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By J. R. Bredell, J. Branehog, C. Coetzee, J. J. van Tonder

A nutating grinding mill, exemplified by the commercial HICOM mill, achieves efficient particle size reduction through the accelerated motion of the material charge within the nutating chamber. This design, characterised by high particle velocities and rapid charge circulation, offers a compact solution compared to tumbling mills. There have been concerns regarding the mechanical reliability of kinetic joints for certain specialised mining applications. Recognising the need to model internal forces in such systems, this study focuses on the uncoupled simulation using multibody dynamics and the discrete element method. The investigation employed a small-scale experimental test mill, referred to as the NuMILL, featuring a cylindrical chamber (140 mm internal diameter, 245 mm height, 6° nutation angle). Instrumented with an array of sensors, the NuMILL provided internal load data for validating the simulations. Experiments were conducted with an empty chamber or filled with charge material. Charge materials included coarse sand or steel pellets with a charge-to-chamber mass ratio of 0.25. The mill speed ranged between 100 to 700 RPM. Multibody dynamics modelled resultant inertial and frictional loads within 5%, while the discrete element method simulated the internal material contact loads with accuracies ranging from 1% to 19%. The combined simulation approach predicted the resultant forces in the critical kinetic joint within a 20% margin. Representing the charge using rigid lumped mass elements was also studied. The presented simulation techniques, validated against experimental data, offer a robust foundation for analysing larger systems, including the industrial-scale HICOM.

Mar 5, 2025

By A. Cambitsis

We show that the prioritization of business improvement and performance management decisions may be significantly improved by taking into account the systemic effects of changes made in a single process area. To do so we build a model of a mining value chain, taking into account consequential downtime that arises from downstream and upstream starvation and choking effects of each activity. Introducing the concept of internal capacity, this approach yields insight into the relative importance of key metrics such as loading and hauling rates. We suggest that a holistic view of this type should be a core component of business improvement and performance management decisions.

Jan 1, 2012

By D. Nardini

The need to move towards faster and safer mine development is a key priority in mining houses globally. In addition to this, South Africa faces an array of challenges introduced by labour-intensive operations; hence a study was conducted to look into improving development rates and labour productivity, specifically in the areas where trackbound flat-end development is required. The aim of the study was to propose a method to deliver fit-for-purpose tracked tunnels that advanced at a rate of one blast per end every single working day. A major part of the study was to optimize the selection of an appropriate trackless fleet and mitigate any constraints introduced with the mechanization. This was done by creating scenarios, based on a typical case study, looking into the aspects around safety, mine design, mine activity cycling, men and machine requirements, service installations, and trackless-trackbound interfaces. The case study was based on a deep-level gold mine developing twin headings with connectings; however, with the move to extend and deepen platinum operations it is foreseen that twin end development will become more prevalent due to ventilation, logistical, and other requirements. With the introduction of the trackless mechanized system suggested, development rates of 56 m of system advance are expected (189 m total development), with on an average of three headings and fixed blast times. Scenarios run with multi-blast conditions yield expectations of 75 m/month advance of the system, significantly higher than the conventional performance seen today.

Jan 1, 2012

By B. Larsen

The Søderberg electrode was invented about 95 years ago by the company Elektrokjemisk, later known as ELKEM. It is still the most commonly used electrode system in submerged-arc furnaces for ferroalloys, platinum smelting, and slag cleaning operations. The size of the furnaces and electrodes has increased over the years and electrode diameters up to 2000 mm are today in use. Such large electrodes can be challenging to manage, and large electrodes are also more demanding when it comes to the electrode paste used in the electrodes. Although electrode sizes and equipment around the electrodes have changed, the main principles of the Søderberg electrode processes are the same. To assists in proper understanding of the electrode processes and to optimize electrode management, Elkem has developed several mathematical tools covering various aspect of the process. Both temperatures and thermal stresses can be calculated during dynamic conditions. The paper demonstrates, with examples, how the tools are used in both education and as practical input to improved electrode performance.

Jan 1, 2012

By D. A. Cowen

Toll and custom treatment of base metal concentrates have been performed for decades as mines and concentrators are developed distant from capital and, energy-intensive smelting and refining facilities. Commercial treatment terms have been developed and refined for over a century, and are negotiated taking into account a complex array of quality and economic factors. Treatment of third-party concentrates in the South African platinum industry is a comparatively recent development, brought about by the opening up of mineral rights and operations being developed that are smaller than the critical size required to sustain capitalization of smelting or refining plant. Published commercial terms have not been demonstrated to reflect the complexities and subtleties of base metal concentrate markets. This paper outlines some key elements and parameter ranges of base metal concentrate treatment contracts, and applies similar principles to typical Merensky and UG2 platinum group element (PGE) concentrates to determine potential value distribution between the mining and treatment parties in tolling arrangements.

Jan 1, 2012

By Z Bagheri, E Moosavi

Identifying the exact borders of geological domains and assessing the uncertainty of these borders are crucial steps in resource estimation. This potential field method was designed to build 3D geological models from data available in geology and mineral exploration, namely the geological map and a digital terrain model, structural data, borehole data, and the geologist’s interpretations. The resource evaluation of a copper ore deposit is often performed in three steps: (i) delimitation of the boundaries of the units corresponding to the various geological formations or ore types; (ii) estimating densities; and (iii) estimation of grades within each unit. In simple cases (e.g., a series of sub-horizontal layers), the geometric model can be built using 2D geostatistical techniques (kriging or co-kriging of the elevations or thicknesses of the various horizons) which also quantify the uncertainty of the model. A 3D model of Cu grade is generated using ordinary kriging; however, the indicator kriging method is run to design a 3D model of rock types by incorporating the drilling results. Block models of geophysical and geological characteristics are included in a similar 3D mesh to investigate their relationships in copper mineralisation.

Sep 1, 2021

By M. S. Shishvan, J. Sattarvand, R. S. H. Meij

Navigating miners during an evacuation using a smart evacuation technology can significantly decrease the evacuation time of an underground mine in case of an emergency. This paper presents a mathematical programming model to calculate the most efficient escape path for each miner as a critical component of the smart evacuation technology. In this model, the total evacuation distance of the crew is minimised and scenarios with blocked pathways, and stamina categories for the miners are simulated. It was found that all the tested scenarios are technically feasible. Using the feature that filters out blocked pathways has no downsides, as safer routes are calculated, and there is no penalty in the computation time. The paper also discusses the social and technical issues that need to be overcome before the algorithm can be implemented as an actual escape solution.

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 A. Melkumyan, M. Balamurali, J. Zigman

Understanding the contribution of geophysical variables is vital for identifying ore indicator regions. Both magnetometry and gamma-rays are used to identify the geophysical signatures of the rocks. Density is another key variable for tonnage estimation in mining, and needs to be re-estimated in areas of change when a boundary update has been conducted. Modelling these geophysical variables in 3D will enable investigation of the properties of the rocks and improve our understanding of the ore. The Gaussian Process (GP) was previously used to generate 3D spatial models for grade estimation using geochemical assays. This study investigates the influence of the following two factors on the GP-based autonomously generated 3D geophysical models: the resolution of the input data, and the number of nearest samples used in the training process. A case study conducted on a typical Hammersley Ranges iron ore deposit using geophysical logs, including density, collected from the exploration holes, is examined in this paper.

Sep 1, 2021

By S. Yaw Amponsah, E. Ben-Awuah, P. M. Takouda, P. Eme

Mining production scheduling is the process of determining the sequence of block extraction over a period of time that will yield the maximum net present value for a mining operation. The block extraction sequence needs to occur under certain resource constraints which presents an NP-hard problem that is computationally intractable and complex to solve for large scale problems using exact mathematical models such as Mixed Integer Linear Programming (MILP). In this paper, we used a metaheuristic optimisation model based on a Genetic Algorithm (GA) to solve the production scheduling problem. The problem definition and resource constraints are formulated using a mining specific GA that enforces block extraction precedence and mining capacity constraints through a normalisation method. A case study is implemented with the GA model, and the results and computational time are compared with that of a MILP model with CPLEX solver which is based on a branch-and-cut algorithm.

Sep 1, 2021

By S. V. Lukichev, O. V. Nagovitsyn, V. V. Laptev

In this paper the authors describe the developed algorithms of short- and medium-term planning of underground mining operations. The algorithms were developed using the example of the flat-back cut-and-fill method, and provide multifactor accounting of mining conditions, technical and technological limitations: state and properties of the rock mass; the current mining situation; mining rules; availability of safety pillars; properties of the filling; additional characteristics of technological objects that complicate the driving of workings; sufficiency of human and technical resources; employee specialisation; existing machinery and equipment; opportunities and limitations of the transport scheme of the mine, and work schedule. The paper is presented by means of a dynamic model of mining development, which shows the sequence of mining operations in the mine workings. Besides animation, the tool provides an opportunity to form tabular and graphical reports.

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 N. Duda, W. Koperska, S. Anufriiev, P. Stefaniak

In mineral processing plants ore extracted from mines is processed to obtain a concentrate with the highest possible concentration of this valuable product. It is a complicated multi-stage process. Usually, the first step is sieving the ore, which is typically done using vibrating mining screens. There is often only a limited number of these machines in the technological chain, which is why their continuous operation, and proper maintenance is crucial for the whole process. Predictive maintenance is the best strategy in this case because it allows for minimising the number and duration of unexpected stoppages. It consists of the prediction of future failures based on available real-time data, and predictive models built upon this data using, for example, machine learning techniques. In this paper we concentrate on feature generation for future predictive maintenance model development for a vibrating screen, based on an example of a KGHM ore processing facility in Polkowice-Sieroszowice. We focus on two types of measurements: bearings temperature and vibrational data. Based on the raw data, the process of more advanced feature engineering is described. By transforming the temperature data we calculate characteristics of bearing cooling and heating dynamics, while based on vibrational data, the aggregated characteristics of the vibrations are derived. Such features are calculated on a long-term basis, and then they are compared to the data related to vibrating screen maintenance to estimate their significance for the prediction of particular failures of the machine.

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 M. Kumral, S. Hmoud

Optimising the configuration of infill drill holes is an important step during mineral exploration campaigns. However, this optimisation process encompassing all possible drilling configurations within a mineral deposit is highly combinatorial and hard to solve in a reasonable time. This paper proposes using spatial clustering algorithms to divide the orebody into contiguous zones and target the best zones with infill drilling in order to reduce the size of this optimisation problem. Several clustering algorithms are assessed in a spatial context on hypothetical datasets. This evaluation shows that the hierarchical density-based spatial clustering application with noise (HDBSCAN) algorithm can be used in combination with the uniform manifold approximation and projection (UMAP) dimensionality reduction algorithm to identify contiguous zones in the orebody.

Sep 1, 2021