Two State Estimators for the Barium Sulfate Precipitation in a Semi-Batch 
Reactor

Mangold, M.; Bück, A.; Schenkendorf, R.; Steyer, Christiane; Voigt, Andreas; Sundmacher, Kai

doi:10.1016/j.ces.2008.05.039

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Two State Estimators for the Barium Sulfate Precipitation in a Semi-Batch Reactor

MPG-Autoren

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Mangold, M.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Bück, A.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Schenkendorf, R.
Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Steyer, Christiane
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

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Voigt, Andreas
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Sundmacher, Kai
Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
Otto-von-Guericke-Universität Magdeburg, External Organizations;

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Zitation

Mangold, M., Bück, A., Schenkendorf, R., Steyer, C., Voigt, A., & Sundmacher, K. (2009). Two State Estimators for the Barium Sulfate Precipitation in a Semi-Batch Reactor. Chemical Engineering Science, 64(4), 646-660. doi:10.1016/j.ces.2008.05.039.

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-9348-A

Zusammenfassung

The on-line determination of particle property distributions by direct measurements is often difficult, because the measurement equations are not invertible or because the inverse problem is ill-posed. If the process is observable, one can use state estimation techniques in order to reconstruct unmeasurable internal states of the process. This is discussed here for a semi-batch precipitation reactor. A Square Root Unscented Kalman Filter and state estimation by online minimisation are studied for the case of a measurable average particle size. Both estimators use a one-dimensional population balance model. The two approaches are compared in simulations. Copyright © 2008 Published by Elsevier Ltd. [accessed February 2, 2009]