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By Abhiram Kumar Verma, Soumya Ranjan Mallick

Generally, waste such as soil and sandstone is used in mines for road construction or haul roads. In many such mines, it is challenging to design haul road pavement to match the dumper capacity. This paper investigates overburden soil to find its utility as a pavement material. It is found that overburden soil is unsuitable pavement material without stabilization; hence, nine composite materials are developed using overburden soil and RBI. Physical, mechanical and microstructural properties of unstabilized and stabilized soil are determined. Statistical analysis is carried out to assess the effect of soil type, RBI and curing period of strength characteristics of unstabilized and stabilized soil. ANOVA tests assess the influence of RBI percentage on soil geotechnical properties, and regression relations are developed. Two-way ANOVA shows the effect of RBI and soil type on soil geotechnical properties, whereas one-way ANOVA shows that the influence of soil type is more than the RBI. The regression relation is developed for CBR, UCS, Poisson’s ratio and modulus of elasticity with a good correlation coefficient. The performance of RBI is more with the finer material and reduces with an increase in the size of soil grains. The performance of the developed composite is better than the earlier developed composite based on overburden with fly ash, lime kiln dust, lime or clinker.

Mar 19, 2024

By Hossein Ghaedi, Hadi Fattahi

Despite the significant role of coal in the economic progress of nations, the occupational and health risks associated with its mining pose a major concern for industry stakeholders. The occurrence of roof collapses in coal mines remains a critical factor leading to substantial loss of life and financial damages for miners. Therefore, accurately predicting the roof fall rate (RFR) holds paramount importance. However, the uncertainty surrounding rock parameters in mines hinders the application of conventional methods to assess roof collapse rates in coal mines. To tackle the challenges associated with predicting roof fall rates in underground coal mines, this study proposes a novel solution by leveraging the Rock Engineering System (RES) method. The investigation is grounded in a dataset comprising 109 data points, encompassing crucial input parameters like depth of cover (DOF), primary roof support (PRSUP), intersection diagonal span (IS), mining height (MH), and coal mine roof rating (CMRR). In the model construction phase, 80% of the data (87 points) were utilized to build the RES model. A critical aspect of this study involves the evaluation of the RES model’s performance against alternative regression techniques, namely linear, power, exponential, polynomial, and logarithmic regression. This comparison was executed using the remaining 24 data points (20% of the dataset) for rigorous evaluation. Employing key statistical metrics such as mean square error (MSE), root mean square error (RMSE), and squared correlation coefficient (R2), the study systematically demonstrated the superior accuracy of the RES-based method compared to other approaches. In conclusion, the outcomes strongly support the efficacy of the RES method in predicting roof fall rates, not only in the specific case studied but also indicating promise for its application in other underground coal projects. This underscores the potential of the RES method as a reliable and versatile tool for forecasting roof fall rates in the complex and critical context of underground coal mining.

Mar 19, 2024

By Xie Junjie, Li Shina, Ma Ruixin, Min Changli, Yin Zichen, Li Kang, Shi Zhuonan

Cesium and rubidium were effectively separated by zinc ferrocyanide coprecipitation method, and the effect of precipitant addition amount on the separation of cesium and rubidium was studied. Under the conditions of Zn/Cs molar ratio of 0.45 and Zn/Fe molar ratio of 1, the precipitation rate of cesium is 84.01% and the separation factor of Cs/Rb is 164.65. The thermogravimetric curves of the sediment were measured, and the effects of roasting temperature, roasting time, and leaching liquid/ solid ratio on the extraction of cesium were studied. Almost all of the cesium was extracted under optimal conditions: roasting temperature of 600 °C, roasting time of 3 h, leaching temperature of 25 °C, leaching time of 0.5 h, and liquid/solid ratio of 5:1 mL/g. After the leaching process, the leach liquor with high concentration of cesium of 80.17 g/L was obtained. Finally, the leach liquor was further purified by solvent extraction, and the purity of the cesium chloride product reached 99.66%.

Mar 17, 2024

By Christos Roumpos, Philip‑Mark Spanidis, Francis Pavloudakis

Coal mining operations are large-scale, long-term frameworks of high complexity. Mining organizations accumulate, develop, and leverage knowledge from diverse scientific and technological fields as a result of these operations. Both practical experience and existing literature indicate that knowledge management (KM) methods within the mining industry primarily target solving immediate technical challenges and operational requirements. However, they are not commonly regarded as strategic tools to enhance the performance and competitiveness of mining companies. Empirical evidence from the Greek mining industry suggests that the management of available knowledge is intricate, less effective, and dysfunctional. This paper presents a methodology based on the principles of quantitative research (QNR), collaboratively conducted with a group of mining experts, to assess the KM landscape in the Greek mining industry. The methodology involves a structured data collection framework using questionnaires, subsequent statistical analysis, a discussion on critical methodological aspects, and an interpretation of results focusing on key aspects useful for planners and designers of KM systems in the mining industry. Finally, the paper concludes by summarizing the methodology’s outcomes and proposing further perspectives for research.

Mar 16, 2024

By Mary Nelima Ondiaka, Kayode Henry Talabi, Oladunni Oyelola Alabi, Fatai Olufemi Aramide, Francis Gitau

Iron ore is essential in steel making; however, high-grade ores have diminished, making low-grade ores inevitable. These lowgrade iron ores need further beneficiation to upgrade the iron content. Currently, traditional physical and chemical methods are utilized and are not environmentally friendly. Bio-beneficiation techniques have emerged as a sustainable alternative for mineral recovery. This review delves into recent bio-beneficiation advancements for enhanced low-grade iron ore recovery using microbes. Research has revealed that bio-beneficiation methods such as bio-leaching, bio-flotation, and bio-flocculation have proven successful in iron recovery from ores. The bio-beneficiation process occurs in mild conditions using bio-reagents derived from microbes and offers a reduction of chemicals used in processing. Bio-beneficiation of iron ore potentially offers a relatively energy-efficient, cost-effective, and environmentally friendly method of maximum iron ore recovery. However, this review has identified a scaling-up difficulty in which a future approach for industrial-use applications is offered following a thorough sustainability assessment. Bio-beneficiation, using microbial processes, provides a viable avenue for maximizing iron ore recovery while tackling the constraints of dwindling high-grade iron ore resources and environmental sustainability.

Mar 16, 2024

By Kyeong Woo Chung, Rina Kim, Ho‑Seok Jeon, Yeongjin Ahn, Byunghun Go, Min‑seuk Kim, Hong‑In Kim, Sujeong Lee, Yuik Eom

The study focused on the recovery of vanadium from a Korean vanadium-bearing titaniferous magnetite (VTM) ore, examining the behaviors of magnetic separation and Na2CO3 roasting-water leaching. Two concentrates were prepared using single-stage dry magnetic separation and three-step wet magnetic separation, resulting in V2O5 grades of 0.8% and 1.0% and major gangue mineral SiO2 contents of 6.5% and 1.7%, respectively. These concentrates were subsequently roasted at 1050 °C with the addition of Na2CO3 and then water-leached at 25 °C. Various stoichiometric ratios of V2O5 to Na2CO3 were tested, ranging from 1:1 to 1:40 of the theoretically required amount. As the amount of Na2CO3 increased, the vanadium leaching efficiency showed fluctuations between 4 and 35% for ratios of 1:1 to 1:10, while it linearly increased from 4 to 82% for ratios of 1:10 to 1:40 in the leaching of the low-grade concentrate. In contrast, the leaching efficiency increased from 47 to 74% for ratios of 1:1 to 1:8 and then sharply rose to 84% at a ratio of 1:10 in the leaching of the high-grade concentrate. Subsequently, it reached a plateau of 92 to 94% for ratios of 1:20 to 1:40. The difference in leaching efficiency was primarily due to the reaction of gangue minerals with Na2CO3, which hindered vanadium leaching from the VTM concentrates. This hindrance was notably more pronounced in the low-grade concentrate with the lower salt ratio, as the gangue minerals exhibited a preference for consuming Na2CO3 over vanadium. The mineral formation was analyzed in detail using XRD and MLA to shed light on the mechanism of different vanadium leaching behavior depending on the concentrate grade and vanadium to salt ratio. Based on the results of this study, it is evident that applying a proper magnetic separation procedure is significant to achieving satisfactory vanadium recovery with less salt amount by removing gangue minerals ahead.

Mar 15, 2024

By R. J. Quevedo, Y. A. Sari, S. D. McKinnon

The large scale of cave mines leads to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method, the effects of which are difficult to account for during the design stages. In this paper we present a case study of the application of a machine learning approach to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile. Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analysed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms, nor the calibration of associated controlling parameters. The proposed random forest model predicted pillar collapses with 80% accuracy despite limited samples to model from. The main contributing factors to collapses were found to be related to available pillar volume, cave front geometry, and time under abutment stress conditions. The effects and interactions of such factors were also studied, showing that careful and improved control over operational conditions can significantly reduce the likelihood of pillar collapses. These conclusions could not have been obtained from stress analysis alone, illustrating the complementary nature of conventional stress analysis and machine learning approaches.

Mar 14, 2024

By J. P. Gouvea

Back-analysis of two mining-induced tremors in 2011 was used to determine a modelling criterion for the entire Klerksdorp Goldfield (i.e., Vaal Reefs region). At that time, most mining operations in the region were owned by AngloGold Ashanti (AGA) and shared many geotechnical similarities. By 2018 AGA had sold off all its Klerksdorp operations to different mining companies, which continued applying the 2011 modelling criterion. As mining activities became more remote, scattered, and isolated, the extraction of isolated blocks of ground and remnants became increasingly necessary. However, the inherited criterion allowed only minimal or no mining activities at these areas. Keeping in mind that safety is the overriding consideration, an innovative numerical modelling approach, which includes the application of peak particle velocity designs, was developed to provide practitioners in the Klerksdorp Goldfield with a suitable criterion to justifiably and safely mine isolated blocks of ground and remnants. This would not always be possible if a conformist approach was used.

Mar 14, 2024

By W. Bendaikha, S. Larbi, A. Ramdane

An oolitic iron ore from Gara Djebilet, Algeria was characterized by X-ray diffraction, scanning electron microscopy, and energy dispersive spectrosopy and the phosphorus distribution between various phases studied. Two ore types were identified – a haematite-dominated, oolitic type and a more massive, magnetite-dominated type. The haematite-dominated ore had a higher phosphorus content, up to 0.6% in the iron oxide minerals, than the magnetite-dominated type (0.3% P). Owing to the very fine-grained texture of the gangue minerals, reducing the phosphorus content to levels suitable for steelmaking (below 0.05% P) by conventiobal means would entail an energyintensive process with a high carbon footprint. Preliminary leaching test work using a sulphurous geothermal water as lixiviant, assisted by solar thermal acceleration, gave very promising results, removing 0.13% of the residual P (or 21.7% removal of total P) from the high-phosphorus oolitic ore.

Mar 14, 2024

By S. Swanepoel, A. V. Cherkaev, Q. G. Reynolds, M. Erwee

Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R2 = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag chemistry. This analysis lays the foundation for implementing Si content control on a ferrochrome smelter.

Mar 14, 2024

By I. Ince, S. Kahraman, M. Rostami, B. Dibavar

The density, porosity, and strength of rocks are fundamental in the design of geo-engineering projects. Determining these properties requires the preparation of smooth core samples, which is usually impossible for soft rocks. Besides, only fragmented samples may be available in some projects. A method for predicting rock properties from rock particles such as drilling debris would therefore be useful. This study was undertaken to develop prediction equations for compressive strength (UCS), tensile strength (BTS), density, and porosity values from the crushability index (CI) obtained from rock fragments. The results showed that UCS, BTS, density, and porosity were strongly correlated with the CI. The physical characteristics and strength of rocks may be predicted with the help of the derived equations. The CI test is a valuable tool for estimating rock properties in drilling projects and in situations where core specimens are not available.

Mar 14, 2024

By Christopher Mark

Six decades ago, the most dangerous job in the USA was mining coal underground. Roof falls were responsible for half of the deaths, killing about 100 miners every year. Fast forward to 2016 and zero roof fall fatalities. Just three miners were killed by roof falls during the following 6 years. How did the mining community achieve this historic goal? This paper starts by analyzing the roof fall fatalities in 1968, categorizing them by their fundamental cause. Then, it shows how each type of roof fall was reduced over time, using snapshots of the fatalities occurring in subsequent decades. Along the way, it evaluates the influence of the regulatory environment, changing mining methods, and better ground control technology. The study found that in 1968 more than half of roof fall fatalities at large mines were attributable to an inadequate safety culture. The immediate effect of the 1969 Coal Mine Health and Safety Act was to reduce the riskiest activities, like needlessly going under unsupported roof. Other hazards, like large roof falls, required technological developments before they were brought under control. Roof Control Plans, which the US Bureau of Mines had been advocating since the 1920s, played a significant role throughout the process.

Mar 12, 2024

By Yao Yevenyo Ziggah, Victor Amoako Temeng, Solomon Evans Kweku Koomson

Tyre hours/life deficit is a major operational challenge facing the mining industry which adversely affects materials production and costs. An accurate forecast of the tyre life is key in addressing this menace. This study for the first time employed the hybrid intelligent technique by utilising three metaheuristic optimisation algorithms, including particle swarm optimisation (PSO), genetic algorithm (GA), and whale optimisation algorithm (WOA), as trainers for the parametric weights and biases to optimise multilayer perceptron neural network (MLPNN) for enhancing prediction of on-site dump truck tyre life in the mine. Four hybrid models known as PSO-MLPNN, WOA-MLPNN, GA-MLPNN, and BP-MLPNN were developed using a total of 157 tyre dataset records obtained from a surface mine in Ghana. In assessing the prediction performances for the models developed, five statistical performance metrics of variance accounted for (VAF), Nash–Sutcliffe efficiency index (NASH), coefficient of determination (r2), mean absolute percentage error (MAPE), and correlation coefficient (r) were utilised. Moreover, ranking, uncertainty analysis and Bayesian information criterion (BIC) techniques were utilised to establish the most effective hybrid model, whereas sensitivity analysis was conducted on the input parameters. Results achieved showed that PSO-MLPNN was the best for prediction because it had the least MAPE value of 1.196% and relatively high values of VAF (99.642%), NASH (0.996), r2 (0.996), and r (0.998). Besides, PSO-MLPNN had the best selection criteria values of 6, 7.1725, and 444.834 for the ranking, uncertainty analysis and BIC respectively. Hence, PSO-MLPNN is recommended for the prediction of on-site dump truck tyre life for the studied mine.

Mar 11, 2024

By Xin Wang, He Shao, Wenpu Li, Yiyi Zheng, Xi Chen, Yurong Gao

This study utilized experimental and numerical simulation methods to investigate and analyze the atomization characteristics of three internal air–water mixed atomizers under different operating conditions. By comparing the research results, the effective dust control working parameters for the application of the selected atomizer in the engineering field were determined. Firstly, the atomization characteristics of the three atomizers were studied using a customized experimental system. The results indicated that nozzle 2 had a wide atomization range, a small atomization distance, and small droplet size; nozzle 3 had a narrow atomization range, large droplet size, and fast atomization speed; and nozzle 1 not only had a large atomization range but also could produce droplets of dry mist size with fast atomization speed, meeting the dust reduction requirements of the Majiliang Coal Preparation Plant. Secondly, Fluent software was used to simulate the atomization process and particle size distribution of the three internal mixed atomizers, determining the working conditions of the atomizers, the required number of atomizers, and the application angle of the atomizers in the internal mixed dust removal system. Finally, the atomized dust control system was applied on-site, achieving a total dust removal efficiency of 90.72% and a respirable dust removal efficiency of 89.75%.

Mar 9, 2024

By Lingyun Zou, Xinghai Lei, Tiezheng Li, Yifei Song, Nan Wang, Yang Li

The stability of key strata is of utmost importance in ensuring safe and effective coal seam mining operations. To minimize disturbance impacts on these strata and ensure their stability, it is crucial to assess the degree of disturbance caused by mining activities. This study proposes the evaluation index “disturbance degree of key stratum (KSDD)” and establishes a quantitative KSDD criterion to measure the degree of disturbance on key strata. To facilitate the assessment process, an intelligent calculation system for KSDD is developed. The classification criteria are derived by combining the measured three-zone heights from multiple mines. The reliability and accuracy of the KSDD quantitative criterion are validated using field measurement data. Furthermore, the KSDD quantitative criterion is applied to the Meihuajing mining area No. 23, and the distribution characteristics of KSDD are analyzed. The findings of this study can serve as a valuable reference for reducing disturbance and enhancing the stability of key strata during subsequent mining operations. Overall, this work contributes to the field of ground control in coal seam mining by providing a comprehensive approach to assess and mitigate the disturbance caused by mining activities on key strata.

Mar 8, 2024

By Dayu Long, Changhong Li, Yu Wang, Juzhou Li, Yongyue Hu

To address the problem of seepage failure that affects the stability of the fine-grained tailings dam, two kinds of prevention and treatment measures were proposed involving composite flocculants and composite curing agents. The orthogonal test was conducted to determine the optimal ratio flocculant (ORF) and the optimal ratio curing agent (ORCG). Then, numerical models were established to evaluate the effects of the prevention and treatment measures on the seepage and stability of the tailings dam. The results show that the mechanical properties and microstructure of the tailings are significantly improved by adding the ORF and ORCG. Furthermore, numerical studies suggest that the addition of the ORF and the ORCG to the tailings improves the factor of safety of the tailings dam, but the addition of the ORCG also increases the height of the phreatic line. It is also speculated that the addition of the ORF can prevent the formation of through failure zone in the tailings dam. Therefore, the optimal measure to prevent seepage failure in the tailings dam is adding an appropriate amount of the ORF, as it reduces the height of the dam’s phreatic line and lowers the risk of seepage failure. The typical area susceptible to seepage failure can be treated by adding the ORCG. However, it is important to ensure adequate drainage of the area and to maintain the phreatic line at a reasonable buried depth.

Mar 4, 2024

By Shakuntala Tudu, Indrajit Paul, Surajit Sinha, Pulibandla Basaveswara Rao, Shaik Mahaboob Basha, Srinivas Dwarapudi

The iron and steel manufacturing sector directly accounts for 7–9% of global CO2 emissions. Raw material preparation, such as iron ore sintering, pelletizing, and cock making, is the major CO2 emitter. As climate change becomes a bigger concern, steel manufacturers need to lower CO2 emissions without hindering efficiency or increasing costs. In recent years, the percentage of iron ore pellets in blast furnaces has increased due to its uniform size, good reducibility, and high tumbler index. Pelletizing is energy-intensive and emits considerable CO2. Around 20% of CO2 emissions during pellet making come from fluxes. In the present work, the authors established a novel CO2- free flux called wollastonite (CaO.SiO2) for sustainable palletization. Pellets with varying percentages of wollastonite (0–6%) are prepared and tested for chemical, physical, and metallurgical properties. Image analysis through an optical microscope is carried out to quantify the phases of fired pellets. SEM–EDS is performed to evaluate the chemical composition of the melt and slag phases. The results showed that the reducibility index (RI) and swelling index (SI) of pellets decreased with an increase in wollastonite dosage. Pellet strength increases with an increase in wollastonite addition up to 1.2% CaO (2.27% wollastonite), and a decrease in strength is observed thereafter. The increase in strength is attributed to the increase in slag bonds. The decrease in strength beyond 1.2% CaO is mainly because of an increase in low melting eutectics and more magnetite content. Pellets with 2.27% wollastonite to get 1.2% CaO showed good physical and metallurgical properties.

Mar 4, 2024

By N. Duru, C. Nesbitt

During the cyanide leaching process, gold and silver ores that come from the transition zones, which typically contains low levels of sulfide, may produce one or more of the following reduced sulfur species: sulfide ( HS−), polysulfides ( Sn 2−), thiocyanate ( SCN−), thiosulfate ( S2O3 2−), trithionate ( S3O6 2−), tetrathionate ( S4O6 2−), pentathionate ( S5O6 2−), hexathionate ( S6O6 2−). The actual speciation of the reduced sulfur species in the circuits will primarily depend on the redox potential, pH, cyanide levels, and existing sulfur species in recycled process water, as well as the presence of other reactive metal species. The development of an accurate analytical method for sulfur species in cyanide leaching units of gold ores where sulfur speciation occurs has an important role in explaining the metal recovery deviations. This study presents the speciation of sulfur species and the development of ion chromatography method to analyze reduced sulfur species thiocyanate ( SCN−), thiosulfate ( S2O3 2−), trithionate ( S3O6 2−), tetrathionate ( S4O6 2−) in cyanide leaching circuits for gold ores. The method developed has resulted in precise quantification of target sulfur species in leach solutions. Method development and validation results were illustrated on analytical graphs containing each species’ peak number, retention time (min), type, width (min), area (mAU*s), height (mAU), and area distribution (%) of the peak.

Mar 2, 2024

By Cheng Xie, Zhihu Li, Xiaoming Sun, Linsen He, Jiaxu Ding, Jinkun Yang, Yong Zhang, Zhenmin Qi

To solve the problem of large deformation of deep soft rock roadway, taking the soft rock roadway of no. 1 coal mine in New Shanghai as an engineering example, the deformation mechanism of surrounding rock of deep soft rock roadway is analyzed by combining theoretical analysis, numerical simulation and field test, and the control countermeasures centering on deep and shallow dense drilling are put forward. Through theoretical derivation, based on the masonry beam theory, surrounding rock structure S-R stability principle and composite beam principle, the length, angle, and spacing of deep and shallow dense drilling are determined. Through numerical simulation, the deformation evolution process of soft rock roadway surrounding rock after dense drilling pressure relief is reproduced, and the influence of drilling parameters (drilling spacing, dip angle) on pressure relief effect is analyzed and compared. The field application test and monitoring verify that the deformation control measures of deep and shallow dense drilling broken roof surrounding rock have a good effect of large deformation control of soft rock roadway, and provide a new support means for the safety and stability control of soft rock roadway.

Mar 2, 2024

By Laura Suarez, Jörgen Kajberg, Pär Jonsén

How does an investigation into the breakage characterization of electric arc furnace (EAF) manganese slags contribute to a more responsible usage of resources throughout the life cycle of steel products and reduction of carbon emissions? The main goal of this study is to increase the knowledge on the mechanical response of manganese slag to open the potential of pyrometallurgical residues to become secondary material in industrial applications such as infrastructure construction, fillers, binders and soil stabilization, among others.

Mar 1, 2024