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A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization

Author

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  • Hu, Hao
  • Lin, Tao
  • Wang, Shaowen
  • Rodriguez, Luis F.

Abstract

Decision making in biomass supply chain management is subject to uncertainties in a number of factors such as biomass yield, procurement prices, market demands, transportation costs, and processing technologies. To better understand such uncertainties requires statistical analysis and data-intensive computing enabled by cyberGIS (aka geographic information science and systems based on advanced cyberinfrastructure and e-science). Therefore, we have developed a cyberGIS approach to optimize biomass supply chains under uncertainties. Our approach (1) designs optimal biomass supply chains from regional to national scale with flexible spatial selection of study areas; (2) performs uncertainty and sensitivity analysis to quantify how various sources of uncertainty in the biomass supply chain contribute to the variation of optimal results; and (3) provides users with online geodesign features. This approach has been implemented as a decision support system through integration of data management, mathematical modeling, uncertainty and sensitivity analysis, scenario analysis, and result representation and visualization. An optimization modeling analysis of 7000 scenarios using Monte Carlo methods has been conducted to quantify the uncertainty and sensitivity impact of various input factors on ethanol production costs and optimal biomass supply chain configurations in Illinois, United States. The results from uncertainty analysis showed that the minimal ethanol production costs range from $2.30 to $3.43gal−1, considering uncertainties from biomass supply, transportation, and processing. The results of sensitivity analysis demonstrated that biomass-ethanol conversion rate was the most influential factor to ethanol production costs while the optimal biomass supply chain infrastructure was sensitive to changes in biomass yield, raw biomass transportation cost, and logistics loss rate. Leveraging high performance computing power through cutting-edge cyberGIS software, what-if scenario analysis has been evaluated to make decisions in case of unexpected events occurring in the supply chain operations.

Suggested Citation

  • Hu, Hao & Lin, Tao & Wang, Shaowen & Rodriguez, Luis F., 2017. "A cyberGIS approach to uncertainty and sensitivity analysis in biomass supply chain optimization," Applied Energy, Elsevier, vol. 203(C), pages 26-40.
    • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:26-40
    DOI: 10.1016/j.apenergy.2017.03.107
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    5. Luo, Li & O'Hehir, Jim & Regan, Courtney M. & Meng, Li & Connor, Jeffery D. & Chow, Christopher W.K., 2021. "An integrated strategic and tactical optimization model for forest supply chain planning," Forest Policy and Economics, Elsevier, vol. 131(C).
    6. De Laporte, Aaron V. & Ripplinger, David G., 2019. "The effects of site selection, opportunity costs and transportation costs on bioethanol production," Renewable Energy, Elsevier, vol. 131(C), pages 73-82.
    7. Guo, Changqiang & Hu, Hao & Wang, Shaowen & Rodriguez, Luis F. & Ting, K.C. & Lin, Tao, 2022. "Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply," Renewable Energy, Elsevier, vol. 186(C), pages 378-393.
    8. Schipfer, Fabian & Kranzl, Lukas, 2019. "Techno-economic evaluation of biomass-to-end-use chains based on densified bioenergy carriers (dBECs)," Applied Energy, Elsevier, vol. 239(C), pages 715-724.
    9. Akhtari, Shaghaygh & Sowlati, Taraneh, 2020. "Hybrid optimization-simulation for integrated planning of bioenergy and biofuel supply chains," Applied Energy, Elsevier, vol. 259(C).
    10. Kang, Yating & Yang, Qing & Bartocci, Pietro & Wei, Hongjian & Liu, Sylvia Shuhan & Wu, Zhujuan & Zhou, Hewen & Yang, Haiping & Fantozzi, Francesco & Chen, Hanping, 2020. "Bioenergy in China: Evaluation of domestic biomass resources and the associated greenhouse gas mitigation potentials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    11. 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.
    12. 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).
    13. Lim, Chun Hsion & Lam, Hon Loong & Ng, Wendy Pei Qin, 2018. "A novel HAZOP approach for literature review on biomass supply chain optimisation model," Energy, Elsevier, vol. 146(C), pages 13-25.
    14. Cooper, Nathanial & Panteli, Anna & Shah, Nilay, 2019. "Linear estimators of biomass yield maps for improved biomass supply chain optimisation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
    16. Biljana Kulisic & Bruno Gagnon & Jörg Schweinle & Sam Van Holsbeeck & Mark Brown & Jurica Simurina & Ioannis Dimitriou & Heather McDonald, 2021. "The Contributions of Biomass Supply for Bioenergy in the Post-COVID-19 Recovery," Energies, MDPI, vol. 14(24), pages 1-31, December.
    17. Ng, Rex T.L. & Maravelias, Christos T., 2017. "Economic and energetic analysis of biofuel supply chains," Applied Energy, Elsevier, vol. 205(C), pages 1571-1582.
    18. Lo, Shirleen Lee Yuen & How, Bing Shen & Leong, Wei Dong & Teng, Sin Yong & Rhamdhani, Muhammad Akbar & Sunarso, Jaka, 2021. "Techno-economic analysis for biomass supply chain: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Ng, Rex T.L. & Kurniawan, Daniel & Wang, Hua & Mariska, Brian & Wu, Wenzhao & Maravelias, Christos T., 2018. "Integrated framework for designing spatially explicit biofuel supply chains," Applied Energy, Elsevier, vol. 216(C), pages 116-131.

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