Linear estimators of biomass yield maps for improved biomass supply chain optimisation

Nathanial Cooper, Anna Panteli and Nilay Shah

Applied Energy, 2019, vol. 253, issue C, -

Abstract: Given the need to shift away from fossil fuels, expanding the role of the bioeconomy is vitally important. Biomass supply chain optimisation is a tool that has been used to help the biomass industry gain a foothold. Biomass supply chain models frequently use the average biomass yield of large areas to calculate overall yield. However, there can be large variation in the biomass yield within those areas, losing useful information. A biomass supply chain optimisation framework has been developed which uses information about the quality of land available by incorporating piecewise linear approximation of the biomass yield distribution into the optimisation. Linear approximations of the biomass yield variability allows the supply chain optimisation model to make more accurate decisions about quantity and location of biomass growth operations, affecting all downstream decisions. A case study of southwest Hungary for potential biomass industry viability has been examined using the framework to illustrate the impact of this yield information in the optimisation. The proposed framework successfully optimised the supply chain while accounting for variability in a spatially distributed resource, found that using the biomass yield estimates reduced the overall land usage by up to 17% in some cases, and improved biomass production by over 7%. Further, it improved biomass output, increasing the quantity of bioproducts which can be produced, and increasing the financial performance, thus demonstrating the importance of including yield variability in the optimisation. This framework could be used for other spatially distributed resources, such as solar insolation or wind availability.

Keywords: Biomass; Supply chain optimisation; Biomass yield; MILP; Biorefinery; Linear approximation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.apenergy.2019.113526

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