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

doi:10.1016/j.cjche.2014.11.029

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (5): 796-803.DOI: 10.1016/j.cjche.2014.11.029

• 过程系统工程与过程安全 • 上一篇下一篇

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

School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

收稿日期:2014-02-17 修回日期:2014-06-23 出版日期:2015-05-28 发布日期:2015-06-26
通讯作者: Kai Song

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

School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

Received:2014-02-17 Revised:2014-06-23 Online:2015-05-28 Published:2015-06-26
Contact: Kai Song

摘要/Abstract

摘要： 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.

关键词: Online model updating, Rubber mixing, Q statistic, Hardness, Rheological parameters, Statistical process control

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.

Key words: Online model updating, Rubber mixing, Q statistic, Hardness, Rheological parameters, Statistical process control

Chunying Zhang, Sun Chen, Fang Wu, Kai Song. A novel Q-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, Kai Song. A novel Q-based online model updating strategy and its application in statistical process control for rubber mixing[J]. Chin.J.Chem.Eng., 2015, 23(5): 796-803.

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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

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