Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes<sup>*</sup>

赵露平; 赵春晖; 高福荣

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

Updated：2026-01-06

- Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*
- Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*
- 中国化学工程学报（英文版） 2012年20卷第6期页码：1191-1197
- Affiliations：
  
  1. State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University,Hangzhou,China,310027
  2. Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon,Hong Kong,China
- Author bio：
- Funds：
  
  Supported by Guangzhou Nansha District Bureau of Economy & Trade;Science & Technology;Information;Project (201103003);the Fundamental Research Funds for the Central Universities (2012QNA5012);Project of Education Department of Zhejiang Province (Y201223159);Technology Foundation for Selected Overseas Chinese Scholar of Zhejiang Province (J20120561)
- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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赵露平, 赵春晖, 高福荣. Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1191-1197.

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1191-1197.
赵露平, 赵春晖, 高福荣. Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1191-1197. DOI：

[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1191-1197. DOI：

摘要

Abstract

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.

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