AI-DRIVEN THERMO-MECHANICAL OPTIMIZATION OF SOLAR-ASSISTED DESALINATION SYSTEMS FOR WATER-STRESSED REGIONS OF SINDH, PAKISTAN

Abstract

Water scarcity in arid regions of Sindh, Pakistan, has intensified due to climate variability, groundwater depletion, and rising demand for freshwater resources. Solar-assisted desalination systems offer a sustainable solution; however, their performance is constrained by thermo-mechanical inefficiencies, heat losses, and unstable output under fluctuating environmental conditions. This study developed an AI-driven thermo-mechanical optimization framework to enhance the efficiency and sustainability of solar-assisted desalination systems. A quantitative simulation-based research design was adopted, integrating computational fluid dynamics (CFD), thermo-mechanical modeling, and machine learning algorithms, including artificial neural networks and optimization techniques. System performance was evaluated under varying climatic conditions representative of Sindh, focusing on thermal efficiency, evaporation rate, heat loss reduction, and freshwater yield. The results revealed that AI-optimized systems significantly outperformed conventional and thermo-mechanical optimized configurations across all performance indicators. Thermal efficiency and freshwater yield increased substantially, while heat losses were minimized. Machine learning models demonstrated high predictive accuracy, with artificial neural networks outperforming other algorithms in capturing nonlinear system behavior. The study concludes that AI-driven thermo-mechanical optimization substantially enhances the performance, adaptability, and sustainability of solar-assisted desalination systems in water-stressed environments. The proposed framework provides a scalable and intelligent solution for addressing long-term water security challenges in Pakistan.