Magnesium is a critical raw material and its recovery
as Mg(OH)2from saltwork brines can be realized via precipitation.
The
effective design, optimization, and scale-up of such a process require
the development of a computational model accounting for the effect
of fluid dynamics, homogeneous and heterogeneous nucleation, molecular
growth, and aggregation. The unknown kinetics parameters are inferred
and validated in this work by using experimental data produced with
a T2mm-mixer and a T3mm-mixer, guaranteeing
fast and efficient mixing. The flow field in the T-mixers is fully
characterized by using thek-εturbulence model implemented in the computational fluid dynamics
(CFD) code OpenFOAM. The model is based on a simplified plug flow
reactor model, instructed by detailed CFD simulations. It incorporates
Bromley’s activity coefficient correction and a micro-mixing
model for the calculation of the supersaturation ratio. The population
balance equation is solved by exploiting the quadrature method of
moments, and mass balances are used for updating the reactive ions
concentrations, accounting for the precipitated solid. To avoid unphysical
results, global constrained optimization is used for kinetics parameters
identification, exploiting experimentally measured particle size distribution
(PSD). The inferred kinetics set is validated by comparing PSDs at
different operative conditions both in the T2mm-mixer and
the T3mm-mixer. The developed computational model, including
the kinetics parameters estimated for the first time in this work,
will be used for the design of a prototype for the industrial precipitation
of Mg(OH)2from saltwork brines in an industrial environment.