104
C. Neugebauer et al.
In both cases, the controller adjusts the porosity within a relatively short time
of about 5 hours smoothly to the new reference value p,I I and keeps it constant.
However, without the d 32 controller, the particle size distribution represented by the
Sauter diameter in the second diagram of Fig. 25 starts oscillating due to the influence
of the porosity on the milling of the oversized particle. If in addition, d 32 control was
applied as described above, the Sauter diameter and the particle size distribution
could also be stabilized.
8 Summary and Outlook
Within this project, new insight into the dynamics of particle formulation by spray
layering in continuously operated horizontal fluidized beds has been obtained. Starting from the single-particle level, using information on the particle recirculation
between functional compartments and multiple chambers by weirs, the effect of
thermal conditions on layer properties, dynamic and steady-state process behavior
has been illuminated. Process models were derived allowing the predictive simulation of particle properties and the dynamic and steady-state behavior. This led to the
derivation of process regime maps, dividing (asymptotically) stable operating points
from unstable operating points. Using this information, control concepts could be
derived and tested—first in simulations and later at a full-scale industrial plant.
Combined information on micro-, meso- and macro-scale behavior, i.e. from single particles to the apparatus, and its implementation in the simulation framework
“Dyssol” allows for model-driven apparatus, process and control design. This enables
the inverse design of process and apparatus starting from particle property requirements, by mathematical optimization.
Acknowledgements Funding of this project within the priority program SPP 1679 “Dynamics
of interconnected solids processes” by Deutsche Forschungsgemeinschaft (DFG) is gratefully
acknowledged. Furthermore, the support and contributions of Christian Dreyschultze, Zhaochen
Jiang, Katja Meyer and Arne Teiwes are gratefully acknowledged.
References
1. Diez, E., Meyer, K., Bück, A., Tsotsas, E., Heinrich, S.: Influence of process conditions on the
product properties in a continuous fluidized bed spray granulation process. Chem. Eng. Res.
Des. 139, 104–115 (2018)
2. Ramkrishna, D.: Population Balances: Theory and Applications to Particulate Systems in Engineering. Academic Press, San Diego, CA (2000)
3. Bück, A., Peglow, M., Naumann, M., Tsotsas, E.: Population balance model for drying of
droplets containing aggregating nanoparticles. AIChE J. 58(11), 3318–3328 (2012)
4. Burgschweiger, J., Tsotsas, E.: Experimental investigation and modelling of continuous fluidized bed drying under steady-state and dynamic conditions. Chem. Eng. Sci. 57(24), 5021–
5038 (2002)
C. Neugebauer et al.
In both cases, the controller adjusts the porosity within a relatively short time
of about 5 hours smoothly to the new reference value p,I I and keeps it constant.
However, without the d 32 controller, the particle size distribution represented by the
Sauter diameter in the second diagram of Fig. 25 starts oscillating due to the influence
of the porosity on the milling of the oversized particle. If in addition, d 32 control was
applied as described above, the Sauter diameter and the particle size distribution
could also be stabilized.
8 Summary and Outlook
Within this project, new insight into the dynamics of particle formulation by spray
layering in continuously operated horizontal fluidized beds has been obtained. Starting from the single-particle level, using information on the particle recirculation
between functional compartments and multiple chambers by weirs, the effect of
thermal conditions on layer properties, dynamic and steady-state process behavior
has been illuminated. Process models were derived allowing the predictive simulation of particle properties and the dynamic and steady-state behavior. This led to the
derivation of process regime maps, dividing (asymptotically) stable operating points
from unstable operating points. Using this information, control concepts could be
derived and tested—first in simulations and later at a full-scale industrial plant.
Combined information on micro-, meso- and macro-scale behavior, i.e. from single particles to the apparatus, and its implementation in the simulation framework
“Dyssol” allows for model-driven apparatus, process and control design. This enables
the inverse design of process and apparatus starting from particle property requirements, by mathematical optimization.
Acknowledgements Funding of this project within the priority program SPP 1679 “Dynamics
of interconnected solids processes” by Deutsche Forschungsgemeinschaft (DFG) is gratefully
acknowledged. Furthermore, the support and contributions of Christian Dreyschultze, Zhaochen
Jiang, Katja Meyer and Arne Teiwes are gratefully acknowledged.
References
1. Diez, E., Meyer, K., Bück, A., Tsotsas, E., Heinrich, S.: Influence of process conditions on the
product properties in a continuous fluidized bed spray granulation process. Chem. Eng. Res.
Des. 139, 104–115 (2018)
2. Ramkrishna, D.: Population Balances: Theory and Applications to Particulate Systems in Engineering. Academic Press, San Diego, CA (2000)
3. Bück, A., Peglow, M., Naumann, M., Tsotsas, E.: Population balance model for drying of
droplets containing aggregating nanoparticles. AIChE J. 58(11), 3318–3328 (2012)
4. Burgschweiger, J., Tsotsas, E.: Experimental investigation and modelling of continuous fluidized bed drying under steady-state and dynamic conditions. Chem. Eng. Sci. 57(24), 5021–
5038 (2002)