Modeling and optimizing of anode-supported solid oxide fuel cells with gradient anode: Part II. Optimization and discussion

Abstract

This article is the second part of a two-part study of the modeling and optimizing of anode-supported solid oxide fuel cells (SOFC) with gradient anode. Based on the 3D numerical model established and validated in part I of this two-part article, two kinds of continuous gradient anodes at the micro-scale level (continuous gradient porosity and continuous gradient particle size) were introduced and described in this article. Two widely used neural networks (NNs), i.e. back propagation neural network (BPNN) and radial basis neural network (RBNN), were tested to find the proper approximation between various microstructure parameters distribution and cell electrical performance. For the optimization of both continuous gradient anodes, BPNN shows a better approximation of the objective function in comparison with RBNN. Based on the BPNN, the genetic algorithm (GA) was used to find the optimal microstructure parameters distribution for the highest electrical performance of SOFC. The optimal porosity distribution was found to be expressed as ε(z) = (0.57–0.70)z29.73/73429.73 + 0.7 (0 µm ≤ z ≤ 734 µm). The optimal particle diameter distribution was expressed as dp(z) = (0.10–0.60)z65.12/73465.12 + 0.6 (0 µm ≤ z ≤ 734 µm). Compared with the experimental maximum output power density (Pmax) of 0.31 W·cm−2 at 800 °C and 125 mL·min−1 in Part I, Pmax of the optimal porosity and particle diameter distribution is increased by 17.10% and 37.42%, respectively. Compared with the optimal porosity distribution, the optimal particle size distribution is more advantageous to improve the cell electrical performance. These results can provide a guidance of microstructure parameters distribution during the experimental fabrication for cell performance enhancement.