LAPSE:2023.3288
Published Article
LAPSE:2023.3288
Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring
Luping Zhao, Xin Huang
February 22, 2023
In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.
Keywords
Batch Process, multi-mode, multi-phase, partial least squares, process monitoring
Suggested Citation
Zhao L, Huang X. Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring. (2023). LAPSE:2023.3288
Author Affiliations
Zhao L: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China [ORCID]
Huang X: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Journal Name
Processes
Volume
10
Issue
1
First Page
43
Year
2021
Publication Date
2021-12-27
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr10010043, Publication Type: Journal Article
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LAPSE:2023.3288
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doi:10.3390/pr10010043
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Feb 22, 2023
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Feb 22, 2023
 
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