Robust multi-objective optimization of methanol steam reforming for boosting hydrogen production

Methanol steam reforming (MSR) has been considered as a promising method for producing pure hydrogen in recent decades. A comprehensive two-dimensional steady-state mathematical model was developed to analyze the MSR reactor. To improving high purity hydrogen production, a triple-objective optimization of the MSR reactor is performed. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed as a robust optimization approach to maximize the three objectives, termed as, methanol conversion, CO selectivity, and H₂ selectivity. The Pareto optimal frontier has also been provided and the ultimate solution of the Pareto front has been found by the three decision-making methods (TOPSIS, LINMAP, and Shannon's Entropy). Among the three distinct decision-making approaches, LINMAP presents better results according to the deviation index parameter. It has been shown that a perfect agreement is available between the plant and simulation data. Operating under the optimum values based on the LINMAP method confirms an almost 47.04% enhancement of H₂ mass fraction compared to the conventional industrial MSR reactor. The predicted results advocate that the key superiority of the optimized-industrial reactor is the remarkable higher production rate of hydrogen compared to the conventional MSR reactor which makes optimized-industrial reactor both feasible and beneficial.