Korean Journal of Chemical Engineering

, Volume 35, Issue 4, pp 826–834 | Cite as

Intelligent control system for extractive distillation columns

  • Thiago Gonçalves das Neves
  • Wagner Brandão Ramos
  • Gilvan Wanderley de Farias Neto
  • Romildo Pereira Brito
Process Systems Engineering, Process Safety


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.


Ethanol Extractive Distillation Artificial Neural Networks Control Set-points 


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  1. 1.
    S. S. Mansouri, M. Sales-Cruz, J. K. Huusom, J.M. Woodley and R. Gani, IFAC Papersonline, 49(7), 735 (2015).CrossRefGoogle Scholar
  2. 2.
    T. Mejdell and S. Skogestad, Ind. Eng. Chem. Res., 30, 2555 (1991).CrossRefGoogle Scholar
  3. 3.
    C. Zhongzhou, M. A. Henson, P. Belanger and L. Megan, IEEE Trans. Control Syst. Technol., 18(4), 811 (2010).CrossRefGoogle Scholar
  4. 4.
    I. A. Udugama, T. Munir, R. Kirkpatrick, B. R. Young and W. Yu, Comput. Aided Chem. Eng., 37, 1613 (2015).CrossRefGoogle Scholar
  5. 5.
    M. Kano, N. Showchaiya, S. Hasebe and I. Hashimoto, Control Eng. Pract., 11, 927 (2003).CrossRefGoogle Scholar
  6. 6.
    F. A. Kalbani and J. Zhang, in 9 th IFCH Symposium on Advanced Control of Chemical Processes, 48, 403 (2015).Google Scholar
  7. 7.
    J.M. Maciejowski, Predictive control with constraints, Prentice Hall, London (2002).Google Scholar
  8. 8.
    S. J. Qin and T. A. Badgwell, Control Eng. Pract., 11, 733 (2003).CrossRefGoogle Scholar
  9. 9.
    N. Sharma and K. Singh, Chem. Eng. Process., 59, (2012).Google Scholar
  10. 10.
    W. L Luyben, Process modeling, Simulation and control for chemical engineers, McGraw Hill, New York (1990).Google Scholar
  11. 11.
    P. Kittisupakorn, T. Charoenniyom and W. Daosud, Eng. J., 18, 145 (2014).CrossRefGoogle Scholar
  12. 12.
    S. Niamsuwan, P. Kittisupakorn and I. M. Mujtaba, Comput. Chem. Eng., 66, (2014).Google Scholar
  13. 13.
    K. Konakom, P. Kittisupakorn and I.M. Mujtaba, Asia-Pac. J. Chem. Eng., 7, 361 (2012).CrossRefGoogle Scholar
  14. 14.
    C.H. Lu, C. C. Tsai, C. M. Liu and Y. H. Charng, Asia-Pac. J. Chem. Eng., 12, 680 (2010).Google Scholar
  15. 15.
    I. D. Gil, J. M. Gomez and G. Rodríguez, Comput. Aided Chem. Eng., 39, 129 (2012).CrossRefGoogle Scholar
  16. 16.
    W. B. Ramos, M. F. de Figueirêdo, K. D. Brito, S. Ciannella, L. G. S. Vasconcelos and R. P. Brito, Ind. Eng. Chem. Res., 55, 11315 (2016).CrossRefGoogle Scholar
  17. 17.
    L. Fortuna, S. Graziani and M. Xibilia, Control Eng. Pract., 13, 499 (2005).CrossRefGoogle Scholar
  18. 18.
    E. Zamprogna, M. Barolo and D. Seborg, J. Process Control, 15, 39 (2005).CrossRefGoogle Scholar
  19. 19.
    M. Dias, A. Ensinas, S. Nebra, R. Maciel Filho, C. Rossell and M. Wolf, Chem. Eng. Res. Des., 87, 1206 (2009).CrossRefGoogle Scholar
  20. 20.
    A. Meirelles and S. Weiss, J. Chem. Technol. Biotechnol., 56, 181 (1992).Google Scholar
  21. 21.
    M. F. Figueirêdo, W.B. Ramos, K. D. Brito and R. P. Brito, Comput. Aided Chem. Eng., 202, 1607 (2015).Google Scholar
  22. 22.
    T. L. Junqueira, M. O. S. Dias, M. Wolf-Maciel, R. Maciel Filho and C. E. V. Rossell, in 9 th Distillation & Absorption Conference, Eindhoven, The Netherlands, 521 (2010).Google Scholar
  23. 23.
    W. L. Luyben, Distillation design and control using Aspen simulation, John Wiley & Sons, New Jersey (2013).CrossRefGoogle Scholar
  24. 24.
    S. Arifin and I. L. Chien, Ind. Eng. Chem. Res., 47, 790 (2008).CrossRefGoogle Scholar
  25. 25.
    W. L. Luyben, Plantwide dynamic simulators in chemical processing and control, Marcel Dekker, New York (2002).Google Scholar
  26. 26.
    B. D. Tyreus and W. L. Luyben, Ind. Eng. Chem. Res., 31, 2625 (1993).CrossRefGoogle Scholar
  27. 27.
    S. Haykin, Neural networks and learning machines, Pearson, New Jersey (2009).Google Scholar
  28. 28.
    L. Fausset, Fundamentals of neural networks: Architectures, algorithms, and applications, Prentice Hall, New Jersey (1994).Google Scholar
  29. 29.
    I. Morsi and L. M. El-Din, Measurement, 47, (2014).Google Scholar
  30. 30.
    O. Nerrand, P. Roussel-Ragot, L. Personnaz and G. Dreyfus, Neural Comput., 5, 165 (1993).CrossRefGoogle Scholar
  31. 31.
    J. L. Elman, Cognit. Sci., 14, 179 (1990).CrossRefGoogle Scholar
  32. 32.
    D. W. Marquardt, SIAM J. Appl. Math., 11, 431 (1963).CrossRefGoogle Scholar

Copyright information

© Korean Institute of Chemical Engineers, Seoul, Korea 2018

Authors and Affiliations

  • Thiago Gonçalves das Neves
    • 1
  • Wagner Brandão Ramos
    • 1
  • Gilvan Wanderley de Farias Neto
    • 1
  • Romildo Pereira Brito
    • 1
  1. 1.Chemical Engineering DepartmentFederal University of Campina GrandeCampina GrandeBrazil

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