Upscaled discrete element modelling of wet granulation in a rotating drum

Wet agglomeration is a complex particulate process involving multiple microscale mechanisms, including coalescence and breakage. Multiscale simulation using Discrete Element Modelling (DEM) can improve the understanding of the underlying mechanisms and facilitate process optimization. However, identifying granules within a dense granular system is challenging. Additionally, DEM is computationally expensive for large-scale simulations. A framework for the identification of granules is first introduced using Graph Network Analysis (GNA). Subsequently, upscaling by coarse graining (CG) is applied to improve the computational efficiency of DEM. The applicability of the CG-DEM with factors CG2 and CG3 in reproducing the granulation characteristics is verified against the original system (CG1), showing good agreement across the scales. Using significantly faster CG-DEM simulations, the influence of material and process parameters on the mean granule volume as well as the process yield, is investigated.