Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors

In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been construc...

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Published inIndustrial & engineering chemistry research Vol. 52; no. 3; pp. 1322 - 1334
Main Authors Kaneko, Hiromasa, Funatsu, Kimito
Format Journal Article
LanguageEnglish
Published American Chemical Society 23.01.2013
Subjects
Accuracy
Autocorrelation
Chemical plant
Computer simulation
Intervals
Mathematical models
Noise
Sensors
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Abstract In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, autocorrelation in process variables, predictive accuracy, and so on. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models and the effects of noise, autocorrelation, TD intervals, and so on on the predictive accuracy. The relationships and the formulas were verified by analyzing simulation data. Furthermore, we analyzed dynamic simulation data and real industrial data and confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.
AbstractList In chemical plants, soft sensors are widely used to estimate process variables that are difficult to measure online. The predictive accuracy of soft sensors decreases over time because of changes in the state of chemical plants, and soft sensor models based on time difference (TD) have been constructed. However, many details of models based on TD remain to be clarified. In this study, TD models are discussed in terms of noise in data, autocorrelation in process variables, predictive accuracy, and so on. We theoretically clarify and formulate the differences of predictive accuracy between normal models and TD models and the effects of noise, autocorrelation, TD intervals, and so on on the predictive accuracy. The relationships and the formulas were verified by analyzing simulation data. Furthermore, we analyzed dynamic simulation data and real industrial data and confirmed that the predictive accuracy of TD models increased when TD intervals were optimized.
Author Funatsu, Kimito
Kaneko, Hiromasa
AuthorAffiliation The University of Tokyo
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  email: funatsu@chemsys.t.u-tokyo.ac.jp
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SubjectTerms Accuracy
Autocorrelation
Chemical plant
Computer simulation
Intervals
Mathematical models
Noise
Sensors
Title Discussion on Time Difference Models and Intervals of Time Difference for Application of Soft Sensors
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