Machine learning optimization of a novel geothermal driven system with LNG heat sink for hydrogen production and liquefaction

Hydrogen production and liquefaction based on geothermal energy is a potential route for the future hydrogen economy. In the current study, a novel integrated system with power generation and cooling capabilities is designed which uses geothermal energy as a heat source and LNG stream as a heat sink. All the generated power by the system is delivered to the PEM electrolyzer to produce hydrogen and liquefied it through a Claude cycle. A comprehensive investigation is carried out to evaluate the performance of the system from a thermodynamic and economic points of view. The analysis shows that the hydrogen production rate is 106.8 kg/h if all the electricity is delivered to PEM electrolyzer. Also, PEM electrolyzer with 93.92 $/h and LNG vaporizer with 5.43 MW have the foremost impact on total cost rate and exergy destruction, respectively. Moreover, a parametric study is performed to understand the effects of input parameters on the performance of the system. In order to optimize hydrogen production rate, total cost rate, and exergy efficiency of the system, a multi-objective optimization process is applied to the system by coupling the artificial neural network with the genetic algorithm. From the optimization procedure, the optimum values of hydrogen production rate, total cost rate, and exergy efficiency are obtained as 154.95 (kg/h), 291.36 ($/h), 23.34%, respectively. At these conditions, cooling capacity and levelized cost of hydrogen are 5.25 MW and 1.827 $/ kg, correspondingly.