Optimal Design and Synthesis of Algae Processing Network under Uncertainty Based on Return on Investment

This article addresses the robust design and synthesis of algae processing networks under feedstock and product price uncertainty. We develop a superstructure of algae processing networks for the production of biodiesel and bioproducts. Based on the superstructure, we proposed a two-stage adaptive robust mixed-integer linear fractional programming problem to maximize the return on investment (ROI) of the algae processing processes under feedstock and product price uncertainty. Due to the fractional term and multi-level optimization structure, the problem cannot be solved directly by any solver. We propose a solution strategy that integrates a parametric algorithm and a column-and-constraint-generation algorithm. Robustness of the optimal solution is controlled by the budget of uncertain parameters, and the obtained robust optimal ROIs range from 6.38% to 7.62%. The robust optimal processing route selects algae cultivation in raceway open ponds, harvesting via centrifugation, supercritical CO₂ extraction, heterogeneously catalysed transesterification, directly combusting the biogas in the remnant treatment process, and poly-hydroxybutyrate upgrading process.