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AISEE 2025, 1(1): 25-35 |
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Dumka P, Pandey N, Mishra D R, Mishra R, Chauhan R. Performance Enhancement and Predictive Modelling of Sponge-Augmented Solar Stills Using Ensemble Learning Techniques. AISEE 2025; 1 (1) :25-35
URL: http://aisesjournal.com/article-1-39-en.html
URL: http://aisesjournal.com/article-1-39-en.html
Jaypee University of Engineering and Technology
Abstract: (225 Views)
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.
Type of Study: Research |
Subject:
General
Received: 2025/07/31 | Accepted: 2025/08/16 | Published: 2025/09/20
Received: 2025/07/31 | Accepted: 2025/08/16 | Published: 2025/09/20
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