Abstract
Basket granulation is a wet granulation process in which the wet formulation is extruded through a perforated screen using a rotor assembly. It is employed in food and pharmaceutical manufacturing as a low-temperature, low-stress extrusion method. A good understanding of this process is crucial for ensuring granule quality and enhancing production efficiency. Employing a design of experiments approach, this study investigates the influence of key process parameters (rotor speed, screen size, and drying time) and formulation characteristics (cohesiveness, consistency, compressibility, and flowability) on granule quality (shape, size, and strength). Principal component analysis (PCA) is also performed to identify dominant wet formulation characteristics. A novel metric, the "Extrudability Factor", is introduced to evaluate extrudability. Using this metric, a regime map indicating suitable granulation conditions is constructed through artificial neural network modeling. Results indicate that granules with an extrudability factor of 70 showed high strength and low attrition.