Potassium-containing feldspars provide a high potential for producing potash, a product with widespread use in agriculture. The present work assesses applying the anti-solvent crystallization method for the purification and recovery of high-purity muriate of potash (KCl) from feldspar leaching solutions. Initially, screening experiments were carried out on a synthetic leaching solution with the aim of analyzing the crystallization behavior of key components. Screening experiments were performed using five anti-solvents, namely methanol, ethanol, acetone, 2-propanol, and ethylene glycol. Acetone and 2-propanol were viable options for crystallization of potassium chloride. Then, the effects of anti-solvent ratio (O/A), time, and anti-solvent addition rate on potassium-chloride crystallization were further investigated using acetone and 2-propanol. A recovery of 83% of potassium was achieved when using acetone at the O/A of 5 with the addition rate of 10 mL/min, at room temperature with a hold time of 180 min. The optimum conditions for 2-propanol were determined to be similar, except for using a 5 mL/min addition rate for 79% recovery. The final muriate of potash products had a purity of over 99.9% using either of the anti-solvent. However, differences in morphology and crystal size of products were observed. Acetone-formed potash crystals were aggregates of cubic crystals with an average size of 3 microns, while 2-propanol-formed potash crystals were 20 microns in size as cubic particles with a hollow core. Despite having almost the same performance in potassium recovery, acetone was found to be a more feasible anti-solvent for potash recovery due to simpler downstream solvent recovery.

By-product metals have a significant potential to bring additional economic benefits to mines. However, a detailed characterization of their distribution is generally required to fulfill this potential and should preferably be integrated into a geometallurgical assessment. This contribution presents a detailed mineralogical and textural investigation of gold-bearing phases at the footwall of the Zn–Pb–Ag–(Cu–Au) Lappberget Deposit, Garpenberg Mine, Sweden, using optical microscopy, scanning electron microscope with energy dispersive spectroscopy (SEM-EDS), electron probe microanalysis (EPMA), laser ablation-inductively coupled plasma mass spectrometer (LA-ICPMS), and bulk chemical analysis applied to drill core and Knelson concentrator samples. Gold is a by-product at the Garpenberg mine, but it is unclear how the mineralogy, occurrence, and distribution of gold-bearing phases impact on gold recoveries during mineral processing. Our results show that Au-dominant electrum is the most abundant gold-bearing phase in the footwall of the Lappberget deposit, occurring strongly associated to sulfides in a variety of textures and grain sizes. Electrum grains commonly occur within sulfide borders, as inclusions, intergrowth and overgrowths of chalcopyrite, pyrite, galena, sphalerite, and pyrrhotite. Gangue minerals may also contain disseminated electrum and inclusions. Electrum grain sizes range from ∼5 µm to 300 µm, predominantly below 100 µm. The potential of sulfide lattice-bound invisible gold in the form of solid-solution gold and colloidal gold was also investigated, showing Au depletion within the analyzed sulfide carriers. The analysis of the concentrate samples from the Knelson gravity concentrator showed 584 and 431 ppm of gold content. High degree of liberation is observed among the gold-bearing phases in the concentrate, and gold recovery is highest among fractions coarser than 106 µm mesh. Pyrite and galena are the most abundant minerals in the concentrate samples. The gold-bearing phases were categorized based on its mineralogy, texture, grain size, and association and their influence on gold processing, especially textures and grain size, which implicates its liberation in milling and recovery by the gravity separator.

The global commitment to decarbonizing the transport sector has resulted in an unabated growth in the markets for electric vehicles and their batteries. Consequently, the demand for battery raw materials is continuously growing. As an illustration, to meet the net-zero emissions targets, the electric vehicle market demand for lithium, cobalt, nickel, and graphite will increase 26-times, 6-times, 12-times, and 9-times respectively between 2021 and 2050. There are diverse challenges in meeting this demand, requiring the world to embrace technological and knowledge advancements and new investments without provoking conflicts between competing goals. The uncertainties in a sustainable supply of battery minerals, environmental, social and governance complexities, and geopolitical tensions throughout the whole battery value chain have shaped the global and regional concerns over the success of transport decarbonization. Here, focusing on the entire value chain of electric vehicle batteries, the approaches adopted by regulatory agencies, governments, mining companies, vehicle and battery manufacturers, and all the other stakeholders are evaluated. Bringing together all these aspects, this literature review broadens the scope for providing multifaceted solutions necessary to optimize the goal of transport decarbonization while upholding sustainability criteria. Consolidating the previously fragmented information, a solid foundation for more in-depth research on existing difficulties encountered by governmental and industrial actors is created. The outcomes of this study may serve as a baseline to develop a framework for a climate smart and resource efficient supply of batteries considering the unique impacts of individual players.

Remote sensing data is a cheap form of surficial geoscientific data, and in terms of veracity, velocity and volume, can sometimes be considered big data. Its spatial and spectral resolution continues to improve over time, and some modern satellites, such as the Copernicus Programme's Sentinel-2 remote sensing satellites, offer a spatial resolution of 10 m across many of their spectral bands. The abundance and quality of remote sensing data combined with accumulated primary geochemical data has provided an unprecedented opportunity to inferentially invert remote sensing data into geochemical data. The ability to derive geochemical data from remote sensing data would provide a form of secondary big geochemical data, which can be used for numerous downstream activities, particularly where data timeliness, volume and velocity are important. Major benefactors of secondary geochemical data would be environmental monitoring and applications of artificial intelligence and machine learning in geochemistry, which currently entirely relies on manually derived data that is primarily guided by scientific reduction. Furthermore, it permits the usage of well-established data analysis techniques from geochemistry to remote sensing that allows useable insights to be extracted beyond those typically associated with strictly remote sensing data analysis. Currently, no generally applicable and systematic method to derive chemical elemental concentrations from large-scale remote sensing data have been documented in geosciences. In this paper, we demonstrate that fusing geostatistically-augmented geochemical and remote sensing data produces an abundance of data that enables a more generalized machine learning-based geochemical data generation. We use gold grade data from a South African tailing storage facility (TSF) and data from both the Landsat-8 and Sentinel remote sensing satellites. We show that various machine learning algorithms can be used given the abundance of training data. Consequently, we are able to produce a high resolution (10 m grid size) gold concentration map of the TSF, which demonstrates the potential of our method to be used to guide extraction planning, online resource exploration, environmental monitoring and resource estimation.

Dry laboratories (dry labs) are laboratories dedicated to using and creating data (they are data-centric). Several aspects of the minerals industry (e.g., exploration, extraction and beneficiation) generate multi-scale and multivariate data that are ultimately used to make decisions. Dry labs and digitalization are closely and intricately linked in the minerals industry. This paper focuses on the instrumentation and infrastructure that are required for accelerating digital transformation initiatives in the minerals sector. Specifically, we are interested in the ability of current and emerging instrumentation, sensors and infrastructure to capture relevant information, generate and transport high-quality data. We provide an essential examination of existing literature and an understanding of the 21st century minerals industry. Critical analysis of the literature and review of the current configuration of the minerals industry revealed similar data management and infrastructure needs for all segments of the minerals industry. There are, however, differences in the tools and equipment used at different stages of the mineral value chain. As demand for data-driven approaches grows, and as data resulting from each segment of the minerals industry continues to increase in abundance, diversity and dimensionality, the tools that manage and utilize such data should evolve in a way that is more transdisciplinary (e.g., data management, artificial intelligence, machine learning and data science). Ideally, data should be managed in a dry lab environment, but minerals industry data is currently and historically disaggregated. Consequently, digitalization in the minerals industry must be coupled with dry laboratories through a systematic transition. Sustained generation of high-quality data is critical to sustain the highly desirable uses of data, such as artificial intelligence-based insight generation.

Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) to showcase our new method. Using various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/models may be more desirable than others, beyond merely cross-validation performance metrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results, the support vector machine algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as compared with non-domained based estimation. Through the adoption of the machine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new data; and simple integration with existing machine learning-based workflows.

Underground mining has historically occurred in surface and near-surface (shallow) mineral deposits. While no universal definition of deep underground mining exists, humanity's need for non-renewable natural resources has inevitably pushed the boundaries of possibility in terms of environmental and technological constraints. Recently, deep underground mining is being extensively developed due to the depletion of shallow mineral deposits. One of the main advantages of deep underground mining is its lower environmental footprint compared to shallow mining. In this paper, we summarise the key factors driving deep underground mining, which include an increasing need for raw materials, exhaustion of shallow mineral deposits, and increasing environmental scrutiny. We examine the challenges associated with deep underground mining, mainly the: environmental, financial, geological, and geotechnical aspects. Furthermore, we explore solutions provided by recent advances in science and technology, such as the integration of mineral processing and mining, and the digital and technological revolution. We further examine the role of legacy data in its ability to bridge current and future practices in the context of deep underground mining.

The interaction and synergic effect of particle size on flotation efficiency were investigated by a comparison study between laboratories (size-by-size flotation modes) and industrial scale operational data (whole mixed size fraction). For this purpose, sampling was done from the feed, concentrate, and tailing of the flotation rougher cells of the Sungun copper processing complex (located in the northwest of Iran). In the size-by-size flotation mode (lab scale), the sample was first subjected to different size fractions, and then flotation tests were performed for each fraction. On an industrial scale, the particle size distribution of feed, concentrate, and tailing of flotation of the rougher stage have been analyzed. According to the results, in the case of industrial flotation mode (whole mixed size fraction), the particles with d80=84 μm were more likely to reach the tailing of flotation, and the particles within the size range of +63-180 μm constituted the highest amount of concentrate particles. In lab flotation mode (size-by-size), the maximum recovery was in the size fraction of +40-60 μm. By comparing the two flotation modes of industrial (whole mixed size fraction) and lab (size-by-size), for fractions <45 μm, the industrial flotation recovery was approximately 40% greater than the lab flotation recovery. However, for fractions >125 μm, the recovery trend was reversed and the lab flotation recovery was greater than the industrial flotation recovery. Coarse particle flotation has significant economic and technological benefits. By improving the recovery of coarse particles during the flotation process, the amount of grinding requirements will be reduced and consequently, it will considerably decrease the amount of energy consumption.

Interakcija i sinergijski učinak veličine čestica na učinkovitost flotacije istraženi su usporednom studijom laboratorijskih (model flotacije po veličini) i operativnih podataka na industrijskoj razini (cijela frakcija miješane veličine). S tom svrhom uzorkovanje je obavljeno iz sirovine, koncentrata i ostataka iz grubih ćelija flotacije u kompleksu Sungun za preradu bakra (koji se nalazi na sjeverozapadu Irana). U načinu flotacije po veličini (laboratorijsko mjerilo) uzorak je najprije podvrgnut razdvajanju po veličini frakcija, a zatim je za svaku frakciju obavljeno ispitivanje flotacije. U industrijskim razmjerima analizirana je granulometrijska raspodjela čestica u sirovini, koncentratu i jalovini grubljih faza flotacije. Prema rezultatima, u slučaju industrijskoga načina flotacije (cijela mješovita frakcija) veća je vjerojatnost da će čestice d80 = 84 μm dospjeti do ostatka flotacije, a čestice unutar raspona veličine od +63 do 180 μm činile su najveću količinu čestica u koncentratu. U laboratorijskome načinu flotacije (po veličini) najveće iskorištenje bilo je u frakciji veličine od +40 do 60 μm. Uspoređujući dva načina flotacije – industrijske (cijela frakcija miješane veličine) i laboratorijske (po veličini), za frakcije <45 μm, industrijska flotacija bila je približno 40 % veća od laboratorijske flotacije. Međutim, za frakcije >125 μm trend iskorištenja bio je obrnut, a iskorištenje laboratorijskom flotacijom bilo je veće od iskorištenja industrijskom flotacijom. Flotacija grubih čestica ima bitne ekonomske i tehnološke prednosti. Poboljšanjem iskorištenja grubih čestica tijekom procesa flotacije smanjit će se potreba za mljevenjem, a posljedično će se znatno smanjiti količina potrošnje energije.

Aitik is a large copper porphyry type deposit located in northern Sweden, currently exploited at an annual rate of approximately 45Mt. The ore’s exceptionally low head grade of 0.25 % Cu and varying degrees of hardness across the entire deposit pose challenges to the two fully autogenous grinding lines, each of which comprises a 25 MW primary autogenous mill in series with a pebble mill. The variability in ore grindability frequently leads to fluctuations in mill throughput. 

Within the framework of a geometallurgical approach, the present study investigated the relationships between ore grindability and modal mineralogy. For this purpose, drill core samples from different lithologies were subjected to Boliden AB’s in-house grindability tests. This fully autogenous laboratory-scale test generates a grindability index mainly related to abrasion breakage, which is a significant breakage mechanism within autogenous mills. The test results suggested divergent degrees of grindability within and across the selected rock types.

A combination of scanning electron microscopy, X-ray powder diffraction, and X-ray fluorescence analyses were performed for the grinding products and bulk mineral samples. The resulting mineralogical and elemental properties were subsequently correlated to the parameters from the grindability tests. It was shown that the main mineral phases, such as plagioclase, quartz, and micas, correlate well with the grindability indices. Similar correlations were found regarding the sample’s chemical composition, attributable to the main mineral phases. A further inverse correlation between the sample’s calculated average Mohs hardness and the grindability indices was established. Moreover, mineral liberation information provided by scanning electron microscopy was associated with the parameters mentioned earlier. The identified relationships between grindability, modal mineralogy, and element grades may help Boliden develop a predictive throughput model for Aitik based on the mine’s block model.

While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data, especially where modern data is considerably different than legacy data, it is an expensive exercise. The risk associated with modernizing such legacy data lies within its uncertainty in return (e.g., the possibility of new discoveries, in primarily greenfield settings). Without any advanced knowledge of yet unanalyzed elements, the importance of re-analyses remains ambiguous. To address this uncertainty, we apply machine learning to multivariate geochemical data from different regions in Canada (i.e., the Churchill Province and the Trans-Hudson Orogen) in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses. Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data (e.g., prospectivity mapping). Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.