Article ID Journal Published Year Pages File Type
227492 Journal of Industrial and Engineering Chemistry 2012 9 Pages PDF
Abstract

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 Ag-ZSM-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.

Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Engineering (General)
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