Modeling for soft sensor systems and parameters updating online |
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| Affiliation: | 1. CAE Team, Display R&D Centre, Samsung Display Co., Ltd., #95 Samsung 2-ro, Giheung-gu, Yongin-City, Gyeonggi-Do 446-711, Republic of Korea;2. School of Chemical Engineering and Advance Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;1. School of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, PR China;2. Department of Chemistry and Biochemistry, University of Delaware, Brown Laboratory, 163 The Green, Newark, DE 19716, USA;1. Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, People''s Republic of China;2. R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 320, Republic of China;1. Engineering Research Center of Process Equipment and its Re-manufacturing, Ministry of Education, Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310032, People''s Republic of China;2. R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li 320, Taiwan, ROC |
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| Abstract: | Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately. |
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| Keywords: | Soft sensor Modeling Substep identification Gauss–Newton Improved recursive algorithms Uniform convergence Bounded convergence |
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