Applicability domains and accuracy of prediction of soft sensor models

Soft sensors are used to estimate process variables that are difficult to measure online. However, the predictive accuracy gradually decreases with changes in the state of chemical plants. Regression models can be updated, but if the model is updated with abnormal data, the predictive ability deteri...

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Bibliographic Details
Published in:AIChE journal 2011-06, Vol.57 (6), p.1506-1513
Main Authors: Kaneko, Hiromasa, Arakawa, Masamoto, Funatsu, Kimito
Format: Article
Language:English
Subjects:
Accuracy
applicability domain
Applied sciences
Chemical engineering
Chemical plants
Deterioration
Estimates
Exact sciences and technology
fault detection
Mathematical models
On-line systems
prediction error
Predictions
process control
Regression
Regression analysis
Safety
Sensors
soft sensor
Thresholds
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Summary:Soft sensors are used to estimate process variables that are difficult to measure online. However, the predictive accuracy gradually decreases with changes in the state of chemical plants. Regression models can be updated, but if the model is updated with abnormal data, the predictive ability deteriorates. In practice, when the prediction error of an objective variable exceeds a threshold, an abnormal situation is detected. However, no effective method exists to decide this threshold. We have proposed a method to estimate the relationships between applicability domains and the accuracy of prediction of soft sensor models quantitatively. The larger the distances to models (DMs), the lower the estimated accuracy of prediction. Hence, the model between DMs and accuracy can separate variations in process variables and y‐analyzer fault. This method was applied to real industrial data. The fault detection ability of the proposed method was better than that of the traditional one. © 2010 American Institute of Chemical Engineers AIChE J, 2011
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.12351