Improvement of Methanol Synthesis Process by using a Novel Sorption-Enhanced Fluidized-bed Reactor, Part II: Multiobjective Optimization and Decision-making Method

In the first part (Part I) of this study, a novel fluidized bed reactor was modeled mathematically for methanol synthesis in the presence of in-situ water adsorbent named Sorption Enhanced Fluidized-bed Reactor (SE-FMR) is modeled, mathematically. Here, the non-dominated sorting genetic algorithm-II (NSGA-II) is applied for multi-objective optimization of this configuration. Inlet temperature of gas phase (Tg), temperature of saturated water (Tshell), total molar flow rate (Ft), diameter of solid adsorbent (ds), mass adsorbent solid to mass catalyst ratio (Mratio) and inlet pressure are selected as the decision variables. The production rate of methanol and selectivity is maximized as two objective functions. The Shannon’s Entropy, LINMAP and TOPSIS methods as the three decision making approaches are applied to select the final solution of Pareto front. The optimization results approved by about 203.63 and 276.65 ton/day methanol production rate and CO2 consumption, respectively, based on LINMAP methods compared with the conventional methanol configuration. The results recommend that consuming optimized-SE-FMR for improvement of methanol production could be feasible and beneficial.