Prediction approach for terminal batch process quality based on process transfer

CHU Fei; CHENG Xiang; DAI Wei; ZHAO Xu; WANG Fuli

doi:10.11949/j.issn.0438-1157.20171388

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Prediction approach for terminal batch process quality based on process transfer

更新时间：2025-12-10

- Prediction approach for terminal batch process quality based on process transfer
- CIESC Journal Issue 6, Pages: 2567-2575(2018)
- 作者机构：
  
  1. 中国矿业大学信息与控制工程学院,江苏,徐州,221116
  2. 东北大学流程工业综合自动化国家重点实验室,辽宁,沈阳,110819
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20171388
  CLC： P274
- Online First：05 June 2018，
  
  Published：05 June 2018
- 稿件说明：
移动端阅览
CHU Fei, CHENG Xiang, DAI Wei, et al. Prediction approach for terminal batch process quality based on process transfer[J]. CIESC Journal, 2018, (6): 2567-2575.
DOI：

CHU Fei, CHENG Xiang, DAI Wei, et al. Prediction approach for terminal batch process quality based on process transfer[J]. CIESC Journal, 2018, (6): 2567-2575. DOI： 10.11949/j.issn.0438-1157.20171388.

摘要

提出了一种基于过程迁移的间歇过程质量预报方法，旨在解决新间歇过程数据不足难以建立准确预报模型的问题。该方法基于多元统计回归分析模型，通过构建相似间歇过程间的共同潜变量空间，将已有相似间歇过程的数据信息迁移应用到未建模的新间歇过程中，实现新间歇过程的快速建模和质量预报。在线应用时，利用在线数据不断更新过程迁移模型；同时，实时估计模型预测误差的置信区间，判断预报模型预测误差的稳定性；为克服相似过程间可能存在的差异给迁移模型带来的不利影响，根据数据相似度逐步剔除相似间歇过程数据。最后，通过仿真实验验证了所提方法的有效性。

Abstract

Adequate process data is the foundation for implementing a data-driven modeling approach. Nevertheless

it is often impossible to meet this requirement for a new batch process. A new quality prediction approach based on process transfer was proposed to establish an accurate prediction model for new batch processes without sufficient data. With application of multivariate statistical regression analysis model

JY-PLS (Joint-Y partial least squares) regression model

this approach realized rapid modeling and quality prediction of new batch processes by construction of common latent variable space between similar batch processes and transfer of present data information from similar batch processes to new and non-modeled batch processes. During online application

the process transfer model was updated with online data and simultaneously estimated confidence interval of prediction error to determine stability of the prediction error. In order to overcome adverse effects on process transfer model caused by possible differences between batch processes

similar process data was eliminated gradually according to data similarity. Finally

effectiveness of the proposed approach was verified by penicillin process simulation.

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