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By J. P. De Carvalho, R. Dimitrakopoulos

An actor-critic reinforcement learning approach is presented to improve the production of an industrial mining complex by defining shovel allocation and adapting the short-term plan given grade-control decisions. Blasthole data updates the simulated orebody models, which are input into a stochastic grade-control method based on a spatially constrained clustering approach that minimizes the profit-loss function. These aspects are embedded in a discrete-event simulator that defines the material flow from faces to processors. A case study at a copper mining complex shows the methods’ ability to adapt to new information, improving the quality of the material feed and increasing cash flow by 23% compared to a baseline case.

Jun 25, 2023

By Ernesto Maldonado, Zach Agioutantis, Jesus Romero

Reliable prediction of dynamic deformations is important when planning to undermine important structures that cannot tolerate large relative deformations and or large horizontal strains. This paper presents examples of dynamic deformation profiles that include subsidence, horizontal displacements, and horizontal strain for a number of points over a retreating longwall panel in the eastern United States. The prediction points are located at different distances from the excavation rib and therefore present a different response in terms of dynamic movements. The formulation is based on the influence function method as implemented in the Surface Deformation Prediction System (SDPS). Results show that the subsidence development curve may be reliably predicted for surface points in the middle of the panel. However, in cases where points may be affected by the magnitude of the edge effect offset, subsidence predictions may be very conservative, that is, higher than actual movements.

Jul 1, 2023

By Kevin Andrews, SETAREH GHAYCHI AFROUZ

Ground control analysis and groundwater inflow assessments for underground coal mines require in-depth field investigations including, but not limited to, geological and geotechnical core logging, downhole geophysical logging, laboratory rock strength testing, and packer (Lugeon) testing. Field and laboratory data collection are time consuming and expensive, and an initial understanding of the expected trends among various geotechnical and hydrogeological parameters can assist with the planning and optimization of geotechnical characterization studies and grouting plans. In this study, a comprehensive database is analyzed, comprised of important rock mass characteristics (hydraulic conductivity, rock quality designation (RQD), and lithology) for coal-bearing rocks in the Central Appalachian Basin. Statistical analysis of the data is completed to establish an enhanced understanding of geotechnical and hydrogeological conditions expected to be encountered as underground coal mining passes near and beneath streams and rivers. The results of the study provide valuable reference material for the coal mining industry for completion of shaft installations, mine stream crossings, environmental assessments, and grouting and mine dewatering designs.

Jul 1, 2023

By Milka Madahana, John Ekoru

Particulate matter is one of the leading pollutants in the mines and in the communities in the areas near or surrounding the mines. The availability of large Clinical and environmental data sets obtained from the mines has brought about the need to use better approaches to analyze the collected data. Prediction and control of particulate matter concentration in the mining environment is essential in minimizing air pollution and protecting the respiratory health of mine workers. The main objective of this research work is to analyze datasets obtained from a mining area using machine learning techniques which will then be followed by the real-time estimation of dust particulate in the mines. Datasets collected over 8 years in an hourly sampling rate from an open cast mine is cleaned and analyzed for trends and patterns. Artificial Neural Networks (ANNs), Support Vector Machines and decision trees techniques are applied to analyze and predict the particulate concentrate. The estimation of particulate concentration using Artificial Intelligence will significantly contribute toward the development of quantitative tools to be used in correlating of cause and effect of dust concentration in mines. The results obtained in this work will form a sub-unit of a respiratory health monitoring system for mine workers. The system is essential in the collection, analysis, and tracking of mine workers’ respiratory health, thus it forms part of an early intervention system for mine workers. From the existing theory, we expect the Artificial Neural Network will perform much better than previously used classical models.

Jun 25, 2023

By Richard Minnitt, Philippe Renard, Christina Dohm, Winfred Assibey-Bonsu, Matthew Aboagye, Julien Straubhaar, Mahlomola I. Mabala

The application of multiple point statistics (MPS) in oil studies has gained traction over the years; however, the use of MPS in mineral resource estimation and classification is still limited. This is despite MPS’ ability to deal with complex and heterogeneous geology better than the commonly applied two-point based methods. The availability and choice of a representative training image (TI) are often cited as the main challenge holding back MPS’ application in mining geostatistical studies. On the other hand, the general approach used in mining whereby early focus is on higher grade areas and lower grades are planned to be mined later presents a massive opportunity for the development of representative TIs. Mined-out areas provide large amounts of historical data that can be used to develop training images or training datasets that can be used to estimate in mining extension areas (often lower in grade), provided low grade areas are underpinned by similar geology analogue. This approach will enable more robust estimates in mine/pit extension areas for both high- and low-grade extensions than with traditional two-point geostatistical methods.

Jun 25, 2023

By Brendan Demich, Linda L. Chasko, Brendan Macdonald, Timothy J. Orr, Jessie J. Mechling, Cassandra Hoebbel, Jason Navoyski, William Helfrich, Jessica Cohen, Paul E. Schmidt, Jennica L. Bellanca

The National Institute for Occupational Safety and Health (NIOSH) in partnership with the Mine Safety and Health Administration (MSHA) developed VR Mine Rescue Training (VR-MRT). It is a training platform that allows mine rescue team members to explore virtual emergency scenarios collaboratively. Team members use a head-mounted display to visualize and interact together with equipment, hazards, and dynamic ventilation. All player actions are recorded and displayed for debriefing. Researchers at NIOSH demonstrated the training platform at various mine rescue competitions, including the International Mine Rescue Competition, and collected feedback. Participants’ responses were overwhelmingly positive. This paper describes the development of the software platform and the results of the usability surveys and interviews.

Jun 25, 2023

By Josiah Pohl, Christopher Utter, Kyle Everly, Akshay Chowdu

Battery Electric Vehicles offer increased energy efficiency for open-pit load & haul operations at a potentially lower overall carbon cost. Electrifying a mine’s fleet, however, presents a unique set of operational challenges. In this study, we explore the effects of electrification on productivity at real operations using a Discrete Event Simulator. The model can simulate multiple shifts, a mixed fleet of diesel and electric trucks, charging and refueling stations, trolley systems, and accurate shovel-truck loading cycles. This study helps determine the trade-offs of adopting electric fleets and how they interact with the layout of today's mines.

Jun 25, 2023

By Alessandro Navarra, Aldo Quelopana

For decades, open-pit mine planning algorithms have incorporated geological uncertainty and other elements or characteristics for increasingly realistic representations. Among these complexities is the detailing of downstream processes that occur inside processing plants. Previous studies have demonstrated that the strategic mine plan and mineral concentrator design are coupled, and should therefore be optimized simultaneously within the same quantitative framework. However, existing frameworks offer limited representation of operational modes. This work demonstrates an approach to resolving this gap, in an optimization based on a parallelized Variable Neighborhood Descent (VND). The resulting algorithm embeds mass-balancing linear programming (LP) using a DantzigWolfe decomposition. A case study of a Zn-Pb deposit is presented to show the performance reached.

Jun 25, 2023

By Bo Hyun Kim, Mark K. Larson

Researchers at the National Institute for Occupational Safety and Health have tested the finite element code, UT3PC, with LaModel to evaluate their results using know case studies. The purpose of the testing was to demonstrate the usefulness and limitations of these programs in evaluating mine layout design. Both of these software packages are simple to use and free for public download. Their use by mine engineers, along with other tools, such as empirical methods, can enhance the assessment of a mine layout design. LaModel can model mining layout on a mine scale or, at least, on a multiple-panel scale. However, this software has limitations in calibrating to conditions at many coal mines in the western United States. The UT3PC software models smaller parts of a mine and is used to evaluate the design of pillars, shafts, or drifts; but the software calculates all components of the stress tensor and provides stresses, displacements, and safety factors throughout the model volume. The two codes are used to evaluate design of one known case, and UT3PC is used to evaluate another case. The results and approaches demonstrated in the testing are intended to aid mine engineers in evaluating site-specific problems and to show that these tools can provide useful information to the mine engineer in evaluating mine layout design.

Jul 1, 2023

By Augusto Andres Torres Toledo, João Felipe Coimbra Leite Costa, Luciano Nunes Capponi, Diego Machado Marques

Short-term mine planning to open pit requires defining diglines to orientate the excavation geometry. In deposits with multiple chemical variables, it is necessary to have more rigorous control over the feeding of the processing plant. This study analyzes the impact of incorporating multiple chemical variables, the risks generated by geological uncertainty, and the number and starting point locations of shovels in the sequential design of diglines. The objective is to maintain and preserve the low-grade variability, required tonnage, and continuity in sequentially selecting blocks. To achieve this, the probability distribution function of each chemical variable is used while attempting to maintain the ore grades as constant as possible. As it is difficult to maintain multiple elements with low variability simultaneously, the relevance of each chemical variable in the economic benefit is identified. Weights are assigned to signal the importance of each element based on its significance in the recovery process. Several diglines, consisting of sequential blocks, were generated on the same seed point to meet the weekly ore demand. Optimizing the stationarity of multiple chemical variables, accessing and reducing the geological uncertainty performed through geostatistical simulation of grades and transfers functions, considering the number and location of shovels, while meeting the operational restrictions of the selected blocks. The optimized digline with the multiple grades as stationary as possible is retained. The process is illustrated in a phosphate mine. The results show that the proposed method is viable and allows for a quick solution.

Jun 25, 2023

By Ademar Lopes, Victor Silva, Heitor Silva, Gustavo Usero, Roberto Rolo, Lucas Pereira, Gabriel Moreira, João Lague, Arthur Endlein, Matheus Feitosa

Grade control models are supposed to provide a higher resolution than long-term or interim models. However, available grids from diamond or reverse circulation drilling do not provide that required resolution. In this study, a geostatistical workflow is proposed to integrate available grade data from production blastholes with its operational parameters taken when drilling downhole. The step of geological logging can be replaced by machine learning models for classifying blastholes into lithologies and hardness classes. Next, indicator and ordinary kriging use the classified blastholes and previous data for estimating the model domains and the grades within each one. The workflow is applied in a world class iron ore mine in Brazil.

Jun 25, 2023

By Amin Mousavi, Soroush Khazaei, Yashar Pourrhimian

As a factory-like method, sublevel caving consists of development activities, stockpiling and production activities that are carried out on different levels independently. Therefore, considering the operation from the development phase to the plant will lead to more reliable production scheduling. This paper presents a mixed integer linear programming framework for the long-term production scheduling of sublevel caving that maximizes the net present value while satisfying development activities, mining, and processing capacities, continuous mining, the allowable number of active mining units, grade blending, and vertical and horizontal sequencing. The model is coded in Jupyter Notebook and is solved using the CPLEX solver.

Jun 25, 2023

By L. E. Salinas-Farran, S. J. Neethling

Key variables in heap leaching include permeability, mechanical stability and mass transport. All of these properties can be manipulated by agglomerating and curing the ore before stacking it into heaps. The chemical and structural evolution of the agglomerates during curing is key to improving agglomerate properties and, ultimately, heap performance. It is possible to image the internal structure of agglomerates using SEM/EDX (e.g. QEMSCAN or MLA), although these methods are destructive and do not represent the 3D structure of the agglomerates. A non-destructive alternative is to image the agglomerates using X-ray Microtomography (XMT). Not only does this provide 3D information, it also allows the same samples to be repeatedly scanned, allowing the time evolution of the agglomerate structure and chemical composition to be tracked. In this paper we compared the behaviour of ores agglomerated using a combination of sulphuric acid and ferric sulphate with ones in which chloride ions are added to the recipe, since chloride leaching is gaining in popularity as a chemical alternative to bioleaching for sulphide ores. We tracked the curing process beyond the typical time scales to see the impact that this has on both the agglomerate structure and the subsequent leaching behaviour. Keywords: Heap leaching, curing extent impact, leaching kinetics, ferric-chloride leaching

Jan 1, 2020

By Z. Tshentu, M. Ngayek, P. Moleko-Boyce

This paper is focused on the development of iridium(IV)-specific quaternary diammonium cations containing electron-donating substituents in the benzyl group of methyl and electron-withdrawing substituents such as triflouromethyl and nitro groups. Quaternary diammonium cations used were tetra- 4-methylbenzyl-1,10-diammonium decane chloride (QuatDMDAMeBnz), tetrabenzyl-1,10- diammoniumdecane chloride (QuatDMDABnz), tetra-3-trifluoromethylbenzyl-1,10- diammoniumdecane chloride (QuatDMDACF3Bnz) and tetra-4-nitrobenzyl-1,10-diammoniumdecane chloride (QuatDMDANO2Bnz). Polyvinylbenzylchloride (PVBC) nanofibres were used as support material, and the functionalised nanofibres were used for separation of Ir(IV) from Rh(III). The sorbent materials were characterised by FTIR and elemental analysis. The efficiency of the quaternary diammonium cations was investigated in a column study under dynamic flow adsorption conditions. Column sorption of [IrCl6]2- and [RhCl5(H2O)]2- on nanofibres functionalised with diammonium cations showed [RhCl5(H2O)]2- was not adsorbed by the sorbent materials while [IrCl6]2- was loaded onto the column. The loading capacities of [IrCl6]2- on the quaternary diammonium sorbent materials increased in the order of F-QuatDMDAMeBnz (3.08 mg/g) < F-QuatDMDABnz (3.39 mg/g) < FQuatDMDACF3Bnz (5.46 mg/g) < F-QuatDMDANO2Bnz (7.10 mg/g). The loading capacity for Ir(IV) with quaternary diammonium cationic nanofibres increased with an increase in the electron-withdrawing nature of the substituent on the quaternising group. The charge delocalising ability of the 4-nitrobenzyl group resulted in the best interaction of the decamethylene diammonium cation with [IrCl6]2-. Keywords: Iridium(IV), rhodium(III), quaternary diammonium, polymer nanofibres, separation

Jan 1, 2020

By S. T. L. Harrison, M. Smart, C. J. Edward

Biooxidation, a biohydrometallurgical process, utilizes a mixed microbial consortium to mediate the dissolution of refractory gold-bearing ores to enhance gold recovery during cyanidation. Recognition of water as a critical resource has prompted improved water management strategies within mining and minerals processing. Bioremediated wastewater from the cyanidation tailings provides a potential water source to supplement the water required for the biooxidation circuit, reducing freshwater extraction. Gold biomining operations have typically been reluctant to recycle this wastewater due to the potential inhibitory effects of cyanide and thiocyanate (SCN-) on the microbial population. Historical process upsets suggest that the microorganisms are sensitive to SCN- concentrations as low as 1 mg/L. However, effective bioremediation processes resulting in effluents with low or negligible SCN- concentrations may allow gold biomining plants the opportunity to recycle available water. Microbial activity as well as alteration in community structure were monitored as a function of increasing SCN- concentration over the range of 0 mg/L to 5 mg/L to investigate the response of the biooxidation community to potential SCN- spikes resulting from upsets in the remediation process. Appropriate microbial growth and ferrous iron oxidation activity with a nominal shift in community structure were observed at SCN- concentrations up to 1 mg/L. Thiocyanate concentrations above 1 mg/L resulted in the complete inhibition of ferrous iron oxidation activity. This was attributed to Leptospirillum ferriphilum’s sensitivity to SCN-. Exposure to a SCN- concentration of 5 mg/L resulted in a lower cell concentration and a significant increase in growth lag time, and the absence of L. ferriphilum from the microbial population. At. caldus and Ap. cupricumulans were still present at 5 mg/L SCN-, although Ap. cupricumulans was unable to oxidise ferrous iron at this SCN- concentration. It was concluded that recycling bioremediated cyanidation tailings wastewater within the biooxidation circuit is feasible; however, to retain process performance, SCN- concentrations should not exceed 1 mg/L. Keywords: Biooxidation, thiocyanate, water recycling, community structure

Jan 1, 2020

By S. C. Maji, U. S. Chattopadyay, S. Chaudhuri, T. Gouri Charan

India is devoid of coking coal resources, which is a vital raw material for coke making. As a result it has to import about 80% coking coal for steel making. Indian National Steel Policy, 2017 has envisaged crude steel production around 300 million tonnes Mt) by the year 2030-31 and forecasts coking coal demand of 161 Mtpa (million tonnes per annum). One way of improving the indigenous resources is to study the performance of existing washeries and to improve the clean coal recovery by adopting suitable technology. A study was carried out, mainly to focus on the fine coal circuit of an existing coking coal washery, to generate the desired quality of clean coal. The operating coking coal washery being studied is presently feeding coal from nine different sources with varying feed ash contents. The coal fines varied in ash content ranging from 19.5% - 43.6%. For treating the coal fines, the washery has operated a spiral for the past few years. Performance studies were carried out, and it was found that the spiral is not efficient in recovering the cleans. Detailed investigations were carried out on the coal fines using flotation techniques, and it was found that to achieve a low ash clean coal, the froth flotation is a much better beneficiation option than a spiral, The studies showed that the recovery of clean coal through a spiral is 67.7% at ash content of 27.0%, while flotation tests showed a recovery of 68.1% at 16.5% ash content. Keywords: Coking coal washery, near gravity material, spiral washer, froth flotation

Jan 1, 2020

By N. Naude, W. P. Roux, O. Kaba

Ceramic filtration plates are used for dewatering slurries with high solid concentrations and particles in the size range of 30 μm to 150 μm. After extensive operating times, the ceramic filter plates at Minas Rio in Brazil begin to blind due to the precipitation of barium sulphate on the surface of the ceramic filter plate, which reduces the overall efficiency of the filtration process. Further research is needed to identify the barium precipitate phases on the blocked filter plates and its thermodynamic and kinetic behaviour in the system. A crystallography analysis was performed on the sample and the results showed that there are two different sets of precipitates formed on the filter plate: grey crystals which were large and scatted on the filter plate and red micro crystals that covered the entire surface of the filter plate. The red microcrystals on the sample were identified with XRD, which indicated that the sample contained barium sulphate in the mineral form barite. Experiments were conducted to determine the optimum conditions under which barium precipitation will occur. The conditions tested as a possible driving force for barium sulphate precipitation were the effect of temperature, pH and concentration. The pH and temperature ranges were pushed to both the lower and higher boundaries. The effect of temperatures as low as 15oC was studied and pH levels of 7 and upwards. The first nuclei started appearing at a pH of 7 but significant growth is promoted at higher pH levels. The X-Ray diffraction analysis confirmed the phase of the precipitates formed, and the orthorhombic crystallographic structure formed is a characteristic of barium sulphate. Keywords: Iron ore, barium sulphate, temperature, pH, concentration, morphology, and ceramic filter plate

Jan 1, 2020

By S. Banisi, S. Zare, M. Mahmoudi, M. Maleki-Moghaddam, M. Yahyaei

As the orebodies become more complex and lower grade, there is an increasing demand from crushing circuits for a finer product. When the product from a crushing circuit does not satisfy the requirements of the downstream processes, the cost of production increases. Thus, enhancing the performance of cone crushers through optimisation of their operating factors becomes important. One of the key factors affecting performance of cone crushers is the filling of the crushing chamber during the operation. The effect of varying the filling of the crushing chamber of a cone crusher was investigated. To assess the crusher performance, parameters such as crusher capacity, power utilisation, and reduction ratio were used. Extensive plant trials were conducted to obtain samples and operational data when the crusher chamber was 50% full (half-full) and 100% full, which is 300 mm above the top of the crusher chamber (super-choked condition). The results indicated a 62% difference in the crushing capacity when the crusher was operated in a super-choked condition compared to the half-full chamber level. In the super-choked condition, the size-specific energy at 12.7 mm was 20 % lower than that of the half-full condition. The Bond ball mill work index (BBWI) of crusher product indicated that the BBWI of the product of the super choked condition is 5.6% less than that of the product of half-full condition. This can be attributed to the generation of micro cracks in particles due to increase in the interparticle breakage when the crusher was operated in the super-choked mode. Keywords: Cone crushers, crushing efficiency, crushing chamber filling

Jan 1, 2020

By Y. Fu, C. Aldrich

This study investigates the use of dense convolutional neural networks to estimate bubble size distributions in flotation froths. 20 videographically captured images of froths from an industrial platinum flotation plant in South Africa were labelled semantically with an image annotation tool, LabelMe (https://github.com/wkentaro/labelme). As a result, pixels in the images could be categorized into three classes, namely ‘bubbles’ (class 0), ‘bubble boundaries’ (class 1) and ‘background’ (class 2). A U-net architecture with a ResNet34 backbone was trained to segment the images via classification of the pixels in the images. 5 of the 20 images were used to validate the performance of the neural network. Explicit inclusion of the bubble boundaries as a class proved to be significantly more effective than training the network with two classes only (i.e. bubbles and background). However, the thickness of the boundary layer regions were critical. If too thin, it was difficult to distinguish between some contiguous bubbles and if too thick, some fine bubbles may not be identified. The results were promising, although there is still significant scope for improvement, among other by making use of more images for training and validation. Keywords: Flotation Froths, Convolutional Neural Networks, Deep Learning, Bubble size Analysis

Jan 1, 2020

By D White, L. Lorenzen

Sampling and metallurgical testwork during various phases of engineering studies is of paramount importance for a robust processing plant design. When developing a process flowsheet, risks in achieving desirable financial outcomes are minimised by ensuring that appropriate sampling and adequate metallurgical testwork support the engineering and cost estimation at each phase of project development. The quality and type of samples used for metallurgical testwork are just as important as the metallurgical testwork itself. The key characteristic required of any sample is that it represents some defined portion of a mineral deposit. Representivity should consider the spatial orientation, lithology, alteration, degree of oxidation, mineralogy, and competency of the ore such as hardness and abrasiveness. Statistically representative sampling procedures and an appropriate metallurgical testwork program should be designed to bring metallurgical understanding up to feasibility study standard and to give a basis for process plant design. The authors have observed that the quality of sampling and quantity of metallurgical testwork carried out during the various phases of engineering studies are often insufficient to truly support a robust plant design. Some designs are based on benchmarks of apparently similar ore bodies or rely on buoyant metal prices supporting cash flows until rectifications can be implemented. The paper will discuss important aspects of sampling and testwork required for engineering studies in designing process plants. Keywords: Sampling, metallurgical testwork, studies, design

Jan 1, 2020