Fracture propagation in complex ore particles is still a largely unexplored subject. Understanding the link between particle microstructure and the nature of breakage is critical to enabling selective breakage, a key development that may enhance the energy efficiency of raw material beneficiation plants. This study introduces a workflow to quantify the modal mineralogy and fracture propagation in complex particles in three-dimensions. The approach integrates automated SEM-EDS mineralogy with high-resolution X-ray computed tomography (XCT), which is classified with a convolutional neural network-based method. Intact ore slabs were first characterised in 2D, and three particles (2-5 mm) were analysed in 3D before and after controlled impact using an automated Short Impact Load Cell (SILC-A). Deep learning models, trained on a dataset informed by mineral texture derived from the SEM-EDS data and XCT grayscale features, enabled the segmentation of XCT volumes into mineralogical and structural classes. Comparative analysis of pre- and post-impact tomograms enabled quantification of fracture extension, evaluated in relation to mineral associations, pre-existing defects, and the absorbed fracture energy during impact. Results demonstrate that this methodology provides a framework for linking the internal grain arrangement within a particle to its mechanical response. As a proof of concept, the analysed particles showed that fracture propagation and breakage response were strongly influenced by intergrowths between mineral phases with contrasting mechanical properties, particularly in domains involving calcite-, albite-, and magnetite-bearing classes. Therefore, this methodology provides a basis for future microstructure-aware studies of comminution that consider selective breakage and energy efficiency.

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.

We outline the potential to adopt geometallurgical concepts during early mineral exploration, particularly during scoping studies, rather than later during feasibility studies or exploitation when costs are higher. The approach is rooted in the increasing capabilities of drill core scanning technologies. Continuous drill core scanning data can now be generated efficiently and at reasonable cost. Validating and calibrating these data with high-resolution quantitative imaging of a suite of localized test samples, e.g., from scanning electron microscopy-based image analysis, allow the mineralogy and microfabric of drill core to be quantified. This quantitative information can then be used for more accurate geologic domaining of a potential orebody. The resulting geologic domain model then provides the basis for sample selection and blending that is essential for representative beneficiation test work. These test results can then be combined with emerging particle-based process modeling techniques that are predictive and can be designed to help understand and tackle metallurgical challenges in unlocking a mineral resource. This will assist in defining geometallurgical domains, using both geologic and technological constraints. However, this ambition is currently limited by several knowledge gaps. Arguably the most crucial issue concerns the forecasting of comminution responses, including particle sizes and compositions, based on the measured mineralogy and microfabric of the ores. Other challenges relate to the resolution and speed of available core scanning technologies and the incorporation of physical constraints into particle-based beneficiation models. Once these issues have been resolved, we expect substantial improvements in the efficiency and predictive power of geometallurgy, which should enable its application during earlier stages of exploration, with greater reliability at each decision stage during a development.

Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract We outline the potential to adopt geometallurgical concepts during early mineral exploration, particularly during scoping studies, rather than later during feasibility studies or exploitation when costs are higher. The approach is rooted in the increasing capabilities of drill core scanning technologies. Continuous drill core scanning data can now be generated efficiently and at reasonable cost. Validating and calibrating these data with high-resolution quantitative imaging of a suite of localized test samples, e.g., from scanning electron microscopy-based image analysis, allow the mineralogy and microfabric of drill core to be quantified. This quantitative information can then be used for more accurate geologic domaining of a potential orebody. The resulting geologic domain model then provides the basis for sample selection and blending that is essential for representative beneficiation test work. These test results can then be combined with emerging particle-based process modeling techniques that are predictive and can be designed to help understand and tackle metallurgical challenges in unlocking a mineral resource. This will assist in defining geometallurgical domains, using both geologic and technological constraints. However, this ambition is currently limited by several knowledge gaps. Arguably the most crucial issue concerns the forecasting of comminution responses, including particle sizes and compositions, based on the measured mineralogy and microfabric of the ores. Other challenges relate to the resolution and speed of available core scanning technologies and the incorporation of physical constraints into particle-based beneficiation models. Once these issues have been resolved, we expect substantial improvements in the efficiency and predictive power of geometallurgy, which should enable its application during earlier stages of exploration, with greater reliability at each decision stage during a development.

Measuring the froth height and depth in froth flotation for the separation of valuable mineral particles is crucial for process optimization. The froth depth is linked to the flotation performance and product quality as it influences various flotation subprocesses such as particle entrainment. Further, it is strongly related to the operating conditions of the flotation cell. Therefore, froth height monitoring is widely used in industry, and can also provide valuable insights for laboratory-scale batch flotation testing. To address the need for precise measurement of the froth surface height and the pulp level in opaque three-phase systems, non-invasive optical or laser-based techniques are required. They allow to investigate the effects of various operating variables on the froth depth in laboratory-scale batch flotation. This experimental study presents a method of optical imaging through the transparent sidewall of a laboratory-scale flotation cell to determine the froth depth by means of advanced image post-processing and analysis. We have demonstrated the performance of this method in a comprehensive study following a full-factorial design of experiments, showing the effects of both hydrodynamic and chemical operating conditions on the froth depth in a batch flotation process. Supplementary measurements using a Dynamic Foam Analyzer confirmed the influence of the chemical conditions on the froth depth. Simultaneously to the optical imaging, in order to provide a comparison to industrial measurement tools, a lidar sensor monitored the froth surface height from above the flotation cell. In conclusion, the proposed imaging method was found to robustly detect the froth phase under the varying experimental procedures of laboratory-scale batch flotation tests, and is similarly applicable to other applications and processes involving foam and froth.

Colloidal silica acts as a multifunctional reagent in the froth flotation process of semi-soluble salt-type minerals, enabling the selective depression of calcite. This study investigates its effect on four key minerals—calcite, scheelite, apatite, and fluorite—using a comprehensive suite of techniques to identify the flotation subprocesses modulated by colloidal silica. This work also aims to determine the specific flotation zones affected by colloidal silica, assessing the influence of its dosage, surface modification, and specific surface area on metallurgical outcomes. Atomic force microscopy revealed mineral-specific surface responses to colloidal silica conditioning: calcite exhibited localized nanoparticle adsorption, whereas apatite underwent a dissolution–reprecipitation mechanism. Scheelite and fluorite, in contrast, showed minimal surface modifications. These differences are attributed to variations in surface reactivity, hydration behavior, and crystallographic structure, with calcite offering a uniquely favorable environment for colloidal silica attachment. Mechanistic insights show that colloidal silica—especially the aluminate-modified type with high specific surface area—influences both the pulp and froth zones by producing small, stable bubbles, enhancing fine scheelite recovery, stabilizing froth, and effectively depressing calcite. In contrast, non-functionalized colloidal silica resulted in poor bubble control and unstable froth. These findings elucidate the subprocess-specific mechanisms by which colloidal silica operates and highlight its potential as a tunable, multifunctional reagent for improving selectivity in the flotation of semi-soluble salt-type minerals.

In froth flotation, overall recovery of the floatable particles consists of true recovery and recovery by entrainment, where entrainment refers to the non-selective recovery of particles in the concentrate. To understand and optimize the flotation process with regard to process conditions, it is essential to distinguish true flotation recovery from overall recovery. The established methods rely on tailored flotation experiments, unrealistic flotation conditions, or using external tracers which can be different in density and crystal structure to the mineral(s) of interest. This study presents an approach to utilize naturally occuring suitable tracers to estimate the entrainment component from overall recovery of individual particles by establishing a relationship between their settling velocity coefficient and recovery probability. Recovery probabilities of individual particles are computed using particle-based separation modelling. The approach is demonstrated for a copper ore, where naturally occurring rutile was used as the tracer to determine the entrained component of the overall recovery of chalcopyrite particles. Laboratory flotation experiments revealed that entrainment accounted for up to 6% of the overall recovery probability of fully liberated chalcopyrite particles in the fine size fractions. This approach provides a practical method for entrainment correction enabling a more accurate evaluation of true flotation recovery.

We introduce a unique approach for faster and more systematic optimization and upscaling of reagent systems in froth flotation processes. By integrating the statistical design of experiments with numerical optimization techniques, our method ensures a seamless transition from laboratory experimentation to pilot and industrialscale implementation. This approach uses comprehensive lab-scale experimental data to assign the effects of reagent system operating parameters and pre-identify optimal conditions. These predictions streamline the optimization algorithm for continuous process improvement, minimizing its convergence issues and, consequently, the test work time and resource consumption. We validated this methodology with a case study on a low-grade scheelite ore, demonstrating a reduction of 60 % in required test work at pilot scale. A four-day industrial campaign resulted in a 16 % increase in concentrate grade without hindering recovery. Additionally, we discuss the influence of variations in ore feed properties on froth flotation performance while optimizing the reagent system. Within this context, we introduce the Grade-Recovery-Performance (GRP) index, a two-dimensional metric to quantify the performance of separation processes according to grade and recovery simultaneously.

Printed circuit boards represent an extraordinarily challenging fraction for the recycling of waste electric and electronic equipment. Due to the closely interlinked structure of the composing materials, the selective recycling of copper and closely associated precious metals from this composite material is compromised by losses during mechanical pre-processing. This problem could partially be overcome by a better understanding of the influence of particle size and shape on the recovery of finely comminuted and well-liberated metal particles during mechanical separation. Here, we propose a workflow to quantify the role of the size and shape of such particles in various separation processes. As a case study, we compare an analytical heavy liquid separation to a new type of eddy current separator. Using X-ray computed tomography, we were able to distinguish metallic and non-metallic phases and determine the size and 3D microstructure of individual particles. For both separation processes, we trained a particle-based separation model that predicts the probability of individual particles to end up in the processing products. In particular, elongated particles were found to display a negative correlation between particle size and sphericity of metallic particles. In line with this correlation, the predicted metal recoveries are positively correlated with particle size but negatively correlated with sphericity in both separation processes. The suggested workflow is easily transferred to other recycling material systems. It allows to quantify the role of 3D geometrical particle properties in separation processes and provide robust predictions for the recoverability of different raw materials in complex recycling streams.

Improvement in resolving hydrodynamic variables in multiphase flows is key to optimizing flotation performance. However, due to equipment complexity and opacity of three-phase systems, in situ measurements become challenging. Therefore, by using a novel multi-sensor approach, the aim of this study is to spatially resolve key hydrodynamic and gas dispersion parameters in a mechanical flotation cell such as superficial gas velocity (Jg), g ), gas holdup (epsilon g), g ), bubble size distribution (BSD), and bubble surface area flux (Sb). b ). A high-resolution inline endoscope (SOPAT), Jg g and epsilon g g sensors were fixed at multiple axial positions in a 6L nextSTEPTM TM flotation cell. This multi-sensor concept has been applied to a simplified benchmark flotation scenario, as part of a binary (pyrite-quartz) flotation test campaign (30 % solid load). Varying operating conditions include tip speed (4.7 - 5.5 m/s), air flow (0.4 - 0.5 cm/s), frother (MIBC: 30 - 60 g/ton), and collector concentrations (PAX: 30 - 60 g/ ton). Sb b is a good indicator of gas dispersion efficiency in flotation, and local measurements indicated that there are significant differences in the local superficial gas velocities which can be measured with our adapted sensor. Real-time bubble size measurements reflected the high shear rates near the rotor-stator region. Overall, the gas flow rate and frother concentration were shown to have the most significant effect on the gas dispersion in the benchmark flotation tests.

Tailings generated during ore processing may host significant residual contents of valuable commodities, including critical metals. The particle properties of the tailings, such as mineralogy, particle size, and the surface liberation of ore minerals, strongly control processing behaviour. This study explores a novel combination of methods for incorporating particle data, derived from automated mineralogy, into geometallurgical models of tailings deposits to better understand their reprocessing potential and the economic feasibility of re-mining. This was achieved through binning of different particle types, geostatistical modelling of particle bin frequencies, and bootstrap resampling to reconstruct particle populations. The spatial distributions of processing-relevant particle properties throughout the tailings deposit were predicted with corresponding uncertainties. There are clear systematic trends in the spatial distributions of different particle types, resulting from the sedimentary-style deposition of the tailings. For instance, the tailings nearer the dam walls comprise coarser, silicate-rich particles, while fine-grained and well-liberated sulphide mineral particles are more abundant in the centre of the tailings deposit. As a result, robust models could be developed for the spatial distributions of particle size and mineralogy, which strongly control the sorting of particles during deposition, and other related properties, such as sulphide mineral grain sizes. Finally, a bulk sulphide flotation process was simulated and acid mine drainage potential estimated using the interpolated particle data. Around 58% of the sulphide minerals present could be recoverable by flotation, with the recoverable sulphide portion decreasing towards the centre of the TSF due to the fine-grained nature of the sulphide minerals. The acid mine drainage potential of the tailings is estimated to be moderate to high, indicating that the carbonate minerals present are not sufficient to neutralise the high acid-generating potential of the sulphide minerals. Overall, this study demonstrates how particle-based geometallurgical models can be developed and utilised for practical applications, with the aim of improving the accuracy of resource and reserve estimations of tailings deposits and the sustainable and responsible management of anthropogenic resources. The methodology proposed here can be easily transferred to other tailings deposits.

Mineral processing encompasses the series of operations used to first liberate the valuable minerals in an ore by comminution, and then separate the resulting particles by means of their geometric, compositional, and physical properties. From a geometallurgical perspective, it is fundamental to understand how ore textures influence the generation of ore particles and their properties. This contribution outlines the processes used to generate and concentrate ore particles, and how these are commonly modelled. A case study illustrates the main ideas. Finally, a brief outlook on the most important research challenges remaining in this branch of geometallurgy is presented.

Mineral separation processes operate on properties of individual particle, which can currently be quantified with 2D characterization techniques, namely 2D automated mineralogy. While 2D automated mineralogy data have driven significant developments in particle-based separation models, this data inherently correspond to 2D slices of 3D objects, which leads to stereological bias in the quantification of geometric particle properties. X-ray micro-computed tomography (& mu;CT) is a 3D imaging technique that can quantify particle geometry. However, & mu;CT only collects limited information regarding material composition, making mineral identification quantification a challenge. To overcome this challenge, we present a workflow that utilizes individual particle histograms and corrects image artefacts caused by & mu;CT measurements, such as partial volume effect. We demonstrate the application of the workflow to perform 3D mineral characterisation of a sulfidic gold ore, where mineral phases that are commonly mistaken with & mu;CT could be distinguished: pyrite and chalcopyrite, gold, and galena. Results were verified by comparison with inductively coupled plasma mass spectrometry and 2D automated mineralogy. As a result, the workflow provides the user with a detailed 3D particle dataset containing the modal mineralogy and surface compositions, size, and geometrical properties of each particle in a sample - essential data for modelling mineral separation processes.

Grinding and flotation processes are often studied independently, despite the well-established grinding influence on flotation performance, which affects not only particle size and thus liberation but also shape and leads to complex changes in pulp chemistry affecting the particle surface properties relevant for selective bubble attachment. Yet, no study jointly investigated these possible causes and many are limited to single mineral flotation. We relate grinding conditions to changes in pulp chemistry and particle surface properties and assess their impact on upgrading. We studied three non-sulfide ores with different feed grades and valuables: scheelite, apatite, and fluorite. These were dry-, wet-, and wet conditioned-ground before flotation in a laboratory mechanical cell. Results were evaluated with bulk- and particle-specific methodologies. The selectivity of the process is higher after dry grinding for the fluorite and apatite ores and irrelevant for the scheelite ore. Variations in flotation kinetics of individual particles associated to their size and shape are not sufficient to explain these results. The higher concentration of Ca2+ and Mg2+ observed in the pulp after wet grinding, altering particle surface properties, better explains the phenomenon. Additionally, we demonstrate how particle shape impacts are system specific and related to both entrainment and true flotation.

Physical separation processes are best understood in terms of the behaviour of individual ore particles.Yet,while different empirical particle-based separation modelling approaches have been developed,their predictive performance has never been tested under variable process conditions.Here,we investigated the predictive performance of a state-of-the-art particle-based separation model under variable feed composition for a laboratory-scale magnetic separation of a skarn ore.Two scenarios were investigated:one in which the mass flow of the different processing streams could be measured and one in which it had to be estimated from data.In both scenarios,the predictive models were sufficiently general to pre-dict the process outcomes of new samples of variable composition.Nevertheless,the scenario in which mass flow could be measured was～4％more precise in predicting mass balances.The process behaviour of minerals present at concentrations above 0.1％by weight could be accurately predicted.Our findings indicate the potential use of this method to minimize the costs of metallurgical testwork while providing in-depth understanding of the recovery behaviour of individual ore particles.Moreover,the method may be used to establish powerful tools to forecast mineral recoveries for partly new ore types at a running mining operation.

Modern analytical techniques used in the minerals processing industry can provide detailed characterization data at the particle level. However, process models that make full use of this information are currently not available, limiting the usefulness of these extensive datasets. This contribution addresses this issue. It presents a novel particle-based approach for process modelling capable of dealing with complete particle datasets and operating without human input. The method provides a probabilistic description for the behavior of individual particles in a given mineral separation unit, based on all measurable particle properties. It is applicable to any separation process that does not modify the physical dimensions of particles, i.e. it does not cover comminution. The method comprises a regularized logistic regression model with a probability adjustment step to accommodate geological variability. Even though this method supports any type of particle-level characterization data, its potential is illustrated here using data obtained by scanning electron microscope based image analysis. Constructed cases demonstrate the efficiency of the method in recreating characteristic recovery trends for magnetic separation, hydrocyclone, and flotation units. In addition, the method was used successfully to reconstruct a real processing plant with three flotation and one magnetic separation circuits. Predicted results of compositions for all the intermediate and product streams correspond well with the results reported from the plant itself. The predicted masses of the products are much affected by the quality of sampling and still require improvement. The case study illustrates that the method proposed here provides a powerful tool to understand and optimize mineral separation processes e and thus increase the resource and energy efficiency of mining operations. (c) 2020 Elsevier Ltd. All rights reserved.

Studies of flotation kinetics are essential for understanding, predicting, and optimizing the selective recovery of minerals and metals through flotation. Recently, much effort has been made to use intrinsic ore properties to model flotation behavior. Particle-based characterization methods, e.g. SEM-based image analysis, have enabled much of this development. However, currently available methods for studies of flotation kinetics can not accommodate single-particle data, resulting in incomplete use of data that is readily available today. In this contribution, a method is introduced to apply kinetic flotation models to individual particles. This method, based on lasso-regularized multinomial logistic regression, allows for an in-depth understanding of particle flotation behavior as a function of all measured particle characteristics. With the proposed method, the joint influences of particle size, shape, as well as modal and surface compositions on the recovery of individual particles can be taken into unprecedented consideration. The results of the simulated particle behavior showed a very good agreement to the outcome of conventional empirical studies and follow well-described froth flotation recovery behavior.