewable fuels requirements will result in an ethanol demand of about 2 billion liters per year. Although imported ethanol can be used to meet this demand, most of the demand for ethanol under the federal and provincial requirements can be met with domestically produced ethanol (Canadian Renewable Fuels Association, 2010). This is due to the abundance of cellulosic materials in Canada including agricultural and forest residues which can be used as feedstock for cellulosic ethanol production. Canada has about 36.4 million hectares (Mha) of crop lands available for agricultural production. More than 85% of these lands are located in the Canadian Prairies including Saskatchewan, Alberta and Manitoba and a small portion in northeastern British Columbia (Sokhansanj et al., 2006a). After harvesting the grain as the primary agricultural product, tonnes of crop residues are left on the fields which could be utilized as renewable energy sources (Kumar and Sokhansanj, 2007). Sokhansanj et al., (2006b) estimated that there would be an annual average of 15 million tonnes (Mt) of straw available for industrial uses like ethanol production on the Prairies after consideration of soil conservation and livestock requirements. Despite of the apparent large quantity of crop residues, the list of operational and under construction ethanol plants in Table 1-1 shows that only one plant uses agricultural residues to produce cellulosic ethanol. This plant is a demonstration facility constructed by Iogen Corporation, a Canadian biotechnology firm specializing in cellulosic ethanol. Currently, there is no commercial-sized cellulosic ethanol plant in Canada that exploits agricultural residues as feedstock. 2

14 Table 1-1: Canadian ethanol plants (Canadian Renewable Fuels Association, 2010) Plant Feedstock Annual Capacity Amaizeingly Green Products L.P. Husky Energy Inc. Lloydminster Enerkem Alberta Biofuels Enerkem Inc. Sherbrooke Pilot Plant Enerkem Inc. Westbury Commercial GreenField Ethanol Inc. Chatham GreenField Ethanol Inc. Johnstown GreenField Ethanol Inc. Tiverton GreenField Ethanol Inc. Varennes (Million liters) Corn 58 Husky Energy Inc. Minnedosa Plant Feedstock Annual Capacity (Million liters) Wheat and Corn 130 Wheat 130 IGPC Ethanol Inc. Corn 162 Municipal Solid Waste Various types of feedstock 36 Iogen Corporation Wheat and barley straw Kawartha Ethanol Inc. Corn 80 Wood waste 5 NorAmera BioEnergy Corporation Corn 195 a North West Terminal Ltd. Corn 230 Permolex Terra Grain Fuels Inc. Wheat 150 a Volumes include industrial alcohol production International, L.P. Corn 27 Pound-Maker Agventures Ltd. Corn 155 Suncor St. Clair Ethanol Plant Wheat 25 Wheat 25 Wheat, wheat starch, corn, barley 2 42 Wheat 12 Corn 400 3

15 There are several obstacles restricting the development of this emerging industry. These obstacles encompass the inefficiencies associated with immature feedstock production practices, marketing and logistics systems, and conversion processes (Fales et al., 2007). These inefficiencies make the cellulosic ethanol industry suffer from the lack of the economies of scale (Klein and LeRoy, 2007). In other words, all aspects of the industry are new and inherently inefficient. Cost reductions in feedstock production, supply systems, and conversion processes will pave the way to establish large-sized cellulosic ethanol plants. Among the above-mentioned inefficiencies, the focus of this research is on the biomass supply chain. The dynamics and stochastic nature of the biomass supply chain make it an unreliable system to deliver large volumes of bulky cellulosic materials to the conversion facility at low costs throughout the year (Cundiff et al., 2009a). The supply system accounts for 35-65% of the total cellulosic ethanol production cost (Fales et al., 2007). In contrast, the feedstock supply costs associated with corn grain-based ethanol made up 8% of the total ethanol production cost in the reference year of 2008 (Hess et al., 2009). Therefore, to accelerate the commercialization of cellulosic ethanol in Canada, the agricultural biomass supply chain must be designed, planned and scheduled in a way that a secure supply of crop residues are delivered to the conversion facility at a minimum cost throughout its business life. 1.2 Problem description A typical agricultural biomass supply chain encompasses all of the operations from biomass producers (farmers in this study) to the gate of the ethanol conversion facility. A general illustration of an agricultural biomass supply chain is depicted in Figure 1-2. The first operations are the harvesting and collection of biomass in which biomass is removed from fields and transported to the nearby storage sites. These operations include cutting, in-field drying, collecting biomass, densifying and transporting it to storage. Biomass can be kept in roadside storage or satellite storage located between the farms and the ethanol plant. Handling and transporting operations include loading biomass onto the transportation vehicles and shipping it to the plant. 4

16 The next operation is receiving in which the arriving truckloads are unloaded at an at-plant storage site. Preprocessing is the last operation in the supply chain before the conversion facility. It may consist of one or more processes including size reduction, fractionation, sorting, and densification (Sokhansanj and Fenton, 2006). It is noteworthy that preprocessing can also take place earlier in the supply chain, at a location between the farmlands and the ethanol plant. For instance, roadside or satellite storage can be a depot in which preprocessing is performed in addition to storing biomass. Biomass producers Harvesting and collecting Storing Handling and transporting Receiving Preprocessing Conversion process Start Point End Point Figure 1-2: Sequence of operations in agricultural biomass supply chain Previous research indicates that in order to achieve economy of scale, large biorefineries capable of handling 5,000-10,000 tons of biomass per day are necessary (Carolan et al., 2007). Daily planning and scheduling of operations to deliver such large quantities of biomass from rural areas to commercial-sized biorefineries would be a cumbersome logistics task. In addition, the specific characteristic of agricultural practices in the Canadian Prairies such as crop rotation, and climatic, geographical and biological factors place additional constraints on the supply chain. For example, the large distribution of the produced residues, low and variable yields, short and variable harvest season, frequent crop rotations, and unfavorable and uncertain weather conditions would result in a highly constrained supply chain with a great level of uncertainty and complexity in the Canadian Prairies. Cereal crops, oilseeds and pulse crops dominate the seeded area in the Canadian Prairies (Sokhansanj et al., 2006a). These crops and their residues are widely distributed across the Prairies. Figure 1-3 illustrates the distribution of produced wheat straw as one of the primary cellulosic materials on the Canadian Prairies. In addition to the large distribution, Figure 1-3 shows the low yield of wheat straw ranging from 1 to 3.5 oven dry tonnes per ha. Wheat straw has also relatively lower bulk density compared to other major cellulosic materials such as corn 5

17 stover, switchgrass and miscanthus (Hess et al., 2009). Similar conditions exist for other crop residues produced in the region. Figure 1-3: Distribution of produced wheat straw on Canadian Prairies (by permission from Agriculture and Agri-Food Canada) These characteristics result in the need for a large supply area to meet the annual feedstock demand of a commercial-scale ethanol plant. A large supply area complicates the management of the supply chain since many farms would be required. In addition, low-bulk density of biomass causes the low utilization rate of storage area, handling and transportation equipment which causes the high costs of these operations. Most of the crops grown in the Canadian Prairies are annual crops, and thus there is only one harvest season in a year. Harvest season is a narrow window constrained by the weather conditions (Cundiff et al., 2009a). Local climate influences the start and the length of grain harvest season (Sokhansanj et al., 2006b). The wheat harvest season may begin early in August to early September and it may take from one month to three months in Saskatchewan (Saskatchewan Ministry of Agriculture, 2011). A short and variable harvest season for harvesting and collecting agricultural residues complicates resource management. For a commercial-sized ethanol plant, many harvesting and collecting machines must operate on a tight schedule to guarantee year-round availability of biomass for the conversion process. Since massive volumes of biomass must be processed during the harvest season, breakdown of machines is likely to occur. 6

18 It also complicates the storage management since large volumes of crop residues must be stored during this short period to secure the year-round delivery to the plant. Thus, crop residues may require being stored for several months. This could result in the significant dry matter losses (DML) of up to 25% in poor storage conditions (Lyschinski et al., 2002). The estimation of DML in storage is difficult as it depends on the weather conditions, moisture content, condition in which biomass is stored and also the duration of storage. In addition, a portion of biomass is lost by machines depending on the sensitivity of biomass to breakage and the efficiency of the machines to process biomass. Another issue related to the harvest season is the dependency of the biomass harvest schedule on the harvest window of grain. Grain is the primary product, and thus, harvest window is scheduled when grain reaches maturity and moisture is optimal (Hess et al., 2009). Farmers prefer to complete the harvest of a mature crop as quickly as possible in time for preparing the land for the following cropping season or to minimize the potential of work stoppage due to cold, humid, or freezing conditions. Thus, the commencement of harvest season and its duration may not be optimal to the harvest and collection of residues. Lack of optimality of the harvest window for agricultural residues could negatively impact the quality and quantity of harvested and collected biomass. To mitigate the negative impacts such as high moisture content, extra operations may be required in the supply chain. These extra operations, such as biomass drying, add up to the complexity of the system and also increase supply costs. Other dominant cellulosic materials such as switchgrass do not have such harvest-related problems. Switchgrass is a perennial grass species with a longer harvest season. For example, Switchgrass is harvested from June to February in the Southeastern US (Ravula, 2007). Thus, sufficient switchgrass must be only stored during three non-harvest months. One of the primary sources of uncertainty in the supply chain is the biomass yield. The net yield depends on the grain yield, straw to grain ratio, soil conservation rate and competitive markets such as livestock feeding and bedding (Sokhansanj et al., 2006a). Grain yield varies based on climate, water, soil type, pests and fertilizer application. In addition, straw to grain ratio varies with location, cultural practices, species, and grain varieties (Boyden, 2001). The amount of residues left on the field required for wind and water erosion control depends on soil texture, field slope and tillage practice. This amount could be variable from 30% to 75% of produced residues in the Prairies (Sokhansanj et al., 2006a). 7

19 Using agricultural residues for livestock feeding and bedding is another parameter that increases the uncertainty in the availability of biomass for ethanol production. Agricultural residues have been used for raising livestock for many years in the Canadian Prairies. Often, due to a higher return on investment, cellulosic ethanol plants have to use the remaining biomass after the consideration of livestock requirements. The net available crop residues on the Prairies after the deduction of livestock feeding has been studied by Sokhansanj et al. (2006b). They showed that the amount of annual cereal straw available on the Prairies for industrial use ranges between 2.3 Mt and 27.6 Mt with the average of 15 Mt. Frequent crop rotation in the Prairies also affects the availability of biomass in the long term. Farmers typically include several crops in rotation. The size of land under each kind of crop (wheat versus barley versus canola, etc.) depends upon a number of factors such as market price, yield expectation, and rotational concerns. As such, it is difficult to know with certainty how much of a crop a farmer may grow in each year. Thus, during the lifetime of the ethanol plant, it would be indefinite which farm will grow which crop and what portion of the land will be assigned to the cultivation of a crop. In addition, farmers may decide to leave the entire produced biomass on the field for soil conservation purposes in some years. Therefore, farmers may change their attitudes toward participating in the biomass supply during the business life of the ethanol plant making the biomass procurement a difficult task in the supply chain. The unfavorable and uncertain weather conditions such as low temperature, sporadic precipitation and frequent droughts in the Canadian Prairies impact the supply chain. The weather conditions affect the availability and performance of machines. The logistics operations, in particular field operations, may be slowed down or shut down due to the unfavorable weather conditions. Another impact of the climatic conditions is on the quantity and quality of biomass delivered to the ethanol plant. Harsh climatic conditions may intensify the dry matter loss and chemical breakdown of the biomass structure and composition. In addition to the specific characteristics of the Prairies, there are other factors that turn the supply chain of crop residues into a complex system. These factors include the different operational window of the logistics operations and also the business model of the supply chain. The conversion facility at the ethanol plant operates 24/7, while the operations upstream of the plant such as field operations and transportation usually have 12 to 14 working hours in a day. Moreover, the transportation system may operate 5/6 days a week. The different operational 8

20 window between logistics operations necessitates at-plant storage. Type, capacity and inventory of at-plant storage should be determined so that the sufficient biomass is delivered to the conversion facility during the off-shift hours of operations at the upstream of the plant. These storage-related decisions have great impacts on the quality, quantity and cost of delivered biomass to the plant. The next complexity of the supply chain is related to its business model. The on-time delivery of the right amount of low-cost biomass to the ethanol plant requires interaction and coordination among the supply actors involved in the supply chain. These actors encompass farmers, hauling contractors and the ethanol plant. Farmers are usually responsible for harvesting and collecting biomass and then transporting it to the storage locations. Thereafter, the stored biomass is hauled to the plant by hauling contractors. Finally, the ethanol plant receives the delivered biomass and converts it to the final product. Farmers and hauling contractors have incompatible preferences on the number and location of storage sites within the supply area as these would affect their total operating cost (Resop et al., 2011). Another differing view exists between farmers and the ethanol plant regarding the supply contracts. The ethanol plant prefers long-term contracts with farmers to secure the availability of biomass for the conversion facility during its economic life. In contrast, farmers are able to sell their products in a spot market and may have little interest in long-term contracts (Cundiff et al., 2009a). The length of contract could be a more serious issue for annual crops such as cereal straw. For a perennial grass such as switchgrass, the contract with switchgrass growers could be as long as years, equivalent to the average life of a stand of switchgrass before reseeding (Judd, 2011). Figure 1-4 illustrates the sources of the complexity and uncertainty in the agricultural biomass supply chain. It is noted that the varying parameters in the agricultural biomass supply chain such as yield, weather condition and harvest season are uncontrollable, thus, planning and scheduling of logistics operations becomes a challenge. In summary, the logistics challenge is how to provide a secure and steady daily feedstock delivery to a commercial-sized ethanol plant at the minimum cost with respect to the dynamics and stochastic nature of the supply chain and also the region-based constraints. 9

21 Grain yield Straw to grain ratio Variability in the availability of biomass for ethanol production Weather conditions Short and variable harvest season Soil conservation rate Competitive markets Stochastic parameters Agricultural biomass supply chain Other stochastic parameters such as machine breakdown and efficiency Crop rotation and annual crops Complex resource and storage management Different operational window of upstream and downstream operations Biomass characteristics Structural complexity Farmer participation rate Dry matter loss and microbial degradation Low-energy content Low-bulk density Distributed natural resource Dependency of biomass harvest schedule on the grain harvest window Incompatible preference between farmers and hauling contractors on the number and location of storage Incompatible preference between farmers and the ethanol plant on the length of supply contracts Figure 1-4: Sources of complexity and uncertainty in an agricultural biomass supply chain 10

22 A thorough review of the relevant literature reveals some studies have considered a static analysis of the supply chain to estimate the available feedstock for bioenergy production and the logistics costs (Clegg and Noble (1987); Allen et al. (1998); Noon and Daly (1996) and Graham et al. (1996)). The developed static models incorporated many assumptions and did not capture the dynamics of the supply chain. Dynamic models have been developed to estimate the amount of biomass delivered to the biorefinery plants and also the associated logistics costs subject to uncertainties such as delays in harvest due to weather, moisture and machine breakdown (Nilsson (1999a); Nilsson and Hansson (2001); Sokhansanj et al. (2006); Sokhansanj et al. (2008b)). However, these models were not able to find the optimal logistics solutions to minimize the delivery cost. Several studies have found the optimal solutions for selection of farms to contract, location and number of storage sites and also the location of the biorefinery plants to minimize the total supply cost (Ekşioğlu et al. (2009); Judd et al. (2010); Zhu et al. (2011); Zhu and Yao (2011) and Judd et al. (2012)). The developed optimization models are mainly at the strategic and tactical levels and thus, the stochastic nature and the part of the complexity and dynamics of the supply chain were neglected in the developed optimization models. Therefore, the optimal solutions do not guarantee the daily fulfillment of the feedstock demand. Finally, a combination of these modeling tools has been used to increase their capabilities to evaluate the biomass supply chain (Freppaz et al. (2004), Ravula (2007) and Berruto and Busato (2008)). However, the literature lacks an integrated modeling approach to explore and analyze a highly constrained agricultural biomass supply chain similar to the one in the Canadian Prairies at both tactical and operational planning levels. The tactical planning level concerns the design of the supply chain to meet the annual biomass demand at the minimum delivery cost. In the operational level, the logistics operations are scheduled on a daily basis to meet the daily demand. This calls for an integration of the optimization approach with the dynamic modeling of biomass flow. It is noted that part of the portfolio of the solutions to improve the performance of the supply chain is associated with the development of more advanced and flexible equipment and technologies. These developments are outside the boundaries of this study. Thus, no future equipment and technologies are included in this study. The focus of this study is on the integration of design and scheduling of the supply chain to meet the feedstock demand of a 11

23 commercial-scale cellulosic ethanol at the minimum cost possible. Note that, it is assumed that the location of the ethanol plant and its daily biomass demand is predetermined. It is also assumed that the delivered biomass meets conversion process quality specifications and thus biomass quality is not considered in modeling and analysis of the supply chain. 1.3 Research objectives and contributions Research objectives The aim of this research is to design and schedule a highly constrained agricultural biomass supply chain to meet the daily biomass demand of a commercial-sized cellulosic ethanol plant. The specific objectives of this study are as follows: 1. To develop a new simulation model that would seek out feedstock from a blend of available biomass in the area surrounding the cellulosic ethanol plant. The developed simulation model incorporates time-dependency, stochastic parameters and regional constraints in the scheduling of the logistics operations to meet the daily biomass demand. 2. To develop a new optimization model to determine the design of the supply system. The design includes the selection of farms, the location of storage sites and also the assignment of the selected farms to the storage sites. It also determines the flow of biomass between farms, storage sites and the plant. The prescribed design and flow ensures the annual fulfillment of the biomass demand at the minimum delivery cost. 3. To integrate the multi-biomass simulation model with the optimization model to ensure an uninterrupted flow of biomass to the ethanol plant at the minimum cost possible. The efficiency of the integrated model is evaluated by applying it to a real-life case study. A sensitivity analysis is conducted to measure the impact of changes in input parameters on the results of the integrated model Research contributions The contributions of this study consist of four parts. The first contribution is that this study provides a detailed source of input data for a commercial-scale bioenergy plant. Due to the 12

24 similarities in the biomass supply chain of different bioenergy products, the gathered data in this study can be used to model and analyze the biomass supply chain for other bioenergy products. This study can be used as a guide to design and schedule the supply chain for agricultural residues, mainly cereal straw which has received less attention compared to other types of cellulosic materials. This study considers both data and model details and provides meaningful information on a constrained supply chain in the Canadian Prairies. This information can be used by decision makers in practical cases. The next contribution is the modeling aspect of this study. Both the developed simulation and optimization models have new features compared to the ones in the literature. The simulation model provides a detailed understating and evaluation of the dynamic and stochastic biomass supply chain on a daily basis. Additionally, the optimization model finds the logistics solutions at the tactical level of the supply chain. The last contribution is on the integration of both developed simulation and optimization models. An iterative procedure is developed to make interaction between both models. The interaction results in feasible and consistent solutions at both tactical and operational levels. The effectiveness of the proposed integrated simulation/optimization model is clearly shown by applying it to a case study. 1.4 Case study A proposed cellulosic ethanol plant located in the Prince Albert region, in north central Saskatchewan was used as the case study in this research. The region is one of the richest agricultural areas in the province and there is an emphasis by the provincial and federal governments on developing the biofuel industry in the region. The total number of farms in the Prince Albert area is 9,647. These farms cover 1,005,572 ha of land. More than 20 different types of crops, such as wheat, oats, barley, dry field peas and canola, are grown in this region. Table 1-2 lists the crop type, the number of farms and the cultivated area in this region in the reference year of This information was provided by Agriculture and Agri-Food Canada (AAFC). 13

25 Table 1-2: Crop type, number of farms and cultivated area for each crop in Prince Albert Crop Type Number of farms Seeded area (ha) Crop Type Number of farms Seeded area Wheat 1 1, ,012 Alfalfa 1, ,272 Oats 1,315 82,916 Dry Field Peas ,247 Barley 1, ,561 Other field crops ,791 Mixed grains 106 6,330 Forage seed 127 8,583 Corn Potatoes Rye ,067 Mustard Seed 28 3,152 Canola 1, ,995 Canary Seed 31 1,616 Flaxseed ,050 Buckwheat 3 24 Other Tame Hay and Fodder crops 1 Includes spring, winter and durum wheat 2 Includes grain corn and corn for silage 3 Fall rye and spring rye 4 Includes solin, safflower, coriander and other spices, etc (ha) ,779 Triticale 35 1,627 In 2008, the Government of Saskatchewan, Iogen Corporation and Domtar Corporation reached an agreement to set the stage for the potential redevelopment of the Prince Albert pulp mill site as a cellulosic ethanol plant. The pulp mill has been closed since April It is located 12 km east of Prince Albert. The plant will be developed by Iogen. The production capacity of the plant is more than 70 million liters (ML) of cellulosic ethanol per year. The conversion facility would process 750 tonnes (t) per day of wheat straw. To procure this amount of wheat straw, Iogen has considered a 160-km supply radius (Iogen Corp., 2009). The details of the case study are provided in Chapter 3. In this study, first the considered supply area by Iogen will be modeled and analyzed using the developed simulation model. Thereafter, the integrated simulation/optimization model is applied to enhance the considered supply area in terms of cost-efficiency and demand fulfillment. Although the developed models are applied to a single specific case study, they can be adapted and applied to other regions, agricultural residues, and bioenergy products. 14

26 It is noted that since the considered agricultural biomass in this study is wheat straw, the term "biomass" refers to "wheat straw" in the rest of the dissertation. In addition, "the plant" refers to "the cellulosic ethanol plant". The term "tonne", or shortly, "t" is used in the dissertation referring to the weight of biomass at the acceptable moisture content for ethanol production (less than 20% w.b.). 1.5 Organization of the dissertation In addition to the introduction chapter, this dissertation includes one chapter on the literature review, one chapter on the case study, two chapters on the developed simulation and optimization models and one chapter of conclusions, limitations of the study, and finally suggestions for future research. The previous studies on the literature are discussed in Chapter 2. They are categorized and reviewed based on the decision-making tools used to find the solutions for different strategic, tactical and operational problems in the biomass supply chain. The strengths and shortcomings of the relevant literature are highlighted in this chapter and an integrated approach is proposed to tackle the shortcomings. The integrated approach includes both simulation and optimization modeling which are discussed in the following chapters. Prior to discussing the developed simulation and optimization models, the details of the case study are explained in Chapter 3. The development of the new simulation model is presented in Chapter 4. The framework of the developed simulation model including the input data, simulation structure and output data are given in this chapter. The developed simulation model is applied to the case study. The discussion on the verification and validation of the simulation model is also provided here. Chapter 5 elaborates on the development of an optimization model to prescribe the design of the supply chain. The integration of the developed simulation in Chapter 4 and the developed optimization model is also discussed in this chapter. The integrated simulation/optimization model is applied to the same case study and the efficiency of the integrated model is shown. The last chapter of this study is assigned to the final conclusions, the strengths and limitations of the study. Finally, several suggestions are given for future research direction. 15

27 Chapter 2. Literature review 2.1 Synopsis The biomass supply chain has been modeled and analyzed in the literature to improve its performance in terms of biomass delivery and the total delivery cost. In this regard, a wide range from strategic to operational decisions have been made, such as the location and capacity of the conversion facility, location of storage sites, inventory and shipment planning, and timing of harvest. Different decision-making tools have been developed to find the solutions for these decisions. Some studies employed static methods including spreadsheets and GIS-based tools. A portion of the relevant studies have exploited the power of simulation modeling mainly for planning and scheduling the operations at the operational level. Another popular tool is optimization modeling mainly used to make optimal decisions at the tactical and strategic levels. A combination of these modeling tools has also been used in the literature. In this chapter, the major relevant studies have been reviewed and categorized based upon the developed decision-making tools. 2.2 Static modeling in the biomass supply chain The initial and simplest tools used in the literature to estimate the logistics costs and biomass delivery are spreadsheets. A number of static spreadsheet models have been developed to calculate the costs of using biomass for bioenergy production, for example by Clegg and Noble (1987), Brundin (1988), Floden (1994), Mitchell (1995), Allen et al. (1998), Mitchell (2000), and Sokhansanj and Turhollow (2002). The developed spreadsheet models have been used as decisions support systems. They have different components such as a user interface, a database, a component that analyzes the data and information and a results screen. The logistics operations such as harvesting, storage and transportation are defined as equations and linked through the programming codes. The spreadsheet models allow the user to develop and evaluate different logistics scenarios through "what if" type questions. GIS-based modeling is another initial tool used to assess the resource availability and estimate the logistics costs. Due to the capability of GIS-based models in terms of storing, managing and 16

28 displaying geospatial data and also providing significant spatial patterns (Noon and Daly, 1996), it has been exploited either as an independent analytical tool or as part of a decision support system (DSS). Noon (1993) and Noon and Daly (1996) developed a GIS-based decision support system called Biomass Resource Assessment Version One (BRAVO). BRAVO assesses the availability of woody biomass including mill residues, logging residues and short rotation woody crops and estimates the delivery cost of biomass to coal-fired plants in the Tennessee Valley Authority (TVA) region. The data inputs in the BRAVO system include a digital map of the road network, a digital map of state and county boundaries and a digital map of plant locations. Graham et al. (1997) applied the BRAVO model to 21 locations in the state of Tennessee to investigate the impact of the biomass demand and the farmer participation rate on the delivered costs. In addition, BRAVO was used to determine the locations of bioenergy plants (Graham et al., 1996). To find the location, the farm gate cost and transport cost were calculated based on spatial data for any specific location in the region under study and the location with minimum total biomass cost was selected as the optimum location. Husdal (2000) developed a raster-based GIS method to minimize the transportation costs by calculating the shortest paths in a network. A raster is a grid of square cells (pixels) where each cell contains a value representing information such as temperature (ESRI, 2009). Resop et al. (2011) developed a raster-based model to assess the potential production of switchgrass and locate satellite storage sites near Gretna and Keysville, Virginia where switchgrass can be grown in a large scale to feed commercial-scale bioenergy plants. Resop et al. (2011) considered several criteria for locating satellite storage sites in the regions under study including: 1) direct road access to ease the transportation of switchgrass from satellite storage locations to the conversion facilities, 2) switchgrass bales can be collected from selected surrounding fields within a 3.2-km radius around storage, 3) minimum 40 ha of switchgrass production available within a 3.2-km radius and, 4) level land areas with average slopes of less than 10% were considered in the process of locating satellite storage sites. Brownell and Liu (2010) developed a heuristics method written in Visual Basic (Microsoft Corp.) to determine the size and number of satellite storage sites. Given the supply area and the location and size of the bioenergy plant in the middle of the supply area, the model starts with an initial satellite storage scenario and changes the size and number of satellite storage sites 17

29 gradually until an optimal scenario is reached. The optimal scenario has the minimum total cost of field, satellite storage and transportation operations. The model was implemented for three different plant sizes: small (2000 tonnes/day), medium (5000 tonnes/day) and large (10000 tonnes/day). The small, medium and large-sized plants require 25, 64 and 105 satellite storage locations, respectively. As reported by Brownell and Liu (2010), it took two days to find the optimal solution. In addition to the computational time drawback, the authors did not elaborate on how their developed method finds the optimal solution while assuring that all of the possible scenarios have been considered. The focus of this study was on satellite storage. At-plant storage was not taken in to account and only the handling system was modeled. The details of some of the studies on the static modeling are given in Table 2-1. The developed static models are easy to develop and implement and can provide a basic understanding of the biomass supply chain, mainly in terms of the amount of produced biomass in a region and the cost of delivered biomass to a bioenergy plant. However, many assumptions were made in the static models, such as those on average yield, average distance between farms and bioenergy plants, and the fixed moisture content across the supply chain. In addition, the dynamic behavior of the supply system such as continuous changes in moisture content, inflow and outflow of biomass from storage and also the functional relationships among many of dependent and independent variables in the supply system were neglected as the static models could not capture the dynamics of the system. Therefore, the obtained results of the analysis cannot be considered as reliable information for decision makers. To improve the quality of the obtained results, the dynamic behavior of the biomass supply system must be taken into account. This requires the deployment of a decision-making approach which enables one to model the time-dependency of the supply chain. 18

30 Table 2-1: Literature review on the static modeling of biomass supply chain Study Method Biomass/Bioenergy Mitchell (1995) type Objective Spreadsheet Short rotation woody crops Estimation of logistics costs Case study/region N/A Findings/important aspects - Better understanding of the viability of growing short rotation woody crops for industrial uses. Allen et al. (1998) Spreadsheet Forest biomass, short rotation coppice, straw and miscanthus/power plant Estimation of biomass delivery cost to the power stations N/A - Delivered cost for large rectangular baled straw would be lower than the cost of other considered biomass types. Sokhansanj and Turhollow (2002) Spreadsheet Corn stover Estimation of corn stover collection costs Midwestern United States - Total collection cost for round baling systems were 9% less than the rectangular baling systems. Noon (1993), Noon and Daly (1996) GIS Mill residues, logging residues and short rotation woody crops (SRWC) Estimation of total purchase and transportation costs Tennessee Valley Authority - Mill residues were the most viable biomass fuel compared to two other types of forest biomass. Graham et al., 1996 GIS Energy crops Location of bioenergy facilities State of Alabama - A conversion facility demanding 600,000 dry t/yr or 27 facilities each requiring 100,000 t with feedstock cost under $35/t could be established. Resop et al. (2011) Raster-based GIS Switchgrass Assessment of the potential production of feedstock and location of satellite storage sites Gretna and Keysville, Virginia - Potential production of switchgrass could supply a hypothetical bioenergy plant with average consumption of t/h within 32- km radius and t/h within 48-km radius. 19

31 2.3 Dynamic modeling in the biomass supply chain Simulation has been widely used in the supply chain networks. This is due to the capability and flexibility of simulation in modeling and evaluating complex dynamic systems, while considering uncertainty and variability in the system (Almeder et al., 2009). Kleijnen (2005) divided the simulation tools used in the supply chain into four types: spreadsheet simulation, system dynamics, discrete-event simulation and business games. Among them, discrete-event simulation is the most powerful simulation tool to model complex stochastic systems (Almeder et al., 2009). Due to the time-dependency and stochasticity of the biomass supply chain, discreteevent simulation has received the most attention among researchers to model and analyze the biomass supply chain (Ebadian et al., 2011). This tool has been mainly used to model the logistics operations in the biomass supply chain at the operational level to estimate the amount of delivered biomass to the bioenergy plant and the associated logistics costs. The simulation modeling was initially used to schedule the farm operations such as selection of forage machinery on a dairy farm (Noel P. Russell, 1983), evaluation of technologies or management practices in forage systems on dairy farms (Savoie et al., 1985), scheduling of labors and equipment for wheat harvesting (Elderen, 1987), and planning of hay harvesting equipment (Axenbom, 1990). One of the initial simulation models developed to model and analyze the biomass logistics is the work of Mantovani and Gibson (1992). They developed a simulation model written in GASP IV (simulation language) to compare harvesting and handling systems for corn stover, hay, and wood residues for ethanol production. They incorporated historical weather data and farmers changing attitude towards harvesting biomass. Ten years of climatological and biomass production data were included in the model. The impact of weather variations and late harvest on biomass availability and equipment cost were discussed. One of the most applicable simulation frameworks developed for designing a biomass delivery system is the Straw HAndling Model (SHAM) (Nilsson, 1999a). SHAM was presented as a dynamic simulation model for analysis of various delivery alternatives only for straw. That is because SHAM was used for analysis of agricultural fields in Sweden where straw is regarded as the prominent renewable energy source. Straw has a considerable potential to meet the energy 20

32 demand in rural areas in Sweden, especially as fuel in district heating plants (Nilsson, 1999a). SHAM simulates the effects of climatic, geographical, and biological factors on the cost of delivering biomass such as field size, transport distances between storage sites and heating plants, and straw yield. Some of the required input data are defined by appropriate random variables, for example the areas of the fields, the start of the harvest season, the arrivals of fields to the simulation model and crop yields. SHAM has been applied to three main regions in Sweden including Svalov, Vara, and Enkoping, as in Nilsson (1999b) and Nilsson (2000). The obtained results of the application of SHAM in these regions showed that the harvesting operation highly depends on the climatic and geographical conditions such as the frequency and duration of precipitation, field size and fraction of the land area with harvestable straw. Another finding was that the employment of management strategies such as prolonging the harvest season, optimal number of balers and transporters, and storage locations could lead to significant cost reductions. Nilsson and Hansson (2001) modified SHAM to incorporate a new crop, reed canary grass (RCG). They evaluated using RCG as feedstock in district heating plants in addition to straw and oil. The obtained results revealed that the total delivered cost could be reduced by using a mix of straw and RCG in suitable proportions instead of solely using straw. This could reduce costs by 15-20%. They also concluded that although RCG is an expensive feedstock (more than three times the cost of straw), the cost savings due to better use of machines and storage space and less consumption of oil as the primary fuel make this crop an attractive complementary fuel. Another comprehensive simulation framework developed to represent various stages of biomass collection, processing, storage, and transport activities is the Integrated Biomass Supply Analysis and Logistics (IBSAL) model. This supply model was developed using the ExtendSim simulation platform available from Imaginethat Inc. (Sokhansanj et al., 2006b). Contrary to SHAM, which was mainly developed for analysis of logistics systems of cereal straw, IBSAL is capable of modeling and analyzing the supply logistics system for a variety of crop residues, such as cereal straw and corn stover and also grasses such as switchgrass. It also calculates different outputs including costs associated with each logistics operation, the quantity of delivered biomass to the conversion facilities, dry matter loss, energy input and carbon emissions and completion time for each operation. 21

33 IBSAL was applied for various biomass types and logistics scenarios as in Sokhansanj and Fenton (2006), Sokhansanj et al. (2006a), Kumar and Sokhansanj (2007), Sokhansanj et al. (2008a), Sokhansanj et al. (2008b), Sokhansanj and Hess (2009), Sokhansanj et al. (2009), Stephen (2008), and Stephen et al. (2010). The details of some of these studies are given in Table 2-2. SHAM and IBSAL are similar in context. However, the issue of dry matter loss is not highlighted in SHAM. IBSAL estimates the dry matter loss as biomass undergoes various operations. In IBSAL, different preprocessing modules such as grinding, pelletizing and briquetting have been developed to assess their effects on the logistics costs and quality of delivered feedstock. There are no similar discussions on the possible preprocessing in SHAM. SHAM considered breakdown of equipment as an activity in the supply chain to shut down the operations, while IBSAL included breakdowns in the machine operational efficiency. Other differences of IBSAL and SHAM include assignment of harvesting equipment to farms, stoppage/delay of operations due to weather conditions and also the length of the harvest season. In addition to SHAM and IBSAL, another simulation model was developed by the Idaho National Laboratory (INL), US Department of Energy (DOE) (Hess et al., 2009). Their simulation model estimated the supply cost of herbaceous lignocellulosic biomass for biofuel production. The considered lignocellulosic biomass types included corn stover, corn cob and switchgrass due to their potential and availability in the US. Three feedstock supply systems were considered in the work of Hess et al. (2009): conventional bale, pioneer uniform, and advanced uniform. The primary difference among these three supply systems is the location of preprocessing operations in the supply chain. In the conventional bale system, the preprocessing occurs in the biorefinery plant, while it is considered at centralized depots in the pioneer uniform supply system, and during harvest and collection in the advanced uniform system. Pushing the preprocessing operation towards the upstream of the supply system eases the handling of a variety of feedstock with the same equipment. This would pave the way for treating biomass as a commodity in the biofuel market. 22

34 Table 2-2: Literature review on the dynamic modeling of biomass supply chain Study Simulation Biomass/Bioenergy Objective Case Findings/important aspects platform type study/region Nilsson (1999 a,b), Nilsson (2000) (Nilsson and Hansson, 2001) Arena Straw Modeling and analyzing the straw fuel delivery systems Arena Straw and reed canary grass (RCG)/ theoretical heating plant Evaluation of the impact of using a mix of straw and reed canary grass in district heating plants Svalov, Vara and Enkoping in Sweden Enkoping - Climatic and geographical factors highly impacted the straw harvesting system. - longer transport distances and lower straw yields were the main reasons for the higher costs of straw handling at Vara and Enkoping compared to Svalov. - Using a mix of straw and reed canary grass would result in reduction in the total cost. - More saving could be achieved using a mix of straw and wood chips, and possibly a small portion of RCG. Sokhansanj and Fenton (2006) ExtendSim Switchgrass and crop residues Calculation of the cost of transporting biomass from roadside storage/ satellite depots to the Biorefinery Canada - Delivery cost mainly depends on the bulk density of the biomass, its moisture content, and the distance to be transported. Kumar and Sokhansanj (2007) ExtendSim Switchgrass Estimation of delivery costs, energy input and emitted CO 2 N/A - Cost of feedstock delivered to a biorefinery for loafing is 18% less than for baling. - Dry matter loss was around 3% to 4% in the delivery system. Sokhansanj et al. (2008b) ExtendSim Straw Comparison of harvest scenarios 1) large square bale 2) round bale 3) loaf 4) dried chops 5) wet chops N/A - The cheapest and most expensive scenarios were loafing ($17.1dt -1 ) and wet chopping ($59.8dt -1 ). - Sensitivity analysis showed 20% reduction in cost by 33% increase in yield. Stephen et al. (2010) ExtendSim Crop residues Investigating the impact of biomass availability on the delivered cost over the service life of a biorefinery. Peace River, Alberta - Agricultural regions such as Peace River are not a good choice of location for a biorefinery due to the annual variability in biomass availability. 23

35 Hess et al. (2009) applied their model to a theoretical plant with a size of 800,000 ton/year and a feedstock supply radius of 80 km for stover and 105 km for switchgrass. The obtained results of the simulation showed that the conventional bale supply system is not able to achieve the 2012 DOE cost target of $34.7 dt -1. The average delivery cost ($/dt -1 ) of stover and switchgrass were estimated to be 55.4 and 49.6, respectively. The obtained results also showed that the pioneer uniform supply system design is not able to achieve DOE cost targets. For example, the total logistics costs for corn cob would be $68.9 dt -1. Hess et al. (2009) conducted a sensitivity analysis. The obtained results revealed that the improvement in equipment efficiency (shredder field speed, baler capacity and efficiency, and harvest window) and biomass properties (bulk density and moisture content) could result in a cost-efficient bale stover supply system. The results for advanced uniform have not been published yet. Similar to IBSAL and SHAM, the developed simulation by Hess et al. (2009) models and evaluates the details of the supply chain but the discussion on whether the model enables to plan and schedule the operations to meet the daily demand has not been provided. In addition, they assumed an equal distance distribution of farms throughout the supply radius. In addition to corn stover, straw and switchgrass as the main agricultural biomass considered for biofuel production, the supply chain of energy crops including miscanthus, reed canary grass, willow and hemp have been studied. Huisman (2003) developed a simulation model to find the minimum supply costs by selecting the best harvesting and storage system for each energy crop. The total cost encompasses harvest, transport, drying and storage costs. Although the framework of the simulation model was illustrated, detailed information on the simulation model and how the model assists in the selection of the optimal supply chain was not given in this work. In addition to the estimation of the logistics costs and delivered biomass, simulation modeling has been used to determine the location of satellite storage sites in the supply area. Cundiff et al. (2009) employed simulation modeling to determine the number of satellite storage locations in the supply system and also the hauling distance from production fields to satellite storage locations. Their simulation study revealed that the allowable hauling distance is 3.2 km (2 mi) based on the total operating time to haul bales from a 16-ha field to satellite storage. The simulation study was carried out within a 50 km (30 mi) radius of Gretna, located in South Central Virginia, a region that has a high potential for switchgrass production. 24

36 Simulation modeling has also been employed to study the supply chain of woody residues. Zhang et al. (2012) developed a simulation model using Arena Simulation Software with a graphical user interface for a biofuel supply chain. Key supply chain activities including biomass harvesting/processing, transportation, and on-site storage were considered in the model. The considered woody biomass included pulpwood and wood residues. The model used the delivered feedstock cost, energy consumption, and GHG emissions as system performance criteria. Zhang et al. (2012) verified the developed model through a series of simulation runs applied to several biofuel facilities in the Lower Peninsula of Michigan. The moisture content was assumed constant (50%) throughout the supply chain. Thus, biomass weight delivered from harvesting areas to the biofuel facility remained the same. The simulation model finds the most and least favorable locations and sizes of bioenergy plants. Modeling of agricultural crops such as corn grain is another area explored by simulation. Arinze et al. (2001) and Sokhansanj et al. (2003) used simulation to model the changes in quality of potash fertilizer and alfalfa cubes during storage and transport. Humphrey and Chu (1992) and Benock et al. (1981) developed a GASP IV-based simulation model to analyze harvesting, transportation, and drying of corn. Although simulation modeling is a powerful tool for detailed evaluation of the supply chain under different circumstances, it is not able to find the best decisions among alternative ones. Therefore, to achieve the optimal decisions on biomass inventory, shipped biomass or location and size of different facilities, the application of mathematical optimization modeling 1 is inevitable (Berruto and Busato, 2008). 2.4 Optimization modeling in the biomass supply chain Mathematical optimization modeling has been broadly used in a variety of industrial and academic fields. However, its application to the agricultural biomass supply chain has been limited. Several researchers have applied the mathematical optimization modeling approach to find the optimal solutions for logistics problems such as monthly working schedule for different logistics operations, the amount of biomass shipped, processed and stored in the supply system, and also the location, number, type and capacity of bioenergy plants and storage sites. 1 Selection of the best alternative from a set of available alternatives with respect to some criteria (INFORMS Computing Society, 1996) 25

37 Cundiff et al. (1997) developed a linear programming (LP) model to design a biomass delivery system for switchgrass producers who provide feedstock for a central plant. The proposed optimization model determined a monthly shipment plan and capacity expansion schedule for each switchgrass producer based on monthly harvest and four different weather scenarios. A mixed-integer linear optimization model was developed by Nagel (2000) to find the most economical and ecological (based on CO 2 emissions) energy supply structure for heating a small rural community. The main decision variable was whether they should build heating systems (individual energy supply), a heating plant, or a co-generation plant. The proposed model was used in the rural municipality of Brandenburg, Germany with 660 inhabitants. The results showed individual heating systems became an attractive option with decreasing prices for biomass. The produced CO 2 could decrease up to 25% by increasing the use of biomass. In addition, biomass would not be an economical feedstock with decreasing prices for fossil fuels. Another application of optimization modeling can be seen in the work of Kaylen et al. (2000). They developed a mathematical programming model to analyze the economic feasibility of producing ethanol from various lignocellulosic biomass materials including agricultural residues, energy crops, wood processing and logging residues in Missouri. They analyzed the tradeoffs between scale economies and transportation costs, and found that the estimated net present value (NPV) of the plant would be maximized at a capacity of 4,360 tons per day, under the conservative assumption that only 10% of available biomass could be used in the plant. Tembo et al. (2003) proposed a mixed integer mathematical programming model for the lignocellulosic biomass-to-ethanol industry. The optimization model determined the most economical source of biomass, timing of harvest and storage, inventory management, biorefinery size, and biorefinery location. It also identified the breakeven price of ethanol for a gasificationfermentation process. The objective function of this model was to maximize the industry s net present value. The optimization model was applied in Oklahoma State where a variety of potential lignocellulosic feedstock including plant residue, indigenous native prairies, and pastures are available. The primary finding of the study was that gasification-fermentation of lignocellulosic biomass to bioethanol may be more economical than fermentation of corn grain with the further development of the conversion process to handle multiple feedstocks and reduction in the capital and operating costs of the plant. 26

38 Due to the high magnitude of potential agricultural biomass in Thessaly, Greece, several studies have been conducted in this area to investigate and optimize different aspects of the biomass supply chain using mathematical programming. Tatsiopoulos and Tolis (2003) applied an LP model to optimize economic aspects of the logistics network by comparing various biomass collection and transportation systems, including the farmer transportation system and third party logistics (3PL) handling system. The model was applied to the cotton biomass cultivated in Thessaly. The study concluded that it would be cheaper if the farmers were engaged in the logistics network. Rentizelas et al. (2009) analyzed the impact of three different biomass storage methods on the total cost of the system. The storage methods included: 1) closed warehouse with biomass drying capability, using heated air (WD), 2) a covered storage facility of a pole-frame structure having a metal roof without any infrastructure for biomass drying (CND), and 3) an ambient storage of biomass, covered only with a plastic film (AS). The model was implemented for the case study of a municipality of Thessaly. The final results showed that simple and cheap storage solutions (AS) could be used for the cheapest biomass type available. Whereas, expensive storage solutions (WD) were a more attractive option for multi-agricultural biomass approach in order to reduce the storage space required. In addition, no drying was required if the arriving biomass to store had a low moisture content (e.g %). Other work done by Rentizelas et al. (2009) was the development of a decision support system (DSS) for multi-biomass energy conversion applications. The DSS provides the investor with information on: 1) the best location to establish the biomass-to-energy facility, 2) the optimal relative size of the base-load combined heat and power (CHP) unit and the peak-load boiler, and 3) the amount and location of each locally available biomass type that should be used. A system-wide modeling of the whole bioenergy system was considered in the DSS to find the global optimal design. The objective function of the optimization model was to maximize the net present value of the investment for the project s lifetime. The DSS was applied to a trigeneration application (electricity, heating and cooling) at a municipality of Thessaly. They concluded that the developed DSS provides visibility to the potential investor regarding the details of the optimum design of the facility and the fuel supply chain, as well as the sensitivity of the investment on a set of investment parameters such as inflation, electricity price and biomass cost. 27

39 To make both long-term decisions (supply chain design-related decisions) and medium and short-term decisions (logistics management decisions), Ekşioğlu et al. (2009) developed a mixinteger programming model. The objective of the mathematical model was to minimize the cost of delivering biofuel by coordinating long- and mid-term decisions and also logistics management of a biorefinery. The long-term decisions were comprised of the number, size and location of biorefineries. The mid- and short-term decisions included the amount of biomass shipped, processed and stored during a certain time period. The State of Mississippi was used as the testing ground of the proposed model. The obtained results revealed that transportation costs and biomass availability are the primary parameters affecting the supply chain-design decisions. In case of low biomass availability, smaller sized biorefineries become commercial. The results also indicated that the supply chain-design decisions were not impacted by changes in biomass collection costs and biomass processing costs. Zhu et al. (2011) proposed a mixed integer linear programming (MILP) model to find the optimal configuration of a biorefinery supply chain. Two types of biomass were transported in the supply chain: switchgrass and residue. Residue was the by-product of the conversion process which was recirculated to the soil. The MILP model prescribed the optimal solutions for both strategic decisions about the supply chain design and tactical decisions on the annual operation schedules. These decisions consisted of whether to establish a biorefinery and an infield/intermediate/at-plant warehouse and their capacities, scheduling the harvest operation for each switchgrass production field, the size of the harvesting team, and transportation flow of switchgrass and residues among the logistics network. The developed model was verified via numerical examples. Due to the large size of the developed MILP model, it was not suitable for solving the large supply area. Moreover, it was mentioned in the study that the monthly fixed costs of operating an intermediate warehouse was assumed to be $60,000. However, no discussion was given on what would happen if the warehouse was kept closed for a month. Moreover, the authors did not elaborate on the components of the fixed cost of the warehouses and their service lives. As an extension of their previous study, in addition to dedicated energy plants such as switchgrass, Zhu and Yao (2011) considered agricultural residues (e.g., corn stalk and wheat stalk) in their MILP model. Thus, the developed MILP model was a multi-commodity network flow model. The model solution covered the harvesting and storage of switchgrass, purchasing of 28

40 stalk and straw, delivering of biomass to biorefineries, processing of biomass in biorefineries, and handling of biomass residue. The numerical study showed that the use of multiple types of biomass could increase the supply of biomass, alleviate the seasonality caused by the nonharvesting season of switchgrass, smooth the biofuel production, and eventually increase the unit profit of biofuel. The model also addressed the dry mass loss of biomass in the supply network due to various operations, fungal degradation, fermentation and breakdown of carbohydrates. Similar to their previous research, the established model was a large-sized optimization model. One of the interesting recent studies is the work of Akgul et al. (2012). They built a multiobjective optimization framework for a hybrid first/second generation biofuel supply chain. The main purpose of the optimization model was to design the hybrid bioethanol supply chain considering economic and environmental objectives, simultaneously. The problem was formulated as a static multi-objective, mixed integer linear programming (MILP) model. In this work, the total daily cost of the supply chain was minimized where the total environmental impact of the supply chain must be less than or equal to the maximum allowed GHG emissions from the supply chain. The proposed model was applied to a case study of bioethanol production in the UK. The environmental impacts were evaluated based on a life cycle analysis (LCA) approach. In addition, the effect of considering carbon tax on the overall environmental emissions was investigated. The results showed that the second generation biofuel production technologies would outperform the first generation ones in terms of the potential GHG savings. In a few recent studies, mathematical optimization modeling has been used to manage satellite storage locations in the biomass supply chain. Judd et al. (2010) developed an integer programming model to determine the optimal number of satellite storage sites and their locations as well as the assignment of production fields to the selected satellite storage sites. In the developed model, the total cost of transporting biomass from farms to satellite storage plus the establishment costs of satellite storage were minimized. The number of satellite storage locations determined by the optimization model was much less than that in Resop et al. (2011) who established a raster-based approach to locate satellite storage sites. However, the developed optimization model did not consider transportation costs from satellite storage sites to the ethanol plant. This cost component would impact the location and number of satellite storage sites within the supply radius. 29

41 In a similar study, Judd et al. (2012) exploited an MILP model to find the optimal number and location of satellite storage sites. The main novelty of the model was the consideration of sharing loading equipment among satellite storage sites. Equipment sharing was compared with the fixed position equipment scenario in which each piece of loading equipment was assigned to only one satellite storage location. The equipment sharing resulted in significant cost saving (14.8%) compared to the fixed position equipment scenario. In addition, three handling systems at the satellite storage sites were compared. Kim et al. (2011) considered uncertainty in their developed optimization model to find the optimal design of the supply chain network. The decision variables included the number, size, and location of two bio-processing units, optimal flow of biomass from forest areas to the conversion units as well as the transportation of the final products to the market. Fourteen variable parameters were considered in the optimization model such as biomass availability, purchase price of biomass and transportation costs. A two-step procedure was developed to find the optimal solutions. The procedure was applied to the Southeastern region of the United States and the final results showed the efficiency of the developed model to mitigate the impact of the variation on the overall profit. Chen and Fan (2012) also considered supply and demand uncertainties in the bioethanol supply chain. A two-stage stochastic programming model was developed to optimize the supply chain for a waste-based ethanol production system. The application of the model to an ethanol plant in California showed that ethanol can be produced at a competitive price of $1.2 per gallon. Another finding was the importance of feedstock diversification to mitigate the annual feedstock fluctuation. The developed model is at yearly-aggregated level and is not appropriate for monthly planning. Most of the developed optimization models focus on strategic and tactical planning levels of the biomass supply chain. These models do not take time-dependency and stochasticity of the supply chain into account in order to find the optimal solutions in a reasonable time. As reported by Ekşioğlu et al. (2009) and Zhu et al. (2011), the developed optimization models might not be solvable for real-world cases. A few of them elaborated on the operational level. Consideration of operational decisions in the optimization modeling increases the computational times. Due to complexity and dynamics of real-world supply chain networks, application of only one method such as simulation or optimization does not guarantee an optimal supply chain network 30

42 (Almeder et al., 2009). Using the combination of these methods under one framework to achieve an optimal operational plan for supply chain networks has recently received attention among researchers, for instance: Truong and Azadivar (2003), Lee and Kim (2002) and Almeder et al. (2009). The combined simulation/optimization model would allow consideration of all decisionmaking levels including strategic, tactical and operational levels. In addition, the timedependency and uncertainty of the supply chain can be considered in the combined approach. 2.5 Combined modeling methods in the biomass supply chain The idea of using a combined simulation/optimization approach in the biomass supply chain is new. To the best of the author s knowledge, the only research in the context of the application of a combined optimization/simulation in the biomass logistics system is the work of Berruto and Busato (2008). They presented a combined approach of simulation and linear programming models to optimize the flow of biomass from the field to a power plant. In the combined approach, first the simulation model projected the performance of a given logistics network under varying input parameters including resource availability, crop yield, field size and shape, and transport distance. The results of the simulation model assisted in developing a linear regression equation. This equation represented the cost per ton of dry matter as a function of the transportation distance for different yields. This equation was then used in the optimization model. Thereafter, the optimization model prescribed the best combination of equipment for each field distance and yield in order to minimize the logistics costs. The considered biomass supply chain was a simple supply network which included forage harvesting, shipping biomass to the plant and unloading to the silos. The combined model had a simple structure and there was no feedback from the optimization model to the simulation model. It is noted that DeMol et al. (1997) developed both simulation and optimization models but these models were not combined. The simulation model, called Biologics (BIOmass LOGIstics Computer Simulation), estimated the logistics costs and energy consumption for different logistics scenarios. The developed optimization was an MILP model to find the annual flow of multi-biomass at the minimum costs. Both models were applied in a proposed bioenergy plant in 31

43 the province of North-Holland, Netherlands. The finding of their research was that the results of the simulation and optimization models were comparable. However, the optimization model can be used to select the optimal supply network or the optimal mixture of biomass types (strategic level) while the simulation model should be used when the supply network is given and it provides more detailed results on the biomass logistics (tactical level). In addition to the combination of simulation and optimization modeling, some studies have used the combination of other approaches. Freppaz et al. (2004) developed a decision support system (DSS) that includes a GIS-based interface, a database and an optimization module. The optimization module determined the size of thermal and electricity plants and also the optimal flow of biomass to the plants. The GIS-based interface was used to plan both forests eligible for biomass collection and sites to locate the energy conversion plants. The parameters of the optimization module were also computed by the GIS-based interface. Ravula (2007) developed a raster-based model to locate satellite storage sites in two separate production regions the Southern Piedmont and Northern Piedmont regions of Virginia. Potential satellite storage locations were determined by converting spatial data into a binary raster where the value of one/zero implies the high/low concentration of biomass inside a cell. Thereafter, the total number of cells within a 3.2-km radius of each cell in the raster was calculated. The calculation showed where the areas of highest and lowest concentration of potential production were located to establish satellite storage. A 3.2-km limitation on procurement regions around each satellite storage location was introduced to reduce bale wagons travel distance due to their low speed (less than 32 km/h (20 mph)). Bale wagons were used to transport the bales on the field to the storage location. Given the location of satellite storage sites, the location of the processing plant was then determined. To this end, each cell in the considered raster became a potential location. Thereafter, the travel time from each satellite storage location to every other cell in the region was computed. Finally, the cumulative travel time to all satellite storage locations was calculated for each cell. The cell with the minimum cumulative travel time was selected as the optimal location for the processing plant. Ravula (2007) also developed a simulation model to schedule the shipment of biomass from satellite storage locations to the processing plant. The simulation model was also used to check the sensitivity of the supply system to variations in land use rate, truck travel times, and plant processing times. 32

44 2.6 Discussion and conclusions Depending on the type of the supply chain decisions, several modeling approaches have been used including static modeling, simulation modeling, optimization modeling and their combination. Each of these modeling approaches has advantages and disadvantages compared to others. Based on the research objectives explained in Chapter 1, the primary decisions made in this research are to design and schedule the highly-constrained agricultural biomass supply chain in the Canadian Prairies. This supply chain should be designed and scheduled to meet the daily demand of a commercial-sized ethanol plant with the minimum delivery cost possible. The simulation modeling can be used to plan and schedule the supply chain as it enables us to consider the dynamics and high level of complexity and uncertainty of the system. However, the estimated costs by the simulation model are not necessarily the minimum ones. The minimum delivery costs can be achieved via the development of an optimization model. The optimization model makes it possible to find the optimal design of the supply chain in which the biomass demand is met at the lowest delivery costs. To avoid a complex and large-sized optimization model, all the uncertainties and complexity of the supply chain cannot be incorporated in the model. Thus, the optimal design cannot guarantee the fulfillment of the daily demand. Therefore, an integration of the modeling tools is required. The literature lacks an integrated modeling approach in which the dynamics and stochasticity of the supply chain have been considered in both design and scheduling of the biomass supply chain. In addition, the details of the supply chain such as farm management, multiple biomass, storage and transportation management, moisture content, delays, and the business model of the supply chain have not been taken into account to fulfill the daily demand of a commercial-scale ethanol plant. Due to the different size and location of farms and their produced biomass, they should be considered as separate suppliers in the supply chain instead of aggregating them at a county or municipality level. Analyzing the supply chain at the farm level provides accurate cost and delivery outputs. It also allows the feedstock manager to help the farms in using their equipment efficiently. In addition, each farmer may grow different crops in a year resulting in the production of multiple biomass types. The supply chain should be able to manage the flow of multi-biomass considering the specific characteristic of each biomass type. 33

45 The storage management plays a key role in meeting the daily demand year-round as the harvest season is short while the conversion facility operates 24/7/365. Biomass gradually enters and leaves storage during the year depending on the harvest schedule, daily biomass demand, weather conditions and also equipment availability. In addition, biomass is prone to degrade during storage. These factors affect the storage costs, the amount of dry matter loss (DML) and the associated costs, storage capacity and inventory. In the relevant literature, the calculation of storage capacities and the associated costs are either based on the optimization models as in Cundiff et al. (1997) or are based on the assumption that storage is filled once or twice per year as in Cundiff et al. (2009), Judd et al. (2012) and Judd et al. (2010). It was also assumed that biomass is available at the storage sites on day one of every year which could not be the case in practice. An accurate estimation of the capacity of storage locations and the associated costs requires tracking of the daily inflow and outflow of biomass in storage. In addition, the daily flow of biomass in storage sites provides an accurate estimation of the amount of DML since the amount of DML in storage depends on the duration of storage time. Thus, the flow of biomass in storage locations must be modeled on a daily basis while considering the time-dependency and uncertainty of the supply chain. Another storage-related issue is the initial inventory in the supply chain. The initial inventory is important to meet the feedstock demand at the beginning of harvest season when there is not sufficient biomass produced available in the system. Initial inventory has not been explored in the relevant literature known to the author. In addition, as stated by Cundiff et al. (2009a), the role of the at-plant storage to avoid any feedstock shortage in the conversion facility has not been fully studied. In addition to storage, the transportation system can be improved by considering the uncertainties in the supply system. To assure the fulfillment of the daily demand, three decisions must be made which influence the fleet utilization: the quantity of daily biomass delivered to the plant, the capacity of at-plant storage and the number of trucks in the supply system. These decisions must be made based on the different operational windows of logistics operation, dry matter loss in the system, and uncertainties such as machine breakdown and weather conditions. Other detailed analyses neglected in the literature are delay times and the continuous track of moisture content. Sokhansanj, Kumar, et al. (2006), Sokhansanj et al. (2008) Sokhansanj et al. 34

46 (2009) and Kumar and Sokhansanj (2007) considered weather-related delay times and moisture content in the supply chain. However, the delays due to the unavailability of machines were not incorporated in these studies. The consideration of these delays is of importance as they may cause bottleneck in the supply chain affecting the planning and scheduling of the machines in the supply chain to meet the daily demand. In addition, the estimation of the moisture content in these studies were done in a discrete fashion in that the moisture content of biomass in a specific day was estimated based on the last recorded moisture content of biomass and the weather data for the respective day. Thus, the time period between the time when the last moisture time was recorded and the respective day were not taken into account. The continuous update of moisture content provides insights for the supply managers whether to take extra actions to manage the moisture content in the system in the case that the moisture content is not within a safe level. In addition, a better estimation of the moisture content assists in better estimation of dry matter loss and the associated costs in the system. Another deficiency of the relevant literature relates to the business model of the agricultural biomass supply chain. Although the general structure of the business model has been discussed in the literature, the duration of contract with farmers and their participation rate have not been taken into account as an input parameter in the modeling and analysis of the supply chain. These parameters impact the number and location of farms in the supply area. In this study, an integrated simulation/optimization model is proposed which alleviates the above-mentioned deficiencies. Prior to explaining the developed integrated model, the details of the case study to which the integrated model was applied, are given in the next chapter. 35

47 Chapter 3. Case study 3.1 Synopsis In this chapter, the case study used in this research to evaluate the efficiency of the developed models, is presented. The case study is a proposed commercial-scale cellulosic ethanol plant located in Prince Albert, in north-central Saskatchewan. Several organizations were contacted to gather the data. Each organization provided specific types of data based on their area of specialty. The data were gathered by searching their websites and communication by , phone calls and in-person meetings. The organizations and the sets of data they provided include: Iogen Corporation: Ethanol plant data Agriculture and Agri-Food Canada (AAFC): Weather data and supply area data Saskatchewan Ministry of Agriculture: Farm data, crop data, and harvest schedule data Idaho National Lab & Oak Ridge National Lab, American Society of Agricultural and Biological Engineers (ASABE): Equipment data and cost data Statistics Canada: Supply area and farm data In addition to these sources, the studies in the literature were used to gather general data such as breakdown and repair data for equipment, grain moisture content and dry matter loss data. The details of the gathered data are explained in this chapter. 3.2 Ethanol plant data Figure 3-1 shows the location of the proposed ethanol plant. The estimated annual ethanol production capacity of the plant is more than 70 ML (Iogen Corp., 2009). The conversion process is a combination of thermal, chemical and biochemical technologies. In addition to the cellulosic ethanol as the main product, the plant would generate green electricity from processed by-products and forestry waste. The service life of the plant is estimated to be 25 years. The cellulosic ethanol is produced from locally-grown cereal straw, mainly wheat straw. Three prevailing types of wheat straw in the region under study include spring wheat (Triticum aestivum), durum wheat (Triticum turgidum) and winter wheat (Triticum hybernum). To reach 36

48 an annual capacity of 70 ML, a daily delivery of 750 t of wheat straw is estimated. No discussion on the preference of one type of wheat straw to another type to deliver to the plant has been given by Iogen. Table 3-1 summarizes the characteristics of the case study. Figure 3-1: Location of the cellulosic ethanol plant, Prince Albert, Saskatchewan (by permission from Iogen Corp.) Table 3-1: Characteristics of the proposed cellulosic ethanol plant Characteristics Bioenergy product Location of plant Annual production capacity Life time Feedstock Daily demand Cellulosic ethanol 12 km east of Prince Albert, north central Saskatchewan 70 million liters (ML) 25 years Wheat straw (winter, spring and durum wheat) 750 t 37

49 3.3 Supply area data To meet the daily straw demand for the conversion facility, a supply area with the radius of 160 km was considered by Iogen (Iogen Corp., 2009). Figure 3-2 depicts the availability of straw within a 160-km supply radius. The yellow polygon represents the supply area which is about 53,800 km 2. As shown in this figure, no wheat straw is available north of Prince Albert. This supply area was produced by the Biomass Inventory Mapping and Analysis Tool (BIMAT) (Stumborg et al., 2008). BIMAT 1 is an Internet-based Geographic Information System (GIS) developed by Agriculture and Agri-Food Canada (AAFC). It estimates the availability of different types of agricultural and forestry residues within a user-defined radius from a userselected geographic point location across Canada. The estimation is based on a gross crop yield and discounted for soil conservation and the use of biomass for animal feeding and bedding. However, BIMAT does not take into account biological and climate constraints on the availability of biomass. Thirty one rural municipalities are located inside 160-km supply radius. Appendix A presents the list of the rural municipalities, their land area, number of wheat farms and distances from the ethanol plant. The locations of rural municipalities in the supply area are shown in Appendix B. Almost 98% of wheat farmers grow spring wheat and the rest produce durum and winter wheat. BIMAT estimates that 1.5 Mt of wheat straw is available within the 160-km radius which is the net available biomass for ethanol production. In other words, this value is the total remaining wheat straw that can be collected and delivered to the ethanol plant, taken into account soil conservation and livestock requirements

50 Figure 3-2: 160-km supply area considered for the proposed cellulosic ethanol plant (by permission from Agriculture and Agri-Food Canada) 3.4 Farm data The most challenging part of the data gathering process was collecting farm data. This is due to the uncertainty in the number of farmers who are willing to participate in the supply system during the business life of the ethanol plant. The average number of wheat farms which can be potential feedstock providers is 3,055 including 2,985 spring wheat, 49 durum wheat and 21 winter wheat farms. These numbers would change with the change in the participation rate. More important, the portion of farmlands that farmers assign to each type of crop in each year is indefinite beforehand since farmers typically include several crops in rotation. Thus, during the lifetime of the ethanol plant, it would be uncertain which farm will grow wheat and what portion of the land is assigned to the cultivation of this crop. Another difficulty in gathering farm data was the location of farms in the supply area. There is no spatial data available on the location of each single farm in the supply area. None of the organizations could provide fine spatial data. The available information is not at the farm and rural municipality levels, but at the crop district level which is a larger aggregation of the smaller rural municipalities. Figure 3-3 shows a map of the crop districts within the province, as well as 39

51 the rural municipalities within each crop district. Prince Albert is in crop district 9AE, and is surrounded by crop districts 9AW, 8B, and 8A. Therefore, due to the uncertainty in the amount of wheat cultivated in the supply area in any given year and also the unavailability of fine spatial analysis, the farms were created randomly inside each rural municipality. Three criteria were used in this regard: Total number of created farms in each municipality does not exceed the number of farms growing wheat in the respective municipality. Total size of created farms in each municipality does not exceed the total land area under cultivation of wheat in the respective municipality. Total produced biomass in all created farms does not exceed the total available biomass within the 160-km supply radius, estimated by BIMAT (1.5 Mt). To assure there is no overlap between the created farms, the origin in each municipality was set as the center of the municipality. A coordinate was then assigned to the center of each created farm. For each new created farm, it was checked whether the new created farm has no overlap with the preceding created farms. If one of the first two criteria is met, no more farms will be created in the respective municipality. If the last criterion is met, the farm creation process terminates. 40

52 Figure 3-3: Crop districts and rural municipalities in the province of Saskatchewan (by permission from Agriculture and Agri-Food Canada) To assign size to each created farm, the historical data on the size of farms in Saskatchewan were used. Table 3-2 shows these ranges. The farm size ranges from 0.4 to 1,620 ha. Roughly 70% of farms have a size between 100 and 900 ha. To assign size to each created farm, a random number is created using a uniform distribution function based on the size ranges in Table 3-2. It is assumed all the created farms are square-shaped. 41