The increasing demand for lithium-ion batteries (LIBs) and the critical need for lithium make the efficient recycling of secondary resources essential. Synthetic Li-bearing phases, some with lithium contents greater than natural sources (e.g., spodumene), can occur in slags produced by the pyrometallurgical recycling of end-of-life LIBs. This study investigates both the composition of synthetic model slags reproducing LIB recycling and the recovery potential of Li-bearing phases using SEM-based automated mineralogy and batch flotation tests, respectively. In particular, the efficacy of a novel zwitterionic collector, punicine, in contrast to the conventional collector, oleic acid, was evaluated with a focus on recovering Li-aluminate as a key engineered artificial mineral (EnAM). The flotation tests demonstrated that punicine provided a higher degree of selectivity for Li-aluminate over gehlenite, along with improved recovery of fine and well-liberated particles. The enhanced performance is attributed to punicine’s unique frothing properties and phase-specific interactions. Our findings highlight punicine’s significant potential as a collector for lithium-bearing EnAMs to advance lithium recovery from complex slag materials. The applied unique methodology supports the study of reagent regimes in relation to the flotation behavior of EnAM phases and the sustainable recycling of LIBs.

The Mesoarchaean West Rand Group displays a layer-cake stratigraphy with lithostratigraphic units correlatable on a basin-wide scale. The ~5 km-thick succession consists of fluvial braidplain and shelf deposits, which range from shallow inner shelf marine orthoquartzites, outer shelf argillites to starved shelf iron-formations. Minor diamictites are of debris flow origin and are possibly related to glacial activity. Three major sequences are present: Sequence I (Hospital Hill Subgroup) is bounded by an angular unconformity at the base of the Orange Grove Formation and a low-angle unconformity at the base of the Promise diamictite. Sequence II (Government Subgroup) extends from the base of the Promise diamictite to a well-defined low-angle unconformity at the base of the Koedoeslaagte Formation. Sequence III (Jeppestown Subgroup) comprises the succession between the Koedoeslaagte Formation and the Maraisburg Formation, up to the low-angle unconformity at the base of the Main Reef. Sequence I was deposited during a period of highstand of sea-level, Sequence II during a period of relative lowstand, and Sequence III during a period of relative highstand coupled with high rates of sediment supply. Isopach, depofacies and palaeocurrent analyses indicate that strata in the western to northwestern parts of the basin were deposited under more proximal sedimentary conditions compared to those in the central or southeastern parts of the basin. There is little relationship between the present outline of the basin and the distribution of depofacies or isopachs of sequences, and it is therefore concluded that the original sedimentary basin was significantly larger in areal extent. Depofacies and thickness distribution, as well as synsedimentary deformation of strata, indicate that the basin was most probably of flexural tectonic origin. These findings strongly support deposition in a wide, shallow, and rather stationary foreland basin, with an axial zone towards the west/northwest and a low amplitude peripheral bulge to the east/southeast. Such shallow foreland basins, with abundant sediment bypassing, are thought to be associated with windward-facing orogenic fronts. High rates of erosion along such fold-thrust belts lead to ineffective loading and advancement of the orogenic front, as well as an oversupply of sediment.

This study investigates the potential of a Falcon Ultra-Fine (UF) concentrator for the recovery of ultrafine scheelite particles from a weathered tailing stockpile of an historical scheelite mine, as it could represent a valuable secondary tungsten resource. The tailings are characterized by a P80 of 17.2 µm, with scheelite grains exhibiting a finer size distribution (P80 = 9.3 µm) and a high liberation degree (82 wt%). A three-stage Falcon concentration circuit was tested under varying operating conditions, including feed tank design, concentration of solids, particle size fraction, G-force and the number of treatment stages. Results showed that separation efficiency is primarily governed by the tank design and the feed particle size distribution, while variations in G-force and concentration of solids produced only minor effects. Hydrocyclone desliming to remove particles finer than 10 µm prior to concentration enhanced the separation selectivity of the Falcon-based concentration flowsheet. However, such pre-treatment of the material will require the implementation of a dedicated process to recover scheelite from the overflow to limit losses. These findings highlight the challenges of effective scheelite separation due to the fineness of these tailings.

Mineralogical data acquired from drillcores provide important constraints for resource estimation, geometallurgical modelling, mineral exploration, and geological interpretation. While hyperspectral imaging is rapidly gaining traction for these applications, it lacks the ability to accurately quantify mineral abundances without extensive calibration data. Here, we build on previous work to demonstrate and benchmark workflows that combine scanning electron microscope (SEM) mineral maps with large-extent multimodal hyperspectral imaging data. The goal is to relate hyperspectral features and mineral abundances using supervised machine learning models, and then apply these models to infer mineralogy across entire drillcores. We adapt the learning process to the non-uniform (unbalanced) composition of most rocks, and achieve reasonable accuracy for most rock-forming minerals. However, we also find that prediction accuracy depends strongly on the representativity of training data—so models often fail to produce accurate maps of rare and accessory minerals. Robust, adaptive and ideally semi-automated sampling approaches might address this shortcoming by identifying locations which ensure optimal coverage of hyperspectral variance. We also emphasise that upscaling from SEM to drillcore scale inevitably involves extrapolation, meaning predictions should always be validated. However, once validated, upscaled mineralogy predictions could provide crucial quantitative data that bridge the scale gap between petrographic observations, metallurgical tests, and geometallurgical models.