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Journal articles on the topic 'Geometallurgey'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Bowell, R. J., J. Grogan, M. Hutton-Ashkenny, C. Brough, K. Penman, and D. J. Sapsford. "Geometallurgy of uranium deposits." Minerals Engineering 24, no. 12 (October 2011): 1305–13. http://dx.doi.org/10.1016/j.mineng.2011.05.005.

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2

Dominy, Simon, Louisa O’Connor, Anita Parbhakar-Fox, Hylke Glass, and Saranchimeg Purevgerel. "Geometallurgy—A Route to More Resilient Mine Operations." Minerals 8, no. 12 (December 1, 2018): 560. http://dx.doi.org/10.3390/min8120560.

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Geometallurgy is an important addition to any evaluation project or mining operation. As an integrated approach, it establishes 3D models which enable the optimisation of net present value and effective orebody management, while minimising technical and operational risk to ultimately provide more resilient operations. Critically, through spatial identification of variability, it allows the development of strategies to mitigate the risks related to variability (e.g., collect additional data, revise the mine plan, adapt or change the process strategy, or engineer flexibility into the system). Geometallurgy promotes sustainable development when all stages of extraction are performed in an optimal manner from a technical, environmental, and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire mine value chain are being established. Geometallurgy has been shown to intensify collaboration among operational stakeholders, creating an environment for sharing orebody knowledge and improving data acquisition and interpretation, leading to the integration of such data and knowledge into mine planning and scheduling. These aspects create better business optimisation and utilisation of staff, and lead to operations that are more resilient to both technical and non-technical variability. Geometallurgy encompasses activities that utilise improved understanding of the properties of ore and waste, which impact positively or negatively on the value of the product, concentrate, or metal. Properties not only include those that impact on processing efficiency, but also those of materials which will impact on other actions such as blasting and waste management. Companies that embrace the geometallurgical approach will benefit from increased net present value and shareholder value.

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3

Pownceby, M. I., and C. Johnson. "Geometallurgy of Australian uranium deposits." Ore Geology Reviews 56 (January 2014): 25–44. http://dx.doi.org/10.1016/j.oregeorev.2013.07.001.

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4

Ellefmo, Steinar L., Kurt Aasly, Aleksandra Lang, Veena S. Vezhapparambu, and Camilo A. M. Silva. "Geometallurgical Concepts Used in Industrial Mineral Production." Economic Geology 114, no. 8 (December 1, 2019): 1543–54. http://dx.doi.org/10.5382/econgeo.4685.

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Abstract Geometallurgy has developed since the 1970s, primarily on metallic ore operations. In parallel, industrial mineral operations have been optimized through detailed deposit knowledge and market development, without making specific reference to geometallurgical concepts. The Norwegian mining industry is dominated by industrial mineral and construction material operations, and, in this paper, key differences between the industrial mineral and the metallic ore sectors are investigated, along with their influence on the development and the use of economic block models and optimization methodologies. Further, the key levers and factors (mining method selection, processing route, scale, sequence, and cutoff policy) for value creation in industrial mineral operations are discussed, along with how and to what extent geometallurgy has been used. It is concluded that the five key levers cannot be used in industrial minerals operations as effectively as they are used in metallic ore operations. In industrial minerals, in situ strength variations are an important parameter in estimating key performance indicators such as recovery and product quality. When modeling the spatial variation in rock strength potential, additivity issues must be resolved by investigating the process the raw material is exposed to. The Norwegian industrial mineral sector has been using elements of geometallurgy but is facing unresolved issues related to strength variations and the use of measurement while drilling data.

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Hoal, Karin E. Olson, and Max Frenzel. "Ores Drive Operations—Economic Geology Is the Foundation of Geometallurgy." SEG Discovery, no. 129 (April 1, 2022): 30–43. http://dx.doi.org/10.5382/geo-and-mining-15.

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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 Economic geology and geometallurgy are intimately linked. Geologists understand the value in knowing the details of ore variability, the formation of mineral deposits, the continuity and the spatial distribution of ore types, and the mineral and textural characteristics that control grades. Beyond exploration and discovery, however, explorers may not recognize that the geologic knowledge developed around a mineral prospect is also essential to miners and metallurgists, reclamation and environmental specialists, and economists and investors who are interested in developing the discovery. Geometallurgy is the interdisciplinary method that links geologic, mineralogical, and geochemical characteristics of mineral deposits to the mining, processing, and metallurgical activities that are involved in the development of mines. Geometallurgy is not a new field, but recent developments in analytical capabilities and the ability to conduct statistical analysis and predictive modeling of large data sets have resulted in geometallurgy becoming a widely used method for optimizing mining operations. While there are many approaches, depending upon the nature of the ore deposit and the mine operating conditions and goals, the most important step explorers can take is to establish partnerships with the other areas of specialization in the project (mining, metallurgy, environmental, economics) and work together to understand the critical factors in order to best develop the deposit. Representative sampling to determine geologic variability and uncertainty and understanding the controls of throughput and recovery in the mining operation are fundamental to optimizing projects. For exploration and prefeasibility timelines, information on ore characteristics and spatial variability can provide a preliminary assessment of how material in a potential ore deposit can be processed.

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6

Dehaine, Q., and K. Esbensen. "Multivariate methods for improved geometallurgy sampling." TOS Forum 2022, no. 11 (May 27, 2022): 411. http://dx.doi.org/10.1255/tosf.167.

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Geometallurgy is at the core of life-of-mine value chain optimisation, with the aim of integrating geoscientific disciplines along with mining engineering and minerals processing. The objective is to link comprehensive geological, geochemical, mineralogical and geotechnical information with metallurgical and mining variability - based on spatially distributed samples. The spatial coverage is a crucial element in this process. Geometallurgy samples are used for metallurgical testing in the service of plant and process design and optimisation. To avoid discrepancies between the expected and actual process performance, geometallurgical test work must be based on representative samples collected and processed in compliance with the Theory of Sampling (TOS). However, even if samples are initially collected to populate a multivariate block model, most of TOS’ recommendations for estimating sampling protocols and sample representativeness is univariate. While the univariate approach is sufficient when a sample must be representative for one property only e.g., for grade estimation, it fails to properly qualify representativeness of a sample which must be representative for multiple properties such as for geometallurgical purposes. Indeed, a geometallurgy sample is considered representative sensu stricto only if its metallurgical behaviour is representative of that of the full zone of the orebody it represents. This can only be achieved if-and-when geo-metallurgical samples are representative for the full set of ore properties that influence process performance. The critical success factor of multivariate representativeness can be assessed using multivariate approaches, such as the multi-variogram, which allow us to summarise the global variability of multiple properties into a single characteristic function. This approach could be optimised by using downstream results from geo-metallurgical process modelling, to select or weight, the individual property contributions to the multi-variogram according to their importance, thereby allowing to optimise a specific geometallurgical sampling procedure in terms of sampling mode, sampling frequency and the number of increments involved according to the overall process performance.

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7

ENDERS, M. Stephen. "Applied Geometallurgy: A Common Sense Approach." Acta Geologica Sinica - English Edition 88, s2 (December 2014): 1272. http://dx.doi.org/10.1111/1755-6724.12380_24.

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8

Hunt, Julie A., and Ron F. Berry. "Geological Contributions to Geometallurgy: A Review." Geoscience Canada 44, no. 3 (October 6, 2017): 103–18. http://dx.doi.org/10.12789/geocanj.2017.44.121.

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Geometallurgy is a cross-disciplinary science that addresses the problem of teasing out the features of the rock mass that significantly influence mining and processing. Rocks are complex composite mixtures for which the basic building blocks are grains of minerals. The properties of the minerals, how they are bound together, and many other aspects of rock texture affect the entire mining value chain from exploration, through mining and processing, waste and tailings disposal, to refining and sales. This review presents rock properties (e.g. strength, composition, mineralogy, texture) significant in geometallurgy and examples of test methods available to measure or predict these properties. Geometallurgical data need to be quantitative and spatially constrained so they can be used in 3D modelling and mine planning. They also need to be obtainable relatively cheaply in order to be abundant enough to provide a statistically valid sample distribution for spatial modelling. Strong communication between different departments along the mining value chain is imperative so that data are produced and transferred in a useable form and duplication is avoided. The ultimate aim is to have 3D models that not only show the grade of valuable elements (or minerals), but also include rock properties that may influence mining and processing, so that decisions concerning mining and processing can be made holistically, i.e. the impacts of rock properties on all the cost centres in the mining process are taken into account. There are significant costs to improving ore deposit knowledge and it is very important to consider the cost-benefit curve when planning the level of geometallurgical effort that is appropriate in individual deposits.RÉSUMÉLa géométallurgie est une science interdisciplinaire qui s’intéresse aux caractéristiques de la masse rocheuse qui influent de manière significative sur l'exploitation minière et le traitement du minerai. Les roches sont des mélanges composites complexes dont les éléments structurant de base sont des grains de minéraux. Les propriétés des minéraux, la façon dont ils sont liés entre eux, et de nombreux autres aspects de la texture des roches déterminent l'ensemble de la chaîne de valeur minière, de l'exploration à l'extraction à la transformation, à l'élimination des déchets et des résidus, jusqu'au raffinage et à la vente. La présente étude passe en revue les propriétés significatives de la roche (par ex. sa cohésion, sa composition, sa minéralogie, sa texture) en géométallurgie ainsi que des exemples de méthodes d'essai disponibles pour mesurer ou prédire ces propriétés. Les données géométallurgiques doivent être quantitatives et localisées spatialement afin qu'elles puissent être utilisées dans la modélisation 3D et la planification de la mine. Elles doivent également être peu couteuses afin d'être suffisamment nombreuses pour fournir une distribution d'échantillon statistiquement valide pour la modélisation spatiale. Une communication efficace entre les différents segments de la chaîne de valeur minière est impérative pour que les données soient produites et transférées sous une forme utilisable et que les duplications soient évitées. Le but ultime est d'avoir des modèles 3D qui montrent non seulement la qualité des éléments précieux (ou minéraux), mais aussi les propriétés de roche qui déterminent l'exploitation minière et le traitement du minerai, de sorte que les décisions concernant l'exploitation minière et le traitement du minerai peuvent être réalisées de façon holistique, c.-à-d. que l’impact des propriétés de roche sur tous les maillons de la chaîne des coûts du processus minier sont prises en compte. Les coûts d’amélioration des connaissances sur le gisement de minerai étant importants, il faut tenir compte de la courbe coûts-bénéfices lors de la planification du niveau d'investissement géométallurgique approprié pour le gisement considéré.

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9

Dehaine, Quentin, Laurens T. Tijsseling, Hylke J. Glass, Tuomo Törmänen, and Alan R. Butcher. "Geometallurgy of cobalt ores: A review." Minerals Engineering 160 (January 2021): 106656. http://dx.doi.org/10.1016/j.mineng.2020.106656.

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10

Hunt, Julie, Ron Berry, Megan Becker, and Regina Baumgartner. "A Special Issue Dedicated to Geometallurgy: Preface." Economic Geology 114, no. 8 (December 1, 2019): 1473–79. http://dx.doi.org/10.5382/econgeo.4688.

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AbstractGeometallurgy is an interdisciplinary field aimed at describing potential ore deposits in terms that mine planners and economists can use to design and run profitable mining operations. The major geologic contribution to the field is defining the spatial variability of potential and active mining resources so that planning and scheduling can accurately predict the economic performance and environmental impact of mining in time to respond efficiently to variations in ore type. This information is needed at the feasibility stage and throughout the mine life. We review the available literature on how geologists have contributed to these predictions in the past. There have been substantial advances in predicting comminution behavior. Prediction of recovery and environmental impacts are less advanced. This introductory paper provides a brief review of geometallurgy and a synopsis of the papers in the Special Issue, along with suggestions on future directions.

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11

Bowell, Rob, Christopher Brough, Andrew Barnes, and Arman Vardanyan. "Geometallurgy of Trace Elements in the Hrazdan Iron Deposit." Minerals 11, no. 10 (October 2, 2021): 1085. http://dx.doi.org/10.3390/min11101085.

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This study presents an evaluation of arsenic and other trace metals in the Hrazdan Iron-Ore project in Armenia using a methodology typically associated with Geometallurgical characterization. The principal host of the trace elements is pyrite and oxidized equivalents. Pyrite is a mineral of elemental concern as it has the potential to generate acidic pH in water that it contacts and thus mobilize metals of concern. In the Hrazdan deposit, there is a general excess of neutralizing carbonate minerals that result in adequate buffering of generated acid and limiting the mobility of metal cations in solution. However, metalloids that form oxyanions species such as those of arsenic or chromium tend to be more mobile in neutral to alkaline mine drainage. From the geometallurgical assessment of the mine waste, the results of the geochemical testwork can be explained and the information used to assess potential issues with mine waste storage, timing of metal release and provide a baseline for mitigation strategies.

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12

Egaña, Álvaro F., Felipe A. Santibáñez-Leal, Christian Vidal, Gonzalo Díaz, Sergio Liberman, and Alejandro Ehrenfeld. "A Robust Stochastic Approach to Mineral Hyperspectral Analysis for Geometallurgy." Minerals 10, no. 12 (December 18, 2020): 1139. http://dx.doi.org/10.3390/min10121139.

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Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.

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13

LanHai, LIU, CHEN Jing, ZHOU TaoFa, ZAHNG YiFan, and LI MengMeng. "The new application of geometallurgy in deportment of gold and critical metals studies." Acta Petrologica Sinica 37, no. 9 (2021): 2691–704. http://dx.doi.org/10.18654/1000-0569/2021.09.06.

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14

Lin, C. L., Ching-Hao Hsieh, Tsend-Ayush Tserendagva, and J. D. Miller. "Dual energy rapid scan radiography for geometallurgy evaluation and isolation of trace mineral particles." Minerals Engineering 40 (January 2013): 30–37. http://dx.doi.org/10.1016/j.mineng.2012.09.001.

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15

Lang, Aleksandra Maria, Kurt Aasly, and Steinar Løve Ellefmo. "Mineral characterization as a tool in the implementation of geometallurgy into industrial mineral mining." Minerals Engineering 116 (January 2018): 114–22. http://dx.doi.org/10.1016/j.mineng.2017.10.021.

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16

Amer, T. E., I. E. El Assay, A. A. Rezk, A. M. El Kammar, A. W. El Manawi, and H. A. Abu Khoziem. "Geometallurgy and processing of North Ras Mohamed poly-mineralized ore materials, South Sinai, Egypt." International Journal of Mineral Processing 129 (June 2014): 12–21. http://dx.doi.org/10.1016/j.minpro.2014.04.005.

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17

Koch, Pierre-Henri, Cecilia Lund, and Jan Rosenkranz. "Automated drill core mineralogical characterization method for texture classification and modal mineralogy estimation for geometallurgy." Minerals Engineering 136 (June 2019): 99–109. http://dx.doi.org/10.1016/j.mineng.2019.03.008.

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18

Tayebi-Khorami, Maedeh, Mansour Edraki, Glen Corder, and Artem Golev. "Re-Thinking Mining Waste through an Integrative Approach Led by Circular Economy Aspirations." Minerals 9, no. 5 (May 10, 2019): 286. http://dx.doi.org/10.3390/min9050286.

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Mining wastes, particularly in the form of waste rocks and tailings, can have major social and environmental impacts. There is a need for comprehensive long-term strategies for transforming the mining industry to move toward zero environmental footprint. “How can the mining industry create new economic value, minimise its social and environmental impacts and diminish liability from mining waste?” This would require cross-disciplinary skills, across the social, environmental, technical, legal, regulatory, and economic domains, to produce innovative solutions. The aim of this paper is to review the current knowledge across these domains and integrate them in a new approach for exploiting or “re-thinking” mining wastes. This approach includes five key areas of social dimensions, geoenvironmental aspects, geometallurgy specifications, economic drivers and legal implications for improved environmental outcomes, and circular economy aspirations, which are aligned with the 10 principles of the International Council on Mining and Metals (ICMM). Applying circular economy thinking to mining waste presents a major opportunity to reduce the liability and increase the value of waste materials arising from mining and processing operations.

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19

Gregory, M. J., J. R. Lang, S. Gilbert, and K. O. Hoal. "Geometallurgy of the Pebble Porphyry Copper-Gold-Molybdenum Deposit, Alaska: Implications for Gold Distribution and Paragenesis." Economic Geology 108, no. 3 (March 7, 2013): 463–82. http://dx.doi.org/10.2113/econgeo.108.3.463.

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20

Parian, Mehdi, Pertti Lamberg, Robert Möckel, and Jan Rosenkranz. "Analysis of mineral grades for geometallurgy: Combined element-to-mineral conversion and quantitative X-ray diffraction." Minerals Engineering 82 (October 2015): 25–35. http://dx.doi.org/10.1016/j.mineng.2015.04.023.

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21

Pereira, Lucas, Sandra Birtel, Robert Möckel, Bruno Michaux, Andre C. Silva, and Jens Gutzmer. "Constraining the Economic Potential of By-Product Recovery by Using a Geometallurgical Approach: The Example of Rare Earth Element Recovery at Catalão I, Brazil." Economic Geology 114, no. 8 (December 1, 2019): 1555–68. http://dx.doi.org/10.5382/econgeo.4637.

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Abstract Geometallurgy aims to develop and deploy predictive spatial models based on tangible and quantitative resource characteristics that are used to optimize the efficiency of minerals beneficiation and extractive metallurgy operations. While most current applications of geometallurgy are focused on the major commodity to be recovered from a mineral deposit, this contribution delineates the opportunity to use a geometallurgical approach to provide an early assessment of the economic potential of by-product recovery from an ongoing mining operation. As a case study for this methodology, possible rare earth element (REE) recovery as a by-product of Nb production at the Chapadão mine in the Catalão I carbonatite complex is used. Catalão I is part of the Alto Paranaíba igneous province in the Goias Province of Brazil. Currently, niobium is produced in the complex as a by-product of the Chapadão phosphates mine. This production is performed in the Tailings plant, the focus of this study. REEs, albeit present in significant concentrations, are currently not recovered as by-products. Nine samples from different stages of the Nb beneficiation process in the Tailings plant were taken and characterized by mineral liberation analyzer, X-ray powder diffraction, and bulk-rock chemistry. The recovery of REEs in each of the tailing streams was quantified by mass balance. The quantitative mineralogical and microstructural data are used to identify the most suitable approach to recover REEs as a by-product—without placing limitations on niobium production. Monazite, the most common rare earth mineral identified in the feed, occurs as Ce-rich and La-rich varieties that can be easily distinguished by scanning electron microscopy (SEM)-based image analysis. Quartz, Fe-Ti oxides, and several phosphate minerals are the main gangue minerals. The highest rare earth oxide content concentrations (1.75 wt % total rare earth oxides) and the greatest potential for REE processing are reported for the final flotation tailings stream. To place tentative economic constraints on REE recovery from the tailings material, an analogy to the Browns Range deposit in Australia is drawn. Its technical flow sheet was used to estimate the cost for a hypothetical REE production at Chapadão. Parameters derived from SEM-based image analysis were used to model possible monazite recovery and concentrate grades. This exercise illustrates that a marketable REE concentrate could be obtained at Chapadão if the process recovers at least 53% of the particles with no less than 60% of monazite on their surface. Applying capital expenditure and operational expenditure values similar to those of Browns Range suggests that such an operation would be profitable at current REE prices.

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22

Wang, Y., and J. D. Miller. "Current developments and applications of micro-CT for the 3D analysis of multiphase mineral systems in geometallurgy." Earth-Science Reviews 211 (December 2020): 103406. http://dx.doi.org/10.1016/j.earscirev.2020.103406.

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23

Parbhakar-Fox, Anita, Nathan Fox, Laura Jackson, and Rebekah Cornelius. "Forecasting Geoenvironmental Risks: Integrated Applications of Mineralogical and Chemical Data." Minerals 8, no. 12 (November 22, 2018): 541. http://dx.doi.org/10.3390/min8120541.

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Management of solid mine wastes requires detailed material characterisation at the start of a project to minimize opportunities for the generation of acid and metalliferous drainage (AMD). Mine planning must focus on obtaining a thorough understanding of the environmental properties of the future waste rock materials. Using drill core obtained from a porphyry Cu project in Northern Europe, this study demonstrates the integrated application of mineralogical and geochemical data to enable the construction of enviro-geometallurgical models. Geoenvironmental core logging, static chemical testing, bulk- and hyperspectral mineralogical techniques, and calculated mineralogy from assay techniques were used to critically evaluate the potential for AMD formation. These techniques provide value-adding opportunities to existing datasets and provide robust cross-validation methods for each technique. A new geoenvironmental logging code and a new geoenvironmental index using hyperspectral mineralogical data (Hy-GI) were developed and embedded into the geochemistry-mineralogy-texture-geometallurgy (GMTG) approach for waste characterisation. This approach is recommended for new mining projects (i.e., early life-of-mine stages) to ensure accurate geoenvironmental forecasting, therefore facilitating the development of an effective waste management plan that minimizes geoenvironmental risks posed by the mined materials.

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Nistor, Mărgărit M., Nicolae Har, Simona Marchetti Dori, Simona Bigi, and Alessandro F. Gualtieri. "Progress in mineralogical quantitative analysis of rock samples: application to quartzites from Denali National Park, Alaska Range (USA)." Powder Diffraction 31, no. 1 (February 17, 2016): 31–39. http://dx.doi.org/10.1017/s0885715615000871.

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This work deals with the determination of the mineralogical composition of three quartzite samples, selected as case study to verify the viability and accuracy of various experimental techniques commonly used in geometallurgy and petrography for the determination of the mineralogical composition of rock samples. The investigated samples are from the North-Eastern side of the Denali National Park (Alaska Range, USA). The mineralogical phase abundance of the samples was determined by digitally assisted optical modal point counting, scanning electron microscopy (SEM) + energy dispersive spectroscopy (EDS) modal and digital image analysis, normative calculation from bulk chemistry calculation, and modal Rietveld X-ray powder diffraction. The results of our study indicate that the results provided by modal optical and SEM digitalized counting seem less accurate than the others. The determination with EDS mapping was found to be inaccurate only for one sample. Agreement was found between the X-ray diffraction estimates and bulk chemistry calculation. For both modal optical and SEM digitalized counting, the statistics was probably insufficient to provide accurate results. The estimates obtained from the various methods are compared with each other in the attempt to attain general indications on the precision, accuracy, advantages/disadvantages of each method.

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Barton, Isabel F., Matthew J. Gabriel, John Lyons-Baral, Mark D. Barton, Leon Duplessis, and Carson Roberts. "Extending geometallurgy to the mine scale with hyperspectral imaging: a pilot study using drone- and ground-based scanning." Mining, Metallurgy & Exploration 38, no. 2 (February 10, 2021): 799–818. http://dx.doi.org/10.1007/s42461-021-00404-z.

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Guntoro, Pratama Istiadi, Yousef Ghorbani, Alan R. Butcher, Jukka Kuva, and Jan Rosenkranz. "Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (µCT)." Natural Resources Research 29, no. 6 (May 11, 2020): 3547–65. http://dx.doi.org/10.1007/s11053-020-09685-5.

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Abstract Texture is one of the critical parameters that affect the process behavior of ore minerals. Traditionally, texture has been described qualitatively, but recent works have shown the possibility to quantify mineral textures with the help of computer vision and digital image analysis. Most of these studies utilized 2D computer vision to evaluate mineral textures, which is limited by stereological error. On the other hand, the rapid development of X-ray microcomputed tomography (µCT) has opened up new possibilities for 3D texture analysis of ore samples. This study extends some of the 2D texture analysis methods, such as association indicator matrix (AIM) and local binary pattern (LBP) into 3D to get quantitative textural descriptors of drill core samples. The sensitivity of the methods to textural differences between drill cores is evaluated by classifying the drill cores into three textural classes using methods of machine learning classification, such as support vector machines and random forest. The study suggested that both AIM and LBP textural descriptors could be used for drill core classification with overall classification accuracy of 84–88%.

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27

Lishchuk, Viktor, Cecilia Lund, and Yousef Ghorbani. "Evaluation and comparison of different machine-learning methods to integrate sparse process data into a spatial model in geometallurgy." Minerals Engineering 134 (April 2019): 156–65. http://dx.doi.org/10.1016/j.mineng.2019.01.032.

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28

Mambwe, Pascal, Michel Shengo, Théophile Kidyanyama, Philippe Muchez, and Mumba Chabu. "Geometallurgy of Cobalt Black Ores in the Katanga Copperbelt (Ruashi Cu-Co Deposit): A New Proposal for Enhancing Cobalt Recovery." Minerals 12, no. 3 (February 26, 2022): 295. http://dx.doi.org/10.3390/min12030295.

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Copper-cobalt deposits in the Central African Copperbelt belong to the Sediment-Hosted Stratiform Copper (SHSC) type and are situated in the Neoproterozoic Katanga Supergroup. This paper describes in detail the geology, geochemistry and hydrometallurgy of cobalt, with a special focus on the Black Ore Mineralised Zone (BOMZ) unit from the Ruashi Cu-Co deposit as a case study. Based on results from fieldwork and laboratory testing, it was concluded that the BOMZ consists of a succession of massive and stratified dolostones, which are weathered into carbonaceous clay dolostones and clays. The Lower “Calcaire à Minéreaux Noirs Formation” (Lower CMN Formation) consists of stratified and finely laminated dolostones, which are weathered at the surface into clayey to siliceous dolostones. The cobalt concentration in the weathering zone is due to supergene enrichment, a process that is linked to the formation of a cobalt cap. The ore consists of heterogenite associated with minor amounts of chrysocolla and malachite. Minor carrollite, chalcopyrite, chalcocite and bornite are present in unweathered fragments. The cobalt grade in both the BOMZ and Lower CMN decreases within depth while the copper grade increases. These grade changes reflect the variation in mineralogy with depth from heterogenite with minor amounts of malachite and chrysocolla to malachite, chrysocolla with traces of heterogenite, spherocobaltite, chalcocite, chalcopyrite, carrollite and bornite. Based on the Cu (100xAS Cu/TCu) and Co ratio (100 xAS Co/TCo), which is related to the ore mineralogy, oxide ores (Cu ratio ≥ 75%) and oxide dominant mixed ores (Cu ratio < 75%, containing the copper sulphide chalcocite) can be differentiated in both the BOMZ and Lower CMN. The absence of talc and the low concentration of Ni, Mn and Fe, on the one hand, and the high-grade Cu in the BOMZ, on the other hand, facilitate the hydrometallurgy of cobalt but require a specific processing. Consequently, the recovery of Co from the BOMZ requires the application of a processing method that is based on sulphuric acid (30 g/L) leaching under reducing conditions (300–350 mV) and the removal of impurities (Cu > 95% and Mn ≈ 99%) from the pregnant leach solution (PLS) by solvent extraction (SX) prior to the precipitation of cobalt as a high-grade hydroxide (40.5%). The sulphuric acid leaching of the BOMZ enabled achieving, after 8 h of magnetic stirring (500 rpm), a highest yield of 93% Co, with other major elements Mn (84%) and Cu (40%). The latter forms a main co-product of the Co exploitation. In contrast, the highest leaching yield for Fe remained smaller than 5%.

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Guntoro, Pratama Istiadi, Yousef Ghorbani, Alan R. Butcher, Jukka Kuva, and Jan Rosenkranz. "Correction to: Textural Quantification and Classification of Drill Cores for Geometallurgy: Moving Toward 3D with X-ray Microcomputed Tomography (μCT)." Natural Resources Research 29, no. 6 (June 19, 2020): 3567. http://dx.doi.org/10.1007/s11053-020-09706-3.

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Kern, Marius, Julian Kästner, Raimon Tolosana-Delgado, Tilman Jeske, and Jens Gutzmer. "The inherent link between ore formation and geometallurgy as documented by complex tin mineralization at the Hämmerlein deposit (Erzgebirge, Germany)." Mineralium Deposita 54, no. 5 (August 21, 2018): 683–98. http://dx.doi.org/10.1007/s00126-018-0832-2.

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Johnson, Curtis L., David A. Browning, and Neil E. Pendock. "Hyperspectral Imaging Applications to Geometallurgy: Utilizing Blast Hole Mineralogy to Predict Au-Cu Recovery and Throughput at the Phoenix Mine, Nevada." Economic Geology 114, no. 8 (December 1, 2019): 1481–94. http://dx.doi.org/10.5382/econgeo.4684.

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Abstract The Phoenix mine and predecessor operations in north-central Nevada have produced an aggregate of 5.2 Moz of gold and 550 million pounds of copper from an Eocene-aged Au-Cu porphyry-related skarn. The complex skarn mineralogy intimately associated with ore-grade mineralization poses significant challenges to blasting, mining, comminution, and process operations. These challenges are rooted in highly variable silicate mineralogy, which manifests as fine-grained, submillimeter grain-size, generally green colored rocks that inhibit accurate identification in the field. Prior to this study, all mineralogical data utilized in Phoenix mine ore control were sourced from blast hole cuttings mapped by ore control geologists in the field—the standard practice at many modern mine sites. At Phoenix, a direct link between mineralogy and mill performance was recognized; however, mineralogical data captured in the field was not sufficient to optimize process operations. To address this, it was determined that analytical work was necessary to quantify fine-grained mineralogy of variable ore types. A visible-near and short-wave infrared (VNIR-SWIR) hyperspectral imaging system provided the ideal tool, as it allows near real-time mineralogical data acquisition and semiquantitative determination of mineral abundances. Multiple iterative studies were conducted to prove that hyperspectral imaging of Phoenix ore types provides results suitable for process optimization. This six-month study described here included hyperspectral imaging of 3,008 blast hole cuttings samples from three pits, and 877 crusher feed, rougher feed, and rougher tails samples. Hyperspectral feature extractions derived from mill samples were paired with associated mill performance data and used to build predictive Au-Cu recovery, grade, and throughput models using multiple linear regression, partial least squares, and deep learning techniques with R-correlation values to observed data of 0.56 to 0.71. Blast hole hyperspectral data were then applied to recovery, grade, and throughput models to calculate predicted recoveries and throughputs that were spatially kriged with excellent correlations to geologic features. The application of VNIR-SWIR hyperspectral imaging to blast hole cuttings is a powerful predictive and diagnostic geometallurgical tool in operations where silicate mineralogy has a strong impact on process operations.

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El Mendili, Yassine, Daniel Chateigner, Beate Orberger, Stéphanie Gascoin, Jean-François Bardeau, Sébastien Petit, Cédric Duée, Monique Le Guen, and Henry Pilliere. "Combined XRF, XRD, SEM-EDS, and Raman Analyses on Serpentinized Harzburgite (Nickel Laterite Mine, New Caledonia): Implications for Exploration and Geometallurgy." ACS Earth and Space Chemistry 3, no. 10 (August 8, 2019): 2237–49. http://dx.doi.org/10.1021/acsearthspacechem.9b00014.

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Philander, C., and A. Rozendaal. "The contributions of geometallurgy to the recovery of lithified heavy mineral resources at the Namakwa Sands mine, West Coast of South Africa." Minerals Engineering 24, no. 12 (October 2011): 1357–64. http://dx.doi.org/10.1016/j.mineng.2011.07.011.

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Kern, Marius, Julian Kästner, Raimon Tolosana-Delgado, Tilman Jeske, and Jens Gutzmer. "Correction to: The inherent link between ore formation and geometallurgy as documented by complex tin mineralization at the Hämmerlein deposit (Erzgebirge, Germany)." Mineralium Deposita 54, no. 5 (March 22, 2019): 699. http://dx.doi.org/10.1007/s00126-019-00874-8.

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Maydagán, Laura, Marta Franchini, Agnes Impiccini, and David Lentz. "Phyllosilicates geochemistry and distribution in the Altar porphyry Cu-(Au) deposit, Andes Cordillera of San Juan, Argentina: Applications in exploration, geothermometry, and geometallurgy." Journal of Geochemical Exploration 167 (August 2016): 83–109. http://dx.doi.org/10.1016/j.gexplo.2016.05.002.

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Ofori-Sarpong, G., T. Okwaisie, and R. K. Amankwah. "Geometallurgical Studies on Gold Ore for Enhanced Comminution and Leaching." Ghana Mining Journal 19, no. 1 (June 30, 2019): 59–65. http://dx.doi.org/10.4314/gm.v19i1.7.

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Many gold processing plants are experiencing challenges as mining pits are becoming deeper, rocks are getting harder and more complex polymetallic and refractory ores are being encountered. The variations in the characteristics of ores lead to deviations from the established parameters, and these affect gold extraction efficiency. This paper presents a study where geological characteristics of the ore types from some mining pits were used to ascertain the influence of ore blends on improving the performances of comminution and leaching circuits. To achieve this, mineralogical, comminution, gravity recoverable gold and leaching investigations were conducted on fresh and weathered ore samples and their blends. Mineralogical study showed that the main rock types associated with the mine pits were dolerite, phyllites, conglomerates and sandstone. The dominant minerals were quartz, plagioclase, with traces of pyrites. The Crushability Work Indices of the rocks were between 30 and 37 KWh/t, which are generally higher than the maximum design value of 31.9 kWh/t, and this situation will pose throughput challenges in that section. The Bond Ball Mill Work Indices of the blends tested were between 16.4 kWh/t and 9.6 kWh/t and a blend ratio of 85% fresh and 15% weathered was found to have a Bond Ball Work Index almost equal to the design value of 14 kWh/t. With gold assays of 2.5 g/t for dolerite, 2.1 g/t for phyllite, 3.7 g/t for sandstone and 3.4 g/t for conglomerate, the gravity recoverable gold was in the order of sandstone 36% > phyllite (31.5%) > dolerite (29.5%) > conglomerate (18%). The overall gold recoveries were in the sequence of conglomerate (95%), sandstone (94%), phyllite (92%) and dolerite (87%). This information could be utilised in developing a proactive plant operations strategies for an operating plant in order to ultimately manage the plant and enhance achievement of set targets. Keywords: Geometallurgy; Ore Blends; Characterisation; Communition Circuit Performance; Gold Recovery

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Boisvert, Laurence, Claude Bazin, Josiane Caron, and François Lavoie. "Development and Testing of a Method to Estimate the Mineral Composition of Ore from Chemical Assays with a View toward Geometallurgy: Application to an Iron Ore Concentrator." Geomaterials 12, no. 04 (2022): 70–92. http://dx.doi.org/10.4236/gm.2022.124006.

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Wright, Joshua, David Lentz, Steven Rossiter, and Phil Garland. "Analysis of Au-Ag Mineralization in the Caribou Base-Metal VMS Deposit, New Brunswick; Examination of Micro-Scale Inter- and Intra-Sulphide Distribution and Its Relation to Geometallurgy." Minerals 6, no. 4 (October 21, 2016): 113. http://dx.doi.org/10.3390/min6040113.

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Gu, Xiaomeng, Andrew Metcalfe, Nigel Cook, Chris Aldrich, and L. George. "Exploratory analysis of multivariate drill core time series measurements." ANZIAM Journal 63 (January 10, 2023): C208—C230. http://dx.doi.org/10.21914/anziamj.v63.17192.

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Demand for mineral resources is increasing, necessitating exploitation of lower grade and more heterogeneous orebodies. The high variability inherent in such orebodies leads to an increase in the cost, complexity and environmental footprint associated with mining and mineral processing. Enhanced knowledge of orebody characteristics is thus vital for mining companies to optimize profitability. We present a pilot study to investigate prediction of geometallurgical variables from drill sensor data. A comparison is made of the performance of multilayer perceptron (MLP) and multiple linear regression models (MLR) for predicting a geometallurgical variable. This comparison is based on simulated data that are physically realistic, having been derived from models fitted to the one available drill core. The comparison is made in terms of the mean and standard deviation (over repeated samples from the population) of the mean absolute error, root mean square error, and coefficient of determination. The best performing model depends on the form of the response variable and the sample size. The standard deviation of performance measures tends to be higher for the MLP, and MLR appears to offer a more consistent performance for the test cases considered. References R. M. Balabin and S. V. Smirnov. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: Benchmarking the robustness on near-infrared (NIR) spectroscopy data”. Analyst 137.7 (2012), pp. 1604–1610. doi: 10.1039/c2an15972d C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. url: https://link.springer.com/book/9780387310732 J. B. Boisvert, M. E. Rossi, K. Ehrig, and C. V. Deutsch. Geometallurgical modeling at Olympic dam mine, South Australia”. Math. Geosci. 45 (2013), pp. 901–925. doi: 10.1007/s11004-013-9462-5 T. Bollerslev. Generalized autoregressive conditional heteroskedasticity”. J. Economet. 31.3 (1986), pp. 307–327. doi: 10.1016/0304-4076(86)90063-1 C. Both and R. Dimitrakopoulos. Applied machine learning for geometallurgical throughput prediction—A case study using production data at the Tropicana Gold Mining Complex”. Minerals 11.11 (2021), p. 1257. doi: 10.3390/min11111257 J. Chen and G. Li. Tsallis wavelet entropy and its application in power signal analysis”. Entropy 16.6 (2014), pp. 3009–3025. doi: 10.3390/e16063009 S. Coward, J. Vann, S. Dunham, and M. Stewart. The primary-response framework for geometallurgical variables”. Seventh international mining geology conference. 2009, pp. 109–113. https://www.ausimm.com/publications/conference->url: https://www.ausimm.com/publications/conference- proceedings/seventh-international-mining-geology- conference-2009/the-primary-response-framework-for- geometallurgical-variables/ A. C. Davis and N. B. Christensen. Derivative analysis for layer selection of geophysical borehole logs”. Comput. Geosci. 60 (2013), pp. 34–40. doi: 10.1016/j.cageo.2013.06.015 C. Dritsaki. An empirical evaluation in GARCH volatility modeling: Evidence from the Stockholm stock exchange”. J. Math. Fin. 7.2 (2017), pp. 366–390. doi: 10.4236/jmf.2017.72020 R. F. Engle and T. Bollerslev. Modelling the persistence of conditional variances”. Econ. Rev. 5.1 (1986), pp. 1–50. doi: 10.1080/07474938608800095 A. S. Hadi and R. F. Ling. Some cautionary notes on the use of principal components regression”. Am. Statistician 52.4 (1998), pp. 15–19. doi: 10.2307/2685559 J. Hunt, T. Kojovic, and R. Berry. Estimating comminution indices from ore mineralogy, chemistry and drill core logging”. The Second AusIMM International Geometallurgy Conference (GeoMet) 2013. 2013, pp. 173–176. http://ecite.utas.edu.au/89773>url: http://ecite.utas.edu.au/89773 on p. C210). R. Hyndman, Y. Kang, P. Montero-Manso, T. Talagala, E. Wang, Y. Yang, M. O’Hara-Wild, S. Ben Taieb, H. Cao, D. K. Lake, N. Laptev, and J. R. Moorman. tsfeatures: Time series feature extraction. R package version 1.0.2. 2020. https://CRAN.R-project.org/package=tsfeatures>url: https://CRAN.R-project.org/package=tsfeatures on p. C222). C. L. Johnson, D. A. Browning, and N. E. Pendock. Hyperspectral imaging applications to geometallurgy: Utilizing blast hole mineralogy to predict Au-Cu recovery and throughput at the Phoenix mine, Nevada”. Econ. Geol. 114.8 (2019), pp. 1481–1494. doi: 10.5382/econgeo.4684 E. B. Martin and A. J. Morris. An overview of multivariate statistical process control in continuous and batch process performance monitoring”. Trans. Inst. Meas. Control 18.1 (1996), pp. 51–60. doi: 10.1177/014233129601800107 E. Sepulveda, P. A. Dowd, C. Xu, and E. Addo. Multivariate modelling of geometallurgical variables by projection pursuit”. Math. Geosci. 49.1 (2017), pp. 121–143. doi: 10.1007/s11004-016-9660-z S. J. Webb, G. R. J. Cooper, and L. D. Ashwal. Wavelet and statistical investigation of density and susceptibility data from the Bellevue drill core and Moordkopje borehole, Bushveld Complex, South Africa”. SEG Technical Program Expanded Abstracts 2008. Society of Exploration Geophysicists, 2008, pp. 1167–1171. doi: 10.1190/1.3059129 R. Zuo. Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China)”. J. Geochem. Explor. 111.1-2 (2011), pp. 13–22. doi: 10.1016/J.GEXPLO.2011.06.012

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Chanturia, Elena. "PROBLEMS OF COMPLEX AND ENVIRONMENTALLY SAFE PROCESSING OF NATURAL AND MAN-MADE MINERAL RAW MATERIALS (PLAKSIN READINGS – 2021): Review." Sustainable Development of Mountain Territories 13, no. 4 (December 20, 2021): 644–54. http://dx.doi.org/10.21177/1998-4502-2021-13-4-644-654.

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In Vladikavkaz (Republic of North Ossetia-Alania) in the period from October 04 – 08, 2021, the Scientific Council of the Russian Academy of Sciences on the Problems of Mineral Enrichment, the Federal State Budgetary Institution of Science “Institute of Problems of Integrated Development of Mineral Resources named after Academician N.V. Melnikova of the Russian Academy of Sciences” (IPCON RAS), the Federal State Budgetary Institution of Higher Education “North Caucasus Mining and Metallurgical Institute (State Technological University (SKGMI (GTU)” held an international conference “Problems of complex and environmentally safe processing of natural and man-made mineral raw materials” (Plaksin Readings – 2021). Plaksin Readings-2021 were attended by 150 representatives from 51 organizations, including 15 academic and 11 industry institutes, 9 large mining and metallurgical companies. The reports were made by scientists from Russia, Kazakhstan, Ukraine, Uzbekistan, Vietnam, Mongolia. Plenary reports were presented by leading scientists in the field of mineral processing: Chanturia V.A., Shadrunova I.V. (Institute of Problems of Integrated Development of Mineral Resources named after Academician N.V. Melnikova RAS, Moscow, Russian Federation); Innovative processes of deep and environmentally safe processing of technogenic raw materials in the context of new economic challenges); DmitrakYu.V. (North Caucasian Institute of Mining and Metallurgy (State Technological University), Vladikavkaz, Republic of North Ossetia-Alania, Russian Federation) “The main directions of scientific activity of SCGMI (GTU) in the study and solution of the problem of complex and environmentally safe processing of natural and technogenic mineral raw materials”; Kurkov A.V., Anufrieva S.I., Temnov A.V. (N.M. Fedorovsky All-Russian Research Institute of Mineral Raw Materials of the Ministry of Natural Resources and Ecology of the Russian Federation, Moscow, Russian Federation) “Prospects for the development and implementation of integrated technologies for processing Subsurface Use waste”; Semyachkov A.I., Pochechun V.A. (Ural State Mining University, Institute of Economics of the Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russian Federation) “Methodological foundations for assessing the impact of mining complexes on the environment”; Alborov I.D., Tedeeva F.G. (North Caucasian Institute of Mining and Metallurgy (State Technological University), Vladikavkaz, Republic of North Ossetia-Alania, Russian Federation) “Environmental aspects of the conservation of technogenic deposits of non-ferrous metals in the North Caucasus”; Masloboev V.A., Makarov D.V., Klyuchnikova E.M. (Institute of Industrial Ecology Problems of the North, FITC KNC RAS, Apatity, Russian Federation) “Sustainable development of the mining complex of the Murmansk region: minimizing technogenic impacts on the environment”; Ustinov I.D. (NPK “Mechanobr-Technika”, St. Petersburg, Russian Federation) “Geometallurgy as the basis of complex processing of mineral raw materials”; Ozhogina E.G., Kotova O.B. (FSBI “All-Russian Research Institute of Mineral Raw Materials named after N.M. Fedorovsky”, Moscow, Russian Federation, FSBI Institute of Geology named after Academician N.P. Yushkin FIT Komi NCUrO RAS, Syktyvkar, Russian Federation) “Technological mineralogy in solving the problem of complex processing of mineral raw materials”. According to the results of the Conference, the importance of the presented results of fundamental and applied research, the high scientific level of reports was noted. The Conference participants agreed that experimental results, scientific developments, and proposed technologies are of undoubted interest and will be in demand in the real sector of the economy. The Conference decided the expediency of supporting the promotion of the initiative to form a state program for the ecological rehabilitation of the land fund and water resources of North Ossetia from the damage caused earlier by the activities of mining enterprises. To provide high-tech industries of Russia with strategic metals (resources) based on complex and environmentally safe processing of natural and manmade mineral raw materials, it is recognized necessary to concentrate the research of leading organizations in the field of mineral processing on the justification and development of innovative processes for extracting valuable components from natural and man-made raw materials.

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Parian, Mehdi, Pertti Lamberg, and Jan Rosenkranz. "Process simulations in mineralogy-based geometallurgy of iron ores." Mineral Processing and Extractive Metallurgy, August 9, 2018, 1–6. http://dx.doi.org/10.1080/25726641.2018.1507072.

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Costa, Fabrizzio R., Guilherme P. Nery, Cleyton de Carvalho Carneiro, Henrique Kahn, and Carina Ulsen. "Mineral characterization of low-grade gold ore to support geometallurgy." Journal of Materials Research and Technology, October 2022. http://dx.doi.org/10.1016/j.jmrt.2022.10.085.

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Lechuti-Tlhalerwa, R., S. Coward, and M. Field. "Embracing step-changes in geoscientific information for effective implementation of geometallurgy." Journal of the Southern African Institute of Mining and Metallurgy 119, no. 4 (2019). http://dx.doi.org/10.17159/2411-9717/588/2019.

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Voigt, M. J., J. Miller, L. Bbosa, R. A. Govender, D. Bradshaw, A. Mainza, and M. Becker. "Developing a 3D mineral texture quantification method of drill core for geometallurgy." Journal of the Southern African Institute of Mining and Metallurgy 119, no. 4 (2019). http://dx.doi.org/10.17159/2411-9717/590/2019.

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Rangel, Leonardo Vasconcellos, Douglas Batista Mazzinghy, Gilberto Rodrigues da Silva, Felipe Seguin, Michelle Fernanda De Lira Teixeira, Sebastião Ubaldino Ferreira Junior, Caio Henrique Ribeiro Vieira, Wanderson Ferreira Borges Junior, Vinicius Campos Silva, and Gabriel Rocha Dimitrov. "Geometallurgy study of the Catalão I Nelsonite bodies aiming to increase the niobium production." Tecnologia em Metalurgia, Materiais e Mineração 17, no. 4 (2020). http://dx.doi.org/10.4322/2176-1523.20202214.

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Golroudbary, Saeed Rahimpour, Andrzej Kraslawski, Benjamin P. Wilson, and Mari Lundström. "Assessment of environmental sustainability of nickel required for mobility transition." Frontiers in Chemical Engineering 4 (January 5, 2023). http://dx.doi.org/10.3389/fceng.2022.978842.

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Nickel (Ni) in batteries (e.g., nickel-metal hydride battery (NiMH), lithium nickel cobalt aluminum oxide (NCA) and lithium nickel manganese cobalt oxide (NMC)) aim to ensure higher energy density and greater storage capacity. Two typical layered nickel-rich ternary cathode materials, NCA and NMC, are commercialized as advanced lithium-ion batteries (LiBs) for electric vehicles (EVs). The technology of those batteries has been improving by steadily increasing the nickel content in each cathode generation. In this study, we consider two types of batteries having a composite cathode made of Li [Ni0.80Co0.1Al0.1]O2, and Li [Ni0.33Mn0.33Co0.33]O2, which are the most common cathode materials for LiBs in EVs since 2010 and their functional recycling is performed. The increasing use of nickel in battery technologies has resulted in the continuous growth of demand for nickel over recent years. Nickel was added to the list of critical materials by the United States Geological Survey (USGS) already in 2021. Unfortunately now, the sustainable supply of nickel is even at higher risk due to the sanctions-related disruption of supplies from Russia. Therefore, enhancing the circularity of nickel starts to be vital for many economies. Demand for recycled nickel is growing, however, a systematic analysis of the sustainability of its recycling is still missing. Therefore, we provide a comprehensive assessment of the sustainability of the global primary and secondary production of nickel. Using system dynamics modelling integrated with geometallurgy principles and by analyzing the processing routes (pyrometallurgical and hydrometallurgical processes), we quantify the key environmental concerns across the life cycle of primary and secondary nickel required for sustainable mobility transition. Energy consumption, water use, and related emissions are assessed for all stages of the nickel supply chain, from mining to recycling. Our analysis shows the possibility of reducing the emissions by around 4.7 mt for GHG, 6.9 kt for PM2.5, 34.3 t for BC, 2.8 kt for CH4, 7.5 kt for CO, 3.3 mt for CO2, 169.9 t for N2O, 3.8 kt for NOx, 11.8 kt for PM10, 104.8 t for POC, 1.6 mt for SOx, and 232.5 t for VOC by engaging in the secondary production of nickel through the recycling of batteries. However, identical growth rate of energy consumption and water use compared to nickel mass flows means no technical progress has been achieved in different stages of the nickel supply chain towards sustainability over the period 2010–2030. Therefore, an improvement in technology is needed to save energy and water in nickel production processes. The results and findings of this study contribute to a better understanding of the necessity for improving closed-loop supply chain policies for nickel.

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