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Drawdown of floating solids in stirred tanks: scale-up study using CFD modeling
Yogesh Waghmare1, Rick Falk, Lisa Graham
1Bend Research Inc., 64550 Research Rd., Bend, OR 97701, United States. ygwaghmare@gmail.com
International Journal of Pharmaceutics
|June 1, 2011
Summary
A new scale-up correlation predicts floating solids drawdown in stirred tanks. This method uses computational fluid dynamic (CFD) simulations and liquid velocity for accurate predictions across various scales.
Area of Science:
- Chemical Engineering
- Fluid Dynamics
- Particle Technology
Background:
- Floating solids drawdown in stirred tanks is crucial for solid-liquid separation processes.
- Accurate scale-up of this operation is challenging due to complex hydrodynamics.
- Existing correlations often rely on empirical parameters, limiting their applicability.
Purpose of the Study:
- To develop a robust scale-up correlation for floating solids drawdown in stirred tanks.
- To utilize computational fluid dynamic (CFD) simulations for predicting drawdown rates.
- To establish a correlation based on fundamental hydrodynamic parameters for broader applicability.
Main Methods:
- Employed Discrete Phase Modeling (DPM) simulations coupled with lab-scale experimental data.
- Developed a semi-empirical correlation relating drawdown rate to average liquid velocity at the free surface.
- Validated the correlation using data from 2L and 10L stirred tanks with varying designs.
Main Results:
- A validated semi-empirical correlation for predicting floating solids drawdown rate was established.
- The correlation demonstrated applicability across different impeller types and tank geometries.
- CFD modeling enabled successful extension of the correlation to pilot and commercial scales (40L to 4000L).
Conclusions:
- The developed CFD-based correlation provides a reliable tool for scaling up floating solids drawdown operations.
- Using average liquid velocity as a key parameter enhances the correlation's versatility.
- This approach offers significant advantages over traditional empirical correlations for process design and optimization.
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