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By P Revell, G Wilkie, P Walters, L Keeney, F Faramarzi

Grade Engineering® is a value-driven strategy aiming to maximise benefits by exploiting intrinsic grade variability of ore deposits. It utilises a multitude of separation techniques to remove low grade material before being introduced to post-blast size reduction processes, i.e. crushing and grinding. At an operational scale, Grade Engineering® techniques can be implemented at the drill-and-blast stage, to control the degree of fragmentation in accordance with the spatial distribution of valuable minerals across an ore domain through differential blasting. It can also exploit intrinsic textural and breakage characteristics of a given ore type to reject uneconomical barren material at coarse sizes, which is known as Natural Deportment. Grade Engineering® can also be employed in the deliver stage of the mining value chain through sensor-based bulk and particle sorting. Each strategy is therefore highly dependent on the responsiveness of an orebody to available techniques. That is, the extent to which Grade Engineering® drivers would be capable of unlocking potential values of currently un-amenable orebodies depends on how detailed the heterogeneous nature of ore domains can be described. This paper considers exploring possible linkages between deportment characteristics of an ore and its competence when broken in a range of energy inputs at a particlebased level. Accordingly, potential opportunity may lie in exploring whether the intensity of input energy (or degree of fineness) can be used as a lever to improve or ‘unlock’ ore amenability to Grade Engineering® techniques. Implications from such a detailed approach offers new opportunities to enhance Grade Engineering® gains through engineering ore competence variability.

Nov 10, 2020

By D Gagnon, P Bouchard, F Vanier, E Soares de Lima Filho, J El-Haddad, M Sabsabi, G J. Wilkie, A Beauchesne, A Hamel, A Blouin, T Vaillancourt, F Boismenu, A Harhira, A Plugatyr, C Padioleau

Quantitative mineralogy is an established discipline in the geosciences and is aimed at providing mineral grade and texture information for geological and mineral processing applications. The two core technologies underpinning Quantitative Mineralogical Analysis (QMA) are Scanning Electron Microscopy (SEM) in combination with Energy-dispersive X-ray Spectroscopy (EDS). Technologies such as QEMSCAN and MLA are now routinely used to optimise the performance of large-scale mineral processing plants in the base and precious metal sectors. One of the major disadvantages of the QMA methods used today is extensive sample preparation requirements which make application of this technique to real-time mineralogical characterization not feasible. A breakthrough has been achieved by the National Research Council Canada in collaboration with CRC ORE by developing a novel Laser Induced Breakdown Spectroscopy (LIBS) based technology capable of real-time mineralogical characterization of process streams without sample preparation. The potential applications of this technology include, but not limited to, in-pit muck piles, underground draw points, cross-belt analysis as well as slurries. This paper describes the development of the LIBS-based technology from a proof-of-concept (TRL2) to the construction of a prototype sensor and its validation in a simulated environment (TRL5). Future work is being planned to test and further validate the LIBS sensor on a mine-site which will progress the technology to its next readiness level (TRL6).

Nov 10, 2020

By J Rutter, D Francois-Bongarcon, M Federov, N Odiar, M Scott

There are two fundamental methods for metalliferous sensing of primary crushed ore available for deployment at operations. These methods can be classed as surface measurement techniques (including XRF, LIBS, NIR, SWIR) or penetrative/semi-penetrative measurement techniques (including PGNAA, PFTNA, GAA, MR). While the individual sensing technologies exploit different physical phenomena to measure metal content, penetrative sensor measurements of grade are often regarded to be superior to surface measurement techniques due to the much larger sampling volume penetrative techniques provide. Surface sensing techniques often utilise simpler, established technologies that have considerable benefits in the speed of analysis, capital costs, ease of deployment, social license to operate and operational safety. This paper investigates whether surface sensing techniques achieve adequate sampling representativity of primary crushed rock mass to provide economic benefit over penetrative sensing technologies for bulk ore sorting applications. The paper details the methodology used to assess whether surface XRF measurements change throughout various stages of comminution, and increased surface exposure, from primary crushed ore (top size of -175mm) to finely crushed product for assay (top size -2mm). The methodology was applied to 2 ore mineralisation styles, across variable metals and grades, to identify any associated opportunities and limitations to surface sensing techniques. The results assist in developing a method to determine if surface sensing and measuring via XRF is an adequate representation of the rock mass for bulk ore sorting applications and identify any associated limitations in particle size distribution, mineralisation or natural deportment strength to guide suitable application of surface sensing techniques.

Nov 10, 2020

By M Nehring, M Kizil, P Knights, A Mousavi, F Sotoudeh

A cut-off grade is used to distinguish an economically viable block of ore from non-economical ones in mining operations. In order to obtain a high Net Present Value (NPV), a strategic and precise determination of cut-off grades (mill feed/marginal, marginal/waste) is required. In recent years, the significant depletion of feed grades and increasing mining operating costs have affected the viability of new and existing mining operations. Hence, the introduction of a pre-concentration system to process ore before sending it the main processing plant has gained increasing attention for its potential improvements in resource exploitation processes, mineral processing, energy consumption and environmental impacts. The primary potential advantage of implementing a pre-concentration system is the improvement of resource utilisation and increased reserves as a result of a reduction in cut-off grade. The objective of this paper is to present a new cut-off grade method, which can be used in the presence of a pre-concentration system for underground metalliferous mining operations. The results of this study illustrate the potential economic benefits of using a pre-concertation system prior to a mineral processing operation as a result of the reduced cut-off grade and increased NPV.

Nov 10, 2020

By N Clarke, J Hunt, A Barros, N Guerrero, L Keeney

Grade Engineering® (GE) involves a range of integrated technologies and operating protocols for improving effective feed grade through early rejection of low value material prior to costly processing activities and has the potential to transform the economics of large, low grade, mining operations. Extensive GE test work by AngloGold Ashanti (AGA) and the Cooperative Resource Centre for Optimising Resource Extraction (CRCORE) determined that the Gramalote gold deposit in Colombia is highly amenable to implementation of grade-by-size screening to upgrade the ore at a production scale. To understand the relationships between geology and the upgrade response drill core and bulk samples were analysed at the Centre for Ore Deposit and Earth Sciences (CODES) as part of a larger CRCORE project to understand geological controls on grade-by-size deportment, i.e., the tendency for specific mineral phases to preferentially fractionate during breakage into different size fractions. This work is focussed on understanding the role of mineralogy and texture in influencing rock breakage and natural fractionation at a range of scales. Detailed logging of Gramalote drill core and mineralogical analyses including hyperspectral logging, portable and bench-scale X-ray fluorescence (XRF), plus low resolution Laser Ablation of intact drill core pieces, combined with data from site (drill logs, geochemistry, petrophysics) were used to evaluate the ore and alteration mineralogy, vein paragenesis and textural variability. This provided information on gold deportment and allowed the identification of favourable host veins. Bulk sample material from different ore grade zones was also analysed. A comparison with the results of GE testing shows that positive upgrade results are produced by rocks with abundant veins, particularly those associated with potassic and/or white mica alteration. This work indicates upgrade response can be predicted from drill core using data collected by a combination of automated systems including hyperspectral data, XRF, and detailed drill core logging.

Nov 10, 2020

By M Pyle, R Ch, T Vizcarra, ramohan, G Lane, G Ballantyne

Preconcentration, through gravity separation (Wallace et al, 2015), magnetic separation and particle size (Clout, 2013), has been used in many historical projects to convert low grade material into ore. More recently, pre-concentration technologies such as bulk sorting (Kurth, 2017), screening (Bearman, 2014) and coarse particle flotation (Vollert et al, 2019) have also demonstrated their ability to progressively reject waste through the value chain. These technologies can not only deliver stepchange improvements to the economics of mining operations, but can transform the environmental and social aspects by minimising water and energy consumption and significantly reduce the quantity of fine tailings produced. However, implementing progressive waste rejection requires technologies and approaches to be combined and applied in the right way to capture the value. From a technical perspective, progressive waste rejection introduces challenges related to the orebody, equipment performance, engineering and understanding the value chain from the resource through to final product. From a management perspective, the key issues are related to people and processes, in particular; motivating the business, challenging the paradigms, and introducing new ways of operating within the constraint of existing management structures to manage risks and minimise lost opportunity costs. This combination of technical and management issues means that progressive waste rejection is a complex undertaking for mining companies, yet the reward for the business and other stakeholders is potentially high and should not be ignored. This paper presents how progressive waste rejection can offer step-change improvements to the business case for high and low grade greenfield and brownfield projects based on case studies completed by Ausenco. The paper then outlines different implementation approaches from concept studies to execution to address the various technical and management challenges outlined above.

Nov 10, 2020

By J Rutter, D La Rosa, P Walters, M Scott, L Keeney

Bulk ore sorting technologies, whether surfaced based or penetrative, have seen an increased uptake by the mining industry for preconcentration application. As with all technologies, going through the technology validation process is key to de-risking the operationalisation of the technologies and to demonstrate and confirm predicted value propositions. The ability to laboratory calibrate cross-belt bulk sensors is standard practise for sensor vendors, however transitioning the technology from laboratory to real-world application is a non-trivial process. The industry has taken two main approaches to trialling and validating bulk sensing technologies in operational environments, namely:  Construct an off-line pilot facility at scale, or  Install bulk sensing systems on production conveyors Each of these two approaches have significant advantages and disadvantages and require different experimental design programs and analysis methodologies. Consideration of material movement, heterogeneity degradation, logistical requirements, and financial investment all factor into which approach is most suitable for an evaluation scenario. CRC ORE in partnership with mining companies, have conducted several bulk sensor pilot trials utilising different sensor technologies employing each of the two main approaches outlined above. This paper describes the challenges faced, learnings obtained and outlines a position on how to transition bulk sensing technologies from the laboratory into the operating environment. The case studies presented cover a range of commodity types in both open pit and underground operations.

Nov 10, 2020

By J Rutter, S Dunham

Technological advances in sensor and material handling technology has enabled a step change in mine operations performance. Sensors capable of monitoring the grade of broken rock mass can be mounted on shovels, truck loading units or conveyors allowing highly precise ore/waste decisions to be made on small parcels of rock, increasing selectivity. This increased selectivity has the potential to remove sub-economic material from the product stream prior to incurring ore treatment costs. Bulk ore sorting is capable of intervening at <10t “pods” on Run Of Mine conveyors, or alternatively at per bucket loading (5-100t) or truck scale (30-350t) parcels of ore. This Grade Engineering technique being developed by CRCORE (Rutter, 2017) can improve mill feed grade, remove deleterious materials and ensure that the high capital intensity ore treatment facility is presented with the best ore feed strategy. While this technology can improve mine economics, estimating the performance of a bulk ore sorting system is challenging. Traditional resource estimation practices such as Ordinary Kriging and Inverse distance weighting (IDW) are based on weighted averages, a process that smooths the grade profile, reducing modelled variance and dispersion. Its purpose is to generate a globally unbiased result but is less suited to generating a model of adequate granularity to enable confidence in local grade heterogeneity. This is an essential component for bulk ore sorting success. Estimating ore sorting performance requires knowledge of the grade-tonnage curve at the scale of the sensor diversion unit (SDU). Evaluation based on an estimator that smooths the SDU distribution will not correctly predict ore/waste separation or economic performance. Alternatives to traditional linear geostatistics have been tested on a variety of mineralisation styles. Additionally, a number of heterogeneity metrics have been developed to assist with evaluation and assessment. The difference between estimation for grade (metal) and modelling for traditional mining block models, versus methods used for predicting heterogeneity and variability are explored.

Nov 10, 2020

By E Amini, N Beaton

Application of ore pre-treatment technologies such as Grade Engineering and in-pit ore treatment require comprehensive analysis to show proven value is being generated by switching away from the current practices. Considering most mining operations last several decades; the pre-treatment technologies must be investigated over life of mine (LOM) to truly show value was generated or lost. Therefore, a value chain flowsheet model has been usually developed to run those analysis/simulations over LOM using a set of defined ore characteristics and the operating equipment capacities. The Integrated Extraction Simulator (IES) has been used for this type of LOM simulations as it has the necessary flexibility to accommodate the whole mining value chain flowsheet in one platform and provide essential computation power to run mass simulations for the given scenarios using the resource block model. This allows to forecast the value chain response over the LOM for all given scenarios in a feasible amount of time. The traditionally developed flowsheets for the analysis/simulations are usually limited to the mineral processing plant and were developed based on a single audit conducted on the plant in a very short time, e.g. several hours. The process plant is being operated with a certain ore at a certain condition during the short audits which would not reveal the true capacity of the operation equipment when ore characteristics changes. Therefore, it would be impossible to define the true capacity of the equipment and identify the constraints along the value chain. In this study, a methodology was developed to identify the constraints along the value chain and implement those in the IES flowsheet using process information. Also, the equipment models were fine-tuned to represent the true capacity of the equipment in the flowsheet using the same data set. The methodology was tested over five process plant in two operations and showed the performance forecast was significantly improved over the traditional methods being used so far. Then those high-fidelity flowsheets were used to evaluate the ore pre-treatment technologies proposed by the relevant resource company experts.

Nov 10, 2020

By A Kobzev, C Robben

Sensor-based particle sorting of gold ores is the masterclass of preconcentration. The technical challenges of a grade in ppm scale makes a direct detection with currently available technology in milliseconds impossible. Consequently, the detection and classification of particles into ore and waste classes relies on detectable proxies which are the foundation for the classification. These must be reliable and repeatable across the ore bodies processed in future. A sensor-based particle ore sorting equipment must therefore be calibrated systematically by understanding the gold deportation in different lithological groups. Also, it must be in best operational state to minimise possible misplacement. The technical challenges are met by a stringent financial regime governing the business case. Many gold ore sorting projects require a stable grade of gold in the rejected waste significantly below 1gr/t. Two examples are introduced in this paper:  The pilot results and operational experience of X-ray transmission (XRT)-based particle ore sorting for pebble sorting of sulphide associated ore;  Laser-based particle ore sorting for pebble circuit of an orogenic, quartz associated gold ore. Usually, sensor-based particle sorters are installed in the crushing section of a plant. However, for large capacity plants, the number of equipment required, and the associated material handling costs result in significant capital and operating expenses for plants with 1200tph run-of-mine capacity. As a conclusion, it was decided to investigate sensor-based particle ore sorting applied to the pebble stream of two existing semi autogenous mills.

Nov 10, 2020

By P Esteves, J L. Bechir, R Galery, C Senna, V Souto

The current moment experienced by the mineral industry is marked by a series of economic, social and environmental challenges. Among the main challenges, it is important to highlight the decreasing in ore grades with the gradual depletion of the existing deposits, the high volatility in international commodity prices and the operational inefficiencies in the mine, beneficiation, and metallurgical plants. In this context, mining companies are continuously looking for alternatives that minimize production costs and maximize overall production. In addition, recent tragedies involving tailings dam collapses in Brazil, reinforced the need for a more responsible, sustainable, and conscious mining, such as aiming the preservation and rehabilitation of the land environment. In this scenario, the Sensor-Based Sorting (SBS) technology is currently getting more space into the mining market. This separation technology aims the removal of gangue material in coarse particle size fractions, prior to comminution and concentration stages. As a consequence, the technology allows a smaller consumption of energy, water, chemical reagents and other inputs. Moreover, the sensor-based sorting is a dry processing technology and favors the reduction of fine material to be sent to the tailings. The aim of this paper is to provide information regarding the technical and economical evaluation of SBS technology applicability to a zinc silicate mining in Brazil. The project evaluation was performed in two stages: laboratory test work and the execution of a pilot plant. The execution of a pilot plant allowed the technology optimization, quantification of downstream process effects, such as a proper economic evaluation about the technology benefits. Keywords: Sensor-Based Sorting; Particle Sorting; Ore Sorting; Preconcentration; capital expenditure (CAPEX); operating expenditure (OPEX); zinc; comminution; dry processing.

Nov 10, 2020

By R Kroukamp, G Rech

CRONIMET Australia in cooperation with Speciality Metals International, the mining lease holder of the Mt Carbine Tungsten mine, have demonstrated the preconcentration and processing of large (>12 million tonnes) tailings and mine waste dumps on site in far north Queensland. The first stage of this project has proven the economic viability of preconcentration using sensor-based particle sorting whilst ensuring high recovery of industry critical tungsten minerals – wolframite and scheelite. Sensor-based sorting technology have enabled the production of a sorter product to feed the existing wet gravity plant ensuring higher recoveries and increasing concentrate production. Processing of 1,000 tonne low grade samples using the TOMRA x-ray transmission (XRT) ore sorter installed on site, has reduced the volume of material entering the downstream wet processing plant by up to 60%. The sorted 10-25mm size range produced the highest upgrade ratio (product/feed) of 24:1 and achieved sorter concentrate grade of 1.52% WO3, sorter waste grades below 0.014% WO3 and metal recoveries of up to 93.4%. The appropriately sized, barren sorter waste material is also sold as aggregate and other quarrying products and thus adding to the overall economic benefit of the project. The utilisation of the sorting technology on site has produced an economically viable project, significantly reduced capital cost, and reduced the time to market of the final tungsten concentrate. The next phase of the project includes design and installation of a larger two sorter plant capable of over 150t/h feedrate.

Nov 10, 2020

By T BoBo, D La Rosa, P Cameron

Efficient pre-concentration using natural and induced deportment starts with effective blast design. Leveraging these characteristics using Size-Based Grade Engineering® techniques requires detailed understanding and measurement of the particle size distribution (PSD) of the Run of Mine (ROM) ore. In turn, blasts must be designed to optimise fragmentation for the pre-concentration method. To be successful, fragmentation outcomes must be predicted and measured as part of the blast design process to account for the response ranking of the rock mass. The upgrade achieved using Size-Based Grade Engineering® methodologies will rely on the proportion of feed that reports to the screen undersize, so particle size distribution of the feed stock is of paramount importance if the potential benefits are to be achieved and maintained. Measuring actual fragmentation in the ROM ore and comparing with predictions allows the modification of subsequent blast designs to optimise fragmentation for pre-concentration scenarios. Automating this process can deliver significant increases in productivity and mine site profits. This paper discusses the importance fragmentation plays in Size-Based Grade Engineering® and explores technologies and methodologies used to measure it, consistently ensuring the best outcomes.

Nov 10, 2020

By E Amini, M Scott

Grade Engineering® involves the planning, integration and operation of coarse preconcentration techniques to reject uneconomic material as early as possible in the mining value chain to improve the quality of ore delivered to mineral processing activities. These preconcentration techniques include screening for natural and induced deportment of grade to finer size fractions during blasting and coarse breakage, sensor-based measurements of grade and mechanised sorting at particle or bulk scales and coarse gravity separation. Over the last decade, the Cooperative Research Centre for Optimising Resource Extraction (CRC ORE), with the support of its participants, has developed a framework for the holistic evaluation of economic and production impacts attributable to these preconcentration techniques. The framework combines the characterisation of preconcentration responses, geostatistical modelling, process modelling and simulation of downstream and upstream production impacts and the optimisation of strategic mine plans to quantify changes in net present value due to different preconcentration strategies. This framework has been applied to evaluate preconcentration techniques at numerous metalliferous operations and can also be used to quantify changes in the environmental metrics for energy, water and steel consumption and emission, tailings and waste rock production. This paper collates the results of these Grade Engineering® studies to show the range and magnitude of economic and environmental impacts attributable to preconcentration. The studies have shown that rejection of uneconomic material prior to mineral processing activities can have a pronounced improvement in energy, water and comminution steel productivity, emission intensity and the profitability of metalliferous mining.

Nov 10, 2020

By H Rossiter, A Maddever, J Market, P Jeanneau, A Virani

It is generally acknowledged that the head grade of the world’s copper resources is declining. Accordingly, there is an increase in the volume of resource material being mined and processed to maintain copper production. This has led to increases in cost base accompanied by reductions in Life of Mine (LOM) Net Present Value (NPV). The higher tonnages of material mined are driven by increased quantities of lower grade material required to satisfy the short term mine model. In turn, this has brought an increased focus on sampling and assay methods, since any error associated with estimating the grade of each block and determining its correct destination serves to magnify the losses that will occur. Presently most open-pit copper mining operations use blasthole sample assays to feed their shortterm model. The problematic nature of blasthole sampling is well documented (Engström, 2017), particularly the poor timeliness of the data and the significant errors that are inherent to the technique. Methods to improve grade control, such as drilling a separate grade control grid, have been proposed but come with their own cost, logistical, and accuracy shortcomings. A recent development in grade estimation is the Pulsed Fast and Thermal Neutron Activation (PFTNA) downhole logging probe. Deployed on an efficient semi-autonomous platform, PFTNA is being aggressively adopted in the Australian iron ore industry. The technology provides in-situ elemental analysis of the geological formation, delivering high accuracy, high efficiency, near realtime data from a sizeable measurement volume. In this paper we consider the application of the technology to copper. Whilst there are several operational components to such a value proposition, we focus on the benefits of improved gradeestimation. A simulated copper mine is used to model the quantum of improvement expected from a reduced grade-estimation error, which reduces the likelihood of misallocating material to the wrong destination, which in turn increases NPV.

Nov 10, 2020

By T Soares, P Esteves, C Senna, V Souto, E Dias

Steinert provides a wide portfolio of Sensor Based Sorting (SBS) technologies, including the possibility to combine different sensor data in the development of the separation applications. This multi sensor approach provides a larger applicability and extensive flexibility for the technology. This effect is emphasized for mining applications, once every ore body presents its own features that can also comprise a large material variability. In this sense, every sorting application is customized and comprises a personalized solution. This means each project development includes a test work stage that approaches from the project discussion, test definition, up to test outcomes and processing development. In relation to very complex and large projects, a sorting pilot scale plant has become a crescent tendency. By having a pilot plant stage can represent a chance to test larger amounts of material from different mining areas, different mining projects, such as to consider different processing targets and application approaches. Based on that, a better understanding about the technology application can guide a more assertive development of large and complex ore sorting projects. The aim of this paper is to provide information about the applicability of the Steinert multi sensor technology at different pilot plants in South America. It consists in a compilation of laboratory test results, plant design and operational results. For that, a chromite, an IOCG deposit and a sulphide gold ore projects are presented. Additionally, the paper discusses the application of different sensor technologies combinations such as various processing targets, thus providing a wider understanding about sorting applications and flexibility.

Nov 10, 2020

By J Rutter, J du Plessis, er, M Scott, D Alex

It is generally acknowledged that bulk ore sorting is an attractive prospect for the mining industry. However, uncertainties relating to the operational performance of sensor technologies, the grade heterogeneity present in analysed material and the physical separation performance of the coupled system complicate the evaluation and deployment of bulk ore sorting strategies at operations. To assist in the quantification of these uncertainties, the Cooperative Research Centre for Optimising Resource Extraction (CRC ORE) and Anglo American have implemented a systematic methodology to evaluate the deployment of sensor technologies for bulk ore sorting. This methodology was implemented at a bulk ore sorting demonstration plant using penetrative prompt gamma neutron activation analysis (PGNAA) and surface x-ray fluorescence (XRF) measurements of base metals, as a proxy for precious metals, at Mogalakwena PGE Mine. The methodology comprises of three phases. The initial phase involves the preliminary investigation of sensor technologies and analysis to reduce identified risks prior to commissioning of the bulk ore sorting plant. The second phase executes a series of experiments to prove the sensing technologies in the operational environment and provide confidence the sensors can drive separation. The final phase demonstrates the production scale bulk ore sorting performance of the plant and validates its performance by campaign of the sorted products at the processing plant. Throughout the operational deployment and evaluation of bulk ore sorting at Mogalakwena, several key lessons were learned. These learnings relate to the calibration requirements of the sensor technologies, the application of surface vs penetrative sensor techniques and the preservation of grade heterogeneity delivered to the sensors. This paper presents the methodology used, and lessons learned, in the operational deployment and evaluation of PGNAA and XRF sensors for bulk ore sorting at Mogalakwena PGE Mine.

Nov 10, 2020

By K Ehrig, V Liebezeit, Y Li, B Pewkliang, M Smith, E Macmillan

Pre-concentration of low grade ores via the rejection of low/no value materials based on fragment size is the subject of many published papers and studies over the past decade. However, the possibility of upgrading should not limited to low grade ores given the high cost of capital, energy, water, reagents, and ultimately tailings storage across the mining industry. Mining and processing facilities at Olympic Dam consist of an: 1) underground mine where ore is extracted via sublevel open stoping and 2) integrated processing plant located on the surface which produces Cu-cathode, Au-Ag bullion and uranium oxide concentrate. Blasted ore is trucked/trained to underground crushers which produce an ore feed with a product size P80 = -145 mm. Crushed ore is then hoisted to the surface and delivered to the ROM pads via an overland conveyor. The typical copper grade of mill feed is approximately 2 wt%. A rapid assessment of the potential to upgrade Olympic Dam mill feed prior to the ROM pads via rejection of low value materials based on fragment size was completed using existing data. Three separate datasets were utilised to assess the deportment of Cu, U, Au across size ranges of 200 mm down to sub-8 m. Cumulative grade vs cumulative mass distribution graphs for each element were produced. This data clearly demonstrates that Cu, U, Au grades remain constant down to ~212 m, which happens to be the liberation size of the sulfides. Hence, no potential to upgrade the ore stream via rejection of +200 mm - to ~212 m sized materials prior to milling. Mineralisation within the Olympic Dam Fe-oxide Cu-U-Au-Ag deposit (IOCG) occurs as disseminated sulfide grains within granite- to hematite-rich breccias. The typical sulfide grain size is sub-200 m that supports the outcomes of the grade deportment by fragment size study.

Nov 10, 2020

By Liming Yuan, Isaac Zlochower, RICHARD THOMAS, Thomas H. Dubaniewicz, Teresa Barone

Mining vehicle manufacturers are developing lithium-ion (Li-ion) battery electric vehicles as an alternative to diesel-powered vehicles. In gassy underground mines, explosion-proof (XP) enclosures are commonly used to enclose electrical ignition sources to prevent propagation of an internal methane (CH4) air explosion to a surrounding explosive atmosphere. Li-ion batteries can create pressurized explosions within sealed enclosures due to thermal runaway (TR). Researchers at the National Institute for Occupational Safety and Health (NIOSH) measured TR pressures of lithiumiron phosphate (LFP) cells as a function of free space within sealed enclosures and observed an inverse power relationship. A well-confined cell produced 294 bar (4260 psia) of pressure during a TR, far exceeding minimum pressure containment specifications for conventional XP enclosures. Results indicate that adding enough free space surrounding LFP cells can reduce TR pressures to levels below that expected for CH4-air ignitions. Measured TR temperatures were below the minimum autoignition temperature of CH4-air.

Nov 6, 2020

By Andrea Brickey, JUDITH BUABA, RICHARD AMOAKO

The global mining industry has recorded significant declines in accident and injury rates attributed to the advancement in technology, increased enforcement, and safety consciousness. A goal of the mining industry is to achieve zero injury and occupational illness on all mine sites, prompting increased research into ways to further reduce mine accidents. A machine learning technique known as multiclass logistic regression is applied on a 10-year injury dataset from the Mine Safety and Health Administration (MSHA) to determine a miner’s susceptibility to a class of injury and to help identify significant risk factors associated with different classes of injury. The data is aggregated based on injury classification to provide statistically relevant results. The analysis identifies specific risk factors that influence a mine worker’s susceptibility to a given class of injury, i.e., non-fatal with no days lost or restricted activity, non-fatal with days lost and/or days of restricted work activity, and fatal and total permanent or partial permanent disability. These factors include miner’s age, mine type (coal vs. non-coal), experience on the current job (years), shift start time, employment type (operator vs. contractor), mining district, and type of accident. The results of the analysis indicate that a miner’s experience on the job, i.e., the number of years worked in a current job, is a significant risk to injury occurrence, even for those with decades of total mining experience. We further show the differences and similarities between the surface and underground mine incidents.

Nov 3, 2020