Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

doi:10.1016/S1004-9541(14)60077-X

Chinese Journal of Chemical Engineering ›› 2014, Vol. 22 ›› Issue (5): 538-548.DOI: 10.1016/S1004-9541(14)60077-X

• 催化、动力学与反应工程 • 上一篇下一篇

Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

曹鹏飞, 罗雄麟

Research Institute of Automation, China University of Petroleum, Beijing 102249, China

收稿日期:2013-06-09 修回日期:2013-12-21 出版日期:2014-05-28 发布日期:2014-05-06
通讯作者: LUO Xionglin,E-mail: luoxl@cup.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China (61104218, 21006127), the National Basic Research Program of China (2012CB720500) and the Science Foundation of China University of Petroleum (YJRC-2013-12).

Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

CAO Pengfei, LUO Xionglin

Research Institute of Automation, China University of Petroleum, Beijing 102249, China

Received:2013-06-09 Revised:2013-12-21 Online:2014-05-28 Published:2014-05-06
Supported by:
Supported by the National Natural Science Foundation of China (61104218, 21006127), the National Basic Research Program of China (2012CB720500) and the Science Foundation of China University of Petroleum (YJRC-2013-12).

摘要/Abstract

摘要： The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.

关键词: soft sensor, Wiener model, modeling, alternate identification

Abstract: The processes of building dynamic and static relationships between secondary and primary variables are usually integrated in most of nonlinear dynamic soft sensor models. However, such integration limits the estimation accuracy of soft sensor models. Wiener model effectively describes dynamic and static characteristics of a system with the structure of dynamic and static submodels in cascade. We propose a soft sensor model derived from Wiener model structure, which is an extension of Wiener model. Dynamic and static relationships between secondary and primary variables are built respectively to describe the dynamic and static characteristics of system. The feasibility of this model is verified. Then the expression of discrete model is derived for soft sensor system. Conjugate gradient algorithm is applied to identify the dynamic and static model parameters alternately. Corresponding update method for soft sensor system is also given. Case studies confirm the effectiveness of the proposed model, alternate identification algorithm, and update method.

Key words: soft sensor, Wiener model, modeling, alternate identification

曹鹏飞, 罗雄麟. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. Chinese Journal of Chemical Engineering, 2014, 22(5): 538-548.

CAO Pengfei, LUO Xionglin. Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification[J]. Chin.J.Chem.Eng., 2014, 22(5): 538-548.

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Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

Soft Sensor Model Derived from Wiener Model Structure：Modeling and Identification

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