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Improving the performance of direct contact membrane distillation (DCMD) for liquid desiccant regeneration has attracted increasing attention. This paper presents multi-objective optimization of a DCMD regenerator to maximize its regeneration capacity (RC) and thermal efficiency (TE) simultaneously when treating a 25–30 wt.% lithium chloride desiccant solution. The key parameters, including initial feed concentration, feed and distillate inlet temperatures and volumetric flow rate were optimized using two methods (i.e. non-dominated sorting genetic algorithm (NSGA-Ⅱ) based method and a fuzzy clustering and weighted cumulative probability distribution (FC-WCPD) technique). The first method obtained an optimal Pareto front, in which the RC and TE were in the ranges of 0.77–0.91 wt.% and 12.2%–13.3%, respectively. The feed and distillate inlet temperatures showed a conflicting effect on enhancing the two objectives, while the initial feed concentration and volumetric flow rate wer ....

Fuzzy Clustering , Genetic Algorithm , Iquid Desiccant Regeneration , Membrane Distillation , Multi Objective Optimization ,