Optimizing Solar Desalination: Integrating Predictive Models with Thermal Energy Storage for Enhanced Efficiency and Sustainability

Abstract

Amidst the growing challenges of water and energy scarcity, the following research presents an innovative solar desalination system that integrates predictive models with advanced thermal energy storage (TES) to optimize freshwater production. The system adjusts feed water flow dynamically, ensuring consistent and continuous top feed water temperature (TBT). Comprehensive mathematical modeling and simulations reveal significant enhancements in system performance, with an optimized TES tank volume of 500 m3 and a solar collector area of 4245 m2. The economic analysis indicates the system is cost-effective, with a production cost of approximately USD 5.86 million, competitive with conventional methods. Additionally, the system achieved an average daily feed water flow rate of 10 kg/s, ensuring stable operation even under fluctuating solar radiation. The system also achieved a global warming potential (GWP) of 2.24 kg CO2-eq per cubic meter, a favorable and environmentally sustainable measure. This study stresses the potential for scalable, sustainable desalination solutions to enhance water security in arid and remote regions.