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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 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. 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 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 Laura Suarez, Jörgen Kajberg, Pär Jonsén

How does an investigation into the breakage characterization of electric arc furnace (EAF) manganese slags contribute to a more responsible usage of resources throughout the life cycle of steel products and reduction of carbon emissions? The main goal of this study is to increase the knowledge on the mechanical response of manganese slag to open the potential of pyrometallurgical residues to become secondary material in industrial applications such as infrastructure construction, fillers, binders and soil stabilization, among others.

Mar 1, 2024

By Min Chen, Qinghua Liang, Weishan Yan, Yuan Feng, Shigang Wang

Various coal mine robots (CMRs) and unmanned aerial vehicles (UAVs) are implemented to explore unknown mines for improving the safety and efficiency of mining. It is challenging for CMRs and UAVs to achieve accurate positioning due to the absence of the Global Positioning System (GPS), poor lighting conditions, and similar geometric features in complex mine scenes. LiDAR-based localization and mapping methods are more accurate than others, while long-time running in large-scale scenarios will introduce nonnegligible cumulative errors. This study presents a semantic-aided Li- DAR simultaneous localization and mapping (SLAM) with loop closure, which leverages the uniqueness of mine intersection structure to establish stable semantic loop closure. Specifically, we propose a semantic intersection descriptor of translation and rotation invariance, which encodes 3D point clouds of the same intersection from different positions and viewpoints into a unified image. By using the semantic descriptor, we can construct a constant loop constraint when the same intersection is revisited from different directions to reduce cumulative drift. We provide experimental validation using large data sets collected in two large underground mines, namely, a simulated Edgar mine deployed in ROS Gazebo and a public underground mine data set provided by ETH. Experimental results show that the proposed method has higher localization accuracy and outperforms the existing LiDAR-based SLAM strategies.

Feb 1, 2024

By Martin Langenderfer, Catherine Johnson, Alexander Douglas

Mine haul roads degrade rapidly due to extreme loads on suboptimal construction materials. Unmanned aerial vehicles (UAV) are suited to quantify large-area road-network conditions, such as surface roughness, defects and grade to optimize remediation of poor conditions and reduce overall costs. Mine haul roads present unique challenges, such as material type and edge characteristics, to automatic road detection that often fails, requiring manual road input. This research work proposes a new method using the road center determined by the principal curve of a haul truck’s path. Analysis grids were created from this center line. A dense point cloud from UAV photogrammetry was generated and multiple linear regression analysis was conducted on each individual grid. The root-mean-square error in each grid indicates the surface roughness, and the change of slope between grids indicates the road grade inconsistencies. This method was applied to 26 road sections, and the results were validated by images taken from the truck operator’s vantage point. Critical defects were identified including excessive pothole formation, corrugation, depressions retaining water, and narrowing of travel lane. The results demonstrate this is a valid method of road identification and quantification of road defects at a mine site.

Apr 1, 2024

By Philani Larrance Ngwenyama, Ronald CW Webber-Youngman

The survival chances of trapped miners in underground mines depend on how quickly they can be located and rescued post-accident. During entrapments, emergency rescue is highly desired but can be hindered by the lack of positioning information when a fit-for-purpose missing-person locator system is not available. Various types of missing-person locator systems are available to aid search-and-rescue operations. From the wide range of the available systems, selecting the most suitable one becomes the next challenge. The available systems have not been measured if they are fit-for-purpose or not, and to what situation. This study proposes user requirements that can be used as a guideline to facilitate the system selection process. These user requirements provide a framework for the design and development of a fit-for-purpose missing-person locator system. The user requirements can also be used for evaluating the performances of a particular system, predict its success, and also drive future research toward developing new technology to enhance the capabilities of the current systems.

Apr 1, 2024

By Kasimcan Koruk, Julian M. Ortiz

A geological model accounting for uncertainties possesses important advantages for resource estimation. Machine learning algorithms (MLAs) employed on multivariate geochemical datasets open up ways to new methodologies for such geological models with ease in comparison to traditional geostatistical methods. This article proposes a two-step MLA with an ensemble implementation to define geological domains and their uncertainties based on geochemical data. The proposed workflow is applied hierarchically on a dataset from a porphyry copper deposit to perform binary classification that can be attributed to alteration domains.

Mar 1, 2024

By Ewan Wingate, Ranjit Prasad, Yanjun Liu

The phosphorus journey begins with the extraction of primary phosphorus in the form of phosphate in apatite through openpit mining and, in some rare cases, underground mining methods. The ores are processed in beneficiation plants to remove impurities before the phosphate rock concentrate is either sold on the open market or converted into phosphoric acid in downstream chemical production facilities by the wet acid process or thermal phosphorus routes. These can in turn be converted into fertilizers, as well as used in the food and electronic industries. The advent of the EV battery has given rise to a new use for purified phosphoric acid in the production of lithium ferro phosphorus (LFP) cathode material for EV battery cathode assemblies. The production of LFP relies on raw materials such as lithium carbonate, lithium hydroxides, iron salts and purified phosphoric acid.

Feb 1, 2024

By Jaeheon Lee, Junmo Ahn, Jae-Yeon Kim, Yoo-Jin Kim, Jiajia Wu, Eun-Woo Kim, Jai-Won Byeon, Jun-Jae Lee

The surge in demand for battery materials, coupled with environmental challenges linked to battery disposal, underscores the need to recycle spent lithium-ion batteries (LIBs). Green recycling approaches, in particular, have garnered attention for battery recycling. In this study, a hydrometallurgical method using a synergetic organic lixiviant combining methanesulfonic acid (MSA) and citric acid (CA) as an alternative to inorganic mineral acids was introduced as a green approach to recycle cathode materials of LIBs. The conditions for leaching and recovery processes were optimized. Cathode materials were regenerated using the recovered materials. Moreover, the leaching performance and economic feasibility of this leaching system were compared with those of various other organic acids.

Feb 1, 2024

By S. J. Schatzel, K. M. Ajayi, E. N. Rubinstein, Z. Khademian

This study simulates the impact of a shale gas well casing breach near a longwall mine. Field studies are conducted to measure mining-induced permeability changes over the abutment pillar of a longwall mine, and a geomechanical model is developed in 3DEC, a three-dimensional numerical modeling code, to predict the aperture of fractures in the overburden at the study site. The predicted aperture values are used to determine mining-induced permeabilities, and the results are compared with the field measurements. These aperture values are provided as inputs into fracture flow code (FFC), which generates a stochastic discrete fracture network (DFN) model for the study site and predicts the potential shale gas flow to the mine. Results from 100 DFN realizations are statistically analyzed using the bootstrapping method to compensate for notable variation in fracture geometry. The results show a significant difference between the gas inflow for nearby panels due to increase in the induced permeability during mining of the second panel. The average gas flow to the mine was calculated as 4.72 × 10 m3/s (49 cfm) for a hypothetical breach at the Sewickley horizon during the first panel mining, 8.97 × 10 m3/s (19 cfm) for a hypothetical breach at the Uniontown horizon during the first panel mining, 2.16 × 10 m3/s (458 cfm) for a hypothetical breach at the Sewickley horizon during the second panel mining, and 8.07 × 10 m3/s (171 cfm) for a hypothetical breach at the Uniontown horizon during the second panel mining. Depending on the mine ventilation system, this could result in methane concentrations exceeding regulatory limits. Hence, these findings provide insights into the potential risk of an unconventional gas well casing breach near a longwall mine under shallow cover.

Feb 1, 2024

By AMIR EHSAN KIANFAR, Moritz Ziegler, TOBIAS HARTMANN, ELISABETH CLAUSEN

Accurate positioning information is a central step in the process of increasing automation and digitalization in the mining industry. Underground mine environments are prime examples for GNSS-denied environments that pose several additional challenges for the employed indoor positioning system (IPS). This work presents an ultra-wideband (UWB)- based system specifically tailored for underground mine environments. Initially, the UWB technology is reviewed and basic concepts of IPS are explained. Both software and hardware are described in detail, including positioning algorithms, electrical circuitry and the mining-compatible enclosure. Finally, the results from field tests in an underground mine are presented and positioning performance and the influence of dilution of precision (DOP) on positioning results are empirically evaluated. The results show that the localization errors are mostly below 1 m. Individual zones of high localization errors are suspected to be caused by noise interference. Significant DOP influence due to the mining drift geometry can be confirmed. The system shows promising localization results for different mining applications, while influences negatively impacting the accuracy are presented as items for future research.

Jan 1, 2024

By Mustafa Kumral, Yuksel Asli Sari

Economic evaluations of underground mining operations are made based on the estimated or simulated grades of the block model as the actual values are not known. Depending on how the planning decisions are made, the risk of the operation can be managed and decreased. This research introduces a mixed-integer linear programming (MILP) model for stope sequencing optimization that considers the uncertainty associated with net present value (NPV) using chance-constrained programming (CCP). The introduced method permits achieving a balance between maximizing NPV and reducing the associated operational risk through transforming the problem into a multiobjective problem, where the objectives are maximizing the NPV and minimizing the risk. This risk is expressed in terms of the standard deviation of project outcomes, taking into account multiple possible scenarios. In a case study, the efficiency of the method was demonstrated as a decision-making tool capable of translating a specified risk or reliability level into a stope sequencing plan.

Jan 1, 2024

By MAURICE SUNKPAL, Taghi Sherizadeh, Kutay E. Karadeniz, Dogukan Guner, Samuel Nowak

In this study, various rib bolting scenarios were investigated to better understand the effectiveness of the support systems. A coal mass constitutive model is implemented to a distinct element method (DEM) code, and rib factor-of-safety (RibFOS) analyses were performed by applying strength reduction for the coal matrix and explicitly modeled face cleats. Ribs with various overburden depths and mining heights composed of solid banded bright coal were considered. Different scenarios such as rib bolt length and number of bolts were studied to quantify the effect of primary rib support density on rib stability. As a result of the unsupported coal rib analyses, a decaying exponential relationship is proposed between the mining height-overburden depth and the RibFOS. The developed coal rib models clearly show the coal-rib bolt interaction and illustrate how the addition of rib bolts restricts the movement of the coal rib. The results reveal that the strength-reduction method in DEM code with the coal-mass model can aid in the development of practical primary sup- port charts, which can help in the rib support design process.

Feb 1, 2024