Using recycled aluminum absorbers and decision tree analysis to increase the thermal performance of solar distiller to produce freshwater

For next-generation renewable energy technologies to be sustainable, especially solar-assisted desalination systems, it is very important to accurately describe and predict how much sunlight will hit the ground. These kinds of gadgets are quite important for places that get a lot of sunlight but still have trouble getting enough fresh water. Recycled aluminum cans (ACs) were used in this experiment as an affordable, high-efficiency thermal absorber upgrade elements an increase temperature sensitivity of potable water output. solar distiller with a single basin (SS). The metal absorbers were painted matte black to make them better at absorbing radiative heat and storing thermal energy in the basin. We thoroughly examined absorber layer three spacing variants (3, 6, and 9 cm) to determine the optimal geometric layouts. We used an analytical method based on Decision Trees (DT) to compare the coated solar still with aluminum cans (CSS). The study first looked at a typical solar still setup without aluminum cans (WAC). Over the course of 2025, experimental testing was conducted in a stable and regulated environment at KLEF in Vijayawada, India. To make sure that the performance was fully evaluated, important environmental and thermodynamic parameters were constantly watched. Hourly freshwater generation, wind speed, ambient temperature, and global sun radiation were all assessed. According to the investigation, black-coated aluminum absorber systems function better than the WAC design. In comparison to the WAC standard (3.549 L m⁻²) and interfacial gaps of 3 cm and 9 cm, this shaped 4.107 and 4.463 L m⁻², respectively. Optimal daily productivity of 6.582 L m⁻² was attained at a 6 cm absorber–basin spacing, outperforming all other configurations. Decision tree modeling revealed potential indices of 0.19, 0.35, and 0.13 for absorber separations of 3, 6, and 9 cm. This shows how good it is at finding good places to work. Using DT modeling to optimize the process boosted the daily distillate output by 41%, especially during the hottest parts of the day. Adding just the 6 cm absorber layer has been enhanced production by 45%. But by using both photothermal and data-driven optimization, performance went boosted by about 60%. The DT method outperformed the RF model (0.15) in terms of classification accuracy at higher potential values (0.31). Its appropriateness as an optimization framework is confirmed by the analysis. In addition to improving solar distillation efficiency, DT-assisted optimization using designed recycled aluminum absorbers advances India’s sustainable water and energy endeavors.