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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 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 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 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

By N Morales, G Nelis

Short-term plans must meet the long-term goals while accounting for shovel selectivity, operational space, and scheduling restrictions. There are several approaches that deal with these issues to deliver a feasible short-term schedule. This paper compares two approaches based on mathematical programming to address the short-term mining cut definition and scheduling in an integrated way. They work by scheduling extraction to comply with long-term production goals, but they differ in how mining cuts are modelled. The first approach schedules Selective Mining Units (SMUs) but aggregates them around predefined locations. The second approach does not work on SMUs; it decides directly what mining cuts are to be used from a pre-defined set of shapes. It assigns a destination and schedules the extraction for the selected mining cuts. We applied both approaches in a real case study to compare their results in terms of profit, mining cut design, expertise needed, profit, and optimisation performance.

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 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. C. Harris, S. J. Gaab, J. Sattarvand, S. Duzgun

Underground mines are inherently dangerous due to potential hazards such as fires, explosions, and caving of rock. Although these events are rare, once they occur a speedy and safe egress of mineworkers is critical to survival. Static evacuation systems use exit signs or directional lines but can get obstructed or be hardly visible in smoky or dusty conditions. This paper presents the investigation of the effectiveness of a smart evacuation technology in underground mining, where a real-time guidance system leads individuals on an optimised path to the egress while avoiding danger zones, compared to conventional practices. Participants simulated an evacuation from a virtual reality mine, using the conventional system and the smart evacuation system with real-time guidance. The highest reduction in evacuation time using the smart method amounted to nearly 40% on average. Moreover, 83% of participants preferred the smart guidance system, and agreed that it could enhance safety.

Sep 1, 2021

By S. Andika, O. A. Putri

Maintaining fleet productivity, which consists of balancing loader and truck performance, has always been a challenging task. An automatic dispatching system (ADS) is implemented to help monitoring fleet condition and assign trucks to a designated loader. Integrated data from real time Global Positioning System, Vehicle Health Monitoring System, and other production related data are acquired to support the ADS. However, the abnormal truck cycle has been the main culprit which resulted in unnecessary loader hanging time and trucks queuing time. Hanging and queuing time accounted for 2.34 and 2.07 minutes respectively per loading cycle or equal to 2,861 loose cubic metres per fleet per day production losses. Using integrated dispatching system data, the authors conducted research to study a truck’s hauling cycle that affects fleet productivity. The data is modelled to obtain the arrival rate pattern in a front loading area by adopting the queue theory algorithm. This paper aims to understand the potential pattern of a truck’s cycle abnormality and to resolve its occurrence.

Sep 1, 2021

By M. Stachowiak, M. Nowicki, K. Walas, A. Skoczylas, P. Stefaniak

In this paper the authors present a walking robot dedicated to the inspection of a belt conveyor’s transportation system in an underground mine. The developed robot can independently diagnose all of the key rotating elements of the belt conveyor (pulleys, bearings, drums, gears, engines) based on vibration and acoustic signals as well as typical RGB (red, green, blue) and thermal images. The article presents key diagnostic procedures applied to real industrial data obtained during robot testing.The work was developed as part of the ‘THING–subTerranean Haptic INvestiGator’ project. The authors presented the main assumptions of the research methodology developed for the needs of technical diagnostics of conveyor elements typical of inspection work performed by mining staff. Based on industrial data, the use of algorithms for elements such as rollers, drive stations, and the conveyor belt were shown. The main assumption of the research is to develop a robot adapted to the conditions of a deep mine, capable of carrying out an autonomous inspection of mining infrastructure. The conducted tests and the developed fault detection algorithms were aimed at preparing the robot for underground tests in the operating conditions of the mine.

Sep 1, 2021

By I. Kaabachi, F. Meunier, N. Morales

In open pit mining, ore and waste are hauled outside of the excavation area by trucks that move along a system of ramps. The design of these ramps must be carefully addressed because they have to comply with geomechanical constraints to ensure the stability of the pit walls, provide access to the different levels and phases, but also maximise the value of the extracted ore and reduce the extraction of waste. Because an optimisation approach able to deal with the design of the full ramp system seems to be out of reach at the moment, this paper addresses the problem of designing one ramp providing access to a single pushback, which has been determined beforehand. The optimisation objective is therefore to find a ramp that gives access to one given potential volume, while minimising its impact on the economic value of the volume. Two versions of the problem are formalised. One version allows the extraction of ore and waste only if this extraction is necessary for the ramp system. This version somehow minimises the intrinsic value of the ramp system. In the second version, it is possible to extract more than is needed by the ramp system. For instance, this takes into account the possibility of attaining ore on the wall opposite to the ramp as soon as the ramp reaches its level. In the setting mentioned above, the contribution of this work is three-fold. First, the computational complexity of the two different versions of the problem is presented and discussed; in particular, they are shown to be NP-hard. Second, compact integer programs modelling these two versions are proposed. Third, an efficient GRASP algorithm able to cope with the large instances of the problem is proposed. To assess the proposed approaches, the models and algorithms are applied to real instances with an off-the-shelf solver, illustrating and discussing their potential and relevance for the two versions of the problem. They are also compared with other approaches from the literature in terms of value and resulting designs.

Sep 1, 2021

By J. Saunders, N. Pretorius, G. Krafft, T. P. Katakwa

Imagine being able to see all the future planned mining activities in real time. This is exactly what SIMS (Short Interval Management System) will enable you to do. SIMS is a real time short interval control system that continuously measures and sets optimised plans for mining production processes. The system utilises algorithm-based software that takes resources (people, equipment, tasks, and workplaces) and gives you the best achievable plan. The algorithm was developed using methods found in the field of metaheuristics. A metaheuristic is a strategy to designed to efficiently search for optimal or near optimal solutions to problems that are very complex computationally. (Glover, 1986) There are several algorithms that have been developed to solve optimisation problems which are difficult to solve using traditional techniques. This has given rise to a field called evolutionary computing. (Simon, 2013) Examples of these techniques are genetic algorithms, particle swarm optimisation, and ant colony optimisation. These methods are used to solve problems in a variety of fields. As an example, Kok and Lane (2012) used particle swarm optimisation for equipment allocation in a mechanised underground mining environment. The first SIMS deployment took place in a platinum underground tabular mechanised mine near Burgersfort (province?) from 2017-2019. This included the development from a paper-based system to a fully operational online system. SIMS then moved to a second site and is currently being tested in a second platinum underground tabular mechanised mine in South Africa. Full deployment of the system is currently being phased in. SIMS was built in response to issues that have been plaguing the mining industry for years. These issues are all aligned into one major problem statement: how can we optimise mining production processes without compromising safety? This product was developed by combining years of mining experience with software development. SIMS is based on a four-pillar concept. Safety, productivity, accountability, and business improvement. SIMS uses a set of pre-configured mining and production rules, and with the resources that are available, to determine valid short term production plans. A background algorithm then determines the optimum plan to be executed. The system continuously produces a set of job cards that are pushed to respective operators on their allocated tablets. The management portal component allows you to view and manage all current and planned work on any connected device available. This allows real time visibility of completed, current, and planned work to operators, service departments and management, i.e., seeing into the future.

Sep 1, 2021

By E. Emami, J. Valencia, J. Sattarvand

The knowledge of the final location of a drillhole is critical in obtaining the expected blasting results. This paper proposes a new methodology to control the drilling accuracy in open-pit mines using aerial images and machine learning. Specifically, it investigates how support vector machines (SVM) and convolutional neural networks (CNN) are useful for automatically detecting blastholes over drone images captured in blasting patterns. Several datasets were obtained from different mines in Nevada (USA) and were used to train and test models. We then processed those images to create orthomosaic and digital elevation representations of the patterns. Thousands of patches were then extracted and augmented to develop SVM and CNN models. The results demonstrate that machine learning is useful for drillhole detection and, in this way, to control the drilling accuracy and use the explosive energy more efficiently.

Sep 1, 2021

By L. Pretorius, S. Uludağ

This paper explores how to apply system dynamics principles to surface mining problems towards understanding the consequences of mining efficiencies. Some simulation examples related to machine and people performance have been used to demonstrate systems engineering concepts to mining engineers. The simulation tool used is Vensim, which is widely used in industrial engineering applicable to systems thinking, and systems dynamics. The science and mathematics behind the system dynamics as well as benefits and typical challenges are discussed to explore the applicability to mining problems. The benefit of having one less drill is easily quantifiable with the developed model which is one of many other measurable benefits, using the developed model. Some of the benefits of the improved quality of drill holes are not fully quantified as this depends on the mine specific inputs. There are examples of quantifying this in the other industries using system dynamics tools.

Sep 1, 2021

By J. P. De Carvalho, R. Dimitrakopoulos

This paper presents an actor-critic reinforcement learning (RL) method to allocate trucks and shovels in open-pit mines, while defining short-term production scheduling of an industrial mining complex, given new information collected during operations. This new data obtained from sensors update the uncertainty of the orebody models. The RL agents interact with the mining complex environment; exploring the solution space, learning actions to improve metal production and equipment utilisation. A case study is presented at a copper mining complex, highlighting the method’s ability to adapt and make informed decisions while collecting new information.

Sep 1, 2021

By Stanisław Prusek, CHONG WEI, Huawei Xu, Jun Wang, Andrzej Walentek, Artur Dyczko, Derek B. Apel

The excavation-induced stresses transferred from the panels or pillars in upper coal seams play a key role in the occurrence of rockbursts in driving roadways during close-distance coal seam mining, which has become a common problem and threat to many underground mines. The authors established a 3D finite-difference numerical model via FLAC3D software using the closedistance coal seam mining conditions in the Zofiówka Mine as a case example to reveal the rockburst mechanism in such conditions. The excavation-induced vertical stresses are analyzed, which provides a basis to build a conceptual model to explain the possible source mechanism of the rockburst under such conditions. Nine typical rockburst indicators considering different contributing factors are then selected as the evaluation criteria to assess rockburst potential in the study site. The results suggest that the superposition of multiple excavation-induced stresses of the main drift and tailgate provides an environment for stress concentration and energy accumulation. The side abutment stress induced by mining in the upper coal seam has a “strengthening” effect to the rockburst occurrence. The great deviatoric stress induced by complex excavating situations is another important exterior cause. A strict calibration procedure should be implemented before using indicators to predict rockburst potential. A methodology was proposed to estimate the location and magnitude of rockbursts combined with numerical modeling, laboratory tests, and feedbacks from the field. The study could provide some references and guidelines for preventing and mitigating rockbursts in underground mines under similar engineering conditions.

Aug 26, 2021

By Scott Rosenthal, David Gilkey, Michael McGivern, Lorri Birkenbuel, Daniel Autenrieth, Ty Murphy, Hadi Aldossari

A safety climate case study was carried out at a surface metal mine where investigators administered the Liberty Mutual Short Scale Safety Climate Survey to 365–368 miners to measure safety climate in consecutive years. Following the baseline safety climate survey in 2019, Foundations for Safety Leadership (FSL) training was conducted with 81 middle to upper management employees at the mine site. Investigators found statistically significant differences in the pre vs. posttraining FSL assessment scores of the middle to upper management employees who attended the training. The follow-up safety climate evaluation was compared to baseline scores and revealed no significant improvement. The overall baseline company safety climate score of 76.38 increased minimally to 76.50 (p-value = 0.616). Investigators also evaluated differences in safety climate between the company’s three major divisions (operations, maintenance, and administration). Both years administration had the highest mean score and operations had the lowest mean score. The authors attributed the statistically significant differences found among the three major divisions to various dissimilarities in their work environments.

Aug 13, 2021