Abstract
The electrical conductivity of Solid Oxide Fuel Cell (SOFC) anode is one of the most important indexes effect the efficiency of SOFC. In order to improve performance of fuel cell systems, it is necessary to have model which modeling the electrical conductivity. In this paper, a model using Support Vector Regression Machine (SVRM) was established to modeling the electrical conductivity of La0.75Sr0.25Cr0.5Mn0.5O3-δ-xCuO (LSCM-xCu) composite anode under two influence factors, including operating temperature (T) and Cu content (x) in LSCM-xCu composites anode. The test result by SVRM support that the generalization ability of SVRM model is with high accuracy. The mean absolute error (MAE) of 4 test samples is 0.32, mean absolute percentage error (MAPE) is 1.05%, multiple correlation coefficients (R 2) is 1.00, which is quite satisfied with the engineering demand. This investigation suggests that SVRM is a powerful tool to be used for optimal designing or controlling the technological process of fuel cell system.
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