Abstract Summary (Max 250 words)
Accurate prediction of particle size distribution (PSD) is critical for the design and scale-up of crystallization processes, often encountered in fine chemical manufacturing. This remains challenging due to the complex coupling between crystallization kinetics and local hydrodynamics. Conventional crystallization models often assume perfect mixing, neglecting spatial heterogeneities in turbulence and particle–flow interactions. These simplifications limit the reliable prediction of process dynamics and PSD, particularly during scale-up and in systems where mixing-driven phenomena such as secondary nucleation are dominant. Although coupled computational fluid dynamics–population balance equation (CFD–PBE) models can capture hydrodynamic effects, their practical use is often hindered by high computational cost. This study investigates the effect of mixing on batch crystallization using a computationally efficient CFD–PBE framework based on compartmental modeling. Turbulent flow in agitated batch crystallizers is described using Reynolds-Averaged Navier–Stokes (RANS) simulations in OpenFOAM, providing spatially resolved fields of velocity, temperature, and turbulent energy dissipation. Millions of CFD cells are coarse grained to 10s of compartments, assuming perfect mixing within each compartment, using a physics-driven clustering strategy based on local energy dissipation rate. PBEs are solved over these compartments using an in-house finite volume solver. In addition, Euler–Euler multiphase CFD simulations are used to assess fluid flow characteristics under highly suspended conditions commonly encountered in industrial crystallization. Case studies show that hydrodynamic heterogeneities can significantly influence crystallization kinetics and PSD evolution. The proposed framework captures these effects at minimal additional computational cost, enabling routine use for crystallization process design and scale-up.
