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Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization Open Access


Other title
Robust identification
Gaussian process regression
EM algorithm
Type of item
Degree grantor
University of Alberta
Author or creator
Ranjan, Rishik
Supervisor and department
Huang, Biao (Chemical and Materials Engineering)
Examining committee member and department
Huang, Biao (Chemical and Materials Engineering)
Prasad, Vinay (Chemical and Materials Engineering)
Chen, Tongwen (Electrical and Computer Engineering)
Department of Chemical and Materials Engineering
Process Control
Date accepted
Graduation date
Master of Science
Degree level
Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a relatively new Bayesian approach to non-parametric data based modelling is investigated in detail. One of the primary concerns regarding the application of such methods is their sensitivity to the presence of outlying observations. Another concern is that their ability to predict beyond the range of observed data is often poor which can limit their applicability. Both of these issues are explored in this work. The problem of sensitivity to outliers is dealt with by using a robust GP regression model. The common approach in literature for identification of this model is to approximate the marginal likelihood and maximize it using conjugate gradient algorithm. In this work, an EM algorithm based approach is proposed in which an approximate lower bound on the marginal likelihood is iteratively maximized. Models identified using this method are compared against those identified using conjugate gradient method in terms of prediction performance on many synthetic and real benchmark datasets. It is observed that the two approaches are similar in prediction performance. However the advantages of EM approach are numerical stability, ease of implementation and theoretical guarantee of convergence. The application of proposed robust GP regression in chemical engineering is also explored. An optimization problem for an industrial water treatment and steam generation network is formulated. Process models are constructed using material balance equations and used for data reconciliation and steady state optimization of the cost of steam production. Since the overall network is under manual operation, a dynamic optimization framework is constructed to find a set point change strategy which operators can use for minimizing steam production cost. Dynamic models for process units and tanks are integrated into this framework. Some of these models are identified using proposed robust GP regression method. Extrapolation ability of identified GP models is improved by applying a suitable GP kernel structure and by using some ad hoc scaling techniques. Based on the application of robust GP regression to an industrial optimization problem, it is shown that non-parametric data-based modelling can be successfully integrated with process optimization objectives.
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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