Machine Learning Based Modeling for Solid Oxide Fuel Cells Power Performance Prediction

This study applies four different types of machine learning methods to model the power performance behaviour of a tubular solid oxide fuel cells (SOFC) under different operating conditions. The corresponding machine learning methods are: artificial neural network (ANN), fuzzy inference system (FIS), support vector machine (SVM), and genetic programming (GP). By using four types of inputs of the SOFC operation: i.e. load current, fuel utilization, inlet air temperature, and air molar flow rate, the task of the corresponding machine learning methods is to predict the stack voltage and outlet temperature values of the corresponding SOFC operation. 1000 input-output data pairings that were generated from the simulations of a physical tubular SOFC model under various operating conditions were used to train the corresponding machine learning models. It was found out from this study that ANN method has slightly better performance in modelling the power performance behaviour of the corresponding SOFC system under various operating conditions.