Estimating product composition profiles in batch distillation via partial least squares regression

The properties of two multivariate regression techniques, principal component analysis and partial least squares (PLS) regression, are exploited to develop soft sensors able to estimate the product composition profiles in a simulated batch distillation process using available temperature measurements. The estimators’ performance is evaluated with respect to several issues, such as pre-processing of the calibration and validation data sets, number of measurements used as sensor inputs, presence of noise in the input measurements, and use of lagged measurements. A simple augmentation of the conventional PLS regression approach is also proposed, which is based on the development and sequential use of multiple regression models. The results prove that the PLS estimators can provide accurate composition estimations for a batch distillation process. The computational requirements are very low, which makes the estimators attractive for on-line use.