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Methods of state estimation for particulate processes

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

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Niemann,  Björn
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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Citation

Mangold, M., Steyer, C., Niemann, B., Voigt, A., & Sundmacher, K. (2006). Methods of state estimation for particulate processes. In W. Marquardt, & C. Pantelides (Eds.), 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering (pp. 1191-1196). Amsterdam: Elsevier.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-9A98-2
Abstract
Determining property distributions of particles online by measurement is difficult in many cases, especially if the particles are in the nanometre range. An alternative may be state estimation techniques, which use information from process simulations in addition to the measurement signals. Two examples of state estimators for particulate processes are presented in this contribution. The first one is an extended Kalman filter based on a population balance model. The second one is a bootstrap filter based on a Monte Carlo simulation. © 2006 Elsevier B.V. All rights reserved. [accessed 2014 January 9th]