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

Journal Article


Abstract


  • 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.

Publication Date


  • 2022

Citation


  • Liu, J., Lin, W., Ren, H., Albdoor, A. K., Hai, F. I., & Ma, Z. (2022). Multi-objective optimization of a direct contact membrane distillation regenerator for liquid desiccant regeneration. Journal of Cleaner Production, 373. doi:10.1016/j.jclepro.2022.133736

Scopus Eid


  • 2-s2.0-85137158395

Web Of Science Accession Number


Volume


  • 373

Abstract


  • 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.

Publication Date


  • 2022

Citation


  • Liu, J., Lin, W., Ren, H., Albdoor, A. K., Hai, F. I., & Ma, Z. (2022). Multi-objective optimization of a direct contact membrane distillation regenerator for liquid desiccant regeneration. Journal of Cleaner Production, 373. doi:10.1016/j.jclepro.2022.133736

Scopus Eid


  • 2-s2.0-85137158395

Web Of Science Accession Number


Volume


  • 373