Compartmental approach for modelling twin-screw granulation using population balances.

Area of Science:

• Chemical Engineering
• Pharmaceutical Technology
• Process Modeling

Background:

• Wet granulation is a critical unit operation in pharmaceutical manufacturing.
• Controlling particle size distribution (PSD) is essential for drug product performance.
• Twin-screw granulators (TSG) offer advanced control over granulation processes.

Purpose of the Study:

• To develop a predictive compartmental population balance model (CPBM) for particle size distribution (PSD) in co-rotating twin-screw granulators (TSG).
• To establish a model that incorporates key process parameters: liquid to solid ratio (L/S) and screw speed.
• To provide a validated tool for optimizing wet granulation in TSG.

Main Methods:

• Development of a five-compartment population balance model accounting for aggregation and breakage.
• Implementation of Kapur's aggregation kernel and the finite volume numerical method.
• Application of Kriging interpolation for empirical parameter estimation and model validation with experimental data.

Main Results:

• The developed CPBM accurately predicts particle size distribution (PSD) in TSG.
• The finite volume method significantly improved solution accuracy over the cell average method.
• Kriging interpolation enabled effective parameter estimation across various L/S and screw speeds.

Conclusions:

• The CPBM is a robust predictive tool for wet granulation in TSG.
• The model successfully correlates process parameters (L/S, screw speed) with PSD outcomes.
• This approach facilitates process optimization and scale-up in pharmaceutical manufacturing.