Quantitative modeling of precipitation processes

Precipitation from the liquid phase is a powerful and common unit operation for the continuous, highly reproducible production of nanoparticles. However, a general, predictive and quantitative modeling framework is still missing due to the inherent multiscale nature of the precipitation process and the complex interplay between the relevant sub-processes. We apply direct numerical simulation of the fluid flow coupled with a population balance framework to investigate the precipitation of stabilized ibuprofen nanoparticles in a T-mixer. Our findings suggest that the Damköhler number (the ratio between the mixing time and solid formation time) determines the precipitation outcome. We demonstrate how the primarily unknown solid formation kinetics can be estimated in the simulations with the guidance of experimental input at a single process condition. We subsequently vary the Damköhler number by changing the inflow rates (global mixing time) and the initial ibuprofen concentration. In doing so, excellent agreement between numerical simulations and experiments in the full particle size distribution at different process conditions (from laminar to turbulent flow and different initial ibuprofen concentrations) is obtained using the beforehand estimated solid formation kinetics. Our model opens avenues for the predictive simulation of particle-formation dynamics and is a stepping stone for the tailored, scalable production of nanoparticles.