journal article·2014-04

Modeling of aggregation kernels for fluidized beds using discrete particle model simulations

Nageswara Rao Narni, Mirko Peglow, Gerald Warnecke, Jitendra Kumar, Stefan Heinrich, J.A.M. Kuipers

Particuology· Volume 13
Publisher page

Abstract

Aggregation is one of the many important processes in chemical and process engineering. Several researchers have attempted to understand this complex process in fluidized beds using the macro-model of population balance equations (PBEs). The aggregation kernel is an effective parameter in PBEs, and is defined as the product of the aggregation efficiency and collision frequency functions. Attempts to derive this kernel have taken different approaches, including theoretical, experimental, and empirical techniques. The present paper calculates the aggregation kernel using micro-model computer simulations, i.e., a discrete particle model. We simulate the micro-model without aggregation for various initial conditions, and observe that the collision frequency function is in good agreement with the shear kernel. We then simulate the micro-model with aggregation and calculate the aggregation efficiency rate. © 2013 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Granular flow and fluidized beds · Mineral Processing and Grinding · Coagulation and Flocculation Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (23)

Show source-provided references
1. Unifying approach to modelling granule coalescence mechanisms10.1002/aic.690430408 ↗
2. Numerical study of segregation using a new drag force correlation for polydisperse systems derived from lattice-Boltzmann simulations10.1016/j.ces.2006.08.054 ↗
3. Aggregation during precipitation from solution: A method for extracting rates from experimental data10.1006/jcis.1996.0530 ↗
4. Modeling agglomeration processes in fluid-bed granulation10.1002/aic.690451005 ↗
5. Review of discrete particle modelling of fluidized beds10.1016/j.ces.2006.08.014 ↗
6. Fluid flow through packed columns
7. Friedlander · 1977
8. High-shear granulation modeling using a discrete element simulation approach10.1016/j.powtec.2005.04.012 ↗
9. Determination of coalescence kernels for high-shear granulation using DEM simulations10.1016/j.powtec.2006.08.002 ↗
10. Goldsmith · 2001
11. Hoomans · 2000
12. Tracer studies of high-shear granulation: II. Population balance modeling10.1002/aic.690470910 ↗
13. A note on particle collision rate in fluid flows10.1063/1.869622 ↗
14. Kinetics of granulation by non-random coalescence mechanism10.1016/0009-2509(72)85048-6 ↗
15. Coalescence of de-formable granules in wet granulation processes10.1002/aic.690460312 ↗
16. Narni · 2009
17. Some modelling aspects of aggregation kernels and aggregation population balance equations
18. Peglow · 2003
19. Similarity size distribution of agglomerates during their growth by coalescence in granulation or green pelletization10.1016/0301-7516(75)90021-6 ↗
20. Mathematical theory of the kinetics of the coagulation of colloidal solutions
21. Building population balance model for fluidized bed melt granulation: Lessons from kinetic theory of granular flow10.1016/j.powtec.2004.04.030 ↗
22. Population balances for particulate processes – A volume approach10.1016/S0009-2509(02)00118-5 ↗
23. Mechanics of fluidization