Application in soft sensing modeling of chemical process based on K-OPLS method

作     者：LI Jun LI Kai 李军;李恺

作者机构：School of Automation and Electrical EngineeringLanzhou Jiaotong UniversityLanzhou 730070China 

出 版 物：《Journal of Measurement Science and Instrumentation》 (测试科学与仪器（英文版）)

年 卷 期：2020年第11卷第1期

页      面：17-27页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China(No.51467008) 

主　　题：kernel method orthogonal projection to latent structures(K-OPLS) soft sensing chemical process 

摘      要：Aiming at the problem of soft sensing modeling for chemical process with strong nonlinearity and complexity,a soft sensing modeling method based on kernel-based orthogonal projections to latent structures(K-OPLS)is *** projections to latent structures(O-PLS)is a general linear multi-variable data modeling *** can eliminate systematic variations from descriptive variables(input)that are orthogonal to response variables(output).In the framework of O-PLS model,K-OPLS method maps descriptive variables to high-dimensional feature space by using“kernel techniqueto calculate predictive components and response-orthogonal components in the ***,the K-OPLS method gives the non-linear relationship between the descriptor and the response variables,which improves the performance of the model and enhances the interpretability of the model to a certain *** verify the validity of K-OPLS method,it was applied to soft sensing modeling of component content of debutane tower base butane(C4),the quality index of the key product output for industrial fluidized catalytic cracking unit(FCCU)and H 2S and SO 2 concentration in sulfur recovery unit(SRU).Compared with support vector machines(SVM),least-squares support-vector machine(LS-SVM),support vector machine with principal component analysis(PCA-SVM),extreme learning machine(ELM),kernel based extreme learning machine(KELM)and kernel based extreme learning machine with principal component analysis(PCA-KELM)methods under the same conditions,the experimental results show that the K-OPLS method has superior modeling accuracy and good model generalization ability.