Mining, Metallurgy & Exploration最新文献

To investigate the injuries caused by multi-point gas deflagration accidents within the complex environment of a mine, this paper conducts a numerical study of the flame propagation of methane explosions in a ventilation door and supporting structures. The effects of ignition source position, number, and changes in the state of the ventilation door were analyzed based on explosion simulation with three different ignition source settings. The results show that the interplay between the increased number of ignition sources and the confining effect of the flame significantly affected the structural evolution of the flame. After crossing the ventilation doors, the flame structure transitions to forms such as umbrella flame, columnar flame, tip flame, or twisted flame. In the early stages of flame propagation, reflected pressure waves are the main cause of changes in flame propagation velocity. As the reaction proceeds, the cause changes to an interaction between the turbulent flame, the chemical reaction, and the reflected pressure wave. The speed of a single ignition source passing through the ventilation door was 172.5 m/s, while the speeds of two ignition sources at increasing distances were 146.6 m/s and 115 m/s, respectively. Therefore, the speed of the flame passing through the ventilation door is inversely proportional to the number of ignition sources and inversely proportional to the distance between the ignition sources. Additionally, with two-point fire sources, the more distorted the vortex distribution, the more twisted the flame propagation shape. This study addresses the lack of research on the flame propagation characteristics of methane explosions in long and narrow confined spaces, which is crucial for gas explosion risk prevention.

Understanding constraints within the raw battery material supply chain is essential for making informed decisions that will ensure the battery industry’s future success. The primary limiting factor for long-term mass production of batteries is mineral extraction constraints. These constraints are highlighted in a first-fill analysis which showed significant risks if lithium-ion batteries are utilised to fully support vehicle electrification and intermittent energy storage. Nickel, lithium, cobalt, and graphite reserves risk 100% depletion with significant consumption of known resources. Furthermore, over 700 new critical mineral mines will need to be developed to meet the required production rates for decarbonisation by 2050. Demand for critical minerals will out-pace mine development timelines even as improvements are made to battery energy density and compositions. Governments and the private sector need to align themselves on decarbonisation goals to establish cooperative agreements on the critical mineral supply chain by reducing the barriers to entry and increasing exploration efforts. Additional measures must also be taken to reduce the demand for critical minerals. Policy such as incentivising public transportation and biking infrastructure can be exploited to drastically reduce the mineral demand placed on the mining industry.

Excavating tunnels has become a widespread practice in the modern world, driven by the need for efficient transportation, subterranean storage, and mineral supply. One challenge encountered during tunnel excavation is the overbreak (OB) phenomenon, particularly prominent when utilizing drilling and blasting techniques. OB poses a risk by increasing operational expenses and compromising workplace safety. Therefore, accurately predicting the occurrence of OB during tunnel excavation is crucial. While various methods exist to forecast OB, traditional approaches like experimental, analytical, numerical, and regression methods face limitations due to uncertainties in geological and geotechnical parameters. In this paper, the use of Teaching–Learning-Based Optimization (TLBO) and Firefly (FF) algorithms is proposed to predict OB, aiming to fully comprehend the physical and mechanical characteristics of the rock mass while considering uncertainties and optimizing project completion in terms of cost and time. The model was constructed using data from three case studies: an Indian mine; the Azad tunnel on the Tehran-North route in Alborz, Iran; and the underground coal mine Tarzareh, comprising 217 data points. Parameters affecting the OB phenomenon in this study include rock mass rating (RMR), specific drilling (SD), perimeter holes powder factor (PPF), and spacing to burden ratio of contour holes (S/B). The dataset was divided into two groups: 80% for training the model and 20% for testing the relationship. To evaluate the model, statistical indices such as squared correlation coefficient (R²), root mean square error (RMSE), and mean square error (MSE) were used. The validation results indicated that the TLBO and FF algorithms performed satisfactorily, demonstrating high accuracy and low error. This suggests that engineers, scientists, and practitioners can benefit from employing intelligent approaches in mining and rock mechanics-related operations, utilizing the accurate model generated by these algorithms.

Accidents involving powered haulage and mobile equipment such as haul trucks often account for the greatest number of fatalities in the mining industry each year. Despite previous analyses that have identified root causes and other contributing factors, there is still a need to better understand the events leading up to these types of accidents, what lessons may be learned, and what strategies can be employed to prevent fatal accidents from occurring. This study examines naturalistic decision-making (NDM) using the critical decision method (CDM). The CDM is a retrospective interview approach used to explore time-limited, high-stakes decision-making that has not been often used in the mining industry. In this study, the CDM is used to obtain more information about what happens prior to, during, and after a potentially fatal situation such as a near-miss event, loss of control, or minor accident involving equipment damage. Researchers captured first-hand accounts from 21 haul truck operators involved in near-miss events from mine sites of various sizes and commodities throughout the USA. These accounts provide rich and detailed narratives from the perspective of haul truck operators themselves and reveal insights into what decisions haul truck operators make, what sensory cues they perceive, and what strategies they employ during challenging and non-routine situations so that haul truck operators can be better prepared in the future. Themes critical to operator decision- making emerged from the data with the top three including, know your truck, situational awareness, and safety first. These themes suggest that haul truck operators need to have a mastery level understanding of how their truck works in order to effectively react, that haul truck operators need to maintain an understanding of conditions and their environment, and that haul truck operators should prioritize safety when making decisions. To support haul truck knowledge acquisition and retention, mine operators may consider providing more detailed and hands on training including practice time in a variety of conditions. To support situational awareness, mine operators may consider investing in collision warning technologies and emphasizing good communication practices. Lastly, mine operators may consider continually emphasizing safety and their commitment to safe practices to help all mine workers internalize safety as a value, thereby reducing or eliminating related conflicts in decision- making. These results, along with potential solutions offered by study participants, can help to inform future research, raise awareness about hidden hazards, and build more creative interventions and realistic training scenarios for use by the industry to address haul truck safety issues.

Acoustic emission (AE) and electromagnetic radiation (EMR) can reflect the precursor information of rock burst and play important roles in rock burst monitoring, early warning, and prevention. However, the existing denoising methods of AE and EMR monitoring signals are poor, and the recognition of precursor information lacks comprehensiveness, accuracy, and real-time. This paper presents a novel method combining adaptive denoising and object detection to realize dynamic recognition of rock burst precursor information. Successive Variational Mode Decomposition (SVMD) adaptively decomposed the AE and EMR monitoring signals such as pulse and intensity into different mode components and Kalman Filter (KF) performed on each mode component to eliminate redundant noise. Furthermore, the YOLOX object detection algorithm recognizes the precursor information in the time–frequency domain after noise removal, including the time interval, frequency band, and energy. The case study illustrates that the precursor response of the AE and EMR monitoring signal in time–frequency domain is highlighted by denoising, and the average accuracy of different types of precursor recognition reaches 96%. Finally, the consistency of the identified precursor information and field records shows the feasibility and effectiveness of the method, which has practical guiding significance for improving the level of rock burst prevention.

This study focuses on examining the separation performance of spirals employed in a classified fine-sized (− 200 + 2 5 µm) chromite ore. Dwindling global reserves of high-grade chromium ores and dispersing minerals in the form of fine particles make it attractive to find alternative and efficient methods for enriching these minerals in an economically as well as environmentally sustainable manner. Experimental tests were conducted on three spiral concentrators with distinct geometries. The effects of pulp density, flowrate, and splitter blade positions on the separation efficiency and enrichment ratios were thoroughly examined. The separation variables were comparatively assessed, and response surface method (RSM) was employed for optimization. The results indicated that optimal separation performance was achieved using a small diameter spiral (Ø: 60 cm), whereas the least effective separation occurred in a large diameter spiral (Ø: 100 cm) with a lower pitch angle. The findings revealed that high flowrates and pulp densities adversely affected the separation efficiencies, and the positions of the splitters significantly impacted the quality of the obtained products.

Millions of tons of solid waste are generated by artisanal and small-scale gold mining in several regions of the world. This study focused on the tailings from the Abu Hamad artisanal gold mine located in northeastern Sudan. The results of the analyses carried out showed that this amalgam waste contained on average 5.5 g/ton of gold, 50 g/ton of mercury, 3.3 g/ton of silver, and 191 g/ton of copper. The particle size distribution was between − 10 and + 300 µm, and the average grain size was about 65 µm. Metal distributions showed that gold and mercury grades increased in fine-grained size fractions. X-ray diffraction analyses showed that quartz is the main constituent mineral phase of these residues. The presence of gold, mercury, and other accessory minerals such as sulfide and oxide minerals was revealed by the SEM–EDS. Microscopic analysis showed that majority of gold particles in these tailings are free while few others were occluded in quartz. The gravity tests carried out showed that the best gold recovery result was 47.18%. Bench scale stirred cyanide leaching tests showed that gold, mercury, copper, and silver can be recovered at 90%, 71%, 32%, and 22%, respectively, in 24 h. These high gold recoveries show that these tailings offer a possible commercial secondary resource for gold mining. These wastes contain high mercury grades, which can cause various environmental and public health problems, that is why new environmentally friendly treatment techniques should be developed to recover gold and mercury from these tailings.

Underground mining accidents have the potential of leaving miners trapped in unknown and life-threatening locations for an extended period of time. The lives of the trapped and unaccounted-for miners are at risk and require emergency rescue. But, the primary tracking systems are highly susceptible to damage during accidents and are most likely to be defunct and inoperable post-accident. This prompted the need for a robust and reliable post-accident communication and locator system. Subsequently, the through-the-earth (TTE) communication systems were developed and tested in underground mines. Under ideal conditions, these systems are capable of post-accident full-duplex two-way voice, text, and data communication and fingerprint detection of the geolocations of the trapped miners. This is achieved through a wireless link established by the transmission of electromagnetic and seismic waves between surface and underground, even in challenged underground environments. Unlike the primary tracking systems, the TTE communication systems do not require extensive shaft-to-workplace backbone infrastructure. This has made the TTE systems to be less susceptible to damage and therefore suitable for post-accident communication. Instead, the Earth’s crust acts as the signal transmission medium which forms an uplink and downlink communication path. This is achieved by injecting an electric current into the ground using electrodes, by transmitting magnetic fields from a radiating loop antenna, or by inducing fingerprint geolocations using seismic waves. Range and data rates are the critical requirements for the effectiveness of these systems and are dependent on factors such as the antenna design, frequency, and rock properties. This study provides a review of the applications of the different types of TTE communication systems, their evolution, factors that affect them, and techniques for improving their efficiencies and capabilities. These systems present the mining industry with an opportunity to improve safety by providing post-accident communication and locating trapped miners as quickly as possible. This will improve their survival chances and ultimately reduce fatality rates in the mining industry.

Composite air leakage from mining-induced fractures is a critical cause of coal spontaneous combustion and gas explosions in a shallow-buried goaf. Physics simulations and numerical calculations were performed to elucidate the dynamic evolution law of air-leakage fractures during mining. The results showed that overburden and surface fractures were the main channels for airflow in the goaf. Additionally, the generation of all fractures was primarily controlled by the key stratum. The dynamic development of overburden fractures was evident during mining, and the fractures underwent opening, closing, and stabilization. The spatial distribution of the overburden fractures was shaped like a double trapezoid. In the low trapezoid, the overall fracture density was high, but the middle fractures were poor because of compaction. In the high trapezoid, horizontal fractures were widely distributed and relatively large, and vertical fractures were mainly distributed on the sides and middle, which were interconnected with the horizontal fractures and penetrated the surface to form composite air-leakage channels. The abundance of fractures from the surface and goaf was the primary cause of multi-source air leakages deep behind the 2421–1 working face in the Baijigou coal mine.

Following the enactment of the Dodd-Frank Act in 2010, specifically Sect. 1502, US companies have been required to report utilizing conflict minerals from the Democratic Republic of Congo (DRC). The conflict mineral coltan, an ore consisting of elements tantalum and niobium, is central to this issue and engenders the need to track and trace the mineral’s supply chain. X-ray fluorescence (XRF) and laser-induced breakdown spectroscopy (LIBS) have been used, in combination with both unsupervised and supervised machine learning, to accurately classify coltan samples with known provenances. Sample spectra were first used as input data into unsupervised machine learning clustering algorithms, upon which dendrogram and constellation plots were generated. The classification achieved via unsupervised machine learning provided the proof of concept necessary to further investigate classification using supervised machine learning algorithms. The sample’s raw spectra were then used to train a supervised machine learning algorithm, consisting of a voting classifier relying on the results from random forest classifier (RFC), linear regression classifier (LRC), support vector classifier (SVC), and multi-layer perceptron classifier (MLPC). The classification achieved using raw spectra was able to achieve accuracies up to ~ 97%. The samples’ raw spectra were pre-processed using principal component analysis (PCA), and the pre-processed data was fed into the same supervised machine learning classifier described above. Accuracies of ~ 98% and ~ 96%, respectively, were achieved. When reviewing the predicted classifications arising from the use of these two different types of spectra, specifically reviewing the confidence score associated with each predicted provenance classification, it was possible to account for the incorrect provenance classifications returned by the voting classifier. If the predicted provenance and associated confidence score obtained via each spectra type was compared to the resulting provenance prediction and confidence score obtained by the other spectra type, and only the prediction with the higher associated confidence score was used, classification accuracies of 100% could be achieved.