Optimizing water purification in double slope solar stills using abc algorithm and machine learning techniques

The double-slope solar still is a promising technology for efficient water desalination using solar energy. It harnesses solar radiation for sustainable freshwater production in arid regions. In this era of intelligent optimization, this study integrates metaheuristic algorithms, computational intelligence, and statistical modeling to enhance performance. The Taguchi method refines experimental design, while machine learning and the Artificial Bee Colony (ABC) algorithm optimize efficiency. The setup includes a 4 mm glass cover, 10 mm basin water depth, glass wool-enhanced fins, and 15 mm heat storage material. Experiments in Tuticorin, Tamil Nadu, validated system performance under real conditions. Results reveal 5380 ml/m²/day (101.51 % efficiency) with Taguchi optimization and 6000 ml/m²/day (113.21 % efficiency) using machine learning. The ABC algorithm produced a lower 4295 ml/m²/day (81.04 % efficiency). This study marks a new era in AI-driven solar desalination for sustainable water purification.