Mining, Metallurgy & Exploration最新文献

The grouting treatment of the old goaf in a coal mine is an essential measure to ensure the safety of the road above it. A novel calculation model is proposed to more accurately determine the appropriate treatment range for the goaf on the road. Compared with traditional mining subsidence calculation models, this new model demonstrates improved fitting to the observed deformation in experimental studies. The deformation of the grouted area is caused by the residual deformation of the untreated goaf areas, and the deformation of the treated area is different from that of the area above the coal wall in the mining stage, which is made by the grouting reinforcement body after grouting treatment. The walking rule (walking probability) of the random medium theoretical walking model is enhanced in this paper to describe this distinction, and a calculation model suitable for quantitative analysis of surface residual deformation in road goaf after grouting reinforcement is established. The standard recommended method, the probability integral method, and a newly derived improved calculation formula are compared in this study. The treatment width predicted by the standard recommended method is the widest, reaching 182 m. The probability integral method predicts a narrower width of 139 m; while the improved calculation formula predicts the narrowest width of 124 m. Compared to the former two, the improved calculation formula not only considers factors such as the depth of the goaf, the overlying strata lithology but also the residual deformation and the grouting reinforcement body. An innovative and effective method for calculating the surface deformation of goaf areas after grouting treatment is developed, thereby offering a basis for designing more precise goaf treatment schemes.

Semi-mobile in-pit crushing and conveying (IPCC) systems can help reduce truck haulage in open-pit mines by bringing the crusher closer to the excavation areas. Optimizing a production schedule with semi-mobile IPCC requires integrating extraction sequence, destination policy, crusher relocation, conveyor layout, and truck fleet investment decisions. A mining complex with multiple mines and IPCC systems should be optimized simultaneously to find an optimal schedule for the entire value chain. An integrated stochastic optimization framework is proposed to produce long-term production schedules for mining complexes using multiple semi-mobile IPCC systems. The optimization model has flexibility to select the crusher locations and conveyor routes from anywhere inside the pits. The framework uses simulated orebody realizations to consider multi-element grade uncertainty and manage associated risk. A hybrid metaheuristic solution approach based on simulated annealing and evolutionary algorithms is implemented. The method is demonstrated using an iron ore mining complex.

In recent decades, the mining industry in the United States has made significant strides in reducing accidents and injuries. While these improvements are commendable, interpreting these statistics can be challenging due to concurrent declines in workforce size, employee hours, productivity, and operating systems. The Mine Safety and Health Administration (MSHA) of the United States has instituted tools like the Pattern of Violation (POV) and Significant & Substantial (S&S) calculator to monitor safety in mines. However, both have their respective limitations. Various risk indices have been proposed to address these limitations, leveraging multiple matrices from MSHA databases. Yet, the primary challenge lies in effectively integrating these diverse matrices into a cohesive risk index. This research endeavors to develop an information entropy–based risk (IER) index through the optimization of weights assigned to these sometimes-conflicting matrices. The seven-dimensional risk indicators considered for IER index computation encompass (a) citations, (b) orders, (c) significant & substantial citations, (d) penalties, (e) incidents with no lost time, (f) lost time injuries, and (g) proposed penalty for violation. The efficacy of the proposed IER index was assessed using data from MSHA’s underground mines spanning from 2011 to 2020. Validation of the IER index was conducted through application of the BIRCH clustering algorithm in tandem with rigorous statistical analysis. The clustering performance was evaluated using the multivariate analysis of variance (MANOVA) test, followed by post hoc analysis. Box plots and univariate analysis of variance (ANOVA) tests were then employed to substantiate the statistical significance of mean differences in IER index values across clusters. The MANOVA test and subsequent post hoc results underscore the successful clustering of the seven-dimensional risk indices across all time periods using the BIRCH algorithm. The ANOVA test unequivocally demonstrates that the mean risk index values of at least one cluster are statistically distinct from the others at a 95% confidence level for all periods. Post hoc analysis further confirms the statistical significance of differences in mean risk indices between clusters. These findings were further supported by the results obtained from the box plots. Finally, the proposed approach was applied to an underground coal mine to illustrate its practical effectiveness. This study demonstrates that the proposed approach can empower mining companies to comprehensively assess their safety performance and implement necessary measures for improvement.

A geometallurgy study aims to link metallurgy and geology to reduce technical risk and enhance the economic performance of a mineral-processing plant. It does so by accounting for variability in a deposit to develop cash flow models with variable throughput rates. High-quality sample selection for metallurgical test work that are representative of the deposit is an essential component of a geometallurgy study, but the large multi-dimensional dataset makes sample selection a daunting task, as classifying the dataset while respecting its heterogeneity is difficult. This paper presents a streamlined approach for sample selection, utilizing statistical analysis techniques in Python. It cuts down time to select samples from around 1200 s per drillhole to about 60 s per drillhole for data classification and from 12 h to 8 h for handpicking samples from the classified dataset, translating to cost savings. The cumulative sum method and k-means clustering method are used in the methodology to elegantly classify the data and select representative samples. The effectiveness of the methodology is demonstrated by presenting data from a pre-feasibility study of a copper-iron mine in which 40 samples were selected for flotation test work.

To investigate the alterations in mechanical properties and damage mechanisms of water-immersed mudstone, the mudstone from the weak interlayer of an open-pit coal mine in eastern Inner Mongolia, China, was selected as the research focus. Mudstone samples with water contents of 0, 4.12%, 7.96%, 10.17%, and 12.73% were obtained through non-destructive immersion tests. Meso-mechanical tests on mudstone with different water contents were conducted using nanoindentation technology and scanning electron microscopy. The findings reveal a noticeable weakening of meso-mechanical properties of mudstone after water immersion. The elastic modulus, hardness, and fracture toughness all show a trend of non-linear attenuation with the increase of water content. Water-immersed mudstone exhibits changes characterized by volume expansion, decreased mineral cementation ability, reduced pore size, and increased quantity. The alteration in mudstone’s internal structure directly reflects the physical reasons for the weakening of mechanical properties after water immersion.

The construction of ore pass systems in underground mines is a high-risk activity, especially in an environment with incompetent rock mass. This study aims to investigate the optimal method for ore pass construction in incompetent rock masses. We evaluated the conventional and raise boring (RB) methods based on safety, efficiency, excavation control, and ground support for ore pass construction. We also performed a stability analysis using analytical Q-raise (Q_R method) and kinematic analysis methods for ore pass construction with a Raise Borer before and after grout injection of the rock mass. As a case study, an ore pass (diameter, 3 m; depth, 100 m) within an incompetent rock mass was considered to gain further insight. The rock mass was characterized according to the classification methods Q Barton, rock quality designation (RQD), rock mass rating (RMR), and geological strength index (GSI). The grout intensity number (GIN) method of grout injection is used. The safety factor (<1.075) obtained before injection was lower than the acceptance criteria in all sections of the rock mass. However, grout injection before Raise Borer excavation resulted in a rock mass safety factor greater than 1.5. Using RB without pre-grouting in the case study indicated that the maximum unsupported diameter (MUSD) of the ore pass was less than the required 3 m. On the contrary, an MUSD of the rock mass post-grouting was equal to or larger than 3 m at all depths.

This paper investigates the flotation of cassiterite (SnO₂) from ore using ricinoleic acid (RA) as a collector which is cheap and environmentally friendly. It is shown that the flotation is significantly enhanced by the activation of lead cations at pH 8. The flotation results are explained and supported by further studies to determine the changes in surface properties (hydrophobicity and surface potentials) and adsorption of RA and lead cations using X-ray photoelectron spectroscopy (XPS), time-of-flight secondary ion mass spectrometry (TOF-SIMS), and FTIR. The results of surface (zeta) potential measurements and TOF-SIMS indicate that the amount of RA anions on the Pb-activated SnO₂ surface was higher than that on the natural SnO₂ surface. The XPS results revealed that RA anions were bound to the Sn atoms on the natural SnO₂ surface. In contrast, RA anions reacted with the Pb atoms instead of Sn atoms on the activated SnO₂ surface, improving the floatability of Pb-activated SnO₂. Pb(RA)₂ precipitation occurred on the Pb-activated surface, and H bonds were formed between two RA anions in Pb(RA)₂, which lead to a tighter assembly of collector species on the SnO₂ surface. The outcomes of this research shed light on the application of the cost-effective and environmentally friendly RA collector in cassiterite flotation.

Whole-body vibration (WBV) is a substantial occupational health and safety hazard to heavy earth-moving machinery (HEMM) operators. There is a need to appraise the effect of WBV jeopardize and the factors influencing the WBV risk exposure on the HEMM operators. Seven machine learning (ML) models were tested on 81 data samples collected from seven underground metalliferous mines. The study considered nine factors which have substantial role behind the intensity of the WBV risk exposure of HEMM operators. RReleifF algorithm was used for dimensionality reduction and ranking the features. Compared to other ML techniques, ANN model was determined to be the most effective approach. The nine considered features were reduced to five features using RReleifF algorithm. The ranking of the five features selected was in order of awkward posture, the machine age, haul road condition, speed, and seat thickness based on their weights. Finally, a predictive equation was developed using the aforementioned five features. This study will help the seven underground mines authority to evaluate the WBV risk exposure effortlessly without the usage of scientific instrument and also helps in adopting immediate control measures to mitigate WBV risk exposure of HEMM operators.

This paper describes a computational and experimental approach to electrodeposition of silicon using a MgF₂-CaF₂-CaO-Y₂O₃-SiO₂ molten salt electrolyte and a yttria-stabilized zirconia solid oxide membrane at the anode. A secondary and tertiary current density distribution model shows anodic current density between 0.5 and 1 A cm⁻² with a fairly even distribution along the anode surface except at the ends of the anodes. Finite element analysis of industrial cell magnetohydrodynamics (MHD) shows electrolyte flow to be 23 times slower compared to a calculated analytical model. The experiments demonstrate formation of highly pure silicon in the melt with particle sizes ranging from a few μm to clusters of 2 ~ 3 mm. Finally, the mechanism of Si formation based on a short thermodynamic analysis was discussed.

Silicomanganese (SiMn) slag is a by-product of ferromanganese and SiMn alloy production poses significant challenges in terms of environmentally sound disposal given its substantial volume. This brief review aims to assess the physicochemical attributes of SiMn slag and explore its potential applications in construction materials recycling. To accomplish this, we systematically evaluated 20 relevant articles, categorizing them into segments covering reutilization methods, key considerations, enhancement strategies, and the recent challenges and prospects associated with SiMn slag reutilization. Our analysis encompassed SiMn slags from five countries, revealing consistent chemical compositions characterized by SiO₂, Al₂O₃, CaO, MnO, MgO, FeO + Fe₂O₃, and K₂O + Na₂O at similar proportions. We identified two distinct types of SiMn slag, i.e., air-cooled and water-quenched, each possessing unique physical properties influencing their suitability for reutilization. SiMn slag has been successfully repurposed into various construction materials, including cement paste, mortar, concrete, alkali-activated matrices, bricks, backfill materials, Mn extracts, and binder/cement. Several critical factors must be considered when reutilizing SiMn slag in construction materials, including cooling methods, moisture content, particle size (fineness), equipment, energy requirements, and cost considerations. To enhance the reutilization process, we propose a structured approach consisting of four key steps, i.e., incoming waste assessment, pre-treatment, physical/chemical treatment, and product development. Furthermore, this review suggests several avenues for future research, including the development of industrial-scale recycling applications, exploring environmentally friendly landfilling methods for SiMn slag, and assessing the practicality and feasibility of SiMn-slag-based products in real-world construction projects.