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Computational tools for soft sensing and state estimation Open Access


Other title
Ensemble Kalman filter
State estimation
Steam Assisted Gravity Drainage (SAGD)
Particle filter
Polymer extrusion
Support vector regression
History matching
Soft sensor
Type of item
Degree grantor
University of Alberta
Author or creator
Balakrishnapillai Chitralekha, Saneej
Supervisor and department
Shah, Sirish (Chemical and Materials Engineering)
Examining committee member and department
Huang, Biao (Chemical and Materials Engineering)
Trivedi, Japan (Civil and Environmental Engineering)
Foss, Bjarne (Engineering Cybernetics, Norwegian University of Science and Technology)
Lynch, Alan (Electrical and Computer Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Department of Chemical and Materials Engineering

Date accepted
Graduation date
Doctor of Philosophy
Degree level
The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science, mathematics and engineering. The main focus of this thesis is on computational tools which can be employed for estimating unmeasured variables in a process using all the available prior information. Specifically, this thesis demonstrates the application of a variety of tools for soft sensing of process variables and uncertain parameters of physiochemical process models, using routine data available from the process. The application examples presented in this thesis come from broad areas where process uncertainty is inherent and includes petrochemical processes, mechanical valve actuators, and upstream production processes in petroleum reservoirs. The mathematical models that are employed in different domains vary significantly in their structure and their level of complexity. In the petrochemical domain, the focus was on developing empirical soft sensors which are essentially nonparametric mathematical models identified using routine data from the process. The Support Vector Regression technique was applied for identifying such nonparametric empirical models. On the other hand, in all the other application examples in this thesis the physical parametric models of the process were utilized. The latter application examples, which cover a major portion of this thesis, demonstrate the application of modern state and parameter estimation algorithms which are firmly grounded on Bayesian theory and Monte Carlo techniques. Prior to the chapters on the application of state and parameter estimation techniques, a tutorial overview of the Monte Carlo simulation based state estimation algorithms is provided with an attempt to throw new light on these techniques. The tutorial is aimed at making these techniques simple to visualize and understand. The application case studies serve to illustrate the performance of the different algorithms. All case studies presented in this thesis are performed on processes that exhibit significant nonlinearity in terms of the relationship between the process input variables and output variables.
License granted by Saneej Balakrishnapillai Chitralekha ( on 2010-11-08T22:47:25Z (GMT): 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 the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein 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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