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Development and neural network optimization of a renewable-based system for hydrogen production and desalination

Author

Listed:
  • Balali, Adel
  • Asadabadi, Mohammad Javad Raji
  • Mehrenjani, Javad Rezazadeh
  • Gharehghani, Ayat
  • Moghimi, Mahdi

Abstract

This study introduces an integrated system based on geothermal and solar energy to provide useful products such as electricity, freshwater, and hydrogen. In the proposed system, a solar unit including parabolic trough collectors has been used to improve the performance of the system and increase the mentioned products. The low-temperature geothermal stream returns to the ground after passing through a thermoelectric generator unit. A combination of Rankine and organic Rankine cycles is used to supply electricity and run the water electrolysis unit. This study aims to address the inefficient heat recovery in a combined solar and geothermal resource system by utilizing waste heat to generate clean hydrogen. A parametric study and sensitivity analysis are considered to investigate the effect of design parameters on the main outputs of the system. After evaluating the system from energy, exergy, and economic perspectives, a multi-objective optimization process with a combination of artificial neural network and genetic algorithm was performed on the system. The optimization problem is divided into two cases (scenarios). Each case considers energy efficiency and total cost rate as objective functions. In addition, hydrogen and freshwater production rates are considered as the third objective function in cases A and B, respectively. The optimization results showed that in case A, the optimal values of exergy efficiency, hydrogen production, and total cost rate are 15.54%, 6.18 kg/h, and 85.9 $/h, respectively. Also, in case B, values of 15.09%, 3527.79 kg/h, and 89.44 $/h are calculated for exergy efficiency, freshwater production, and total cost rate, respectively. A case study conducted in Tehran revealed that the peak production of various products is observed in May, which leads to the production of 623.14 kW of electricity, 5.35 kg/h of hydrogen, and 1948.9 kg/h of freshwater in the base condition.

Suggested Citation

  • Balali, Adel & Asadabadi, Mohammad Javad Raji & Mehrenjani, Javad Rezazadeh & Gharehghani, Ayat & Moghimi, Mahdi, 2023. "Development and neural network optimization of a renewable-based system for hydrogen production and desalination," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012715
    DOI: 10.1016/j.renene.2023.119356
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