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

• Published in: Energy conversion and management Vol. 254; p. 115266
• Main Authors: Mehrenjani, J. Rezazadeh, Gharehghani, A., Sangesaraki, A. Gholizadeh
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.02.2022; Elsevier Science Ltd
• Subjects: Artificial neural networks; Cooling; Economic analysis; Exergy; Genetic algorithms; Geothermal energy; Geothermal power; Heat; Heat sinks; Hydrogen; Hydrogen production; Hydrogen-based energy; Liquefaction; Liquefied natural gas; LNG regasification; Machine learning; Multi-generation system; Multi-objective optimization; Multiple objective analysis; Neural networks; Optimization; Organic Rankine cycle; Performance evaluation; Thermodynamics; Vaporizers; LNG regasification; Multi-objective optimization; Geothermal energy; Hydrogen production; Organic Rankine cycle; Multi-generation system
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2022.115266

[Display omitted] •A novel geothermal driven system with LNG heat sink is designed.•Comprehensive energy, exergy and economic analysis of the cycle is performed.•Thermo-economic optimization through coupling ANN with GA is carried out.•Optimum exergy efficiency and total cost rate are 23.34 % and 291.36 $/h. 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 $/h and LNG vaporizer with 5.43 MW have the foremostimpact on total cost rate and exergy destruction, respectively. Moreover, a parametric study is performed to understand the effects of input parameters on the performance of the system. In order to optimize hydrogen production rate, total cost rate, and exergy efficiency of the system, a multi-objective optimization process is applied to the system by coupling the artificial neural network with the genetic algorithm. From the optimization procedure, the optimum values ​​of hydrogen production rate, total cost rate, and exergy efficiency are obtained as 154.95 (kg/h), 291.36 ($/h), 23.34%, respectively. At these conditions, cooling capacity and levelized cost of hydrogen are 5.25 MW and 1.827 $/ kg, correspondingly.