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Milling is an essential unit operation used for particle size reduction in solid oral dosage manufacturing. The breakage of particles in a comil is due to the intense shear applied on the particles between impeller and the screen. Breakage also occurs due to the impact from a rotating impeller. Particles exit the mill based on their size relative to the aperture size of the screen bores. This study was set up to understand the working of the comil better. A new CPP (Critical Process Parameter), in the form of batch loading was identified. It was found that there are two different regimes (quasi static regime and impact regime) in which a comil generally operates, and the effect of the CPP’s (batch loading and impeller speed) on these regimes was studied. Knowledge of the effect of upstream operations on a particular unit operation is of significant importance, especially for pharmaceutical industry. For this reason, the effect of granulation variables such as liquid-to-solid ratio, granulator impeller speed and the amount of binder in the formulation were analyzed. Milled particle size distribution and other critical quality attributes such as bulk density, friability, and porosity were studied. Batch loading and the interaction effect of batch loading with impeller speed are significant parameters that affect the quality attributes of the mill. Predictive regression models were developed for throughput of the mill, milled product bulk density and milled product tapped density (with an R2 of 0.987, 0.953, 0.995 respectively) to enable their use in downstream process modeling.

This work is concerned with the semi-mechanistic prediction of residence time metrics using historical data from mono-component twin screw wet granulation processes. From the data, several key parameters such as powder throughput rate, shafts rotation speed, liquid binder feed ratio, number of kneading elements in the shafts and the stagger angle between the kneading elements were identified and physical factors were developed to translate those varying parameters into expressions affecting the key intermediate phenomena in the equipment, holdup, flow and mixing. The developed relations were then tested across datasets to evaluate the performance of the model, applying a k-fold optimization technique. The semi-mechanistic predictions were evaluated both qualitatively through the main effects plots and quantitatively through the parity plots and correlations between the tuning constants across datasets. The root mean square error (RMSE) was used as a metric to compare the degree of goodness of fit for different datasets using the developed semi-mechanistic relations. In summary this paper presents a new approach at estimating both the residence time metrics in twin screw wet granulation, mean residence time (MRT) and variance through semi-mechanistic relations, the validity of which have been tested for different datasets.