Mining

A case study of designing a waste dump was conducted for the iron mine located in the Bulacan area, Philippines. Iron ore mines generate a relatively high amount of waste, and at the study mine, the constrained waste dumping area of 3 hectares necessitated a higher dump design, leading to potential stability issues. Additionally, the waste dump is projected to be situated on an inclined surface; subsequently, there is a concern about dump stability. Therefore, this study aims to find the optimum waste dump design by assessing its stability, and a geometrical configuration was conducted to optimize the bench parameters. Numerical modeling of the finite difference method (FDM) was used to estimate the distribution of the Factor of Safety by simulating several models. Models with steeper base inclinations (>12°) demonstrate progressive instability, as demonstrated by pre-assessment. The statistical analysis results show that the total model simulations with a 45-degree slope angle have a significantly high probability of failure of 38.2%. Whereas models with 35-degree and 40-degree slope angles have probabilities of failure calculated as 0.3% and 6.5%, respectively. Therefore, results suggest that the general slope angle should be kept at 40 degrees or less. Moreover, the results show that an average of 0.02 points drops in FoS for each 2.5 m of increment in dump height. Regarding geometrical setup, four benches with 7.5 m of berm would be preferable for the waste dump design of the case study. Overall, the effect of an inclined surface as a base was discussed, the effect of a gradual increase in dump height was outlined, and the significance of the dump slope angle on dump design was highlighted. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

Technological advances are prompting mining companies to explore new options to enhance the efficiency of activities such as drilling, blasting, ventilation, and the loading and hauling of ore and waste. The emergence of digital environments, such as the Metaverse, allows companies in mining and other industrial sectors to simulate or predict scenarios in real time, generate ideas, and propose solutions before implementing them in the real world. There are various risks associated with the Metaverse and virtual worlds; however, there is insufficient information about the potential threats that could impact the Mining Metaverse. This investigation aims to establish a preliminary model for the efficient integration of the Metaverse into the mining industry. It highlights its potential by referencing previously adopted technologies such as virtual reality (VR), augmented reality (AR), and the Internet of Things (IoT) in mining and other sectors. It also seeks to identify and explain the risks associated with using a Mining Metaverse, considering constraints that will be valuable not only to the Australian mining industry but also on a global scale. Full article

Geometallurgy is an approach that utilizes predictive models that can support business decisions, mitigate risks, and enhance production efficiency. To develop an accurate geometallurgical model, it is essential to understand the behavior of each lithology within the ore body through geometallurgical testing. In this context, the present study aims to evaluate the performance of bench-scale tests conducted on the main mining fronts of a zinc mine operation located in Brazil. The mineral processing plant was designed to process lead and zinc sulfide ores without material stockpiling, where all ores extracted from the underground mine are immediately processed. The geometallurgical characterization was conducted through the following steps: sampling, crushing, grinding, and flotation. The recovery, concentrate, and tailing contents during the flotation stages of galena and sphalerite were analyzed. A mineralogical characterization using a Mineral Liberation Analyzer (MLA) was performed to assess the degree of particle liberation and mineral associations within the studied mining fronts. The results indicate that a higher degree of pyrite liberation leads to greater metallurgical recovery of mineralized bodies A (breccia-hosted orebody), B (sphalerite-rich doloarenite orebody), and C (upper replaced stratiform orebody). Among these, mineralized body C presents the highest recovery in the zinc and lead stages, with 99.5% and 86.2%, respectively. Full article

In this study, we model a solution surface, with each point having nine components using artificial intelligence (AI), to optimise the effects of ground vibration during blasting operations in an open-pit diamond mine. This model has eight input parameters that can be adjusted by blasting engineers to arrive at a desired output value of ground vibration. It is built using the best performing artificial neural network architecture that best fits the blasting data from 100 blasting events provided by the Debswana diamond mine. Other AI algorithms used to compare the model’s performance were the k-nearest neighbour, support vector machine, and random forest—together with more traditional statistical approaches, i.e., multivariate and regression analyses. The input parameters were burden, spacing, stemming length, hole depth, hole diameter, distance from the blast face to the monitoring point, maximum charge per delay, and powder factor. The optimised model allows for variations in the input values, given the constraints, such that the output ground vibration will be within the minimum acceptable value. Through unconstrained optimisation, the minimum value of ground vibration is around 0.1 mm/s, which is within the vibration range caused by a passing vehicle. Full article

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