Parameter collaborative analysis and multi-objective optimization of a nanofluid filtered solar membrane distillation system using heat pump.

The distributed solar powered membrane distillation (SPMD) system for water/electricity co-production is a valuable solution for relieving energy pressure in remote areas but it suffers from a complicated operation mechanism. This paper performs a comprehensive parameter analysis and user-oriented optimization on a nanofluid filtered solar membrane distillation system using heat pump to enhance its competitiveness. Response surface methodology is employed to examine the interactions of the influencing factors on the system performance, and the plots reveal significant interactions, particularly on equivalent specific thermal energy consumption (eSTEC). An artificial neural network is integrated with genetic algorithm to perform single and multi-objective system performance optimization. The results show that maximum system exergy efficiency, permeate flux and minimum eSTEC are 19.39 %, 35.8 kg·m−2·h−1 and 463.5 kWh·m−3, respectively. Furthermore, the technique for order preference by similarity to an ideal solution is utilized, based on performance indicator weight derived from the analytical hierarchy process, to offer a user-oriented optimal solution. It demonstrates that after optimization, the system exergy efficiency and permeate flux are improved by 9.9 % and 30.2 %, respectively, along with a reduction of eSTEC by 5.7 %. This research contributes to advancing solar-to-water/electricity efficiency and offering valuable insights for the development of SPMD systems. • Comprehensive analysis and user-oriented optimization on a new SPMD is conducted. • RSM employed to examine interactions of influencing factors on system performance. • ANN integrated with GA performs single and multi-objective system optimization. • Exergy efficiency & permeate flux improved by 9.9 % & 30.2 %, eSTEC reduced by 5.7 %. [ABSTRACT FROM AUTHOR]