Inferential disturbance observer based control of a binary distillation column

Abstract

This paper presents an integrated disturbance observer based control and inferential control of a binary distillation column. Disturbance observers have been shown being capable of providing excellent disturbance attenuation. Research into the application of inferential control methods in distillation columns have achieved a much higher quality of control in comparison to the feedback control using composition analyzers which possess substantial measurement delays. This paper takes the novel approach of integrating two frequency domain disturbance observers with multiple tray temperature based inferential estimations of top and bottom product compositions. In order to cope with colinearities in tray temperature measurements, principal component regression (PCR) and partial least square (PLS) regression are used in the soft sensor development. The number of principal components in the PCR model and the number of latent variables in the PLS model are determined using cross validation. The frequency domain disturbance observers have been designed by optimizing the value of the filter time constants within a first order filter, in order to maximize disturbance rejection. The proposed control approach is applied to a binary distillation column for separating methanol and water mixture.