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Skip to Search Results- 4Bayesian networks
- 1Acid-catalyzed fructose conversion
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- 1Bayesian networks based soft sensors
- 1Bitumen visbreaking
- 1Causality analysis
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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 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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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...