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http://dx.doi.org/10.5012/bkcs.2010.31.04.808

Gas Phase Oxidation of Toluene and Ethyl Acetate over Proton and Cobalt Exchanged ZSM-5 Nano Catalysts- Experimental Study and ANN Modeling  

Hosseini, Seyed Ali (Department of Applied Chemistry, Faculty of Chemistry, University of Tariz)
Niaei, Aligholi (Department of Applied Chemistry, Faculty of Chemistry, University of Tariz)
Salari, Dariush (Department of Applied Chemistry, Faculty of Chemistry, University of Tariz)
Jodaei, Azadeh (Department of Applied Chemistry, Faculty of Chemistry, University of Tariz)
Publication Information
Abstract
Activities of nanostructure HZSM-5 and Co-ZSM-5 catalysts (with different Co-loading) for catalytic conversion of ethyl acetate and toluene were studied. The catalysts were prepared by wet impregnation method and were characterized by XRD, BET, SEM, TEM and ICP-AES techniques. Catalytic studies were carried out inside a U-shaped fixed bed reactor under atmospheric pressure and different temperatures. Toluene showed lower reactivity than ethyl acetate for conversion on Co-ZSM-5 catalysts. The effect of Co loading on conversion was prominent at temperatures below $400^{\circ}C$ and $450^{\circ}C$ for ethyl acetate and toluene respectively. In a binary mixture of organic compounds, toluene and ethyl acetate showed an inhibition and promotional behaviors respectively, in which the conversion of toluene was decreased at temperatures above $350^{\circ}C$. Inhibition effect of water vapor was negligible at temperatures above $400^{\circ}C$. An artificial neural networks model was developed to predict the conversion efficiency of ethyl acetate on Co-ZSM-5 catalysts based on experimental data. Predicted results showed a good agreement with experimental results. ANN modeling predicted the order of studied variable effects on ethyl acetate conversion, which was as follows: reaction temperature (50%) > ethyl acetate inlet concentration (25.085%) > content of Co loading (24.915%).
Keywords
Catalytic oxidation; Cobalt exchanged ZSM-5; Volatile organic compounds; ANN modeling;
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