The high demand of lithium for electromobility and energy storage requires an efficient and sustainable optimization of the current extraction from natural lithium brine process. In this context, this study develops a simultaneous Mg²⁺ and B³⁺ extraction system, which could be a contribution to the elimination of precipitation stages and the generation of high purity lithium brine. This dual system composed of a dialkylphosphate ionic liquid and a 2-ethylhexanol/kerosene mixture as magnesium and boron extractors, respectively, was optimized by a mathematical and statistical method known as response surface methodology (RSM). The RSM method identified, through a small number of experiments, the interdependencies of the parameters involved in the extraction process, and calculated their optimal values to achieve the maximum extraction efficiency of Mg²⁺ and B³⁺. The optimization was performed from two approaches. The first, maximizing the extraction of Mg²⁺ and B³⁺, resulted in experimental efficiencies of 99.17% of Mg²⁺, 99.36% of B³⁺ and 23.54% of Li⁺; whereas the second, in which lithium co-extraction was minimized, yielded efficiencies of 83.56% Mg²⁺, 99.22% B³⁺ and 3.36% Li⁺, results that demonstrate the high extraction capacity of the system, as well as proving the validity of the statistical model.

电动汽车和储能对锂的高需求需要对从天然锂卤水工艺中提取的电流进行有效和可持续的优化。在此背景下，本研究同时开发了 Mg ²⁺和 B ³⁺提取系统，这可能有助于消除沉淀阶段和生成高纯度锂盐水。该双系统由磷酸二烷基酯离子液体和 2-乙基己醇/煤油混合物组成，分别作为镁和硼提取剂，通过称为响应面法 (RSM) 的数学和统计方法进行了优化。RSM 方法通过少量实验确定了提取过程中涉及的参数的相互依赖性，并计算出它们的最佳值以实现Mg ²⁺和B ³⁺的最大提取效率。优化是通过两种方法进行的。第一，最大限度地提取Mg ²⁺和B ³⁺，导致实验效率为 99.17% 的 Mg ²⁺、99.36% 的 B ³⁺和 23.54% 的 Li ⁺；而第二个，其中锂共提取最小化，产生了 83.56% Mg ²⁺、99.22% B ³⁺和 3.36% Li ⁺的效率，结果证明了系统的高提取能力，并证明了有效性的统计模型。