Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes

Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignora...

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Bibliographic Details
Published inChinese journal of chemical engineering Vol. 20; no. 6; pp. 1191 - 1197
Main Author 赵露平 赵春晖 高福荣
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2012
Subjects
Algorithms
batch process
Chemical engineering
Injection molding
Least squares method
Monitoring
multi-phase
On-line systems
partial least squares
Phase transformations
quality prediction
Regression
transition
产品质量
分析基
注塑成型工艺
相位信息
相变
质量预测
间歇过程
quality prediction
multi-phase
partial least squares
batch process
transition
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ISSN1004-9541
2210-321X
DOI10.1016/S1004-9541(12)60607-7

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Summary:Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares （PLS） method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.
Bibliography:ZHAO Luping 1,2 , ZHAO Chunhui 1, ＊＊ and GAO Furong 1,2 1 State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China 2 Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares （PLS） method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.
11-3270/TQ
multi-phase; transition; partial least squares; quality prediction; batch process
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content type line 23
ISSN:1004-9541
2210-321X
DOI:10.1016/S1004-9541(12)60607-7