Predictive Modelling of Lithium Mineral Grades from Chemical Assays for Geometallurgical Applications

Routine chemical assays, which are more readily available than direct mineralogical analyses, offer a rapid and cost-efficient approach of estimating mineral grades for geometallurgical modelling. This paper addresses the prediction of ore minerology from chemical assays for lithium-bearing pegmatites by implementing and comparing two element-to-mineral conversion (EMC) approaches: (1) mass balance techniques using two calculation variants and (2) machine learning methods (MLM). Both routines of the mass balance approach achieved satisfactory R2 values exceeding 0.8, although calculation routine 1 was unable to automatically differentiate between the two lithium-bearing phases (spodumene and cookeite). Of the eight algorithms applied for the MLM approach, the top three performing models achieved R2 values greater than 0.6 for both training and testing datasets, with slightly lower error evaluation metrics compared to the mass balance approach. Based on data accuracy requirements across the Mine Value Chain, the mass balance approach is suitable for the feasibility and operational stages, while the MLM approach meets the minimum data accuracy requirements of the scoping and pre-feasibility stages. However, it should be noted that the mass balance approach is limited to deposits with simple mineral assemblages while the MLM approach can handle deposits with greater elemental overlap among minerals.