Optimizing the Various Operating Parameters of Active Solar Still for Water Purification

ABSTRACT This study includes experimental research on an active solar still designed for the Vidarbha region, aiming to enhance water output and energy efficiency. The authors used the Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), artificial bee colony (ABC), differential evolution (DE), and particle swarm optimization (PSO) for testing the still's performance. Productivity, thermal efficiency, heat loss coefficients, incident solar energy, and evaporation energy were measured under different conditions. The results show that PSO outperforms other algorithms in terms of productivity, thermal efficiency, and energy efficiency. This study demonstrates that optimization strategies significantly enhance the performance of solar stills in arid regions. Future studies explore hybrid algorithms and advanced materials to further improve solar still efficiency. This research aids in designing long‐term water desalination options for sites with great solar potential. The proposed PSO model outperforms all the existing models in all criteria. The proposed model has a water productivity of 4.44 L/m²/day, indicating high water production efficiency. The PSO model achieved a thermal efficiency of 4.72% and has the lowest heat loss coefficient cover of 2.6 W/m² K for improved heat retention. PSO has the highest rate of energy efficiency, 4.93%, and the lowest cost per liter of water, 2.52, indicating the overall beneficial value of optimizing the performance of the solar still.