Artificial Neural Networks based thermodynamic and economic analysis of a hydrogen production system assisted by geothermal energy on Field Programmable Gate Array |
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Affiliation: | 1. Department of Mechanical Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Turkey;2. Department of Electrical Electronics Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Turkey;3. Department of Mechatronics Engineering, Afyon Kocatepe University, 03200 Afyonkarahisar, Turkey;4. Department of Electrical, Kırklareli University, Kırklareli 39000, Turkey;1. Department of Science, Education Faculty, Afyon Kocatepe University, 03200 Afyon, Turkey;2. Department of Mechatronics Engineering, Faculty of Technology, Suleyman Demirel University, Cunur, West Campus, Isparta, Turkey;3. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ont. L1H 7K4, Canada;4. Faculty of Mechanical Engineering, Yildiz Technical University, Istanbul, Turkey;1. Chemical Engineering Faculty, Urmia University of Technology, Urmia, Iran;2. Chemical Engineering Department, Faculty of Engineering, Urmia University, Urmia, Iran;3. ITM-CNR, c/o University of Calabria, Via Pietro Bucci, Cubo 17/C, 87036 Rende, CS, Italy |
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Abstract: | In this study, the thermodynamic and economic analysis of a geothermal energy assisted hydrogen production system was performed using real-time Artificial Neural Networks on Field Programmable Gate Array. During the modeling of the system in the computer environment, a liquid geothermal resource with a temperature of 200 °C and a flow rate of 100 kg/s was used for electricity generation, and this electricity was used as a work input in the electrolysis unit to split off water into the hydrogen and oxygen. In the designed system, the net work produced from the geothermal power cycle, the overall exergy efficiency of the system, the unit cost of the produced hydrogen and the simple payback period of the system were calculated as 7978 kW, 38.37%, 1.088 $/kg H2 and 4.074 years, respectively. In the second stage of the study, Feed-Forward Artificial Neural Networks model with a single hidden layer was used for modeling the system. The activation functions of the hidden layer and output layer were Tangent Sigmoid and Linear functions, respectively. The system was implemented on Field Programmable Gate Array using the Matlab-based model of the system as a reference. The maximum operating frequency and chip statistics of the designed unit of Field Programmable Gate Array based geothermal energy assisted hydrogen production system were presented. The result can be used to gain better knowledge and optimize hydrogen production systems. |
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Keywords: | Geothermal energy Hydrogen production Artificial neural networks Field programmable gate arrays |
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