ERA

Download the full-sized PDF of Just-in-time and Adaptive Methods for Soft Sensor DesignDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3MP4VW4S

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Just-in-time and Adaptive Methods for Soft Sensor Design Open Access

Descriptions

Other title
Subject/Keyword
Just-in-time
Soft sensor
Adaptive
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Ma, Ming
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Zhao, Qing (Electrical and Computer Engineering)
Shah, Sirish (Chemical and Materials Engineering)
Department
Department of Chemical and Materials Engineering
Specialization
Process Control
Date accepted
2014-12-18T09:55:50Z
Graduation date
2015-06
Degree
Master of Science
Degree level
Master's
Abstract
In many industrial processes, critical variables cannot be easily measured on-line: they are either obtained from hardware analyzers which are often expensive and difficult to maintain, or carried out off-line through laboratory analysis which cannot be used in real time control. These considerations motivate the design of inferential sensors or so-called soft sensors to infer process quality variables in real time from on-line process measurements. Numerous modeling techniques have been proposed and successfully applied to soft sensors for many industrial processes. Despite the popularity of these techniques in industry, development and implementation of soft sensors are still challenging due to complexity of industrial processes. The main contribution of this thesis is the development of several soft sensing methods that can achieve and maintain satisfactory performance while handling multi-mode, nonlinear and time-varying problems. Real time identification of local process model, also known as Just-in-time (JIT) modeling, is a special modeling technique for design of infinite-mode soft sensors. It is widely used in dealing with nonlinear and multi-mode of industrial processes. The performance of JIT model depends on parameters of the similarity function as well as the structure and parameters of the local model. A Bayesian framework is proposed to provide a systematic method for real time parameterization of the similarity function, selection of the local model structure, and estimation of the corresponding model parameters in JIT modeling methods. Another challenging issue in JIT modeling is the selection of most relevant samples from database by considering input-output information. Thus, a new input-output similarity function is defined and integrated into a Bayesian framework for JIT modeling. To cope with time-varying behaviour of processes, on-line adaptation is usually integrated in the implementation procedure. Although there are a number of publications dealing with adaptation of soft sensors, few of them have considered the adaptation of nonlinear grey-box models which are popular in process industry. Thus, a new adaptation mechanism for nonlinear grey-box models is proposed based on recursive prediction error method (RPEM). Adaptive data preprocessing and cautious update strategy are integrated to ensure robustness and effectiveness of the adaptation. The effectiveness and practicality of the proposed methods are verified using data from industrial processes. Some of the proposed methods have also been implemented for industrial applications.
Language
English
DOI
doi:10.7939/R3MP4VW4S
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.
Citation for previous publication
M. Ma, S. Khatibisepehr and B. Huang, A Bayesian framework for real-time identification of locally weighted partial least squares, AIChE Journal (2014).

File Details

Date Uploaded
Date Modified
2015-06-15T07:01:18.759+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (PDF/A)
Mime type: application/pdf
File size: 2235499
Last modified: 2015:10:21 23:52:32-06:00
Filename: Ma_Ming_201412_MSc.pdf
Original checksum: f26a2483331fb3e7087b32b7638810fa
Activity of users you follow
User Activity Date