Fractional metrics, such as return on investment (ROI), are widely used for performance evaluation, but uncertainty in the real market may unfortunately diminish the results that are based on nominal parameters. This article addresses the optimal design of a large-scale processing network for producing a variety of algae-based fuels and value-added bioproducts under uncertainty. We develop by far the most comprehensive processing network with 46,704 alternative processing pathways. Based on the superstructure, a two-stage adaptive robust mixed integer fractional programming model is proposed to tackle the uncertainty and select the robust optimal processing pathway with the highest ROI. Since the proposed problem cannot be solved directly by any off-the-shelf solver, we develop an efficient tailored solution method that integrates a parametric algorithm with a column-and-constraint generation algorithm. The resulting robust optimal processing pathway selects biodiesel and poly-3-hydroxybutyrate as the final fuel and bioproduct, respectively.
- Mixed integer fractional programming
- Superstructure optimization
- Two-stage adaptive robust optimization
ASJC Scopus subject areas
- Chemical Engineering(all)
- Environmental Engineering