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Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains

  • S.I.: Agriculture Analytics, BigData and Sustainable Development
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Abstract

A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31\(\%\) of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6\(\%\) and 91.63\(\%\) on average, respectively.

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Acknowledgements

This material is based upon work that is supported by the National Institute of Food and Agriculture (NIFA), U.S. Department of Agriculture (USDA), under Award No. 2014-38502-22598 through South Central Sun Grant Program and by the USDA/NIFA Hispanic Serving Institutions Education Grants Program No. 2015-38422-24064. The authors would like to thank Dr. Mohammad Roni, research scientist in the Bioenergy and Renewable Energy group at Idaho National Laboratory, for his valuable technical and practical advice.

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Authors and Affiliations

  1. Texas Sustainable Energy Research Institute and Mechanical Engineering Department, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA

    Mario Aboytes-Ojeda & Krystel K. Castillo-Villar

  2. Department of Industrial Engineering, Clemson University, 277-C Freeman Hall, Clemson, SC, 29634, USA

    Sandra D. Eksioglu

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  1. Mario Aboytes-Ojeda
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  2. Krystel K. Castillo-Villar
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  3. Sandra D. Eksioglu
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Correspondence to Krystel K. Castillo-Villar.

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Appendix

Appendix

Table 12 and 13 show the coordinates of the solution to the no quality-related properties and quality-related properties problems correspondingly.

Table 12 Coordinates of selected facilities (no quality-related properties)
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Table 13 Coordinates of selected facilities (problem 10)
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Aboytes-Ojeda, M., Castillo-Villar, K.K. & Eksioglu, S.D. Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains. Ann Oper Res 314, 319–346 (2022). https://doi.org/10.1007/s10479-019-03477-8

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