Optimization for Preparation Conditions of Mn-Ce Catalyst Based on BP Artificial Neural Network Model
Three influencing factors (roasting temperature, roasting time, and metal ratio) which affect the preparation conditions of Mn-Ce catalysts for catalytic wet air oxidation was investigated. A BP artificial neural network model was established, in which the input conditions were selected as roasting temperature, roasting time, and metal ratio, and the output condition was TOC removal of n-butyric. The highest TOC removal was regarded as the optimization aim, along with constraints of each factors bounds. The model validation results showed that there was only less than 5％ of average relative deviation existed between the values of BP model predicted and experimental ones. The determination coefficient between the fitting curve and the Nash-Suttcliffe simulation efficiency coefficient (NSC) were 0.8324 and 0.8116 (>0.80) respectively, indicating the model predicted well. Meanwhile, two-factor and three-factor optimization of Mn-Ce catalyst preparation was executed through genetic algorithms, and the value of TOC removal over catalytic wet air oxidation of n-butyric could increased by more than 10％ compared to the experimental one under the optimal reaction conditions.
catalytic wet air ozidation n-butyric acid Mn-Ce catalyst BP artificial neural network optimization
Yu Li Yan Hu Shuang Zheng Xianyuan Du Jiangling Wang
Energy and Environmental Research Center North China Electric Power University Beijing,China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）