Mining, Metallurgy & Exploration最新文献

United States federal laws mandate that mining companies ensure a safe workplace, implement approved training programs, and promptly report work-related injuries. The mining industry’s commitment to innovation reflects a history of adopting technological advancements to enhance environmental sustainability, workplace safety, and vocational training. The objective of this research was to develop an augmented reality (AR) system for heavy equipment operators (HEOs) in surface mining. The developed system has the potential to enhance mine safety, training, and data-driven decision-making, which presents a significant step toward a more sustainable, effective, and technologically driven mining training, contributing to the industry’s evolution and growth. The AR Training System leverages Microsoft’s Power Platform and HoloLens 2 capacities to provide operators with detailed, immersive training guides for three mining equipment including bulldozers, motor graders, and end dump trucks. These AR guides combine 3D objects, informative images, and videos to enhance learning and safety. The system also provides an efficient approach to data collection during HEO training, having the potential to modify the training guides based on user performance. The system was developed and applied via a case study in a surface mine in the southern United States.

The thermal conductivity (TC) of rocks is an essential parameter for geothermal investigations and heat transport modeling under the ground. Although Turkey has a remarkable geothermal potential, investigation of rocks’ thermal conductivity has been very limited. The aim of this study is to investigate the relationships between TC and significant engineering parameters (uniaxial compressive strength (UCS), point load index (PLI), ultrasonic pulse velocity (UPV), indirect tensile strength (BTS), Schmidt hammer rebound number (R), Leeb hardness (H_L), density, and apparent porosity) of basalt samples. In addition to the engineering properties, TC correlated with the serpentinization of olivine and some chemical elements (O and Si). The study area was divided into three categories (A1, M2, and M3) according to the alteration zones with stratigraphically different levels. Petrographic thin section studies, SEM (scanning electron microscopy), and EDS (energy dispersive spectroscopy) analyses were also carried out to recognize the particles. This study demonstrated that the thermal conductivity values depend on the engineering properties of basalts due to the progressive serpentinization of olivine minerals. Serpentinization of olivine was found approximately 10% for A1 basalts, while this value was around 80% for M3. A strong relation was found between TC and serpentinization of olivine minerals for all samples and average A1, M2, and M3. The most significant factors affecting the serpentinization are proximity to the volcano cone and fault contact.

Safety in conjunction with production is a reality achieved in underground mining, where roof or side falls can have devastating effects on operations. A precise understanding of the roof structure is crucial for designing effective support systems that mitigate ground-fall risks. A key finding underscores the significance of this understanding. Sub Surface Profiler Ground-Penetrating Radar (SSPGPR) technology, utilizing real-time data and wirelessly transmitted signals, plays a pivotal role in achieving accurate knowledge of the roof structure. Geotechnical approaches, incorporating SSPGPR algorithms, facilitate continuous recording of sub-horizontal reflections through the lithology, optimizing roof support with accurate images of unexplored rock structures. The technology’s practical application in the Saoner group of underground mines highlights its effectiveness in mapping various zones within the roof rock strata, aiding excavation and support methods. SSPGPR is instrumental in detecting unmined strata profiles not evident in borehole data during exploration, emphasizing its transformative impact on efficiency and safety in underground mining. The correlation between fault zones mapped by SSP and ground faults further validates its effectiveness.

In the current study, two algorithms, custom XG Boost (CXGBA) and improved genetic XG Boost algorithm (IGXGBA), have been chosen to create an empirical formula for the simultaneous prediction of the mean fragmentation size (MFS) and the peak particle velocity (PPV) with sourced datasets of geo-blast parameters such as spacing burden ratio (S/B), stemming length (T), decking length (DL), firing pattern (FP), total quantity of explosive (TE), maximum charge per delay (MCD), measuring distance (MD), joint angle (JA), joint spanning height (JSP), joint set number (Jn), and rock compressive strength. Advanced technical combinations like K-10 cross-validation, and grid search executed along genetic algorithm processes with a high mutation rate to XGBoost algorithm. All algorithms were executed using Python programming in the Google Colab platform. The results unveiled that IGXGBA is superior and effective in-terms of metric R², RMSE, and MAPE in predicting MFS and PPV. A WEB APP called Bhanwar Blasting Formula (BBF) was created utilizing Google Cloud Platform (GCP) and FLASK APP to benefit practicing mining engineers to predict blasting results easily from the site itself and identify optimization.

In recent decades cyanide is the most widely used for the extraction of gold and silver, for being economical and efficient, however, other alternatives have been considered because of its toxicity to the environment, for this reason in this work we study a new leaching agent that seeks to be a viable alternative to cyanide, which is commercially called DEZO and is considered ecological due to the low quantity of the main complexing agent which is cyanate, and other components such as sodium oxide, nitrogen, ammonium, calcium, iron, which is used for gold and silver extractions. For the development of the study a gold and silver ore provided by the mining company "Las Chispas", located in Arizpe, Sonora, Mexico, was used. The ore contains 15.50 g/T of Au and 1550 g/T of Ag. Leaching was carried out at moderate pressures using sodium cyanide and DEZO as lixiviants for Au and Ag extraction. XRD and SEM–EDS analyses confirm the presence of quartz, fluorite and argentite species. Pressure leaching was performed using NaCN, with conditions of T = 70 °C and P = 0.62 MPa, NaCN [300 mg/L], -270 mesh, 20% solids, time 1 h and 600 rpm, obtaining 98.3% extraction of Au and only 8.8% of Ag. Next, pressure leaching was performed using the DEZO eco-friendly lixiviant, with conditions of T = 70 °C and P = 0.62 MPa, NaCNO [300 mg/L], -270 mesh, 20% solids, time 1 h and 600 rpm, obtaining 93.9% Au extraction and only 7.7% Ag. Subsequently, the adjustment of the shrinking core model was performed by varying the temperature in the pressure leaching, the activation energy (Ea) using both leaching reagents (NaCN and DEZO) was less than 20 kJ/mol, which defines that the gold and silver leaching are controlled by diffusion through the product layer.

Samarium is a rare earth element that exhibits variable valence states of + 2 and + 3. In this work, we present the reduction products obtained through calciothermic reduction of SmF₃ at various molar ratios of Ca to SmF₃. The crystal structure, morphology, elemental distribution, and chemical valence of the reduction products were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS). The results show that SmF_2.028 and CaF₂ are the sole reduction products obtained under molar ratios of 0.5, 1, 1.5, and 2 for Ca to SmF₃, whereas some unreacted metallic Ca is detected in the products at a molar ratio of Ca to SmF₃ of 2. The samarium ions in the reduction products exhibit mixed valence states with a relative content of approximately 9:1 for Sm³⁺ and Sm²⁺. Notably, the large amount of adsorbed oxygen present in the products oxidizes Sm²⁺ to Sm³⁺.

Hydrometallurgical processes generate large volumes of aqueous effluents, which are being treated and disposed in tailings ponds. Effluent desalination, i.e., clean water recovery for reuse in process circuits, is key to attain a zero liquid discharge future in the industry. In this study, we report on the use of hydrate-based desalination (HBD) to treat a synthesized effluent from the zinc industry. HBD is an innovative, energy-efficient, and sustainable desalination technology, capable to treat hydrometallurgical effluents to recover water in the form of gas hydrates by consuming CO₂. Water recovery and total dissolved solids (TDS) removal efficiency of 42 ± 2% and 60 ± 4% were achieved in a three-stage HBD process. Further, CO₂ nanobubbles (NBs) were tested as a sustainable kinetic promoter of the process. The desalination outcomes verified that CO₂ NBs played a crucial role in enhancing the kinetics of the process. Specifically, the presence of CO₂ NBs resulted in a notable increase in water recovery, which reached 60 ± 2%, accompanied by a TDS removal efficiency of 53 ± 1% in a three-stage HBD process.

This paper addresses the surrounding rock control problem of deep roadways in broken soft rock. The 21914 working face haulage roadway in the Zhangshuanglou coal mine was taken as a case study. The deformation characteristics and failure mechanisms of the roadway surrounding rock were analysed via theoretical analysis and numerical simulation. A classification support technology was proposed and then applied to the studied roadway. This study indicated that the high stresses, mining disturbances and mechanical properties of soft rock resulted in large deformations developed over long periods, leading to the destruction of the deep roadway in the soft rock. The failure depth of the upper goaf floor was 15.84 m, and the development radius of the plastic zone during roadway excavation was 9.55 m. The roadway deformation was positively correlated with the thickness of the interbedded fractured coal and negatively correlated with the thickness of the fractured sandy mudstone. This paper proposed a classification support technology with the main steps of surrounding rock status identification, parameter determination and graded support; the chief support measures were the addition of grout, bolts, anchor cables, steel strips, steel beams and trapezoidal sheds. The field work showed that classification support could effectively restrain the large deformation of the surrounding rock. This research can provide a reference for the stability control of other roadways under similar conditions.

The western part of the Moroccan Anti-Atlas comprises numerous copper occurrences hosted within various sedimentary rocks, all containing low-grade copper concentrations. This study aims to assess the feasibility of using a near-infrared (NIR) sorting system to efficiently process these low-grade resources. In essence, it involves evaluating the potential of short-wave infrared (SWIR) spectroscopy and machine learning models to classify ore fragments into waste or concentrate based on their SWIR spectral characteristics. In order to conduct this study, the SWIR reflectance of 475 rock samples from the Tizert deposit was measured. Mineralogical analysis was performed, using X-ray diffraction and scanning electron microscopy, to understand the mineralogy of the samples and its relationship to SWIR spectra. Chemical analysis was also performed to categorize samples based on their copper content. Several machine learning models, including partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) were evaluated based on both lithology and copper content characteristics. Among these, PLS-DA yielded the most favorable results, achieving an 84% accuracy in lithologies classification and 90% accuracy in classifying samples based on their copper content, utilizing a 0.2% cutoff grade. This laboratory-scale study validates the effectiveness of SWIR spectroscopy as a prominent tool for pre-concentrating sedimentary copper deposits. It enables the production of a concentrate with a copper content of 1.49% and waste with 0.12%, resulting in an upgrading rate of 43% from the feed, which originally has a copper grade of 1.04%.

A worker’s personal exposure to respirable dust in occupational environments has traditionally been monitored using established methodologies which entail the collection of an 8-h representative sample that is sent away for laboratory analysis. While these methods are very accurate, they only provide information on the average exposure during a specific time period, generally a worker’s shift. The availability of relatively inexpensive aerosol sensors can allow researchers and practitioners to generate real-time data with unprecedented spatial and temporal granularity. Low-cost dust monitors (LCDM) were developed and marketed for air pollution monitoring and are mostly being used to help communities understand their local and even hyper-local air quality. Most of these integrated sensing packages cost less than $300 per unit, in contrast to wearable or area dust monitors specifically built for mining applications which have been around for decades but still average around $5000 each. At the National Institute for Occupational Safety and Health (NIOSH), we are leveraging the power of high-volume data collection from networks of LCDM to establish baseline respirable hazard levels and to monitor for changes on a seasonal basis as well as following any application of control technologies. We have seen the effective use and advantages of monitoring live data before, during, and after events like shift changes, operational changes, ventilation upgrades, adverse weather events, and machine maintenance. However, many factors have prevented a systematic adoption of LCDMs for exposure monitoring: concern for their analytical performance, the complexity of use, and lack of understanding of their value are some factors. This contribution outlines a 1-year case study at a mine in Wisconsin, USA, covering the installation, maintenance, data visualizations, and collaboration between NIOSH researchers and the industrial hygiene professionals at the mine.