Multi-objective optimization of solar-driven hollow fiber membrane dehumidification system based on MOPSO

Yukui Men, Yanfang Dong, Si Zeng, Caihang Liang and Xiaoman Tong

Energy, 2024, vol. 304, issue C

Abstract: The solar-driven hollow fiber membrane dehumidification (SHFMD) system is a renewable, energy-saving, and highly effective dehumidification system. It provides an effective solution to the problem of air-entrained liquid droplets during the traditional solution dehumidification process. The system's mathematical model was established and the reliability of the mathematical model was verified by experimental data. To enhance the comprehensive performance of the system, the multi-objective particle swarm optimization (MOPSO) algorithm is used in this paper to optimize the multi-objective parameters of the system. The objective function is the total exergy destruction of the system and the dehumidification capacity of the system. The optimization variables are cold water temperature, cold water flow rate, solution flow rate, air flow rate, and hot water flow rate. The optimization is performed using the multi-objective particle swarm optimization (MOPSO) algorithm to obtain the Pareto front, and the points of the Pareto front are normalized. Finally, the optimal trade-off point of the Pareto frontier is obtained. Each operating parameter of the optimal point of the Pareto front is also analyzed. The results show that the air flow rate and cold water flow rate have the most influence on the conflict between the total exergy destruction of the system and the dehumidification capacity of the system.

Keywords: Solar energy; Liquid dehumidification; Hollow fiber membrane; Particle swarm algorithm; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224018589
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:304:y:2024:i:c:s0360544224018589

DOI: 10.1016/j.energy.2024.132084

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().