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Journal of Pharmaceutical Innovation

, Volume 7, Issue 3–4, pp 181–194 | Cite as

DOE-Based CFD Optimization of Pharmaceutical Mixing Processes

  • Thomas Hörmann
  • Daniele Suzzi
  • Siegfried Adam
  • Johannes G. KhinastEmail author
Research Article

Abstract

Fluid mixing and homogenization are key manufacturing processes in the pharmaceutical industry that in an industrial setting are typically optimized and adapted using empirical techniques rather than numerical methods. In the recent years, in silico techniques have increasingly attracted interest due to the many advantages and the increased information content. Computational fluid dynamics, for example, have often been applied to mixing problems. Although numerical flow simulations are nowadays common for simple applications, more complex cases (e.g., industrial mixing) still require much work to achieve reliable results with reasonable resources. In our work, we present an efficient procedure for optimizing the mixing performance of an unbaffled tank for pharmaceutical applications. The optimization objectives were the position of an impeller in the tank defined by the bottom clearance, the eccentricity of the impeller, the angle of the impeller shaft, and the impeller rotational speed. In order to generate a regression model for prediction of the optimal performance, design of experiments was used. Our optimization study showed that the impeller eccentricity had significantly more impact on mixing performance than the shaft angle, that the impeller speed was the main driver for the power input and the average shear forces, and that the bottom clearance may have strongly impacted the flow in the bottom tank area.

Keywords

Mixing CFD simulation DOE Optimization 

Notation

C

Bottom clearance, in meters

C0

Bottom clearance in base position, in meters

D

Impeller diameter, in meters

E

Off-center distance, in meters

E0

Off-center distance in base position, in meters

f

Elliptic relaxation function

g

Gravitational acceleration, in meters per square second

H

Filling level, in meters

IT

Turnover rate, per second

k

Turbulence kinetic energy per unit mass, in square meters per square second

M

Torque, in newton meters

N

Impeller rotational speed, in revolutions per minute

P

Power input by an impeller, in watts

Q

Volumetric flow rate, in cubic meters per second

Q2

Goodness of prediction

r

Distance between center points, in meters

R2

Goodness of fit

Re

Reynolds number

T

Tank diameter, in meters

TM

Mean tank diameter (cone-shaped tank), in meters

Vi

Cell volume of the computational cell i, in cubic meters

Vtot

Total volume of the domain, in cubic meters

x

x-coordinate, in meters

y

y-coordinate, in meters

α

Impeller shaft angle, in degrees

ß

Cone angle, in degrees

γav

Volumetric mean shear rate, per second

γi

Shear rate of a mesh cell i, per second

ζ

Normalized velocity scale

v

Kinematic viscosity, in square meters per second

ρ

Density of the liquid, in kilograms per cubic meter

φ

Rotating angle concerning the z-axis, in radians

ψ

Rotating angle concerning the y-axis, in radians

ω

Angular impeller velocity, per second

Notes

Acknowledgments

This work was performed as part of the K1 Competence Center program of the Federal Ministry of Transport, Innovation and Technology (BMVIT) and the Federal Ministry of Economy, Family and Youth (BMWFJ) and was funded by the Austrian Research Promotion Agency (FFG), the State of Styria, and Styrian Business Promotion Agency (SFG).

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Thomas Hörmann
    • 1
  • Daniele Suzzi
    • 1
  • Siegfried Adam
    • 1
  • Johannes G. Khinast
    • 2
    Email author
  1. 1.Research Center Pharmaceutical Engineering GmbHGrazAustria
  2. 2.Institute for Process and Particle EngineeringGraz University of TechnologyGrazAustria

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