Biotechnology and Bioprocess Engineering

, Volume 24, Issue 1, pp 191–205 | Cite as

CFD-DEM Simulation of the Fluidized-bed Granulation of Food Powders

  • Ju-Eun Kim
  • Young Mi ChungEmail author
Research Paper


In this study, the granulation behavior of food powders was simulated by taking into account the changes in the rheological properties and water content of the powders. Several food powders were studied and classified into self-agglomerating or non-agglomerating powders depending on the presence of a glass transition temperature. Cricket powder, which is incapable of self-agglomeration, was mixed with dextrin powder to enable the granulation process. Cricket + dextrin powder was used as a simulation model, and the glass transition temperature of dextrin and a newly derived evaporation model were adopted in our simulation studies. Similar tendencies were observed in both the experimental and simulation results. Our granulation model can describe all of the following: nucleation, granulation based on the powder properties and changes in particle water content due to evaporation; these behaviors have not been modeled in this way previously.


FBG CFD DEM food powders glass transition temperature evaporation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Litster, J. D., K. P. Hapgood, J. N. Michaels, A. Sims, M. Roberts, and S. K. Kameneni (2002) Scale-up of mixer granulators for effective liquid distribution. Powder Technol. 124: 272–280.CrossRefGoogle Scholar
  2. 2.
    Fries, L., S. Antonyuk, S. Heinrich, D. Dopfer, and S. Palzer (2013) Collision dynamics in fluidised bed granulators: A DEMCFD study. Chem. Eng. Sci. 86: 108–123.CrossRefGoogle Scholar
  3. 3.
    Sen M., D. Barrasso, R. Singh and R. Ramachandran (2014) A multi-scale hybrid CFD-DEM-PBM description of a fluid-bed granulation process. Processes 2: 89–111.CrossRefGoogle Scholar
  4. 4.
    Christakis, N., J. Wang, M. K. Patel, M. S. A. Bradley, M. C. Leaper, and M. Cross (2006) Aggregation and caking processes of granular materials: continuum model and numerical simulation with application to sugar. Adv. Powder Technol. 17: 543–565.CrossRefGoogle Scholar
  5. 5.
    Langlet, M., M. Benali, I. Pezron, K. Saleh, P. Guigon, and L. M. Komunjer (2013) Caking of sodium chloride: Role of ambient relative humidity in dissolution and recrystallization process. Chem. Eng. Sci. 86: 78–86.CrossRefGoogle Scholar
  6. 6.
    Ennis, B. J., G. Tardos, and R. Pfeffer (1991) A microlevel-based characterization of granulation phenomena. Powder Technol. 65: 251–272.CrossRefGoogle Scholar
  7. 7.
    Peng, Z., E. Doroodchi, and G. Evans (2010) DEM simulation of aggregation of suspended nanoparticles. Powder Technol. 204: 91–102.CrossRefGoogle Scholar
  8. 8.
    Braumann, A., M. J. Goodson, M. Kraft, and P. R. Mort (2007) Modelling and validation of granulation with heterogeneous binder dispersion and chemical reaction. Chem. Eng. Sci. 62: 4717–4728.CrossRefGoogle Scholar
  9. 9.
    Caparino, O. A., C. I. Nindo, J. Tang, and S. S. Sablani (2017) Rheological measurements for characterizing sticky point temperature of selected fruit powders: An experimental investigation. J. Food Eng. 195: 61–72.CrossRefGoogle Scholar
  10. 10.
    Palzer, S. (2005) The effect of glass transition on the desired and undesired agglomeration of amorphous food powders. Chem. Eng. Sci. 60: 3959–3968.CrossRefGoogle Scholar
  11. 11.
    Jaya, S. and H. Das (2009) Glass transition and sticky point temperatures and stability/mobility diagram of fruit powders. Food Bioproc. Tech. 2: 89–95.CrossRefGoogle Scholar
  12. 12.
    Avilés, C. A., E. Dumoulin, and C. Turchiuli (2015) Fluidised bed agglomeration of particles with different glass transition temperatures. Powder Technol. 270: 445–452.CrossRefGoogle Scholar
  13. 13.
    Kunii, D. and O. Levenspiel (1991) Fluidization Engineering. 2nd ed., pp. 68–71. Butterworth-Heinemann, Massachusetts, USA.Google Scholar
  14. 14.
    Gantt, J. A. and E. P. Gatzke (2005) High-shear granulation modeling using a discrete element simulation approach. Powder Technol. 156: 195–212.CrossRefGoogle Scholar
  15. 15.
    Verkoeijen, D., G. A. Pouw, G. M. H. Meesters, and B. Scarlett (2002) Population balances for particulate processes—a volume approach. Chem. Eng. Sci. 57: 2287–2303.CrossRefGoogle Scholar

Copyright information

© The Korean Society for Biotechnology and Bioengineering and Springer 2019

Authors and Affiliations

  1. 1.School of Energy, Materials & Chemical EngineeringKorea University of Technology and EducationCheonanKorea

Personalised recommendations