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.
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