Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

doi:10.1016/j.cjche.2017.06.013

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (1): 137-143.DOI: 10.1016/j.cjche.2017.06.013

• Process Systems Engineering and Process Safety • 上一篇下一篇

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Pengfei Cao¹, Xionglin Luo², Xiaohong Song³

1 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
2 Research Institute of Automation, China University of Petroleum, Beijing 102249, China;
3 State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China

收稿日期:2016-10-31 修回日期:2017-06-07 出版日期:2018-01-28 发布日期:2018-03-01
通讯作者: Pengfei Cao,E-mail address:cpfskdzdh@sina.com
基金资助:
Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2016RCJJ046) and the National Basic Research Program of China (2012CB720500).

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Pengfei Cao¹, Xionglin Luo², Xiaohong Song³

1 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China;
2 Research Institute of Automation, China University of Petroleum, Beijing 102249, China;
3 State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo 255000, China

Received:2016-10-31 Revised:2017-06-07 Online:2018-01-28 Published:2018-03-01
Contact: Pengfei Cao,E-mail address:cpfskdzdh@sina.com
Supported by:
Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents (2016RCJJ046) and the National Basic Research Program of China (2012CB720500).

摘要/Abstract

摘要： Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way, and the stochastic Newton recursive (SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm (DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

关键词: Soft sensor, Modeling, Characteristics separation, System identification, Double auxiliary models

Abstract: Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way, and the stochastic Newton recursive (SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm (DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

Key words: Soft sensor, Modeling, Characteristics separation, System identification, Double auxiliary models

Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics[J]. Chinese Journal of Chemical Engineering, 2018, 26(1): 137-143.

Pengfei Cao, Xionglin Luo, Xiaohong Song. Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics[J]. Chin.J.Chem.Eng., 2018, 26(1): 137-143.

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Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

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