Solid Oxide Fuel cell (SOFC): Modeling and Testing Reliability Using Neural Network

The main objective of this paper is testing the reliability of a two dimensional numerical model of Solid Oxide Fuel cell using COMSOL (FEMLAB 3.1) software against neural network model. In the proposed study, two layers feed forward neural network was examined for the purpose of modeling the Solid Oxide Fuel Cell (SOFC) system. The examined neural network model with one hidden layer of five nodes was trained with the Levenberg-Marquardt back propagation algorithm. The presented feed forward neural network model is fitted very well with the experimental data and proved to outperform a numerical model. The various outcomes of this application indicate that numerical simulation of SOFC by using FEMLAB 3.1 needs minor modifications. A more general investigation into the potential role of neural network in modeling SOFC is conducted in this research.