Multi-objective optimization of a direct contact membrane distillation regenerator for liquid desiccant regeneration

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 were near their lower limits. Despite nearly identical results being obtained, the FC-WCPD technique can directly compute the compromised optimal solution without the aid of a multi-criteria decision-making process in comparison with the NSGA-II-based method. The multi-objective optimization can effectively improve the overall performance of the DCMD regenerator as compared to the single-objective optimization, i.e. 4.9% higher in TE and 1.1% lower in RC than that optimizing the RC only; a 16.9% increase in RC and a 3.8% decrease in TE when compared to that optimizing the TE only.

Automated summary

This paper presents a multi-objective optimization approach for the DCMD regenerator used in liquid desiccant regeneration. The aim is to simultaneously maximize the regeneration capacity and thermal efficiency. The optimization results show that the feed and distillate inlet temperatures have conflicting effects on the two objectives, while the initial feed concentration and volumetric flow rate are near their lower limits. The multi-objective optimization approach significantly improves the overall performance of the DCMD regenerator.