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Skip to Search Results- 37Huang, 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)
Results for "supervisors_tesim:"Huang, Biao (Chemical and Materials Engineering)""
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Fall 2011
The large number of control loops in a modern industrial plant poses a serious challenge for operators and engineers to monitor these loops to maintain them at optimal conditions continuously. Much research has been done on control loop performance assessment and monitoring of individual...
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Spring 2019
In the oil sands extraction process, bitumen (crude oil) is separated from the sands in the Primary Separation Vessel (PSV) through a water-based gravity separation process. The interface between froth (crude oil) and middlings (water and sand) is the most important control variable in the PSV...
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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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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
Processes in industry usually encounter time varying time delays as well as outliers in measurement data. These make identification of the process a challenging problem. Thus, a reliable estimation of the time delay and a correct estimation of the noise to include outliers are essential to...
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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 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...