Economic and environmental optimization of a large scale sustainable dual feedstock lignocellulosic-based bioethanol supply chain in a stochastic environment

Atif Osmani and Jun Zhang

Applied Energy, 2014, vol. 114, issue C, 572-587

Abstract: This work proposes a two-stage stochastic optimization model to maximize the expected profit and simultaneously minimize carbon emissions of a dual-feedstock lignocellulosic-based bioethanol supply chain (LBSC) under uncertainties in supply, demand and prices. The model decides the optimal first-stage decisions and the expected values of the second-stage decisions. A case study based on a 4-state Midwestern region in the US demonstrates the effectiveness of the proposed stochastic model over a deterministic model under uncertainties. Two regional modes are considered for the geographic scale of the LBSC. Under co-operation mode the 4 states are considered as a combined region while under stand-alone mode each of the 4 states is considered as an individual region. Each state under co-operation mode gives better financial and environmental outcomes when compared to stand-alone mode. Uncertainty has a significant impact on the biomass processing capacity of biorefineries. While the location and the choice of conversion technology for biorefineries i.e. biochemical vs. thermochemical, are insensitive to the stochastic environment. As variability of the stochastic parameters increases, the financial and environmental performance is degraded. Sensitivity analysis shows that levels of tax credit and carbon price have a major impact on the choice of conversion technology for a selected biorefinery. Biochemical pathway is preferred over the thermochemical as carbon price increases. Thermochemical pathway is preferred over the biochemical as the level of tax credit increases. In addition, bioethanol production in the US is shown to be unviable without adequate governmental subsidy in the form of tax credits.

Keywords: Biofuel supply chain; Biomass conversion technologies; Facility location; Stochastic MILP; Sample Average Approximation; Sustainability (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:114:y:2014:i:c:p:572-587

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DOI: 10.1016/j.apenergy.2013.10.024

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