Ann-assisted optimization of solar-driven hybrid desalination systems with thermal energy storage for sustainable freshwater supply: a comparative 3E analysis of MED-TVC and MSF technologies in iranian cities

This study optimizes and evaluates solar-driven hybrid desalination systems in four different cities in Iran (Tehran, Shiraz, Kerman, and Bandar Abbas), integrating parabolic trough solar collectors (PTSC) with multi-effect desalination (MED) or multi-stage flash (MSF) technologies. Thermal energy storage (TES) units address intermittent sunlight, thereby ensuring sustainable freshwater production. Sobol global sensitivity analysis reveals that solar radiation is the dominant parameter, contributing 38–45% to output variance, while the collector mass flow rate and HTHE pinch point exhibit moderate nonlinear interactions (Δ = 0.110–0.139), with the Hybrid + MSF system showing stronger parametric coupling than Hybrid + MED, confirming that multi-variable optimization is essential for identifying optimal operating conditions. Utilizing artificial neural networks (ANN) and genetic algorithms (GA) for multi-objective optimization, the research balances exergy efficiency, freshwater production, and cost. Results indicate that a Hybrid + MSF system demonstrates superior energy efficiency (48.66%) and freshwater output (149.9 kg/s) compared to Hybrid + MED (44.85% and 70.3 kg/s). Supply-demand matching analysis further reveals that a single Hybrid + MSF unit can meet 2.9–6.4% of the daily freshwater demand in Shiraz, Kerman, and Bandar Abbas during peak production months, compared to 1.4–3.0% for Hybrid + MED