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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

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 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 M. Balamurali, K. M. Seiler

Modelling stockpiles is a key factor of a project’s economics and operation in mining, because not all the mined ores are not able to mill for many reasons. Furthermore, the financial value of the ore in the stockpile needs to be reflected on the balance sheet. Therefore, automatically tracking the boundaries of the stockpile facilitates the mine scheduling engineers to calculate the tonnage of the ore remaining in the stockpile. This paper suggests how the dynamic of stockpile shape changes caused by dumping and reclaiming operations can be inferred using polygon models. The paper also demonstrates how the geometry of stockpiles can be inferred in the absence of reclaimed bucket information, in which case the reclaim polygons are established using the digger’s global positioning system (GPS) positional data at the time of truck loading. This work further compares two polygon models for creating 2D shapes.

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 M. S. Shishvan, J. R. van Duijvenbode, L. M. Cloete, M. W. N. Buxton

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define ore fingerprints. VNIR/SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and collocated XRF data were clustered to form material types (fingerprints). The material types were related to an Equotip-BWi correlation. These correlations can be used to extrapolate a hardness signature and generate a BWi proxy for different blocks. The combined fingerprints and BWi proxy can assist as a tool for enhancing the prediction of comminution behaviour. They can explain specific domain-related hardness variations. For example, one material type could be separated into a softer (~15-18 kWh/t), and harder (>20 kWh/t) material blend. This was accomplished using the commonly overlooked VNIR region at 605 nm. This outcome has significance for blending strategies.

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 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