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Vol: 56(70) No: 4 / December 2011 Multivariable Predictive Control for the Secondary Processes in a Cryogenic Carbon Isotope Separation Column Cristina I. Pop Technical University of Cluj-Napoca, Department of Automatic Control, 26-28 Gh. Baritiu Str., 400027 Cluj-Napoca, Romania, e-mail: Cristina.Pop@aut.utcluj.ro Bogdan Muresan Technical University of Cluj-Napoca, Department of Automatic Control , 26-28 Gh. Baritiu Str., 400027 Cluj-Napoca, Romania, e-mail: Bogdan.Muresan@aut.utcluj.ro Eva H. Dulf Technical University of Cluj-Napoca, Department of Automatic Control , 26-28 Gh. Baritiu Str., 400027 Cluj-Napoca, Romania, e-mail: Eva.Dulf@aut.utcluj.ro Clara Ionescu Ghent University, Department of Electrical energy, Systems and Automation Technologiepark, 913, 9052 Gent, Belgium, e-mail: ClaraMihaela.Ionescu@UGent.be Robain De Keyser Ghent University, Department of Electrical energy, Systems and Automation Technologiepark , 913, 9052 Gent, Belgium, e-mail: Robain.DeKeyser@UGent.be Clement Festila Technical University of Cluj-Napoca, Department of Automatic Control, 26-28 Gh. Baritiu Str., 400027 Cluj-Napoca, Romania, e-mail: Clement.Festila@aut.utcluj.ro Keywords: predictive controller, multivariable time delay systems, robustness, modeling errors Abstract The secondary processes in a pilot plant carbon isotope cryogenic separation column are multivariable, strongly coupled time delay processes. First principles modeling for these processes may result in mathematical models too complex for control purposes. Practice has shown that experimental identification proves to be enough for an accurate model. However, the resulting models may be susceptible to identification errors due to truncations, approximations, etc. Thus, the control algorithm designed must cope with modeling errors in a robust manner. The proposed control strategy for the secondary processes in a cryogenic carbon isotope separation column is based on a self-adaptive predictive controller, with intrinsic time delay compensation. The simulations presented show that the predictive controller is also robust to significant time delay, gain or time constants estimation errors, solving at the same time the disturbance rejection problem. References [1] J.E Normey-Rico, E.F. Camacho, Dead time compensators: a survey, Control Engineering Practice, vol. 16, pp. 407- 428, 2008. [2] Q. G. Wang, B. Zou, Y. Zhang, Decoupling Smith Predictor design for multivariable systems with multiple time delays, Chemical Engineering Research and Design, 78, pp. 565–572, 2000. [3] C.-I. Pop, E.-H. Dulf, R. De Keyser, C.M. Ionescu, B. Muresan, Cl. Festila,Predictive Control of the Multivariable Time Delay Processes in an Isotope Separation Column, 6th IEEE International Symposium on Applied Computational Intelligence and Informatics, pp. 29 – 34, 19-21 mai 2011, Timisoara. [4] M. Galvez-Carrillo, R. De Keyser, C. Ionescu, Application of a Smith Predictor based Nonlinear Predictive Controller to a Solar Power Plant, 7th IFAC Symposium on Nonlinear Control Systems, South Africa, 2007. [5] R. Sivakumar, K. Suresh Manic, V. Nerthiga, R. Akila, K. Balu, Application of Fuzzy Model Predictive Control in Multivariable Control of Distillation Column, International Journal of Chemical Engineering and Applications, Vol. 1, No. 1, 2010. [6] A. Rueda, S. Cristea, C. De Prada, R. De Keyser, Non-linear Predictive Control for a Distillation Column, Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain, December 12-15, 2005. [7] T. Guo, J. Lu, W. Xiang, W. Ding, T. Zhang, Dynamic Modeling and Multivariable Model Predictive Control of the Air Separation Columns in an IGCC Power Plant, Proceedings of the World Congress on Engineering and Computer Science, Vol. II, San Francisco, USA, 2009. [8] D. Axente, M. Abrudean, A. Baldea, Isotope Separation 15N, 18O, 10B, 13C by isotopic exchange (in Romanian), Casa Cartii de Stiinta, Cluj-Napoca, 1994. [9] C.I. Pop, Cl. Festila, E.H. Dulf, Optimal control of the carbon isotopes cryogenic separation process, Chemical and Biochemical Engineering Quarterly, 24, pp.301-307, 2010. [10] M. Gligan, E.H. Dulf, M. L. Unguresan, Cl. Festila, Preliminaries regarding general modeling of the cryogenic distillation with application to (13C) isotope separation, IEEE International Conference on Automation, Quality and Testing, Robotics, vol. 1, pp.155 – 158, 2006. [11] R. De Keyser, Model Based Predictive Control, In: UNESCO Encyclopedia of Life Support Systems (EoLSS), (Eolss Publishers Co Ltd), article 6.43.16.1, Oxford, 2003. [12] De Keyser R., Van Cauwenberghe A., „A self-tuning multistep predictor application“, Automatica, 17, 167-174, 1981. [13] De Keyser R., Ionescu C., The Disturbance Model in Model Based Predictive Control, IEEE Conference on Control Applications (CCA’2003), Istanbul, Turkey, CD paper CF-001472. |