期刊
ENVIRONMENTAL TECHNOLOGY
卷 34, 期 11, 页码 1377-1384出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/09593330.2012.750381
关键词
NO; (x); SCR; activated carbon; response surface methodology; artificial neural network
资金
- University of Tabriz, Iran
- Iranian Nanotechnology Initiative
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: 10.2v/v% HNO3, 6.1wt% 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 R-2 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据