Mining, Metallurgy & Exploration最新文献

Rare earth elements, as strategic resources, have garnered global attention. Among these elements, bastnaesite stands out as one of the most abundant rare earth resources. It has various production processes, with carbochlorination being one of the most effective for rare earth recovery. We propose a carbochlorination process for bastnaesite using aluminum chloride produced in situ from alumina, which serves as the fluorine-fixing agent, and coke, which serves as the reducing agent. In the carbochlorination process, to prevent raw material from splashing during the reaction in the packed bed, a binder is typically added, and a reducing agent is used for balling. The impact of various binders on the strength of bastnaesite pellets was investigated, and the bonding mechanisms of the binders were analyzed and discussed. With pellet strength as the primary focus, an experimental investigation was conducted on the factors affecting binder addition, raw material particle size, water addition, and drying temperature. The results indicated that a raw material particle size of 100 mesh, a binder additive amount of 3%, a water addition of 11%, and a drying temperature of 100 ℃ were optimal experimental conditions. Under these conditions, the dry and wet ball drop strengths were 52.5 times and 10.5 times greater, respectively, and the wet and dry compressive strengths were 760.71 N/cm² and 2.79 N/cm², respectively. To reduce experimental costs, the composite binder and its doping ratio were explored. Finally, pellets prepared with the three binders were selected for experimental verification of carbochlorination.

This research proposes empirical models to estimate pillar strength by adopting multilinear regression and artificial neural network approaches for rock salt mines of the Salt Range, Punjab, Pakistan. The field data of a total of 168 pillars was collected from three (03) selected rock salt mines being operated by Pakistan Mineral Development Corporation. The field work included geometry of pillars, Schmidt rebound hardness (SRH), uniaxial compressive strength (UCS), fracture spacing, fracture condition, joint-orientation, groundwater state, weathering effects, blasting effects, and mining-induced stress. The dataset collected from the field for each rock salt pillar was further utilized to determine rock quality designation (RQD), rock mass rating (RMR), mining rock mass rating (MRMR), design rock mass strength (DRMS), and pillar strength (({sigma }_{p})). The modeling was done using a dataset of 150 columns, and the remaining data of 18 pillars was left for validation purposes. The proposed ANN and MLR models have R-square (R²) values of 95.35% and 91.61%, respectively. Further, the prediction performance of the ANN model was also compared with that of multilinear regression (MLR). It was found that the ANN model outperformed the MLR model.

To reduce the environmental footprint of hydrometallurgical processing of black mass from spent lithium-ion batteries (LIBs), a green leaching system based on glycine and sodium metabisulfite (Gly-SMS) was proposed. The novel leaching system was validated using black mass from end-of-life batteries and manufacturing scrap from battery producers, representing the two dominant black mass types processed in the market. The leaching study demonstrated that the highest cobalt and lithium recoveries of 100% and 99.8% were achieved under optimal conditions. The leaching mechanism revealed that the dissolution of LiCoO₂ in the Gly-SMS solution followed the shrinking core model. The apparent activation energies for cobalt and lithium were determined as 48.05 kJ/mol and 41.51 kJ/mol, respectively, indicating a surface chemical reaction controlling mechanism. The leachate was then processed by an acidification-precipitation technique with oxalic acid as the precipitant to remove cobalt. Glycine complexes with metal ions by zwitterionic ligand and recycles in the leaching-precipitation circuit, reducing the reagent cost. Compared to other studies, this leaching system has near-neutral operating conditions and is cost-effective, making it an economically viable alternative for treating cathode materials from spent LIBs.

Tenuous dilution estimates in underground mine production scheduling continue to cause significant variations between schedule forecasts and actual production. This arises partly from the inference of dilution from predecessor stopes’ performance, disregarding that these stopes would have undergone multiple intermediate design changes between scheduling and actual mining. The resultant drill and blast-influenced dilution factors gradually lose its robustness over longer planning horizons or when applied to greenfield or brownfield expansions that do not have prior performance data. To overcome this problem, a new methodology is proposed to predict dilution in underground sub-level open stoping (SLOS) using basic geological, geotechnical and stope design attributes available in the early stage of mine planning. The method utilises principal component analysis (PCA), classification and regression tree (CART) algorithm and stepwise selection and elimination (SSE) analysis. First, SSE analysis was conducted to identify the most important independent variables to be used with the CART algorithm (i.e., the SSE-CART model) to provide a predictive model. PCA analysis was then performed, and the new principal components were used to propose a new comparative model (i.e., the PCA-CART model). Low R² values were observed for both models, necessitating the consolidation of dilution categories to increase the models’ prediction bandwidth. The hybrid PCA-CART model outperformed the SSE-CART model with overall F1 score prediction accuracy of 72% and target dilution category prediction accuracy of over 93% against SSE-CART’s 70% and 72%, respectively. Importantly, this study revealed a 13% minimum underestimation of dilution relative to the original design stopes.

The development of autonomous shearer height adjustment technology, a crucial component of generalized mining automation, is covered in this study. This study examines the main technical development research in the two directions of coal-rock interface detection and memory cutting in order to investigate the development of shearer auto-height adjustment technology. The development of five methods, such as image recognition method, is introduced in detail in coal rock identification. It lists the shortcomings of each approach and provides an overview of the major variables influencing the advancement of shearer auto-height adjustment technology. Based on the current state of height adjustment technology development and the demand for coal mine intelligence, the following development outlook for auto-height adjustment of shearers is suggested: integrating a variety of cutting-edge technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data (Big Data), along with the safety mechanism, to create a more complete and effective auto-height adjustment system for shearers. The article concludes by highlighting ongoing research in this area, which uses data expansion to address the issue of poor data quality while also allowing for the combination of machine learning algorithms, data expansion by the appropriate network model to train high-quality and high-precision models, and the development of memory cutting technology to create a comprehensive, continuous, and accurate independent height adjustment control system of the shearer.

The use of Artificial Intelligence (AI), particularly of Artificial Neural Networks (ANN), in alerting possible scenarios of methane explosions in Colombian underground mines is illustrated by the analysis of an explosion that killed twelve miners. A combination of geological analysis, a detailed characterization of samples of coal dust and scene evidence, and an analysis with physical modeling tools supported the hypothesis of the existence of an initial methane explosion ignited by an unprotected tool that was followed by a coal dust explosion. The fact that one victim had a portable methane detector at the moment of the methane explosion suggested that the ubiquitous use of these systems in Colombian mines could be used to alert regulatory agencies of a possible methane explosion. This fact was illustrated with the generation of a database of possible readouts of methane concentration based on the recreation of the mine atmosphere before the explosion with Computational Fluid Dynamics (CFD). This database was used to train and test an ANN that included an input layer with two nodes, two hidden layers, each with eight nodes, and an output layer with one node. The inner layers applied a rectified linear unit activation function and the output layer a Sigmoid function. The performance of the ANN algorithm was considered acceptable as it correctly predicted the need for an explosion alert in 971.9 per thousand cases and illustrated how AI can process data that is currently discarded but that can be of importance to alert about methane explosions.

This study integrated geophysical methods (ground magnetics, electrical resistivity, and induced polarization measurements) in conjunction with fire assay and inductively coupled plasma-atomic emission spectrometry techniques to delineate orogenic gold mineralization potential zones in the Kushaka greenschist belt. Different edge detection filters and a 3D Euler deconvolution technique were applied to magnetic data to delineate geologic structures that control orogenic gold mineralization in the study area. VOXI Earth Modeling™ software was applied to induced polarization and electrical resistivity data to generate gold mineralized targets in the study area. Based on the geochemical findings in this study, orogenic gold mineralization in the belt is associated with galena, sphalerite, monazite, bastnaesite, and manganese oxide minerals and has a metamorphic origin. The total magnetic field results indicate that NE-SW and NW–SE trending structures are primarily associated with gold assay hotspots, indicating that orogenic gold mineralization in this belt is connected to Pan-African orogenic events. Fractured zones with disseminated gold-sulfide deposits and hydrothermal alteration halos exhibit low resistivity and high chargeability signatures. However, the occurrence of disseminated gold-sulfide deposits that infilled quartz veins and fractured zones in the intensely silicified metasedimentary rocks exhibit high chargeability and high resistivity signatures. The produced gold mineralized targeting model correlates well with geologic structures, metasedimentary rocks, and gold hotspots, indicating that lithologies and geologic structures preferentially control orogenic gold mineralization in the belt. Hence, the information gathered in this study would assist miners and academia in determining the drill-hole locations for future gold exploration programs in the area.

Mine coal flow transportation has some typical features of long-distance and complex environments. The transportation equipment usually adopts the mode of constant speed, which makes a large amount of energy waste. To solve these problems, the characteristics of the coal flow transportation system are analyzed. Based on a principal component analysis-convolutional neural network (PCA-CNN), the operation parameters optimization method of coal flow transportation equipment is proposed. Taking the transport time, transport cost, and equipment utilization of belt conveyors and other equipment as the optimization objectives, the multi-objective functions are established, and the operation parameters such as transport speed, transport distance, and equipment start-up time are optimized. The PCA and the CNN are respectively used to determine the weight of each objective function and iteratively train the practical production data samples under multiple constraints. The fully connected layer of CNN is constructed by the Lagrange multiplier method. The optimal production mode and operation parameters of the coal flow transportation equipment are obtained, satisfying the multi-objective functions and constraints. Finally, the practical engineering case is simulated by Plant Simulation, and the operation parameters of the coal flow transportation equipment are compared before and after optimization. The research results show that the objective function of each experiment is optimized to some degree. Furthermore, comprising other common algorithms, the advantages and effectiveness of the based-CNN operation parameters optimization method are verified. These have an important guiding significance for energy-saving and efficient coal flow transportation equipment operation.

Grinding media influence the energy consumption and efficiency of the grinding process during the calculation of the Bond Work index (BWi), a well-known method for selecting comminution equipment, evaluating milling efficiency, and calculating required milling power. Traditional grinding tests often choose steel balls as the grinding media, but ceramic balls are used widely currently with their high efficiency in grinding. This study aims to calculate the Bond Work index with steel and ceramic balls and explore the equation for the BWi of mixed grinding media (steel and ceramic balls). This paper also proposes a conversion equation of BWi between the mixed grinding media (steel and ceramic balls) and conventional media (steel balls). The results combined the advantages of ceramic and steel balls to improve the grinding capacity and reduce energy consumption.

Samarium (Sm), as one of the rare earth elements (REEs), has gained significant attention in the production of SmCo magnets due to their high corrosion and oxidation resistance, as well as their high-temperature stability. SmCo magnets find applications in various industries, including but not limited to national defense, aerospace, military, and medical equipment. Sm and Co have been classified as a critical metal due to its economic importance and supply risk. Recovering Sm from SmCo magnets is an effective method to ensure a stable supply. The present study investigates an integrated hydrometallurgical treatment and combustion process for the preparation of rare earth oxide (Sm₂O₃) powders from SmCo. Initially, SmCo powders is exposed to nitric acid, and the resulting slurry is selectively oxidized at 250 °C to obtain Sm(NO₃)₃, Co₂O₃, and Fe₂O₃. Subsequently, the selectively oxidized powders are leached with water to extract Sm. Sm₂O₃ powders are produced from the obtained leaching solution using an energy- and time-efficient solution combustion process. In this process, once the ignition point of the leaching solution-citric acid complex is reached, combustion occurs and concludes within a short time. The combusted powders are then calcined at different temperatures to produce crystalline Sm₂O₃ powders. Finally, the optimal conditions for the production of Sm₂O₃ are identified, and the produced powder is characterized through XRD and FESEM analysis.