Both the mineralogy and geochemistry of coal mine waste presents environmental and social challenges while simultaneously offering the potential source for recovery of metals, including critical raw materials (CRMs). Assessing these challenges and opportunities requires effective waste management strategies and comprehensive material characterization. This study deals with the integration of analytical data obtained from various portable sensor technologies. Infrared reflection spectroscopy (covering a wide wavelength range of 0.4 to 15 µm), and geochemical x-ray fluorescence (XRF) were utilized to differentiate between samples belonging to various geological lithologies and quantify elements of interest. Therefore, we developed a methodological framework that encompasses data integration and machine learning techniques. The model developed using the infrared data predicts the Sr concentration with a model accuracy of R2 = 0.77 for the testing dataset; however, the model performances decreased for predicting other elements such as Pb, Zn, Y, and Th. Despite these limitations, the approach demonstrates better performance in discriminating materials based on both mineralogical and geochemical compositions. Overall, the developed methodology, enables rapid and in-situ determination of coal mine waste composition, providing insights into waste composition that are directly linked to potential environmental impact, and the possible recovery of economically valuable metals.@en

This chapter provides a comprehensive review of techniques, instruments, and methods suitable for mine residue sampling and characterization, using the potential recovery of critical raw materials (CRMs) from bauxite residues as an example. The sampling methods address diverse strategies for assessing the suitability of CRMs recovery, including screening, detailed characterization, determination of acid rock drainage generation and wastewater, and the implementation of a geometallurgical approach. The methods for characterizing mine residues are a selection of geochemical, mineralogical, and other techniques that can be used either in field environments (e.g., portable X-ray fluorescence, infrared spectroscopy) or in the laboratory (e.g., inductively coupled plasma-based techniques, scanning electron microscopy) to study the main properties of the waste material. Other techniques used for the remote characterization, such as earth observation are also addressed. Approaches for data analytics and the impact of digitalization in the characterization of mine residues are briefly discussed. Overall, this chapter aims to help practitioners and researchers to implement better practices in the sampling and characterization for the revalorization of mine residues.

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Multi-element (ME) datasets provide comprehensive geochemical signatures of an orebody and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and to identify target-pathfinders. However, little effort is made in using these data to explain comminution or recovery characteristics. This paper describes an agglomerative hierarchical clustering approach applied to ME data from the Tropicana Gold Mine, Australia, and investigates the relationship between the resultant classes and run-of-mine comminution and recovery parameters. First, it is demonstrated how an industry scale ME dataset is prepared for clustering. The preparation consists of verifying the absence of interlaboratory and intralaboratory bias between measurements, centred log-ratio transformation (clr), normalisation and principal component analysis (PCA). Afterwards, the first case study indicate that the clustering separation is primarily driven by geochemical differences caused by major rock-forming mineral signatures (felsic vs mafic, alteration vs no alteration, chert or quartz lithologies, unmineralised vs mineralised material). This case study separates the ME dataset into five unmineralised and two Au-mineralised material classes. The second case study continues with the two identified mineralised material classes and further separates these samples into five new classes. These classes are explored geochemically and by using the spatial context (within domains) better matched with metallurgical test results. It is found that domain-related material class proportions assist in interpreting different processing proxies such as the Equotip hardness (Leeb), Bond Work index (BWi), Axb, and processing recovery and reagent consumption. Knowledge of the processing parameters per domain and class composition can be used to infer such characteristics in the absence of standard metallurgical tests. This new approach of gaining insights into comminution and recovery parameters through geochemical analysis demonstrates the benefit of the conceptualised material fingerprinting concept.

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Cobalt (Co) mine production primarily originates from the sediment‐hosted copper (Cu) deposits of the Democratic Republic of Congo (DRC). These deposits usually consist of three ore zones with a supergene oxide ore blanket overlying a transition zone which grades into a sulphide zone at depth. Each of these zones display a mineral assemblage with varying gangue mineralogy and, most importantly, a distinct state of oxidation of the mineralisation. This has direct implications for Cu and Co extraction during mineral processing as it dictates which processing method is to be used (i.e., leaching vs. flotation) and affects the performance of these. To optimise resource effi-ciency, reduce technical risks and environmental impacts, comprehensive understanding of varia-tion of ore mineralogy and texture in the deposit is essential. By defining geometallurgical ore types according to their inferred metallurgical behaviour, this information can serve to classify the re-sources and improve resource management. To obtain insight into the spatial distribution of mineral grades, it is necessary to develop techniques that have the potential to measure rapidly and, preferably, within the mine at relatively low‐cost. In this study, the application of portable Fourier transformed infrared (FTIR) spectroscopy is investigated to measure the mineralogy of drill core samples. A set of samples from a sediment‐hosted Cu‐Co deposit in DRC was selected to test this approach. Results were validated using automated mineralogy (QEMSCAN). Prediction of gangue and target mineral grades from the FTIR spectra was achieved through partial least squares regression (PLS‐R) combined with competitive adaptive reweighted sampling (CARS). It is shown that the modal mineralogy obtained from FTIR can be used to classify the ore according to type of mineralisation and gangue mineralogy into geometallurgical ore types. This classification supports selection of a suitable processing route and is likely to affect the overall process performance.

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Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and co-located pXRF data were clustered to discriminate material types (fingerprints). The hardness of each type was further explored using collocated BWi, Axb, Equotip rebound hardness and penetration rate datasets, but also by considering spatial contextual relationships and the within material type variability. The Tropicana orebody example gave a good illustration of how a phengitic-epidote K-feldspar rich domain (schistosity and softer, ∼15–18 kWh/t) separated from a harder (>20 kWh/t), shorter wavelength phengitic plagioclase-rich feldspar dominated domain. Exploring the within material type differences using the white mica composition (wAlOH) and a new w605 spectral feature demonstrated how the effects of shearing were captured within material types. Such findings will ultimately improve the understanding of the constitutive material hardness and have significance for process optimisation and blending strategy design.

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TRIM4Post-Mining is a H2020/RFCS-funded project that brings together a consortium of European experts from industry and academia to develop an integrated information modelling system. This is designed to support decision making and planning during the transition from coal exploitation to a revitalized post-mining landscape, enabling infrastructure development for agricultural and industrial utilization, and contributing to the recovery of energy and materials from coal mining dumps. The smart system will be founded upon a high-resolution spatiotemporal database, utilizing state-of-the-art multi-scale and multi-sensor monitoring technologies that characterize dynamic processes in coal waste dumps related to timely, dependent deformation and geochemical processes. It will integrate efficient methods for operational and post-mining monitoring, comprehensive spatiotemporal data analytics, feature extraction, and predictive modelling; this will allow for the identification of potential contamination areas and the forecasting of geotechnical risks and ground conditions. For the interactive exploration of alternative land-use planning scenarios—in terms of residual risks, technical feasibility, environmental and social impact, and affordability—up-to-date data and models will be embedded in an interactive planning system based on Virtual Reality and Augmented Reality technology, forming a TRIM—a Transition Information Modelling System. This contribution presents the conceptual approach and main constituents, and describes the state-of-the-art and detailed anticipated methodological approach for each of the constituents. This is supported by the presentation of the first results and a discussion of future work. An anticipated second contribution will focus on the main findings, technology readiness and a discussion of future work.@en

The recent Circular Economy Action Plan for Europe1 considers mine waste a secondary source of minerals. These deposits contain potentially economic concentrations of Critical Raw Materials (CRMs), such as Al, Li, Co and REE, which are strategic for the global economy and energy transition. However, there are significant knowledge and technological gaps that hinder their successful recovery. The INCO-Piles 2020 project2 is currently working on the recapitulation, establishment and development of innovative technologies for the sustainable extraction of CRMs from the residuals of mining activities, focusing on Regional Innovation Scheme (RIS) strategic areas. The project includes the definition of potential applications, best practices, and the promotion of technology transfer through round tables that count with international experts' participation.The first Round Table, a hybrid event held in December 2020 with 73 experts from 23 countries, addressed the challenges in recovering CRMs from tailings. The discussions were based on three topics: (1) challenges in sampling and characterisation from mining residue, (2) extraction and processing challenges, and (3) economic and environmental challenges. Regarding the first topic, one of the most significant issues is the inherent heterogeneity of mine waste deposits, which is a product of the mine processing and deposition methods, and the post-depositional weathering reactions. The lack of historical data, particularly for old deposits, hampers the understanding of such processes. A second challenge concerns the specific type of information required for assessing the CRMs potential. Representative geochemical and mineralogical data must be collected and interpreted at different scales (i.e., from individual minerals to tens of meters tall waste rock piles and tailings). The collection of representative samples faces issues related to the accessibility to the mine waste sites, the coverage and the sample contamination (i.e., material mixing) related to sample recovery methods. The scalability can be addressed by a combination of laboratory analyses, in-the-field surveys and remote sensing techniques. Current innovations in the combination of modern analytical instruments for geochemistry and mineralogy (e.g., pXRF, LIBS and portable infrared spectrometers) and the implementation of machine learning and artificial intelligence techniques will contribute to closing the knowledge and technology gaps.Lastly, the discussions included the potential hazards faced during the characterisation and re-intervention of old-sites. Well-known mine wastes issues related to human health, environment and license to operate that can hinder a characterisation campaign must be properly considered before the commencement of a CRMs recovery project. The participants also identified transversal challenges for the three discussion topics, such as the need for regulation and professionals with an appropriate background.All the insights discussed during this First Round Table will serve as a baseline for defining the best practices for characterisation and sampling of CRMs in mine wastes and contributing to increasing the sustainability in the supply of mineral resources and improving old mining sites' environmental quality. 1 EU Circular Economy Action Plan https://ec.europa.eu/environment/circular-economy/ 2 INCO-Piles is a two-year project funded by EIT RawMaterials. More information: https://site.unibo.it/inco-piles-2020/en@en

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define ore fingerprints. VNIR/SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and collocated XRF data were clustered to from material types (fingerprints). The material types were related to an Equotip-BWi correlation. These correlations can be used to extrapolate a hardness signature and generate a BWi proxy for different blocks. The combined fingerprints and BWi proxy can assist as a tool for enhancing the prediction of comminution behaviour. They can explain specific domain-related hardness variations. For example, one material type could be separated into a softer (~15-18 kWh/t), and harder (>20 kWh/t) material blend. This was accomplished using the commonly overlooked VNIR region at 605 nm. This outcome has significance for blending strategies.@en

Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines.

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Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO2, Al2O3, and Fe2O3 concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO2, Al2O3, and Fe2O3 mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications. @en

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations.@en

Material attributes (e.g., chemical composition, mineralogy, texture) are identified as the causative source of variations in the behaviour of mineral processing. That makes them suitable to act as key characteristics to characterise and classify material. Therefore, vast quantities of collected data describing material attributes could help to forecast the behaviour of mineral processing. This paper proposes a conceptual framework that creates a data-driven link between ore and the processing behaviour through the creation of material “fingerprints”. A fingerprint is a machine learning-based classification of measured material attributes compared to the range of attributes found within the mine’s mineral reserves. The outcome of the classification acts as a label for a machine learning model and contains relevant information, which may identify the root cause of measured differences in processing behaviour. Therefore, this class label can forecast the associated behaviour of mineral processing. Furthermore, insight is given into the confidence of available data originating from different analytical techniques. Taken together, this enhances the understanding of how differences in geology impact metallurgical plant performance. Targeted measurements at low-confidence unit processes and for specific attributes would upgrade the confidence in fingerprints and capabilities to predict plant performance.

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In response to the growing demand for sustainable products and services, the kaolin calcination industry is developing practices that optimise the use of resources. The main challenges include more efficient use of raw materials and a reduction in the energy consumed by the calcination furnace. An opportunity to achieve this lies in the optimisation of the calcination process. This can be done by giving real-time feedback on the quality parameters of the generated calcined kaolin. This study proposes the use of infrared spectroscopy as a monitoring technique to determine the chemical properties of the calcined kaolin product. The basis of the monitoring system is the measurement of the kaolin soluble alumina content as one of the most important quality parameters; this property is an indicator of the over- or under-use of raw materials and energy during the calcination process and can advise the operations regarding the optimisation of the working conditions of the furnace. The implementation of an infrared-based monitoring system would lead to increased efficiency in the production of calcined kaolin.

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Metallurgical attributes are often omitted from the mine to metal valuation models since they are either absent or unreliable. However, recent developments in sensor technology indicate the potential to collect information on metallurgical properties directly or by measurement of proxies. Integrating this information back into the resource model would provide the necessary means to move towards a more comprehensive and reliable evaluation model. To obtain truly optimized mining decisions it is necessary to consider the metallurgical attributes since they are indicated as root cause of changing plant performance. Therefore, a better metallurgical characterization of the plant feed over time is required, which allows for a more optimal selection of process control settings. Different material types have varying effects on machine performance in the comminution circuit. This makes it possible to refer a performance change as a response to different geological attributes. Hence, the corresponding geological machine behaviour can be controlled by defining effects of behavioural geology. This paper introduces a framework containing data fusion of sensor responses which resemble geological attributes and subsequent multivariate time series machine behaviour characterization for improved process control in the comminution circuit. The conceptual framework’s approach is that process control in future will be supervised by profound knowledge from sensor data indicating geological behaviour. The use of multivariate time series deep learning is proposed to create innovative process control. This innovative control is then a response to a combination of advanced sensor data (XRF, LIBS, FTIR, etc.) with more traditional sensor data (throughput, density, etc.). These advanced sensors provide more knowledge about material specific properties in the form of discoverable events. This new knowledge is important in the vision of behavioural geology, to better understand the influence of geological behaviour on machine performance.@en

Sustainability is often defined as: the ability to continue a defined behavior indefinitely. However, considering the nature of mining operations, this cannot be meant with the phrase “Sustainability in Mining”. Sustainability in the mining industry should be understood in the same way as sustainability in environmental science: meeting the resources and services needs of current and future generations without compromising the health of the ecosystems that provide them. A number of aspects of this are addressed in this chapter: use of energy, use of water, land disruption, reducing waste (involving solid waste, liquid waste, and gaseous waste), acid rock drainage when dealing with sulfide minerals, and restoring environmental functions at mine sites after mining has been completed. To do everything in an environmentally sound way is costly, but in the end necessary. Regarding this, it is concluded that governmental regulations concerning emission of waste, storage of waste and re-use of the land after mining are essential to provide a sustainable form of mining and mineral processing.@en

The increasing advances in sensor technology have resulted in greater availability of sensor data for a wide range of applications. One such application is raw material characterization in mining operations. Sensor technologies operate over certain range of the electromagnetic spectrum and provide information on several aspects of material properties. The sensitivity and the material properties the instrument detects and measures varies from sensor to sensor. The purpose of this study was to synthesize and evaluate the use of sensor technologies for characterization of a polymetallic sulphide deposit in “Reiche Zeche” underground mine. This paper discusses the material characterization methodology using sensor technologies, demonstrates how it fits within the Real-Time Mining (RTM) framework, identifies the interface for both software and hardware requirements and defines the gaps and limitations of application of sensors. It provides a brief overview of the use of sensor and data fusion for material characterization to convey a high-level context in raw material characterization. The sensor technologies considered in this study include RGB imaging, visible–near infrared (VNIR), short wave infrared (SWIR), mid-wave infrared (MWIR), long-wave infrared (LWIR) and Raman spectroscopy. The required information from sensor data in mining operations is not limited to grade control applications. Information on co-occurring minerals or elements are also important for definition of requirements in mineral processing, to identify indirect proxies of elements/minerals of interest, to understand the formation of minerals, to define requirements for blasting parameters, to improve safety and to define requirements for environmental monitoring of toxic material. In view of these points, there is a need for combinations of sensors to achieve a near complete description of material composition and properties. The methodological approaches developed for information extraction from each sensor data and fused data are presented. This includes both direct mineral fingerprinting and indirect proxies using spectral data. The efficient sensor data processing methods and the acquired results from the use of individual sensor and the fused data are summarized. Overall, the acquired results from the use of each sensor technology and the data fusion approach significantly contributed to an improvement of data quality and illustrate the efficiency of use of sensors in the mining industry. However, some of the observed limitations include lack of system robustness, a need for test case specific mineral libraries, the need for development of an integrated principled tool for efficient data collection, processing and knowledge generation. Going forward, automated material characterization is possible with robust system design (exemplified by portable and ruggedized system) and efficient software (test case specific mineral libraries) that can be developed using a combined sensor signal.@en

Near-infrared (NIR) and short-wavelength infrared (SWIR) hyperspectral imagery can be used to detect certain alteration minerals. At epithermal deposits, the formation of alteration minerals is, in theory, related to the mineralisation of gold and silver. In order to provide foundations for developing sensor-based sorting applications at a mine that exploits such a deposit, it was investigated if NIR-SWIR hyperspectral imagery can be used to distinguish between ore and waste particles by characterising the alteration mineralogy. Maps were produced from the NIR-SWIR hyperspectral images of 827 drill core samples that show mineral occurrences, mineral absorption feature intensities and characteristics of the iron oxide mineralogy. Partial least squares discriminant analysis (PLS-DA) was applied to the information contained in these maps to investigate if this information can be used to discriminate between ore and waste. The results showed that NIR-SWIR hyperspectral imagery could be used to segment a population of waste samples by detecting occurrences of pyrophyllite, dickite and/or illite. This result can be explained by the fact that these minerals are commonly deposited further away from the ore-bearing epithermal veins, while the absence of SWIR-active minerals or detected occurrences of alunite are more closely associated with these structures. The ability to identify waste with NIR-SWIR spectral sensors means there is potential that sensor-based sorting can be used to remove this waste from mineral processing operations. Additional research is still required to assess the economic feasibility of such a sensor-based sorting application.

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In the production of calcined kaolin, the on-line monitoring of the calcination reaction is becoming more relevant for the generation of optimal products. In this context, this study aimed to assess the suitability of using infrared (IR) spectroscopy as a potential technique for the on-line characterization of the
calcination of kaolin. The transformation of kaolin samples calcined at different temperatures were characterized in the short-wave (SWIR) spectra using the kaolinite crystallinity (Kx) index and the depth of the water spectral feature (1900D). A high correlation between the standard operational procedure
for the quality control of calcined kaolin and the Kx index was observed (r = -0.89), as well as with the 1900D parameter (r = -0.96). This study offers a new conceptual approach to the use of SWIR spectroscopy for the characterization the calcination of kaolin, withdrawing the need of using extensive laboratory techniques.@en