Dynamic Optimization Of A Mma With Vac Copolymerization Reactor

Increasing worldwide market competitiveness and reduced profit margins are pressing chemical and process industries to move towards a predictive control approach, based on first-principles mathematical models, as well as plant dynamic optimization. In this perspective, the paper focuses on the development of a nonlinear model predictive control (NMPC) to manage the copolymerization process of methyl methacrylate (MMA) with vinyl acetate (VAc), consisting of a jacketed continuous stirred tank reactor, a separator, and a recycle loop. This system presents a highly complex behavior, thus making difficult the success of controllers based on linear models. A detailed differential and algebraic mathematical model consists of 53 equations and is implemented in Fortran 90/95 to simulate the plant and setup the NMPC. The numerical solution is performed by using IMSL library. NMPC is proved to be superior to a linear model predictive control approach and appears to hold a considerable promise for such a reactor system. Copyright © 2009, AIDIC Servizi S.r.l.1713831388Bemporad, A., Morari, M., Robust Model Predictive Control: A Survey (1999) Robustness in Identification and Control. Lecture Notes in Control and Information Sciences, 245. , Eds. Garulli A, Tesi A. and Vicino A, Springer, BerlinCongalidis, J.P., Richards, J.R., Ray, W.H., Feedforward and feedback control of a solution copolymerization reactor (1989) AIChE J, 35, pp. 891-907Haeri, M., Beik, H.Z., Application of extended DMC for nonlinear MIMO systems (2005) Comput. Chem. Eng, 29, pp. 1867-1874Lima, N.M.N., Maciel Filho, R., Embiruçu, M., Wolf Maciel, M.R., A cognitive approach to develop dynamic models: Application to polymerization systems (2007) J. Appl. Polym. Sci, 106, pp. 981-992Manenti, F., Rovaglio, M., Integrated multilevel optimization in large-scale poly(ethylene terephthalate) plants (2008) Ind. Eng. Chem. Res, 47, pp. 92-104Maner, B.R., Doyle III, F.J., Polymerization reactor control using autoregressive-plus volterra-based MPC (1997) AIChE J, 43, pp. 1763-1784Özkan, G., Hapoglu, H., Alpbaz, M., Non-linear generalized predictive control of a jacketed well mixed tank as applied to a batch process - a polymerization reaction (2006) Appl. Therm. Eng, 26, pp. 720-726Soroush, M., Kravaris, C., Nonlinear control of a batch polymerization reactor: An experimental study (1992) AIChE J, 38, pp. 1429-144

Development Of Dynamic Models And Predictive Control By Fuzzy Logic For Polymerization Processes

This work presents the development of a predictive hybrid controller (PHC) based in fuzzy systems for polymerization processes. These reactions have typically a highly non linear dynamic behavior, thus making the performance of controllers based on conventional internal models to be poor or to require a lot of effort in controller tuning. The solution copolymerization of methyl methacrylate and vinyl acetate in a continuous stirred tank reactor is used to illustrate the performance of the proposed controller. It is introduced the development of a methodology for the design of the predictive controller based on functional fuzzy dynamic models of Takagi-Sugeno type. These models present an excellent capacity to represent dynamic data and this feature is explored in the proposed hybrid controller. Moreover, they allow the inclusion of qualitative or operational information of the process. Gaussian membership functions are used for the fuzzy sets and model determination (rules number and model parameters) is obtained from the process database. The treatment of these data for the fuzzy model determination is carried out by means of algorithms of subtractive clustering and least squares. The kinetic parameters and reactor operating conditions are obtained from the literature and a mathematical model is considered as a virtual plant for data generation and process identification. The modeling by the fuzzy approach showed to have a good potential for the processes representation. The PHC controller was compared to the dynamic matrix controller (DMC) to the regulatory and servo problems. The obtained results showed that the proposed control is robust and it requires less computational time than the conventional predictive controllers, being an interesting alternative to attack control problems in complex chemical processes.ABDELAZIM, T., MALIK, O.P., Identification of nonlinear systems by Takagi-Sugeno logic grey box modeling for real-time control (2005) Control Engineering Practice, 13, pp. 1489-1498ALEXANDRIDIS, A.P., SIETTOS, C.I., SARIMVEIS, H.K., BOUDOUVIS, A.G., BAFAS, G.V., Modelling of nonlinear process dynamics using Kohonen's neural networks, fuzzy systems and Chebyshev series (2002) Computers and Chemical Engineering, 26, pp. 479-486CERRADA, M., AGUILAR, J., COLINA, E., TITLI, A., Dynamical membership functions: An approach for adaptive fuzzy modeling (2005) Fuzzy Sets and Systems, 152, pp. 513-533CHEN, B., LIU, X., Reliable control design of fuzzy dynamic systems with time-varying delay (2003) Fuzzy Sets and Systems, pp. 1-26CHIU, S., A cluster estimation method with extension to fuzzy model identification (1994) IEEE, pp. 1240-1245CHIU, S., Method and software for extracting fuzzy classification rules by subtractive clustering (1996) IEEE, pp. 461-465CONGALIDIS, J.P., RICHARDS, J.R., RAY, W.H., Feedforward and feedback control of a solution copolymerization reactor (1989) AIChe Journal, 35 (6), pp. 891-907. , JuneDOUGHERTY, D., COOPER, D.A., Practical Multiple Model Adaptive Strategy for Multivariable Model Predictive Control (2003) Control Engineering Practice, 11, pp. 649-664GUIAMBA, I.R.F., MULHOLLAND, M., Adaptive Linear Dynamic Matrix Control Applied to an Integrating Process (2004) Computers and Chemical Engineering, 28, pp. 2621-2633HABBI, H., ZELMAT, M., BOUAMAMA, B.O., A dynamic fuzzy model for a drum-boiler-turbine system (2003) Automatica, 39, pp. 1213-1219HAERI, M., BEIK, H.Z., Application of Extended DMC for Nonlinear MIMO Systems (2005) Computers and Chemical Engineering, 29, pp. 1867-1874MANER, B.R., DOYLE III, F.J., Polymerization reactor control using autoregressive-plus volterra-based MPC (1997) AIChe Journal, 43 (7), pp. 1763-1784. , JulyPARK, M., RHEE, H., Property Evaluation and Control in a Semibatch MMA/MA Solution Copolymerization Reactor (2003) Chemical Engineering Science, 58, pp. 603-611PASSINO, K.M., YURKOVICH, S., (1998) Fuzzy Control, , Addison-Wesley-Longman, Menlo Park, CARAMASWAMY, S., CUTRIGHT, T.J., QAMMAR, H.K., Control of a Continuous Bioreactor Using Model Predictive Control (2005) Process Biochemistry, 40, pp. 2763-2770ROSS, T.J., (2004) Fuzzy Logic with Engineering Applications, , John Wiley & Sons Ltd, Second EditionSALA, A., GUERRA, T.M., BABUSKA, R., Perspectives of fuzzy systems and control (2005) Fuzzy Sets and Systems, 156, pp. 432-444SANTOS, L.O., AFONSO, P.A., CASTRO, J.A., OLIVEIRA, N.M., BIEGLER, L.T., On-line Implementation of Nonlinear MPC: An Experimental case study (2001) Control Engineering Practice, 9, pp. 847-857SCHNELLE, P.D., ROLLINS, D.L., Industrial Model Predictive Control Technology as Applied to Continuous Polymerization Processes (1998) ISA Transactions, 36 (4), pp. 281-292SILVA, J. 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