Mining, Metallurgy & Exploration最新文献

Gibbsite-type bauxite is the main material for alumina extraction by Bayer process globally, while the iron in red mud is difficult to use for the high alumina content. Therefore, the efficient separation of iron and alumina is the premise for the resource utilization of red mud. In this work, the separation of iron and alumina in red mud containing 47.45% Fe and 11.58% Al₂O₃ was studied through reduction roasting followed by magnetic separation. The analysis methods of XRD, VSM, SEM, and EDS were used to investigate the phase transformation of red mud during reduction roasting. Results show that hematite can be firstly reduced into magnetite, and alumogoethite into magnetite and alumina. Then, the magnetite reduction undergoes the process of Fe₃O₄→FeO→Fe, while alumina can react with FeO to form hercynite. The hercynite is ultimately reduced into metallic iron and alumina at elevated temperature. The specific saturation magnetization of reduced product is closely related to its main minerals, that is, the specific saturation magnetization of magnetite and metallic iron is higher than that of wustite and hercynite. The mass ratio of Fe to Al₂O₃ in magnetic concentrate increases with roasting temperature, from 4.55 at 600 °C to 10.27 at 1200 °C. Therefore, efficient separation of iron and alumina in red mud could be achieved through deep reduction-magnetic separation.

Strategic mine planning centers on solving cut-off grade selection, capacity planning, and block sequencing. Even though significant knowledge has been accumulated on mine planning over the last decades, there is still potential to add value to mineral sourcing by addressing various aspects. To this end, this paper addresses two issues. The effects of (1) grade/metal distribution within a mineral deposit and (2) the economies of scale (EoS) are explored in cut-off grade selection and capacity planning. In doing so, the interdependency between cut-off grade selection and capacity planning is also considered. A case study is implemented on a metallic deposit whose grade distribution exhibits lognormal distribution to detect if grade/metal distribution influences cut-off grade selection. Also, based on the same ore tonnage and metal quantity, six different datasets are generated with different shape and scale factors. The research outcomes indicate that deposits with lower shape and scale factors of lognormal distribution are more sensitive to metal price and discount rate changes because slight cut-off grade variations significantly change net present value (NPV). While the NPV of the deposit with the largest shape factor is $3,208,112,841 with a cut-off grade of 0.058 oz/tonne, the NPV of the deposit with the smallest shape factor is $93,617,240 with a cut-off grade of 0.027 oz/tonne. Furthermore, the case study is directed to investigate the effect of EoS on a project’s value, with a specific emphasis on the ratio of variable cost to total cost (capacity factor). Two different regression analyses are conducted based on the proposed model for optimal capacity planning and cut-off grade selection, respectively. In the first one, the absolute standardized beta values for EoS of mining and mineral processing costs are 0.736 and 0.425, meaning that capacity planning is highly sensitive to the EoS of mining and mineral processing operating costs. Meanwhile, the absolute standardized beta value for grade variability is 0.054 which means that the effects of grade variability and metal distribution are almost negligible for capacity planning. However, EoS is the most critical variable for capacity optimization. In the second regression analysis, the standardized beta values for grade variability and EoS of mineral processing operating cost are 0.573 and 0.522, so their effects on cut-off grade selection become vital.

There are a large number of thick alluvium and thin bedrock deposits at depth in the Henan coal base. The degree of surface mining is high, and the surface environment is seriously damaged. Using indoor experiments, numerical simulation, and field measurement, it is studied the influence of the mechanical properties of thick alluvium on the surface subsidence characteristics of thick alluvium thin bedrock deposits at depth, taking Zhaogu No. 2 coal mine as the research background. The results show that the compressive strength and elastic modulus of the alluvial layer are linearly negatively correlated with the alluvial layer particle size, linearly positively correlated with the confining pressure, and nonlinearly positively correlated with the loading rate. The thickness of the alluvial layer has the greatest influence on the displacement of surface subsidence and the range of surface subsidence. The advancing mining rate of the working face has the greatest influence on the surface horizontal displacement. The mechanical properties of the alluvial layer affect the evolution characteristics of the caving arch and towering roof beam. The influence range of the 11012 working face is about 550 m, β is about 55.5°, and the maximum subsidence rate of the surface is 12 mm/day. The mining process and separation grouting can be adjusted to control the overburden displacement to reduce surface disturbance and protect the surface ecological environment. The research results of this paper can provide a research basis for the study of surface subsidence under similar geological conditions.

In this study, a MSWI bottom ash sample was assessed to evaluate the feasibility of various physical beneficiation processes in concentrating valuable elements prior to chemical leaching. The raw sample was initially assayed to determine the content and economic value of various metals present in the material. The potential recoverable value (PRV) of the sample was calculated, and the result showed that the total PRV of the sample was 483 $/ton, with Ti, Sc, Fe, Cu, and Zn being the most valuable metals. Next, various physical separation processes, including size fractionation, froth flotation, magnetic separation, and gravity separation, were conducted to determine the extent to which the valuable elements can be concentrated. The results were compiled into an element-by-beneficiation enrichment ratio (er) matrix that was used to develop suitable beneficiation flowsheets for further consideration. The result clearly show delineation of four products, including a Fe-rich product that can be isolated by magnetic separation (er = 5.0), a Cu/Zn-rich product that can be isolated by flotation (er = 5.3 to 9.4), a Sc-rich product that can be isolated by gravity separation (er = 0.6), and a Ti–rich product that is produced in the residue. Lastly, the leachability of valuable elements from the bottom ash sample was determined by acid leaching tests. The results indicated that it is viable to employ hydrometallurgical methods to recover and purify the valuable metals. This work provides a reference for the recovery of valuable metals from MSWI bottom ash from both the technical and economic aspects.

The top-down extraction sequence in the sublevel open stope method leaves rib pillars to support the excavations, so the stability of the pillars and stopes makes dilution control critical in this method. This work introduces an integrated methodology for the net profit and geomechanical optimization of the layout of open stopes and rib pillars with the use of a genetic algorithm. Parameters such as the minimum size of stopes, minimum size of pillars, maximum accepted dilution, and maximum acceptable percentage of pillar failure need to be informed by the user. A profit function capable of attributing economic value to the geometric set analyzed, including the geomechanical performance, is established. The geomechanical performance of the geometric sets is obtained by autonomous numerical models in the FLAC3D software and includes the average percentage of pillar failure and the potential dilution. The algorithm is verified using a case study of a mining panel of an underground gold mine with a top-down mining sequence. An 8% increase in net profit was obtained relative to the engineer’s design method, considering 70% of hangingwall support efficiency for both methods. The percentage of pillar failure decreased threefold. When considering no hangingwall support, the net profit increase is 22% relative to the engineer’s design method. The proposed methodology proved that it is possible to carry out an integrated optimization, considering the costs inherent to mining and the cost of the geomechanical performance, reducing the need for secondary support compared to the engineer’s methodology.

The accurate prediction of blast-induced ground vibration due to underground ring blasting is a prominent need for ensuring the safety of structures. Different site-specific empirical equations are available for the prediction of ground vibration. These empirical equations are best suited when the monitoring and blasting locations are present in the same medium. The change in the medium alters the behavior of wave propagation. Hence, existing empirical equations have limitations in peak particle velocity (PPV) prediction when the blasting location is an underground hard rock mine and the monitoring location is ground surface. This is because the underground metal mine comprises different levels having void in the form of excavated stope or paste-filled stope. It is very difficult to predict the magnitude of PPV on the surface in such instances. Therefore, this study has been carried out to predict the PPV at surface due to underground blasting. In this paper, PPV data was recorded at surface for 207-ring blasts. Furthermore, the PPV has also been measured at different underground locations for 47-ring blasts. Different empirical equations along with k-nearest neighbor (KNN) and random forest (RF) model of machine learning technique were developed for the prediction of PPV. Most of the empirical models have higher accuracy in the prediction of PPV at an underground location. This shows that scaled distance-based empirical predictors are best suited when the monitoring and blasting media are the same. However, the empirical models do not predict PPV accurately when the monitoring location is ground surface and the blast is conducted underground. The machine learning models are better suited for PPV prediction in such cases. Based on the analysis performed for the case study site, RF model predicts PPV at surface with the highest accuracy. The coefficient of determination and root mean square error for RF model used for predicting PPV at ground surface are 0.94 and 0.438 mm/s respectively. The RF-based model is also the best suited among all the models for predicting PPV at underground locations as well.

Recovery of metals from coarse ore particles is a great challenge for heap leaching operations of low-grade precious metal resources. Internal fractures of ore particles like micro-cracks can facilitate subsurface transportation of solution inside particles and leaching of valuable minerals. A plant-scale high-pressure grinding roll (HPGR) unit at Coeur Mining’s Rochester mine was operated at selected specific pressures to crush and prepare various products for crack analysis using high-resolution X-ray computed tomography (XCT). 3D shapes of cracks inside ore particles for selected size fractions reveal different damage mechanisms during HPGR crushing regarding particle size and crushing pressure. Close-to-surface cracks formed by abrasion were found in small 2.5 mm particles. Impact cracks with a high aspect ratio were found in particles of larger size and higher HPGR pressure. The volumetric percentage of internal fractures and the specific crack surface areas of selected ore particles are compared to give quantitative conclusions on particle damage by compression during HPGR crushing.

Significant factors believed to be responsible for tailings dam failure which can lead to loss of lives, destruction of properties and assets or damage to the environment are the mechanics of material characteristics and static liquefaction. It is therefore essential to understand the tendency for the likely occurrence of liquefaction in tailings dam as well as mechanics behind the behaviour of this tailing materials (inevitable products of ore mining, mineral and metallurgical processes). This study aims to present the mechanical behaviour and susceptibility to liquefaction of a gold tailings sample. Series of oedometer tests were conducted to determine the compression behaviour and the possibility of transitional behaviour or not. Index and physical tests were conducted to determine engineering grading descriptors which was directly linked to compression behaviour and comparisons made with other tailings. Microstructure was studied to understand the effects of fabric on the behaviour of samples and the chemical composition and mineralogy were also investigated. The compression paths slowly converge and the gold tailings might be said to be less transitional. The gold tailing is less dependent on initial state. The loosest gold tailing is more susceptible to liquefaction and compared to other gold tailings, this sample is less susceptible to liquefaction.

The bottom-blown smelting technology has been widely adopted in non-ferrous metal smelting industries. The largest bottom-blown smelting furnace used in copper smelting was numerically simulated to improve the stirring effect in the molten bath by optimizing the oxygen injector arrangement and blowing parameters. The results show that a small injector spacing leads to gas jet coalescence, which is detrimental for smelting efficiency, increases copper loss and shortens furnace service life. Three schemes were proposed to improve the uneven stirring and reduce the gas jet coalescence by increasing the axial spacing of the injectors, the radial installation angle and the gas injection angle. Changing the axial spacing of the injectors can significantly reduce the gas jet coalescence, yielding the best stirring effect. The results of simulation suggested that when the axial spacing of the injectors was increased from 0.380 m to 0.610 m, the mean melt velocity in the mixing zone increased to 0.243 m/s, which was 20.9% higher than that before the optimization. Meanwhile the (varvec{RSD}) (relative standard spatial deviation of melt velocity) decreased from 123% to 84%. In the actual production, the matte content in the smelting slag decreased from 6.57% to 3.12% after changing the axial spacing of the injectors from 0.380 m to 0.610 m.

Glauconite is a clay mineral that is most probably mined with phosphate and represents a challenge for phosphate upgrading by flotation. Reverse flotation is a common method for removing silica and silicate from siliceous phosphate. Thus, a systemic study was conducted to determine the effect of clay and its interactions with siliceous phosphate ore components on its cleaning by flotation. The flotation of each mineral (phosphate, quartz, and clay) was tested in terms of product recovery using quaternary amine at pH 9. In addition, the settling behavior of each mineral was investigated with and without quaternary amine. Binary and tertiary systems were studied to investigate the interactions between the main ore-containing minerals. The results of each mineral flotation test showed that quartz has the highest floatability, with low floatability of both phosphate and glauconite by quaternary amine. The presence of glauconite does not affect the phosphate recovery, whereas glauconite reduces the quartz floatability, which deteriorates the quartz removal from phosphate. Finally, the interaction mechanisms in the different systems are illustrated.