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The mining industry is confronted with substantial challenges in achieving environmental sustainability, particularly regarding water usage, waste management, and dam safety. The increasing global demand for minerals has led to increased mining activities, resulting in significant environmental consequences. By 2025, an estimated 19 billion tons of solid tailings are projected to accumulate worldwide, exacerbating concerns over their management. Tailings storage facilities represent the largest water sinks within mining operations. The mismanagement of water content in tailings can compromise their stability, leading to potential dam failures and environmental catastrophes. In response to these pressing challenges, the mining industry is increasingly turning to innovative solutions such as tailings dewatering and water reuse/recycling strategies to promote sustainable development. This review paper aims to (I) redefine the role of mine tailings and explore their physical, chemical, and mineralogical characteristics; (II) discuss environmental concerns associated with conventional disposal methods; (III) explore recent advancements in dewatering techniques, assessing their potential for water recovery, technical and economic constraints, and sustainability considerations; (IV) and present challenges encountered in water treatment and recycling within the mining industry, highlighting areas for future research and potential obstacles in maximizing the value of mine tailings while minimizing their environmental impact. [ABSTRACT FROM AUTHOR]

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance. [ABSTRACT FROM AUTHOR]

Energy consumption represents a significant operating expense in the mining and minerals industry. Grinding accounts for more than half of the mining sector's total energy usage, where the semi-autogenous grinding (SAG) circuits are one of the main components. The implementation of control and automation strategies that can achieve production objectives along with energy efficiency is a common goal in concentrator plants. However, designing such controls requires a proper understanding of process dynamics, which are highly complex, coupled, and have non-deterministic components. This complex and non-deterministic nature makes it difficult maintain a set-point for control purposes, and hence operations focus on an optimal control region, which is defined in terms of desirable behavior. This paper investigates the feasibility of employing machine learning models to delineate distinct operational regions within in an SAG mill that can be used in advanced process control implementations to enhance productivity or energy efficiency. For this purpose, two approaches, namely k-means and self-organizing maps, were evaluated. Our results show that it is possible to identify operational regions delimited as clusters with consistent results. [ABSTRACT FROM AUTHOR]

This paper attempts to define a name for an area of science and technology that encompasses the acquisition, processing and application of spatial data in the mining industry. A comparative study of the evolution of spatial data exchange methods between Geographic Information Systems (GISs) and General Mining Planning (GMP) software is carried out to define the problem and name it. Subsequent modifications of the acronym GIS towards the specialisation of its application in mining are then reviewed. This is followed by the identification of three terminological postulates designed to formulate constraints and rules for the creation of a new definition. The subsequent analysis identifies the nomenclatural basis of the research area of geomatics and determines its applicability in the context of mining. The results of the research made it possible to formulate a new definition of "mining geomatics". The final section of the article presents an initial proposal for an inventory of the basic concepts of mining geomatics in the form of a Body of Knowledge for mining geomatics. [ABSTRACT FROM AUTHOR]

The concepts currently applied to the sampling of broken minerals and particulate materials were recognised over 100 years ago. The first integrated theory of sampling for the mineral industries was proposed by Gy, who brought together means of estimation of the variance of sampling due to both the particulate nature of a broken ore and the time variation of composition of process streams. However, his theory deals only with sampling variance and cannot determine the full sampling distribution. The full distribution of the composition of potential samples arising from their particulate nature is developed in this paper based solely on the assumption that particle numbers in correct samples follow independent Poisson distributions. The enabling mathematical method is the use of the characteristic function of the sampling distribution. The practical means of calculating the full distribution completes the theory of sampling of particulate materials. The method is applied to a sample of gold ore to calculate the full sampling distribution and effectively clarifies several difficulties when sampling this material. The method has general applicability to all particulate materials, including foodstuffs. [ABSTRACT FROM AUTHOR]

Standard hydraulic breaking hammers are widely used for crushing oversized blasted materials and concrete structures demolition in industry. These hammers, installed in on-surface working excavators or stationary manipulators at the dumping points of underground conveyors, provide the required limited sizes of bulk materials and enable the safe operation of other equipment (screens, crushers). In parallel, hydraulic hammers have an alternative—fully electric hammers. This paper aims to review existing linear electric motor (LEM) hammers as an energy-saving and eco-friendly solution in industry. Global market analysis is presented with potential branches of LEM hammers. Several aspects for implementation—design optimization, dynamics simulation, machine control, and performance estimation—are considered. Different case studies for LEM-hammer application are given. The preliminary measurements are demonstrated on the electric hammer of Lekatech Company, which is intended for the mining industry and construction demolition. Experiments showed that depending on the impact frequency, type of rock, and shape of the crushing tool, the time to fracture varies significantly. Optimal parameters exist for every case, for which adjusting requires online hammer control. [ABSTRACT FROM AUTHOR]

The rapid wear of conical picks used in rock cutting heads in the mining industry has a significant economic impact in cost effectiveness for a given mineral extraction business. Any mining facility could benefit from decreasing the cost along with a substantial durability increase of a conical pick; thus, the hardfacing method of production and regeneration should be taken into account. In order to automatize the regeneration, the wear rate assessment is necessary. This paper presents a methodology used to create a 3D photogrammetric model of most of the commercially available tangential-rotary cutters in their before and after abrasive exploitation state. An experiment of three factors on two levels is carried out to indicate the proper setup of the scanning rig to obtain plausible results. Those factors are: light level, presence of polarizing filter and the distance from the scanned object. The 3D scan of the worn out specimen is compared to the master model via algorithm developed by the authors. This approach provides more detailed information about the wear mechanism and can help either in roadheader cutting head diagnostics or to develop a strategy and optimize the toolpath for the numerically controlled hardfacing machine. [ABSTRACT FROM AUTHOR]

Development and operations (DevOps), artificial intelligence (AI), big data and edge–fog–cloud are disruptive technologies that may produce a radical transformation of the industry. Nevertheless, there are still major challenges to efficiently applying them in order to optimise productivity. Some of them are addressed in this article, concretely, with respect to the adequate management of information technology (IT) infrastructures for automated analysis processes in critical fields such as the mining industry. In this area, this paper presents a tool called Pangea aimed at automatically generating suitable execution environments for deploying analytic pipelines. These pipelines are decomposed into various steps to execute each one in the most suitable environment (edge, fog, cloud or on-premise) minimising latency and optimising the use of both hardware and software resources. Pangea is focused in three distinct objectives: (1) generating the required infrastructure if it does not previously exist; (2) provisioning it with the necessary requirements to run the pipelines (i.e., configuring each host operative system and software, install dependencies and download the code to execute); and (3) deploying the pipelines. In order to facilitate the use of the architecture, a representational state transfer application programming interface (REST API) is defined to interact with it. Therefore, in turn, a web client is proposed. Finally, it is worth noting that in addition to the production mode, a local development environment can be generated for testing and benchmarking purposes. [ABSTRACT FROM AUTHOR]

The lithium-ion battery (LIB) has the advantages of high energy density, low self-discharge rate, long cycle life, fast charging rate and low maintenance costs. It is one of the most widely used chemical energy storage devices at present. However, the safety of LIB is the main factor that restricts its commercial scalable application, specifically in hazardous environments such as underground coal mines. When a LIB is operating under mechanical and electrical abuse such as extrusion, impact, overcharge and overheating, it will trigger thermal runaway and subsequently cause fire or even an explosion. According to the relevant requirements in IEC60079, the explosion-proof protection of LIB can be adapted to the working environment of high dust and explosive gas environments such as in the mining face of coal production. This paper presents an overview of the LIB-relevant technology, thermal runaway, safety and applications in the general mining industry with implications to establish a theoretical and technical basis for the application of high-capacity LIBs in the industry. These then promote intelligent, safe and efficient production not only for the coal mine industry but also for non-coal applications. [ABSTRACT FROM AUTHOR]

Selected and tested poplar clones are very suitable biomass resources for various applications such as biofuels, the pulp and paper industry as well as chemicals production. In this study, we determined the content of lignin, cellulose, holocellulose, and extractives, syringyl to guaiacyl (S/G) ratio in lignin, and also calculated higher heating values (HHV) among eight examined clones of Populus grown on three different experimental sites. The highest lignin content for all the examined sites was determined in 'I-214' and 'Baka 5' clones, whereas the highest content of extractives was found in 'Villafranca' and 'Baka 5' clones. The highest S/G ratio for all the examined sites was determined in 'Villafranca' and 'Agathe F' clones. The chemical profiles of main wood components, extractives, and the S/G ratio in lignin were also influenced by both the experimental site and the clone × site interaction. Higher heating values, derived from calculations based on the contents of lignin and extractives (or lignin only), were in close agreement with the previously published data. The highest heating values were found for 'Baka 5' and 'I-214' clones. The optimal method of poplar biomass utilization can be chosen on basis of the lignocellulosics chemical composition and the S/G ratio in lignin. [ABSTRACT FROM AUTHOR]

The concept of mining or extracting valuable metals and minerals from technospheric stocks is referred to as technospheric mining. As potential secondary sources of valuable materials, mining these technospheric stocks can offer solutions to minimise the waste for final disposal and augment metals' or minerals' supply, and to abate environmental legacies brought by minerals' extraction. Indeed, waste streams produced by the mining and mineral processing industry can cause long-term negative environmental legacies if not managed properly. There are thus strong incentives/drivers for the mining industry to recover and repurpose mine and mineral wastes since they contain valuable metals and materials that can generate different applications and new products. In this paper, technospheric mining of mine wastes and its application are reviewed, and the challenges that technospheric mining is facing as a newly suggested concept are presented. Unification of standards and policies on mine wastes and tailings as part of governance, along with the importance of research and development, data management, and effective communication between the industry and academia, are identified as necessary to progress technospheric mining to the next level. This review attempts to link technospheric mining to the promotion of environmental sustainability practices in the mining industry by incorporating green technology, sustainable chemistry, and eco-efficiency. We argue that developing environmentally friendly processes and green technology can ensure positive legacies from the mining industry. By presenting specific examples of the mine wastes, we show how the valuable metals or minerals they contain can be recovered using various metallurgical and mineral processing techniques to close the loop on waste in favour of a circular economy. [ABSTRACT FROM AUTHOR]

The collapse of abandoned room-and-pillar mines is often violent and unpredictable. Safety concerns often resulted in mine closures with no post-mining stability evaluations. As a result, large amounts of land resources over room-and-pillar mines are wasted. This paper attempts to establish an understanding of the long-term stability issues of goafs (abandoned mines). Considering progressive pillar failures and the effect of single pillar failure on surrounding pillars, this paper proposes a pillar peeling model to evaluate the long-term stability of coal mines and the associated criteria for evaluating the long-term stability of room-and-pillar mines. The validity of the peeling model was verified by numerical simulation, and field data from 500 pillar cases from China, South Africa, and India. It is found that the damage level of pillar peeling is affected by the peel angle and pillar height and is controlled by the pillar width-height ratio. [ABSTRACT FROM AUTHOR]

This paper reviews current trends in wearable device technology, and provides an overview of its prevalent and potential deployments in the mining industry. This review includes the classification of wearable devices with some examples of their utilization in various industrial fields as well as the features of sensors used in wearable devices. Existing applications of wearable device technology to the mining industry are reviewed. In addition, a wearable safety management system for miners and other possible applications are proposed. The findings of this review show that by introducing wearable device technology to mining sites, the safety of mining operations can be enhanced. Therefore, wearable devices should be further used in the mining industry. [ABSTRACT FROM AUTHOR]

The commercial application of copper bioleaching, an environmentally-friendly approach for low-grade and secondary mineral resources recycling, has increased worldwide since the 2000s. As the world's second-largest economic entity and the largest developing country, China has the largest demand for metal resources, significantly advancing the theory and industrial technology of copper bioleaching. This paper reviews the exploration and application of copper bioleaching in China. Two typical bioleaching applications and technological processes, bioheap leaching at the Zijinshan Copper Mine and bioheap leaching at the Dexing Copper Mine, are introduced. The considerable research completed by researchers is summarized, especially focusing on the isolation and identification of leaching bacteria, the bioleaching mechanism and interface reactions, multistage percolation behavior, bioleaching system reconstruction, the multiphysics coupled model, and enhanced copper bioleaching from waste printed circuit boards (WPCBs). Based on this investigation in China, key trends and prospects in copper bioleaching--such as efficiency improvement, environmental protection, and improved technology applications--are proposed. [ABSTRACT FROM AUTHOR]

Material management in opencast mines is concerned with planning, organizing, and control of the flow of materials from their extraction points to destinations. It can be strongly affected by operational decisions that have to be made during the production process. To date, little research has focused on the application of simulation modeling as a powerful supportive tool for decision making in such systems. Practical experiences from implementing a simulation model of a mine for the operational support on an industrial scale are not known to the authors. This paper presents the extension of a developed stochastic simulation model by the authors from a conceptual stage (TRL4) to a new Technology Readiness Level (TRL 6) by implementing it in an industrially relevant environment. A framework for modeling, simulation, and validation of the simulation model applied to two large opencast lignite mines is presented in detail. Operational implementation issues, experiences, and challenges in practical applications are discussed. Furthermore, the strength of applying the simulation modeling as an operational decision support for material management in coal mining is demonstrated. Results of the case studies are used to describe the details of the framework, and to illustrate the strength and limitations of its application. [ABSTRACT FROM AUTHOR]

In the last few years, jargon, such as machine learning (ML) and artificial intelligence (AI), have been ubiquitous in both popular science media as well as the academic literature. Many industries have tried the current suite of ML and AI algorithms with various degrees of success. Mineral processing, as an industry, is looking at AI for two reasons. First of all, as with other industries, it is pertinent to know if AI algorithms can be used to enhance productivity. The second reason is specific to the mining industry. Of late, the grade of ores is reducing, and the demand for ethical mining (with as little effect on ecology as possible) is increasing. Thus, mineral processing industries also want to explore the possible use of AI in solving these challenges. In this review paper, first, the challenges in mineral processing that can potentially be solved by AI are presented. Then, some of the most pertinent developments in the domain of ML and AI (applied in the domain of mineral processing) are discussed. Lastly, a top-level modus operandi is presented for a mineral processing industry that might want to explore the possibilities of using AI in its processes. Following are some of the new paradigms added by this review. This review presents a holistic view of the domain of mineral processing with an AI lens. It is also one of the first reviews in this domain to thoroughly discuss the use of AI in ethical, green, and sustainable mineral processing. The AI process proposed in this paper is a comprehensive one. To ensure the relevance to industry, the flow was made agile with the spiral system engineering flow. This is expected to drive rapid and agile investigation of the potential of applying ML and AI in different mineral processing industries. [ABSTRACT FROM AUTHOR]

Deep sea mining tailings disposal is a new environmental challenge related to water pollution, mineral crust waste handling, and ocean biology. The objective of this paper is to propose a new tailings disposal procedure for the deep sea mining industry. Through comparisons of the tailings disposal methods which exist in on-land mining and the coastal mining fields, a new tailings disposal procedure, i.e., the submarine-backfill-dam-reuse (SBDR) tailings disposal procedure, is proposed. It combines deep sea submarine tailings disposal, backfill disposal, tailings dam disposal, and tailings reuse disposal for the deep sea mining industry. Then, the analytic network process (ANP) method is utilized to evaluate the performances of different tailings disposal methods. The evaluation results of the ANP show that the new proposed tailings disposal procedure is the most suitable for the deep sea mining industry. [ABSTRACT FROM AUTHOR]

Mining is currently experiencing a rapid growth in the development and uptake of automation and other new technologies (such as collision detection systems); however, they are often developed from a technology-centred perspective that does not explicitly consider the end-user. This paper first presents a review of the technologies currently available (or near-market) and the likely human factors issues associated with them. The second part of the paper presents a potential long term strategy for research and development that aims to maximise the safety and health benefits for operators of such new technologies. The strategy includes a four stage research and development process, this covers: better understanding the needs for technology, user requirements and risk/cost analysis; human element design, procurement and deployment processes; evaluation and verification of the strategy; and dissemination of it to relevant stakeholders (including equipment manufacturers, mine site purchasers and regulators). The paper concludes by stressing the importance of considering the human element with respect to new mining technologies and the likely benefits of adopting the type of strategy proposed here. The overall vision is for mining to become safer and healthier through effective user-centred design and deployment of new technologies that serve both operator needs and the demands of the workplace. [ABSTRACT FROM AUTHOR]

This paper describes selected results from over a dozen collaborative projects led by Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia spanning a 30-year history of developments in satellite, airborne, field and drill core sensing technologies and how these can assist explorers to measure and map valuable mineral information. The exploration case histories are largely from Australian test sites and describe how spectral sensing technologies have progressed from early "niche creation" systems, such as the field PIMA-II (Portable Field Mineral Analyzer) and airborne Geoscan, HyMap™ and OARS-TIPS (Operational Airborne Remote Sensing - Thermal Infrared Profiling Spectrometer) systems and drill-core HyLogger™ systems, to the current expanding array of pubic and commercial mineral mapping sensors, including the ASTER (Advanced Spaceborne Thermal Emission and Reflectance Radiometer) satellite system which has acquired imagery spanning the entire Earth's land surface (<83°latitude). These sensors are delivering voluminous spectral data from different parts of the visible to the thermal infrared (400 to 14,000 nm) spectrum at different spectral, radiometric and spatial resolutions. Two critical exploration challenges are central to the case histories, namely: (i) can surface cover, such as vegetation, regolith or transported materials, be characterized and accounted for so that the target geology is accurately revealed; and (ii) does this revealed geology show evidence of alteration footprints to potential economic mineralization. Spectrally measurable minerals important to solving these challenges include white micas, kaolinite and garnets, with measurement of their respective physicochemistries being key. For example, kaolin disorder is useful for mapping transported versus weathered in situ materials, while the chemical substitution in white micas and garnets provide vectors to potential economic mineralization. Importantly, appropriate selection of the optimum sensor/data type for a given geological application depends primarily on the level of detail/accuracy of the mineral information required by the user. A major opportunity is to now harness the many sensor/data types and deliver to users consistent, accurate mineral information products, that is, creation of a number of valuable global mineral product standards. As part of this vision, CSIRO has been developing improved sensor/data calibration processes and information extraction methods that for example, unmix the target mineralogy from green and dry vegetation cover in remote sensing data sets. Emphasis to date has been on generating public spectral-mineral product standards, especially at ASTER's limited but geologically-valuable spectral resolution. The results are showing that scalable, global, three-dimensional (3D) mineral maps are achievable which will only improve our ability to more accurately characterize regolith and geological architecture, increase our understanding of formative processes and assist the discovery of new economic mineral systems. [ABSTRACT FROM AUTHOR]

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. [ABSTRACT FROM AUTHOR]

A performance monitoring study of an electric rope shovel operating in an open pit coal mine was conducted. As the mining industry moves toward higher productivity, profitability and predictability, the need for more reliable, productive and efficient mining shovels increases. Consequently, it is critical to study the productivity of these machines and to understand the effect of different operational parameters on that. In this paper a clustering analysis is performed to classify shovel digging effort and behaviour based on digging energy, dig time and payload per pass. Then the influence of the operator on the digging efficiency and productivity of the machine is analyzed with a focus on operator technique during digging. A statistical analysis is conducted on different cycle time components (dig time, swing time, return time) for different operators. In addition to time components, swing and return angles as well as loading rate and mucking rate are observed and analyzed. The results of this study help to understand the effect of different operators on the digging productivity of the shovel and then to set the best operator practice. [ABSTRACT FROM AUTHOR]

This paper presents a review of measurement while drilling (MWD) technology as applied to the mining industry, describes its development path, provides a global review of literature on this topic, and outlines further trends of development for research on MWD application in drilling and blasting (D&B) operations at mining enterprises. The current review serves as a starting point for anyone interested in the research or application of MWD technology in Mining and Construction. In the paper, the authors examine major works of researchers in this area, describe current state of the art, and propose a way to improve MWD for drilling equipments. The paper contains examples of technology application in various processes, associated with drilling and mining operations, describes approaches and problems of MWD system utilization, revealed in the course of data collection and analysis of drilling processes. The study also presents a summary of existing approaches in the area of data validation and verification, applied up to the present day to cope with the problems of global MWD use in Mining and Construction. The authors outline future areas of study which are of interest and deserve the attention of the scientific community and researchers working on the development of MWD technology. [ABSTRACT FROM AUTHOR]

The mining industry plays an extremely important strategic role in China's economic and social development. In the new era of pursuing circular/green/efficient development, the evaluation of the total factor environmental efficiency (TFEE) of China's mining industry is essential for alleviating resource waste and environmental pollution. The Epsilon-Based Measure (EBM) model effectively solves the shortcomings of radial and non-radial DEA models. In addition, the Malmquist–Luenberger (ML) index can measure the dynamic change of efficiency value. Combining the EBM model and the ML productivity index, this paper evaluates the TFEE from the static and dynamic perspective in China's 31 provincial mining industries over the period 2007–2016. The Theil index is employed to reveal the root of the overall provincial TFEE gap (OGTFEE) in China's mining industry. The results show that the average total factor static environmental efficiency (TFSEE) of China's provincial mining industry exhibits a low score of 0.6589 and with significant spatio-temporal differences. The provincial TFEE gap within four major areas (WGTFEE), especially that in east and west areas, is the main cause of the OGTFEE in China's mining industry. Technical change contributes more to the TFEE decline in China's mining industry. There are differences in improving the TFEE among China's 31 provincial mining industries, and corresponding countermeasures can be formulated accordingly. This study provides theoretical and practical basis for the clean and green development of China's mining industry. [ABSTRACT FROM AUTHOR]

Mechanical systems (as belt conveyors) used in the mining industry, especially in deep underground mines, must be supervised on a regular basis. Unfortunately, they require high power and are spatially distributed over a large area. Till now, some elements of the conveyor (drive units) have been monitored 24 h/day using SCADA systems. The rest of the conveyor is inspected by maintenance staff. To minimize the presence of humans in harsh environments, we propose a mobile inspection platform based on autonomous UGV. It is equipped with various sensors, and in practice it is capable of collecting almost the same information as maintenance inspectors (RGB image, sound, gas sensor, etc.). Till now such experiments have been performed in the lab or in the mine, but the robot was controlled by the operator. In such a scenario the robot is able to record data, process them and detect, for example, an overheated idler. In this paper we will introduce the general concept of an automatic robot-based inspection for underground mining applications. A framework of how to deploy the inspection robot for automatic inspection (3D model of the tunnel, path planing, etc.) are defined and some first results from automatic inspection tested in lab conditions are presented. Differences between the planned and actual path are evaluated. We also point out some challenges for further research. [ABSTRACT FROM AUTHOR]

In the context of planning the exploitation of an open-pit mine, the final pit limit problem consists of finding the volume to be extracted so that it maximizes the total profit of exploitation subject to overall slope angles to keep pit walls stable. To address this problem, the ore deposit is discretized as a block model, and efficient algorithms are used to find the optimal final pit. However, this methodology assumes a deterministic scenario, i.e., it does not consider that information, such as ore grades, is subject to several sources of uncertainty. This paper presents a model based on stochastic programming, seeking a balance between conflicting objectives: on the one hand, it maximizes the expected value of the open-pit mining business and simultaneously minimizes the risk of losses, measured as conditional value at risk, associated with the uncertainty in the estimation of the mineral content found in the deposit, which is characterized by a set of conditional simulations. This allows generating a set of optimal solutions in the expected return vs. risk space, forming the Pareto front or efficient frontier of final pit alternatives under geological uncertainty. In addition, some criteria are proposed that can be used by the decision maker of the mining company to choose which final pit best fits the return/risk trade off according to its objectives. This methodology was applied on a real case study, making a comparison with other proposals in the literature. The results show that our proposal better manages the relationship in controlling the risk of suffering economic losses without renouncing high expected profit. [ABSTRACT FROM AUTHOR]

Mobile robots are increasingly being used in challenging outdoor environments for applications that include construction, mining, agriculture, military and planetary exploration. In order to accomplish the planned task, it is critical that the motion control system ensure accuracy and robustness. The achievement of high performance on rough terrain is tightly connected with the minimization of vehicle-terrain dynamics effects such as slipping and skidding. This paper presents a cross-coupled controller for a 4-wheel-drive/4-wheel-steer robot, which optimizes the wheel motors' control algorithm to reduce synchronization errors that would otherwise result in wheel slip with conventional controllers. Experimental results, obtained with an all-terrain rover operating on agricultural terrain, are presented to validate the system. It is shown that the proposed approach is effective in reducing slippage and vehicle posture errors. [ABSTRACT FROM AUTHOR]

Multiple criteria decision making (MCDM) is a supporting tool which is widely spread in different areas of science and industry. Many researchers have confirmed that MCDM methods can be useful for selecting the best solution in many different problems. In this paper, two novel methods are presented and applied on existing decision-making processes in the mining industry. The first method is multiple criteria ranking by alternative trace (MCRAT) and the second is ranking alternatives by perimeter similarity (RAPS). These two novel methods are demonstrated in decision-making problems and compared with the ranking of the same alternatives by other MCDM methods. The mining process often includes drilling and blasting operations as the most common activities for exploitation of raw materials. For optimal blasting design it is important to select the most suitable parameters for the blasting pattern and respect characteristics of the working environment and production conditions. By applying novel methods, how to successfully select the most proper blasting pattern respecting all conditions that must be satisfied for economic aspects and the safety of employees and the environment is presented. [ABSTRACT FROM AUTHOR]

Poland still relies largely on coal for energy generation, which creates environmental problems connected to the mining process, particularly the issue of saline waste water discharge. Membrane-based zero liquid discharge systems can be employed to recover important resources from coal mine waste waters, such as demineralized water, salt, magnesium hydroxide, and gypsum. In this paper, we present a historical overview of proposed membrane-based solutions for the Polish coal mining industry and discuss possible further areas of research. [ABSTRACT FROM AUTHOR]

Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the 'dimensionality curse', which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system. [ABSTRACT FROM AUTHOR]

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area. [ABSTRACT FROM AUTHOR]

Centrifugal air classifiers are often used for classification of particle gas flows in the mineral industry and various other sectors. In this paper, a new solver based on the multiphase particle-in-cell (MP-PIC) method, which takes into account an interaction between particles, is presented. This makes it possible to investigate the flow process in the classifier in more detail, especially the influence of solid load on the flow profile and the fish-hook effect that sometimes occurs. Depending on the operating conditions, the fish-hook sometimes occurs in such apparatus and lead to a reduction in classification efficiency. Therefore, a better understanding and a representation of the fish-hook in numerical simulations is of great interest. The results of the simulation method are compared with results of previous simulation method, where particle–particle interactions are neglected. Moreover, a validation of the numerical simulations is carried out by comparing experimental data from a laboratory plant based on characteristic values such as pressure loss and classification efficiency. The comparison with experimental data shows that both methods provide similar good values for the classification efficiency d50; however, the fish-hook effect is only reproduced when particle-particle interaction is taken into account. The particle movement prove that the fish-hook effect is due to a strong concentration accumulation in the outer area of the classifier. These particle accumulations block the radial transport of fine particles into the classifier, which are then entrained by coarser particles into the coarse material. [ABSTRACT FROM AUTHOR]

Understanding how mining-induced strata movement, fractures, bed separation, and ground subsidence evolve is an area of great importance for the underground coal mining industry, particularly for disaster control and sustainable mining. Based on the rules of mining-induced strata movement and stress evolution, accumulative dilatation of mining-induced unloading rock mass is first proposed in this paper. Triaxial unloading tests and theoretical calculation were used to investigate the influence of elastic dilatation of mining-induced unloading rock mass on the development of bed separation in the context of district No. 102 where a layer of super-thick igneous sill exists in the Haizi colliery. It is shown that the elastic dilatation coefficient of mining-induced unloading hard rocks and coal were 0.9~1.0‰ and 2.63‰ respectively under the axial load of 16 MPa, which increased to 1.30~1.59‰ and 4.88‰ when the axial load was 32 MPa. After successively excavating working faces No. 1022 and No. 1024, the elastic dilatation of unloading rock mass was 157.9 mm, which represented approximately 6.3% of the mining height, indicating the elastic dilatation of mining-induced unloading rock mass has a moderate influence on the development of bed separation. Drill hole detection results after grouting, showed that only 0.33 m of the total grouting filling thickness (1.67 m) was located in the fracture zone and bending zone, which verified the result from previous drill hole detection that only small bed separation developed beneath the igneous sill. Therefore, it was concluded that the influences of elastic dilatation of mining-induced unloading rock mass and bulking of caved rock mass jointly contributed to the small bed separation space beneath the igneous sill. Since the accurate calculation of the unloading dilatation of rock mass is the fundamental basis for quantitative calculation of bed separation and surface subsidence, this paper is expected to be a meaningful beginning point and could provide a useful reference for future, related research. [ABSTRACT FROM AUTHOR]

The comminution of ores consumes a high portion of energy. Therefore, different pretreatment methods of ores prior to their comminution are considered to reduce this energy. However, the results of pretreatment methods and their technological development are scattered in literature. Hence, this paper aims at collating the different ore pretreatment methods with their applications and results from published articles, conference proceedings, and verified reports. It was found that pretreatment methods include thermal (via oven, microwave, or radiofrequency), chemical additive, electric, magnetic, ultrasonic, and bio-milling. Results showed that the chemical pretreatment method has been used at an industrial scale since 1930, mainly in cement production. The microwave pretreatment results showed positive improvements at pilot scale mining applications in 2017. The results of ore pretreatment using electric and ultrasonic methods showed up to 24% and 66% improvement in energy consumption, respectively. The former and the latter have been piloted for gold and carbonate ore, respectively. Findings also showed that magnetic, radiofrequency, and bio-milling methods have not led to significant reductions in comminution energy. Based on energy reduction, safety, costs, stage of application, and downstream benefits, microwave and electrical pretreatment methods may be focused for applications in the mining industry. [ABSTRACT FROM AUTHOR]

Linear regression is often used as a diagnostic tool to understand the relative contributions of operational variables to some key performance indicator or response variable. However, owing to the nature of plant operations, predictor variables tend to be correlated, often highly so, and this can lead to significant complications in assessing the importance of these variables. Shapley regression is seen as the only axiomatic approach to deal with this problem but has almost exclusively been used with linear models to date. In this paper, the approach is extended to random forests, and the results are compared with some of the empirical variable importance measures widely used with these models, i.e., permutation and Gini variable importance measures. Four case studies are considered, of which two are based on simulated data and two on real world data from the mineral process industries. These case studies suggest that the random forest Shapley variable importance measure may be a more reliable indicator of the influence of predictor variables than the other measures that were considered. Moreover, the results obtained with the Gini variable importance measure was as reliable or better than that obtained with the permutation measure of the random forest. [ABSTRACT FROM AUTHOR]

This paper examines the possibility of using basalt-based glass ceramics for construction of structural parts of equipment in metallurgy and mining. An ultrasonic vibration method with a stationary sample pursuant to the ASTM G32 standard was used to evaluate the possibility of the glass ceramic samples application in such operating conditions. As the starting material for synthesis of samples, olivine–pyroxene basalt from the locality Vrelo–Kopaonik Mountain (Serbia) was used. In order to obtain pre-determined structure and properties of basalt-based glass ceramics, raw material preparation methods through the sample crushing, grinding, and mechanical activation processes have been examined together with sample synthesis by means of melting, casting, and thermal treatment applied for the samples concerned. The mass loss of samples in function of the cavitation time was monitored. Sample surface degradation level was quantified using the image analysis. During the test, changes in sample morphology were monitored by means of the scanning electronic microscopy method. The results showed that basalt-based glass ceramics are highly resistant to cavitation wear and can be used in similar exploitation conditions as a substitute for other metal materials. [ABSTRACT FROM AUTHOR]

The rapid mapping and characterization of specific porphyry vein types in geological samples represent a challenge for the mineral exploration and mining industry. In this paper, a methodology to integrate mineralogical and structural data extracted from hyperspectral drill-core scans is proposed. The workflow allows for the identification of vein types based on minerals having significant absorption features in the short-wave infrared. The method not only targets alteration halos of known compositions but also allows for the identification of any vein-like structure. The results consist of vein distribution maps, quantified vein abundances, and their azimuths. Three drill-cores from the Bolcana porphyry system hosting veins of variable density, composition, orientation, and thickness are analysed for this purpose. The results are validated using high-resolution scanning electron microscopy-based mineral mapping techniques. We demonstrate that the use of hyperspectral scanning allows for faster, non-invasive and more efficient drill-core mapping, providing a useful tool for complementing core-logging performed by on-site geologists. [ABSTRACT FROM AUTHOR]

The gob-side entry layout is popular at two-entry longwall mine sites in China for the benefit of improving the coal recovery rate. Currently, two methods have been widely used to develop gob-side entries, including gob-side entry retaining and gob-side entry driving. Gob-side entry retaining maximizes the recovery rate by pillarless mining but increases the difficulty in gob-side entry support. Also, this method has limited applications in hard roof conditions. The gob-side entry driving mine site uses the rib pillar to separate the gob entry and the gob area of the previous panel, which leads to additional coal losses. The waste is more intolerable in large-cutting-height panels and longwall top coal caving panels as the Chinese government limits the minimum recovery rate of longwall panels using these mining methods. In this paper, a new gob-side entry layout method, termed gob-side pre-backfill driving, is established to overcome the shortcomings of the existing methods. The new method eliminates rib pillar losses and enhances gob-side entry stability. The feasibility of gob-side pre-backfill driving is studied by numerical modelling and a field trial at Changcun Mine in China. The results indicate that gob-side pre-backfill driving is an alternative for gob-side entry development. This method is practical and also has the potential to bring significant economic benefits to the mining industry. [ABSTRACT FROM AUTHOR]