The use of first principles model for evaluation of adaptive soft sensor for multicomponent distillation unit
| Autor: | Sigurd Skogestad, Andrei Torgashov |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Observational error Mean squared error Computer science General Chemical Engineering Constrained optimization Estimator 02 engineering and technology General Chemistry Soft sensor law.invention 020901 industrial engineering & automation 020401 chemical engineering law A priori and a posteriori 0204 chemical engineering Algorithm Distillation Advanced process control |
| Zdroj: | Chemical Engineering Research and Design. 151:70-78 |
| ISSN: | 0263-8762 |
| Popis: | Traditionally, soft sensors are developed based on measurement data only, but here we consider an adaptive soft sensor that uses data generated from a fitted, first principles model of the distillation columns. The contribution of the paper is a procedure for moving window soft sensor design that incorporates a priori knowledge, which is especially suitable when the training sample is small and contains measurement errors. In addition, we propose a continuous adaptation of all model parameters based on new data, instead of the usual procedure of only updating the bias. The accuracy of the predicted product quality is investigated by calculating the coefficient of determination and root mean squared error for the test sample. Several approaches were considered, and we found that a constrained optimization approach was superior. The constraints on the model parameters of soft sensors are derived from a fitted, rigorous distillation unit model. The improved estimator quality resulted in the successful industrial application of advanced process control systems. |
| Databáze: | OpenAIRE |
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