NEURAL-NETWORK AS A TOOL FOR CATALYST DEVELOPMENT

This article introduces a novel information science technique, an artificial neural network, which will possibly be a powerful tool for catalyst development. As an example of synergistically generated catalytic functions, the neural network has been applied to the estimation of acid strengths of mixed oxides, and it has been shown that not only the interpolation but also the extrapolation of given acid strength data is possible within reasonable experimental error. The neural network also has been successfully applied to the estimation of catalytic performance, such as the catalytic activity of a series of lanthanide oxides in the oxidation of butane and the selectivities to various products in the oxidative dehydrogenation of ethylbenzene on a series of promoted SnO2 catalysts. On the basis of these results, some remarks are given on its application to catalyst development.