This study uses a combined experimental and modelling approach to predict key filtration metrics for needle-like crystallized products based on their PSSD. Such models can significantly reduce the time and cost associated with resource-intensive experimental trials. In the modelling phase, a two-step approach is proposed. First, a non-equilibrium Monte Carlo (MC) model simulates the cake’s formation, using in-house software. Second, fluid flow through the compacted cake’s final structure at steady-state conditions is modelled with computational fluid dynamics (CFD), in OpenFOAM. Compared to discrete element method (DEM)-CFD, which performs iterative CFD simulations on multiple packings, this framework requires less computational effort. Additionally, compared to forced-based DEM, MC requires fewer input parameters. This MC-CFD framework was applied to several case studies, examining PSSD characteristics including shape (spherical and needle-like), polydispersity, and operating conditions such as pressure drop. In the experimental phase, populations with varied PSSDs were created and characterized using novel imaging-based techniques.2 Constant pressure filtration experiments were performed using a purpose-built filtration rig, with the results compared to simulations.
This work introduces a predictive tool that minimizes experimental reliance, enabling faster and more cost-effective filterability assessments in research and development. It also lays the groundwork for expanding into modelling washing and drying processes.
References