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- 1Alencar, J.
- 1Balakrishnapillai Chitralekha, Saneej
- 1Chan, D. H.
- 1Khosbayar, Anudari
- 1Ma, Ming
- 1Morgenstern, N. R.
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- 9Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
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- 3Huang, Biao (Chemical and Materials Engineering)
- 1Ardakani, Masoud (Electrical and Computer Engineering)
- 1Huang, Biao (Chemical and Materials Engineering)
- 1Huang, Biao (Department of Chemical and Materials Engineering)
- 1Leung, Juliana (Civil and Environmental Engineering - Petroleum Engineering)
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1994
Morgenstern, N. R., Alencar, J., Chan, D. H.
Abstract: The paper presents the results obtained in the finite element simulation of 8 years of construction of a section of Syncrude's tailings dyke, which is located in northern Alberta and has been used to store oil sand mining waste. The site investigation for the construction of this dyke...
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Spring 2011
Balakrishnapillai Chitralekha, Saneej
The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science,...
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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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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 2014
In distributed sensing systems, measurements from a random process or parameter are usually not available in one place. Also, the processing resources are distributed over the network. This distributed characteristic of such sensing systems demands for special attention when an estimation or...
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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 2022
Processing of complex feedstocks for the production of value-added chemicals and fuels is industrially important. The lack of a priori knowledge of the innumerable species and the reaction pathways governing their conversion, has posed challenges to monitoring these processes. Although,...
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Spring 2022
Hydraulically fractured horizontal wells are widely adopted for the development of tight or shale gas reservoirs. The presence of highly heterogeneous, multi-scale, fracture systems often renders any detailed characterization of the fracture properties challenging. The discrete fracture network...
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Probabilistic Models for Process Monitoring and Causality Analysis with Industrial Applications
DownloadFall 2019
Process monitoring involves ensuring that the process systems are run safely and operated in the most profitable manner. On the other hand, causal modelling involves studying the causal interactions among the variables in a process system. The knowledge of these interactions is useful in process...
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Spring 2021
For efficient process control and monitoring, accurate real-time information of quality variables is essential. To predict these quality (or slow-rate) variables at a fast-rate, in the industry, inferential/soft sensors are often used. However, most of the conventional methods for soft sensors do...