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- 1Adaptive
- 1Bayesian inference
- 1Closed-loop identification
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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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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 2024
In the process industry, certain quality variables cannot be measured regularly due to technical limitations or economic constraints. Consequently, the industry relies on laboratory analysis to measure such quality variables. However, laboratory analysis introduces long time-delays in obtaining...