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Skip to Search Results- 40Huang, Biao (Chemical and Materials Engineering)
- 5Forbes, Fraser (Chemical and Materials Engineering)
- 2Li, Zukui (Chemical and Materials Engineering)
- 2Prasad, Vinay (Chemical and Materials Engineering)
- 1Afacan, Artin (Chemical and Materials Engineering)
- 1Forbes, J.Fraser (Chemical and Materials Engineering)
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Spring 2016
In data-driven modelling, model accuracy relies heavily on the data set collected from target process. However, various types of measurement noise exist extensively in industrial processes and the data obtained are usually contaminated. If the influence of measurement noise is neglected, both the...
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Spring 2024
The advent of Industry 4.0 integrates advanced digital technologies and Artificial Intelligence (AI) into system engineering. This research explores the potential of AI in smart automation for industries, bridging it with physics-informed approaches, particularly through Explainable Artificial...
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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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Fall 2018
The objective of this work is to study the problems that arise in state estimation for severely nonlinear systems. In practice, many processes are nonlinear, accompanied by uncertain parameters. The complexity of the model causes the probability density function (PDF) of the states to deviate...
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Fall 2015
A large volume of literature exists on fault detection and isolation for industrial processes. In a general view, these various methods may be divided into process model based and process history based fault diagnosis. In both classes, there has been a recent focus on extracting the temporal...
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Spring 2013
Parameter estimation of a dynamic system is an important task in process systems engineering. The utilization of an augmented system offers the approach of estimating process states and parameters simultaneously. In practice, the parameters often satisfy certain constraints which should be...
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Spring 2015
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...
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Spring 2019
Predictive modeling has proven to be a valuable tool in process industry to estimate hard-to-measure variables that cannot be measured online. Those variables usually require LAB analysis to be quantified, which is time-consuming and costly. Predictive modeling can be used for both regression and...
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Spring 2020
Machine learning (ML) has shown great potential to create tremendous value and growth to all sectors around the world, enhancing productivity, health, and longevity of humanity. ML differentiates itself from all previous methods through its adaptive and self-learning capabilities. In recent...