(380c) Optimal Design of County-Level Hydrocarbon Biorefinery Supply Chains Under Uncertainty: A Case Study for the State of Illinois Using Spatially-Explicit Model

Hydrocarbon biorefinery technologies, which convert biomass to hydrocarbon biofuels such as gasoline and diesel, have been considered as promising approaches to replace traditional fossil-fuel-based energy generation technologies. Hydrocarbon biofuels can be potentially used without significant changes of the current fuel distribution and utilization, including pipelines, pumping stations, and vehicles. Compared with their conventional counterparts, hydrocarbon biofuels provide vehicle similar to or better performance.[1] Renewable Fuel Standards (RFS) has put a target of producing 36 billion gallons of biofuels in 2022.[2] In order to achieve this goal in a relatively short time, it is appealing to investigate design of supply chain network for biofuels. Currently, several mathematical models have been developed to address this issue.[3, 4] However, most of them are deterministic approaches that assume all problem parameters can be exactly known in advance. In practice, the hydrocarbon biorefinery supply chain may be affected by a variety of uncertainties, such as seasonal and geographical fluctuation of biomass supplies, population growth and unexpected events.[5] It is necessary to develop a comprehensive model for the optimal design of hydrocarbon biorefinery supply chain under the presence of uncertainties. Another critical issue is the existence of potentially high financial risk when uncertainties are involved in. Risk management should be taken into consideration in the final design of hydrocarbon biorefinery supply chain to reduce the impacts of unexpected outcomes of uncertain parameters.[6]

In this paper, we study the county-level hydrocarbon biofuel supply chain for the State of Illinois. A spatially explicit model is developed to integrate decision making across multiple temporal and spatial scales and simultaneously predict optimal network design, technology selection, capital investment, production operations, and logistics management decisions under hydrocarbon biofuel demand and biomass supply uncertainties. The objective of the model is to minimize the total expected annualized cost and financial risk that is quantified by conditional value-at-risk (CVaR)[7] and downside risk.[8] The State of Illinois consists of 102 counties. Thus, the supply chain network in this case study has 102 harvesting sites, 102 hydrocarbon biorefinery location candidates and 102 demand zones. Three major types of biomass are considered, which are agricultural residues, energy crops, and wood residues. Two types of hydrocarbon biofuels, diesel and gasoline, are produced and shipped to demand zones. We consider two major conversion technologies: gasification followed by FT synthesis and fast pyrolysis followed hydroprocessing. The capacities for both of them have three levels: 1-50 MGY (million gallons per year), 50-100 MGY, and 100-200 MGY.

Case study in the State of Illinois with sample size of 100 scenarios is solved and the minimum total expected annualized cost is $2,822.565MM. It corresponds to a minimum supply chain cost of biofuels is 4.00 $/GEG (gasoline-equivalent gallon). In the optimal solution, 11 biorefineries with capacities ranging from 62 MGY to 200 MGY are built in 11 counties that have high population density. Six of them are in the north part of Illinois, another four are located in central Illinois and the one left is built in southern Illinois. For each biorefinery, biomass used is supplied by local harvesting sites or those in adjacent 1 to 8 counties. The number of counties that are involved in supplying biomass to hydrocarbon biorefineries ranges from 23 to 56. When biomass supply decreases or biofuel demand increases, more counties join biomass supply network, leading to more inter-county transportation. According to production capacity and biofuel demand, hydrocarbon biofuels of each biorefinery are shipped by trucks to satisfy local demand or demand in other counties. The biorefinery with capacity of 80 MGY in Cook County can only meet local demands because of the highest demands of biofuels in Chicago area. On the contrary, as a result of the lowest demands in southern Illinois, the biorefinery in Wayne County with capacity of 125 MGY is able to meet biofuel demands in as many as 28 southern counties. When biomass supply reduces or biofuel demand increases too much, the number of counties whose demand can be met by each biorefinery shrinks a lot. Affected by yields of two technologies using different kinds of biomass that are geographically representative, most of biorefineries choose fast pyrolysis except the plant in La Salle County that adopts gasification. The inventory levels of biofuel products and biomass feedstock in a year always reach peaks in November and decrease gradually in other months, this trend is consistent with the seasonality of typical biomass resources corn stovers in the State of Illinois. Increase of either biofuel demand or biomass supply contributes to a high level of biomass inventory. Biofuel inventories are more sensitive to demand, they fall down sharply when demand grows greatly. In the optimal solution after financial risk management, some of biorefineries shift from counties with high population to counties with rich biomass resources to reduce the risk of supply disruption. Based on the optimal solutions of hydrocarbon biorefinery supply chain, counties with both high population density and abundant biomass resources are perfect locations of biorefineries.

References

[1]        F. Q. You and B. Wang, "Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed-Centralized Processing Networks," Industrial & Engineering Chemistry Research, vol. 50, pp. 10102-10127, 2011.

[2]        EIA, "U.S. Energy Information Administration," 2011.

[3]        A. Dunnett, C. Adjiman, and N. Shah, "A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential," Biotechnology for Biofuels, vol. 1, pp. 1-17, 2008.

[4]        B. Aksoy, H. Cullinan, D. Webster, K. Gue, S. Sukumaran, M. Eden, and N. Sammons, "Woody biomass and mill waste utilization opportunities in Alabama: Transportation cost minimization, optimum facility location, economic feasibility, and impact," Environmental Progress & Sustainable Energy, vol. 30, pp. 720-732, 2011.

[5]        B. H. Gebreslassie, Y. Yao, and F. Q. You, "Design under Uncertainty of Hydrocarbon Biorefinery Supply Chains: Multiobjective Stochastic Programming Models, Decomposition Algorithm and A Comparison between CVaR and Downside Risk," Submited to AIChE Journal 2012.

[6]        F. Q. You, J. M. Wassick, and I. E. Grossmann, "Risk Management for a Global Supply Chain Planning Under Uncertainty: Models and Algorithms," AIChE Journal, vol. 55, pp. 931-946, 2009.

[7]        S. Uryasev and R. T. Rockafellar, "Conditional Value-at-Risk: Optimization approach," in Stochastic Optimization: Algorithms and Applications. vol. 54, S. Uryasev and P. M. Pardalos, Eds., ed, 2001, pp. 411-435.

[8]        G. Eppen, and and R. Martin, "A scenario approach to capacity planning," Oper Res, vol. 37, pp. 517–527, 1989.