Mining, Metallurgy & Exploration最新文献

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.

Managing uncertainty is a core challenge in mine planning. Mine planners often represent various planning variables, such as equipment performance and geological parameters, as random variables due to inherent uncertainties. This paper looks at geological uncertainty and its impact on mine planning. Some traditional approaches to manage this uncertainty include using conditional simulations or mathematical programming in the planning process. Drilling additional holes, despite its cost, is a common method to reduce uncertainty using additional samples to reduce deposit variance. In this paper, we first outline an ore blending optimization model which uses chance-constrained programming to manage property limit risk when selecting the order of ore feed into a processing facility. In coal mining, in tactical planning horizons, the order of coal seam removal is usually predetermined, allowing a blending model to ensure optimal feed properties. Using chance-constrained programming allows us to blend the uncertainties from geological models to maximize plant output while adhering to property constraints. We use the chance-constrained blending model to determine the value of additional information from infill drilling. The model prioritizes drilling locations that reduce uncertainty and improve blending outcomes. A case study on a coking coal mine in Queensland, Australia, demonstrates the model’s application, highlighting significant improvements in blending by reducing the variance of high-quality blocks. The study concludes that targeting high-quality blocks for variance reduction can better accommodate lower-quality material, offering a more valuable approach than the traditional focus of reducing uncertainty in low-quality blocks. This approach provides insights for improving mine planning strategies and showcases the potential of chance constraints in optimizing ore blending under uncertainty.

The presence of joints in rocks significantly impacts the mechanical behavior and stability of a slope. A better comprehension of the relationship between jointed rock masses and slope stability has been made possible by recent advances in machine learning algorithms and numerical modelling. The purpose of this research is to predict the stability of slopes in the presence of different types of joints (parallel deterministic, cross jointed, Baecher, Veneziano, and Voronoi) with the help of classification-based machine learning algorithms. In order to achieve this goal, 40,290 different cases have been utilized, following numerical simulation using shear strength reduction (SSR) technique in RS2. Geomechanical properties, parameters defining slope geometry, structural properties of joints including properties of filling materials, and the influence of certain external factors have been considered. For these datasets, classification algorithms such as random forest, k-nearest neighbor, support vector machine, logistic regression, decision tree, and Naive Bayes have been utilized. Additionally, the synthetic minority oversampling technique (SMOTE) has been implemented in order to address imbalanced class problems. The results exhibit an encouraging level of accuracy, with random forest and decision tree both achieving 0.98 as an overall accuracy.

The excavation of steeply dipping ore deposits using sub-level mining techniques with delayed backfill can cause stress relaxation and concentration in the stope hanging wall and footwall at deeper depths. Designing adequate crown pillars that can withstand significant horizontal stress and prevent the collapse of the hanging wall and footwall is crucial for ensuring safe mining operations. This study developed a methodology for predicting the appropriate crown pillar thickness for depths between 510 and 1000 m based on 240 non-linear numerical models with Mohr–coulomb elastoplastic failure criteria under plane strain conditions with five parameters affecting crown pillar stability. A precise and reliable empirical equation has been devised to compute the safety factor (SF) of the crown pillar. The equation has a high predictive capability with an R² value of 0.85. Design charts were developed for various geo-mining conditions and working depths to estimate the optimal crown pillar thickness.

This study developed a Gaussian process regression (GPR) model to predict and optimize blast fragmentation at Wolongan Mine by using the primary data from the mine and secondary data from other mines. The blast data comprised 125 datasets, each containing seven blast design parameters as inputs and the muckpile mean fragment size as the model output. Additionally, the study developed artificial neural networks (ANNs), support vector regression (SVR), and multiple linear regression (MLR) models, and compared their prediction performances with the GPR model. The models’ accuracies were evaluated using five statistical metrics, including coefficient of determination (({R}^{2})), root mean square error (RMSE), variance accounted for (VAF), mean absolute bias error (MABE), and mean absolute percentage error (MAPE). The GPR model outperformed the other models, with ({R}^{2}), RMSE, VAF, MABE, and MAPE values of 0.9302, 0.0487, 93.2670, 0.0383, and 13.9405, respectively, for the test data. Based on the top-down correlation and Kendall’s coefficient of concordance analyses on the four sensitivity analysis methods used, the study found that the in situ block size and Young’s modulus of the rock were the most important parameters affecting fragmentation. Using the GPR model, the study showed that reducing the blast burden by 13–23% could decrease the mean fragment size of the muckpile at Wolongan Mine by 6–12%, leading to a significant reduction in the percentage of boulders.

Sophisticated models have progressively been developed to address the challenges related to long-term, open-pit mine planning under conditions of geological uncertainty. Prior research has acknowledged that strategies for mine planning and the design of mineral concentrators are interdependent; thus, it is highly desirable to optimize them together. However, achieving detailed holistic optimization of the entire mineral value chain remains unresolved because of the inherent limitations associated with mathematical formulations and computational processing capacity. This paper details a method that contributes to bridging these limitations by employing a novel parallelized variable neighborhood descent approach combined with an embedded mass–balance component using linear programming techniques refined through Dantzig–Wolfe decomposition. This approach is exemplified through a case study of a gold deposit, which illustrates the enhanced performance capabilities of the new algorithm. The findings demonstrate significant improvements in the optimization process for mine planning, providing a stronger link between the mine’s output and processing plant’s capabilities.

The Ochre-Umm Greifat area is one of the Red Sea areas with high concentrations of iron and zinc, which is formed from hydrothermal solutions as a result of the structural activity that occurred in the Red Sea Zone during the Pleistocene period. These deposits are also accompanied by deposits of low- to high uranium grade. In addition to Zn, Pb, and Cu anomalies, particularly in fault zones and their branches affecting the study area, although there are numerous zinc minerals in the Ocher-Greifat area, uranium minerals are scarce, with only one mineral, compreignacite, being recorded and the majority of the uranium being present as an adsorbed element on iron and/or clay stones. In addition, uranothorite is extremely rare and occurs as fine grains embedded in rocks. A technological sample was taken from an iron-rich clay area in a fault zone and was found to assay 700-ppm uranium. The leachability of uranium from the used sample was investigated using an alkaline solution based on the chemical and mineralogical composition of the used sample. The selected ore is treated with Na₂CO₃ and NaHCO₃ in the presence of H₂O₂ as oxidant. Many digestion factors are studied and optimized. Under the optimum leaching conditions, the uranium dissolution efficiency is around 84%. For the uranium separation, the pH of the leach liquor is adjusted at 10, then subjected to a solvent extraction step using 4% Aliquat^®336/kerosene in the presence of isodecanol as third-phase prevention. The loaded organic solvent was then treated with NaOH/H₂O₂ solution as a stripping solution. Finally, the resultant solution is subjected to a precipitation step using ammonia solution.

This paper presents engineering design and equipment selection for a successful temporary spot cooling installation used to support underground shaft sinking. The cooling system operated over a 22-month period from August 2021 to June 2023. The need for cooling was driven by the depth of shaft sink, which started from greater than 1900 m below surface. The system was subject to many of the common challenges preventing the widespread use of underground spot cooling, including limited process water and dewatering capability, heat rejection equipment placement in the path of blasting fumes, limited airflow quantity for heat rejection, and layout constraints due to the existing and upcoming mine services installations and construction. Use of hybrid cooling towers allowed for increased heat rejection capacity from evaporative cooling while maintaining a fully closed-loop condenser water circuit. Skid-mounting of all components allowed for easy placement and relocation. Use of HDPE piping lashed to existing ground support allowed for maximum layout flexibility and minimized installation time. Performance, operational features, and additional lessons learned, including feedback from operations personnel, are shared.

The optimum fragmentation size measures the quality of a blasting operation. Boulders or large fragments can result in more costs because they need secondary blasting, while small fragments can result in ore loss and dilution. Therefore, it is very significant to accurately predict the mean block size to reduce production costs and enhance efficiency. Due to the shortfalls of the empirical models, scholars have been inclined toward artificial intelligence (AI) techniques for fragmentation size prediction over the decades. Firstly, in this study, three tree-based models, i.e., the random forest (RF), extra-trees (ET), and CatBoost (CB), are employed for basic prediction. The model uses eight parameters, seven input parameters, and mean block size (MBS) as the output parameter. Secondly, their performance and hyper-parameters were fine-tuned using Bayesian optimization: tree-structured Parzen estimators (TPE) algorithm using Optuna. Among the three models, the TPE-ET model showed superior performance with the following metric scores on the training dataset: 0.9896, 0.0184, and 0.0003, and on the test dataset with the following metric scores: 0.9463, 0.0415, and 0.0017, i.e., R², RMSE, and MSE, respectively. In conclusion, analysis by the SHapley Additive ExPlanations approach shows that elastic modulus significantly impacts the model’s prediction of rock fragmentation.