Evaporative cooling performance prediction and multi-objective optimization for hollow fiber membrane module using response surface methodology

The proposed hollow fiber membrane-based evaporative cooler (HFMEC) is expected to be an alternative to the conventional direct evaporative cooler because of its advantages such as the isolation of air from liquid water and the large specific surface area. For the common counter-flow HFMEC with many influencing parameters, it is a bit laborious or even incompetent to rely on experiment or numerical simulation for the parametric study and optimization. Therefore, this study aims to develop accurate and rapid performance prediction models for the proposed HFMEC with the statistical method. An experimental test system for a counter-flow HFMEC was set up. 120 sets of simulations were carried out based on the experimentally validated numerical model and the response surface methodology. Five accurate and practical empirical equations were derived using simulated data: the considered eight input factors consisted of four operating parameters and four membrane module design pa-rameters; the five output responses included the outlet air temperature, outlet air relative humidity, saturation effectiveness, cooling capacity per unit volume, and COP. These simplified equations were adopted to facilitate parameter sensitivity analysis and multi-objective optimization. A case study on the regional applicability of the counter-flow HFMEC demonstrated the ability of the derived equations to conveniently make performance predictions. The results indicated that the regression models could contribute to the rapid performance pre-diction of the counter-flow HFMEC, aiding in optimization and design.

Automated summary

This study aims to develop accurate and rapid performance prediction models for the proposed hollow fiber membrane-based evaporative cooler (HFMEC). Five accurate and practical empirical equations were derived using experimental and simulated data. These equations can be used for parameter sensitivity analysis and multi-objective optimization. A case study demonstrated the ability of these equations to conveniently make performance predictions, aiding in optimization and design.