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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

By S. Andika, O. A. Putri

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

Sep 1, 2021

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

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

Sep 1, 2021

By I. O. Flores, A. Anani, H. Li

Current technological advancement has increased the exploitation of deeper ore bodies using underground methods. In certain cases, the mineral deposit starts near the surface and continues deep within the earth's crust. Such deposits are best mined with both underground and surface methods. Two main techniques are used to determine the optimal transition depth from surface to underground - a disintegrated approach and an integrated approach. Both methods have limitations in their use to determine the optimal transition depth. The objective of this research is to investigate the conditions in which an integrated approach to the transition depth problem is superior to a disintegrated approach. Three binary integer linear programming models are developed for this purpose: a surface and underground model for the disintegrated instance and an integrated model. A case study is performed to validate the models, with a comparative sensitivity analysis to obtain operational insights.

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 C. Musingwini, A. S. Nhleko, P. M. Muke

Open-pit mine planning includes the determination of a long-term open-pit mine production scheduling (OMPS) plan. The OMPS plan is generally a three-dimensional (3D) block extraction sequence that seeks to maximise the net present value (NPV) of future cash flows, while complying with technical and operational constraints. The OMPS problem is computationally complex because it involves the analysis of large datasets, while simultaneously incorporating both multiple hard and soft constraints. The OMPS problem has been solved using deterministic or exact approaches, heuristic, and metaheuristic models. When compared to heuristic and metaheuristic models, deterministic approaches tend to take much longer to solve the OMPS problem when the number of constraints increases. Therefore, this paper presents a metaheuristic model which utilises a genetic algorithm (GA) framework to solve the OMPS problem. The GA-based model was applied to the Newman1 and Zuck Small mining instances, which are publicly available from the online MineLib database. The NPVs for the OMPS plan generated using the GA-based model for the two case studies were compared to the best-known published MineLib solutions obtained using the TopoSort heuristic model at the time of this study. For the Newman1 instance the equivalent annual NPVs were approximately US$6.6 million for the GA-based solution and US$5.1 million for the TopoSort solution. For the Zuck Small mining instance, the equivalent annual NPVs were approximately US$99.8 million for the GA-based solution and US$102.5 mil for the TopoSort solution. These results show that the GA-based model produced comparable solutions to some of the best-known solutions, thus, indicating strong potential application of GA-based models to solve OMPS problems.

Sep 1, 2021

By Hongsheng He, Xiaofang Lv, Jianfeng Wang

This paper provided an overview of characteristics of limonite ores as well as their influence on sintering performance. Evaluation experiments focusing on basic characteristics, granulation characteristics, and high temperature sintering characteristics had been conducted by using two types of limonite ores, then the principles of high effective utilization technology of limonite ores were discussed, and the sintering pot tests were conducted. Limonite ores possessed low total iron content, high combined water content, high SiO2 content, large average particle size, low adhering particles proportion, high porosity, high water absorption rate, short water absorbing time, poor granulation ability, high melt formation ability, low melt fluidity ability, low melt penetration ability, high melt absorption ability, low bonding phase strength, and high forming ability of SFCA. By taking the technical measures of ore-proportioning optimization, increasing the proportion of burntlime and segmentation granulation, the usage of limonite ores could reach 50% in sintering.

Aug 27, 2021

By Zhian Liang, Xiong Huang, Weixin Wu, Lifeng Cui, Zhuanhua Wei, Guoyao Li, Xiao Ni, Junrong Wang, Shuiping Zhong

ZJ 201 (modified starch) is a non-toxic, odorless, and biodegradable polymeric depressant, which selectively depressed galena in batch flotation tests while chalcopyrite was not significantly affected. In a plant trial, copper-lead separation was successful for the first time without using dichromate. Moreover, the plant process could be changed to sequential Cu-Pb flotation using ZJ 201 for higher grade lead concentrate. A copper concentrate was obtained with a Cu grade of 24.91% and a Pb grade of 5.11%, which recovered 76.6% of the copper from the original ore; a lead concentrate was also obtained with a Pb grade of 21.90% and a Cu grade of 5.31%, which recovered 52.6% of the lead. In addition to the effective copper-lead separation observed in the plant trial, another improvement observed is that gold was recovered mostly in copper concentrate, which could be sold for higher prices. Au grade reached 230.64 g/t, and its recovery reached 65.8% in the copper concentrate. Au grade in lead concentrate was only 18.90 g/t.

Aug 27, 2021

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

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

Aug 26, 2021

By 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 F. N. Siddiquie, Mohd Shaif, Rajiullah Khan, Juned Alam

Manganese ore bodies of Banswara Mn belt were classified into stratiform and stratabound types, based on their mode of occurrence, distribution, and their chemical characters. These ore bodies generally composed of braunite, pyrolusite, cryptomelane/ psilomelane, wad, etc. The stratiform-type Mn ore bodies are conformable with the associated host rocks (Mn phyllite, etc.) and occurred in the form of layers, bands, etc., which are variable in their shape and size. These ore bodies are also strongly folded with the enclosing host rocks. The others, which are stratabound-type Mn ore bodies, also showed an intimate association with the associated host rocks. These types of ore bodies occurred in thin layers, pockets, lenses, stringers, etc. The classified ore bodies are confined with Kalinjara formation of Aravalli Supergroup of Paleoproterozoic age. The ore bodies are structurally controlled and well associated with phyllite, schist, limestone, quartzite, etc. The stratiform-type ore bodies constitute a marginal to low Mn grade and are also characterized by lower Mn content (26.19–39.24%) along with the Mn/Fe ratio of 7.16–12.23, while the stratabound-type Mn ore bodies are rich in Mn (35.92–49.64%) along with the higher Mn/Fe ratio of 14.15–23.97. However, the poor Mn grade along with high siliceous impurities of Banswara Mn deposits is subjected to them for intermittent mining activities in the past and, consequently, did not attract the scientific and industrial community for the evaluation of these resources. Therefore, the authors attempted this study in a scientific and systematic manner (mode of occurrence/distribution and possible methods for evaluation/exploration) to evaluate the potential of existed and newer Mn deposit resources in the Banswara Mn belt. The whole findings would be very much helpful for exploration/ mining geologist along with other concerned, to completely evaluate these Mn deposits in the future.

Aug 16, 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