Wet granulation multiscale modelling accelerated via CG-DEM

Wet agglomeration is a complex particulate process driven by multiple microscale mechanisms. Discrete Element Modeling (DEM) enables a deeper understanding of these mechanisms and supports process optimization. However, DEM simulations become computationally expensive at larger scales. Moreover, identifying granules within dense systems remains challenging. This study introduces a framework for granule identification using Graph Network Analysis (GNA). To enhance computational efficiency, a Coarse-Grained (CG) DEM approach is implemented. The accuracy of the CG upscaling by factors 2 and 3 is first validated against the original system (CG1) to ensure it effectively reproduces the granulation characteristics. Leveraging the speed of CG-DEM, this sets the basis for studying the effects of other material and process parameters (e.g. surface tension and rotation speed) on key granulation metrics like mean granule volume, size distribution or granulation yield.