Ai-Optimized Hybrid Pv-T-Pcm Solar Desalination System: A Breakthrough in Efficiency and Yield

Abstract

This study explores advanced methodologies to enhance solar still efficiency by integrating forced circulation, hybrid photovoltaic-thermal (PV-T) systems, and phase change materials (PCMs). A novel multi-objective optimization framework is developed using computational fluid dynamics (CFD) and machine learning (ML) to maximize distillate yield while minimizing energy consumption. Experimental validation confirms a 50–85% increase in freshwater production compared to conventional solar stills. The study also introduces adaptive nanofluid cooling and AI-driven predictive control for real-time performance optimization. A techno-economic analysis demonstrates the feasibility of deploying these systems in off-grid communities, aligning with Sustainable Development Goal (SDG) 6 for clean water access.