Experimental investigation, optimization and IoT-based monitoring for enhanced yield in single basin solar still systems

• Published in: Desalination and water treatment Vol. 324; p. 101424
• Main Authors: Jeyam Jacqueline, S., Venugopal, D., Joe Patrick Gnanaraj, S.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2025; Elsevier
• Subjects: Ant colony optimization (ACO); Differential evolution (DE); Fresh water production; Grey wolf optimizer (GWO); IoT based monitoring; Metaheuristic; Particle swarm optimization (PSO); Solar still; Taguchi technique; Thermal energy storage; Solar still; Metaheuristic; Particle swarm optimization (PSO); Fresh water production; Grey wolf optimizer (GWO); Differential evolution (DE); Thermal energy storage; IoT based monitoring; Taguchi technique; Ant colony optimization (ACO)
• ISSN: 1944-3986; 1944-3994; 1944-3986
• DOI: 10.1016/j.dwt.2025.101424

The solar still technology is a renewable source that produces freshwater by utilizing the sunlight. Yet, its productivity could be improved by means of maximizing important design variables. The paper gives a comprehensive approach that combines experimental validation, Taguchi-based parametric analysis, metaheuristic optimisation, and IoT-based observing system to enhance the performance of a single basin solar still. The study to determine the basin geometry, wick material, heat storage medium and the water depth were assessed on three levels using a Taguchi L9 orthogonal array. Five metaheuristic algorithms, that is, PSO, GA, GWO, ACO, and DE were used to further optimize the outputs. The maximum configuration of the algorithms always consisted of chambered basin with fins, polished black granite heat store and black cotton fabric wick, and 15 mm depth of water in a basin. PSO showed the fastest and the most stable solution, among them. Optimized system showed a maximum yield of 5200 ml/m2/day, and an average of 5040 ml/ m2/ day; this is a 93.87 percent increase over a conventional flat basin still. The ratio of output latent heat to input solar energy was given as thermal efficiency of 41.2 %. The novelty was the creation of an IoT-centered mobile monitoring system that allows monitoring the temperature of a basin, solar irradiance, and volume of a distillate in real-time and it offers much needed assistance to the automation of the logging, distant diagnostics, and the connection of eventual smart control. This method illustrates a proven scalable process route to smart solar desalination systems suitable to be implemented in water-deficient regions.