Mining

Detecting surface mineral indicators with high spatial precision remains a significant challenge in mineral exploration, particularly in remote or geologically complex regions such as Eastern Kazakhstan. This study addresses this challenge by integrating high-resolution multispectral imagery from Unmanned Aerial Vehicles (UAVs) to map iron oxide distributions, key indicators of ore mineralization such as copper porphyry at the Kyzylkiya mining site in Eastern Kazakhstan. The novelty of this study is the development of a statistical fuzzy logic model that integrates UAV-derived spectral indices, including the Normalized Difference Vegetation Index (NDVI) and targeted band ratios, to generate probabilistic maps of iron oxide presence at a fine spatial resolution of 5.29 cm. This approach enhances prediction accuracy by incorporating uncertainty and variability in spectral responses. The model’s output was validated through a multi-stage process involving independent multispectral datasets and ground-truth sampling, achieving an overall accuracy of 80%. The results reveal concentrated iron oxide anomalies in the northeast and northwest of the study area, underscoring the method’s effectiveness. This integrated UAV-fuzzy logic framework demonstrates a scalable and cost-effective solution for early-stage mineral exploration and can be adapted to similar geological settings globally. Full article

Mining (1) operations in remote areas (2) face significant challenges related to energy supply, high fuel costs, and limited infrastructure. This study investigates the potential for achieving energy independence (3) and resilience (4) in such environments through the integration of renewable energy sources (5) and battery–electric mining equipment. Using the “Studena Vrila” underground bauxite mine as a case study, a comprehensive techno-economic and environmental analysis was conducted across three development models. These models explore incremental scenarios of solar and wind energy adoption combined with electrification of mobile machinery. The methodology includes calculating levelized cost of energy (LCOE), return on investment (ROI), and greenhouse gas (GHG) reductions under each scenario. Results demonstrate that a full transition to RES and electric machinery can reduce diesel consumption by 100%, achieve annual savings of EUR 149,814, and cut GHG emissions by over 1.7 million kg CO₂-eq. While initial capital costs are high, all models yield a positive Net Present Value (NPV), confirming long-term economic viability. This research provides a replicable framework for decarbonizing mining operations in off-grid and infrastructure-limited regions. Full article

Occupational heat stress represents an increasing challenge to safety and operational performance in underground mining, where elevated temperatures, humidity, and limited ventilation are common. This study proposes an integrated framework to analyze the systemic impact of heat stress on human reliability in mining operations. We conducted a systematic literature review to identify empirical studies addressing thermal exposure, extracting key operational functions for modeling. These functions were structured using the Functional Resonance Analysis Method (FRAM) to reveal interdependencies and performance variability. Human reliability was evaluated using Fuzzy CREAM, which quantified the degree of contextual control associated with each function. Finally, we applied the Gaussian Analytic Hierarchy Process (AHP) to prioritize functions based on thermal impact, contextual reliability, and systemic connectivity. The results showed that functions involving subjective or complex judgment, such as assessing thermal stress or identifying psychophysiological indicators, exhibited lower reliability and higher vulnerability. In contrast, monitoring and control functions based on standardized procedures were more stable and resilient. This combined approach identified critical points of systemic fragility and offers a robust decision-support tool for prioritizing thermal risk mitigation. The findings contribute to advancing the scientific understanding of heat stress impacts in mining and support the development of targeted interventions to enhance human performance and safety in extreme environments. Full article

The purpose of this work was to obtain specialized enrichment cultures from an original extreme acidophilic consortium of extremely acidophilic microorganisms and to study their microbial community composition and biotechnological potential. At temperatures of 25, 35, 40 and 50 °C, distinct enrichments of extremely acidophilic microorganisms used in the processes of bioleaching sulfide ores were obtained using nutrient media containing ferrous sulfate, elemental sulfur and a copper sulfide concentrate as nutrient inorganic substrates, with and without the addition of 0.02% yeast extract. The microbial community composition was studied using the sequencing of the V3–V4 hypervariable region of the 16S rRNA genes. The different growth conditions led to changes in the microbial composition and relative abundance of mesophilic and moderately thermophilic, strict autotrophic and mixotrophic microorganisms in members of the genera Acidithiobacillus, Sulfobacillus, Leptospirillum, Acidibacillus, Ferroplasma and Cuniculiplasma. The dynamics of the oxidation of ferrous iron, sulfur, and sulfide minerals (pyrite and chalcopyrite) by the enrichments was also studied in the temperature range of 25 to 50 °C. The study of enrichment cultures using the molecular biological method using the metabarcoding method of variable V3–24 V4 fragments of 16S rRNA genes showed that enrichment cultures obtained under different conditions differed in composition, which can be explained by differences in the physiological properties of the identified microorganisms. Regarding the dynamics of the oxidation of ferrous ions, sulfur, and sulfide minerals (pyrite and chalcopyrite), each enrichment culture was studied at a temperature range of 25 to 50 °C and indicated that all obtained enrichments were capable of oxidizing ferrous iron, sulfur and minerals at different rates. The obtained enrichment cultures may be used in further work to increase bioleaching by using the suitable inoculum for the temperature and process conditions. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

The use of high-pressure grinding rolls (HPGRs) is increasing in the ore industries as advanced technology is available for this type of comminuting. There are important parameters in these devices, which have many effects on productivity. One of the main reasons for damage on the rolls and, therefore, decreases in the machine’s productivity and efficiency is surface wear. This phenomenon must be carefully understood so that it can be controlled as much as possible through the readjustment and optimization of the effective parameters. In this research, the wear mechanism of HPGRs in a production line for iron ore concentrate was investigated. The results showed that there was greater wear at the center of the rolls and that changes to the chemical and physical properties of the incoming iron compared to the design condition reduced the rolls’ lives. The results showed a failure to perform appropriate mechanical adjustment and improper repair and maintenance. Full article

This study presents a methodology developed in collaboration with the Colombian National Mining Agency, aimed at enhancing the economic and productive activity of small-scale miners in Colombia through the promotion of associativity. Despite persistent challenges in the formalization and sustainable development of the artisanal mining sector, fostering associative models offers a pathway towards a more sustainable mining industry, aligned with current national policies. The proposed roadmap, designed to achieve this objective, is divided into three sequential phases. The first, the Baseline Survey, focuses on comprehensively understanding the initial socio-economic and operational conditions of mining communities. This is followed by Participatory Strategic Planning, which involves projecting the long-term role and development of mining associative figures. Finally, the Annual Operational Planning and Execution phase concentrates on the concrete implementation of activities designed to achieve sustainable organizational goals. During the design and initial implementation of this roadmap, we found that continuous support and tailored training programs are essential for mining communities. These programs are critical for fostering the development of collective skills and strengthening community ties within mining organizations. The findings highlight that by strengthening collective capabilities and community ties, mining organizations can enhance their self-management capacities and significantly contribute to the economic development of their regions. This approach addresses key challenges in the sector by promoting a more organized and resilient operational framework. The implementation of a participatory methodology, coupled with specific organizational strengthening programs, coordinated execution, and continuous monitoring, provides a viable route towards a more sustainable and formalized small-scale mining sector in Colombia. This roadmap offers a practical framework for fostering self-managed and economically contributing mining organizations. Full article

The present study aimed to estimate the contribution of the mining and mineral processing steps of lithium concentrate production in Brazil to the Global Warming Potential (GWP100) using an LCA perspective. No previous national study was identified that quantified national GHG emissions in mining and beneficiation operations for lithium ores. This study is considered original and aims to contribute to filling this gap. The functional unit was 1 ton of lithium carbonate equivalent (LCE) in the mineral concentrate. The contribution to GWP100 was estimated at 1220 kg of CO_2eq per ton of LCE, of which 262 kg originated from foreground processes. In the background processes, the largest contribution was 456 kg of CO_2eq from emissions in the production of ammonium nitrate, used in the manufacture of mining explosives. An analysis of substituting electricity sources in the product system showed a reduction of 22.7% and 14.7% in the estimated global warming impact when using wind or solar power, respectively. Full article

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