Slags from the metallurgical recycling process are an important source of resources classified as critical elements by the EU. One example is lithium from Li-ion battery recycling. In this context, the thermodynamic properties of the recycled component system play a significant role in the formation of the Li-bearing phases in the slag, in this case, LiAlO₂. LiAlO₂ crystal formation could be engineered and result in varying sizes and occurrences by different metallurgical processing conditions. This study uses pure ingredients to provide a synthetic model material which can be used to generate the valuable phase in the slag, or so-called engineered artificial minerals (EnAMs). The aim is to investigate the crystallisation of LiAlO₂ as an EnAM by controlling the cooling conditions of the model slag to optimise the EnAM formed during crystallisation. Characterisation of the EnAMs is an important step before further mechanically processing the material to recover the valuable element Li, the Li-bearing species, respectively. Investigations are conducted using powder X-ray diffraction (XRD), X-ray fluorescence (µXRF), and X-ray Computer Tomography (XCT) on two different artificial lithium slags from MnO-Al₂O₃-SiO₂-CaO systems with different cooling temperature gradients. The result shows the different EnAM morphology along the height of the slag, which is formed under different slag production conditions in a semi-pilot scale experiment of 5 kg. Based on the different EnAM morphologies, three defined qualities of the EnAM are identified: granular, dendritic, and irregular-shape EnAM.

The increasing use of lithium-containing materials highlights the urgent need for their recycling to preserve resources and protect the environment. Lithium-containing slags, produced during the pyrometallurgical process in lithium-ion battery recycling, represent an essential resource for lithium recovery efforts. While multiple methods for lithium recycling exist, it is crucial to emphasize environmentally sustainable approaches. This study employs dry forced triboelectrification (FTC) to recover valuable components from slag powder, commonly known as engineered artificial minerals (EnAMs). The FTC method is used to change the charge of the target material and achieve a neutral state while other materials remain charged. The downstream electrostatic separator enables the charged particles to be separated from the target material, which in this study is lithium aluminate. The results show that the method is effective, and lithium aluminate can be successfully enriched.

Slags containing critical minerals concentrated in artificial phases, so-called engineered artificial minerals (EnAMs), are a novel source of critical raw materials. To liberate the EnAMs, the slags need to be comminuted, reducing the size of the particles. This work investigated the dependence of the breakage behavior on particle size and mineral structure during the comminution of an EnAM-containing slag. Piston-die experiments were performed for particles in the 3 mm to 5 mm size range. Nanoindentation and two-roller breakage tester experiments were performed for those in the 50 µm to 200 µm size range. The investigations were accompanied by X-ray computed tomography (XCT) and scanning electron microscope/energy dispersive X-ray spectroscopy (SEM/EDX) measurements as well as a micro X-ray fluorescence analysis to examine the mineral microstructure. It was found that the commonly assumed exponential connection between particle size and strength differed in the two size ranges. This behavior can be linked to different grain and cluster sizes, which were found in the investigation of the mineral microstructure. In addition to particle size, it was found that mineral structure plays an important role when characterizing the breakage behavior.

Efficient recovery of critical raw materials such as lithium from metallurgical slags requires optimized liberation of target phases during comminution. To determine effective mechanical process parameters for target phase recovery, an in-depth understanding of the characteristics of slag particles is crucial. For this purpose, modern tomography techniques, such as computed tomography (CT), can provide high-resolution 3D images of micrometer-sized slag particles. However, analysis of such CT images poses challenges, such as insufficient grayscale contrast between mineral phases and partial- volume effects. This paper presents a scalable workflow for accurate phase- and particle-wise 3D characterization of particle systems by correlating 3D CT images with 2D mineral maps. For this purpose, high-resolution scanning electron microscopy (SEM) slices are registered in 3D CT images and used as ground truth to train 3D convolutional neural networks (CNNs) for the segmentation of individual particles and mineral phases. This approach addresses the principal challenges of obtaining CT-based mineralogical characterizations, allowing for the particle-wise 3D characterization of complex slag systems with minimum manual labeling effort. The trained CNNs are then applied to CT images of particle systems with different particle sizes (from 63 μm to 100 μm and from 100 μm to 250 μm) of a lithium-bearing slag with LiAlO2 as the target phase. Although virtual cross-sections of the predicted 3D segmentations show excellent agreement with mineral liberation obtained from 2D validation SEM-EDS data, the derived 3D mineral liberation statistics differ significantly from 2D estimates. In particular, our results show that the 2D analyses significantly overestimate mineral liberation compared to the 3D characterization. By addressing this stereological bias, the correlative 3D characterization workflow provides essential insights required to tailor pyrometallurgical and mechanical processing parameters to improve the recovery of raw materials.

The engineered artificial mineral (EnAM) concept aims to concentrate target elements in tailored mineral phases with defined composition, grain size, and shape through controlled slag chemistry and crystallisation. This study evaluates the transfer of LiAlO₂ formation as a Li-bearing EnAM from a 5 kg laboratory-scale slag to a 125 kg pilot-scale slag. The pilot-scale slag was produced in a 300 L top-blown rotary converter and subjected to controlled cooling. A multiscale characterisation workflow combining micro-X-ray fluorescence (µXRF), X-ray computed tomography (XCT), SEM-EDX and electron backscatter diffraction (EBSD) was used to investigate EnAM distribution, morphology, intergrowth and grain structure. LiAlO₂ was formed at pilot scale, with a phase fraction of 7.5–10.5 wt. % compared with 11.2–14.4 wt. % at laboratory scale. µXRF revealed substantially larger EnAM clusters at pilot scale, whereas EBSD showed that these clusters consist of finer, more irregular LiAlO₂ grains. Nevertheless, both scales exhibited a comparable size-shape progression from smaller, compact clusters toward larger, less compact structures. XCT and SEM-EDX further revealed intergrowth of LiAlO₂ with Mn-Al spinel. The results demonstrate the feasibility of transferring EnAM formation to pilot scale while showing that scale-up modifies EnAM yield, clustering, intergrowth, and individual grain properties.

Efficient recovery of critical raw materials such as lithium from metallurgical slags requires optimized liberation of target phases during comminution. To determine effective mechanical process parameters for target phase recovery, an in-depth understanding of the characteristics of slag particles is crucial. For this purpose, modern tomography techniques, such as computed tomography (CT), can provide high-resolution 3D images of micrometer-sized slag particles. However, analysis of such CT images poses challenges, such as insufficient grayscale contrast between mineral phases and partial-volume effects. This paper presents a scalable workflow for accurate phase- and particle-wise 3D characterization of particle systems by correlating 3D CT images with 2D mineral maps. For this purpose, high-resolution scanning electron microscopy (SEM) slices are registered in 3D CT images and used as ground truth to train 3D convolutional neural networks (CNNs) for the segmentation of individual particles and mineral phases. This approach addresses the principal challenges of obtaining CT-based mineralogical characterizations, allowing for the particle-wise 3D characterization of complex slag systems with minimum manual labeling effort. The trained CNNs are then applied to CT images of particle systems with different particle sizes (from 63 μm to 100 μm and from 100 μm to 250 μm) of a lithium-bearing slag with LiAlO₂ as the target phase. Although virtual cross-sections of the predicted 3D segmentations show excellent agreement with mineral liberation obtained from 2D validation SEM-EDS data, the derived 3D mineral liberation statistics differ significantly from 2D estimates. In particular, our results show that the 2D analyses significantly overestimate mineral liberation compared to the 3D characterization. By addressing this stereological bias, the correlative 3D characterization workflow provides essential insights required to tailor pyrometallurgical and mechanical processing parameters to improve the recovery of raw materials.