Multi-objective optimization to explore the potential optimal thermal and flow performance of a packed bed humidifier

To maximize the energy effectiveness and minimize the pressure drop of the packed bed humidifier filled with corrugated wire mesh packing, potentially, multi-objective optimization is implemented based on response surface method and genetic algorithm. One geometric parameter and four thermodynamic parameters are considered as the design variables, and the objective functions include energy effectiveness and pressure drop in Box-Behnken design. Then a reliable quadratic regression model of objective function is formed based on the 46 groups of experimental design schemes. The analysis results of variance indicate that pressure drop model shows a higher accuracy compared with energy effectiveness model. It is found that liquid-gas ratio, combination of liquid-gas ratio and inlet air temperature are respectively the most significant factors for energy effectiveness among the independent and interactive parameters, while it is specific surface area and combination of liquid-gas ratio and specific surface area for pressure drop, according to the Prob > F values. Besides, an optimal parameter group is obtained more quickly and several Pareto-optimal points are given after natural evolution. In addition, the actual optimal energy effectiveness of 0.94 and pressure drop of 0.34 Pa are simultaneously gained via introducing the optimal parameter group into the established mathematical models. Furthermore, it is also demonstrated that energy effectiveness improves by 40.30% and pressure drop reduces by 66.99%, respectively, compared with the best results before optimization. Analysis of humidification process by finite difference method