13 Morphological Modelling and Simulation …
471
more sophisticated than of 2D images, the proposed methods help to understand morphological influences on important phenomena like crystal growth, agglomeration
and breakage.
Another aspect of the work was the modeling of crystallization under consideration of the influence of morphology. Using a high dimensional population balance
model the important influence of multiple surfaces with different growth rates on crystal growth was demonstrated. For the effective solution of such a model, novel numerical methods have been developed. It is common practice to neglect the combined
influence of agglomeration and growth on the surface area of crystal populations. It
was herein demonstrated that this introduces high errors in such models. A method
was proposed to parametrize a population balance model that considers both the
evolution of crystal volume and area correctly. Because such models are still numerically challenging and computationally expensive even if not included in a flowsheet,
they are currently not suitable for simulation of integrated flowsheets. Therefore, a
simpler model was included in the simulation platform Dyssol. It is demonstrated
that Dyssol can be used to efficiently calculate integrated crystallization processes.
References
1. Kovaˇ cevi´ c, T., Reinhold, A., Briesen, H.: Identifying faceted crystal shape from threedimensional tomography data. Cryst. Growth Des. 14(4), 1666–1675 (2014)
2. Kovaˇ cevi´ c, T., Schock, J., Pfeiffer, F., Briesen, H.: Shape identification of primary particles in
potash alum aggregates using three-dimensional tomography data. Cryst. Growth Des. 16(5),
2685–2699 (2016)
3. Kovaˇ cevi´ c, T., Briesen, H.: Determining the full geometric complexity of crystal aggregates
via Monte Carlo Simulation. In: PARTEC: International Congress of Particle Technology,
Nuremberg, pp. 19–21 (2016)
4. Kovaˇ cevi´ c, T., et al.: Disorientation angle distribution of primary particles in potash alum
aggregates. J. Cryst. Growth 467, 93–106 (2017)
5. Heisel, S., Kovaˇ cevi´ c, T., Briesen, H., Schembecker, G., Wohlgemuth, K.: Variable selection
and training set design for particle classification using a linear and a non-linear classifier. Chem.
Eng. Sci. 173, 131–144 (2017)
6. Kovaˇ cevi´ c, T., Briesen, H.: Simulations of crystal aggregation and growth: towards correct
crystal area. AIChE J. 65(5) (2019)
7. Kulozik, S., Buchholz, M., Kovaˇ cevi´ c, T., Haus, J., Heinrich, S., Briesen, H.: Dynamic flowsheet
simulation of a crystallization reactor with material recovery via a hydrocyclone. In: PARTEC
2019, Nuremberg, VDI Verlag (2019)
8. Reinhold, A., Briesen, H.: High dimensional population balances for the growth of faceted
crystals: combining Monte Carlo integral estimates and the method of characteristics. Chem.
Eng. Sci. 127, 220–229 (2015)
9. Reinhold, A., Schorsch, S., Mazzotti, M., Briesen, H.: Modeling and measurement of abraded
particles. Powder Technol. 271, 134–140 (2015)
10. Ma, C.Y., Wang, X.Z., Roberts, K.J.: Morphological population balance for modeling crystal
growth in face directions. AIChE J. 54(1), 209–222 (2008)
11. Reinhold, A., Briesen, H.: Convex geometry for the morphological modeling and characterization of crystal shapes. Part. Part. Syst. Charact. 28(3–4), 37–56 (2012)
12. Borchert, C., Sundmacher, K.: Morphology evolution of crystal populations: modeling and
observation analysis. Chem. Eng. Sci. 70, 87–98 (2012)
471
more sophisticated than of 2D images, the proposed methods help to understand morphological influences on important phenomena like crystal growth, agglomeration
and breakage.
Another aspect of the work was the modeling of crystallization under consideration of the influence of morphology. Using a high dimensional population balance
model the important influence of multiple surfaces with different growth rates on crystal growth was demonstrated. For the effective solution of such a model, novel numerical methods have been developed. It is common practice to neglect the combined
influence of agglomeration and growth on the surface area of crystal populations. It
was herein demonstrated that this introduces high errors in such models. A method
was proposed to parametrize a population balance model that considers both the
evolution of crystal volume and area correctly. Because such models are still numerically challenging and computationally expensive even if not included in a flowsheet,
they are currently not suitable for simulation of integrated flowsheets. Therefore, a
simpler model was included in the simulation platform Dyssol. It is demonstrated
that Dyssol can be used to efficiently calculate integrated crystallization processes.
References
1. Kovaˇ cevi´ c, T., Reinhold, A., Briesen, H.: Identifying faceted crystal shape from threedimensional tomography data. Cryst. Growth Des. 14(4), 1666–1675 (2014)
2. Kovaˇ cevi´ c, T., Schock, J., Pfeiffer, F., Briesen, H.: Shape identification of primary particles in
potash alum aggregates using three-dimensional tomography data. Cryst. Growth Des. 16(5),
2685–2699 (2016)
3. Kovaˇ cevi´ c, T., Briesen, H.: Determining the full geometric complexity of crystal aggregates
via Monte Carlo Simulation. In: PARTEC: International Congress of Particle Technology,
Nuremberg, pp. 19–21 (2016)
4. Kovaˇ cevi´ c, T., et al.: Disorientation angle distribution of primary particles in potash alum
aggregates. J. Cryst. Growth 467, 93–106 (2017)
5. Heisel, S., Kovaˇ cevi´ c, T., Briesen, H., Schembecker, G., Wohlgemuth, K.: Variable selection
and training set design for particle classification using a linear and a non-linear classifier. Chem.
Eng. Sci. 173, 131–144 (2017)
6. Kovaˇ cevi´ c, T., Briesen, H.: Simulations of crystal aggregation and growth: towards correct
crystal area. AIChE J. 65(5) (2019)
7. Kulozik, S., Buchholz, M., Kovaˇ cevi´ c, T., Haus, J., Heinrich, S., Briesen, H.: Dynamic flowsheet
simulation of a crystallization reactor with material recovery via a hydrocyclone. In: PARTEC
2019, Nuremberg, VDI Verlag (2019)
8. Reinhold, A., Briesen, H.: High dimensional population balances for the growth of faceted
crystals: combining Monte Carlo integral estimates and the method of characteristics. Chem.
Eng. Sci. 127, 220–229 (2015)
9. Reinhold, A., Schorsch, S., Mazzotti, M., Briesen, H.: Modeling and measurement of abraded
particles. Powder Technol. 271, 134–140 (2015)
10. Ma, C.Y., Wang, X.Z., Roberts, K.J.: Morphological population balance for modeling crystal
growth in face directions. AIChE J. 54(1), 209–222 (2008)
11. Reinhold, A., Briesen, H.: Convex geometry for the morphological modeling and characterization of crystal shapes. Part. Part. Syst. Charact. 28(3–4), 37–56 (2012)
12. Borchert, C., Sundmacher, K.: Morphology evolution of crystal populations: modeling and
observation analysis. Chem. Eng. Sci. 70, 87–98 (2012)