Computational flow models for crystallization processes

Crystallization is a key separation and purification method governed by thermodynamics, kinetics, and multiphase fluid dynamics. Supersaturation generation, nucleation, crystal growth, aggregation, and breakage determine particle size and shape and are described using population balance models (PBMs). Coupled with computational fluid dynamics (CFD), PBMs enable spatially resolved predictions of particulate behavior in real reactors. This review outlines fundamental crystallization kinetics and PBM formulations, including nucleation and growth expressions, aggregation and breakage kernels, and moment-based solution methods. It also highlights how multiphase CFD represents turbulence, mixing, and phase interactions in stirred tanks, tubular reactors, static mixers, and impinging jets, supporting process design and scale-up. Key computational challenges include stiffness, micro-mixing, reaction–equilibrium networks, and parallelization, along with cost-reduction strategies such as compartment models and CFD-informed reactor representations. Emerging machine-learning tools accelerate parameter estimation and surrogate modeling, with applications from inorganic precipitation to pharmaceutical crystallization