NETE Automatic Control on Batch and Continuous Distillation Columns

Abstract

Distillation is fundamental in Chemical
Engineering. It is a highly complex and non-linear process.
Therefore, developing intelligent control systems for distillation
columns is challenging. These control techniques are based on
previous knowledge and intuitive rules. In this work, several
control strategies, such as IMC, Gain Scheduling, Expert, Fuzzy
(Mamdani and Sugeno) and Neural-Network Control are applied
to control a simulated distillation column for batch and
continuous processes, and their performance is compared with a
traditional PI controller. The controlled variable was the distillate
molar fraction using as manipulated variable the reflux ratio. All
control strategies were tested with respect set-point changes in
two scenarios: without and with disturbances. The best control
strategy was the Neural-Network, using a NARMA-L2 controller.
This control has a good disturbance rejection and a fast set-point
tracking with a smooth control action.