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Modeling and Development of Soft Sensors with Particle Filtering Approach Open Access


Other title
EM algorithm
Particle Filter
Soft Sensor
Type of item
Degree grantor
University of Alberta
Author or creator
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Prasad, Vinay (hemical and Materials Engineering)
Zhao, Qing (Electrical and Computer Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Master of Science
Degree level
Limitations of measurement techniques and increasingly complex chemical process render difficulties in obtaining certain critical process variables. The hardware sensor reading may have an obvious bias compared with the real value. Off-line laboratory analysis with high accuracy can only be obtained every certain period, sometimes even with time delay. Soft sensors are inferential methods that provide real-time estimation for those critical variables. This thesis deals with modeling, on-line calibration and implementation issues that are associated with soft sensor development. In chemical industries, processes are often designed to perform tasks under various operating conditions. In order to deal with modeling difficulties rendered by multiple operating conditions, the Expectation-Maximization (EM) algorithm is applied to deal with the identification problem of nonlinear parameter varying systems. The existing model is updated using the latest observation data. The particle filter based Bayesian method is proposed in this thesis to synthesize different sources of measurement information. An augmented state is constructed to deal with processes with time delay problem.
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.
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