Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation

Abstract

The granulation of traditional Chinese medicine (TCM) has attracted widespread attention, there is limited research on the high shear wet granulation (HSWG) and wetting mechanisms of sticky TCM powders, which profoundly impact the granule size distribution (GSD). Here we investigate the wetting mechanism of binders and the influence of various parameters on the GSD of HSWG and establish a GSD prediction model. Permeability and contact angle experiments combined with molecular dynamics (MD) simulations were used to explore the wetting mechanism of hydroalcoholic solutions with TCM powder. Machine learning (ML) was employed to build a GSD prediction model, feature importance explained the influence of features on the predictive performance of the model, and correlation analysis was used to assess the influence of various parameters on GSD. The results show that water increases powder viscosity, forming high-viscosity aggregates, while ethanol primarily acted as a wetting agent. The contact angle of water on the powder bed was the largest and decreased with an increase in ethanol concentration. Extreme Gradient Boosting (XGBoost) outperformed other models in overall prediction accuracy in GSD prediction, the binder had the greatest impact on the predictions and GSD, adjusting the amount and concentration of adhesive can control the adhesion and growth of granules while the impeller speed had the least influence on granulation. The study elucidates the wetting mechanism and provides a GSD prediction model, along with the impact of material properties, formulation, and process parameters obtained, aiding the intelligent manufacturing and formulation development of TMC.

Graphical Abstract