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Discrete stochastic programming to address biomass yield variability and feedstock quality

Calderon Ambelis, Heydi Jeaneth
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Abstract

While advances in improving biomass yields and conversion technologies will contribute toward the U.S. energy security goals, the lack of a large-scale stable supply of feedstock could limit this biobased venture. Therefore, optimizing logistics for collecting, storing, combining feedstock, and address potential supply risks are critical to facilitate a biobased industry and offset non-renewable sources consumption. This project determined the land to contract for a five-year biomass supply subject to the risk of year to year variation in feedstock availability of two dedicated energy crops that could be blended to meet carbohydrate and ash requirements. For this purpose, I built a discrete stochastic programming model that minimized costs subject to the inherent variability of biomass yield, quality specifications, and assumed plant capacity. This research introduced a risk management approach to address the risk of year to year biomass yield variability and contributes to the creation of a market for bioenergy sources in Oklahoma.

Date
2020-07
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