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By V Matousek, A Sellgren, R Visintainer, G McCall

A 4-component model for settling slurry pipe flow has been previously described by Wilson et al (2006) and Sellgren et al (2014) to predict pipeline friction loss (hydraulic pressure gradient) over a range of slurry compositions: from fine to coarse particle size, narrow to broad particle size distribution, and low to high solids concentration. The method applies a weighted average of established pipeline models for various settling slurry flow regimes, according to the volume fraction of solids falling within the applicable size range for each model. Further development of the model was undertaken by Visintainer et al (2017a, 2017b), based on a comprehensive set of laboratory tests in 203 mm (8 inch) and 103 mm (4 inch) pipelines, and it was also adapted to the modelling of slurry pump performance derates. However, this work was all performed with solids having a specific gravity near 2.65, as is typical for many mineral processing and dredging applications. The goal of the present work is to test the applicability of these models for settling slurries having a higher solids specific gravity, as may often be seen in the mineral processing of iron ore deposits. To that end, a test program was carried out in a 103 mm (4 inch) pipe loop using various compositions of an iron ore product having a solids specific gravity of 4.75 and containing both coarse and fine solids. By screening and flushing operations, a range of particle size distributions were created having different proportions of the coarse and fine elements. In all, 19 tests were performed with d50 particle sizes ranging from 50 μm to 3.2 mm and delivered solids concentrations from 10 per cent to 43 per cent by volume. Particle size distributions varied from very narrow to very broad, with d85/d50 ratios ranging from 1.75 to 26. Pipeline pressure gradient and pump performance data were collected and used to test the applicability of the previously developed 4- component models and to propose improvements to the models for the handling of high-density solids.

Nov 8, 2021

By T Wang, D Wang

Photogrammetry-based techniques supported by traditional mapping tools are more frequently used for open pit geotechnical mapping. It fundamentally involves the collection of geological, structural and rock mass data from pit batters when safe to do so. These are essential components for refining and implementing continuous improvements to geotechnical models. The geotechnical model is used to review bench performance, optimise geotechnical design parameters, interpretation and analysis of pit slope stability. This paper is a case study of implementing different pit mapping methods at Mount Carlton gold mine, North Queensland. It critically reviews the tools required, including the Brunton compass, mobile application Clino, geotechnical data mapping software CloudCompare (2015) and Sirovision software. The review compares the image precision, structural measurements and data analysis. The review also underlines mine site management philosophy for safe geotechnical data collection to identify in-pit geotechnical hazards and the mitigation of potential slope instability. Sirovision is a remote sensing system for site geology mapping and interpreting geotechnical characteristics of exposed mining faces. The system utilises off-the-shelf digital SLR cameras to capture stereo photographs of the rock face and uses the latest image processing technology to generate accurate 3D models. These 3D models are digitally analysed using computational geometry to extract unbiased and accurate geological and geotechnical data. CloudCompare processes high accuracy 3D dense cloud rock surfaces reconstructed with photogrammetry software to extract the geological planes using the FACET or Compass plugins. The major discontinuity sets can be observed and the orientation (dip/dip direction) obtained from the algorithm is accurate and reliable for geotechnical assessment, including kinematic and rock slope stability analysis.

Nov 29, 2022

By E J. Hill, F Huang, A Otto

Geochemical data from drill core samples is used in mineral exploration to enable geologists to understand subsurface geology. However, it is challenging to analyse these data sets rigorously and consistently. Manual data interpretation relies on the knowledge and experience of logging geologists. When multiple geologists are involved, it is possible to introduce inconsistencies in logged lithology type and scale of logging (‘splitters’ versus ‘lumpers’). Machine learning (ML) is a powerful tool for analysing high dimensional data (ie data with many variables) and large data sets (ie data with many samples). However, ML methods applied to drill hole samples do not incorporate spatial information and this can result in high misclassification rates and small-scale ‘noisy’ results. We demonstrate the use of multiscale spatial analysis (wavelet tessellation) as a pre-ML step to analyse spatial information. This step helps reduce misclassification and filter out unwanted small-scale variation in the data. In an experiment, we used wavelet tessellation and supervised ML to predict litho-geochemical rock types from geochemistry data. The data set is from the Valhalla uranium deposit near Mount Isa, northern Queensland. First, geochemical knowledge and statistical analysis was used to select appropriate geochemical elements for litho-geochemical classification. Second, the elements were processed using multiscale spatial analysis. Third, we trained a Random Forest model with the data set and found that the model performs worse in predicting rare rock samples than common ones. We applied over-sampling and down-sampling methods in aim to overcome this issue. As a conclusion, the combination of multiscale spatial analysis (to define rock unit boundaries) and Random Forests (to classify rock units) is a good method for rapid and accurate analysis of drill hole geochemistry.

Mar 22, 2022

By R Yong, A McEwan, D G. Miljak, D Milinkovic, A Curtain, P J. Coghill, R Stefulj, B Lovric, G Roberts

The CSIRO has pioneered the development and use of certain classes of magnetic resonance (MR) for mining applications. The MR technique is a radio frequency-based spectroscopy that provides highly quantitative measurement of selected ore minerals. MR has many inherent advantages for bulk sensing in mining applications, including deep sensing penetration into most bulk ores with the use of non-contact sensors that are robust and tolerant of demanding mine conditions. The highly discriminating nature of the technique also provides the very desirable attribute of robust and simple calibration, that can often be performed prior to sensor installation. Rapid sensing (within seconds) at very low grade (few hundred ppm Cu) has been demonstrated, as well as sensing at high tonnage rate. Besides sorting applications, MR may also be applied to preconcentration applications involving very large volume sensing including: (i) haul truck sensing using portal arrangements, where truck destination assignment may be dynamically determined based on the truck grade, (ii) in-pit gantries used for loading trucks that may similarly be equipped with sensors for truck assignment and (iii) deep mine bench sensing, recently progressed through a CRC ORE project, where local bench grade is determined at the few-metre lateral scale and at depth prior to blasting, to aid selective blasting and ore extraction. MR sensor prototypes have been developed for each of these applications and have been tested in laboratory or limited field conditions. To inform the value proposition for truck or bench sensing, initial analysis of truck assignment improvement based on an analysis of in-situ mine bench grade has been performed for a porphyry style deposit. The prototype sensors, preliminary results and future technology development and objectives will be described.

Nov 10, 2020

By M Badawi, Y Foucaud, C Veloso, J Laine, A C. Araujo

Iron is one of the most exchanged commodities worldwide. Its use for steelmaking explains the growing demand in the metal which remains mainly produced by the mining and the beneficiation of iron ores. Flotation is the most used mineral separation technique worldwide, processing several billion tonnes (Bt) of ore annually, including, in some cases, hematite and magnetite ores. Based on the physicalchemical properties of mineral surfaces, flotation aims to selectively adsorb molecules that render mineral surfaces either hydrophobic or hydrophilic, known as collectors and depressants, respectively. Hydrophobic particles are then recovered in the froth by the injection of gas bubbles travelling up in the cell and attaching the hydrophobic particles. However, despite the popularity and the good performances of flotation, mining industry faces new challenges of decreasing grades and increased mineralogical complexness in ores. Many flotation variables, especially when related to reagent adsorption, remain poorly understood and it becomes necessary to gain knowledge to enhance the existing processes. Molecular modelling techniques like the density functional theory or molecular dynamics simulations (ie Classical molecular dynamics or Ab initio Molecular Dynamics) currently represent powerful tools for different applications in material sciences, like catalysis or depollution purposes. They have recently been successfully applied in the flotation area, with an increasing number of publications dealing with this topic every year. These tools provide crucial information on bulk and surface properties as well as on the adsorption mechanisms of reagents on mineral surfaces, key step in the flotation process, and synergetic effect of collector mixtures. A better understanding of the adsorption mechanisms of flotation reagents, provided by molecular simulations, will help to finely choose flotation reagents, and even lead to the development of new reagents formulations. Overall, these tools enable the discovery of practical and industrial solutions to improve the flotation process, which will undoubtedly help the mining industry to face its future challenges.

Nov 8, 2021

By N J. Potter, N W. Brand, J M. F Clout

The increasing demand for iron ore with low concentrations of trace elements, puts increasing pressure on iron ore producers for high-quality material. Understanding the mineralogy, petrography, chemistry, and mineral association of iron ore deposits is becoming increasingly important to provide iron ore products that meet the ever-increasing standards of steel mills. Limited attention has been paid to documenting new orebodies beyond bulk RC drill chip assays and product specification data. Therefore, limited information is known about the spatial distribution of minor and trace elements at the hand specimen scale, and elemental deportment within different ore and gangue minerals. In this study, samples from high-grade microplaty hematite, supergene-enriched martite-goethite and hematite iron ores are examined for their elemental and mineral associations. These samples contain distinctive minor and trace element distributions, with the mapping of deleterious elements (eg Zn, As, Cu) enabling the deportment and abundance within the ore and gangue minerals to be assessed. Results from this study show a diverse range of minor and trace mineralogy that are present in typical iron ore samples, in abundances below the detection limit of routine XRD scans, that host the deleterious elements, including but not limited to, sphalerite (Zn and S), chalcopyrite (Cu and S), xenotime (P and Y), apatite (P and Ca), cryptomelane (K and Mn), barite (Ba and S), alkaline feldspar (K, Al and Si) zircon (Zr and Si) and chromite (Cr and Al). The presence of these minerals and associated deleterious elements, even at low concentrations, has implications for producers, with the ongoing restrictions and tightening of environmental constraints in shipped iron ore products. By understanding the distribution and location of these elements within the iron ore, steps can be considered to maintain increasingly higher quality iron ore supply around the world.

Nov 8, 2021

By P Velletri, N Gaylard

The management of wear surfaces in mining operations has remained largely unchanged since the inception of mining. Sacrificial wear liners are installed on the inside of chutes, hoppers and other equipment used to transfer bulk ore to protect the asset from abrasive wear caused by the flow of material. These liners are generally replaced at fixed intervals which may be determined based on historical wear rates or may be driven by other maintenance requirements for that asset. This approach does not always yield satisfactory results, especially for critical assets where the impact of downtime on production can be substantial. For such assets, a condition-based maintenance strategy has the potential to significantly increase productivity and reduce the total cost of ownership associated with the asset. However, for such a strategy to be successful, timely information on the actual condition (remaining thickness) of the wear liners is essential. This paper describes the development and implementation of a wear monitoring system that can be used to remotely and continuously report on remaining liner material thickness in real time. It details some of the challenges that were overcome when developing the system, including the design of a patented thickness measuring system that can be used in a variety of liner materials and is tolerant to the presence of ore on the wear surface. The paper also explains how a cloud-based approach enables data from the wear sensors to be integrated into external systems such as a data historian, which has been demonstrated through a collaborative project with one of the largest iron ore producers in the Pilbara. This ability to combine real time liner thickness information with other data sources, such as throughput, provides the foundation for the transition to a condition-based maintenance strategy.

Nov 8, 2021

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 E Shilov, R Dixon, L Tovey

BHP Western Australian Iron Ore (WAIO) operations use a suite of slope monitoring systems which are deployed to operational areas based on the ground control risk. Slope Radars are one system used within WAIO, having recently deployed and integrated a network of 22 radar systems across the operations. The integration required the development of supporting network architecture, infrastructure, virtualised hosting, security protocols and a technology support model. This has enabled data transfer between the physical radar systems located in open pits, to on-site offices, a vendor supported remote control room in Indonesia and the WAIO Integrated Remote Operations Centre for data analysis and monitoring of system and displacement alarms 24/7. The typical technical skillset of a Geotechnical Engineer does not include the knowledge and capability to understand and develop the supporting IT infrastructure required for establishing an integrated radar monitoring network. This paper will provide a discussion of the technology considerations for integrating radar systems and highlight the importance of the developed partnerships between internal geotechnical and technology teams and the external vendor team to ensure a reliable, robust and scalable slope monitoring solution.

Nov 29, 2022

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 M W. Chen, I Canbulat, S Saydam, C G. Zhang

The height of fracturing (HoF) measures the propagation distance of continuous fracturing from the mining horizon and is also termed the height of cracking (Hebblewhite, 2020; Khanal et al, 2019) or hydrologically the height of groundwater drainage (Tammetta, 2013). In longwall practice, HoF is an important predictive indicator for assessing the dewatering effect of panel extraction on the shallow aquifer and surface flows. The HoF determination is basically dependent on field measurement works, typically using surface extensometer, borehole televiewer imaging, piezometer monitoring, packer testing, borehole breakout logging, and isotopic tracer monitoring (Brown and Walsh, 2022; Corbett, 2022; Hebblewhite, 2020; Holla and Armstrong, 1986; Mills and O’Grady, 1998; Walsh et al, 2022a, 2022b). These methods or tools have been widely applied to observe rock fracturing and groundwater responses to longwalls at Australian coalmines, for example, Dendrobium Mine (Brown and Walsh, 2022; Walsh et al, 2022b), Appin Mine (Walsh et al, 2022a), and Springvale Mine (Corbett, 2022). In Chinese mines, borehole televiewer imaging and packer testing are more frequently used due to convenient instrumentation, quick testing, and not requiring long-term maintenance of the instrumentation boreholes. The HoF determination also involves further raw data interpretations, for which specialist software for data extraction and quantitative criteria for bounding the fracture transition and termination are necessary. By combining the measured fracture distribution and longwall parameters regarding panel geometry and geological settings, several HoF models have been developed for conceptualising the understanding of strata depressurisation and providing mine engineers fast calculation tools for HoF prediction. The Mackie model considered the height of fracturing as the upper bound of fractured zone and revealed that within the fractured zone there are highly connected fractures and accordingly porosity enhancement and groundwater depressurisation, where the fracture network can be less transmissive from the mining horizon up (Hebblewhite, 2020). The fractured zone shares similar fracturing regime but different heights in varying models: HoF is 30 to 58 times of the mining height (t) based on Australian, Soviet and UK experience (Kendorski, 1993, 2006), and 21 to 33 t according to the field measurements in NSW Central Coast, Australia (Galvin, 2016). Some Australian longwall mines adopt Ditton model (Ditton and Merrick, 2014) (Equations 1.1 and 1.2 in Table 1) sourcing from NSW coalfield experience for HoF prediction, while Liu model (Liu, 1981) (Equations 2.1 to 2.4 in Table 1) developed from 27 coalmines in China is frequently used by Chinese coalmines.

Nov 29, 2022

By T Takayama, M Matsumura, Y Konno

The steel industry accounts for 14 per cent of domestic CO2 emission in Japan, and iron ore sintering process occupies approximately three per cent. While Fe content in iron ore is gradually decreasing. So, the amount of concentrated iron ore will increase in the future. We have focused on magnetite concentrate as sinter materials, because magnetite can be easily magnetically beneficiated. However, it is experienced that blending magnetite ore resulted in increasing Fe2+ component in the iron ore sinter, which results in lower reducibility. Therefore, promoting the oxidation reaction from magnetite to hematite in the sintering process is countermeasure for reducibility. In addition, this promotion causes increase of oxidation heat, which results in high sinter strength and reduction of coke. It is assumed that the oxidation of magnetite tends to proceed in the cooling stage, where the oxygen partial pressure increases after the combustion of fine coke, but there are few studies on the effect of heat profile, especially cooling path. The purpose of this study is to design the effective sintering heat profile to promote oxidation of magnetite ore, especially, to investigate the effect of cooling path on sintered ore microstructure. In this study, magnetite ore and magnetite reagent were respectively mixed with calcium carbonate reagent. Then the mixed samples were pressed and formed into cylindrical tablets for simulating the adhesive layer of a pseudo particle. Through examining influence of maximum temperature and cooking rate on oxidation reaction of magnetite, we confirmed that the oxidation reaction of magnetite is suppressed at above 1350°C for maximum temperature, compared to 1300°C. Sintering at lower temperature is desirable because hematite is stable below 1350°C in the Fe2O3-CaO phase diagram.

Nov 8, 2021

By I Canbulat, J Emery, L Yao and C Zhang

This paper documents a process of data analytics and machine learning applied to coalmine roof convergence data. It follows a publication documenting the formation of the database. For each set of analyses Python scripts were utilised to extract data from an SQL database, with subsequent analyses also executed in Python, utilising open-source tools such as scikit-learn. Preliminary analyses with classic machine learning algorithms included multiple logistic regression, K-nearest neighbours, decision tree, random forest, support vector machine, kernel support vector machine, artificial neural network (ANN), naïve Bayes and multiple linear regression. Results indicated that relationships were complex and non-linear, with the artificial neural network most suitable. An ANN model was then refined through a standard process of hyperparameter optimisation and data augmentation to arrive at a final model. Due to the black box nature of the ANN further insight into how the parameters interacted was sought and found with Shapley additive explanations (SHAP values). SHAP values utilise Game Theory, where each input parameter or feature is a ‘player’ while the data set is the ‘team’. The SHAP value is the impact of each player on the target value, essentially establishing the contribution of each input parameter. The results from all analyses are delivered in the paper, together with insights, applications, strengths, and limitations of the methodology.

Nov 29, 2022

By S Mitra, D O’Dea, N Barrett, T Honeyands, P Zulli

The steel industry continues to explore and demonstrate technologies that decrease its greenhouse gas emissions, especially in blast furnace ironmaking where alternate operating conditions will be required. As the furnace has been optimised over many years, it is approaching the minimum fuel rate possible with coke and Pulverised Coal Injection (PCI). Alternative operating conditions are therefore required to further decrease greenhouse gas emissions. Because of this, hydrogen has been proposed as a reducing agent in the blast furnace. Injection of hydrogen through the tuyeres was assessed using a comprehensive 2D blast furnace global model. The model was calibrated to a blast furnace operating with PCI as a base case and various approaches to hydrogen injection assessed, with a particular focus on displacing PCI. The maximum hydrogen injection rate of 19.5 kg H2 per tonne of hot metal was predicted, limited by decreases in the raceway adiabatic flame temperature and top gas temperature. Internal blast furnace conditions, such as gas composition and reduction degree were also predicted. Holistic analysis was performed to estimate the effectiveness of hydrogen injection on reduction of greenhouse gas emissions. Based on these results, the changes in blast furnace operation and emissions under hydrogen injection are discussed.

Nov 8, 2021

By J Plinke

The imperative to operate sustainably requires all equipment and processes to operate at their maximum possible efficiency. Achieving this ideal state requires a thorough understanding, not only of the process itself, but also of the tools available to design and assess potential improvements. This paper presents a case where the widely employed Discrete Element Method (DEM) toolset proved insufficient to model problematic material flow during the process of optimising wet screening performance. A Smoothed Particle Hydrodynamics (SPH) model was found to more accurately replicate the flow characteristics of the physical system. Site observations identified biased loading of the screen decks to cause inefficiencies in the wet screening of an iron ore processing circuit. Two numerical modelling approaches were pursued with the aim to select the most suitable one and continue the optimisation process based on that selected model. Selection criteria were established, based on the minimum requirements of being able to replicate the problem, including the main flow dynamics deemed to cause the issue. The DEM model was found to over-simplify the slurry flow dynamics within the scrubber and no laterally biased flow was introduced by the scrubber’s rotation to the otherwise symmetric system. Since DEM was not able to accurately replicate the existing problem, it was deemed an unsuitable method to assess the effect of any changes to the system. The second approach consisted of a SPH model. This model was deemed to map the flow within the scrubber with sufficient accuracy to address the problem, as it produced biased screen loading comparable to that reported from site. Changes to the wet chute, delivering material from the scrubber to the screen, aimed to neutralise the non-centrality introduced via the scrubber, were then successfully designed and assessed using the SPH model.

Nov 8, 2021

By P Revell, A Jenkin

This paper presents an analysis of significant Australian-based R&D programs focussed on the minerals industry, which was commissioned by CRC ORE. The landscape analysis may assist organisations with R&D management issues including program creation, program/partner selection, portfolio management, and public policy settings. The dataset was collated early in 2020, and contains programs being conducted by or with Australian-based ‘Research Service Providers’ (RSPs), most notably Universities and CSIRO. It does not include R&D being conducted in-house by mining and METS companies, or by them with other organisations that are not RSP’s. The dataset contains 65 R&D programs, with total annual cash funding of $229m (70% from industry and 30% from government). The total funding compares unfavourably with the combined R&D expenditure of Australian universities and CSIRO of over $12b, and minerals industry export revenues of over $200b. The R&D landscape is analysed by program numbers and funding, technical domain, geography, and funding source. Areas of relative strength are noted in ‘mineral processing’, CSIRO Minerals, and the CRC program. Areas of relative weakness are noted in ‘bulk materials handling and logistics’, ‘tailings and waste management’, and ‘social and community’. The field of preconcentration is noted as a current area of R&D strength, but it is under significant threat with the impending completion of CRC ORE. The overall R&D landscape is characterised by a majority of small-scale programs, low levels of industry coordination, a low focus on step-change innovation, and total funding lower than should be expected for such a large critical industry. It is hoped that key stakeholders in the Australian minerals industry, particularly governments, and mining and METS companies, reflect on the analysis and act to address holistic weaknesses.

Nov 10, 2020

By S Hapugoda, A Edenton, L Lu

The blast furnace cohesive zone has a profound impact on hot metal productivity and quality, fuel consumption, operational stability and the lining life of the blast furnace. The high temperature properties of ferrous burden materials determine the configuration and location of the cohesive zone and have therefore attracted particular attention. While numerous standard testing methods are in use to assess the quality of ferrous burden materials for the blast furnace, these simple tests are generally conducted under fixed conditions, which often represent the extreme situations encountered by the burden materials in the blast furnace. Furthermore, none of these simple tests provides information on the high temperature properties of the burden materials, such as their softening and melting properties. CSIRO is equipped with a fully computerised testing facility to characterise the high temperature properties of ferrous burden materials under realistic conditions similar to those encountered in the blast furnace. This paper will first review the CSIRO softening and melting test method and then discuss the softening and melting properties of a variety of blast furnace burden materials, including sinter, pellets and lump ore, and their implications for blast furnace operation.

Nov 8, 2021

By C Garrido, J Barrera, H Alvarez, L Perez, S Ramirez

In recent years, there has had a greater recurrence of studies and projects that consider tailings filtration followed by dry stacking to reduce the consumption of fresh water, the space required for disposal, and, eventually, to reduce the costs of the project. In addition, this technique could be required to meet new environmental restrictions to extend the life-of-mine of existing plants or to enable the development of new projects, especially in areas of high social interest. This article is intended to describe the most relevant technical aspects of the design and implementation of an iron ore tailings filtering system and its final overland disposal as well as physical and metallurgical surveys, and laboratory and pilot testing. Furthermore, it is presented how these activities are inserted in the project development to rationalise the resources. The case under study considers an iron mineral wet magnetic concentration processing plant. The outcome tailings are dewatered by means of thickeners, where the overflow water is recycled back to the plant. The underflow slurry is pumped into intermediate storage tanks before proceeding with further dewatering and final disposal. The project consists of a tailings filtration plant, belt conveying systems, a temporary stockpiling system, trucking loading and transportation system, moisture-conditioning facilities (spreading and airing), and final disposal facilities (compacting). Due to the stickiness of the filtered tailings that governs the belt conveying system design, the operating moisture range must be established. Besides, proper technical aspects such as planning, executing, and analysing both the laboratory and the pilot testing data to validate process design parameters; selecting filters; defining spare filtering capacity; handling the out-of-range filtrated moisture; sizing the belt conveying system; configuring the equipment involved, must be considered.

Nov 8, 2021

By B Banerjee, N Kumar, S Parihar

Rampura Agucha Pb-Zn Mine (RAM) is located about 200 km to the south-west of Jaipur in the state of Rajasthan, India. Open pit mining operations commenced in 1988 and continued to 2018. RAM open pit footprint is the largest in India with length of 2000 m, width 1500 m and depth of 400 m. Development of an underground mine under the operating pit commenced in 2013, through trial mining longhole open stoping with cemented backfill was established as a preferred mining method. Nine years in operations underground mine has reached the production capacity of 4.5 MT, which represent more than 70 per cent of the ore volume mined by the pit. RAM is currently the world second largest underground Pb-Zn mine.

Nov 29, 2022

By M Lak, R Amiri, A Haratian, M Asghari, S Haji

Dry magnet separators are used to upgrade and beneficiate a wide variety of minerals such as hematite and magnetite materials. In recent years advances in dry magnetic separators have improved their performance. Fine particle separation is one of the main areas which still remain weak. This paper will present a new design of fine particle drum separators which used for particle size less than 3 mm. A big problem in processing of iron ore is the grade and recovery of mineral values in the fines fraction. The new design high efficiency drum separator is developed to handle fine feeds. In this drum separators both of the drum shell and magnetic yoke rotate in opposite direction. the results showed that when the material feed size was = 1298 μm, drum shell speed was 42.5 rev/min, magnetic yoke speed was 100 rev/min and the maximum intensity of magnetic field was at = 30° the feed grade increased from 50.87 to 65.67 in concentrate box, increase to 60.45 in middle box and tail grade was 24.44. The total achieved recovery for both concentrate and middle box was 81.89 per cent.

Nov 8, 2021