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A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions

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  • Yazdanparast, R.
  • Jolai, F.
  • Pishvaee, M.S.
  • Keramati, A.

Abstract

Drop-in biofuels, which are functionally equivalent to petroleum-based transportation fuels and fully compatible with existing petroleum infrastructure, have been widely used in developed countries in recent decades as an effective means of reducing the greenhouse gas (GHG) emissions emitted by fossil fuels. The most challenging obstacles to biofuel development are the widespread access to cheap fossil fuels, high costs of constructing biorefineries, and potential disruptions in the biofuel supply chain (SC). This paper proposes a practical optimization model for the development of drop-in biofuels using the existing petroleum infrastructure. The model takes potential supply and production disruptions into account, and investigates four proactive strategies: flexible supply contracts, infrastructure fortification, alternative production routes, and prepositioning of emergency inventory to improve overall SC resilience. Furthermore, a risk-averse two-stage stochastic program is developed to cope with uncertainty where a conditional value-at-risk (CVaR) is used as the risk measure. By integrating the planning and operational decisions in this decision-making framework, we explore the best available solutions to ensure both economic and environmental sustainability. In the end, the applicability of the proposed model is verified through a real-world case study. The results indicate that the proposed strategies are effective in minimizing logistical costs.

Suggested Citation

  • Yazdanparast, R. & Jolai, F. & Pishvaee, M.S. & Keramati, A., 2022. "A resilient drop-in biofuel supply chain integrated with existing petroleum infrastructure: Toward more sustainable transport fuel solutions," Renewable Energy, Elsevier, vol. 184(C), pages 799-819.
    • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:799-819
    DOI: 10.1016/j.renene.2021.11.081
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    References listed on IDEAS

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    4. Hyeongjun Lim & Sojung Kim, 2024. "Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production," Energies, MDPI, vol. 17(20), pages 1-19, October.
    5. Oppong, Francis & Zhongyang, Luo & Li, Xiaolu & Song, Yang & Xu, Cangsu & Diaby, Abdullatif Lacina, 2022. "Methyl pentanoate laminar burning characteristics: Experimental and numerical analysis," Renewable Energy, Elsevier, vol. 197(C), pages 228-236.

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