Model Predictive Inferential Control of a Distillation Column


Sharif University of Technology


Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive controller, is developed for an industrial multicomponent distillation column. First, composition control by direct measuring composition is used. This method because of large sampling delay has a poor performance. The selection of the temperature measurement points is done for indirect control of the column. The use of temperature loop leads to an offset in the composition; due to the fact that the temperature set-point must be changed when feed disturbances occurred. An artificial neural network estimator is designed to estimate the product compositions from tray temperature measurements. A model predictive controller is used to control column composition based on composition estimates. The performance of the developed inferential model predictive control system is tested for set-point tracking and load rejection.


1- Weber,Sh. and Brosilow,C. (1972). “The use of secondary measurements to improve control.” AICHE.
Vol.18, No.3, PP. 614-623.
2- Brosilow,C. and Joseph,B. (1978). “Inferential control of process, PartI, Steady State Analysis and
Desighn.”AICHE.,Vol.24, PP.485-492.
3 Mejdell,T. and Skogestad,S. (1991). “Estimation of distillation compositions from multiple temperature
Measurements using Partial least squares regression.” Ind. Eng.Chem.Res., Vol.30, PP. 2543-2555.
4 Mejdell,T. and Skogestad,S. (1993). “Output estimation using multiple secondary measurements: High purity
distillation.” AICHE., Vol.39, No.10, PP. 1641-1653.
5- Kano,M., Miyazaki,K., Hasebe,Sh. And Hashimoto,I. (2000). “Inferential control system of distillation
compostion using dynamic partial least squares regression.” Journal of Process Control ,Vol.10,PP.157-156.
6- Kano,M., Showchaiya,N., Hasebe,Sh. And Hashimoto,I. (2003). “Inferential control of distillation
compositions: Selection of model and control configuration.” Control Engineering Practice, Vol.11, PP.927-
7- Tzu-Ming,Y.,Huang,M. and Huang.,C. (2003). “Estimate of process composition and plantwide control from
multiple secondary measurements using artificial neural networks.” Computers and Chemical Engineering,
Vol. 27, PP. 55-72
8- Bahar,A. and Ozgen,C. (2004). “Artificial neural network estimator design for the inferential model predictive
control of an industrial distillation column.” Ind.Eng.Chem.Res., Vol.43, PP. 6102-6111.
9- Singah,V., Gupta,I. and Gupta,H. (2008). “ANN based estimator for distillation-inferential control.” Chemical
Engineering and Processing4,Vol.44, PP. 785-795.
10- Boyd,D.; M, (1975). “Fractionation column control.” Chemical Engineering Progress. Vol.71, No.6, PP. 55-
11-Luyben,W.L. (1972). “Profile position control of distillation columns with sharp temperature Profiles.”
AICHE ,Vol.18, No.1, PP.238-240.
12- Buckley,P.S., Luyben, W.L. and Shunta,J.P. (1985). Design of distillation column control systems,
Instrumental Society of America, Research Triangle Park, NC,1985.
13-Clarke,D.W., Mohtadi, C. and Tuffs, P.S. (1987). “Generalized predictive control-partI.” The Basic
Algorithm. Automatica, Vol.23, 137.
14- Bulsari,A. (1995). Neural Network for Chemical Engineers. Elsevier Science B.V.
15- Rossiter, J. (2003). "A Model-Based Predictive Control:A Practical Approach.CRC Press.
16- Ljung L. (1987). System Identification: Theory for the User. Prentic-Hall.