Developing global reaction rate model for CO oxidation over Au catalysts from past data in literature using artificial neural networks

Abstract

In this work, the literature for CO oxidation kinetics over Au based catalysts was analyzed using artificial neural networks to test the possibility of developing global reaction rate models representing the entire literature. A database was constructed using the data obtained from nineteen papers published between the years 1997 and 2011; then, the reaction rate was modeled as a function of catalyst preparation and operational variables by using neural networks. Next, global reaction rate equations in the form of power law were developed for each support type by the help of the neural network model, and the order of reaction with respect to each reactant and the parameters of Arrhenius relation were estimated. These power law models were successfully validated by using the information reported in the literature; hence, it was concluded that they can be used for the initial estimation of the reaction rates in the absence of more specific rate equations.