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Real-Time Optimization for Large Scale Processes: Nonlinear Model Predictive Control of a High Purity Distillation Column

In: Online Optimization of Large Scale Systems

Author

Listed:
  • Moritz Diehl

    (Universität Heidelberg, Interdisziplinäres Zentrum für wissenschaftliches Rechnen)

  • Ilknur Uslu

    (Universität Stuttgart, Institut für Systemdynamik und Regelungstechnik (ISR))

  • Rolf Findeisen

    (Universität Stuttgart, Institut für Systemtheorie technischer Prozesse (IST))

  • Stefan Schwarzkopf

    (Universität Stuttgart, Institut für Systemdynamik und Regelungstechnik (ISR))

  • Frank Allgöwer

    (Universität Stuttgart, Institut für Systemtheorie technischer Prozesse (IST))

  • H. Georg Bock

    (Universität Heidelberg, Interdisziplinäres Zentrum für wissenschaftliches Rechnen)

  • Tobias Bürner

    (Universität Heidelberg, Interdisziplinäres Zentrum für wissenschaftliches Rechnen)

  • Ernst Dieter Gilles

    (Universität Stuttgart, Institut für Systemdynamik und Regelungstechnik (ISR)
    Max-Planck-Institut für Dynamik komplexer technischer Systeme)

  • Achim Kienle

    (Max-Planck-Institut für Dynamik komplexer technischer Systeme)

  • Johannes P. Schlöder

    (Universität Heidelberg, Interdisziplinäres Zentrum für wissenschaftliches Rechnen)

  • Erik Stein

    (Max-Planck-Institut für Dynamik komplexer technischer Systeme)

Abstract

The purpose of this paper is an experimental proof-of-concept of the application of NMPC for large scale systems using specialized dynamic optimization strategies. For this aim we investigate the application of modern, computationally efficient NMPC schemes and realtime optimization techniques to a nontrivial process control example, namely the control of a high purity binary distillation column. All necessary steps are discussed, from formulation of a DAE model with 164 states up to the final application to the experimental apparatus. Especially an efficient real-time optimization scheme based on the direct multiple shooting method is introduced. It is characterized by an initial value embedding strategy, that allows to immediately respond to disturbances, and real-time iterations, that dovetail the optimization iterations with the real process development. Using this scheme, sampling times of 10 seconds are feasible on a standard PC. This shows that an efficient NMPC scheme based on large scale DAE models is feasible for the real-time control of a pilot scale distillation column.

Suggested Citation

  • Moritz Diehl & Ilknur Uslu & Rolf Findeisen & Stefan Schwarzkopf & Frank Allgöwer & H. Georg Bock & Tobias Bürner & Ernst Dieter Gilles & Achim Kienle & Johannes P. Schlöder & Erik Stein, 2001. "Real-Time Optimization for Large Scale Processes: Nonlinear Model Predictive Control of a High Purity Distillation Column," Springer Books, in: Martin Grötschel & Sven O. Krumke & Jörg Rambau (ed.), Online Optimization of Large Scale Systems, pages 363-383, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-04331-8_20
    DOI: 10.1007/978-3-662-04331-8_20
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