Abstract
In this work, two methanol-reforming gas fuel cell systems coupled with an electrochemical hydrogen pump (EHP), namely, EHP pre-coupling and post-coupling fuel cell systems were proposed to achieve performance enhancement. A back propagation neural network surrogate model was established for the two systems to accurately describe system behavior, and a second-generation multi-objective non-dominated genetic algorithm was utilized for addressing the multi-objective optimization problem encompassing net output power, carbon mass-specific emission and the levelized cost of electricity. The linear non-weighted multi-attribute preference and technique for order preference by similarity to ideal solution multi-attribute decision-making methods were employed to select the optimal solutions. The optimized EHP pre-coupling fuel cell system exhibited lower carbon emissions (0.69 kgCO2/(kW·h)) and higher net output power (126.02 kW) compared to previous studies. While the EHP post-coupling system achieves a lower levelized cost of electricity (0.36 USD/(kW·h)), and stronger system stability (total efficiency reduction less 1 %). Both EHP-coupling systems offer viable process enhancements for fuel cell systems.
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