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Bayesian Methods for On-Line Gross Error Detection and Compensation Open Access

Descriptions

Other title
Subject/Keyword
Gross Error Detection
Bayesian Inference
Data Reconciliation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Gonzalez, Ruben
Supervisor and department
Dr. Biao Huang, Chemical and Materials Engineering
Examining committee member and department
Dr. Bob Koch, Mechanical Engineering
Dr. Vinay Prasad, Chemical and Materials Engineering
Department
Department of Chemical and Materials Engineering
Specialization

Date accepted
2010-09-30T16:19:39Z
Graduation date
2010-11
Degree
Master of Science
Degree level
Master's
Abstract
Data reconciliation and gross error detection are traditional methods toward detecting mass balance inconsistency within process instrument data. These methods use a static approach for statistical evaluation. This thesis is concerned with using an alternative statistical approach (Bayesian statistics) to detect mass balance inconsistency in real time. The proposed dynamic Baysian solution makes use of a state space process model which incorporates mass balance relationships so that a governing set of mass balance variables can be estimated using a Kalman filter. Due to the incorporation of mass balances, many model parameters are defined by first principles. However, some parameters, namely the observation and state covariance matrices, need to be estimated from process data before the dynamic Bayesian methods could be applied. This thesis makes use of Bayesian machine learning techniques to estimate these parameters, separating process disturbances from instrument measurement noise.
Language
English
DOI
doi:10.7939/R3WD4K
Rights
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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