(49b) Compartmental Modelling of Wet Granulation in Twin Screw Using Population Balance

Wet granulation is one of the major steps in improving powder properties (flowability, friability, and size) in different industries including mining, fertilization, food production and pharmaceutical manufacturing [1,2]. Twin screw is continuous manufacturing operational unit used by pharma companies for granule production. Process parameters like liquid to solid ratio (L/S) and screw speed can influence the granule size produced from the twin screw granulator (TSG).

In this study, a compartmental population balance model (CPBM) is developed as a predictive tool of particle size distribution (PSD) produced from wet granulation in a co-rotating twin-screw granulator (TSG). The CPBM is derived in terms of liquid to solid ratio (L/S) and screw speed representing the main process parameters of the TSG. This model accounts for aggregation and breakage of the particles occurring in five compartments of the TSG with inhomogeneous screw configurations (3 conveying zones and 2 kneading zones). The experimental data is obtained for the granulation of microcrystalline cellulose in a 12 mm TSG at various L/S ratios and screw speeds. Kapur kernel is implemented for the first time as an aggregation kernel for the TSG and is compared against the commonly used sum kernel where Kapur kernel shows better fitting with 58% improvement. Also, finite volume numerical method (FVNM) that is adapted for solving the CPBM in TSG shows a dramatic improvement in solution accuracy compared to commonly used cell average numerical method (CANM). Moreover, Kriging interpolation is used to interpolate for new values of the empirical parameters at new L/S and screw speeds. The aggregation rate appeared to be higher in the conveying zones at low and medium L/S ratios while breakage appeared to be higher in the kneading zones for all L/S ratios and screw speeds. The final CPBM in terms of L/S and screw speed is validated using the experimental data.

References:

[1] A. Chaudhury, H. Wu, M. Khan, and R. Ramachandran, “A mechanistic population balance model for granulation processes: Effect of process and formulation parameters,” Chem. Eng. Sci., vol. 107, pp. 76–92, 2014.

[2] A. Faure, P. York, and R. C. Rowe, “Process control and scale-up of pharmaceutical wet granulation processes: A review,” Eur. J. Pharm. Biopharm., vol. 52, no. 3, pp. 269–277, 2001.