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Just-in-time and Adaptive Methods for Soft Sensor Design Open Access


Other title
Soft sensor
Type of item
Degree grantor
University of Alberta
Author or creator
Ma, Ming
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Zhao, Qing (Electrical and Computer Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Master of Science
Degree level
In many industrial processes, critical variables cannot be easily measured on-line: they are either obtained from hardware analyzers which are often expensive and difficult to maintain, or carried out off-line through laboratory analysis which cannot be used in real time control. These considerations motivate the design of inferential sensors or so-called soft sensors to infer process quality variables in real time from on-line process measurements. Numerous modeling techniques have been proposed and successfully applied to soft sensors for many industrial processes. Despite the popularity of these techniques in industry, development and implementation of soft sensors are still challenging due to complexity of industrial processes. The main contribution of this thesis is the development of several soft sensing methods that can achieve and maintain satisfactory performance while handling multi-mode, nonlinear and time-varying problems. Real time identification of local process model, also known as Just-in-time (JIT) modeling, is a special modeling technique for design of infinite-mode soft sensors. It is widely used in dealing with nonlinear and multi-mode of industrial processes. The performance of JIT model depends on parameters of the similarity function as well as the structure and parameters of the local model. A Bayesian framework is proposed to provide a systematic method for real time parameterization of the similarity function, selection of the local model structure, and estimation of the corresponding model parameters in JIT modeling methods. Another challenging issue in JIT modeling is the selection of most relevant samples from database by considering input-output information. Thus, a new input-output similarity function is defined and integrated into a Bayesian framework for JIT modeling. To cope with time-varying behaviour of processes, on-line adaptation is usually integrated in the implementation procedure. Although there are a number of publications dealing with adaptation of soft sensors, few of them have considered the adaptation of nonlinear grey-box models which are popular in process industry. Thus, a new adaptation mechanism for nonlinear grey-box models is proposed based on recursive prediction error method (RPEM). Adaptive data preprocessing and cautious update strategy are integrated to ensure robustness and effectiveness of the adaptation. The effectiveness and practicality of the proposed methods are verified using data from industrial processes. Some of the proposed methods have also been implemented for industrial applications.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
M. Ma, S. Khatibisepehr and B. Huang, A Bayesian framework for real-time identification of locally weighted partial least squares, AIChE Journal (2014).

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