Diego Toro

Complementing the steady state modeling of an ideal solar evaporation pond, the present study is concerned with the dynamic modeling of the ideal solar evaporation pond for Li extraction from brines. The approach conceptualizes the pond as a sequence of discrete particles of brine of suitable size with continuous feeding and discharging considering water evaporation events over time. The discrete dynamic model produces a dynamic concentration profile over time, which converges to a steady-state concentration profile equal to that predicted by the ideal solar evaporation law. This discrete dynamic particle model allows for variations in feed flow rate, evaporation rate per unit area, feed concentration, and pond area. This approach allows the visualization of concentration perturbations propagating at a finite speed through the system exhibiting a hyperbolic nature. The model was validated using average lithium, boron, and magnesium concentrations obtained from an industrial pond system at the Salar de Atacama, Chile, in the year 2023. Therefore, this discrete dynamic model constitutes a versatile tool for analyzing and optimizing the performance of solar evaporation pond systems under variable operating conditions.

In the coming decades, anticipated population growth is projected to escalate the demand for essential resources such as NaCl, KCl, and LiCl, which are critical for human consumption, agriculture, and battery production. A substantial proportion of these salts is produced from brines using solar evaporation ponds. This article presents a one-dimensional surrogate mathematical model of an ideal solar evaporation pond working at a steady state. The ideal pond considers only water evaporation, with a uniform evaporation rate per unit area. The model’s equation, or the ideal solar evaporation law, allows calculating the ion concentration profile in an ideal pond just given the feed and discharge concentrations. The validation of the law was conducted with industrial data collected in the year 2023 in a lithium recovery plant throughout 15 ponds in series at the Salar de Atacama, Chile. The results verified that the model could accurately predict the monthly concentration profiles (R² in the range 0.9646 to 0.9864) if lithium does not precipitate in the pond. The model provides accurate values of pond inventories and area requirements for designing stages. The model’s relevance extends beyond the lithium industry to encompass any solar evaporation processes for salt recovery or solution concentration.