Coupling a thermoelectric-based heat recovery and hydrogen production unit with a SOFC-powered multi-generation structure; an in-depth economic machine learning-driven analysis

Abstract

This study presents a comprehensive technical, environmental, and economic analysis of a thermal power plant utilizing solid oxide fuel cells (SOFC) to meet urban demands for electrical power, fresh water, and hydrogen. The integrated system includes SOFC with anode and cathode recycling, multi-effect desalination, a power generation cycle with a heat recovery unit using a thermoelectric generator, and a hydrogen compression unit. A detailed parametric analysis was conducted to identify optimal conditions for key outputs such as total cost rate and exergy efficiency, employing genetic algorithms and artificial neural networks. According to the economic evaluation, the SOFC stack accounts for 65.11 % of total costs at 139.8 $/h, with the inverter contributing 11.9 %. The environmental analysis shows that the proposed system emits the least CO2 per energy unit compared to SOFC/GT and SOFC/GT/RC systems. The parametric study indicates that increasing the pressure ratio enhances the output power of the SOFC and the production of the gas turbine. However, this also leads to higher compressor consumption, thereby reducing net power. Furthermore, increasing the current density results in greater production of electricity, hydrogen, and freshwater, while also raising the exhaust gas temperature, which aids in the desalination process. The optimization results show an exergy efficiency of 61.38 % and a total cost rate of 132.9 $/h, with artificial neural networks reducing optimization time from 124 h to 14 min.

Keywords

• Solid oxide fuel cell (SOFC)
• Waste heat recovery
• Multi-effect desalination (MED)
• Thermoelectric generator (TEG)
• Machine learning-based optimization