详细信息

Batch process monitoring based on just-in-time learning and multiple-subspace principal component analysis  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Batch process monitoring based on just-in-time learning and multiple-subspace principal component analysis

作者:Lv, Zhaomin[1];Yan, Xuefeng[1];Jiang, Qingchao[1]

机构:[1]E China Univ Sci & Technol, Ministiy Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份:2014

卷号:137

起止页码:128

外文期刊名:CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录:;EI(收录号:20240915628405);WOS:【SCI-EXPANDED(收录号:WOS:000341470500014)】;

基金:The authors gratefully acknowledge the support of the following foundations: the 973 project of China (2013CB733605), National Natural Science Foundation of China (21176073) and the Fundamental Research Funds for the Central Universities.

语种:英文

外文关键词:Batch process monitoring; Just in time learning; Multiple subspace; Principal component analysis

摘要:Batch or fed-batch process monitoring is a challenging task because of its characteristics such as batch-to-batch variations, inherent time-varying dynamics, and multiple operating phases. Thus, a new batch process monitoring method based on just-in-time learning (JITL) and multiple-subspace principal component analysis (MSPCA) is developed. Based on offline one batch normal data, the division algorithm of multiple subspace is proposed, in which mutual information (MI) and K-means are employed to derive the segmentation rule of variable subspace and then the variables are divided into several subspaces according to the segmentation rule of variable subspace. At online monitoring, the training data set for modeling is obtained by JITL and separated into each subspace according to the segmentation rule of variable subspace. Principal component analysis is employed to build the model in each subspace, and all components are retained to calculate T-2 statistics. A unique probability index is obtained by Bayesian inference (BI) as the decision fusion strategy of T-2 statistics of all subspaces. A simple numerical example is used to show the advantages of the proposed MSPCA method. The feasibility and effectiveness of JITL-MSPCA is demonstrated by fed-batch penicillin fermentation. (C) 2014 Elsevier B.V. All rights reserved.

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