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Multicomponent granular mixing in a Bohle bin Blender-Experiments and simulation.

Koyel Sen1, Natasha Velez2, Carl Anderson2

  • 1Department of Pharmaceutical Sciences, University of Connecticut, USA.

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|February 15, 2020
PubMed
Summary

This study used Discrete Element Method (DEM) modeling and experiments to analyze pharmaceutical powder mixing in a Bohle bin blender. DEM simulations optimize blending protocols by revealing how parameters like fill percentage affect mixing efficiency.

Keywords:
Bohle bin blenderDiscrete element modelGranular bondMixing rateMulticomponent mixingPowder mixing

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Area of Science:

  • Pharmaceutical Engineering
  • Computational Fluid Dynamics
  • Materials Science

Background:

  • Optimizing powder mixing is crucial for pharmaceutical product uniformity and efficacy.
  • Bohle bin blenders are widely used, but understanding their mixing dynamics requires advanced simulation.
  • Pharmaceutical formulations often involve cohesive granular materials, complicating mixing processes.

Purpose of the Study:

  • To investigate the mixing and segregation behavior of pharmaceutical granular materials in a Bohle bin blender.
  • To develop and validate a Discrete Element Method (DEM) model for simulating granular flow and mixing.
  • To assess the impact of critical processing parameters on mixing efficiency within the blender.

Main Methods:

  • Experimental study of a multicomponent pharmaceutical blend in a Bohle bin blender.
  • Development of a Discrete Element Method (DEM) model incorporating material properties like particle size and density.
  • Simulation of granular flow, assessing mixing degree (Relative Standard Deviation) under varying operational parameters (loading, rotation rate, fill percentage).

Main Results:

  • Numerical simulations showed radial mixing is faster than axial mixing due to blender geometry.
  • The DEM model accurately predicted mixing behavior compared to experimental data.
  • Critical processing parameters significantly influence the mixing dynamics and efficiency.

Conclusions:

  • The DEM model provides valuable insights for optimizing mixing protocols in Bohle bin blenders.
  • This approach enables the development of tailored mixing strategies for diverse pharmaceutical formulations.
  • DEM modeling serves as a powerful tool for enhancing process understanding and control in pharmaceutical manufacturing.