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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 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 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 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 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. E. Rossi, C. Badenhorst

Conditional bias occurs when the expected value of the true grade conditional on the estimated grade is not equal to the estimated grade. It is a quantification of how the average of estimates differs from the true value. Conditional bias is present in all resource estimates due to the unavoidable properties of kriging, and indeed all linear estimation (regression) methods. In a mining resource estimation context, the degree of conditional bias is dependent on the spacing of the conditioning data; more data leads to the expectation of less conditional bias This paper discusses conditional bias from two perspectives: 1. 2. Estimates seeking to eliminate, as much as possible, conditional bias; and Estimates that accept a larger amount of conditional bias in resource models, in order to achieve a specific outcome. The problem of conditional bias in estimation can only be fully appreciated if the objective or purpose of a resource model is understood. There are instances where conditional bias should be avoided because it is not reasonable to make decisions using estimates that are known to be wrong in expected value; for example, when making a final decision in grade control with respect to the destination of the material. In most instances in the mining resource estimation context, however, the estimated values will not be “final estimates”, but “interim estimates”, meaning that in the future more data will be obtained, and the ”interim estimate” will then be updated. In this context, the most useful resource model is an interim model that is able to predict the final model estimates, despite not having all the data available that will be collected in the future. This is the objective, for example, of a recoverable “long-term” model developed prior to operating a mine, for example at the feasibility stage. It attempts to predict the ore that a processing plant will receive, well into the future. In such cases, it can be unequivocally demonstrated that conditional bias is not only unavoidable, it is a necessity.

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 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 J. Githiria, D. Wachira

The conventional truck and shovel haulage system has become expensive to operate in most mines due to current strict environmental conditions, increase in depth and size of open-pit mines, increase in labour costs, and fuel costs. These factors have motivated the search for alternative material haulage options in open-pit mines. In-pit crushing and conveying (IPCC) system has been regarded as a plausible alternative in material haulage systems. However, usage of IPCC has been limited to a few mines due to limited knowledge of IPCC performance, and other technical factors of the system. This paper investigates, and evaluates the productivity performance of four types of IPCC systems (fixed, semi-fixed, semi-mobile, and fully mobile IPCCs) using mine productivity index (MPi). Using a model developed by MATLAB, the MPi for the four systems is computed using input variables such as availability, utilisation, performance and number of trucks and shovels in a limestone quarry. The mine productivity index for fixed, semi-fixed, semi-mobile, and fully mobile IPCC is obtained as 67.13%, 69.02%, 70.52%, and 71.52% respectively. In conclusion, the fully mobile in-pit crushing and conveying system is the most appropriate IPCC in the quarry, due to its better productivity performance compared to the other three IPCC systems and the truck and shovel system currently in use.

Sep 1, 2021

By Julián M. Ortiz, Antoine Caté, Oy Leuangthong, Ilkay S. Cevik

Mineral resource classification relies on the expert assessment of a qualified person (QP) to determine which blocks of a 3D mineral resource model are classified as measured, indicated, or inferred. The decision is often based on a combination of quantitative parameters related to the estimation process and qualitative decisions based on previous experience or preconceptions not captured in the numerical model. As such, the procedure is subject to inconsistency, that is, blocks with similar qualities may end up in different categories, mainly due to the subjective nature of the approach. In this paper, we present a methodology to assist the qualified person in this task by clustering blocks with similar parameters and then classifying them into categories in a consistent and automatic manner that only requires the specification of a few hyper-parameters. The result is a consistent classification into resource categories comparable to the result of a classification done by a qualified person but fully consistent and generated in a short time frame. The procedure begins with repeatedly sub-sampling the block model to determine a distance matrix using an unsupervised random forest approach and then clustering the blocks using the associated distance matrix. Then, all the blocks in the model are classified using a supervised random forest approach, which gives a probability of belonging to each class. The initial class can be determined from the class probabilities. Support vector classification with a radial basis function kernel is utilized to smooth the boundaries between classes and define the final classification. An approach to tune the hyper-parameters of smoothing is provided. The methodology is demonstrated with two examples from two gold deposits. Results are comparable to the classification done by the project qualified person using conventional methods.

Aug 17, 2021

By Wang Xuewen, Bao Qingbao, Xie Jiacheng, Yan Zewen

This study puts forward a virtual memory cutting method based on the prediction of AFC shape. The integrated key technologies include the generation of virtual teaching cutting curve, the height adjustment strategy of front and rear drum, cutting height adjustment strategy, the reciprocating cycle of cutting technology and so on. Through the two algorithms, SVM and ELM, the form of the next working cycle can be obtained by rolling prediction using the form information of AFC in the first several circulations. Based on the obtained dynamic fluctuation of the form in the vertical and horizontal working faces, the position and cutting information of the shearer during its operation can be controlled and predicted in advance, the memory cutting template can be adjusted and modified in time. Employing this method, the more stable, accurate and timely memory cutting of the shearer under the uneven coal seam could be realized.

Aug 16, 2021

By Greg J. Hasenfus, Allison M. Haines, Joshua M. Silvis

The floodable volume of longwall gob can be of great importance in developing accurate coal mine operation plans, particularly for mine water management and environmental control strategies. Given the inaccessibility and difficulty in direct measurement of gob characteristics following gob recompression, hydrologic, geomechanical, and spatial data and principles were used to solve for the porosity and height of the residual gob using a mathematical model framework and bulking-based techniques. Based on this approach, gob porosity of Appalachian longwalls appears to be tighter than previously reported, ranging between 4.99 and 14.31%. Residual gob height, as normalized with respect to mine extraction height, ranges between 1.90 and 2.45. A novel, bedding-based bulking approach is ampliative with respect to existing roof caving and bulking fundamentals, as it has greater explanatory value compared with the traditional, lithologic-based bulking application. The efficacy of using the residual gob characteristics determined by the mathematical model and bedding-based bulking is examined using a causal comparative method, whereby time series prediction of historical groundwater rebound is compared with observed mine pool elevation data. Prediction accuracy is significantly improved using a prototype model to calculate void volume of longwall gob, specifically, the dependence of porosity of the gob material upon the longwall width to overburden depth ratio for movement basins of subcritical and critical panel widths. The potential implication of these findings toward hydrologic, geomechanical, and ventilation studies of longwall gob could be significant.

Aug 13, 2021

By R. Simon, P. Pagé, L. Li, P. Yang

The Mathews-Potvin stability method is widely used in the Canadian mining industry as a starting point to determine the maximum dimensions of mine stopes. However, it cannot be applied to inclined (more frequently encountered) mine stopes without conducting numerical modelling to obtain the stress factor A, defined as a function of the ratio of unconfined compressive strength of intact rock to the induced principal stress on the exposed stope walls. The need to conduct numerical modelling significantly limits the application of the Mathews-Potvin method. In addition, given its empirical nature and main application for preliminary design, it is deemed undesirable to conduct numerical modelling, especially elaborate modelling. Alternatively, theoretical methods can provide a much simpler and quicker way to estimate stresses around stopes and the corresponding stress factors. Over the years, a large number of studies have been conducted to estimate stresses around openings excavated with various crosssections. However, theoretical or graphical solutions remain unavailable for mine stopes that typically consist of horizontal floor and roof, and two parallel inclined walls (hangingwall and footwall). To remedy this situation, a series of numerical simulations is first performed for openings with vertical and inclined walls, including typical stopes commonly encountered in underground mines. A group of empirical solutions is then formulated to estimate the induced principal stresses at the roof centre and mid-height of the stope walls. The validity and predictability of the proposed solution have been verified using additional numerical simulations. The proposed solution can thus be used to calculate stresses and the resultant stress factors A around typical mine stopes with any inclination angle and height to width ratio, under any in-situ stress state, without conducting numerical modelling.

Aug 1, 2021

By P. C. Pistorius, D. Kruger, Y. Kacar

Because of its superior properties, stainless steel has been widely used for many applications. Nonmetallic inclusions can influence the quality of stainless steel products. A set of samples was used to track the response of inclusion composition during processing of a heat of titanium-bearing 18% chromium steel, from deoxidation after decarburization, to the solidified slab. The oxide inclusions responded as expected to additions of deoxidizers and calcium treatment, changing from silica to alumina and spinels, and then to calcium aluminates. The samples confirmed that titanium nitride can nucleate on liquid calcium aluminate during steel solidification.

Aug 1, 2021

By B. P. Watson, D. P. Roberts, R. A. Lamos

The Upper Group 2 (UG2) chromitite reef is a shallow-dipping stratiform tabular orebody in the South African Bushveld Complex, which strikes for hundreds of kilometres. Mining is extensive, with depths ranging from close-to-surface to 2 500 m. Pillars are widely used to support the open stopes and bords. Little work has been done in the past to determine the strength of pillars on the UG2 Reef and design was done using formulae developed for other hard-rock mines. This has led to oversized pillars with consequent sterilization of ore. In this paper we describe a back-analysis of stable and failed UG2 pillars on the Bushveld platinum mines, and provides a strength formula for UG2 pillars. The formula may be used cautiously on all Bushveld platinum mines with similar geotechnical, geometrical, and geomechanical conditions to the pillars in the database.

Aug 1, 2021

By B. P. Watson, A. Carpede, W. O. Kekana, N. Fernandes, W. Theron, M. P. Mahlangu, G. Betz

The research described in this paper was done to confirm the Upper Group 2 (UG2) PlatMine peak pillar strength formula (Watson et al., 2007), which was determined from a back-analysis of failed and unfailed pillars. Underground measurements were made on a stable pillar that was loaded by firstly reducing it’s length and then by mining the surrounding pillars until pillar failure took place. The pillar was instrumented with suitably positioned strain cells and closure meters, which allowed both the average pillar stress and strain to be determined. The paper describes the methodology applied to identify a suitable position for the instrumentation, as well as the results. A stress/strain curve is presented for a UG2 pillar with a w/h ratio of 2.0, at Booysendal Platinum Mine. The measured pillar strength was similar to the predicted strength using the PlatMine pillar strength formula for UG2 pillars. The PlatMine formula has been successfully implemented on Booysendal Platinum Mine, and about 3 670 pillars have been cut without a single failure. An additional revenue of US$1.3 billion was calculated for the 25-year life of the mine as a direct result of the improved pillar design, given the January 2020 platinum group metals basket price. An extended life of mine and better mining efficiencies will also be realized.

Aug 1, 2021

By Yun-tao Xin, Yu Yi, Kang Yan, Gang Li, Xiao-dong Lv

This study investigated the selective removal of arsenic from high-arsenic dust in alkaline systems and the effects of different leaching conditions. The results indicated that the liquid–solid ratio, NaOH concentration, and sulfur dosage had a significant influence on the process. The leaching efficiency of arsenic reached 99.57%, while that of lead was as low as 0.03% under appropriate conditions. In particular, the addition of sulfur can effectively promote the leaching of arsenic and reduce the dissolution of lead in the solutions. An artificial neural network was used to model the leaching process. It consisted of a back-propagation artificial neural network model with a “6–10–2” structure that could effectively simulate and predict the value with more than 99% accuracy. Based on the difference in weights of the different parameters in the neural network model, the relative importance of the parameters related to arsenic and lead leaching efficiency was obtained, which followed the order of NaOH concentration, liquid–solid ratio, sulfur dosage, temperature, time, and stirring speed.

Jul 29, 2021