Inferential Active Disturbance Rejection Control of a Distillation Column using Dynamic Principal Component Regression Models

Abstract

This paper presents a multivariable inferential active disturbance rejection control (ADRC) method for product composition control in distillation columns. The proposed control strategy integrates ADRC with inferential feedback control. In order to overcome long time delay of gas chromatography in measuring product compositions, static and dynamic estimators for product compositions have been developed. The top and bottom product compositions are estimated using multiple tray temperatures. In order to overcome the colinearity issue in tray temperatures, principal component regression is used to build the estimator. The proposed technique is applied to a simulated methanol-water separation column. It is shown that the proposed control strategy gives good setpoint tracking and disturbance rejection control performance.