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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Cracking the code: Spatial heterogeneity as the missing piece for modeling granular fluidized bed drying.

Tuur Vandeputte¹, Michael Ghijs¹, Thomas De Beer²

¹BIOMATH, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000 Ghent, Belgium; Laboratory of Pharmaceutical Process Analytical Technology, Department of Pharmaceutical Analysis, Ghent University, B-9000 Ghent, Belgium.

International Journal of Pharmaceutics

|April 21, 2024

Summary

This summary is machine-generated.

This study enhances pharmaceutical fluid bed drying models by adding granule segregation, improving predictions of moisture content and temperature for better drug product development.

Keywords:

Continuous manufacturing Fluidized bed drying Mathematical modeling Twin-Screw Wet Granulation

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Area of Science:

Pharmaceutical Manufacturing
Chemical Engineering
Process Modeling

Background:

Twin-screw wet granulation is crucial for drug product development, with fluid bed dryers impacting critical quality attributes.
Accurate modeling of pharmaceutical manufacturing processes is essential for optimization and quality assurance.

Purpose of the Study:

To improve the predictive accuracy of fluid bed drying models in pharmaceutical manufacturing.
To integrate a new model layer accounting for granule segregation during fluid bed drying.

Main Methods:

An existing compartmental model was enhanced by incorporating a new layer describing granule segregation behavior.
Probability distributions for the vertical position of granules within size classes were modeled.
Model predictions were validated against experimental data for moisture content and granule bed temperature.

Main Results:

The refined model demonstrated significantly improved prediction accuracy compared to previous models.
Accurate predictions of residual moisture content were achieved for an untrained formulation.
Simultaneous accurate predictions of granule bed temperature validated the model's structural integrity.

Conclusions:

The enhanced model provides a powerful tool for predicting pharmaceutical fluid bed drying behavior.
This advancement facilitates optimization of manufacturing parameters and ensures drug product quality.
The improved predictive capability holds significant promise for accelerating pharmaceutical product development.