Performance optimization of CO2 hydrogenation to methanol in the fixed-bed reactor with fractal fins

Abstract

CO₂ hydrogenation to methanol has attracted considerable attention as a promising pathway toward sustainable development. This study introduced a novel fixed-bed reactor featuring a fractal-fin cooling structure, providing an effective strategy to overcome heat-transfer limitations. A computational fluid dynamics model incorporating detailed reaction kinetics was rigorously developed to investigate the performance characteristics of the reactor. Furthermore, a multi-objective optimization framework integrating response surface methodology with the non-dominated sorting genetic algorithm-II was established. The results indicated that, compared with the adiabatic reactor, the reactor with finned-tube cooling effectively increased methanol yield by 29%. Increasing the fin number and the fractal angle progressively enhanced the reactor’s heat transfer capability, resulting in a higher methanol mole concentration and a lower CO mole concentration. Additionally, as the inlet temperature increased, CO₂ conversion gradually rose and eventually stabilized, while methanol selectivity continuously declined. A higher temperature difference along the finned-tube wall was found to improve methanol selectivity. The operating parameters of the reactor were optimized to maximize both methanol selectivity and CO₂ conversion, and the Pareto-optimal solution set exhibited significant improvements in both metrics compared with the base-case conditions.