Droplet freeze desalination high fidelity modeling for sustainable water purification and diverse applications

Abstract

Freeze desalination (FD) is an environmentally clean water purification technique that holds a great promise for low-energy consumption and with unlimited brine's salinity levels. The brine cooling induces freezing, rendering separation and removing contaminants. A high-fidelity molding using CFD is employed to explain the purification of ice from saline droplets in a sub-cold environment under natural free-convection. Comparisons were made with analytical predictions derived from experimentally acquired data through brine cooling in bulk and droplet scales. The procedural sequence employed facilitated the model's ability to accurately represent the ion diffusion and phase-change phenomena across various temperatures (−15 °C and − 25 °C), concentrations, partition & heat-transfer coefficients, and droplet volumes (3 μL to 9 μL). The salt concentration ranged from seawater (35 g/L) to RO (70 g/L) salinity levels. The model successfully accommodates the experimental temperatures observed during the equilibrium between the solid and liquid phases. The achieved desalination efficiency ranges from 20 % to above 40 %. Lastly, machine learning is implemented to identify the principal experimental parameters influencing desalination efficiency. In the suggested system, dynamic salinity/impurity and temperature profiles enable natural free convection brine cooling research. The current framework can study the purification/concentration efficiency of diverse range of mixtures subjected to freeze crystallization.