Optimizing Return on Investment in Biomass Conversion Networks under Uncertainty Using Data-Driven Adaptive Robust Optimization

摘要

Bioconversion networks provide a means of turning raw biomass feedstock into useful biochemicals and fuels. These networks can display not only how crops and plants can be converted into bioproducts, but also how organic waste and other unconventional feedstocks can be used to create useful products as well. In order to determine the economic feasibility of the conversion pathways in these networks, an economic measure of design profitability such as return on investment must be used. Given a bioconversion network containing 216 technologies and 172 materials/compounds, we propose a two-stage adaptive robust mixed integer fractional programming model capable of measuring the economic success of bioconversion technology pathways. The proposed approach yields the processing pathways with optimal return on investment subject to minimum demand and maximum capacity constraints.