Performance evaluation of IT-AP-SOFC ammonia fueled and validation using machine learning

The limitation of fossil energy sources and environmental concerns have caused efforts to use clean and sustainable energy sources. The solid oxide fuel cell with intermediate working temperature and ammonia fuel is one of the promising sources to replace conventional energy sources. On the other hand, the use of fuel cell performance prediction methods with appropriate and high accuracy and speed is significantly important. In this research, first, the solid oxide fuel cell with ammonia fuel, electrolyte leakage, and average working temperature are numerically simulated. Then the effective input parameters are selected to calculate the performance of the fuel cell in different conditions. In this regard, after generating a sufficient data set, different machine learning algorithms are used to predict the objective functions, including the power density and the maximum temperature of the fuel cell. The results indicate the complexity of predicting the power density of the fuel cell compared to the maximum temperature. It was also observed that the XG Boosting method with R2 equal to 0.99 has the best efficiency in predicting the parameters of maximum temperature and power density.