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By V. W. Lumadi, S. Nyasha

The mining industry has yet to fully accept and embrace the strategic role of technology and innovation in successful business planning and execution. In this study, we empirically determine the factors that influence acceptance of technology solutions in the mining industry in South Africa, assess critical success factors influencing adoption of advanced technology in the South African mining industry, and explore challenges faced by the mining industry in South Africa in adopting advanced technology in its operations. Thematic content analysis is employed in the data analysis. Results show that critical success factors to the adoption of mining technology include learning culture, knowledge sharing, high labour costs, and behaviour of other firms in the industry; commonly identified challenges include inadequate engagement with external stakeholders, uncertainties, the cyclical nature of the sector, and high risk related to the adoption of unproven technology and performance systems focused on volumetric production. The study encourages mining companies to implement usage of technology. This can be properly done through regular engagement with the Minerals Council South Africa.

Apr 14, 2024

By K. Karaman

This study investigated the effect of surface roughness on Schmidt rebound hardness (RL). Four different test surfaces of rock samples were studied: natural, ground, cut surfaces, and core samples. There was significant variability of standard deviation based on the RL on the natural surface, which indicated high roughness of the rock surface, whereas surface polishing caused a significant decrease in standard deviation. ISRM and ASTM methods were compared to estimate unconfined compressive strength (UCS) for different testing surfaces. RL obtained from the cut surface was found to be more reliable than those obtained from other testing surfaces for the prediction of UCS; however, regression and ANOVA analyses revealed that the ISRM method gave a more accurate UCS estimation of rocks with highly rough surfaces. It was also shown that RL values obtained from a cut surface were significantly higher than those obtained from core samples. Therefore, a comparison between RL values obtained from core samples and cut surfaces was made based on previous studies. This study statistically showed that estimated UCS values are not statistically significant if Schmidt rebound tests are not performed on similar surfaces. In addition, the mineral grain sizes of the studied rocks, different testing surfaces compared with those in literature, and standard deviation from RL are evaluated and discussed. The Schmidt hammer technique is a rapid, inexpensive, and straightforward method for estimating UCS for preliminary assessment; however, roughness of the surface should be eliminated if variations are shown in the surface rebound hardness.

Apr 14, 2024

By N. C. Steenkamp

Lightning has been observed in dust clouds following open-pit blasting. It is proposed that the occurrence of this phenomenon is related to the physical composition and characterization of the mineral fines that comprise the bulk of the dust cloud. Silicate minerals, which are susceptible to fine fracturing during blasting, generate the initial charge. This is further enhanced by collisions and friction during the turbulent upcast in the post-blast dust cloud. Varying size fractions result in different rates at which these particles drop out of suspension and create a secondary temperature gradient. This results in the creation of two charged zones and increases the potential of developing a discharge in the form of lightning. The ideal prevailing meteorological conditions need to be windless to minimize the effect of particle dissipation and have sufficient moisture toenhance the potential of generating lightning.

Apr 14, 2024

By Martin Langenderfer, Catherine Johnson, Alexander Douglas

Mine haul roads degrade rapidly due to extreme loads on suboptimal construction materials. Unmanned aerial vehicles (UAV) are suited to quantify large-area road-network conditions, such as surface roughness, defects and grade to optimize remediation of poor conditions and reduce overall costs. Mine haul roads present unique challenges, such as material type and edge characteristics, to automatic road detection that often fails, requiring manual road input. This research work proposes a new method using the road center determined by the principal curve of a haul truck’s path. Analysis grids were created from this center line. A dense point cloud from UAV photogrammetry was generated and multiple linear regression analysis was conducted on each individual grid. The root-mean-square error in each grid indicates the surface roughness, and the change of slope between grids indicates the road grade inconsistencies. This method was applied to 26 road sections, and the results were validated by images taken from the truck operator’s vantage point. Critical defects were identified including excessive pothole formation, corrugation, depressions retaining water, and narrowing of travel lane. The results demonstrate this is a valid method of road identification and quantification of road defects at a mine site.

Apr 1, 2024

By Philani Larrance Ngwenyama, Ronald CW Webber-Youngman

The survival chances of trapped miners in underground mines depend on how quickly they can be located and rescued post-accident. During entrapments, emergency rescue is highly desired but can be hindered by the lack of positioning information when a fit-for-purpose missing-person locator system is not available. Various types of missing-person locator systems are available to aid search-and-rescue operations. From the wide range of the available systems, selecting the most suitable one becomes the next challenge. The available systems have not been measured if they are fit-for-purpose or not, and to what situation. This study proposes user requirements that can be used as a guideline to facilitate the system selection process. These user requirements provide a framework for the design and development of a fit-for-purpose missing-person locator system. The user requirements can also be used for evaluating the performances of a particular system, predict its success, and also drive future research toward developing new technology to enhance the capabilities of the current systems.

Apr 1, 2024

By Xuebin Liu, Yi Shang, Can Zhou, Hongqiu Zhu, Junbo Du

In the neutral leaching process of zinc hydrometallurgy, the pH value directly affects the leaching rate of zinc calcine. However, due to the complex mechanism of the leaching process and frequent fluctuation of working conditions, it is difficult to control the pH value by manual control. Therefore, this study proposes a fuzzy control method based on rule extraction under multiple working conditions, and analyzes the pH control performance. Firstly, the different operating states are classified according to process mechanisms and the Pearson method. Secondly, under the framework of the active learning algorithm, the approximate linear dependence (ALD) method is introduced to iteratively select samples with rule information in the original sample set for manual annotation. Then, the support vector machine (SVM) method is used to extract fuzzy control rules in different working conditions. Finally, a fuzzy rule set is established according to the samples mapped by the support vector. The simulation results based on industrial data show that the proposed method has better control performance than conventional fuzzy control and proportional–integral–derivative (PID) control.

Apr 1, 2024

By Ricardo Oliveira Gallart de Menezes, Claudio Gerheim Porto, Reiner Neumann

The Jiboia heavy mineral sands deposit is a new discovery associated with the São Francisco River delta on the northern coast of Sergipe state. The identified resources are part of the much larger mineralized trend. Mineralization is hosted in paleodunes deposited on Holocene marine terraces (Fig. 1). The dunes are up to 30 m above sea level, and mineralization occur from 6 m above sea level upward. The main ore minerals constitute pseudorutile, ilmenite, zircon, rutile and monazite. This work presents a thorough characterization of the mineralogy and granulometric distribution of the Jiboia orebody.

Apr 1, 2024

By Arman Hazrathosseini, Ali Moradi Afrapoli

Fleet management systems (FMSs), as a pivotal part of any surface mining operation, need to evolve from conventional to intelligent systems because of both Mining 4.0 requirements and some shortcomings inherent in conventional methods. However, this novel transformation needs to be investigated technically and strategically. To this end, previously published research works on intelligent FMSs are explored to track the latest status of these third-generation frameworks within the surface mining context. Their underlying models are then compared in terms of five categories of allocation and dispatching features to pinpoint the technical gaps ignored. Having drawn the future lines of research, the present work then leverages the popular SWOT analysis method to outline the strengths, weaknesses, opportunities, and threats associated with the advent of these intelligent FMSs in mines of future. By and large, the analysis indicates that advantages outweigh disadvantages. Solutions are offered to address the existing weaknesses and threats.

Apr 1, 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 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

By Kasimcan Koruk, Julian M. Ortiz

A geological model accounting for uncertainties possesses important advantages for resource estimation. Machine learning algorithms (MLAs) employed on multivariate geochemical datasets open up ways to new methodologies for such geological models with ease in comparison to traditional geostatistical methods. This article proposes a two-step MLA with an ensemble implementation to define geological domains and their uncertainties based on geochemical data. The proposed workflow is applied hierarchically on a dataset from a porphyry copper deposit to perform binary classification that can be attributed to alteration domains.

Mar 1, 2024

By Ankit Jha, Javad Sattarvand, Aaron Young

Blockchain technology is one of the prominent disruptive technologies that are being implemented in several disciplines. The application of blockchains in the mining and mineral industry can provide utility to several processes especially when the industry is undertaking digital transformation. The governing principles of blockchain technology are applicable in undertaking transformation in several processes within the mining industry. This study investigates the applications of blockchain technology within the realms of the mining industry and identifies key areas of implementation. A systematic review of publications on academic platforms relating to blockchain technology in the mining industry did not retrieve a considerable number of articles. Consequently, a thorough review of white papers and articles authored by individuals on use cases within the mining industry is compiled and presented in this review article. This review article identifies all the potential areas within the mining industry where blockchains could provide value and improve processes through their implementation. The broad area of implementation of blockchains includes supply chain, traceability in spares parts, contract management, regulatory compliance and cybersecurity.

Mar 1, 2024

By S. Afrouz, C. Keles, W. R. Reed, E. Sarver, T. Akinseye, A. Greth, L. Pan, F. Animah

The resurgence of severe lung disease among U.S. coal miners has not only highlighted the need for improved dust controls, but also the need for an improved understanding of dust characteristics and monitoring strategies [1]. Following up on prior work by the National Institute of Occupational Safety and Health (NIOSH) to demonstrate a novel wet dust collection system on a roof bolter [2], the current study explores the effect of that system on dust characteristics and uses interlaboratory comparisons to comment on analytical methods. Results indicate the wet system did not appreciably affect the composition or size of respirable dust in the vicinity of the operating roof bolter. However, the system does appear to have substantial benefits for the operator. NIOSH has already shown it can reduce the respirable dust mass concentration to which the operator is exposed during dustbox cleanout [2,3], which is a frequent maintenance activity that can re-aerosolize dust, and new results here indicate the silica and silicates content in this dust may also be reduced, thereby reducing the overall hazard. Moreover, regarding analytical methods, excellent correlation was demonstrated between measurements of quartz mass, taken first by NIOSH and then by a university laboratory, using direct-on-filter Fourier transform infrared (FTIR) analysis. This method is the basis for envisioned “end of shift” silica monitoring (quartz is the predominant form of crystalline silica in coal mines) [4]. For particle-level analysis by scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), there was more variability in results generated by two independent university laboratories.

Mar 1, 2024

By JIAQI JIN, Chen-Luh Lin, Amanda N. Erskine, Jan D. Miller, Shijie Wang

The liberation and exposure of valuable mineral grains in ore particles are of significant importance in understanding the efficiency of flotation separations. A flotation feed sample from the Kensington concentrator was scanned by micro Xray computed tomography (micro-XCT), to determine the liberation and exposure of auriferous pyrite grains. The analysis results suggest a satisfying extent of liberation for particle sizes smaller than 212 μm. Theoretical flotation recovery of pyrite for selected particle size fractions was predicted from the three-dimensional liberation and exposure analysis. The plant flotation tail was also characterized by micro-XCT to identify liberated and partially liberated pyrite particles that might not be collected into the concentrate. However, liberation analysis of the tail sample revealed that the majority of liberated pyrite was recovered in the plant flotation circuit. Trainable Weka, a machine learning segmentation approach, was compared with the Watershed thresholding segmentation for the 106 × 45 μm particle size fraction scanned at a voxel size of 1.85 μm. Improved segmentation of small particles was found using the machine learning method.

Mar 1, 2024

By M. C. Bekker, T. Govindasamy

The potential benefits of implementing the Last Planner System (LPS) on mining infrastructure projects were explored through two case studies in the South African mining industry. The first part of the study involved measurement of the impact of LPS through earned value techniques on secondary project data. The second part sought to establish the lean construction success factors leading to improvements in construction performance. This was done by means of a research questionnaire distributed to the project owner’s teams, contracted parties, and the LPS facilitators. The results revealed a positive correlation between LPS application and planned percentage complete. However, the performance achieved during the LPS pilot was not sustained. The success factors and benefits considered most evident in the case study pilot differed among the three stakeholder groups. The findings are expected to guide construction stakeholders to better define performance measures and focus on factors required to make LPS implementation more effective in the South African mining sector.

Feb 12, 2024

By S. W. Robertson, M. T. Golela, S. Mxinwa

South Africa has one of the largest Cr-related industries in the world and generates large amounts of Cr-containing smelter dusts. It is normal practice to scrub Cr from the furnace off-gas in water, and to recover the Cr by adsorption, followed by recycling of the loaded adsorbent into the furnace. A study was conducted to find a low-cost adsorbent that is capable of removing sufficient Cr from the solutions generated by scrubbing. The test work focused on the evaluation of different clay minerals (attapulgite, bentonite, and kaolinite) as adsorbents for Cr. Cr(VI) could only be adsorbed after prereduction with e.g. ascorbic acid (ASA). An 80% removal of Cr(VI) could be achieved from a solution containing 20 mg/L Cr using bentonite clay after reduction with ASA. Attapulgite and kaolinite adsorbed less than 55% Cr after reduction with ASA. Both ASA and ferrous salts were found to be suitable reducing agents. The use of acid mine drainage (AMD) was also investigated as a low-cost alternative reducing agent, as AMD usually contains iron. The Cr(VI) reduction potential of the AMD was determined by redox titration against a solution containing 50 mg/L Cr(VI). Studies show that AMD can potentially be used as a reducing agent in a Cr removal process if it is available on site.

Feb 12, 2024