Neural network aided design of Pt-Co-Ce/Al2O3 catalyst for selective CO oxidation in hydrogen-rich streams

In this study, the design of Pt-Co-Ce/Al2O3 catalyst for the low temperature CO oxidation in hydrogen streams was modeled using artificial neural networks. The effects of five design parameters, namely Pt wt.%, Co wt.%, Ce wt.%, calcination temperature and calcination time, on CO conversion were investigated by modeling the experimental data obtained in our laboratory for 30 catalysts. Although 30 points data set can be considered as small for the neural network modeling, the results were quite satisfactory apparently due to the fact that the experimental data generated with response surface method were well balanced over the experimental region and it was very suitable for neural network modeling. The success of neural network modeling was more apparent when the number of data points was increased to 120 by using the time on stream as another input parameter. It was then concluded that the neural network modeling can be very helpful to improve the experimental works in catalyst design and it may be combined with the statistical experimental design techniques so that the successful models can be constructed using relatively small number of data points.