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Bayesian Solutions to Multi-model Inferential Sensing Problems Open Access


Other title
Bayesian Soft Sensor
Multiple Models
Inferential Sensor
Type of item
Degree grantor
University of Alberta
Author or creator
Khatibisepehr, Shima
Supervisor and department
Huang, Biao (Chemical & Materials Engineering)
Examining committee member and department
Prasad, Vinay (Chemical & Materials Engineering)
Huang, Biao (Chemical & Materials Engineering)
Lee, Jay H. (Chemical & Biomolecular Engineering)
Shah, Sirish L. (Chemical & Materials Engineering)
Zhang, Hong (Computing Science)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Doctor of Philosophy
Degree level
In many industrial plants, development and implementation of advanced monitoring and control techniques require real-time measurement of process quality variables. However, on-line acquisition of such data may involve difficulties due to inadequacy of measurement techniques or low reliability of measuring devices. These concerns motivate the design of inferential sensors to infer process quality indicators from real-time measurable process variables. Development and implementation of inferential sensors entail many challenges that are often addressed in a rather ad hoc manner. Although many of the encountered challenging issues are interconnected, most of the existing solutions are disjoint. The main contribution of this dissertation is development of an integrative and holistic Bayesian inferencing paradigm to provide general and integrated solutions to certain outstanding inferential sensing problems. The core component of an inferential sensor is the process model which is identified through first-principles and process data analysis. The problem of model identification from contaminated data is formulated under a hierarchical Bayesian framework to simultaneously consider different aspects of data analysis and inferential modeling. A Bayesian approach is developed for identification of multi-modal systems switching among non-linear continuous-state dynamics. The proposed procedure provides a framework to accommodate the overlapping operating regions, facilitate the inclusion of prior knowledge about the operating conditions, and include a global adaptation mechanism within the envelope of previously identified operating conditions. Real-time identification of inferential models can be viewed as a special modeling technique for design of multi-model inferential sensors with infinite number of local models. A Bayesian framework is developed to provide a systematic and computationally feasible method for real-time similarity function parametrization and model structure selection in just-in-time/space modeling methods. One of the practical challenges faced in implementation of inferential sensors is to assess the accuracy of their real-time predictions. A data-driven Bayesian approach is proposed to capture conditional dependence of the reliability of inferential sensor predictions on characteristics of the input space and reliability of the empirical process model. The practicality and validity of the proposed Bayesian frameworks are verified using data from various simulation configurations, experimental set-ups, and industrial processes.
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
Khatibisepehr, S., B. Huang and S. Khare (2013). Design of inferential sensors in the process industry: A review of Bayesian methods. Journal of Process Control. in press.Khatibisepehr S., B. Huang, E. Domlan, E. Naghoosi, Y. Zhao, Y. Miao, X. Shao, S. Khare, M. Keshavarz, E. Feng, F. Xu, A. Espejo and R. Kadali (2013). Soft sensor solutions for control of oil sands processes. The Canadian Journal of Chemical Engineering 91(8), 1416-1426Khatibisepehr, S. and B. Huang (2013). A Bayesian approach to robust process identification with ARX models. AIChE Journal 59(3), 845-859Khatibisepehr, S. and B. Huang (2012). A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry. Journal of Process Control 22(10), 1913-1929Khatibisepehr S., B. Huang, F. Ibrahim, J.Z. Xing and W. Rao (2011). Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources. Computational Biology and Chemistry 35(2), 69-80

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