Multi objective optimization of novel phase change material-based desalination system using genetic algorithms

Maximizing efficiency in desalination systems is necessary for addressing global water scarcity. This study focuses on modeling and optimizing a novel desalination system using genetic algorithms, emphasizing four key efficiencies: overall thermal, parabolic collector, exergy, and solar still parameters. A Box-Behnken experimental design, coupled with Response Surface Methodology (RSM), was utilised for performance prediction and optimization. The main objective is to maximize desalination system's efficiencies through effective control parameter and optimization. The novelty of this study lies in applying a genetic algorithm for multi-objective optimization of desalination efficiencies while systematically evaluating influence of inlet parameters. This approach addresses a key research gap by integrating multi-variable interactions in solar desalination efficiency analysis. The Multi objective optimization analysis showed that maximum overall thermal efficiency, parabolic collector efficiency, exergy efficiency of parabolic collector and solar still are found as 82.17 %, 68.56 %, 3.35 % and 22.57 % respectively. Optimization performed by RSM identifies that affects exergy efficiency of parabolic collector and solar still get maximum value at T_w of 74 °C, T_g of 39.9 °C, T_a of 34.7 °C, T_in of 29.6 °C and T_out of 65.6 °C. Additionally, genetic algorithms and response surface methodology were employed to optimize design parameters, leading to an overall thermal efficiency improvement and more effective desalination processes.