Distillation variability prediction

Abstract

A novel technique is proposed to predict product variabilities for distillation columns. The technique uses industrial disturbance data and applies signal processing techniques to extract its amplitude and frequency information. This information is combined with the closed-loop Bode plot for the same disturbance as a function of frequency to predict closed-loop product variabilities for the column. The closed-loop Bode plot is obtained using a linear dynamic model of the process. The approach is demonstrated using a binary distillation colunm, a C3 splitter and a multicomponent distillation column, a depropanizer. Four different designs of both columns were considered. A thorough study of the approach is carried out to verify the accuracy and the shortcomings of the approach. The potential of the approach as a quantitative tool for configuration selection was also explored. For this purpose, nine different distillation configurations were analyzed which indicated that this approach can be successfully used for distillation configuration selection.