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

In this research, an efficient method of lithium extraction from alpha-spodumene by thermal treatment with NH4HF2 was optimized. Temperature (T), alpha-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 comparedto obtain a predictive model of the system. Both models showed good concordance with the experimental data (R-2 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, T-2, and T-3 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 degrees C, m = 1:17.5, and t = 100.6 min. Experimental lithium extractions of 96.45 +/- 3.68% were obtained at 157 degrees C using a molar ratio of 1:17.5 for 100 min. The products of the thermal treatment were LiF, (NH4)(3)SiF6 center dot F, (NH4)(3)AlF6, NH3, and H2O. After a water leaching step, the silicon in the sample was separated, obtaining ( NH4)(3)SiF6 center dot F as a by-product. Finally, the solid products were leached with H2SO4 10% (v/v) to solubilize all lithium. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

In this study, an efficient method of lithium extraction from alpha-spodumene by thermal treatment with NH4HF2 was optimized. Modeling results were compared using least squares and artificial neural networks to obtain a predictive model with high concordance to experimental data. The system was further optimized using response surface methodology to maximize Li extraction and minimize operational parameters.