Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design

Abstract

Abstract This work presents a novel methodology for integrated multi-objective superstructure optimization and multi-criteria assessment. The method is tailored for sustainable process synthesis utilizing mixed-integer linear programming (MILP). The six-step algorithm includes 1) superstructure formulation, 2) criteria definition and implementation, 3) criteria weighting, 4) single-criterion optimization, 5) reformulation and 6) multi-criteria optimization. It is automated in the O pen s U perstruc T ure mo D eling and O ptimizati O n f R amework (OUTDOOR) and tested on integrated power-to-X and biomass-to-X processes for methanol production. Three criteria are considered, namely net production costs (NPC), net production greenhouse gas emissions (NPE) and net production fresh water demand (NPFWD). The optimization indicates NPC of 1307 €/tMeOH with NPE of −2.23 t CO 2 / t MeOH ${\text{t}}_{{\text{CO}}_{2}}/{\text{t}}_{\text{MeOH}}$ and NPFWD of −3.42 t H 2 O / t MeOH ${\text{t}}_{{\text{H}}_{2}\text{O}}/{\text{t}}_{\text{MeOH}}$ for an optimal trade-off plant. The plant configuration features low-pressure alkaline electrolysis for hydrogen supply, absorption-based CO2 capture and steam production from methanol purge gas for internal heat supply. Conducted variation and sensitivity analyses indicate that methanol costs can drop to about 500 €/tMeOH if electricity is free of charge, or to 805 €/tMeOH if biogas is available at large quantities, if a least-cost process layouts are considered. However, all performed multi-criteria analyses imply a robust optimal process design utilizing electricity-based methanol production.