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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 andO    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 €/t MeOH  with  NPE  of −2.23t   CO  2    /   t  MeOH${\text{t}}_{{\text{CO}}_{2}}/{\text{t}}_{\text{MeOH}}$and  NPFWD  of −3.42t    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 CO 2  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 €/t MeOH  if electricity is free of charge, or to 805 €/t MeOH  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.

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