Mining

In a dynamic global market, platinum-group metals (PGMs), particularly palladium, are in high demand across various industries due to their unique properties. Palladium plays a crucial role in environmentally friendly technologies, such as catalytic converters, which mitigate harmful automotive emissions. Additionally, it is essential for clean energy production, particularly in hydrogen generation, which makes palladium a critical resource for building a sustainable and secure supply chain. This study evaluates the prospects of the palladium market through strategic analysis, focusing on the Russian mining and metals company PJSC MMC Norilsk Nickel. The research employs strategic and industry analysis methods to examine palladium production, market dynamics, and technological advancements, as well as emerging applications in the context of a green economy. The article analyzes the economics of palladium production, including price volatility driven by stringent environmental regulations and the rising adoption of electric vehicles. The palladium market faces challenges such as a constrained resource base, supply disruptions due to sanctions, price instability, and growing demand from key sectors, particularly the automotive industry. Nevertheless, innovation-driven trends offer promising opportunities for market growth, aligning with sustainable development principles and the transition toward a green, low-carbon economy in both established and emerging markets. As a key scientific contribution, this study proposes a modified methodological approach to industry analysis, enabling the assessment of a mining and metals company’s competitive sustainability in the palladium market over the medium and long term. Furthermore, the research models the life cycle of palladium as a commodity, considering evolving market trends and the rapid development of new industries within the green economy. Full article

Real-time mining (RTM) has become increasingly significant in response to the growing need for sustainable mineral resource extraction, driven by global population growth and technological progress. This innovative approach addresses critical challenges, such as declining ore grades, deeper and less accessible deposits, and rising energy costs, by integrating advanced online grade monitoring, data analysis, and process optimization. By employing real-time grade control, dynamic mine planning, and production optimization, it enhances the efficiency of resource extraction while minimizing environmental and social impacts. Originally proposed about a decade ago, RTM has gained attention for its potential to revolutionize the industry. This review examines recent advancements in closed-loop concepts, emphasizing the integration of advanced sensors and data analytics to enable continuous monitoring and adaptive decision making across the mining value chain. It highlights the role of online sensor technologies in providing high-resolution data for process optimization and evaluates various mining optimization techniques. The paper also explores data assimilation methods, such as Kalman filters and artificial intelligence (AI), showcasing their ability to continuously update models and reduce operational uncertainties. Ultimately, it proposes a comprehensive framework for adaptive, data-driven mining operations that promote sustainable development, enhance profitability, and improve decision-making capabilities. Full article

This study was conducted on a copper porphyry deposit located in Espinar, Cusco (Peru), with the objective of developing and comparing predictive models for processing capacity in SAG grinding circuits. A total of 174 samples were used for the JK Drop Weight Test (JKDWT) and 1172 for the Bond Work Index (BWi), along with 36 months of operational plant data. Three modeling methodologies were evaluated: DWi-BWi, SGI-BWi, and SMC-BWi (Mia, Mib), all integrated into a geometallurgical block model. Validation was performed through reconciliation with actual plant data, considering operational constraints such as transfer size (T₈₀) and maximum throughput (TPH). The model based on SMC parameters and BWi showed the best predictive performance, with a root mean square error (RMSE) of 143 t/h and a mean relative deviation of 1.5%. This approach enables more accurate throughput forecasting, improving mine planning and operational efficiency. The results highlight the importance of integrating geometallurgical and operational data to build robust models that are adaptable to ore variability and applicable to both short- and long-term planning scenarios. Full article

The progress toward energy transition has made it essential to secure large quantities of critical metals to meet both short- and long-term demand, driving the exploration of new approaches, such as deep-sea mining (DSM). This study conducts a bibliometric analysis to examine the current scientific landscape of DSM, identifying trends, critical factors, and research gaps through a combined PESTEL and bibliographic analysis covering co-authorship, co-citation, co-occurrence, and bibliographic coupling. This comprehensive approach not only highlights emerging areas but also helps guide research efforts toward priority topics that support the advancement of DSM toward more sustainable exploitation. The results provide a general overview of recurrent themes and underexplored areas, serving as a basis for future research. While significant progress has been made in the environmental, technological, political, and legal dimensions, there remains a major gap in studies addressing the economic and social aspects of DSM, which account for less than 14% of the literature analyzed. This imbalance limits the integration of a truly sustainable framework, underscoring the need to promote interdisciplinary approaches and foster synergies among organizations and countries to build a more balanced and holistic understanding. Full article

The former Provence coal basin, closed since 2003, has been monitored by a microseismic network since 2008. The objective is to detect the precursor signs of a brittle subsidence that would be caused by the collapse of the old underground mining works. Since the start of monitoring, no subsidence has affected the risk areas, and nearly 4000 events with a local magnitude between −3 < M_L < 3 have been recorded. One sector in particular, called the Fuveau swarm, located outside the risk zones and therefore outside the brittle subsidence hazard zones, has attracted attention since 2012 because it was the subject of several seismic episodes felt in 2010, 2012, 2014, late 2016–early 2017, and August 2017. Since 2017, it has been established that the observed seismicity cannot be explained only by instability phenomena in the old mining works. The most likely hypothesis is that of the remobilization of faults hydraulically connected to the mining works, with seismic activity that is closely linked to variations in the groundwater level, which are themselves influenced by pumping and effective rainfall. This paper shows, through multiplet analysis method of the seismic data recorded by the monitoring network stations, that part of the seismicity in the monitoring areas is also due to the reactivation of tectonic faults. This conclusion is based on the concordance between the location of the multiplets and the orientation of the main faults mapped in the studied areas, as well as on the fact that the strongest events belong to these multiplets. This finding underscores the need to integrate fault reactivation into seismic monitoring strategies, beyond the current focus on mining-induced instabilities. This conclusion leads us to recommend revising the list of post-mining hazards, as post-mining seismic risk is often overlooked in many European regulations. Full article

Despite the rapid improvement in the availability and resolution of real-time electricity data, budget development processes in mining have remained relatively unchanged. Currently, there is no standard for the evaluation of mine electricity cost budgets. This study aims to determine whether forecasting processes used by mines produce budgets of sufficient quality and resolution to be used as a tool for daily energy- and cost management. A literature review was conducted to determine a set of best practices for electricity budgeting on mines. These findings were used to develop a survey to evaluate the current state of budgeting processes on South African mines. Surveys were conducted at 41 mine business units. Survey results were processed and analyzed and found that there are significant shortcomings in complying with the identified best practices. The majority of mines produced forecasts in lower resolutions than actual available data, thereby reducing their usefulness as energy management tools. The methods currently employed by mining sites are not scalable and are vulnerable to human error. Only 7% of participating business units’ budgets passed the identified best practices. Adherence to best practices, identified in this paper, will assist mines in improving electricity cost forecasts for more proactive- and sustainable energy management. This will also assist the industry in aligning with the UN Sustainable Development Goals (SDGs) of Affordable and Clean Energy (SDG 7), Industry, Innovation, and Infrastructure (SDG 9), and Responsible Consumption and Production (SDG 12). Full article

The navigation of Unmanned Ground Vehicles (UGVs) in underground mining environments is critical for enhancing operational safety, efficiency, and automation in hazardous and constrained conditions. This paper presents a thorough review of path-planning algorithms employed for the navigation of UGVs in underground mines. It outlines the key components and requirements that are essential for an effective path planning framework, including sensors and the Robot Operating System (ROS). This review examines both global and local path-planning techniques, encompassing traditional graph-based methods, sampling-based approaches, nature-inspired algorithms, and reinforcement learning strategies. Through the analysis of the extant literature on the subject, this study highlights the strengths of the employed techniques, the application scenarios, the testing environments, and the optimization strategies. The most favorable and relevant algorithms, including A*, Rapidly-exploring Random Tree (RRT*), Dijkstra’s, Ant Colony Optimization (ACO), were identified. This paper acknowledges a significant limitation: the over-reliance on simulation testing for path-planning algorithms and the computational difficulties in implementing some of them in real mining conditions. It concludes by emphasizing the necessity for full-scale research on path planning in real mining conditions. Full article

Slope failures in open-pit mining pose significant operational and safety issues, underscoring the importance of implementing effective stability monitoring frameworks for early hazard detection to allow for timely intervention and risk mitigation. This systematic review presents a comprehensive synthesis of existing and emerging methods and technologies used for slope stability monitoring in open-pit mining, including both remote sensing and in situ methods, as well as advanced technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and Wireless Sensor Networks (WSNs). Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, a total of 49 studies were selected from a collection of four engineering databases, and a comparative analysis was conducted to determine the underlying differences between the various methods for open-pit slope stability monitoring in terms of their performance across key attributes, such as monitoring accuracy, spatial and temporal coverage, operational complexity, and economic viability. Their juxtaposition highlighted the notion that no universally optimal slope stability monitoring system exists, due to a series of compromises that arise as a result of inherent technological limitations and site-specific constraints. Notably, remote sensing methods offer large-scale, non-intrusive monitoring, but are often limited by environmental factors and data acquisition infrequency, whereas in situ methods provide high precision, but suffer from limited spatial coverage and scalability. This review further highlights the capacity of emerging methods and technologies to address these limitations, providing suggestions for future research directions involving the integration of multiple sensing technologies for the enhancement of monitoring capabilities. This study provides a consolidated knowledge base on open-pit slope stability monitoring methods, technologies, and techniques, to guide the development of integrated, cost-effective, and scalable slope monitoring solutions that enhance mine safety and efficiency. Full article

This research aims to define an operational plan for the sustainability of small-scale artisanal gold mining (ASGM) in Northeast Antioquia, Colombia. A qualitative approach with a descriptive scope was used, for which a documentary or bibliographical review technique was made. Accordingly, articles, theses, books and institutional documents were reviewed as any contribution to the research topic. Likewise, this documentation contributed to defining aspects for elaborating the operational plan. Based on the reviewed sources, the need was found to propose an operational plan for this area to contribute to sustainability. Based on the sustainability analysis of ASGM in the zone, three common factors could be identified within the various positions proposed: the environmental, economic, and socio-cultural dimensions. With these, different needs were recognised in the area of study that still need action. An operational plan was devised to address these challenges and support the sector’s long-term sustainability. Full article

This research investigates the engineering properties of sandstone through tests performed prior to and following heat treatment. This study concentrated on evaluating the physical and mechanical properties, such as density, compressive strength, and wave velocities, in conjunction with the mineralogical, Cerchar hardness, and basic geochemical characteristics of the rock. Heat treatment was conducted at different temperatures (35 °C, 200 °C, 400 °C, 600 °C, and 800 °C), followed by analyses utilizing X-ray fluorescence (XRF), thin section analysis, and scanning electron microscopy (SEM) techniques. The results were analyzed to assess the influence of heat treatment on rock properties, utilizing Design Expert software for data evaluation. Numerical analysis with FLAC3D was conducted to validate the observed values at various temperature levels, further investigating the impact of the treatment on the engineering properties of sandstone. A significant finding was the reduction in strength, particularly correlated with a decrease in primary wave velocity, which is associated with an uneven distribution of strength within the rock. Increased temperature results in stress concentrations that facilitate crack formation, while variations in grain size significantly influence crack propagation. This study highlights the substantial influence of temperature on the compressive strength and general material properties of sandstone. Full article

A low-grade uranium-gold polymetallic ore is associated with many rare elements, such as beryllium (Be), zirconium (Zr), thorium (Th), and cerium (Ce). It has potential development and utilization value. In order to improve the development and utilization rate of a low-grade uranium-gold polymetallic ore, beryllium (Be) in low-grade uranium-gold polymetallic ore was extracted by a combined method of (NH)₂SO₄ and Al₂(SO₄)₃. The effects of different concentrations of (NH₄)₂SO₄ solution on the leaching of beryllium (Be) in low-grade uranium-gold polymetallic ore with different particle sizes after sieving were studied; microstructure and physicochemical analyses were carried out. The leaching mechanism of beryllium (Be) was revealed. The experimental results showed that when the low-grade uranium-gold polymetallic ore in (NH)₂SO₄ solution is 6 g/L and Al₂(SO₄)₃ is 3 g/L, the particle size of the ore sample is 0.01 mm, the concentration of beryllium (Be) in the leaching solution reaches 0.521 mg/L after 3 days of leaching, the concentration of beryllium (Be) in the leaching solution of the sample without Al₂(SO₄)₃ solution is 0.007 mg/L, and the leaching rate of beryllium (Be) reaches 98.6%. SEM and XRD analyses showed that the silicate composition in the sample after leaching was obviously destroyed compared with the control group when the (NH)₂SO₄ solution was 6 g/L, which increased the contact area on the surface of the ore sample and promoted the leaching of beryllium (Be) in the uranium ore sample. The research results lay a theoretical foundation for the development and extraction of beryllium (Be) associated with low-grade uranium-gold polymetallic ore. Full article

Mining significantly impacts terrestrial ecosystems despite its importance to the global economy. As part of soil ecosystems, fungi are highly responsive to environmental and human-induced drivers, shifting community composition and structure. Indeed, fungi play a key role in maintaining ecosystem resilience. Thus, we aim to address the question of whether soil fungal communities maintain similar ecological functions despite changes in community composition due to the impact of mining across ecosystems. To evaluate the ecological role of fungi across four ecosystems with varying iron mining impact levels, we used the FUNGuild database to assign functional guilds at the genus level. Co-occurrence network and ordination analyses were used to infer ecological relationships among fungal taxa and visualize the correlation between edaphic properties and fungal communities. A total of 22 functional guilds were identified, with dung saprotrophs, wood saprotrophs, fungal parasites, plant pathogens, ectomycorrhizal fungi, animal pathogens, and endophytes being the most abundant. Soil properties such as pH, organic matter, texture, and nutrients drive taxonomic and functional shifts. Our findings indicate that while mining activities shift fungal community compositions across ecosystems, the profiles of functional guilds show overlap between highly, moderately, and lowly impacted ecosystems, indicating functional redundancy. Network analysis reveals that highly connected hub taxa contribute to ecological redundancy across ecosystems and might act as a buffer against environmental disturbances. Our findings emphasize the important ecological role of soil fungi and indicate a potential for using fungal communities as bioindicators of ecological recovery in post-mining landscapes. From a mining and restoration perspective, this offers a low-cost, ecologically meaningful tool for monitoring soil recovery and guiding reclamation efforts. Full article

Induced seismicity resulting from mining activities is one of the major challenges faced by the mining industry. Although such events have been documented for over a century in countries with extensive mining traditions, such as Canada, Australia, and Chile, their impact has intensified over time. This increase is primarily attributed to the greater extraction depths, where elevated stress levels and environmental conditions heighten the likelihood of rockburst occurrences. Seismic events within mines lead to significant human casualties and substantial infrastructure damage, necessitating the implementation of various safety protocols. Among these, seismic indicators are employed to identify periods when high-magnitude seismic events are most likely to occur through the analysis of parameters such as magnitude, energy, time, and decay rate. In this context, the present study aims to utilize the accumulated frictional energy generated by microearthquakes within the Bobrek mine, Poland, as a seismic indicator (variation of frictional energy in time), establishing its correlation with the occurrence of high-magnitude seismic events exceeding the background activity. Thousands of combinations of seismic parameters were tested to maximize the performance of this frictional energy-based indicator, parameters such as moment magnitude, frictional energy, and rock properties. The optimal set of parameters was determined using the Piece Skill Score (PSS) and subsequently applied to the Accumulated Frictional Heat (AFH) methodology. According to the results, the seismic indicator forecasts 86.6% of events with magnitudes M_w ≥ 2.3, with an average forecasting time of 9.76 h, indicating that, on average, these events can be anticipated approximately 10 h before their occurrence. Full article

The increasing production of iron ore has led to the accumulation of iron ore tailings (IOTs), which pose significant environmental and safety risks when stored in tailings dams. This study investigates the potential of IOTs as a precursor in alkali-activated binder systems, aiming to provide a sustainable solution for mining waste management. Industrial calcium carbide lime and sodium silicate (Na₂SiO₃) were used as activators in varying concentrations (Na₂SiO₃: 10%, 15%, 20%, 25%, and 30%; carbide lime: 5%, 7.5%, and 10%), with curing conditions of 23 °C for 7 days. Techniques including unconfined compressive strength tests, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and metal leaching tests were employed to evaluate the mechanical performance and environmental safety of the alkali-activated binders. The results reveal that a mixture containing 20% Na₂SiO₃ and 10% carbide lime achieved the highest compressive strength of 0.33 MPa at 7 days. The binder also showed negligible metal leaching, meeting environmental safety standards. These findings confirm the viability of using IOTs in the development of durable, eco-friendly construction materials, offering a scalable and sustainable solution for the management of mining waste and promoting circular economy principles in the construction sector. Full article

The automation of mining mobile equipment is a topic of considerable interest, as it has the potential to significantly reduce the number of accidents and implement the so-called zero-entry mining concept, which would eliminate the need for any human presence on the mine site. Nevertheless, the current state of robotics and automation technology does not yet meet the requirements for the implementation of fully autonomous operations in mines. Autonomous mining equipment continues to operate under the supervision of humans, and a considerable number of mining equipment has not yet been automated. This indicates the necessity of identifying novel strategies to increase the safety of mining operations through the utilization of robotics and automation technologies. One potential solution to address this challenge is to increase the involvement of humans in autonomous mining operations. This could entail integrating human decision-makers into the decision-making loops of autonomous mining equipment. To this end, we propose the paradigm of autonomous collaborative mining, wherein humans and autonomous machines work together in a collaborative manner to increase the safety and efficiency of mining operations. We analyze the enabling factors required to implement this paradigm and present the case of autonomous loading using LHDs based on the autonomous collaborative mining paradigm. Full article

Predicting and analyzing the stability of underground stopes is critical for ensuring worker safety, reducing dilution, and maintaining operational efficiency in mining. Traditional stability graphs are widely used but often criticized for oversimplifying the stability phenomenon and relying on subjective classifications. Additionally, the imbalanced nature of stope stability datasets poses challenges for traditional machine learning and statistical models, which often bias predictions toward the majority class. This study proposes a novel methodology for developing site-specific stability graphs using probabilistic modeling and machine learning techniques, addressing the limitations of traditional graphs and the challenges of imbalanced datasets. The approach includes rebalancing of the dataset using the Synthetic Minority Over-Sampling Technique (SMOTE) and feature selection using permutation importance to identify key features that impact instability, using those to construct a bi-dimensional stability graph that provides both improved performance and interpretability. The results indicate that the proposed graph outperforms traditional stability graphs, particularly in identifying unstable stopes, even under highly imbalanced data conditions, highlighting the importance of operational and geometric variables in stope stability, providing actionable insights for mine planners. Conclusively, this study demonstrates the potential for integrating modern probabilistic techniques into mining geotechnics, paving the way for more accurate and adaptive stability assessment tools. Future work includes extending the methodology to multi-mine datasets and exploring dynamic stability graph frameworks. Full article

This study presents a constitutive model for simulating the behavior of weak rock masses under various stress conditions, including the effects of pore pressure and temperature. Addressing the limitations of existing models in accurately representing the complex anisotropic response of these materials, the model utilizes Monte Carlo simulations to integrate stress anisotropy, pore pressure effects, and deviatoric stress states. This approach aims to capture the impact of geological factors such as foliation and jointing on the mechanical behavior of weak rock masses, which are often characterized by low strength and high deformability. Five rock types (claystone, mudstone, sandstone, shale, and siltstone) were simulated, generating 1000 cases per type with variability modeled using Weibull distributions. Statistical validation, employing the Kolmogorov–Smirnov test and Q–Q plots, demonstrated a strong agreement between simulated and experimental data. The results suggest that the proposed model can effectively predict deformation patterns in weak rock masses, offering potential applications in mining, geothermal energy extraction, and other engineering projects involving these challenging geological formations. Full article

Rock fracturing by blasting is the most common and efficient method of rock fragmentation in mining operations. The fragmentation size affects the productivity and costs of downstream operations and is influenced by the rock mass and blast design encountered. The encountered rock mass is the unmodifiable parameter in blasting. Therefore, blasting improvements can be achieved through blast design, which includes explosive selection, geometrical design, and initiation sequencing and delays. Stress wave interactions between blastholes can improve or diminish fracturing. The analysis conducted in this study through numerical modelling indicates an improvement in blast outcomes with appropriate delay and sequencing in some cases. The optimum delay ensures the formation of fractures on the succeeding blasthole and constructive interactions with the stress wave from the preceding blasthole, increasing the stress pulse and fracturing. While it is insignificant in intact rock blasting, the firing sequence is vital when blasting through the contacts of soft and hard rocks or joints, depending on the infill material. Sequential initiation and the firing direction do not improve fracturing in all cases; for example, when blasting through an empty joint, the joint acts as a free face with minimum to no interaction of the stress wave from adjacent charges. In such cases, simultaneous initiation can be used with caution based on the intensity of induced vibrations. Full article

The growing demand for green and, therefore, sustainable technologies present new challenges for our society. The European Union (EU) identified the critical raw materials (CRMs) and strategic raw materials (SRMs) necessary for these technologies and introduced policies to reduce reliance on external suppliers, which includes investigating the recovery of CRMs from extractive waste. This study assesses the recovery potential of mine waste collected in the Traversella mine district (Piedmont, Italy), known for its polymetallic Fe-Cu-W deposit. The characterization of waste rock samples involved chemical and mineralogical analyses, revealing metallic-bearing minerals such as magnetite and scheelite. Laboratory-scale magnetic and gravity separation tests were carried out and compared. Magnetic separation resulted in a recovery of 75.4% of Fe, 72.3% of Cu, and 83.7% of W, with a weak concentration. Instead, gravity separation produced high-grade Fe (67.6%) and W (1289 ppm) concentrate with lower recovery rates. Full article