Empirical prediction model based process optimization for droplet size and spraying angle during pharmaceutical...

Area of Science:

• Pharmaceutical Engineering
• Chemical Engineering
• Materials Science

Background:

• Fluidized bed granulation is crucial for pharmaceutical manufacturing.
• Binder atomization significantly impacts granule properties.
• Predicting droplet size and spray angle is essential for process control.

Purpose of the Study:

• To develop empirical models for predicting droplet size and spray angle.
• To investigate the influence of binder viscosity, atomization pressure, and spray rate.
• To validate the predictive accuracy of the developed models.

Main Methods:

• Response surface central composite design (CCD) for experimental planning.
• Analysis of Variance (ANOVA) to assess factor significance.
• Stepwise regression analysis for model development.
• Experimental validation comparing predicted and measured values.

Main Results:

• Established a droplet size prediction model with R²=0.93 and RMSE=10.10.
• Developed a spraying angle prediction model with R²=0.82 and RMSE=3.69.
• Achieved prediction errors below 10% for both droplet size and spraying angle.

Conclusions:

• The empirical models accurately predict droplet size and spray angle in binder atomization.
• Validated models provide a reliable tool for optimizing pharmaceutical fluidized bed granulation.
• Findings are significant for enhancing process understanding and control in drug manufacturing.