Optimization of Humidification-Dehumidification solar desalination Unit: Comparative analysis

Water is essential for life on Earth. By turning saline water into drinkable water, desalination technology may help solve water scarcity. The present work uses artificial neural networks and the response surface approach to discuss freshwater production through a humidification-dehumidification solar-powered desalination unit with a pneumatic sprayer. The compressed air flow rate, air pressure, and time on productivity desalinated by the proposed system are investigated. The designed artificial neural networks model indicated that responses are close to the observed findings. It is shown that the generated models can be utilized to predict productivity and accumulated productivity within the investigation area. The optimum parameters for maximum accumulated productivity are 150 L/min compressed air flow rate, 7 bar air pressure, and 16o'clock to achieve the maximum accumulated freshwater productivity of 19.4 L/day. The comparative results reveal higher precision in the artificial neural networks model than in the response surface methodology approach.