Discrete element method study of particle size and shape in a twin screw wet granulator

This study presents a numerical investigation of granulation in a twin screw wet granulator (TSWG) using the discrete element method (DEM). The Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm was applied to classify and group particles into granules, while statistical methods were applied to determine the achievement of steady state in the DEM simulations. The objective was to understand particle granulation behaviors, focusing on particle size distribution (PSD) and aspect ratio under low and high liquid-to-solid (L/S) ratios. Analyses of particle granulation rate, relative magnitudes of forces, distributions of particle collisions, and particle trajectories were conducted to provide a comprehensive understanding of the TSWG system. The DEM model incorporated a capillary liquid bridge model to simulate particle cohesion at the individual particle level, and quantitative comparisons against experiments showed good agreement with RMSE of ∼ 3-5 vol% across compartments, a near-constant mean AR (∼0.65) with low RMSE (∼0.03-0.09). Qualitatively, early bimodality at low L/S ratio and peak-splitting at the outlet were reproduced computationally. The study provides insights into the granule formation mechanisms, functions of each compartment within the TSWG, and the potential of DEM simulation for predicting granulation behaviors.