AI in Sustainable Energy and Environment

en Performance Enhancement and Predictive Modelling of Sponge-Augmented Solar Stills Using Ensemble Learning Techniques عمومى General پژوهشي Research In this study, an attempt has been made to compare the performance of a conventional solar still (CSS) with a modified solar still (MSS) augmented with sponges. Two identical single-slope solar stills have been built for this purpose, with sponges placed in the basin area to enhance capillary action-induced storage capacity and water surface area. Outdoor experiments were conducted under real weather conditions at Raghogarh, Guna (24° 39′N, 77° 19′E), India, for a basin water mass of 40 kg. The experimental results demonstrated a noticeable boost in distillation and efficiency when the CSS was modified with sponges. The cumulative distillate yield of MSS was observed to be 16.38% higher than that of CSS. The presence of sponges improved the overall efficiency of the still by 16.39%, reaching 36.86% compared to 31.67% for CSS. Sponges contributed to an 18.31% increase in distillate yield during the daytime and a 9.78% increase during nighttime, indicating their effectiveness in improving heat retention and capillary-assisted evaporation. Furthermore, multiple machine learning models were evaluated for predicting the performance of solar stills. Among the tested models, XGBoost demonstrated superior accuracy with an R² value of 0.9693 and minimal standard deviation, highlighting its robustness and reliability. The results highlight that integrating sponges with a conventional solar still can significantly enhance its efficiency and productivity, making it a viable solution for improving freshwater production.   Solar Still, Artificial Intelligence, Desalination, Sustainability, Sponges 25 35 http://aisesjournal.com/browse.php?a_code=A-10-351-2&slc_lang=en&sid=1 Pankaj Dumka p.dumka.ipec@gmail.com 1003194753284600152 1003194753284600152 Yes Jaypee University of Engineering and Technology Nitesh Pandey niteshpandey46974@gmail.com 1003194753284600153 1003194753284600153 No Jaypee University of Engineering and Technology Dhananjay R. Mishra dm30680@gmail.com 1003194753284600154 1003194753284600154 No Jaypee University of Engineering and Technology Rohit Mishra rohit.mishra@juetguna.in 1003194753284600155 1003194753284600155 No Jaypee University of Engineering and Technology Rishika Chauhan rishikaster@gmail.com 1003194753284600156 1003194753284600156 No Jaypee University of Engineering and Technology