Search Results (140)

Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations. Full article

This study investigates the application of Bayesian statistical methods to analyze and predict the dimensional changes in torsion bars made from 20CrMnTi alloy steel during carburizing heat treatment. The process parameters, including a treatment temperature of 920 °C followed by oil quenching, were selected to optimize surface hardness while maintaining core toughness. The dimensional changes were measured pre- and post-treatment using precise caliper measurements. Bayesian statistics, particularly conjugate normal distributions, were utilized to model the dimensional variations, providing both posterior and predictive distributions. These models revealed a marked concentration of the posterior distributions, indicating enhanced accuracy in predicting dimensional changes. The findings offer valuable insights for improving the control of carburizing-induced deformations, thereby ensuring the dimensional integrity and performance reliability of torsion bars used in high-stress applications such as pneumatic clutch systems in mining ball mills. This study underscores the potential of Bayesian approaches in advancing precision engineering and contributes to the broader field of statistical modeling in manufacturing processes. Full article

This study was conducted at a fixed monitoring site in the southern dump of the large-scale Antaibao open-pit coal mine of China Coal Pingshuo, using long-term monitoring methods. Based on data from 2019 and 2024 in the reclaimed area of the Pingshuo open-pit coal mine, all seedlings and saplings within the Robinia pseudoacacia L. + Ulmus pumila L. + Ailanthus altissima (Mill.) Swingle mixed forests were studied to analyze changes in their abundance and the driving factors influencing their survival rates from 2019 to 2024. The main conclusions are as follows: (1) The species composition of seedlings and saplings remained unchanged but the number of seedlings increased significantly. The majority of newly recruited seedlings were U. pumila., accounting for 92.22% of the total new seedlings, whereas R. pseudoacacia had the highest mortality rate among seedlings. The distribution patterns of seedling-to-sapling transition, sapling-to-tree transition, and seedling–sapling mortality were generally consistent with the overall distribution of seedlings and saplings at the community level. (2) At both the community and species levels, the optimal models for seedling and sapling survival were the height model and the biological factor model. Overall, survival rates of both seedlings and saplings showed a significant positive correlation with height. (3) The biological factors affecting the survival of U. pumila saplings were the basal area (BA) at breast height and the number of conspecific adult trees. The former was significantly negatively correlated with U. pumila seedling survival, while the latter was positively correlated. For R. pseudoacacia seedlings, the key biological factors were the number of heterospecific adult trees and the number of heterospecific seedlings. The former was significantly negatively correlated with survival, whereas the latter was significantly positively correlated. The primary factor influencing sapling survival was sapling height, which showed a significant positive correlation. Full article

Determining the Bond grindability test in a ball mill is one of the most commonly used methods in the mining industry for measuring the hardness of ores. The test is an essential part of the Bond work index methodology for designing and calculating the efficiency of mineral grinding circuits. The Bond ball mill grindability test has several restrictions, including the sample’s initial particle size distribution (PSD). This paper presents a method for calculating the Bond work index when the Bond ball mill grindability test is performed on samples with non-standard PSD. The presented equation includes a correction factor (k) and is applicable only for P₁₀₀ = 75 μm. The defined method is then compared with methods proposed by other researchers, and conclusions are drawn as to which method results in less deviation. The presented model resulted in a mean square error of 0.66%. Full article

This study examined significant changes in phosphogypsum, a byproduct of the phosphoric acid industry, induced via mechanical activation through intensive grinding using a planetary ball mill. Alterations in crystallinity, surface area, and zeta potential were monitored using X-ray diffraction, Brunauer–Emmett–Teller analysis, zeta potential measurements, X-ray photoelectron spectroscopy, and scanning electron microscopy. The severe grinding of this mining waste led to the conversion of gypsum (CaSO₄·2H₂O) to anhydrite (CaSO₄), an increase in surface area from 5.8 m²/g to 17.8 m²/g, and a decrease in pore radius from 76.6 nm to 9.3 nm. The zeta potential shifted as the isoelectric point changed from pH 8.5 to pH 4.3. These modifications enhanced the material’s potential as a cost-effective and eco-friendly adsorbent for wastewater treatment. The enhanced adsorption capabilities for Cd and Pb were evaluated, revealing a higher adsorption capacity (~40 mg/g for both) and removal efficiency (~90% for Cd and ~80% for Pb) for activated phosphogypsum. The adsorption process followed the Freundlich isotherm and pseudo-second-order kinetic model, indicating its physisorption nature and spontaneous thermodynamic characteristics, and highlighting its potential for wastewater treatment. The mechanically activated adsorbent demonstrated over 90% desorption efficiency over five cycles, ensuring effective regeneration and reusability for Cd and Pb removal. Real tannery wastewater was treated using mechanically activated phosphogypsum at pH 6 and 70 °C for 60 min, achieving a 94% Cd and 92% Pb removal efficiency, with an overall heavy metal removal efficiency of up to 83%. This study demonstrates the sustainable utilization of phosphogypsum, contributing to green wastewater management and environmental protection. Full article

Thermoelectric materials have considerable potential in the mitigation of the global energy crisis, through their ability to convert heat into electricity. This study aims to valorize natural resources, and potentially reduce production costs, by incorporating tetrahedrite–tennantite (td) ores from the Portuguese Iberian Pyrite Belt into synthetic samples. The ore samples were collected in a mine waste at Barrigão and as “dirty-copper” pockets of ore from the Neves Corvo mine. Subsequently, high-energy ball milling and hot pressing were employed in the production of thermoelectric materials. These are characterized by XRD, SEM/EDS, and thermoelectrical properties. The complete dissolution of the dump material sulfides with the synthetic tetrahedrite constituents led to an increase in the amount of the tetrahedrite–tennantite phase, which was made up of a tetrahedrite–tennantite–(Fe) solid solution. The thermoelectric characterization of these materials is provided, revealing that most of the combined synthetic ore samples displayed better results than the pristine tetrahedrite, mostly due to higher Seebeck coefficient values. Furthermore, the best thermoelectric performance is achieved with 10% of ore, where a power factor of 268 µW.K⁻².m⁻¹ is reached at room temperature. Full article

The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failure risks. Traditional maintenance strategies often fail to prevent unexpected downtimes, safety hazards, and economic losses. As a response, industries are integrating predictive monitoring technologies, including machine learning, the Internet of Things, and digital twins, to enhance early fault detection and optimize maintenance strategies. This Systematic Literature Review analyzes 166 high-impact studies from Scopus and Web of Science, identifying key trends in fault detection algorithms, hybrid AI models, and real-time monitoring techniques. The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. Despite these advancements, challenges such as data standardization, model scalability, and system interoperability persist, requiring further research. Future work should focus on real-time AI applications, explainable models, and academia-industry collaboration to accelerate the implementation of intelligent maintenance solutions, ensuring greater reliability, efficiency, and sustainability in mining operations. Full article

In recent years, significant increases in premature failures of rock bolts that are attributed to stress corrosion cracking (SCC) have been observed in underground reinforcement systems, which pose serious safety concerns for underground operations. A multitude of studies have focused on understanding the environmental factors, such as the composition of the corrosive medium, temperature, and humidity, in promoting the SCC of rock bolts, but the SCC failure mechanism associated with microstructural changes is still unclear due to the complexity of the underground environments. To understand its failure mechanism and develop effective mitigation strategies, this study evaluated different testing conditions, employing pin-loaded and bar-loaded coupon tests using representative specimens. The tests were conducted in an acidified sulfide solution. The failure characteristics and crack paths of the failed specimens were examined. It was observed that the steel with lower carbon content exhibited a reduced susceptibility to SCC. The subcritical cracks observed in the specimens were influenced by the microstructure of the material. SCC was observed not only on the original surface of rock bolts, which featured mill scale and decarburization, but also on freshly machined surfaces. Evidence for the occurrence of hydrogen-induced SCC was identified and discussed. The proposed testing methods and the obtained results contribute to a deeper understanding of SCC in rock bolts as well as promote the development of more durable materials for underground mining applications, ultimately enhancing the safety and reliability of rock bolt systems. Full article

Tailings generated by mining account for the largest world-wide waste from industrial activities. As an element, copper is relatively uncommon, with low concentrations in sediments and waters, yet is very elevated around mining operations. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901 and 1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. About 10 MMT formed a beach that has migrated 7 km from the original Gay pile to the Traverse River Seawall. Another 11 MMT are moving underwater along the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Here we use remote sensing techniques to document geospatial environmental impacts and initial phases of remediation. Aerial photos, multiple ALS (crewed aeroplane) LiDAR/MSS surveys, and recent UAS (uncrewed aircraft system) overflights aid comprehensive mapping efforts. Because natural beach quartz and basalt stamp sands are silicates of similar size and density, percentage stamp sand determinations utilise microscopic procedures. Studies show that stamp sand beaches contrast greatly with natural sand beaches in physical, chemical, and biological characteristics. Dispersed stamp sand particles retain copper, and release toxic levels of dissolved concentrations. Moreover, copper leaching is elevated by exposure to high DOC and low pH waters, characteristic of riparian environments. Lab and field toxicity experiments, plus benthic sampling, all confirm serious impacts of tailings on aquatic organisms, supporting stamp sand removal. Not only should mining companies end coastal discharges, we advocate that they should adopt the UNEP “Global Tailings Management Standard for the Mining Industry”. Full article

Geometallurgical programmes are crucial for designing mineral processing plants that maximise comminution throughput. However, the variability of complex ore bodies, such as platinum group element (PGE) deposits, poses challenges in developing these programmes into profitable mine-to-mill production. This paper investigates the geological characteristics of different lithologies hosting the complex PGE orebody located in the Northern Limb of the Bushveld igneous complex in South Africa and assessed their impact on metallurgical efficiency in comminution circuits. Regression machine learning techniques were employed to analyse the ore mineralogical dataset from two lithologies (feldspathic pyroxenite and pegmatoidal feldspathic pyroxenite) and predict the Bond Work Index (BWI), a key comminution parameter for calculating processing plant throughput. The results indicated that BWI is strongly influenced by Chlorite, silicates, iron oxides, and the relative density of the PGE deposit. Using both simulated and laboratory-measured throughput values, a particle swarm optimisation (PSO) algorithm was applied to maximise the plant’s comminution throughput through tactical blending of low-grade and high-grade ore stockpiles. The PSO algorithm was shown to be an effective tool for stockpile management and tactical mine-to-mill operation in response to feed mineralogical variability. This first-time innovative approach addresses complex geological uncertainties and lays the groundwork for future geometallurgical studies. Potential areas for further research include incorporating additional lithologies for tactical ore stockpile blending and optimising parameters critical for ore mineral flotation. Full article

This study analyzes the distribution of operating costs along the value chain of an open-pit copper mine with a focus on key operational units or operations such as drilling, blasting, loading, hauling, stockpiling, blending, crushing, milling, and flotation. Using process costing analysis, key cost drivers were identified, and their individual contributions to total expenses were quantified. Results revealed that comminution processes dominate the operational cost structure, with milling accounting for 6.18 USD/ton, representing 59.1% of total operating costs, and crushing costing 1.15 USD/ton, that is, 11% of total operating expenditure. The study also highlighted several opportunities for cost reduction and enhanced mining sustainability through strategies such as energy consumption optimization, the use of alternative energy sources, and optimized blast design. Finally, valuable insights aimed at promoting sustainable resource utilization, improved cost efficiency, and data-driven decision-making in mining operations are offered to mine planners and operators. This is eventually expected to lay the foundation for benchmarking work on the establishment of a baseline and standards for similar mining operations. Full article

Currently, in the grinding areas of Peruvian mining operations, there are linings made of various materials such as rubber and steel, all of which have a limited lifespan and eventually wear out. Understanding the wear behavior of these linings has a direct impact on mill performance. There are several solutions for measuring wear, making wear projections, and, most importantly, knowing the replacement date so that the mining company can schedule a plant shutdown. However, these solutions are not swift, as traveling to the mine, returning to the company, processing the data in software, and generating reports take 3 to 10 days depending on the workload of each supplier of these linings. Mining companies seek solutions to monitor the condition of their linings and avoid plant shutdowns as they disrupt production. The primary objective of this tool is to quickly and accurately predict the wear and removal of mill linings with user safety as a top priority. The product design and development process followed the methodology proposed by Ulrich and Eppinger, which includes (a) identifying customer needs, (b) planning, (c) developing product concepts, (d) system-level design, (e) detailed design, and (f) testing and refinement. Key metrics for design were defined through 50 surveys. Additionally, two focus groups with mill lining experts and user testing were conducted, allowing for the refinement and validation of the initial concept. The tool prototype was modeled in 3D, sensors and other electrical mechanisms were purchased, and an LED screen was programmed for data reading. Methodologies such as TRIZ, SCAMPER, and Canva were incorporated, facilitating a well-designed product with attention to detail. Finally, the final characteristics of the digital comb, ranging from 10′ to 25′, were defined and tested on mill linings, and with the help of the Weir Projection application, wear history and projections were rapidly generated. When compared with other measurement tools, minimal differences were found within a range of ±2 mm. Therefore, it is concluded that the prototype assists in quickly scheduling mill lining requirements in advance. Full article

The overload problem in semi-autogenous grinding (SAG) mills is critical in the mining industry, impacting the extraction of valuable metals and overall productivity. Overloads can lead to severe operational issues, including increased wear, reduced grinding efficiency, and unscheduled shutdowns, which result in financial losses. Various strategies have been employed to address SAG mill overload, from real-time monitoring to predictive modeling and machine learning techniques. However, existing methods often lack the integration of domain-specific knowledge, particularly in handling class imbalance within operational data, leading to limitations in predictive accuracy. This paper presents a novel approach that integrates convolutional neural networks (CNNs) with physics-informed neural networks (PINNs), embedding physical laws directly into the model’s loss function. This hybrid methodology captures the complex interactions and nonlinearities inherent in SAG mill operations and leverages domain expertise to enforce physical consistency, ensuring more robust predictions. Incorporating physics-based constraints allows the model to remain sensitive to critical overload conditions while addressing the challenge of imbalanced data. Our method demonstrates a significant enhancement in prediction accuracy through extensive experiments on real-world SAG mill operational data, achieving an F1-score of 94.5%. The results confirm the importance of integrating physics-based knowledge into machine learning models, improving predictive performance, and offering a more interpretable and reliable tool for mill operators. This work sets a new benchmark in the predictive modeling of SAG mill overloads, paving the way for more advanced, physically informed predictive maintenance strategies in the mining industry. Full article

Mining is a crucial sector in the global economy, providing essential materials for various industries, including construction, electronics, and energy. However, traditional mining practices often have significant negative impacts on the environment. Therefore, integrating sustainable practices into mining has become vital. Grinding is a crucial stage in the mineral processing industry, essential in liberating valuable minerals from ore. However, it is also one of the most energy-intensive processes in mining operations, consuming a substantial amount of electricity. Understanding and optimising electricity consumption in the grinding process is essential for enhancing energy efficiency and reducing operational costs. The relationship between electricity consumption in the grinding process and the Bond Work Index (BWI) is a crucial aspect of mineral processing and energy management in the mining industry. Understanding this relationship helps optimise grinding operations and improve energy efficiency. This review paper continues a previous work, where possible alternative modified methods for estimating the BWI in a Bond ball mill are presented. An analysis of selected methods is also provided to assess and obtain an accurate value of the BWI, which is essential in the grinding process. The methods for estimating the BWI using the wet method are presented. It is shown how the BWI can be estimated using dynamic elastic parameters and how changes in the Bond ball mill affect the BWI value. New equations for calculating the BWI and alternative procedures for evaluating the BWI in samples of non-standard size are proposed. The paper presents a comparative analysis of all presented methods. Full article

Open-pit mining remains the dominant method for copper extraction in current operations, with blasting playing a pivotal role in the efficiency of downstream processes such as loading, hauling, crushing, and milling. This study assesses the impact of surface blast design parameters on the performance of a comminution circuit processing a copper-bearing ore. The analysis focuses on important design parameters such as burden, spacing, stemming, and powder factor, evaluating their influence on the fragment size distribution and downstream comminution circuit performance. Using the Kuz-Ram model, four novel blast designs are compared against a baseline to predict the size distribution of rock fragments (X80). Key performance indicators throughput and specific energy consumption are calculated to evaluate the comminution circuit performance. Results demonstrated that reducing the X80 from 500 mm to 120 mm led up to a 20% increase in throughput and a 29% reduction in total specific energy consumption. Furthermore, achieving finer particle sizes through more intensive blasting contributed to a reduction in total operating costs by up to 12%. These findings provide valuable insights for optimizing blast design to improve comminution circuit performance, contributing to sustainable mining practices by reducing energy consumption, operating costs, and the environmental footprint of mining operations. Full article