4E analysis and machine learning optimization of a geothermal-based system integrated with ejector refrigeration cycle for efficient hydrogen production and liquefaction

Abstract

This study presents a new geothermal-based system for power generation and liquid hydrogen production. Depending on the network requirements, part of the generated power is supplied to the network and the remaining part enters the PEM electrolyzer to produce hydrogen. The produced hydrogen is then pre-cooled by the ejector cooling cycle before entering the liquefaction cycle. The ejector refrigeration cycle not only uses geothermal waste heat but also facilitates the liquefaction process through precooling hydrogen; therefore, it significantly improves the overall performance of the integrated system. In addition, the modified liquefaction cycle is suggested, which is superior to common ones. After simulating, some key parameters are identified and a comprehensive parametric research is carried out to investigate the thermodynamic, economic and environmental performance in different conditions. The optimization procedure is implemented through a genetic algorithm approach and the objective functions are defined. The results reveal that the double flash power cycle has the greatest effect on exergy destruction rate and total cost rate. In the optimal case, the total cost rate, the liquefied hydrogen rate, the total exergy efficiency, net power generation, and CO2 reduction rate are determined as 181.71 $/h, 59.92 kg/h, 25.27%, 4.03 MW, and 1421.2 kg/h, respectively. Also, coupling an artificial neural network with genetic algorithm significantly lessens optimization time.