Thermodynamic Optimization of a Solar Heliostat-PCM Multigeneration System via ANN-GA: Combined Production of Power, Freshwater, Hydrogen, and Methane

The increasing demand for clean energy, fresh water, and renewable energy carriers necessitates the development of integrated multi-purpose solar systems. This study presents a solar-based multi-purpose energy generation cycle capable of simultaneously producing electricity, fresh water, hydrogen, and methane. The system integrates PCM thermal energy storage with performance optimization using a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) framework. Additionally, an isothermal methanation reactor design with dual-role thermal management using Therminol VP1 is employed, which eliminates the need for external cooling utilities. The parametric analysis revealed that increasing the H₂/CO₂ ratio beyond the baseline decreases both energy and exergy efficiencies while increasing operational costs. Under baseline conditions, the cycle achieved an energy efficiency of 26.27% and an exergy efficiency of 19.33% with a cost rate of $120.19 per hour. Furthermore, replacing NaNO₃/NaOH with KNO₃/NaNO₃ in the thermal storage system reduced both the volume and cost of the PCM tank, providing additional economic benefits. The results confirm the technical and economic viability of the proposed system for deployment in high-DNI coastal regions such as Chabahar port.