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Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways

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  • Ge, Yuntian
  • Li, Lin
  • Yun, Lingxiang

Abstract

Cellulosic biofuel is considered a promising alternative to traditional fossil fuels. Currently, the commercialization of cellulosic biofuel is proceeding at a slower pace than expected due to the high cost of the cellulosic biofuel supply chain (CBSC), especially in the manufacturing process. In this paper, a new model for a four-echelon CBSC incorporating 12 conversion pathways is proposed to minimize total supply chain cost by considering multiple time periods, various biomass types and spatial distributions, biomass and biofuel inventories, logistics, and different demand levels. Case studies from the state of Illinois in the U.S. are used to demonstrate the effectiveness of the proposed model. It is manifested that the model can select appropriate conversion pathways for biorefineries given the abundance of local resources to minimize the overall cost. Compared to the two cases adopted a single conversion pathway, the optimal result achieves 14.6% and 4.6% reductions on the unit biofuel cost, respectively. In addition, the results of sensitivity analysis indicate that the biorefinery construction cost and the biofuel throughput are the main economic drivers in the current supply chain design.

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  • Ge, Yuntian & Li, Lin & Yun, Lingxiang, 2021. "Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314914
    DOI: 10.1016/j.apenergy.2020.116059
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    References listed on IDEAS

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    2. Edgar Gutierrez-Franco & Andres Polo & Nicolas Clavijo-Buritica & Luis Rabelo, 2021. "Multi-Objective Optimization to Support the Design of a Sustainable Supply Chain for the Generation of Biofuels from Forest Waste," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    3. Jabbarzadeh, Armin & Shamsi, Meisam, 2025. "Designing a resilient and sustainable multi-feedstock bioethanol supply chain: Integration of mathematical modeling and machine learning," Applied Energy, Elsevier, vol. 377(PB).
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    5. Yunusoglu, Pinar & Ozsoydan, Fehmi Burcin & Bilgen, Bilge, 2024. "A machine learning-based two-stage approach for the location of undesirable facilities in the biomass-to-bioenergy supply chain," Applied Energy, Elsevier, vol. 362(C).
    6. Maryam Roudneshin & Amanda Sosa, 2024. "Optimising Agricultural Waste Supply Chains for Sustainable Bioenergy Production: A Comprehensive Literature Review," Energies, MDPI, vol. 17(11), pages 1-23, May.

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