Journal
JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY
Volume 18, Issue 6, Pages 2083-2091Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jiec.2012.06.002
Keywords
Volatile organic compounds; Ethyl acetate; ZSM-5; Catalyst design; Genetic algorithm; Artificial neural network
Funding
- University of Tabriz, Iran
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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.
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