Abstract
This paper summarizes recent developments and applications of dynamic real-time optimization (D-RTO). A decomposition strategy is presented to separate economical and control objectives by formulating two subproblems in closed-loop. Two approaches (model-based and model-free at the implementation level) are developed to provide tight integration of economical optimization and control, and to handle uncertainty. Simulated industrial applications involving different dynamic operational scenarios demonstrate significant economical benefits.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
T. Backx, O. Bosgra, and W. Marquardt. Towards intentional dynamics in supply chain conscious process operation. In FOGAPO 98, Snowbird, Utah. http://www.lpt.rwth-aachen.de/Publication/Techreport/1998/LPT-1998-25.html, 1998.
L.T. Biegler, A.M. Cervantes, and A. Wächter. Advances in simultaneous strategies for dynamic process optimization. Chem. Eng. Sci., 24:39–51, 2002.
A.E. Bryson and Y.-C. Ho. Applied Optimal Control. Taylor & Francis, Bristol, PA, 1975.
C. Büskens and H. Maurer. Sensitivity analysis and real-time optimization of parametric nonlinear-programming problems. In M. Grötschel, S. O. Krumke, and J. Rambau, editors, Online Optimization of Large Scale Systems, pages 3–16. Springer, 2002.
M. Diehl, H.G. Bock, J.P. Schioder, R. Findeisen, Z. Nagy, and F. Allgower. Realtime optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. Journal of Process Control, 12(4):577–585, 2002.
G. Dünnebier, D. van Hessem, J.V. Kadam, K.-U. Klatt, and M. Schlegel. Prozessführung und Optimierung von Polymerisationsprozessen. Chemie Ingenieur Technik, 76(6):703–708, 2004.
A.V. Fiacco. Introduction to Sensitivity and Stability Analysis in Nonlinear Programming. Academic Press, New York, 1983.
J.F. Forbes. Model structure and adjustable parameter selection for operations optimizations. PhD thesis, McMaster University, 1994.
A. Helbig, O. Abel, and W. Marquardt. Structural concepts for optimization based control of transient processes. In F. Allgöwer and A. Zheng, editors, Nonlinear Model Predictive Control, pages 295–311. Birkhäuser, Basel, 2000.
J.V. Kadam and W. Marquardt. Sensitivity-based solution updates in close-loop dynamic optimization. In S.L. Shah and J.F. MacGregor, editors, Proceedings of the DYCOPS 7 conference, 2004.
J.V. Kadam, W. Marquardt, M. Schlegel, O. H. Bosgra T. Backx, P.-J. Brouwer, G. Dünnebier, D. van Hessem, A. Tiagounov, and S. de Wolf. Towards integrated dynamic real-time optimization and control of industrial processes. In I. E. Grossmann and C. M. McDonald, editors, Proc. of FOGAPO 2003, pages 593–596, 2003.
J.V. Kadam, W. Marquardt, M. Schlegel, R.L. Tousain, D.H. van Hessem, J. van den Berg, and O.H. Bosgra. A two-level strategy of integrated optimization and control of industrial processes: a case study. In J. Grievink and J. v. Schijndel, editors, European Symposium on Computer Aided Process Engineering-12, pages 511–516. Elsevier, 2002.
J.V. Kadam, B. Srinivasan, D. Bonvin, and W. Marquardt. Optimal grade transition in industrial polymerization processes via NCO tracking. Submitted to: AICHE J., 2005.
M.L. Luyben and W.L. Luyben. Essentials of Process Control. McGraw-Hill, New York, USA, 1997.
T.E. Marlin and A.N. Hrymak. Real-time optimization of continuous processes. In Chemical Process Control-V: Proceedings of the 5 th International Conference of Chemical Process Control, volume 93 of AIChE Symposium Series: 316, pages 156–164, 1997.
S.J. Qin and T.A. Badgwell. A survey of industrial model predictive control technology. Control Engineering Practice, 11:733–764, 2003.
J.B. Rawlings. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3):38–52, 2000.
M. Schlegel and W. Marquardt. Detection and exploitation of the control switching structure in the solution of dynamic optimization problems. Journal of Process Control, 16:275–290, 2006.
M. Schlegel, K. Stockmann, T. Binder, and W. Marquardt. Dynamic optimization using adaptive control vector parameterization. Comp. Chem. Eng., 29:1731–1751, 2005.
B. Srinivasan and D. Bonvin. Dynamic optimization under uncertainty via NCO tracking: A solution model approach. In C. Kiparissides, editor, Proc. BatchPro Symposium 2004, pages 17–35, 2004.
B. Srinivasan, D. Bonvin, E. Visser, and S. Palanki. Dynamic optimization of batch processes II. Role of measurements in handling uncertainty. Comp. Chem. Eng., 27:27–44, 2003.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kadam, J.V., Marquardt, W. (2007). Integration of Economical Optimization and Control for Intentionally Transient Process Operation. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-540-72699-9_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72698-2
Online ISBN: 978-3-540-72699-9
eBook Packages: EngineeringEngineering (R0)