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Spring 2022
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this work, focusing on fixed design linear regression with Gaussian noise and a...
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Camera based Primary Separation Vessel Interface Level Detection and Estimation Utilizing Markov Random Field based Image Processing
DownloadSpring 2017
The level of the froth middling interface in primary separation vessel plays an important role in overall bitumen recovery in conventional oil sands bitumen extraction process. To maintain the interface within a certain range of level, the accurate measurement is always desired. Online camera...
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Fall 2013
The utility system plays an important role in efficient plant operations of chemical processes. In this thesis, economic optimization of steam utility system is investigated in detail. The objective is: 1) to calculate the optimal generation amount of steam and electricity under uncertainty in...
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EM Algorithm for Electricity Pool Price Prediction and Errors-in-variables Process Identification
DownloadSpring 2016
In this thesis, under the EM algorithm framework, a multiple model approach is developed towards electricity price prediction, and the identification problem for errors-in-variables (EIV) systems is studied. Alberta's electricity price, which shows high volatility and erratic nature, is...
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Spring 2012
Limitations of measurement techniques and increasingly complex chemical process render difficulties in obtaining certain critical process variables. The hardware sensor reading may have an obvious bias compared with the real value. Off-line laboratory analysis with high accuracy can only be...
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Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization
DownloadFall 2015
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...
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Robust Gaussian Process Regression with a mixture of two Gaussian distributions as a noise model
DownloadSpring 2018
Increasingly many complex processes from the different fields of biological systems, engineering or econometrics are often required to be controlled. Hence, in such cases, we deal with identification of underlying complex processes which is essential for control design, optimization, and process...
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Fall 2019
Measurements in the process industry can arrive with fast or slow sampling rates. Fast measurements such as flowrate and temperature are sampled frequently and are obtained instantly after sampling. The slow measurements, which are usually related with chemical quality variables such as product...