multiobjective optimization of fluidized-bed methanol reactor for simultaneous intensification of methanol production rate and selectivity

In this study, non-dominated sorting genetic algorithm-ii (nsga-ii) is used for multi-objective optimization of the fluidized bed methanol synthesis reactor. inlet temperature of gas phase (tg), temperature of saturated water (tshell), total molar flow rate (ft) and inlet pressure of gas phase (pt) are selected as decision variables. methanol production rate and selectivity is maximized as two objective functions. the shannon’s entropy, linmap and topsis methods as three decision making approaches are used to select the final solution of pareto front. the optimization results enhanced about 55.4% and 48% methanol production rate and selectivity based on linmap methods compared with the non-optimized fluidized bed methanol configuration. the results suggest that utilizing of the optimized-fbmr for enhancement of methanol production could be feasible and beneficial.