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By I. J. Geldenhuys

South Africa’s mining and metals industries have an illustrious history dating back to the Late Iron Age. We live in a complex and data-intensive world that has already fundamentally changed how we work and live. The outbreak of the global COVID-19 pandemic in 2020 resulted in immeasurable suffering worldwide, but the crisis also accelerated many technological developments and altered the way we think about, and experience, technology and work. Problem-solving requires new and rapidly changing skills to manage the data tsunami. Whether at the rock face or on the processing plant, the fundamentals have not changed, although how we interact with minerals and their properties has changed dramatically. Sustainable processing and design are non-negotiable if we genuinely want to achieve the aim of the cradle-to-cradle principle. As we venture forth, what can scientists, engineers, technologists, and mathematicians learn from history? History is data presented in a context; sometimes, the context is also important. We exist in an age of data, with most of the world’s data created in only the past two years. History can enhance our understanding of the present and enhance our future outcomes. Lessons from history can help provide insights into making ethical and sustainable choices related to technology or engineering in the mining and metallurgical industry. Shifting the perspective of mining and metallurgy professionals from a narrow focus on complex technical solutions towards a broader context for problem-solving and designs that includes the entire ecosystem is crucial. In other words, using or reflecting on historical process development is, at its core, systems thinking.

Sep 1, 2021

By B. McFadzean, M. Tadie, T. Dzingai, M. Becker

Ores from a single deposit may exhibit extensive variability in their mineralogy and texture. The ability to quantify this variability and link it to mineral processing performance is one of the primary goals of process mineralogy. This study focuses on the effect of alteration in three platinum group element ore samples from the Great Dyke in Zimbabwe – two of which were more pristine compared to the third, which was locally classified as ‘oxidized’ ore. These ores are known to be characterized by varying degrees of alteration, resulting in numerous challenges in flotation and affecting both grade and recovery. Alteration, by near-surface oxidation, of the valuable base metal sulphides and platinum group minerals resulted in lower flotation recoveries of Cu, Ni, Pt, and Pd. Evidence of incipient oxidation was more readily observed in the base metal sulphide assemblage than the platinum group mineral assemblage, even though the loss in recovery (because of oxidation) was most significant for Pd. Alteration through hydration resulted in a significant increase in mass pull and dilution of concentrate grade through the inadvertent recovery of naturally floating gangue comprising composite orthopyroxene and talc particles. In this study, the amount of naturally floating gangue was more strongly correlated with the talc grain size distribution than the grade of talc in the flotation feed. The oxidation and hydration alteration reactions are not necessarily mutually exclusive, although one may be more dominant than the other, giving rise to ore variability.

Sep 1, 2021

By N. Duda, P. Śliwiński, P. Stefaniak, B. Jachnik

Ventilation and air-conditioning are some of the most energy-consuming processes in deep underground mining. Additionally, the energy consumption of these processes systematically increases along with the expansion of the mine. Therefore, proper energy efficiency assessment and consumption forecasts of these systems are particularly important in modern mines for ongoing tracking and planning of the likely costs. In the article, a short-term prediction model for surface air–conditioning stations is described. This model takes into account dependencies that were identified, based on the data from the supervisory control and data acquisition (SCADA) systems. These dependencies between the response value and the external conditions are then incorporated into the model. This way, for example, the ambient temperature or the total number of specific days of the week (with sometimes significantly lower or higher than the weekly average energy consumption levels) are included. Taking into account these dependencies, and e.g. forecasted average monthly temperatures allow us to include the seasonality of energy consumption in the presented model. In the article, a sample energy efficiency analysis of the A/C system based on an example of the KGHM Polish Copper Polkowice-Sieroszowice mine is performed. The results of the solution proposed in the article are then evaluated, and compared to the currently functioning, more basic forecasting methods. However, given the general nature of the dependencies used in the model, similar solutions could be used in other case studies.

Sep 1, 2021

By H. Dudycz, A. Skoczylas, P. Stefaniak, P. Pyda

In the era of Industry 4.0, technologies such as Big Data (BD) and the Internet of Things (IoT) play a key role, especially for multi-site manufacturing companies. In such companies, the challenge is to collect and process massive amounts of data from industrial devices and extract key information from this data in real-time for ad hoc decision-making. The ability to analyse data from industrial devices translates directly into streamlined production processes, cost optimisation, prevention of unplanned events, and safety during operation. In this context, particular attention should be paid to Big Data Analytics (BDA), whose main source is the Industrial Internet of Things (IIoT). The purpose of this paper is to propose an IT system architecture incorporating BD and the IoT for a mining company. The paper presents results from a literature review focusing on available BD and the IoT solutions for industry (mining in particular) for the purposes of the acquisition, integration, and advanced analysis of data. The advantages and disadvantages of the different solutions were indicated and gaps in the accessibility to dedicated functionality are identified. Based on the analysis of a multi-site mining company, the main challenges faced by such companies planning to implement BD and IoT solutions are identified. Also, a proposal for an IT system architecture incorporating BD and IoT for a mining company is described in the paper. The contribution of the article consists of the identification of the challenges faced by mining companies planning to implement BD and the IoT solutions, and the proposal of an IT system architecture that allows BDA whose main source is the IIoT.

Sep 1, 2021

By Z. Levinson, R. Dimitrakopoulos

A mining complex is an engineering system where raw materials are extracted from several mines, processed, transported, and sold to the market. The uncertainty and local variability of material grades and their locations within the related mineral deposits are critical for determining the optimal life-of-mine extraction sequence, destination policy, operating modes, and capital investment decisions, which define the production schedule of a mining complex. Infill drilling is used to further characterise a mineral deposit and reveals information that changes the representation of the resource model and potentially the resulting optimal long-term production schedule. A self-learning reinforcement learning approach is proposed to determine the optimal drilling locations that maximise the net-present value of the long-term production schedule and the associated financial forecasts by adapting the schedule to additional information. A case study at a copper mining complex demonstrates a $70 M increase in project value by collecting drilling information with a $1 M budget.

Sep 1, 2021

By M. Salimov, J. Sattarvand

The conventional visualisation method of remote equipment is through the installation of one or more cameras on the machine, and sending a live feed to the operator’s flat display. However, the performance of the teleoperation can be enhanced using better visualisation through virtual reality (VR). This paper presents the process of the design and manufacturing of a robotic platform that will be used in the next phase of the project for a VR-assisted teleoperated excavator. The robot consists of a loading boom and arm arrangement installed on the undercarriage of a CAT300-9D. All hydraulic cylinders are replaced with electric actuators, and crawling motors are substituted by electric motors. The pieces of the boom and the arm, as well as the bucket were designed in Autodesk Inventor, and manufactured from aluminium sheets using a desktop and CNC machine. Parts are joined by pins and welding as needed. An Arduino-based control system is developed for the robot, and motor controllers are used to convert the power commands from the microcontroller to the high-amperage actuators. The control system is capable of communicating with a wireless Play Station controller to operate remotely. The manufactured robotic excavator is planned to be utilised in multiple autonomy research projects.

Sep 1, 2021

By M. Z. Naghadehi, F. F. Nasategay, J. Sattarvand, R. Battulwar, E. E. Gohari

After the recent increase in catastrophic failures of tailings dams such as the Burmadinho, Brazil failure in 2019, the mining industry has actively engaged in the stabilisation of tailings dams. Most of the tailings storage facility (TSF) guidelines suggest a laborious action that can be facilitated through imaging by unmanned aerial vehicles (UAVs). Although UAVs are not unknown to most mining operations, automated rill detection through aerial image processing is a new concept. This study utilises images from sites in the U.S. and employs semantic segmentation with a UNet architecture to detect location and size of these rills. The developed model can be used as a tool for monitoring surface erosion of TSFs.

Sep 1, 2021

By F. Navarro, F. Soto, G. Díaz, E. Jélvez, G. Morales, N. Morales

The increasing availability of tools on the web provides a great opportunity for the research community to boost investigation by accessing computational resources from all over the world. For example, data analytics libraries implemented in languages like Python can be accessed through many open and commercial platforms, therefore providing researchers with a common set of tools or shared datasets. Unfortunately, that is not the case with research in resource assessment and mine planning, where the most standardised tools are commercial applications designed to work on the desktop, but there are not many standard platforms for research. As a result, researchers from different groups often need to start from scratch in order to develop their work, and rarely share implementations details. Similarly, apart from a few publicly available examples, there is a lack of common datasets that are validated and can be used to compare results between different approaches. In this paper, we introduce a platform that provides tools for resource assessment and mine planning, which can be accessed remotely utilising Jupyter Notebooks. In its current version, the platform provides (i) definition and implementation of basic structures for managing samples and models (loading, saving, adding or removing columns, etc.), (ii) definition of experimental and modelled variograms for spatial continuity analysis, (iii) definition of standard interpolation methods in resource evaluation (inverse distance and kriging), (iv) definition of standard problems in mine planning (ultimate pit and precedence constrained production scheduling), (v) generation of 2D standard graphs like grade distribution, conditional means, tonnage-grade, and production plans (vi) a set of datasets, which can be used for case studies, and (vii) a set of notebooks with examples and documentation. In this paper, we introduce the platform and show some examples that illustrate some of the possibilities that it has for research and education in the area of resource assessment and mine planning.

Sep 1, 2021

By M. S. Shishvan, S. Klerkx, J. Sattarvand

The inaccurately drilled blastholes from the planned locations cause changes in the spatial distribution of explosive energy; some areas of the blast may receive a surplus of explosive energy, while others see a reduction. This results in less uniform fragmentation, reduces the efficiency of mining operations and increases costs. Additionally, it can lead to slope stability issues, flyrock, and uneven benches. To mitigate these problems, this study attempts to reduce differences in the explosive energy distribution (EED) through an optimization approach that adjusts the height of explosive columns in blastholes. Two optimization algorithms, the Genetic Algorithm (GA) and Tabu Search (TS), are tested. The latter is preferred for its local search neighbourhood strategy that gives superior computation times. Applications on different blast patterns result in improvements of 0.53–1.54%, or 2.14–3.94% when only the affected blocks are considered.

Sep 1, 2021

By G. Akdogan, C. Bergmann, C. J. Coetzee, Q. G. Reynolds, S. J. Baumgartner

Submerged arc furnaces (SAF) are used extensively in the production of ferrochrome, where charge material – consisting of mixtures of carbonaceous reductants, fluxes such as silica, and ore in the form of pellets – is fed through chutes into the vessel. The feed typically forms a thick bed or ‘burden’ of solid particulates above a bath of molten material below. This burden layer surrounds three electrodes, which are typically arranged in a triangular configuration in circular SAFs. A major challenge in the industry is the availability of sought-after reductants, volatile market conditions, and increasing electricity tariffs in South Africa. A good understanding of the physical properties of the raw materials in the charge is therefore of great value in order to best deal with the reductants available at any given time, and thereby maximising furnace throughput. This paper presents a study of the effects of different particle properties on particle flow behaviour relevant to SAF operations. Discrete element model (DEM) simulations were used to estimate the sensitivity of the particulate flow of beds of charge material to properties such as the coefficients of friction, particle shape and size, and others. The present study lays the groundwork for the development of full DEM models of the SAF that will greatly improve understanding of the flow of complex mixtures of solid particles in the submerged arc furnace, and how the relative physical properties of each component in the charge can influence electrical behaviour of the burden.

Sep 1, 2021

By S. F. Böde, K. Böde

This paper focuses on developing a comprehensive and pragmatic information technology (IT) strategy for low-margin construction activities. This forms part of the realignment of the general business strategy, and refocusing the operational model. The main elements of the IT strategy formation are analysis of the current IT environment in terms of software and hardware, carrying out a gap analysis, and developing individual functional digitalisation strategies in construction execution based on Building Information Modelling (BIM), business administration processes, communication, business intelligence (BI) and reporting, and data security. Further, substantial emphasis is given to the sustainable strategy implementation in terms of hardware and software readjustments, their integration, user training, and defining the way forward.

Sep 1, 2021

By M. Kumral, S. Li, M. Balci, Z. Shao

Main haulage roads play an important role in the performance of underground mines because of the cost of transportation of the extracted ore, equipment, and workers. During mine development, these roads are designed through manual tools or CAD-based software. However, the optimisation of these roads is still a complex problem due to some constraints such as minimum turning radius and maximum gradient. By utilising Dubins path theory, three elevation cases; namely low, medium and high are defined, considering gradient constraint as well as the minimum turning radius in a three-dimensional space. The shortest path between two points can then be found by the three-dimensional Dubins path algorithm with elevation constraint. As a further step, a three-dimensional Dubins path algorithm with elevation constraint for multiple points is also developed for multi-point cases. The effectiveness of these algorithms is tested through case studies in order to determine the optimal path between different points.

Sep 1, 2021

By P. Stefanek, N. Duda, B. Jachni, P. Stefaniak, B. Bursa

The Zelazny Most Tailings Storage Facility (TSF) is the largest reservoir of post–flotation tailings in Europe. The total amount of tailings from the mines in Lubin, Rudna, and Polkowice are stored each year at the disposal facility, which reaches approximately 30 million tons. It is crucial to accurately monitor and maintain such a huge storage facility, for the safety and economics of the entire region. In general, the monitoring network installed at the Zelazny Most TSF includes four groups of components: (a) geotechnical network (VW pore pressure transducers and inclinometers), (b) hydrological network (open piezometers, water level gauges, drainage discharge measurements, (c) geodetic survey network (e.g. surface and in-depth benchmarks, laser measurements, GPS measurements within GEOMOS network and (d) seismic network (e.g. accelerometer stations, cone penetration tests.) Apart from the above, chemical water analyses, tests of soil samples, monitoring of spigotting processes, and monitoring of relief wells’ discharge are also conducted. In total, approximately 2900 monitoring instruments are installed within the area of the Zelazny Most TSF. Such large amounts of data from the various information sources makes manual data analysis by the engineers almost impossible. In this paper we discuss a machine learning driven approach to improve the quality of the monitoring and maintenance of such facilities. We focus on the seismic cone penetration test (SCPT) data analysis. First, we carefully describe the collected data, its quality and availability. Then we discuss possible events that can occur in the TSF that need to be predicted using SCPT. Based on the information obtained in these two steps, the applicability of several machine learning methods is estimated. Some of them are then tested on available historical data. The algorithms described in this article will be further developed in the IlluMINEation project (H2020).

Sep 1, 2021

By T. J. Otto, I. D. Ungerer, G. C. Lindeque, T. J. van Heerden

Anglo American Kumba Iron Ore (Kumba) continues to implement technologies focussed on improving safety and productivity; this forms part of the groups FutureSmart Mining initiative. The paper provides insights into the implementation of three technology solutions that improves the productivity of heavy mining equipment (HME). Enhancements to the short term mine planning (STMP) process, the effective use of a fleet management system (FMS) and the deployment of drones or Remotely Piloted Aircraft Systems (RPAS) are discussed. The implementation of these and other technologies contributed to a 13% and 22% increase in the overall equipment efficiency (OEE) of the primary HME at Kumba’s Sishen and Kolomela mines respectively. Four key success factors (KSF) are highlighted namely that Kumba’s technology implementation drive is a key enabler to the company’s strategy, has a strong focus on value-add, is underpinned by a holistic and integrated implementation approach and is people-centric.

Sep 1, 2021

By K. M. Seiler

Automation in mining requires accurate maps of road networks on site. Because roads on open-cut mines are dynamic in nature and continuously changing, manually updating road maps is tedious and error-prone. This paper investigates the possibility of automatically deriving an accurate representation of the road network using GPS data available from haul trucks operating on site. We present an overview of approaches proposed in literature and test the performance of publicly available methods on GPS data collected from trucks operating on site. Based on shortcomings seen in all tested algorithms, a post-processing step is developed which geometrically analyses the created road map for artefacts typical of free-drive areas on mine sites and significantly improves the quality of the final road network graph.

Sep 1, 2021

By M. Z. Naghadehi, E. Emami, J. Sattarvand, R. Battulwar

In this paper, a methodology for a computerised recognition of joint sets on 3D point cloud models of rock masses is presented. The process starts with classifying joints on a 3D rock mass surface through training a deep network architecture and validated using manually labeled datasets. Then, individual joint surfaces are identified using the Density-Based Scan with Noise (DBSCAN) clustering algorithm. Subsequently, the orientations of the identified joint surfaces are computed by fitting least-square planes using the Random Sample Consensus (RANSAC). Finally, the joint planes are classified into different joint sets, and the dip direction and dip angle for each set are calculated. The performance of the proposed methodology has been evaluated using a case study. The results show that the presented procedure is fast, accurate, and robust.

Sep 1, 2021

By T. Bohmer, L. Auret, N. Pillay, Z. Horn, P. Rambalee, A. Haasbroek

As part of a broader digital technology-enabled transformation programme, a major mining company is systematically implementing Advanced Process Control (APC) solutions and associated instrumentation throughout their operations. This paper highlights the approach and benefits of this holistic approach for a specific instrumentation installation, and APC installation at a South African coal processing plant. For the coal processing plant under consideration, a systematic approach was followed. Process sections/units are considered from upstream to downstream processing. APC solutions are designed for each process section and each process section is interrogated in terms of availability of sufficient process measurements to ensure stable, capable, and ultimately, optimised performance. Where insufficient process measurements are available, instrumentation upgrades are implemented. Finally, APC solutions are implemented, and evaluated in terms of productivity, safety, health, and environmental benefits. For the flotation section, an opportunity was identified to install froth sensors. The installed froth sensors provide measurements on froth velocity, bubble size, height, colour, and stability. The improved visibility that the froth sensors provided on the flotation process resulted in quick gains in terms of productivity: for example, instability in the feed to the flotation section could be quantified. This resulted in targeted process troubleshooting in an upstream thickener, and subsequent improvement of process stability, and reduced feed variance. The measurements from the froth sensors were further integrated in an APC solution. Proportional mass pull contributions per cell and per line can be inferred from the froth sensors, serving as controlled variables, while pulp level set points serve as the manipulated variables. On-off tests indicated that the advanced process control solution provided increases in yield and product quality.

Sep 1, 2021

By Z. Saw, C. Poveda, N. L. Mai

Optimising the throughput of a mining network comprising all equipment from shovels to semi-autogenous grinding (SAG) mills is a complicated task, yet the potential benefits can be significant. Key modelling challenges stem from the sheer number of possible routes which material can travel, different equipment operating under different constraints, and the rate of SAG mills being calculated through a nonlinear function of the blended materials’ rock attributes. Nevertheless, this is an ideal onapplication area of mathematical programming, a branch of operations research. This paper describes an application of mixed integer linear programming (MILP) to optimise the mining equipment network of one of the world’s largest copper mines.The model takes as input a year-long monthly mine schedule of shovels in different pits, pushbacks, sequences and faces which produce material to be moved through a network of crushers, conveyors, silos, distributors, stockpiles and SAG mills, ultimately outputting a daily operational plan for all equipment that maximises the throughput of the network. All operational constraints and business rules are considered, including three quality attributes of copper ore. Comparing model results with actual data shows an improvement of 4.47% in network throughput tonnage. Additionally, although the model is solved using a free, open-source solver, optimal solutions are still achieved within a practical timeframe.

Sep 1, 2021

By F. M. Ramadhan, M. R. Pratama

Monitoring open-pit mine road conditions has become very critical nowadays. There are several benefits to in conducting research on this field such as opportunity to improve truck’s speed which directly influences truck’s productivity. This research aims to provide a semi real-time, and sensors-based automatic road damage detector that predicts the road quality through road damage exact location tracing, road damages auto-classification, and real-time instruction in the form of work order format to mining road maintenance, and repair authorities through mobile apps and maintenance unit’s in-cabin display. It will also provide the maintenance/repair activity performance evaluation via integrated dashboards. The artificial neural network algorithm is applied during the process of data training regarding classifying road damages and severities which resulted in a valid model. The road damage classification model has a precision level of 80.5% in predicting whether the road is damaged or not. Using this approach, we are expecting to visualise a road quality map of all active roads on mine site.

Sep 1, 2021

By R. Minnitt, C. Morgan, D. Fourie

The problems associated with outliers and their adverse effects on resource estimation in deep level South African gold mines are presented, and solutions suggested in available literature are reviewed. Traditional Witwatersrand type outlier capping uses the 99th percentile grade as the upper limit, a somewhat blunt approach. The occurrence of high and low grade areas in mining operations and the estimation of resources using geostatistical techniques require that the assumption of stationarity be upheld. Honouring the stationarity of data requires that mining areas be subdivided into appropriately sized and spaced domains. In order to overcome high-grade conditionally biased block estimates, the overly strong influence of extreme grades is mitigated by using naturally occurring grade sub-domains, by identifying outliers within the grade sub-domains, allocating relevant capping limits, and weighting them in a post-estimation process to. Rather than applying a capping value across a domain it applies a maximum cap based on the extreme value and its range of influence; the method also ensures that relevant capping occurs even in lower grade sub-domains. The proposed method for capping outliers recognises that metal contained in high grade chip samples should not be over-capped, and based on the grade of surrounding samples. The post-processing method of capping outliers does not interfere with the kriging equations. The method is simple, elegant, and can be easily applied. An advantage is that the output from the kriging process is not overwritten, allowing for comparison between original kriging results and post-processing results.

Sep 1, 2021