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Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system

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  • Kang, Seongwhan
  • Heo, Seongmin
  • Realff, Matthew J.
  • Lee, Jay H.

Abstract

This research suggests a three-stage model framework for the design of a microalgae-based biofuel supply chain to meet the goal of economic commercialization. First, the design stage decides the spatial layouts and dimensions of each scale of biorefineries and economic analyses are done to estimate the capital costs and operating costs for different design options. Using the spatial dimensions determined in the first stage, the second stage selects the candidate locations for the biorefineries using an geographic information system (GIS) based site evaluation. This stage screens the available land area for the biorefineries, and also reduces the computational burden of the latter stage. In the mathematical optimization stage, a mixed-integer linear programming optimization model is formulated to make multi-period strategic and tactical decisions of the supply chain under the total cost minimization objective. Since the formulated problem is computationally intensive, a two-stage decomposition solution strategy is proposed to solve the problem in a reasonable time. The model framework is demonstrated through a case study cast in Texas, U.S., with a time horizon of ten years, using the suggested decomposition method. As a result, the minimum fuel selling price (MFSP) of microalgae-based biodiesel is calculated as $10.92/galbiodiesel, which is about three times higher than the current biodiesel price $3.51/galbiodiesel. To reduce the cost, the strategies of biomass storage and maximum delivery are investigated to deal with the productivity fluctuation. In the scenario analysis, the underutilization of production capacities is alleviated resulting in reduced MFSP of $7.90/galbiodiesel, $7.92/galbiodiesel, and $7.43/galbiodiesel respectively. Clearly, for economic feasibility, the production cost should be further reduced by developing more cost-efficient technologies and integrating high-value coproducts into the biorefinery portfolio.

Suggested Citation

  • Kang, Seongwhan & Heo, Seongmin & Realff, Matthew J. & Lee, Jay H., 2020. "Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system," Applied Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:appene:v:265:y:2020:i:c:s0306261920302853
    DOI: 10.1016/j.apenergy.2020.114773
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    6. Olli-Jussi Korpinen & Mika Aalto & Raghu KC & Timo Tokola & Tapio Ranta, 2023. "Utilisation of Spatial Data in Energy Biomass Supply Chain Research—A Review," Energies, MDPI, vol. 16(2), pages 1-23, January.
    7. Senocak, Ahmet Alp & Guner Goren, Hacer, 2023. "Three-phase artificial intelligence-geographic information systems-based biomass network design approach: A case study in Denizli," Applied Energy, Elsevier, vol. 343(C).
    8. 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).
    9. Islam Hassanin & Matjaz Knez, 2022. "Managing Supply Chain Activities in the Field of Energy Production Focusing on Renewables," Sustainability, MDPI, vol. 14(12), pages 1-33, June.

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