Evaporative cooling systems face limitations in producing cold water due to their dependence on ambient air conditions, air-water cross-contamination, and large equipment size. This study introduces a vacuum-assisted hollow fiber membrane integrated evaporative water cooler as a potential solution. To facilitate its deployment in applications such as building air conditioning and industrial processes, a robust design optimization framework was constructed. A numerical model was established and experimentally verified. Combining the response surface method, three regression models were derived for convenient performance prediction. The six input design parameters encompass operating conditions and membrane specifications. The three output performance metrics are outlet water temperature, coefficient of performance, and cooling capacity per unit volume. Multi-objective optimization using a genetic algorithm, under two inlet water temperature conditions, yielded well-balanced cooler designs with superior cooling performance, energy efficiency, and compactness. The regression models demonstrated high prediction accuracy, with R2 surpassing 0.93. Compared to the original designs, the parameter combinations identified through the ideal point method realized over 40% reduction in outlet water temperature, over 66% increase in coefficient of performance, and over 138% enlargement in cooling capacity per unit volume. Furthermore, validation of the obtained Pareto fronts confirmed the practical reliability of the design references.

A design optimization framework for vacuum-assisted hollow fiber membrane integrated evaporative water coolers