Quantifying the Amount of Lithium-Bearing Minerals in Fine Matrix

Abstract

Lithium is a highly sought-after resource currently. Among other things, its use in lithium-ion batteries used extensively in electric vehicles, mobile phones and other electronics keeps the demand high. The demand for lithium is even projected to double in the coming years. It is safe to say that lithium will continue to remain an important resource for the future. Because of this increasing demand, the supply of lithium has grow. This supply can increase by expanding current mines, or opening new ones. Lithium can be found in lithium-caesium-tantalum pegmatites, a rare subcategory of granitic pegmatites. In these pegmatites, the lithium-containing minerals spodumene, petalite and lepidolite are most commonly mined. These minerals can be found in the form of larger crystals, but in recent years more attention has come to lithologies where quartz and lithium-containing minerals have been found growing side-by-side in smaller crystals.
This thesis aims to propose two models to quantify the amount of lithium-bearing minerals in fine matrix. These models are created by using Fourier-Transform Infrared Spectroscopy (FTIR) data of powder mixtures consisting of petalite and quartz for the first model, and spodumene and quartz for the second model. This approach using powders allows for simple and fast data acquisition which aids model development. The models considered in this thesis are linear regression, ridge regression and support vector regression. Linear regression produces the best results. Both models have shown to accurately predict the respective weight percentages of petalite and spodumene in powder mixtures with quartz. The spodumene model, when applied to a rock which contains both spodumene and quartz in fine matrix, produces similar results compared to Laser-Induced Breakdown Spectroscopy (LIBS) and thin section analysis, which shows that FTIR models created using powder mixtures can be utilized for rock samples as well. To expand on the results presented in this thesis, further testing of the models presented here, and looking into models that can predict mixtures of three or more mixtures is recommended.