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B. Izadkhah,S.R. Nabavi,D. Salari,T. Mahmuodi Badiki,N. Caylak,A. Niaei 한국공업화학회 2012 Journal of Industrial and Engineering Chemistry Vol.18 No.6
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.
Khataee, A.R.,Fathinia, M.,Zarei, M.,Izadkhah, B.,Joo, S.W. Korean Society of Industrial and Engineering Chemi 2014 Journal of industrial and engineering chemistry Vol.20 No.4
Oxidation of phenol in aqueous media using supported TiO<SUB>2</SUB> nanoparticles coupled with photoelectro-Fenton-like process using Mn<SUP>2+</SUP> cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn<SUP>2+</SUP> concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R<SUP>2</SUP>=0.949).
A.R. Khataee,주상우,M. Fathinia,M. Zarei,B. Izadkhah 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.4
Oxidation of phenol in aqueous media using supported TiO2 nanoparticles coupled with photoelectro-Fenton-like process using Mn2+ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn2+ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R2 = 0.949).