Multi-objective performance optimization of a Maisotsenko-cycle humidification-dehumidification desalination system using Pareto analysis and TOPSIS

Humidification-dehumidification (HDH) desalination is attractive for decentralized freshwater production, but its operating variables create conflicting water-output and energy-performance objectives. This study couples a previously validated thermodynamic model of a water-heated open-air open-water HDH system employing a Maisotsenko-cycle humidifier with a multi-objective genetic algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The novelty of this work lies in optimizing the operating conditions of a validated system by including ambient conditions and operating parameters in the optimization problem, which produces a climate-conditioned family of optimum solutions. Ambient temperature, relative humidity, mass flow ratio, and saline-water inlet temperature were varied to maximize freshwater productivity, gained-output ratio (GOR), and recovery ratio (RR) while minimizing specific energy consumption (SEC). The genetic algorithm generated 18 non-dominated solutions, spanning 4.18–8.64 kg/h in productivity, 1.46–2.16 in GOR, 3.35–5.12% in RR, and 305.08–445 kWh/m 3 in SEC. For comparison, published HDH and M-cycle studies report GOR values of 0.8–3.31 and SEC values of approximately 370–745 kWh/m 3 . TOPSIS selected representative compromise-performance, energetic, and production-priority operating points. The compromise-performance point produced 6.41 kg/h at GOR = 1.95, RR = 4.02%, and SEC = 337.26 kWh/m 3 . The energetic point achieved the highest GOR (2.13) and lowest SEC (310.47 kWh/m 3 ), whereas the production point achieved the highest productivity (8.63 kg/h) and RR (5.09%) among the selected scenarios. Within the obtained Pareto set, saline-water inlet temperature showed the strongest statistical association with all four performance indicators, suggesting its primary role in transitioning between energy-efficient and high-production operating points.