文献类型：期刊文献

英文题名：Development of a Free Radical Kinetic Model for Industrial Oxidation of p-Xylene Based on Artificial Neural Network and Adaptive Immune Genetic Algorithm

作者：Qian, Feng[1];Tao, Lili[1];Sun, Weizhen[2];Du, Wenli[1]

机构：[1]E China Univ Chem Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]E China Univ Chem Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：51

期号：8

起止页码：3229

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000300854800004)】；

基金：This research was supported by Major State Basic Research Development Program of China (973 Program: 2012CB720500), National Natural Science Foundation of China (Key Program: U1162202), National Natural Science Foundation of China (General Program: 20876044), Shanghai Key Technologies R&D Program (10dz1121900), the 111 Project (B08021), Shanghai Leading Academic Discipline Project (B504).

语种：英文

摘要：A novel kinetic model based on the free radical mechanism is used to simulate the oxidation of p-xylene (PX) in a continuous stirred-tank reactor (CSTR) under industrial operating conditions. Because this kinetic model cannot provide appropriate prediction of the influence of the reaction factors, such as catalyst concentrations, water concentrations, and temperatures, on the kinetic parameters for oxidation of PX in the laboratory semibatch reactor (SBR), the kinetic parameters that are highly nonlinear of the reaction factors are estimated by a back-propagation neural network (BPNN). Furthermore, correction coefficients are introduced to accurately evaluate the kinetic parameters based on Adaptive Immune Genetic Algorithm (AIGA) due to the significant difference between the nature of PX oxidation conducted in the laboratory SBR and in the industrial CSTR. The model with the evaluated optimum kinetic parameters is obtained, and its efficiency is validated via comparison with industrial data.