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DEVELOPMENT OF A SOFT SENSOR FOR A BATCH DISTILLATION COLUMN USING LINEAR AND NONLINEAR PLS REGRESSION TECHNIQUES
Eliana Zamprogna1, Massimiliano Barolo1 and Dale E. Seborg2
1 DIPIC, Department of Chemical Engineering, University of Padova
Via Marzolo 9, 35131 Padova PD (Italy)
2 Department of Chemical Engineering, University of California
Santa Barbara, CA 93106 (U.S.A.)
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.
Keywords: batch distillation, soft sensing, composition estimation, partial least squares
Session slot T-We-A11: Process Identification and Estimation/Area code 7a : Chemical Process Control
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