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Design of Cellulosic Ethanol Supply Chains with Regional Depots Rex Ng, and Christos Maravelias Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.5b03677 • Publication Date (Web): 30 Nov 2015 Downloaded from http://pubs.acs.org on December 6, 2015
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Industrial & Engineering Chemistry Research
Design of Cellulosic Ethanol Supply Chains with Regional Depots Rex T. L. Ng1,2, Christos T. Maravelias*,1,2 1Department
of Chemical and Biological Engineering and 2DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI 53706, USA
Abstract. The conversion of lignocellulosic biomass to fuels has the potential to reduce our dependence on fossil fuels. To ensure biomass supply meets biofuel demand, it is necessary to have an effective biomass supply network. Towards this end, the concept of regional biomass processing depot, where biomass is pretreated and/or densified to a higher density intermediate, has been introduced to improve the performance of supply network in terms of costs and emissions. In this article, we develop a mixed-integer non-linear programming model for the capacity and inventory planning problem of biofuels supply chain including depots. Importantly, the proposed model accounts for variable locations of depots, which is a subject that has not been studied in the literature. In addition, our models account for biomass selection and allocation, technology selection and capacity planning at depots and biorefineries, and biomass seasonality. Keywords: Biofuel; biorefinery; cellulosic ethanol; distributed biomass processing; depot
1 Introduction Production of fuels from lignocellulosic biomass has been identified as a promising alternative renewable energy strategy to meet the increasing energy demand. Various types of lignocellulosic biomass are available, from agricultural wastes such as corn stover, bagasse and rice wheat; wood wastes such as hardwood and softwood chips; energy crops such as switchgrass and micanthus; and urban waste such as municipal solid waste. Lignocellulosic biomass consists of three major components: cellulose, hemicellulose and lignin. Cellulose and hemicellulose are converted into sugars while lignin is used for heat and power generation. Lignocellulosic biomass is generally sent for pretreatment to reduce the moisture content and size of biomass particles, remove lignin, convert hemicellulose into fermentable sugar, etc. A number of excellent review papers on the pretreatment of lignocellulosic biomass have been published1,2. Pretreatment of biomass generally can be divided into physical (e.g., mechanical comminution3, drying and densification4), physicochemical (e.g., ammonia fiber explosion5,6, liquid hot water7 and steam explosion with SO28), chemical (e.g., dilute acid9, alkaline10–12 and organosolv13) pretreatments.
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Pretreated biomass can be converted into cellulosic biofuel via various conversion platforms which can be classified as biochemical, thermochemical and catalytic14. In the biochemical platform, pretreated biomass is sent to enzymatic hydrolysis to convert cellulose and hemicellulose into sugar monomers. It is then followed by microbial fermentation for the production of the ethanolcontaining fermentation broth15. The fermentation broth is purified via distillation and molecular sieve adsorption processes to obtain high purity ethanol. In the thermochemical platform, pretreated biomass can be converted into syngas via indirect16 or direct17 gasification. Raw syngas produced from gasification is conditioned to meet downstream requirements. Conditioned syngas is converted to alcohols via alcohol synthesis and the produced mixtures are separated and purified18. The catalytic conversion platform offers the advantages of higher selectivity and yield at relatively mild conditions19–23. In one strategy, for example, cellulose is converted catalytically to γvalerolactone (GVL), which is further reduced to butene and then butene oligomers24,25. In another strategy, both the cellulose and hemicellulose fractions are simultaneously converted into sugars using a GVL solvent containing a dilute acid catalyst26,27. Fermentable sugars that contain GVL, lignin and humins are separated from CO2-based extraction prior to microbial fermentation28. Ethanol is produced from the fermentation of biomass-derived sugars. The concept of integrated biorefinery emerges as an integration of several biomass conversion technologies to maximize the economic potential29. Techno-economic assessments of integrated biorefineries based on biochemical30–32, thermochemical33,34 and catalytic28,35,36 platforms have been widely conducted. In addition to economic aspects, environmental factors37,38 and social concerns such as process safety39 and occupational health40 as well as multi-player design of integrated biorefineries based on cooperative game approaches41–43 have been explored. There is a number of systems-level analyses for the design of integrated biorefineries28,44–46. A number of studies focusing on the design of the entire cellulosic biofuel supply chains (SCs) have also appeared in the literature. Comprehensive reviews have been provided by An et al.47, Awudu and Zhang48, Sharma et al.49, Yue et al.50 and Garcia and You51. Approaches for cellulosic biofuel supply chain design can be categorized into two types as illustrated in Figure 1. In the centralized configuration (Figure 1a), biomass is directly transported to the biorefinery and pretreated on site. Decisions typically considered in these models include biomass selection and allocation at farms, and technology selection and capacity planning at the biorefinery. The objective is the maximization of profit, or minimization of cost, of the entire biofuel SC52–57.
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Figure 1: Cellulosic biofuel supply chain configuration
On the other hand, the concept of collection facility or regional biomass processing depot58 (referred to in this study as a ‘depot’) has been introduced to improve the handling efficiency of biomass as well as reduce transportation cost and CO2 emissions. As shown in Figure 1b, biomass is pretreated and/or densified to a higher density intermediate for ease of transportation and storage. Though biomass can be shipped directly to the biorefinery if this leads to better economic or environmental benefits. For the distributed configuration, additional decisions related to the depot such as facility location, technology selection and capacity planning are considered59–63. Bowling et al.61 showed that the distributed configuration has lower cost than the centralized configuration. The aforementioned works focus on the profitability of the cellulosic biomass SC. Environmental and social aspects have also been considered. For example, carbon footprint (greenhouse gases emission) from the transportation and production activities has been incorporated in the SC models38,64–67. Energy footprint, agricultural land footprint, water pollution footprint and water footprint have also been considered68,69. In terms of social impact, the risks associated with the biofuel SC (e.g., work-related human casualties69,70 and financial risk71) have been studied. In addition, jobs created during the construction and operation phases for the entire SC have been the subject of research in social sustainability72,73. Furthermore, multi-period inventory models have been proposed to account for different types of biomass74–76 and several studies have been carried out to determine the optimal design of cellulosic biofuel SC under uncertainty (e.g., biomass availability, fuel demand, product prices, product yield) via stochastic programming approaches71,77–80. Finally, multi-player designs of the cellulosic biofuel SC based on cooperative game approaches have been proposed to account for individual and contractive interests of the participants in the entire SC81,82.
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Despite all the extensive research in the area, the design of biofuel SC with depots has received limited attention; depots were not studied at all or assumed to be installed in predetermined locations. However, while the location of farms is given and the selection of the biorefinery location can be made from a list of predetermined locations that meet certain criteria (e.g., proximity to city, blending depot, etc.), the optimal number and therefore location and capacity of depots is unknown. In fact, the efficiency of the biofuel SC depends heavily on these decisions. Accordingly, the goal of this paper is to develop a general multi-period optimization model for the design of cellulosic biofuel SC considering the option of regional depot installation in conjunction with the optimization of their number, location, and capacity. The remainder of this paper is structured as follows: Background on cellulosic biofuel SC and a formal problem statement are given in Section 2. In Section 3, we present a detailed model formulation. In Section 4, we discuss how the model can be modified to handle different features. A case study is presented to illustrate the applicability of the proposed model in Section 5. We use lowercase Greek letters for parameters; Latin letters for variable; lowercase Latin italic letters for subscripts; and uppercase U/L in superscript for upper/lower bounds.
2 Background 2.1 Cellulosic Biofuel Supply Chain Figure 2 shows a superstructure for a cellulosic biofuel SC. Biomass from different farms is transported to depots and/or biorefineries. At the depot, biomass can be densified directly without any other treatment (Pretreatment A) or it can be pretreated via ammonia fiber explosion (AFEXTM) and densified83 (Pretreatment B). At the biorefinery, biomass and untreated intermediates (red and blue lines) should be pretreated (Pretreatment 1). All pretreated intermediates (grey and purple lines) are then sent for conversion (Conversions 1 and 2) to ethanol. 2.1.1
Inventory Planning
Due to seasonal biomass availability, some biomass types are only available at certain seasons. Multi-period inventory planning is therefore necessary to ensure constant supply of biofuel. Figure 3 illustrates the material balance of biomass at the depot for a one year horizon divided into four periods (seasons). It assumed that biomass is shipped and converted evenly during each period; i.e., inventory fluctuations caused, for example, by a large shipment are not considered. To avoid the underestimation of inventory cost and also maintain an inventory level that would allow the execution of the same plan the next year, we adopt the concept of “cyclic” inventory balance, where inventory levels at the beginning and end of horizon are identical.
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Figure 2: Superstructure for a cellulosic biofuel supply chain. Biomass from different farms is shipped to depots or the biorefinery (red arcs); densified intermediate (blue arcs) and biomass are sent for pretreatment to the biorefinery; pretreated intermediates from depots (grey arcs) and the biorefinery itself (purple arcs) are converted to biofuel (green arc). t=1
Inventory holding level, S
(a)
t=2
t=3
t=4
Biomass, F 45
60
10 Consumption level, G
65 35
20
60
50
50
30 25 30
(b) 40 Inventory level , S (kt)
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30 20 10 1
2
3
4 Time period , t
Figure 3: Inventory planning of biomass: (a) material balance represented as flow balance in a network, and (b) resulting inventory level. Biomass from different farms (denoted by F, red line) is shipped to the depot; biomass is consumed (denoted by G, orange line) or held as inventory (denoted by S, dashed green line). Inventory levels at the end of each period shown as black boxes.
2.1.2
Depot Location and Transportation Distance
To account for transportation cost, different distance measurements (e.g., flow path distance57,62, rectilinear distance54 and straight line distance53,59,61) and transportation modes84 (e.g., roadway, railway, waterway, pipeline, etc.) have been used. Flow path distance is the exact traveling distance between two points based on the existing roadway or railway networks. The flow path distance between two points can be obtained from Google Maps85 and ArcGIS86. Rectilinear distance is the
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distance along two edges, perpendicular to each other, connecting two SC nodes. It is a good estimation in the United States where the interstate highway as well as the state roadway systems are developed based on approximate rectilinear patterns. Straight line distance is the shortest distance between two points. It can be estimated either by great-circle distance or Euclidean distance methods. The great-circle distance is calculated using Haversine formula87 or Spherical Law of Cosines which utilizes the available longitude and latitude information; whereas the Euclidean distance is calculated using the Pythagorean formula88. Tortuosity factor89, which depends on the road infrastructure, is usually considered when estimating the distance between two points using the great-circle or Euclidean distance measurement. 2.2 Problem Statement In this work, we consider a one-year horizon divided into time periods ∈ = 1,2,3,4 . The problem we study can be posed in terms of the following sets, subsets and parameters: a) Compounds ∈ with unit price , inventory holding unit costs , fixed/variable distance unit cost / , and fixed/variable CO2 emission due to transportation / .
i. ii. iii. iv. v.
Biomass feedstocks ⊂ with CO2 emission during biomass collection . Intermediates ⊂ produced at depots.
Intermediates ⊂ produced at biorefineries.
Products ⊂ with minimum , and maximum , product demand.
By-products ⊂ .
b) Farms ∈ with x-Cartesian coordinate !" , y-Cartesian coordinate #" , and biomass availability $,", .
c) Potential depots % ∈ &.
d) Biorefineries ' ∈ ( with x-Cartesian coordinate !) and y-Cartesian coordinate #) .
e) Technologies * ∈ + with conversion coefficient ,,- ,. , CO2 emission coefficient . , and production cost /. .
i. ii. iii.
Pretreatment/densification technologies at depots + ⊂ +.
Pretreatment technologies at biorefineries + ⊂ +. Conversion technologies + 0 ⊂ +.
Given the geographically distributed biomass availability and the locations of farms and potential biorefineries, our goal is to determine the optimal number, size, and location of depots, the selection and capacity of biorefineries, as well as the operating levels and inventories of all SC nodes.
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3 Proposed Model In this section, we present a multi-period mixed-integer nonlinear programming (MINLP) model for the design of biofuel SC with regional depots. We introduce the following binary decision variables: • • •
12 /1) =1 if depot k/biorefinery l is selected.
4.,2 /4.,) =1 if technology m at depot k/biorefinery l is selected.
5",2 /5",) /52,) =1 if transportation along arc j → k/j → l/k → l is selected.
and the following nonnegative continuous decision variables: • • • • • • • • •
6,", : harvested amount of compound ∈ at farm j during period t.
7,",2, /7,",), /7,2,), : amount of compound i shipped along arc j → k/j → l/k → l during period t. 8,", /8,2, /8,), : inventory level of compound at SC node j/k/l at the end of period t.
9,), : total sales of ∈ ∪ from biorefinery l during period t.
;,2,., : consumption of compound i by technology m at depot k during period t.
