Comparison of Contact and Non-Contact in Single-Slope Solar Desalination Systems: Experimental Insights and Machine Learning Predictions

Solar desalination systems turn saltwater water into freshwater, which helps to overcome water scarcity. In this study, the effects of direct (contact) and indirect (non-contact) interactions with water in solar desalination were evaluated. The emphasis was on changing water depths to better understand the performance variances. Contact systems have a direct interface between thermal absorption materials and water, whereas non-contact systems avoid material–water contact to increase longevity. The experiments at two elevated water depths (3 cm and 4 cm) were conducted in a single-slope solar desalination system. The productivity of both touch and non-contact systems was investigated in June, August, and October 2024 to gather sufficient data for the training and testing of various machine learning models used to predict the distillate. Surprisingly, the non-contact structure system produced 15% and 8% more distillate than the contact system at 3 and 4 cm water depths, respectively. This insightful result will aid in building efficient and sustainable solar desalination technologies. The comparative study gives information on the trade-off between contact and non-contact techniques, with implications for future advances in solar-powered desalination technology. Among all the machine learning techniques, random forest regression achieved the highest coefficient determination (R2 train of 0.89 and R2 test of 0.95 for the non-contact structure system and R2 of train 0.85 and R2 test of 0.98 for the contact structure system). Machine learning techniques improve solar desalination by allowing for predictive insights and efficient maintenance, ultimately leading to sustainable water production.