Improving the Performance of a Continuous Stirred Tank Reactor Using Moving Horizon State Estimation and Model Predictive Control

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Abstract:

Since chemical reactors are utilized to produce specific and valuable products, concentration of products should be regulated at a specified level. As a disturbance input, a change in the inlet concentrations can vary the product concentration. So, in order to regulate the product concentration, the inlet concentrations and the product concentration should be measured. However, measurement of concentration encounters some problems such as high cost and time delay. For compensation of these failures, estimation of concentration has been proposed. In this work, the inlet concentration and the product concentration of a continuous stirred-tank reactor (CSTR) are estimated based on the moving horizon state estimation (MHSE), and the product concentration is regulated based on the model predictive control (MPC). Simulation results indicate that the proposed strategy improves the performance of the CSTR compared with the method in which the inlet concentration is not estimated.

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Advanced Materials Research (Volumes 403-408)

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3454-3460

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November 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] W. L. Luyben, Chemical reactor design and control, John Wiley & Sons, Inc.: New Jersey, 2007, pp.122-123.

Google Scholar

[2] C. Qu. Cheryl and J. Hahn, Computation of arrival cost for moving horizon estimation via unscented Kalman filtering, J. Proc. Cont. vol. 19, p.358–363, (2009).

DOI: 10.1016/j.jprocont.2008.04.005

Google Scholar

[3] V. Becerra, P. Roberts and G. Griffiths, Applying the extended Kalman Filter to systems described by nonlinear differential-algebraic equations, Cont. Eng. Prac. vol. 9, p.267–281, (2001).

DOI: 10.1016/s0967-0661(00)00110-6

Google Scholar

[4] S. A. Middlebrooks, Modelling and control of silicon and germanium thin film chemical vapor deposition, Ph.D. dissertation, University of Wisconsin-Madison, (2001).

Google Scholar

[5] V. Prasad, M. Schley, L. P. Russo and B.W. Bequette, Product property and production rate control of styrene polymerization, J. Proc. Cont. vol. 1, p.353–372, (2002).

DOI: 10.1016/s0959-1524(01)00044-0

Google Scholar

[6] R. Gudi, S. Shah and M. Gray , Multi rate state and parameter estimation in an antibiotic fermentation with delayed measurements, Boitech. Bioeng. Vol. 44, p.1271–1278, (1994).

DOI: 10.1002/bit.260441102

Google Scholar

[7] J. Rawlings and B. Bakshi, Particle filtering and moving horizon state estimation, Comp. Chem. Eng . vol. 30, p.1529–1541, (2006).

DOI: 10.1016/j.compchemeng.2006.05.031

Google Scholar

[8] F. Daum , Nonlinear filters: Beyond the kalman filter, IEEE. Aerosp. Electron. Syst. Mag. vol. 20, p.57–69, (2005).

DOI: 10.1109/maes.2005.1499276

Google Scholar

[9] M. Chaves, and E. Sontag, State-estimators for chemical reaction networks of Feinberg-Horn-Jackson zero deficiency type, Euro. J. Cont. vol. 8, p.343–359, (2002).

DOI: 10.3166/ejc.8.343-359

Google Scholar

[10] M. J. Tenny and J. Rawlings, Efficient moving horizon estimation and nonlinear model predictive control, in Proc. Am. Contr. Conf., Anchorage, USA, 2002, May 8-10.

DOI: 10.1109/acc.2002.1025355

Google Scholar

[11] E. L. Haseltine and J. B. Rawlings, Critical evaluation of extended Kalman filtering and moving horizon estimation, Ind. Eng. Chem. Res. vol. 44, p.2451–2460, (2005).

DOI: 10.1021/ie034308l

Google Scholar

[12] P. Kuhl, M. Diehl, T. Kraus, J. P. Schloder and H. G. Bock, A real-time algorithm for moving horizon state and parameter estimation, Comp. Chem. Eng . vol. 34, pp.1016-1045, (2010).

Google Scholar

[13] J. Prakash and R. Senthil, Design of observer based nonlinear model predictive controller for a continuous stirred tank reactor, J. Proc. Cont. vol. 18, p.504–514, (2008).

DOI: 10.1016/j.jprocont.2007.08.001

Google Scholar

[14] X. W. Zhang, S. H. Chan, H. Ko. Ho, J. Li, G. Li and Z. Feng, Nonlinear model predictive control based on the moving horizon state estimation for the solid oxide fuel cell, Int. J. Hyd. Ene. vol. 33, p.2355 – 2366, (2008).

DOI: 10.1016/j.ijhydene.2008.02.063

Google Scholar