Integration of economical optimization and control for intentionally transient process operation

Lecture Notes in Control and Information Sciences

Volumn 358, Issue 1, 2007, Pages 419-434

  Kadam, Jitendra V ^a,b   Marquardt, Wolfgang ^b  

^a ExxonMobil Chemical Company   (Belgium)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

NONLINEAR SYSTEMS; PREDICTIVE CONTROL SYSTEMS;

CONTROL OBJECTIVES; DECOMPOSITION STRATEGY; ECONOMICAL BENEFITS; OPERATIONAL SCENARIO; OPTIMIZATION AND CONTROL; REAL-TIME OPTIMIZATION; TIGHT INTEGRATIONS; TRANSIENT PROCESS;

MODEL PREDICTIVE CONTROL;

EID: 36048944234     PISSN: 01708643     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-72699-9_34     Document Type: Article

References (21)

1. T. Backx, O. Bosgra, and W. Marquardt. Towards intentional dynamics in supply chain conscious process operation. In FOCAPO 98, Snowbird, Utah. www.lpt.rwth-aachen.de/Publication/Techreport/1998/LPT-1998-25.html, 1998.
2. L.T. Biegler, A.M. Cervantes, and A. Wächter. Advances in simultaneous strategies for dynamic process optimization. Chem. Eng. Sci., 24:39-51, 2002.
3. A.E. Bryson and Y.-C. Ho. Applied Optimal Control. Taylor & Francis, Bristol, PA, 1975.
4. 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.
5. M. Diehl, H.G. Bock, J.P. Schloder, 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.
6. 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.
7. A.V. Fiacco. Introduction to Sensitivity and Stability Analysis in Nonlinear Programming. Academic Press, New York, 1983.
8. J.F. Forbes. Model structure and adjustable parameter selection for operations optimizations. PhD thesis, McMaster University, 1994.
9. 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.
10. 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.
11. 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 FOCAPO 2003, pages 593-596, 2003.
12. 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.
13. 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.
14. M.L. Luyben and W.L. Luyben. Essentials of Process Control. McGraw-Hill, New York, USA, 1997.
15. th International Conference of Chemical Process Control, volume 93 of AIChE Symposium Series: 316, pages 156-164, 1997.
16. S.J. Qin and T.A. Badgwell. A survey of industrial model predictive control technology. Control Engineering Practice, 11:733-764, 2003.
17. J.B. Rawlings. Tutorial overview of model predictive control. IEEE Control Systems Magazine, 20(3):38-52, 2000.
18. 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.
19. M. Schlegel, K. Stockmann, T. Binder, and W. Marquardt. Dynamic optimization using adaptive control vector parameterization. Comp. Chem. Eng., 29:1731-1751, 2005.
20. 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.
21. 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.