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Particle Filter for Bayesian State Estimation and Its Application to Soft Sensor Development Open Access


Other title
Bayesian State Estimation
Particle Filter
Oil Sands Extraction
Soft Sensor
Type of item
Degree grantor
University of Alberta
Author or creator
Shao, Xinguang
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Prasad, Vinay (Chemical and Materials Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Zhang, Hong (Computing Science)
Mhaskar, Prashant (Chemical Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Doctor of Philosophy
Degree level
For chemical engineering processes, state estimation plays a key role in various applications such as process monitoring, fault detection, process optimization and model based control. Thanks to their distinct advantages of inference mechanism, Bayesian state estimators have been extensively studied and utilized in many areas in the past several decades. However, Bayesian estimation algorithms are often hindered by severe process nonlinearities, complicated state constraints, systematic modeling errors, unmeasurable perturbations, and irregular with possibly abnormal measurements. This dissertation proposes novel methods for nonlinear Bayesian estimation in the presence of such practical problems, with a focus on sequential Monte Carlo sampling based particle filter (PF) approaches. Simulation studies and industrial applications demonstrate the efficacy of the developed methods. In practical applications, nonlinear and non-Gaussian processes subject to state constraints are commonly encountered; however, most of the existing Bayesian methods do not take constraints into account. To address this inadequacy, a novel particle filter algorithm based on acceptance/rejection and optimization strategies is proposed. The proposed method retains the ability of PF in nonlinear and non-Gaussian state estimation, while taking advantage of optimization techniques in handling complicated constrained problems. Dynamical systems subject to unknown but bounded perturbations appear in numerous applications. Considering that the performance of the conventional particle filter can be significantly degraded if there is a systematic modeling error or poor prior knowledge on the noise characteristics, this thesis proposes a robust PF approach, in which a deterministic nonlinear set membership filter is used to define a feasible set for particle sampling that guarantees to contain the true state of the system. Furthermore, due to the imperfection of modeling and the nature of process uncertainty, it is important to calibrate process models in an adaptive way to achieve better state estimation performance. Motivated by a question of how to use the multiple observations of quality variables to update the model for better estimate, this thesis proposes a Bayesian information synthesis approach based on particle filter for utilizing multirate and multiple observations to calibrate data-driven model in a way that makes efficient use of the measured data while allowing robustness in the presence of possibly abnormal measurements. In addition to the theoretical study, the particle filtering approach is implemented in developing Bayesian soft sensors for the estimation of froth quality in oil sands Extraction processes. The approach synthesizes all of the existing information to produce more reliable and more accurate estimation of unmeasurable quality variables. Application results show that particle filter requires relatively few assumptions with ease of implementation, and it is an appealing alternative for solving practical state estimation problems.
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
Xinguang Shao, Biao Huang, Jong Min Lee, Constrained Bayesian State Estimation - a Comparative Study and a New Particle Filter Based Approach, Journal of Process Control, 20(2), pp.143-157, 2010.Xinguang Shao, Biao Huang, Jong Min Lee, Fangwei Xu, Aris Espejo, Bayesian Method for Multirate Data Synthesis and Model Calibration, AIChE Journal, 57(6), pp. 1515-1525, 2011.Xinguang Shao, Fangwei Xu, Biao Huang, Aris Espejo, Estimation of Bitumen Froth Quality Using Bayesian Information Synthesis: An Application to Froth Transportation Process, The Canadian Journal of Chemical Engineering, in press, 2012.Xinguang Shao, Biao Huang, Jong Min Lee, Practical issues in particle filters for state estimation of complex chemical processes, the 15th IFAC Symposium on System Identification, IFAC SYSID, 2009.

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