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Permanent link (DOI): https://doi.org/10.7939/R3NP1WQ1P

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DATA DRIVEN SOFT SENSOR DESIGN: JUST-IN-TIME AND ADAPTIVE MODELS Open Access

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Other title
Subject/Keyword
Adaptive Linear regression
Just-In-Time
Locally weighted regression
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Sharma, Shekhar
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Liu, Jinfeng (Chemical and Materials Engineering)
Tavakoli, Mahdi (Electrical & Computer Engineering)
Sharp, David (Chemical and Materials Engineering)
Huang, Biao (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2015-09-28T08:19:14Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
A number of industrial processes involve variables that cannot be reliably measured in real time using online sensors. Many such variables are required as inputs in control schemes to ensure safe and efficient plant operation. Laboratory analysis, which is a reliable method of measuring these variables, is slow and infrequent. Thus, mathematical models called soft sensors which can estimate these hard to measure variables from the abundantly available online process measurements have been used in a number of industrial applications. Among the various soft sensor applications of online prediction, process monitoring, fault detection and isolation, the focus of this thesis is on online prediction and parameter estimation applications. Just-In-Time (JIT) modeling is a unique framework wherein a local model is created every time a prediction is required. One of the most critical components of JIT models is the similarity criterion which determines the data used in the local models and their associated weights. To handle nonlinear and time varying systems simultaneously under the JIT framework, a new similarity metric which incorporates time, along with the traditional space distance, to evaluate sample weights, is proposed. Further, a query based method to determine the bandwidth of the local models adaptively, as an alternative to the offline global method, is also developed. Next, the distance-angle similarity criterion used in modeling dynamic systems under the JIT technique is studied. An improved weighing scheme is then proposed which enables a more accurate selection of data for local modeling and provides a better interpretation of results. Again, for this proposed weighing scheme also, an alternative to the global bandwidth estimation, called the point-based method, is proposed. In the field of online soft sensor prediction and parameter estimation applications, adaptive linear regression algorithms such as recursive least squares and moving window least squares are widely used because of their simplicity and ease of implementation. However, these methods are not robust to outlying values. We develop a new robust and adaptive algorithm with a cautious parameter update strategy. The proposed algorithm is also quite flexible and a number of variants are easily formulated. Finally, advantages of the methods are clearly illustrated by applications to numerical examples, experimental data and industrial case studies.
Language
English
DOI
doi:10.7939/R3NP1WQ1P
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. 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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