The hybrid system consists of a molten carbonate fuel cell and Stirling engine, which is now seen as more expensive but clean and innovative energy source. This study explores how to optimize the anode and cathode partial pressures, temperature, and current density of the fuel cell. A multi-objective evolutionary algorithm integrated with the nondominated sorting genetic algorithm (NSGA-II) approach is employed to obtain Pareto fronts in each case scenario. The optimization process is carried out using a genetic algorithm in MATLAB software, resulting in the highest efficiency of 87.34% for the hybrid system. The study starts by analyzing the irreversible work and efficiency, then moves on to the reversible work and efficiency of the hybrid system and its components. It also looks at the voltage of the molten carbonate fuel cell in both irreversible and reversible states. The efficiency of the hybrid system in its irreversible state is 77.52%, while in the reversible state with the maximum reversible work of the fuel cell, it reaches 89.23%.
Alayi et al. (Thu,) studied this question.