Unraveling Optimal Biomass Processing Routes from Bioconversion Product and Process Networks under Uncertainty: An Adaptive Robust Optimization Approach

Abstract

A bioconversion product and process network converts different types of biomass to various fuels and chemicals via a plethora of technologies. Reliable bioconversion processing pathways should be designed considering the effect of uncertain parameters, such as biomass feedstock price and biofuel product demand. Given a large-scale bioconversion product and process network of 194 technologies and 139 materials/compounds, we propose a two-stage adaptive robust mixed-integer nonlinear programming problem. The model allows for decisions at the design and operational stages to be made sequentially and considers budgets of uncertainty to control the level of robustness. Nonlinearity in this model appears in the first-stage objective function, and the second-stage problem is a linear program. We efficiently solve the proposed problem with a tailored algorithm. The robust optimal solutions corresponding to various uncertainty budgets show that the minimum total annualized cost is more sensitive to biofuel demand uncertainty compared to biomass feedstock price uncertainty.