School of Mining Engineering (ETDs)
Permanent URI for this communityhttps://hdl.handle.net/10539/37974
Browse
14 results
Search Results
Item Back analysis of previously stooped Panels to improve the safety and productivity of future stooping operations at new Denmark colliery(University of the Witwatersrand, Johannesburg, 2024) Gonsalves, RicardoNew Denmark Colliery (NDC) is a Seriti-owned underground coal mine in the Mpumalanga Province. It started stooping operations towards the end of 2018 and has since stooped 50 panels safely across all three of its shafts. NDC selected the NEVID method, which is known for its safety and success in neighbouring mines. Initially stooping panels in the 1100 block, NDC encountered challenges due to narrow panel spans, leading to limited goafing and heightened abutment stresses on surrounding pillars and infrastructure. This necessitated additional support for strategic pillars to ensure long-term stability. To address these challenges, comprehensive research was conducted, which included back-analysis of stooped panels and numerical modelling. Findings revealed that goafing was influenced more by horizontal stress concentrators, such as geological structures than panel spans. Goafing at NDC has primarily been defined by low-angle shear failures, which only extend a couple of metres into the immediate sandstone roof. This is known as partial goafing, which typically results in high abutment stresses. Since complete goafing is unlikely due to the depth below surface and the competent roof material, the high abutment stresses needed to be managed by increasing the width of the barrier pillars between panels and leaving a sufficient number of stopper pillars at the end of a stooped panel. Numerical modelling was used to validate NDC's current design strategy of stooping panels and to determine the width of new barrier pillars. The derived strategy includes an increase in barrier pillar width and stand-off distances, to ensure the long-term stability of main developments. iv The current stooping strategy which was informed by comprehensive research and modelling, has proven effective in both safety and stability.Item Improving grade estimation using machine learning: a comparative study of ordinary kriging against machine learning algorithms(University of the Witwatersrand, Johannesburg, 2024) Akpabio, AniekanThis study investigated the efficiency of machine learning (ML) methods in the accurate prediction of ore grades, placing them in direct comparison with the established Ordinary Kriging (OK) methodology, a mainstay in geostatistical analysis. Utilising a dataset from a complex platinum group elements (PGE) deposit, the research assessed a suite of ML algorithms—namely, Random Forest (RF), Decision Trees (DT), Support Vector Regression (SVR), and particularly 𝑘- Nearest Neighbours (𝑘NN). The latter is highlighted for its adeptness in assimilating spatial data correlations intrinsically, echoing the insights from Nwalia's analytical explorations. The research engages with detailed swath plot analyses, comparative metric evaluations, and a nuanced understanding of spatial continuity, to illustrate the distinct advantages and operational competencies of the models. 𝑘NN, with its reliance on local data proximities and non-parametric nature, alongside RF, with its ensemble-based approach, emerged as capable in point estimate predictions. These models adeptly delineated local grade variations, demonstrating a high degree of reliability to the observed data and outperforming the OK model in both precision and accuracy. Further, the study examined block estimate predictions, a cornerstone in practical mining and resource estimation, where both 𝑘NN and RF demonstrated a commendable ability to generalise predictions over larger spatial extents. This translates into significant potential for enhancing mineral resource estimation processes, tailoring them to the granular specifics of a given ore body, and refining block model accuracy to inform more strategic mining operations. While the results endorse the ML methodologies as robust alternatives to traditional geostatistical techniques, the research also highlights the nuanced nature of these predictions. Factors such as the ore body's heterogeneity, the appropriateness of the variogram model, and the interplay between prediction scale and algorithmic performance are examined, offering a critical lens through which the suitability of each method is assessed. iv The research suggests that while some models like LR and SVR are bounded by linear assumptions and hyperparameter sensitivities, non-linear models such as DT and RF can innately navigate the complex, multifaceted layers of geological data. The comprehensive evaluation extends to propose a novel set of performance metrics designed to capture the intricacies of grade prediction, thereby aligning closely with the operational demands and decision-making processes in the mining industry.Item An investigation into equity market timing practices by South African mining companies(University of the Witwatersrand, Johannesburg, 2024) Matumba, LindelaniThis research examines the practice of equity market timing among 30 Johannesburg Stock Exchange (JSE)-listed mining companies from 2006 to 2022. Mining companies, characterised by their capital-intensive nature, rely on management for optimal capital management, which includes both the acquisition of capital through debt or equity and its optimal allocation. The concept of equity market timing, introduced by Wurgler and Baker in the 1990s, suggests that company management may engage in timing the equity market when they perceive their stock to be mispriced. This study incorporated control variables such as market-to-book value (a relative valuation metric that investors use to assess a company's market value in relation to its book value), asset tangibility, degree of leverage, and profitability. Panel regression analysis, utilising both fixed effects and random effects, revealed that market-to-book value was not statistically significant at the 5% level. The overall R-squared value was 58.8%. Given the lack of significance for market-to- book value and asset tangibility, it is recommended to consider other capital structure theories, such as the pecking order or trade-off theory. Additionally, incorporating variables like interest rates and other macroeconomic factors could help address the potential for omitted variable bias.Item Assessing the policies for legalising artisanal and small-scale mining in south africa(University of the Witwatersrand, Johannesburg, 2024) Komape, Ledile Jane; Marshall, T. R.This research examines the regulatory framework of artisanal and small-scale mining in South Africa, discussing issues around whether the current policies are up to the challenge of managing the realities and expectations of artisanal and small-scale miners. The research was conducted through a survey of three focus groups across four areas in South Africa using structured questionnaires and interviews. Data collection involved contacting individuals at the Department of Mineral Resources and Energy, Mine Health and Safety Council, and Mining Qualifications Authority, as well as Artisanal and Small-Scale Miners and mine representatives, and conducting interviews at their offices, homes, or workplaces based on their preferences. Data collected from the three focus groups reveal a disconnect between the goals of the policies and how the artisanal and small miners’ communities experience them, emphasising the need for effective policy implementation, comprehensive education initiatives, and avoidance of unrealistic expectations. Key recommendations of the research include the adoption of digital technologies for monitoring, fostering cooperative models, and encouraging international collaboration between local and foreign operators. It underscores the importance of creating and applying inclusive, equitable and sustainable policies to improve the socio-economic and environmental conditions of artisanal and small-scale miners in South Africa.Item Assessing the Challlenges in the Valuation of Early-Stage Secondary Diamond Deposits(University of the Witwatersrand, Johannesburg, 2024) Ganda, Nair da Conceição de Oliveira Gavião; Marshall, Tania R.Diamond mining is a fundamentally important part of the economy in many countries. Globally, some of these countries are home to early-stage alluvial diamond projects that attract significant interest from investors. Often, these investors need to understand the project’s value to make informed decisions. However, valuing early-stage alluvial projects is a complex and challenging process. This research report identifies and assesses the challenges associated with the valuation of early-stage alluvial projects through a case study of a project in Angola. For the case study, a valuation exercise was conducted using both the Cost Approach and the Market Approach. The research identified challenges specific to the Cost Approach, such as data availability and compliance with internationally recognised Resources and Reserves reporting codes. Likewise, challenges specific to the Market Approach included estimating current commodity prices and checking the performance of alluvial diamond properties on an applicable stock exchange. Additionally, it became clear that complications related to both approaches, such as experience and resource estimation methodologies, need to be addressed before a final valuation range can be determined. Although there are several difficulties, the valuation of early-stage alluvial projects is still possible. Nonetheless, these challenges impact the accuracy, consistency, and interpretation of the valuation results. Therefore, becoming familiar with these challenges and the recommendations made in the report will help valuators avoid potential pitfalls and contribute significantly to the field by guiding more informed decision-making in the valuation of early-stage alluvial diamond projects.Item An assessment of the Angolan mineral taxation regime: considerations for possible improvements on government´s revenue(University of the Witwatersrand, Johannesburg, 2024) Africano, N´djamila Hilifavale Borges; Mtegha, HudsonAngola is host to 36 of the 51 critical minerals in the world and ranks third in mineral exports, totalling over USD 1 billion in 2020, and third in diamond production; Botswana and South Africa hold the top two slots, respectively. These untapped opportunities make the Angolan mining industry an excellent place to invest despite the mining industry contributing less than 1% to GDP and has yet to become a driver of economic diversification. In June 2022, Angola joined the EITI, bringing a welcome improvement in the transparency of the sector´s governance and reform, intended to attract new investors. The study evaluates the effectiveness of the Angolan mineral fiscal system as a tool for maximising revenue for the benefit of its citizens and securing investment (local and foreign) to promote linkages and broad-based national growth and development. Four objectives were examined in this study: (i) Conduct a situational analysis of the current fiscal regime through a comparative analysis of headline rates in regional and international countries; (ii) Qualitative evaluation of the effectiveness and efficiency of the mineral fiscal regime; (iii) Analyse the tax revenues raised by the mining industry between 2011-2021; (iv) Make possible recommendations to improve the current mining tax regime. The study employed a descriptive survey design with a qualitative and quantitative approach for data collection and analysis. The main findings include: (i); Angola's political economy setting resembles that of a hegemonic government characterised by an institutionalised one-party regime whereby the implications on the mineral fiscal regime are multifaceted, affecting investment, regulation, revenue sharing, and sustainability; (ii) Both mineral royalty and corporate income tax rates, are within regional and international norms and have consistently contributed a significant share of the government's direct tax revenues over the last eleven years; (iii) Prevailing fiscal regime can be improved through a combination of tax instruments such as resource rent-tax or profit-based royalty with a basic ad valorem tax system; (iv) However, Angola’s primary challenges point to a possible absence of enforcement and compliance mechanisms for both the mining code iii and the sector fiscal framework, as well as the need to strengthen government agency capacity to oversee and gather fiscal contributions from the sector. In light of these findings, it is recommended to (i) Improve the sector's mining code and fiscal legislative framework and enforce it; (ii) Conduct a study to analyse the effects of all government taxes (direct tax, indirect tax and non-tax instruments and tax incentives) on both the industry and the government´s treasury; and (iii) Conduct further studies on the proposed optimal mineral fiscal regime. Finally, an effective, efficient, and transparent mineral fiscal system can only exist first and foremost through intentional collaboration and alignment of objectives among the sector’s stakeholders.Item A Data Science Framework for Mineral Resource Exploration and Estimation Using Remote Sensing and Machine Learning(University of the Witwatersrand, Johannesburg, 2023-08) Muhammad Ahsan, Mahboob; Celik, Turgay; Genc, BekirExploring mineral resources and transforming them into ore reserves is imperative for sustainable economic growth, particularly in low income developing economy countries. Limited exploration budgets, inaccessible areas, and long data processing times necessitate the use of advanced multidisciplinary technologies for minerals exploration and resource estimation. The conventional methods used for mineral resources exploration require expertise, understanding and knowledge of the spatial statistics, resource modelling, geology, mining engineering and clean validated data to build accurate estimations. In the past few years, data science has become increasingly important in the field of minerals exploration and estimation. This study is a step forward in this field of data science and its integration with minerals exploration and estimation. The research has been conducted to develop a state-of-the-art data science framework that can effectively use limited field data with remotely sensed satellite data for efficient mineral exploration and estimation, which was validated through case studies. Satellite remote sensing has emerged as a powerful modern technology for mineral resources exploration and estimation. This technology has been used to map and identify minerals, geological features, and lithology. Using digital image processing techniques (band ratios, spectral band combinations, spectral angle mapper and principal component analysis), the hydrothermal alteration of potential mineralization was mapped and analysed. Advanced machine learning and geostatistical models have been used to evaluate and predict the mineralization using field based geochemical samples, drillholes samples, and multispectral satellite remote sensing based hydrothermal alteration information. Several machine learning models were applied including the Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Support Vector Regression (SVR), Generalized Linear Model (GLM), and Decision Tree (DT). The geostatistical models used include the Inverse Distance Weighting (IDW) and Kriging with different semivariogram models. IDW was used to interpolate data points to make a prediction on mineralization, while Kriging used the spatial autocorrelation to make predictions. In order to assess the performance of machine learning and geostatistical models, a variety of predictive accuracy metrics such as confusion matrix, a receiver operating characteristic (ROC) curve, and a success-rate curve were used. In addition, Mean Absolute Error, Mean Square Error, and root mean square prediction error were also used. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values. The experimental results also showed that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised to generate more accurate predictions. For the case study of copper prediction, the results showed that the RF model exhibited the highest predictive accuracy, consistency and interpretability among the three ML models evaluated in this study. RF model also achieved the highest predictive efficiency in capturing known copper (Cu) deposits within a small prospective area. In comparison to the SVM and CNN models, the RF model outperformed them in terms of predictive accuracy and interpretability. The evaluation results have showed that the data science framework is able to deliver highly accurate results in minerals exploration and estimation. The results of the research were published through several peer reviewed journal and conference articles. The innovative aspect of the research is the use of machine learning models to both satellite remote sensing and field data, which allows for the identification of highly prospective mineral deposits. The framework developed in this study is cost-effective and time-saving and can be applied to inaccessible and/or new areas with limited ground-based knowledge to obtain reliable and up- to-date mineral information.Item Slope Failure Prediction at Husab Open Pit Mine in Namibia(University of the Witwatersrand, Johannesburg, 2023-12) Thikusho, Christine Runguro; Watson, Bryan P.The study is focused on Domain D at Husab Mine in Namibia. The purpose of the study was to improve prediction of pending slope failures for planar and wedge configurations. Planar and wedge failures are similar in that little strain is required to initiate failure. Slope monitoring systems such as ground based radars, interferometric synthetic aperture radar and prisms were reviewed from the available literature. The data from the mine’s satellite monitoring data and the ground-based radar instruments was analysed. Slope prediction methods were used to back-analyse the failures, to determine if failure prediction times were possible. A case study was incorporated from the neighbouring Rössing Uranium mine, to supplement the data. The data utilised for the study was downloaded from the slope monitoring instruments on the mine i.e., the interferometric synthetic aperture radar, ground-based radar and tension crack data. The following slope failure predictive tools were investigated; the strain deformation approach; the inverse velocity method; the slope gradient method; the acceleration and velocity approach; and Displacement/Time plots. The back-analysis work done proves that the following slope failure predictive methods were able to predict failure at least 3 days before failure: velocity, cumulative displacement and inverse velocity. It appears that the Husab mine failure mechanism is not as brittle as previously assumed and failures are not necessarily instantaneous. Therefore, failures should be identified early, and the necessary risk mitigation measures implemented proactively. The ability of back analysing large volumes of stored data is important in the study of failure prediction.Item Pit Optimisation of Vondeling Quarry by Understanding Geotechnical Parameters Determined at Zoutkloof Quarry(University of the Witwatersrand, Johannesburg, 2023-11) Mukwevho, Tshinanne Matty; Pillay, OhveshlanThe purpose of the study was to investigate the geotechnical parameters at Zoutkloof quarry and how they affect stability and the mine planning process. The geological features of Zoutkloof and Vondeling are similar, hence the lessons learned while mining Zoutkloof quarry can be used when mining Vondeling quarry. Zoutkloof quarry has already reached its limits and is no longer operational. It is important that mine planning considers the critical geotechnical parameters. The main reason for this consideration is to keep slope walls stable, employees and equipment safe, and to continue mining the ore in an economical manner. The methodology of the research incorporated highlighting the literature in the public domain on geotechnical considerations in open pit mining. Evaluating geotechnical parameters such as groundwater, rock mass strength, slope angle and monitoring; and additionally, showed scheduling of mining blocks from 2007 to 2008 formed part of methodology in the research. The results analysis indicated that the strategies implemented to control groundwater were successful to keep the production benches dry and walls stable. Good understanding of the discontinuities and the rock mass strength enabled the quarry to be divided into ground control districts. Kinematics analysis for possible failures was done and the results showed that there was no probability of failure on planar mode. However, there were minor possibility that failure can occur on wedge and toppling mode. Yearly mining scheduling was completed, focusing on tonnage and quality requirements. During this period, Zoutkloof had minimum waste mined and the quarry had narrowed significantly which required the operational team to work within mine design specifications to maintain safety and slope angles. Some resources had to be compromised as it was not practical to exploit them safely. The research concluded its findings as successful because Zoutkloof quarry was mined completely with approximately 10 slope failures that resulted with no injuries to employees or damage to equipment. The factors of safety (FOS) were evaluated to be well above one and slopes remained stable until mining ceased. The research also made recommendations that can be implemented while the Pretoria Portland Cement (PPC) continue to mine Vondeling quarry to aid same success as Zoutkloof while being cost effective.Item Selection of an Optimal Tunnel Development Method for an Underground Mine Travelling Way Using an Analytic Hierarchy Process(University of the Witwatersrand, Johannesburg, 2024-01) Meyer, Berto; Musingwini, Cuthbert; Tholana, Tinashe; Sihesenkosi, NhlekoThis research study was conducted at the Marikana Operations (Marikana) which are situated within the Western Limb of the Bushveld Complex (BC) located in the North West province of South Africa. The Marikana Operations mine platinum group metals (PGM) using conventional underground mining methods. The Marikana Operations are owned by Sibanye-Stillwater. The PGM conventional underground mining entails extracting a shallow-dipping narrow reef horizon which is accessed via a network of development workings. Within this layout, a travelling way is an inclined tunnel that connects lateral development workings with the workings on the reef horizon. At Marikana the conventional hand-held mining method is used to excavate travelling ways. However, there was a trial process completed that proved the viability of the inverse drop raising method of excavating travelling ways at Marikana. With more than one viable tunnelling method being available for travelling way development, the need arose to select an optimal travelling way development method for Marikana. The selection is a multi-criteria decision making (MCDM) process because it requires the simultaneous consideration of several factors when evaluating the different alternatives or options. Commonly applied tunnelling techniques were reviewed leading to the selection of both the hand-held drill and blasting method and the inverse drop raising method as applicable to the conventional underground mining environment and specifically to travelling way development. Thereafter, Multi Criteria Decision Analysis (MCDA) techniques were reviewed, and the Analytic Hierarchy Process (AHP) was selected as the MCDA technique to be used as the selection tool for the research, due to its several advantages such as the ability to detect inconsistencies in judgements and provision to remedy the inconsistencies. After the application of AHP, the inverse drop raising method scored 7.2% higher than the hand-held method with a 53.6% versus 46.4% score. The inverse drop raising method was therefore selected as the optimal method to develop travelling ways at Marikana. With the approach that both these methods are currently being executed at Marikana, the inverse drop raising method is nearly twice as expensive as the conventional hand-held method. If PGM prices become a constraint, the method might not be sustainable if executed the way that it is done at the Marikana Operations from a cost perspective. It is suggested that further research should be done to see how the method can be executed more cost-effectively.