Intelligent control system for extractive distillation columns

• Published in: The Korean journal of chemical engineering 2018, 35(4), 217, pp.826-834
• Main Authors: das Neves, Thiago Gonçalves, Ramos, Wagner Brandão, de Farias Neto, Gilvan Wanderley, Brito, Romildo Pereira
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biotechnology; Catalysis; Chemistry; Chemistry and Materials Science; Control systems; Distillation; Ethanol; Ethylene glycol; Industrial Chemistry/Chemical Engineering; Materials Science; Neural networks; Process Safety; Process Systems Engineering; 화학공학
• ISSN: 0256-1115; 1975-7220
• DOI: 10.1007/s11814-017-0346-0

We developed and implemented an intelligent control system to be used in an extractive distillation column that produces anhydrous ethanol using ethylene glycol as solvent. The concept of artificial neural networks (ANN) was used to predict new setpoints after disturbances, and proved to be a fast and feasible solution. The developed control system receives data from temperature, flowrate and composition measurements of the azeotrope feed, and the ANN estimates the new set-points of the controllers to maintain 99.5 mol% of ethanol at the top and less than 0.1mol% at the bottom; feed composition was also estimated using an ANN. All ANN were trained to provide output data corresponding to an optimized operating condition. The results showed that the intelligent control system can predict a new operating condition for any disturbance in the column feed and presented superior performance when compared with the control system without ANN.