Inferential Active Disturbance Rejection Control of a Distillation Column

Abstract

Distillation columns are the major energy consumers in petrochemical and chemical industry and their efficient operation is essential for energy saving and product quality enhancement. This paper presents an inferential active disturbance rejection control (ADRC) method for product composition control in distillation columns. The proposed control strategy integrates ADRC with inferential feedback control. Tray temperatures are used to estimate the top and bottom product compositions which are difficult to measure on-line without time delay. In order to overcome the colinearity in the tray temperature data, principal component regression (PCR) is used to build the soft sensors, which are then integrated with ADRC. In order to overcome static control offsets caused by the discrepancy between soft sensor estimations and the true compositions, intermittent mean updating is used to correct PCR model predictions. The proposed technique is applied to a simulated methanol-water separation column.