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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. Kaabachi, F. Meunier, N. Morales

In open pit mining, ore and waste are hauled outside of the excavation area by trucks that move along a system of ramps. The design of these ramps must be carefully addressed because they have to comply with geomechanical constraints to ensure the stability of the pit walls, provide access to the different levels and phases, but also maximise the value of the extracted ore and reduce the extraction of waste. Because an optimisation approach able to deal with the design of the full ramp system seems to be out of reach at the moment, this paper addresses the problem of designing one ramp providing access to a single pushback, which has been determined beforehand. The optimisation objective is therefore to find a ramp that gives access to one given potential volume, while minimising its impact on the economic value of the volume. Two versions of the problem are formalised. One version allows the extraction of ore and waste only if this extraction is necessary for the ramp system. This version somehow minimises the intrinsic value of the ramp system. In the second version, it is possible to extract more than is needed by the ramp system. For instance, this takes into account the possibility of attaining ore on the wall opposite to the ramp as soon as the ramp reaches its level. In the setting mentioned above, the contribution of this work is three-fold. First, the computational complexity of the two different versions of the problem is presented and discussed; in particular, they are shown to be NP-hard. Second, compact integer programs modelling these two versions are proposed. Third, an efficient GRASP algorithm able to cope with the large instances of the problem is proposed. To assess the proposed approaches, the models and algorithms are applied to real instances with an off-the-shelf solver, illustrating and discussing their potential and relevance for the two versions of the problem. They are also compared with other approaches from the literature in terms of value and resulting designs.

Sep 1, 2021

By M. M. Ali, C. Musingwini, R. A. Phillips

The long-term open-pit mine production scheduling problem (OPMPSP) is typically solved in two broad stages. The first stage entails optimisation of the ultimate pit limit, which delimits the economical final pit and maximises the undiscounted value of the deposit. The second stage entails optimisation of the long-term open-pit mine production scheduling, which maximises the discounted value of the deposit. The long-term OPMPSP is a type of an NP-hard problem, such as the travelling salesman problem (TSP), because it is intrinsically complex to solve since it incorporates both hard and soft constraints. Therefore, metaheuristic approaches have been used to find global optimum solutions to the long-term OPMPSP. This paper presents a metaheuristic approach utilising a dual interchange algorithm (DIA) to solve the long-term OPMPSP. The DIA is a parent algorithm which supervises two subservient algorithms, namely, simulated annealing (SA) and Tabu search (TS). The DIA selects the better solution generated in each run from the two algorithms, and simultaneously penalises the algorithm that produced an inferior solution, while rewarding the one that produced a better solution. The DIA was tested on the three-dimensional Arizona Copper Deposit (KD) dataset, which is publicly available for research purposes from the online MineLib repository. It was set to generate net present values (NPVs) in five-minute intervals and the solution improved until it plateaued at about 30 minutes of runtime. The NPVs compared favourably to those obtained on the same KD dataset but obtained over 24-hour runtimes, when solved using simulated annealing and Tabu search algorithms, and the NPV generated from the work of Muñoz (2012) using the modified TopoSort as found on the MineLib repository at the time of this study. Therefore, this paper demonstrates that DIAs can be developed and used to solve the three-dimensional long-term OPMPSP.

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 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 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 J. P. De Carvalho, R. Dimitrakopoulos

This paper presents an actor-critic reinforcement learning (RL) method to allocate trucks and shovels in open-pit mines, while defining short-term production scheduling of an industrial mining complex, given new information collected during operations. This new data obtained from sensors update the uncertainty of the orebody models. The RL agents interact with the mining complex environment; exploring the solution space, learning actions to improve metal production and equipment utilisation. A case study is presented at a copper mining complex, highlighting the method’s ability to adapt and make informed decisions while collecting new information.

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 J. Pelders, F. Magweregwede, S. M. Rupprecht

Increased depths of conventional gold and platinum mines, longer travelling times as working places move further from the shaft, and ancillary activities taking place in the mining cycle reduce the useable time spent at them workface. Current shift lengths of 8 hours 20 minutes may no longer be adequate ton complete required mining activities.The impact of mineworker commuting times on shift work is also not well understood. The aim of this research was to draft a framework to optimize shift cycles in order to maximize productivity, health, safety, and wellness in the South African mining sector. The framework was informed by a review of the literature, case studies at a local gold mine and platinum mine, and a workshop with industry experts. The findings indicate potential advantages of extended shift lengths and continuous operations, such as improved productivity, asset utilization, and employee morale. However, concerns included fatigue and the associated safety hazards, particularly when working longer shifts, night shifts, and numerous consecutive shifts. Job demands, personal factors, and commuting times are further considerations when scheduling shifts. The ideal shift system for the mining sector should holistically consider and balance operational requirements, health and safety, and employees’ and their families’ requirements, and suit the unique needs of each operation. Changes in shift cycles will require a structured change management process, which involves all stakeholders, and could help to enhance the viability of the South African mining sector.

Aug 1, 2021

By L. Ntlhakana, K. Khoza-Shangase, G. Nelson, I. Maposa

Occupational noise-induced hearing loss (ONIHL) is associated with various risk factors in South African mines. We investigated the association between standard threshold shifts (STSs) and exposure to noise and platinum mine dust (PMD), using demographic data, five years of annual audiometry screening results, and noise and dust exposure data. Miners’ age, sex, percentage hearing loss (PLH), and dust and noise exposure data were gathered, and a linear mixed effects regression model used to predict STS. Average occupational exposure levels to noise and dust were calculated from recorded measurements. A total of 12 692 records were analysed. Most miners were male (89.6%) and more than 50% were younger than 41 years. More than 70% were exposed to > 85 dBA noise and 58% were exposed to 1.5–2.99 mg/m3 PMD. Changes in hearing levels ranged from 8.3 dBHL at baseline (2014/2015) to 10 dBHL in 2016 (STS), with no changes thereafter. The model showed that the estimated effect on STS for males was 27% and 21% higher than for females, for the left and right ear, respectively The estimated effect of age, PLH, noise exposure and years of exposure on STS was < 10% for each variable. There was no statistically significant association between PMD and STS. The combined effects of age, sex, years of exposure to noise, and noise exposure levels, and strength of associations can be used to predict STS for this group of miners. Our findings may be used to measure the efficacy of the mine’s hearing conservation programme.

Aug 1, 2021

By R. Simon, P. Pagé, L. Li, P. Yang

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

Aug 1, 2021

By D. Orynbassar, N. Madani

This work addresses the problem of geostatistical simulation of cross-correlated variables by factorization approaches in the case when the sampling pattern is unequal. A solution is presented, based on a Co-Gibbs sampler algorithm, by which the missing values can be imputed. In this algorithm, a heterotopic simple cokriging approach is introduced to take into account the cross-dependency of the undersampled variable with the secondary variable that is more available over the entire region. A real gold deposit is employed to test the algorithm. The imputation results are compared with other Gibbs sampler techniques for which simple cokriging and simple kriging are used. The results show that heterotopic simple cokriging outperforms the other two techniques. The imputed values are then employed for the purpose of resource estimation by using principal component analysis (PCA) as a factorization technique, and the output compared with traditional factorization approaches where the heterotopic part of the data is removed. Comparison of the results of these two techniques shows that the latter leads to substantial losses of important information in the case of an unequal sampling pattern, while the former is capable of reproducing better recovery functions.

Aug 1, 2021