Design of a composition estimator for inferential control of distillation columns

Abstract

In distillation column control, secondary measurements such as temperatures and hows are widely used in order to infer product composition. This paper addresses the design of the linear static estimators using the secondary measurements for estimating product compositions of distillation columns. Based on the unified framework for the estimator design, the relationships among various static estimators are discussed in terms of the estimator structure. It is shown that the projection estimator is equivalent to the regression estimators in the special cases. Since the projection estimator heavily depends on the measured inputs such as reflux flow and heat input to the reboiler due to its structural characteristic, the estimation performance is far more sensitive to measurement noise and nonlinearity of them, compared with the regression estimators based on the PCR or PLS method. It is also found that the use of the measured inputs leads to performance deterioration of both the projection and regression estimators because of their nonlinear effects on the product compositions especially in high-purity columns. Design guidelines for the PCR and PLS estimators are presented by analyzing the results of the simulation studies on a high-purity column example. The estimator based on the guidelines is robust to sensor noise and has a good predictive power.