A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

Chunying Zhang; Sun Chen; Fang Wu; Kai Song

doi:10.1016/j.cjche.2014.11.029

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A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing

过程系统工程与过程安全 | Updated：2026-01-06

- A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing
- A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing
- 中国化学工程学报（英文版） 2015年23卷第5期页码：796-803
- Affiliations：
  
  School of Chemical Engineering and Technology, Tianjin University,Tianjin,China,300072
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.11.029
  中图分类号：
- 纸质出版：2015
- Accepted：
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Chunying Zhang, Sun Chen, Fang Wu, 等. A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing[J]. 中国化学工程学报（英文版）, 2015,23(5):796-803.

-based online model updating strategy and its application in statistical process control for rubber mixing[J]. Chinese Journal of Chemical Engineering, 2015, 23(5): 796-803.
Chunying Zhang, Sun Chen, Fang Wu, 等. A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing[J]. 中国化学工程学报（英文版）, 2015,23(5):796-803. DOI： 10.1016/j.cjche.2014.11.029.

-based online model updating strategy and its application in statistical process control for rubber mixing[J]. Chinese Journal of Chemical Engineering, 2015, 23(5): 796-803. DOI： 10.1016/j.cjche.2014.11.029.

摘要

Abstract

To overcome the large time-delay in measuring the hardness of mixed rubber

rheological parameters were used to predict the hardness. A novel Q-based model updating strategy was proposed as a universal platform to track time-varying properties. Using a few selected support samples to update the model

the strategy could dramatically save the storage cost and overcome the adverse influence of lowsignal-to-noise ratio samples.Moreover

it could be applied to any statistical processmonitoring systemwithout drastic changes to them

which is practical for industrial practices. As examples

the Q-based strategy was integrated with three popular algorithms (partial least squares (PLS)

recursive PLS (RPLS)

and kernel PLS (KPLS)) to form novel regression ones

QPLS

QRPLS and QKPLS

respectively. The applications for predicting mixed rubber hardness on a large-scale tire plant in east China prove the theoretical considerations.

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