A transient simulation for a novel solar-geothermal cogeneration system with a selection of heat transfer fluids using thermodynamics analysis and ANN intelligent (AI) modeling

Today, due to the reduction of fossil resources and on the other hand the high environmental pollution of these resources, it is necessary to pay attention to alternative resources, including clean and available renewable resources. In this study, solar and geothermal energy sources are combined, generating electricity, cooling, freshwater, and hydrogen, along with selecting a suitable heat transfer fluid for parabolic trough solar collectors. This system consists of parabolic trough solar collectors, a steam Rankine cycle, a steam Rankine cycle with an organic Rankine cycle, a proton exchange membrane electrolyzer, and a reverse osmosis desalination unit. To analyze the performance of solar collectors, Therminol 59, Marlotherm SH, Syltherm 800, and Therminol VP1 heat transfer fluids are selected. Solar radiation intensity, solar collector mass flow rate, turbine and pump efficiency, evaporator pinch-point temperature, and organic Rankine cycle turbine inlet temperature were investigated as design parameters affecting system performance. Maximizing energy efficiency and reducing cost rate were selected as two objective functions and determined using a multi-objective sorting genetic algorithm (NSGA-II). The results showed that Therminol 59 has the highest energy efficiency and net power output compared to other heat transfer fluids. Also, the proposed system produces 1140 kW of electricity in the optimal state, and in the best state, it has an energy efficiency of 32.39% and a cost of 36.32 $/GJ.