A PLS-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Both linear and nonlinear versions of PLS are employed and their estimation performance is compared. Several issues are addressed such as the selection of the most appropriate model input variables, and the effect of augmenting the original process data with lagged measurements. A novel PLS approach is also proposed that provides for the development of multiple PLS models for different time intervals during the batch operation.

Development of a Soft Sensor for a Batch Distillation Column Using Linear and Nonlinear PLS Regression Techniques

BAROLO, MASSIMILIANO
;
2002

Abstract

A PLS-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Both linear and nonlinear versions of PLS are employed and their estimation performance is compared. Several issues are addressed such as the selection of the most appropriate model input variables, and the effect of augmenting the original process data with lagged measurements. A novel PLS approach is also proposed that provides for the development of multiple PLS models for different time intervals during the batch operation.
2002
IFAC Proceedings Volumes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/1333440
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