Modeling and thermoeconomic analysis of new polygeneration system based on geothermal energy with sea water desalination and hydrogen production

In order to maximize heat recovery through cascading processes, this study presents the development of an advanced polygeneration system that combines liquefied natural gas (LNG) and geothermal power generation. The importance of this system is highlighted by the rising need for sustainable energy solutions that can generate a variety of outputs, including power, hydrogen, freshwater, and thermal energy. By using sensitivity-based optimizations, the suggested solution seeks to improve thermodynamic, financial, and environmental performance. With strong R-squared values and high predictive accuracy, the Random Forest machine learning model predicts exergy efficiency, freshwater production, unit specific product cost (USPC), net present value (NPV), and environmental impact. By reducing the irreversibility of important components, the system minimizes its impact on the environment while achieving electrical efficiency of 14.38 %, energy efficiency of 23.12 %, and exergy efficiency of 27.97 %. A USPC of 5.37 $/GJ and a NPV of 15.48 M$ support the system's economic performance and show that it is feasible in a market with favorable conditions. The Grey Wolf Optimization (GWO) algorithm, which directs the system's optimization process, demonstrates a competitive trade-off between exergy efficiency, freshwater production, costs, NPV, and environmental impact. This system's practical uses are best suited for coastal, island, or industrial areas with LNG and geothermal infrastructure, as it can offer a combined energy solution that lowers infrastructure costs and advances energy sustainability in general.