This research introduces a sustainable solar desalination setup that employs repurposed Aluminum–Tin (Al–Sn) coatings extracted from used drink cans as a medium for thermal energy storage. The configuration incorporates a 3D-printed polypropylene layer for enhanced insulation, effectively reducing thermal losses and improving heat preservation. In comparison with a standard solar still, this innovative model demonstrated a 34% improvement in distilled water output (3.65 L per day) and attained an overall efficiency of 20.3%. The sustainable selection of Al–Sn films offers both waste-valorization and low-carbon manufacturing advantages over conventional materials such as paraffin wax and nanofluids. Advanced machine learning models—RNN, Random Forest, and XGBoost—were implemented to optimize geometric and thermal parameters, where RNN demonstrated the best predictive accuracy (MSE = 0.15, accuracy = 93.5%). Overall, the work combines circular-economy material reuse with AI-driven optimization to advance the design of efficient and environmentally responsible solar desalination systems.
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