Modelling of fuel composition influences on solid oxide fuel cell performance by artificial neural network

There are many mathematical models of the singular solid oxide fuel cell (SOFC). SOFC performance modelling is related to the multiphysic processes taking places on the fuel cell surfaces. Heat transfer together with electrochemical reactions, mass and charge transport are conducted inside the cell. There are many parameters which impact the cell working conditions, e.g. electrolyte material, electrolyte thickness, cell temperature, inlet and outlet gas compositions at anode and cathode, anode and cathode porosities ect. The Artificial neural Network (ANN) can be applied to stimulate an object.s behaviour without an algorithmic solution merely by utilizing available experimental data. The ANN is used for modelling singular cell behaviour. The optimal network architecture is shown and commented. The error back-propagation algorithm was used for an ANN training procedure.