Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery

Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery


  • Idris Bin Ibrahim Mechanical Engineering Department, Universiti Teknologi Petronas Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan, Malaysia



Distillate Purity/Impurity; Product Concentrations; Simplified Model; Nonlinear Model Predictive Control.


This paper presents a detailed procedure to develop a mathematical modelling and simulation of a distillation column for a real feedstock from a condensate processing plant as an initial step of a project feasibility study. The mathematical model of overall dynamics is established on the dynamic continuity equations of the mass and the energy for each unit operation where the mass and the energy can accumulate. The paper provides a case study tutorial for a typical petroleum refinery engineering design. The dynamic analysis and controller for the distillation systems are extremely complicated due to their nonlinearity and multivariable. A nonlinear model predictive control (NMPC) computational scheme for with soften constraints is developed to verify the applicable ability of a direct NMPC controller for a distillation column dealing with the disturbance and the model-plant mismatch as the influence of the plant feed disturbances.


[1] PetroVietnam Gas Company, Condensate Processing Plant Project – Process Description Document No. 82036-02BM-01. PetroVietnam, pp. 1-54 (1999).
[2] Kehlen, H., and Ratzsch, M., “Complex Multicomponent Distillation Calculations by Continuous Thermodynamics”. Chem. Eng. Sci. Vol. 42, no.2, pp. 221-232 (1987).
[3] Franks, R. G.E., Modeling and Simulation in Chemical Engineering. Wiley-Interscience, N.Y., (1972).
[4] Nelson, W. L., Petroleum Refinery Engineering. Auckland McGraw-Hill, (1982).
[5] Joshi, M. V., Process Equipment Design. New Delhi, Macmillan Company of India, (1979).
[6] McCabe, W. L. , and Smith J. C., Unit Operations of Chemical Engineering. N.Y McGraw-Hill, (1976).
[7] Wuithier, P., Le Petrole Raffinage et Genie Chimique. Paris Publications de l’Institut Francaise du Petrole, (1972).
[8] Stephanopoulos, G., Chemical Process Control. Prentice Hall International, (1984).
[9] Katsuhiko Ogata, Model Control Engineering. Prentice-Hall International (1982).
[10] Papadouratis, A., Doherty, M., and Douglas, J.“Approximate Dynamic Models for Chemical Process Systems”. Ind. Eng. Chem. Res. Vol. 28, no.5, pp. 546-522 (1989).
[11] Skogestad, S., and Morari, M. “The Dominant Time Constant for Distillation Columns”. Comp. Chem. Eng. Vol. 11, no. 7, pp. 607-617 (1987).
[12] Marie, E., Strand, S. and Skogestad S., “Coordinator MPC for Maximizing Plant Throughput”. Comp. Chem. Eng., Vol. 32, no. 1-2, January 2008, Pages 195-204 pp. 195-204 (2008).
[13] Vu Trieu Minh, John Pumwa, "Fuzzy logic and slip controller of clutch and vibration for hybrid vehicle", Asian Journal of Control, Volume 11, Issue 3, Pages 526-532, 2013.
[14] Vu Trieu Minh, Fakhruldin Mohd Hashim, "Adaptive teleoperation system with neural network-based multiple model control", Mathematical Problems in Engineering, Volume 2010, pages 1-15, 2010.
[15] Vu Trieu Minh, Fakhruldin Mohd Hashim, "Fault detection model?based controller for process systems", Asian Journal of Control, Volume 13, Issue 3, Pages 382-397, 2011.




How to Cite

Ibrahim, I. B. (2019). Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery: Multivariable Nonlinear Model Predictive Control for a Petroleum Refinery. International Journal of Innovative Technology and Interdisciplinary Sciences, 2(4), 275–289.