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.