Emerging criticality: Unraveling shifting dynamics of the EU's critical raw materials and their implications on Canada and South Africa

Critical Raw Materials (or CRMs) are materials that are in high demand, difficult to replace and whose supply is prone to disruption. Various nations have defined CRM lists, although terminology, supporting data and assessment frameworks differ. The European Union (EU) has the longest published history of CRM lists with the first one published in 2011, followed by 3-year revisions. In this study, we analyze CRM designation trends over time by using the EU's five CRM lists to deduce the driving factors. Overall, the number of CRMs have increased by 1.67 new CRMs per year from 2011 to 2023, with the number of new CRMs yet to reach a plateau. Our analysis also reveals issues that could affect the value of the CRM lists including: (1) a hidden two-stage process with transparency issues; (2) static baselines with regards to criticality; (3) an overemphasis on ideology versus pragmatism; (4) a lack of differentiation between CRMs and strategic raw materials (SRMs); (5) a lack of foresight; and (6) a lack of consideration for extrinsic risks and system behaviour. Given these issues, we provide suggestions to improve the CRM assessment methodology and discuss the implications for the EU and the minerals industry. Subsequently, we extend our findings to Canada and South Africa, which are nations in the early stages of CRM framework creation. We find that Canada has more time to realize its CRM framework as compared to the EU, and that South Africa may be faced with a bifurcating reality of extra-national and national needs. Our findings also highlight serious geopolitical implications with the ensuing competition for resources likely resulting in the formation of economic blocs, clubs or cartels. Finally, improvements to the methodology resulting in more predictable outcomes would better incentivize the minerals industry to lower investment risk and ensure a smooth and pragmatic green energy transition

Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing storage facilities (TSFs), because they are not static due to sedimentation, compaction and changes associated with hydrospheric and lithospheric processes (e.g., erosion, saltation and mobility of chemical constituents). In this paper, we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin (South Africa). Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF, we trained a machine learning model to predict in-situ gold grades. Subsequently, we deployed the model to the Lindum TSF, which is 3 km away, over a period of a few years (2015-2019). We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF. Additionally, we were able to infer extraction sequencing (to the resolution of the data), acid mine drainage formation and seasonal migration. These findings suggest that dynamic mineral resource models and live geochemical monitoring (e.g., of elemental mobility and structural changes) are possible without additional physical sampling

Dry laboratories – Mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

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.Validerad;2023;Nivå 2;2023-01-09 (hanlid);Funder: Swedish government; Centre for Advanced Mining and Metallurgy (CAMM)</p

Assessing cobalt supply sustainability through production forecasting and implications for green energy policies

Transitioning to a decarbonized and circular economy is paramount for climate change mitigation and sustainable development. In this paper we assess the global production trends of cobalt, an energy-transition metal (ETM), and its supply sustainability. Accurate production forecasting of ETMs is essential to understand the dynamics of energy supply security and adequately plan for a change from fossil fuel energy to renewable energy production. Evaluations of market concentrations demonstrate that cobalt is a high-risk market characterized by production fluctuations and supply-chain complexities. We forecast the cobalt production using several methods. Results from both of the Auto Regressive Integrated Moving Average (ARIMA) and Holt's methods show a linear increase in world cobalt production for the short term, while a Hubbert model predicts a world production decline beginning in the late 2010s. These predictions, coupled with geopolitical, socio-environmental, and techno-economic influences on the market, reinforce the concern regarding cobalt supply sustainability. Although alternative avenues for sourcing cobalt, such as secondary urban mining and stockpiling exist, they are unlikely to become major suppliers in the short term, which highlights the need to accurately forecast primary production. Increasing interests in critical raw materials (CRMs) in policy spheres also heightens the necessity to anticipate the future of cobalt supply as governmental entities acknowledge the imbalance of CRMs in international trade. Well-researched and well-designed policies, that incorporate environmental sustainability and non-discriminatory economic growth, can facilitate an equitable shift to a greener and more circular economy. At the forefront of this shift should be ethical environmental and resource governance that recognizes the inequalities in socio-economic development and energy-transition, and mandates for a just transition towards a low carbon future.Validerad;2021;Nivå 2;2021-10-21 (beamah);Forskningsfinanziär: National Research Foundation (NRF) Thuthuka (121973)</p

Machine Learning-Based Delineation of Geodomain Boundaries: A Proof-of-Concept Study Using Data from the Witwatersrand Goldfields

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.Funder: Department of Science and Innovation (DSI)-National Research Foundation (NRF) Thuthuka Grant (UID: 121973); DSI-NRF CIMERA; Wits Mining Institute (WMI)</p

Deriving big geochemical data from high-resolution remote sensing data via machine learning: Application to a tailing storage facility in the Witwatersrand goldfields

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&amp;apos;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.Validerad;2023;Nivå 1;2023-04-12 (hanlid);Funder: Department of Scienceand Innovation (DSI) - National Research Foundation (NRF) ThuthukaGrant (121,973); SI-NRF CIMERA; Centre for Advanced Miningand Metallurgy (CAMM), Luleå University of Technology; Sibanye-Stillwater Ltd</p

Repurposing legacy metallurgical data Part I : A move toward dry laboratories and data bank

Advancements in modern mineral processing has been driven by technology and fuelled by market economics of supply and demand. Over the last three decades, the demand for various minerals has steadily increased, while the mineral processing industry has seen an unavoidable increase in the treatment of complex ores, continuous decline in plant feed grade and poor plant performance partly due to blending of ores with dissimilar properties. Despite these challenges, production plant data that are routinely generated are usually underutilised. In this contribution and aligned with the direction of the 4th industrial revolution, we highlight the value of legacy metallurgical plant data and the concept of a dry laboratory approach. This study is presented in two parts. In the current paper (Part I), a comprehensive review of the potential for the combination of modern analytical technology with data analytics to generate a new competence for process optimisation are provided. To demonstrate the value of data within the extractive metallurgy discipline, we employ data analytics and simulation to examine gold plant performance and the flotation process in two separate case studies in the second paper (Part II). This was done with the aim of showcasing relevant plant data insights, and extract parameters that should be targeted for plant design and performance optimisation. We identify several promising technologies that integrate well with existing mineral processing plants and testing laboratories to exploit the concept of a dry laboratory, in order to enhance pre-existing mineral processing chains. It also sets the passage in terms of the value of innovative analysis of existing and simulation data as part of the new world of data analytics. Using data- and technology-driven initiatives, we propose the establishment of dry laboratories and data banks to ultimately leverage integrated data, analytics and process simulation for effective plant design and improved performance.Validerad;2020;Nivå 2;2020-09-29 (alebob)</p

Moving towards deep underground mineral resources: Drivers, challenges and potential solutions

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.Validerad;2023;Nivå 2;2023-01-16 (sofila);Funder: Department of Science and Innovation-National Research Foundation (South Africa) Thuthuka Grant (grant no.121973) </p

Dry laboratories:mapping the required instrumentation and infrastructure for online monitoring, analysis, and characterization in the mineral industry

Abstract 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