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

Moritz Diehl, Ilknur Uslu, Rolf Findeisen, Stefan Schwarzkopf, Frank Allgöwer, H. Georg Bock, Tobias Bürner, Ernst Dieter Gilles, Achim Kienle, Johannes P. Schlöder and Erik Stein
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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)
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

A chapter in Online Optimization of Large Scale Systems, 2001, pp 363-383 from Springer

Abstract: 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.

Keywords: Extend Kalman Filter; Model Predictive Control; Distillation Column; Manipulate Variable; Distribute Control System (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-04331-8_20

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DOI: 10.1007/978-3-662-04331-8_20

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