Solid oxide fuel cell is a promising tool for distributed power generation systems. This type of power system will experience different conditions during its operating life. The present study aims to simulate mathematically a direct internal reforming planar type anode supported solid oxide fuel cell considering mass and energy conservation equations along with a complete electrochemical model. Two main reactions, namely water–gas shift reaction and methane steam reforming reaction, are considered as two dominant reactions occurring in a fuel cell. Such a model may be employed to examine the effect of different operating conditions on main solid oxide fuel cell parameters, such as temperature gradients, power, and efficiency. Furthermore, using such mathematical model, a multi-objective optimization procedure can be applied to determine maximum cell efficiency and output power under constraints such as the allowable temperature difference and limited operating potential. The selected design variables are air ratio, fuel utilization, average current density, steam to carbon ratio, and pre-reforming rate of methane. It has been revealed that any increase in pre-reforming rate of methane and steam to carbon ratio of the entering fuel will lead to efficiency penalty and more uniform temperature distribution along the cell. In addition, the more average current density increases, the less electric efficiency is achieved, and on the other hand, the more temperature difference along the cell is seen. Besides, it is shown that some interesting and important relationships as useful optimal design principles involved in the performance of solid oxide fuel cells can be discovered by Pareto based multi-objective optimization of the mathematically obtained model representing their electric performance. Such important optimal principles would not have been obtained without the use of both mathematical modeling and the Pareto optimization approach.
Modeling of solid oxide fuel cell (SOFC) electrodes from fabrication to operation: Microstructure optimization via artificial neural networks and multi-objective genetic algorithms
