Experimental and Numerical Study to Enhance Granule Control and Quality Predictions in Pharmaceutical Granulations

Area of Science:

• Pharmaceutical Manufacturing
• Chemical Engineering
• Materials Science

Background:

• Pharmaceutical industry requires strict control over product characteristics for reproducible wet granulation.
• Developing systematic models is crucial for understanding and optimizing granulation processes.

Purpose of the Study:

• To develop and validate a discrete element method (DEM) model for simulating wet granulation.
• To integrate an artificial intelligence (AI) tool for enhanced process analysis and granule quality assessment.

Main Methods:

• Simulating particle behavior using DEM, considering material properties, equipment geometry, and operational conditions.
• Incorporating particle interactions and cohesion to predict granule formation and quality.
• Utilizing an EDEMpy AI tool for post-treatment analysis of agglomerate size distribution.

Main Results:

• The DEM model accurately reproduced experimental granule growth kinetics from a high-shear granulator.
• The AI tool effectively monitored and analyzed granule size distributions for process efficiency assessment.
• Validation confirmed strong agreement between numerical simulations and experimental data.

Conclusions:

• The developed DEM model demonstrates significant potential for predicting and controlling granule quality in pharmaceutical wet granulation.
• This approach enhances precision and efficiency in pharmaceutical manufacturing.
• The integration of AI tools further supports process optimization and quality assurance.