Design and optimization of Bi-metallic Ag-ZSM5 catalysts for catalytic oxidation of volatile organic compounds

A neural network model was coupled with genetic algorithm to find an optimal catalyst for elimination of volatile organic compounds (VOCs). The model was based on simultaneous investigation of catalyst formulation, preparation condition, and loaded metal atomic descriptors as representative of each metal, which enables us to evaluate catalyst composition with much fewer experimental data. We have investigated oxides of first transition metal series (V, Cr, Mn, Fe, Co, Ni, Cu and Zn) as a promoter for AgZSM-5 catalyst. Three optimum catalysts, Fe-Ag-ZSM-5, Ni-Ag-ZSM-5, and V-Ag-ZSM-5 were found to have more catalytic activity for VOC (ethyl acetate) oxidation than Ag-ZSM-5. (C) 2012 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved.