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 99.5mol% 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

Mansouri SS, Sales-Cruz M, Huusom JK, Woodley JM, Gani R, IFAC Papersonline, 49(7), 735 (2015)
Mejdell T, Skogestad S, Ind. Eng. Chem. Res., 30, 2555 (1991)
Zhongzhou C, Henson MA, Belanger P, Megan L, IEEE Trans. Control Syst. Technol., 18(4), 811 (2010)
Udugama IA, Munir T, Kirkpatrick R, Young BR, Yu W, Comput. Aided Chem. Eng., 37, 1613 (2015)
Kano M, Showchaiya N, Hasebe S, Hashimoto I, Control Eng. Practice, 11, 927 (2003)
Kalbani FA, Zhang J, in 9th IFCH Symposium on Advanced Control of Chemical Processes, 48, 403 (2015).
Maciejowski JM, Predictive control with constraints, Prentice Hall, London (2002).
Qin SJ, Badgwell TA, Control Eng. Practice, 11, 733 (2003)
Sharma N, Singh K, Chem. Eng. Process., 59, 9 (2012)
Luyben WL, Process modeling, Simulation and control for chemical engineers, McGraw Hill, New York (1990).
Kittisupakorn P, Charoenniyom T, Daosud W, Eng. J., 18, 145 (2014)
Niamsuwan S, Kittisupakorn P, Mujtaba IM, Comput. Chem. Eng., 66, 2 (2014)
Konakom K, Kittisupakorn P, Mujtaba IM, Asia-Pac. J. Chem. Eng., 7, 361 (2012)
Lu CH, Tsai CC, Liu CM, Charng YH, Asia-Pac. J. Chem. Eng., 12, 680 (2010)
Gil ID, Gomez JM, Rodriguez G, Comput. Aided Chem. Eng., 39, 129 (2012)
Ramos WB, Figueiredo MF, Brito KD, Ciannella S, Vasconcelos LGS, Brito RP, Ind. Eng. Chem. Res., 55(43), 11315 (2016)
Fortuna L, Graziani S, Xibilia M, Control Eng. Practice, 13, 499 (2005)
Zamprogna E, Barolo M, Seborg DE, J. Process Control, 15(1), 39 (2005)
Dias MOS, Ensinas AV, Nebra SA, Maciel R, Rossell CEV, Maciel MRW, Chem. Eng. Res. Des., 87(9A), 1206 (2009)
Meirelles A, Weiss S, J. Chem. Technol. Biotechnol., 56, 181 (1992)
Figueiredo MF, Ramos WB, Brito KD, Brito RP, Comput. Aided Chem. Eng., 202, 1607 (2015)
Junqueira TL, Dias MOS, Wolf-Maciel M, Filho RM, Rossell CEV, in 9th Distillation & Absorption Conference, Eindhoven, The Netherlands, 521 (2010).
Luyben WL, Distillation design and control using Aspen simulation, John Wiley & Sons, New Jersey (2013).
Arifin S, Chien IL, Ind. Eng. Chem. Res., 47(3), 790 (2008)
Luyben WL, Plantwide dynamic simulators in chemical processing and control, Marcel Dekker, New York (2002).
Tyreus BD, Luyben WL, Ind. Eng. Chem. Res., 31, 2625 (1993)
Haykin S, Neural networks and learning machines, Pearson, New Jersey (2009).
Fausset L, Fundamentals of neural networks: Architectures, algorithms, and applications, Prentice Hall, New Jersey (1994).
Morsi I, El-Din LM, Measurement, 47, 5 (2014)
Nerrand O, Roussel-Ragot P, Personnaz L, Dreyfus G, Neural Comput., 5, 165 (1993)
Elman JL, Cognit. Sci., 14, 179 (1990)
Marquardt DW, SIAM J. Appl. Math., 11, 431 (1963)