In-line agglomeration degree estimation in fluidized bed pellet coating processes using visual imaging

Area of Science:

• Pharmaceutical Engineering
• Process Analytical Technology (PAT)

Background:

• Pellet agglomeration in fluidized bed coating negatively impacts product yield and quality.
• Current monitoring methods are often off-line, delaying necessary process interventions.

Purpose of the Study:

• To develop and validate an in-line visual imaging system for monitoring pellet agglomeration during fluidized bed coating.
• To enable real-time process control and improve product quality.

Main Methods:

• Utilized visual imaging through an observation window for contactless data acquisition.
• Developed efficient image analysis algorithms for automatic recognition of pellets and agglomerates.
• Conducted seven pilot-scale Wurster coating processes under varied conditions.

Main Results:

• In-line agglomeration degree trends were successfully tracked, revealing process dynamics.
• A strong positive correlation (R²=0.99) was found between in-line estimations and off-line sieve analysis.
• The system demonstrated feasibility for real-time agglomeration monitoring.

Conclusions:

• The developed visual imaging system provides accurate, in-line monitoring of pellet agglomeration.
• Early detection of agglomeration allows for timely interventions, ensuring product quality and process efficiency.