Burning Side Reaction Model of the INVISTA Oxidation Process Using a Radial Basis Function Neural Network Integrated with Partial Mutual Information-Least Square Regression

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN

Chao Chen¹  Xuefeng Yan¹ 

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology

关键词: Burning Side Reaction;    INVISTA Oxidation Process;    Radial Basis Function Neural Network;    Partial Mutual Information;    Cluster;   

DOI  :  10.1252/jcej.14we212

来源: Maruzen Company Ltd

【 摘 要 】

References(64)The mechanism of the burning side reaction in the INVISTA oxidation process is complex, nearly unknown, and difficult to model. In this study, a radial basis function neural network (RBFNN) was used to model the burning side reaction based on the data collected from the INVISTA process. Over the past decades, clustering methods have been used to improve the ability of several RBFNN models to determine more efficient structures. However, these RBFNN models determine the RBFNN structure without considering the prediction accuracy of the model. To elucidate the optimal RBFNN structure and obtain a satisfactory burning side reaction model, RBFNN integrated with partial mutual information-least square regression (PMI-LSR) is proposed. PMI-based selection takes the correlation between the hidden layer and the output layer into account and eliminates redundant information in the selected hidden layer neurons to improve RBFNN prediction accuracy. Sammon’s nonlinear map is used to illustrate the distribution of the selected hidden layer centers. This distribution differs from the uniform distribution of the cluster centers obtained using cluster methods. The burning side reaction model developed by PMI-LSR-RBFNN is better than those obtained by several cluster based RBFNN variants.

【 授权许可】

Unknown