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By P. C. Pistorius, M. P. Maphutha, J. D. Steenkamp

Advanced high-strength steels (AHSS) are sophisticated materials being developed by the steel industry to mitigate challenges related to the performance of motor vehicles. To meet the requirements of AHSS, the ferromanganese alloys (FeMn) utilized in the production of the steel are required to contain acceptable levels of unwanted impurities, i.e. P, S, N, H, and C. The focus of the current study was to investigate dephosphorization of ferromanganese to produce a low-P alloy that could be effectively utilized in the production of AHSS. The study involved conducting laboratory-scale testwork to study the efficiency of CaO-based slag systems to dephosphorize FeMn alloys. The addition of Na2O, CaF2, and BaO to MnO-CaO-SiO2 slag was considered. The test work was carried out in a 25 kW induction furnace at temperatures of 1350°C, 1400°C, and 1450°C. The P partition coefficient (Lp) remained small at <1, which is an indication that dephosphorization had not been achieved. The baseline slag, comprising 40%CaO-40%SiO2-20%MnO, reported higher Lp values. Addition of Na2O and CaF2 did not show any further benefit. Substituting half of the CaO by BaO, resulted in similar Lp values to those of the baseline slag under conditions of 1350°C and 1450°C at 30 minutes. In summary, based on the Lp values obtained, the conditions investigated with the CaO-based slags appeared to have been unfavourable for dephosphorization of FeMn alloys, as most of this impurity element remained in the alloy.

Sep 1, 2021

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

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

Sep 1, 2021

By 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 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 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 S. Blancher, A. Perroton, H Smith, T. Nzima, T. Wallmach, V. Dieudonné

Chromite beneficiation is generally performed by crushing and density separation using spirals. At the Dwarsrivier Chrome mine spiral efficiencies seam to vary, the reason for which is not evident. This sometimes results in the production of low-grade products, which are sold at relatively low prices. This paper represents a Qemscan® study that investigated mineralogical reasons for these variations. Variations in liberation of chromite particles as well as variations in chromite compositions were investigated. The results showed that the so-called low grade chromite product can be improved to become a high grade chromite product by means of a refinement in mineral processing. A detailed Qemscan® database (SIP file) was developed to account for variations in chromite chemistries. It can be shown that chemical variations with respect to Cr, Al, Mg and Fe in chromite particles reporting to the scavenger tails are larger than in the high grade product. A combination of these chemical variations together with a decrease in liberation of chromite particles in the scavenger tails can potentially help to recover more chromite particles from this specific process stream.

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 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 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 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 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 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 A. Melkumyan, K. Silversides

Chemical assays collected from blast holes have the potential to greatly improve the local accuracy of grade models. Assays must be assigned to the correct domain or they will introduce errors. Boundary surfaces created from the widely spaced exploration holes (~50m) do not provide sufficient resolution to correctly delineate domain boundaries between closely spaced (3-5m) blast holes. This paper demonstrates a probabilistic method for classifying production blast holes near domain boundaries. Gaussian processes models are applied to identify the assays and assay ratios most suited to distinguishing particular domains. These models are combined with the exploration based surfaces to classify the blast holes that can be confidently assigned to a particular domain. A reiterative method is used, where information from the blast holes closest to the exploration holes is used to label blast holes further away. This is tested on two domain boundaries in a banded iron formation-hosted iron ore mine.

Sep 1, 2021

By N. Madani, S. Abulkhair

In multivariate geostatistics, it is common to have different types of complexities between variables of interest. In this context, an inequality constraint is an example of complex bivariate relationships. Unfortunately, traditional co-kriging and co-simulation algorithms cannot reproduce this type of bivariate complexity, leading to the overestimation of disturbing elements. This paper proposes a new algorithm based on a hierarchical sequential Gaussian co-simulation framework, integrated with inverse transform sampling, to model inequality constraints between variables. First, the proposed methodology's validity was evaluated by applying it to a real case study from an iron deposit, with an inequality constraint between iron and aluminum oxide. Then the simulated results were compared with a conventional hierarchical co-simulation algorithm to investigate the effect of inverse transform sampling on the quality of the co-simulation. The results showed that the proposed algorithm can reproduce an inequality constraint between variables.

Sep 1, 2021