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By M. Frenzel, K. Bachmann, M. Rudolph, R. Tolosana-Delgado, E. Schach, M. Buchmann, L. Pereiraa, K. G. van den Boogaart, N. Kupka

Geometallurgy - the science of predicting the behaviour of ores along the value chain based on their geological and mineralogical characteristics - has barely moved beyond process minerology studies. These studies typically link process behaviour to the mineralogical and microstructural characteristics of pristine or processed ores in a qualitative or semi-quantitative manner. Eventually, a trial-and-error optimisation of the operation is achieved. However, to deliver on its promise, geometallurgy should progress beyond this current approach to a fully quantitative understanding of process behaviour, including the various uncertainties involved, namely predictive geometallurgy. One of the barriers currently hindering this development is the neglect of most ore characterisation data obtained in geometallurgical studies. This is mostly due to the large extent of this information exceeding the capacity of conventional software. To address this challenge, we developed a data mining platform consisting of an SQL database, an R front-end in-house package, and back-ends to interpret data from several analytical instruments. This platform enables the use of the statistical and data mining power of the R open-source environment for any geometallurgical study. Potential applications range from ore characterisation to process understanding and forecasting, and from geostatistical prediction to operational control and optimisation. The scripting abilities of R allow for the: 1) processing of many streams in loops, thus freeing the user from repeating tedious click-and-drop tasks; 2) computation of virtually any derivate or aggregate quantity from the data; 3) distribution of the work among several processor clusters; and 4) keeping of a log file for the calculations done. This contribution presents the building blocks of this platform, and illustrates with several examples its potential to enable predictive geometallurgy. Keywords: Data mining, entropy, kernel methods, LASSO regression, predictive geometallurgy

Jan 1, 2020

By M. J. Latva-Kokko, CA Snyders, J. R. Peltola, M. K. Kostiainen

It is well known that carbon management is essential for minimising soluble gold losses in carbonin- leach (CIL) and carbon-in-pulp (CIP) circuits. Even so, carbon measurements remain predominantly a manual operation with a low amount of control and automation around CIL and CIP reactors. Carbon inventory, i.e. knowing the carbon concentration in each reactor, is crucial for good carbon management. CarbonSense, a novel in-situ measurement system, was therefore developed for continuous monitoring of the carbon concentration in each reactor and paves the way for more advanced control in gold operations. The carbon management and optimisation system is based on Outotec® HSC Sim dynamic process simulations where each process component is modelled in detail. Generated models are validated with real data from a particular operation. The simulator will allow for the carbon control sequence to be realistically tested and the efficiency optimised by controlling the feed and discharge pumps in a hazard- and risk-free process environment. Furthermore, by combining CarbonSense and Outotec® gold courier (online soluble gold measurements) continuous feed-forward and feedback information can be obtained. The optimiser will then define the best running parameters based on the cost function of gold production versus intrinsic factors such as utility costs and adsorption time. Eventually, the application can be installed parallel with a real process island and it can be continuously calibrated with real-time process data. The dynamic model will then create future predictions for the entire process island. The example case presents a CIL circuit, which is simulated using Outotec® HSC Chemistry Sim and operated through Outotec® ACT (Advanced Control Tool) application. Keywords: Carbon-in-leach, CIL, CarbonSense, carbon management, automated control, simulation

Jan 1, 2020

By Y. Zhang, F. Zhu, Y. Zhao, L. Dong, Y. Li

In order to obtain a more suitable separation medium for fine coal, in this paper a pulsed fluidised bed was used to measure the pressure fluctuation, density characteristics, bubble diameter and rising velocity of Geldart A and B particles in the bed by controlling the pulsation frequency and gas velocity, in order to study the fluidisation effect and two-phase distribution of the bed. The results indicated that with the increase of pulsation frequency, the breakthrough point on the fluidisation characteristic curve caused by particle adhesion disappeared, and there was an optimal frequency, which made the minimum fluidisation velocity of particles reach the minimum. In addition, the bubble rising velocity was also simulated. The relationship between them was established using a model, and the error was within 5%. This provides an effective way to predict the bubble velocity, study the distribution of bubble phase, and improve the stability of bed density. Keywords：Frequency, flow velocity, pressure fluctuation, bed density, bubble

Jan 1, 2020

By I. Y. Motovilov, S. A. Telkov, M. B. Barmenshinova, Z. S. Abisheva

As a rule, removing waste rock at the beginning of the process improves the conditions for the flotation of minerals, and also reduces the operating costs of crushing, grinding and flotation. Lead-zinc ores in the Shalkiya deposit of the Republic of Kazakhstan are classified as difficult for enrichment, but taking into account their significant reserves, they are one of the main potential ores for the production of lead and zinc. Under industrial conditions, for processing ores of the Shalkiya deposit, a selective flotation concentration scheme with the production of lead and zinc concentrates is recommended. However, according to the recommended scheme, it is not possible to obtain high-quality concentrates of lead and zinc, with high metal recovery. The paper presents the results of studies of preliminary gravitational enrichment of lead-zinc ore in the Shalkiya deposit. Based on the fractional analysis for the particle size range of -40+8 mm, Henri- Reingard enrichment curves were constructed, separation density (2730 kg /m3) was established to isolate the light fraction with the lowest possible lead and zinc content, and gravity enrichment indices (0.7-0.71) were determined. It was found out that when using gravitational enrichment of the original large-sized ore in the Shalkiya deposit, tails are released at the beginning of the technological process with a yield of up to 20% with the extraction of silica up to 30%, calcite up to 11% and carbonaceous matter up to 12%. As a result of the studies, it was found that the beneficiation of crushed lump ore could be conducted in heavy suspensions. One of the positive aspects of the use of gravitational enrichment is the reduction of energy costs for grinding the heavy fraction, which was experimentally proved in this article. Keywords: Lead-zinc ores, Shalkiya deposit, fractional analysis, gravitational enrichment, floating fraction, sunken fraction, separation density

Jan 1, 2020

By T. Moodleya, I. Govender

Despite the widespread use of DEM to simulate particle processes, comparatively little work has been done to evaluate its accuracy. One of the reasons is a lack of available measurement techniques that can capture dynamic information at the momentum transfer timescale which is lower than 10-6 s - typical of the kinetics occurring within the drum. This work makes use of Positron Emission Particle Tracking (PEPT), an in-situ method capable of measuring 3D kinematic data at 1000 frames per second. PEPT measurements are performed using 3 mm and 5 mm mono-size glass beads in a 300 mm diameter rotating drum at critical speeds that are relevant to industry. We undertake systematic ‘tuning’ of DEM with a view to optimising the agreement between experiment and simulation. To ensure an equitable comparison scale, both the DEM and PEPT data are coarse grained within representative volume elements. This work seeks to validate DEM predictions against experimentally derived PEPT kinematic measurements. Keywords: PEPT, nuclear imaging, DEM, Tumbling Mills

Jan 1, 2020

By R. V. Peche

Since 1952, Bond’s equation has been used as a standard to predict the specific energy of grinding for a given duty. The Bond’s equation considers that the exponent is constant for all conditions. Hukki, in 1961, pointed out that the exponent is a function of the particle size, and concluded that the Bond’s equation applies successfully for an intermediate range of sizes; out of this range the Bond’s equation leads to high error as demonstrated in this paper. More recently, in 2004, S. Morrell presented a new formula in which the exponents vary with the feed and product size. This paper is based on Hukki’s approach and presents a new general formula, in which the exponent varies only with the feed particle size. This new equation has been proven in 82 observable cases in Peru and other countries around the world, involving ball mills and vertimills in different configurations over a wide range of feed and product sizes, work indices and mill diameters; in most cases, the predicted value has an error less than 10% with regards to the observed value and no need for any correction factor. Keywords: Grinding, specific energy, particle size, work index, variable exponent

Jan 1, 2020

By C. Steyn, J. D. le Roux

A dynamic model is used to represent the single-stage closed grinding mill circuit at Mogalakwena North Concentrator. The circuit consists of a ball mill closed by a hydrocyclone. The circuit is modelled according to a semi-mechanistic non-linear dynamic model available in literature. An algebraic routine is used to fit model parameters to steady-state process data. Preliminary validation results against dynamic data indicate that the model is capable of capturing the main dynamic trends of the plant. Since classical system identification techniques proved insufficient to produce accurate models; the non-linear model can be used as a reliable source for model-predictive controller design. Keywords: Grinding, modelling, process control, parameter estimation

Jan 1, 2020

By J. E. Dickinson, R. Campbell, M. J. Cole, K. P. Galvin, M. C. Cole

It is common practice in the beneficiation of iron ore ultrafines to apply reverse flotation, and hence float the gangue minerals, leaving behind a high-grade iron ore product. Given the difficulty in floating particles finer than 20 μm, especially those finer than 10 μm, it is necessary to subject the feed to desliming ahead of flotation. There are two problems with this approach. Firstly, given the known inefficiencies of cyclones, desliming needs to be aggressively applied to particle sizes upwards of 45 μm to ensure all the offending ultrafine gangue is removed. Secondly, desliming also removes a significant portion of the valuable iron ore. Thus, a significant portion of the ultrafines, including the iron ore, is lost to the tailings. This paper examines the direct flotation of iron ore fines using the Reflux Flotation Cell. The RFC operates using a concentrated bubbly zone, in the absence of a conventional froth zone, for delivering strong counter current washing through an inverted fluidised bed of bubbles ahead of the product discharge. A system of parallel inclined channels in the lower part of the cell provides enhanced segregation of the bubbles from the tailings, preventing the loss of air bubbles into the tailings. Experiments conducted under semi-batch conditions produced encouraging results with concentrates of ~65% Fe grade in a single flotation stage. Comparatively, up to five successive batch flotation stages were required when using a laboratory scale batch mechanical flotation cell to achieve similar Fe grades. For a given grade, lower Fe recoveries were typically achieved by the RFC. This was attributed to the effective prevention of ultrafine particles succumbing to hydraulic entrainment under a positive bias flux, as well as an increasing feed volume involved in the semi-batch operation using inverted fluidisation water for cleaning. Overall, similar grade-recovery curves were produced, however, significantly higher grades were obtained by the RFC and with fewer separation stages. Keywords: Desliming flotation, iron ore, modified release analysis, reflux flotation cell

Jan 1, 2020

By T. Bui, T. Wallmach, S. B. Blancher, A. Back, M. Pierre, V. Delarue, A. Kanzari

This study investigates nickel laterite porosity and its impact on geometallurgical modelling. We performed a detailed mineralogical study on a set of Ni-laterite samples originating from New Caledonia. Samples were analysed after heavy liquid separation by automated SEM (QEMSCAN®). Laboratory and QEMSCAN® modelling results do not correlate well. Ideal physical mineral properties such as mineral densities used in QEMSCAN® modelling do not match the actual physical densities of minerals. The presence of empty or water-filled pores can represent important amounts in minerals and change the overall density of a particle. An adequate modelling of real density separation of minerals and particles should consider the water-filled pores. The first step towards a better simulation was to integrate pores larger than 10 μm in diameter filled with water (density of 1 g/cm3) in the QEMSCAN® database. The results approached, but did not match the physical laboratory results. The second step was to identify nanometer to micrometer sized pores, which could not be identified by QEMSCAN®. It was possible to identify nanometric pores in enstatite, Fe-oxides, serpentine, talc and quartz, and radial aggregates of fibrous Fe-oxyhydroxides with high resolution SEM (HR-SEM). The quantification of those micro-sized pores was performed on 2D HR-SEM images using image-processing techniques developed in Python to calculate the nanometer to micrometer scale pores in each mineral. Despite the limitation of converting 2D imaging into 3D objects, in our case pore sizes and porosities, we were able to calculate a mean value of porosity for each mineral. The integration of this porosity in the calculation of particle densities yielded a good match between the QEMSCAN® model and the laboratory experiment results. Despite the complexity of a strongly weathered material, such as Ni-laterites, a good knowledge of the ore makes it possible to build reliable processing models. Keywords: Mineralogy, geometallurgy, nickel laterites, SEM, QEMSCAN®, porosity, density

Jan 1, 2020

By C. Bergmann, V. Ross

The concept of underground ore processing, in which valuable ore and waste are separated at a coarse particle size to improve the economics of operations and minimise the environmental impact, seems attractive from a sustainability perspective. Perceived benefits of such an operation include energy and cost savings due to a reduced hoisting load, a reduced footprint of surface operations, and using material for backfill that has not been contaminated by process chemicals. Progress in the South African mining industry in this area has however been below expectation, and opportunities for underground processing in deep, narrow reef operations were therefore explored by an Excel-based simulation with sensor-based sorting (SBS) as concentration technology. Various PGM mining scenarios incorporating factors such as the mining rate, depth of operation, and effects of dilution and particle size on the metallurgical efficiency, were compared. Whilst these initial results can be regarded as indicative only, they have shown that the economics of such an operation are driven by selective mining at significantly higher rates than achievable with conventional methods. Also, the cost of preparing, conveying and placing rock backfill will need to be lower than that of hoisting waste and depositing it on a surface dump. Another aspect to be addressed is whether ore sorting machines would be able to operate under harsh conditions underground and what provision would need to be made to ensure their efficient operation. Further investigations should focus on refining costs, incorporating environmental considerations in the modelling of economics and sustainability, and compare greenfields with brownfield scenarios. Keywords: Ore sorting, hoisting, backfill, mining rate, economics simulation

Jan 1, 2020

By S. Banisi, M. R. Yarahmadi, S. R. Mortazavi

Nowadays the development of automatic on-line sampling systems has enabled large amounts of data to be available to plant managers. These data are inevitably prone to random and sometimes systematic errors. Data reconciliation methods are used to deal with random errors. In these methods, the measured data are adjusted in accordance to their error, so that the mass conservation law is satisfied at the same time. Measured data may also contain systematic errors, using these data in mass balancing software will result in inaccurate or even irrational results. In this paper three methods for detecting systematic errors (global test, node constraint test and maximum power constraint test) were presented. A step by step method to locate the systematic errors was demonstrated. Then, the new version of Movazen©, a mass balancing software, was introduced and used to locate systematic errors in the measurements taken from the Sarcheshmeh pilot plant copper flotation circuit. Keywords: Mass balancing, data reconciliation, random error, systematic error

Jan 1, 2020

By G. Mahieu, L. Perez-Barnuevo, P. Barbeau-Verreault, O. Gravel

Raglan mine is a copper-nickel mining operation, part of Glencore, and located at the northern border of the province of Quebec, in Canada. The concentrator is fed by a mix of four different underground mines, thus it must accommodate periodic variations in run-of-mine mineralisation and composition. In order to concentrate nickel and copper sulphides, the flotation circuit also includes a regrind step at the cleaner stage. This leads to challenges in maintaining optimal recoveries and in diagnosing potential disturbances. This paper advocates for the combination of automated mineralogical analyses, reconciliated mass balances from diagnostic sampling campaigns and innovative multidimensional visualisation to assist in tackling these challenges. Through this approach, it has been possible to quantify the regrind step influence on the liberation of valuable mineral particles in the final flotation concentrate. The methodology also allows for virtual sieving of the products in order to track unit loss in the flotation process. The proposed analysis scheme presents an opportunity to easily diagnose flotation circuit performance based on mineralogical information. Keywords: Flotation diagnostics, multi-dimensional visualisation, mineral liberation analysis

Jan 1, 2020

By J Taguta, B. McFadzean, P. E. Ngoepe, M. G. Mulaudzi

Computational modelling methods and isothermal titration calorimetry (ITC) were employed to investigate the interaction of a pyrite surface with thiol collectors. Modelling work was conducted in the presence and absence of water. Microcalorimetry was employed to investigate the resultant energy of interaction pyrite as a result of mineral-collector interactions. Sodium ethyl xanthate (SEX), diethyldithiocarbamate (DTC) and diethyl dithiophosphate (DTP) were investigated in this study. The results showed that the calculated surface energies and morphologies for FeS2 showed that (100) and (111) are the most stable surfaces. The morphology also indicated the appearance of the (100) and (111) surface facets and no presence of others. The calculated adsorption energies between thiol collectors and the pyrite surface were always more exothermic than the experimentally determined ones. When comparing different ligand types with the same alkyl chain length, the heat of adsorption of diethyl-DTC was greater than that of ethyl xanthate and diethyl-DTP for pyrite. Experimentally, the diethyl DTP barely adsorbed onto the pyrite surface and this may be due to a different mechanism of action. In computational calculations, water adsorption was found to reduce the reactivity of the Fe atom for the interaction with thiol collectors, and bring the adsorption energies closer to the magnitude of the experimental values. Keywords: Computational modelling, microcalorimetry, experimental and thiol collectors, adsorption energies

Jan 1, 2020

By O. I. Rychkova, M. N. Sycheva, E. V. Poryadochnova, G. V. Sedelnikova

Deposits of copper-porphyry type occupy a leading position in the mineral resource base of the world copper industry. They are characterized by large and gigantic scales, often complex composition, their development is carried out in an open way. The raw material base of the Russian copper industry is mainly sulfide copper-Nickel and copper-pyrite ores. Exploration of copper-porphyry deposits and the creation of long-term large mining facilities on their basis is one of the most promising and priority tasks for the Russian industry. This task is especially acute for the copper enterprises of the Urals in connection with the depletion of copper - pyrite ore reserves. In recent years, Russia has carried out a large complex of studies on the composition and technology of processing of copper-porphyry ores of different subtypes. In this work the complex poor copper-molybdenum-gold ore of one of the deposits is investigated. The chemical and mineral composition of the ore containing 0.49% copper, 0.020% molybdenum, 0.39 g/t gold, 5.85 g/t silver was determined. The forms of finding metals and their distribution by size classes are established. The dependence of extraction of metals into concentrates of collective-selective flotation on the size of ore grinding, pH of the medium, reagent consumption is shown. Under optimal flotation conditions, copper concentrate with 18.6% copper content and 92.32% extraction and molybdenum concentrate with 21.5% molybdenum content and 73.4% extraction were obtained. With a high concentration of molybdenum in selective flotation products, it is possible to conclude about the expected molybdenum concentrate with a content of 45% and a recovery of 70%. The associated extraction of gold and silver into copper concentrate is 76.8 % and 73.4%. Keywords: Collective-selective flotation, copper, molybdenum, porphyry ore, grinding, pH, reagents

Jan 1, 2020

By B. S. Agbenuvor, R. Morrison, V. Jokovic, C. Antonio

Body breakage usually involves breakage of particles by compression between two surfaces or impacted against a rigid surface, with the latter of interest in this current study. This mode of breakage has been identified to be the most elementary size reduction process and the most effective breakage process in comminution devices. It has been found in the literature that the progenies produced from this form of breakage possess a residual velocity at which they move, after the breakage event. In the pursuit of efficiently utilising applied comminution energy, a laboratory-scale breakage device has been developed to deliver precise specific impact breakage using the concept of a fast-moving hammer and a slow-moving particle to effect breakage. The residual velocities of the progenies from the breakage event may be used to generate secondary breakage without further addition of energy if the particle encounters a stiff obstacle such as a steel plate at right angles to its path. To measure the extent of secondary breakage, the steel wall of the device was lined with a rubber material to suppress the second set of breakage events. The performance of the new device is analysed, based on calculated t10 values and measured residual velocities of the moving daughter particles. Testing involving anvils showed a much finer PSD and higher t10 compared to the test involving rubber lining. This finding is corroborated by the measured residual velocities of the progenies. The resulting energies calculated from the measured residual velocities of the progeny were found to be approximately 42-88% of the primary specific impact energy. This retained energy was found to be sufficient for secondary breakage. Keywords: Impact breakage, residual velocity, secondary breakage, energy utilisation

Jan 1, 2020

By D. T. Jordan, L. C. Coetzee, G. Kalenga, T. Mahlangu, W. J. Shipman

Although online free cyanide analysers are widely available and used in gold leaching operations worldwide, reliable automated free cyanide concentration control is not consistently achieved in many of these operations. Effective cyanide concentration control is complex owing to amongst others, the long residence times and ore feed variations. Model Predictive Control (MPC) has been proven to provide superior cyanide concentration control over traditional proportional-integral-derivative (PID) control, leading to savings in operational costs and reduced leachable gold losses. In the first example reported in this paper, MPC has improved control of the free cyanide concentration by keeping it within the required range 73% of the time during testing, compared to the previous PID controller that only achieved this target 30% of the time. This represents a 143% improvement. Another example demonstrates a 22% reduction in cyanide usage as a result of using MPC. MPC functionality is rarely available on the programmable logic controllers (PLCs) and control systems installed at gold plants, hence it is implemented on a personal computer (PC) networked to the cyanide analyser and plant infrastructure. Mintek has developed an embedded MPC that installs into the Mintek Cynoprobe analyser, adding MPC functionality without the need for additional PC and network infrastructure, reducing installation costs. This allows plants to minimise their expenditure on additional infrastructure while gaining the benefits of superior MPC technology for multiple cyanide dosage points. This paper will present results showing that the embedded MPC performs as well as Mintek’s existing PC-based StarCS MPC controller, making it a viable alternative for infrastructure-constrained gold plants. Keywords: Model predictive control, gold cyanidation

Jan 1, 2020

By N. Naudé, J. de Villiers, C. P. Bergmann

It is proposed that the physical and chemical composition of individual mineral particles are an appropriate basis from which to model the performance of minerals processing equipment. Such a particle-based approach enables modelling across disciplines such as comminution, classification and flotation and automatically generates the traditional mass and chemistry balances. The general approach followed is to generate full grade-recovery curves for a separator by sampling product and tailings at varying mass yields. AutoSEM characterisation of each sample provides particle data that is used to reconcile mineral deportment and liberation to the measured size and assay data. In this way, partition surfaces can be generated and modelled. In this paper, an empirical model for the performance of a gravity spiral concentrator treating fine chromite ore is proposed. The model is based on the combination of two partition surfaces and is able to accommodate both forward and reverse gravity concentration effects per size fraction. The results show that this approach is well suited to represent spiral performance for the cases evaluated. The effect of two feed particle size distributions on the performance of the Multotec SC21 and SC25 spirals is demonstrated. Simulations are run in an Excel environment, linked to Python-enabled routines for enhanced data management and calculation speed. Keywords: Spiral concentrator, gravity separation, particle-based modelling

Jan 1, 2020

By F. Wang, P. Rosario, A. Vazirizadeh

Advances in data science, machine learning, and artificial intelligence are transforming mining and mineral processing, and making them more algorithm intensive. The paradigm is shifting from one of detection and control to one of prediction and optimisation. To build up reliable prediction and optimisation models these steps should be passed: collect, aggregate, cleanse, and process large amounts of complex, structured, and unstructured data as a first stage. Next, the right tools must be used to extract meaningful features from the operational data for the build-up of a prediction model. A grinding circuit is one of the challenging areas in terms of determining its overall efficiency and the prediction of product quality. In a processing plant, some of the KPIs are typically unmeasurable or infrequently measured for real-time application, e.g. ore grindability and grind size. However, these variables are required to calculate the grinding circuit efficiency and to model the operation performance. In this paper, advanced analytic tools have been used to extract one selected KPI (cyclone overall P80 or final grind size) and its relationship with measured variables for an industrial dataset. The results illustrate that the performance of the grinding circuit can be predicted for real-time monitoring by means of measured data. Keywords: Grinding circuit, data-driven, machine learning, process modelling, hybrid modelling

Jan 1, 2020

By G. Asbjörnsson, E. Hulthén, P. Papadopoulou, C. M. Evertsson

Crushing plant managers need to assess, manage, and communicate the environmental impact induced by their crushing plant operations as a step towards the sustainable development of the company. However, performing reliable environmental impact calculations of crushing plants can be challenging when the availability of operational and emission data is low, and when the company’s environmental expertise is not adapted and connected to plant operations (or does not currently exist). To address these challenges, this paper proposes a practical approach where the lifecycle assessment methodology (LCA) is combined with process simulations in a web-based tool. To describe what functionality such a tool should have to be incorporated into a company’s workflow, the system that surrounds a crushing plant is analysed. Further, a case study with a three-stage crushing plant is introduced to showcase how the simulated power draw results produced with the beta version of the tool can be compared to measured sensor data from the crushing plant for one operational day. The results indicate that the simulation tool can provide an adequate estimation of the specific energy for the different material fractions and give an overview of the environmental impact of the process caused by electricity and explosives consumption. Plant managers and operators can use this tool to calculate the environmental impacts of their plant and simulate how different operational strategies influence them. Keywords: LCA, environmental impact, coarse comminution, crushing plant

Jan 1, 2020

By S. Adams, J. R. C. von Holdt, J. L. Broadhurst

Fugitive dust from mineral extraction and processing activities can significantly influence air quality, the environment, and the health and quality of life of workers and surrounding communities. Poor engagement and communication between experts and lay persons, disciplinary silos and polarised viewpoints have made it difficult to develop a holistic understanding of the complex health issues associated with environmental emissions of mine dusts, and consequently to design meaningful and integrated approaches to address such issues. The collaborative Global Challenges Research Fund (GCRF) Mine Dust and Health Network, hosted by Minerals to Metals at the University of Cape Town, serves as a collaborative think-tank to inform research directions within and across disciplines, government policy and regulations, health monitoring programmes at public clinics, industry best practice, community healthcare and impact prevention programmes. It focuses initially on the southern African region, but with the intention to expand to mining-intensive developing countries within the rest of Africa, South America and Asia. This paper outlines the approach and activities of this global transdisciplinary and intersectoral network and presents some of the key outputs and achievements to date. Keywords: Mine dust, Global Challenges Research Fund Mine Dust and Health Network

Jan 1, 2020