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Parameter Identification For Continuous Fluidized Bed Spray Agglomeration

1
Institute for Automation Engineering; Otto-von-Guericke University, D-39106 Magdeburg, Germany
2
Institute of Process Engineering; Otto-von-Guericke University, D-39106 Magdeburg, Germany
3
Process Synthesis and Process Dynamics; Max-Planck-Institute for Complex Technical Dynamics, D-39106 Magdeburg, Germany
4
Moscow Power Engineering Institute, National Research University, 111250-Moscow, Russia
5
Institute of Particle Technology, Friedrich-Alexander University Erlangen-Nürnberg, D-91058 Erlangen, Germany
*
Author to whom correspondence should be addressed.
Processes 2018, 6(12), 246; https://doi.org/10.3390/pr6120246
Received: 30 October 2018 / Revised: 26 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Recent Advances in Population Balance Modeling)
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Abstract

Agglomeration represents an important particle formation process used in many industries. One particularly attractive process setup is continuous fluidized bed spray agglomeration, which features good mixing as well as high heat and mass transfer on the one hand and constant product throughput with constant quality as well as high flow rates compared to batch mode on the other hand. Particle properties such as agglomerate size or porosity significantly affect overall product properties such as re-hydration behavior and dissolubility. These can be influenced by different operating parameters. In this manuscript, a population balance model for a continuous fluidized bed spray agglomeration is presented and adapted to experimental data. Focus is on the description of the dynamic behavior in continuous operation mode in a certain neighborhood around steady-state. Different kernel candidates are evaluated and it is shown that none of the kernels are able to match the first six minutes with time independent parameters. Afterwards, a good fit can be obtained, where the Brownian and the volume independent kernel models match best with the experimental data. Model fit is improved for identification on a shifted time domain neglecting the initial start-up phase. Here, model identifiability is shown and parameter confidence intervals are computed via parametric bootstrap. View Full-Text
Keywords: population balance modeling; continuous fluidized bed spray agglomeration; parameter identification; identifiability population balance modeling; continuous fluidized bed spray agglomeration; parameter identification; identifiability
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Golovin, I.; Strenzke, G.; Dürr, R.; Palis, S.; Bück, A.; Tsotsas, E.; Kienle, A. Parameter Identification For Continuous Fluidized Bed Spray Agglomeration. Processes 2018, 6, 246.

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