title: Modelling and optimization of Mn/activate carbon nanocatalysts for NO reduction: comparison of RSM and ANN techniques.
abstract: A response surface methodology (RSM) involving a central composite design was applied to the modelling and optimization of a preparation of Mn/active carbon nanocatalysts in NH3-SCR of NO at 250 degrees C and the results were compared with the artificial neural network (ANN) predicted values. The catalyst preparation parameters, including metal loading (wt%), calcination temperature and pre-oxidization degree (v/v% HNO3) were selected as influence factors on catalyst efficiency. In the RSM model, the predicted values of NO conversion were found to be in good agreement with the experimental values. Pareto graphic analysis showed that all the chosen parameters and some of the interactions were effective on response. The optimization results showed that maximum NO conversion was achieved at the optimum conditions: 10.2 v/v% HNO3, 6.1 wt% Mn loading and calcination at 480 degrees C. The ANN model was developed by a feed-forward back propagation network with the topology 3, 8 and 1 and a Levenberg-Marquardt training algorithm. The mean square error for the ANN and RSM models were 0.339 and 1.176, respectively, and the R2 values were 0.991 and 0.972, respectively, indicating the superiority of ANN in capturing the nonlinear behaviour of the system and being accurate in estimating the values of the NO conversion.
authors.affiliation: Department of Applied Chemistry and Chemical Engineering, Faculty of Chemistry, University of Tabriz, Tabriz, Iran.
authors.forename: Seyed Mahdi
authors.initials: SM
authors.lastname: Mousavi
authors.forename: Aligholi
authors.initials: A
authors.lastname: Niaei
authors.forename: Dariush
authors.initials: D
authors.lastname: Salari
authors.forename: Parvaneh Nakhostin
authors.initials: PN
authors.lastname: Panahi
authors.forename: Masoud
authors.initials: M
authors.lastname: Samandari
mesh_terms: D000393:Air Pollutants; D000465:Algorithms; D000704:Analysis of Variance; D002244:Carbon / Q000737:chemistry*; D007220:Industrial Waste; D008345:Manganese / Q000737:chemistry*; D008962:Models, Theoretical; D053758:Nanoparticles / Q000737:chemistry*; D016571:Neural Networks, Computer; D009589:Nitrogen Oxides / Q000032:analysis / Q000737:chemistry*; D010084:Oxidation-Reduction; D012044:Regression Analysis; D012107:Research Design; D013696:Temperature
chemical_list: D000393:Air Pollutants; D007220:Industrial Waste; D009589:Nitrogen Oxides; D008345:Manganese; D002244:Carbon