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Skip to Search Results- 1325Department of Chemical and Materials Engineering
- 3Department of Biomedical Engineering
- 2Department of Biological Sciences
- 2Department of Laboratory Medicine and Pathology
- 1Department of Civil and Environmental Engineering
- 1Department of Human Ecology
- 2Bendrich, Michelle
- 2El-Thaher, Nayef
- 2Fadic Eulefi, Anton
- 2Hoseinzadeh Hejazi, Sayed Alireza
- 2Hosseininejad, Seyed Shaham Aldin
- 2Karkooti, Amin
- 44Xu, Zhenghe (Chemical and Materials Engineering)
- 40Huang, Biao (Chemical and Materials Engineering)
- 34Liu, Qingxia (Chemical and Materials Engineering)
- 27Henein, Hani (Chemical and Materials Engineering)
- 27Zeng, Hongbo (Chemical and Materials Engineering)
- 20Gupta, Rajender (Chemical and Materials Engineering)
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Fall 2015
A number of industrial processes involve variables that cannot be reliably measured in real time using online sensors. Many such variables are required as inputs in control schemes to ensure safe and efficient plant operation. Laboratory analysis, which is a reliable method of measuring these...
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Data Mining and Knowledge Discovery for Process identification and multivariate monitoring using spectroscopy: application to low temperature bitumen visbreaking
DownloadFall 2016
Data mining and knowledge discovery is a systematic process of identifying useful information from a data set where there is no or limited information about the underlying process. In this study, data mining and other learning methods are used cohesively to model a low temperature visbreaking...
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Spring 2018
Steam-assisted gravity drainage (SAGD) is an enhanced oil recovery (EOR) technology widely used in Canada. Data available in SAGD industrial processes contain valuable information for monitoring, soft sensing, control, and optimization. This thesis focuses on data mining and optimization in the...
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Data Quality Assessment for Closed-Loop System Identification and Forecasting with Application to Soft Sensors
DownloadFall 2012
In many chemical plants, data historians store thousands of variables at fast sampling rates. Much of this collected data is routine operating data that could easily be used for system identification and forecasting, especially in the design of soft sensors. Currently, there is no framework for...
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Fall 2013
Time consuming offline laboratory analysis and high cost hardware measurement techniques render difficulties in obtaining the important quality variables in real time application. Near-infrared (NIR) spectroscopy is widely used as a process analytical tool (PAT) in chemical processes, providing...
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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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Fall 2023
With the advances made in machine learning and data science, data-driven modeling and optimization techniques have garnered significant attention in recent years. However, despite the availability of various data-driven methods for addressing optimization problems under uncertainty, their...
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Spring 2014
Green House Gases (GHG) contribution to global warming has led to extensive research into reduction of emission of the GHG. Transportation, as a main contributor to GHG, faces a major challenge in researching and developing of new technologies with the aim of reducing the carbon foot print. The...
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Fall 2021
This thesis aims to investigate, develop and advance solution techniques for optimization under uncertainty in process control, scheduling and operations research applications. Decision rule methods offer a rich and flexible framework for solving these classes of problems. Recent literature has...