Low-temperature lithium extraction from α-spodumene with NH4HF2: Modeling and optimization by least squares and artificial neural networks

Abstract

In this research, an efficient method of lithium extraction from α-spodumene by thermal treatment with NH4HF2 was optimized. Temperature (T), α-spodumene:NH4HF2 molar ratio (m), and reaction time (t) were studied using a two-level univariate strategy. The results were modeled using least squares (LS) and artificial neural networks (ANN) and then compared to obtain a predictive model of the system. Both models showed good concordance with the experimental data (R² of 0.9881 and 0.9957, respectively) and with each other. The ANOVA of the cubic model indicated that T, m, t, and the interactions Tt, T², and T³ were significant. Finally, the system was optimized using response surface methodology to maximize Li extraction and minimize operational parameters. The desirability function predicted an extraction value of 95.48 ± 2.50% for T = 156.7 °C, m = 1:17.5, and t = 100.6 min. Experimental lithium extractions of 96.45 ± 3.68% were obtained at 157 °C using a molar ratio of 1:17.5 for 100 min. The products of the thermal treatment were LiF, (NH4)3SiF6·F, (NH4)3AlF6, NH3, and H2O. After a water leaching step, the silicon in the sample was separated, obtaining (NH4)3SiF6·F as a by-product. Finally, the solid products were leached with H2SO4 10% (v/v) to solubilize all lithium.