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Production targets are the basis for almost every decision made by operations personnel in the daily operation of a concentrator. It is the production targets that determine how the plant is operated and therefore determine its efficiency. However, to achieve maximum plant efficiency production targets must be relevant to the process and achievable.In RoseberyÆs case, for many years, production targets were taken from budget data which, with the exception of mill feed tonnes and grades were carried forward from the previous budget. The zinc concentrate grade target was fixed at 57 per cent Zn, a long-term average; it was the accepted æstandardÆ for Rosebery. Historical data showed this target was never consistently achieved and plant efficiency was highly variable.Investigations into the possible causes for the variable performance ruled out plant and mechanical issues. Despite the Rosebery concentratorÆs age the plant and equipment are more than serviceable and reliable. The mineralogy of the ore and specifically the iron content of the sphalerite was found to be the most significant determinant of zinc concentrate grade. It was found with increasing frequency that the zinc grade of the sphalerite in the ore was below the zinc concentrate grade target. Consequently on these occasions attempts to meet the required concentrate target destabilised the process resulting in plant inefficiencies.Using the correlation between iron and zinc, data modelling was able to accurately predict the maximum zinc concentrate grade achievable for any ore type. By using on stream analysis instrumentation the operations personnel are now provided with constantly updated concentrate grade targets. By knowing the mineralogical limits of the ore at the time it is being treated process stability is maintained allowing plant efficiency to be optimised. This has resulted in a step change in plant performance and a significant increase in revenue.CITATION:Harris, G R, 2012. Developing and implementing ore-specific targets for the Rosebery Concentrator, in Proceedings 11th AusIMM Mill OperatorsÆ Conference, pp 321-324 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Oct 29, 2012

By D Connelly

There is now a new generation of mobile crushing and screening plant systems available which have been developed based on the motivation of reducing truck haulage. Newly designed mobile crushing and screening plant systems have the advantages of mobility, flexibility, economy and reliable performance, making this system very appealing for small- to medium-sized projects or projects where a number of resources are separated by distance.A brief review of the past history and experience with transportable crushing plants provides an insight into some of the issues with transportable plants in mineral processing. A closer look at a simple model based on exploiting small but high-grade orebodies suggests the concept has merit, however there are many more engineering, logistics, legal, technical and management aspects which have limited the application of such a concept in the resource sector. This paper explores the concept in detail and endeavours to analyse the preferred commodities and scenarios under which the concept could be used and the economic and logistical limitations for iron ore. The paper also looks at the risk assessment criteria to be applied in selecting the type of resource projects where the model could be applied successfully. Other aspects considered in this paper are the project planning requirements, technical and economic feasibility, bankability and operability. Reference is made to the operating experience of contractors at a number of sites and feedback from the resource companies as managers of the projects.CITATION:Connelly, D, 2013. Mobile crushing and screening plant applications for small- to medium-sized iron ore projects, in Proceedings Iron Ore 20 13, pp 349-354 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 12, 2013

By N Steenkamp, L Furstenberg, W Cele, M Richter

Managing productivity and improving efficiency for a healthy return on capital employed have become key mining industry strategies under prevailing tough global market conditions. However, achieving this in a multisystem environment such as a mining operation can be daunting. The field of discrete event simulation offers some relief, giving engineering teams insight into multivariable systems and stochastic processes. It allows for integrated system thinking and domain expertise to be woven into project thinking fibre. Directed system optimisation and therefore improved efficiency becomes possible. Furthermore, capital discipline accrues from optimal upgrade investments. This paper illustrates the practical application of this approach by reviewing a typical case study for a direct shipping iron ore operation. Dynamic simulation was used to establish maximum processing plant capacity under varying material characteristics and identify capital-efficient upgrades for increasing production.The study started with descriptive statistical analysis of existing data. Discrete equipment and system capacities were defined, followed by static plant mass balance calculations to establish bottlenecks and overall static capacity. Equipment ‘uptime’ and ‘downtime’ analyses established system and overall plant availability and utilisation. From this information, a dynamic simulation model could then be developed and initially calibrated against actual plant performance to verify its robustness. Once confirmed to accurately represent existing plant dynamics, system capacity response to variable changes could be tested to ultimately determine maximum plant capacity. Additionally, plant optimisation was incorporated to arrive at optimal upgrade points. Maximum capacity and necessary upgrades were established as a function primarily of feed size distribution. Additionally, the interfacing effects of rehandling feed material to the final product grade and potentially to life-of-mine were established as important outcomes. The results of the study illustrate the value of using dynamic simulation models to realistically and cost-effectively increase throughput.CITATION:Cele, W, Furstenberg, L, Richter, M and Steenkamp, N, 2015. Capacity evaluation of iron ore processing facilities utilising dynamic simulations, in Proceedings Iron Ore 2015, pp 597–604 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By M A. González, J A. Romero, D L. Fuentes, C J. Poblete, O Abdrashitova

"Robustness can be defined as the ability of a system to resist change without adapting its initial stable configuration. Robust design methodology, which is widely accepted in the manufacturing industry, allows the establishment of robustness and reliability metrics in scenario analysis.In the mining industry, a mine can be considered as a system with design parameters such as processing plant and mining capacities. Traditionally, a limited number of scenario iterations are performed in an attempt to determine optimal capacity values. An important strategic decision such as process plant capacity in particular is made during the very early stages of a mining study, often based on a low level of information. Lack of time and tools to run and analyse a larger number of possible scenarios for different design parameter combinations are usually the main reasons for not performing a full analysis. In addition, the uncertainty of other mining study input parameters creates more risk in decision-making.With advances in technology, it is now possible to automate the process of generating several thousands of scenarios in an affordable and timely manner. This allows performing the results analysis and assessing robustness and reliability criteria within a reasonable time frame. It is possible to generate a surface of results based on the net present value (NPV) for each scenario. This surface will reflect the efficiency level of the capture of the economic rent for each production scale. In addition, each scenario can be tested under diverse conditions of volatility of other external variables such as prices, costs or recoveries for example.This paper aims to establish a methodology that applies robustness criteria to determine the best scenario on several surfaces of value, which is then assessed with real data from a mining project. Obtained results demonstrate the advantages of using the proposed methodology in dealing with the above-mentioned multidimensional problems. It allows better decision-making on key design parameters and better control of the risks of a strategic mine plan.Citation:Poblete, C J, González, M A, Romero, J A, Fuentes, D L and Abdrashitova, O, 2016. Use of Robust Design Methodology for Production Scale Definition in Open Pit Mining, in Proceedings Ninth AusIMM Open Pit Operators' Conference 2016, pp 284–291 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 15, 2016

By A Bax, G Wardell-Johnson, A Giblett, W Staunton

This paper presents a number of key considerations for process development testwork as a reliable basis for the inclusion of gravity separation in gold recovery flowsheets. Operating data, experiences and numerous case studies are also discussed in support of identifying best practice principles for gravity circuit design.CITATION:Giblett, A, Bax, A, Wardell-Johnson, G and Staunton, W, 2013. A review of best practice in gravity circuit design and operation, in Proceedings MetPlant 2013 , pp 272-284 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 15, 2013

By Y Shougu

Many mine accidents will affect the mine ventilation system when they occur. At present, there is a lack of monitoring means to capture the occurrence of accidents in time by monitoring the ventilation system, so as to take emergency measures to reduce the loss of personnel and property. Pressure wave is an elastic wave existing in air. Most accidents in mine will cause obvious changes of pressure wave. It is an innovative idea to monitor the occurrence of accidents in mine by measuring pressure wave. The development and functions of the pressure monitoring device and related software are introduced in detail. The pressure wave monitoring test is carried out in San Xavier underground mining laboratory, and the D-wave and S-wave waveform data are collected. The waveform characteristics are analysed, and the main frequency distribution of the pressure wave is found to be within 1 Hz by using FFT. At the same time, the influence of the propagation distance of the pressure wave on the waveform is analysed. The propagation velocity of D-wave and S-wave are roughly calculated, and they are 279.6 m/s and 397.3 m/s respectively. S-wave is slightly faster than D-wave. Although there are errors and uncertainties in the test, the test verified the feasibility of the existence and collection of pressure waves in the ventilation roadway, and provides a method for the future study of the propagation of pressure waves in the mine ventilation system.

Oct 12, 2022

Real-time data collection and plant historians are becoming common place in mining and processing plants. This vast quantity of data must be turned into easily accessible and actionable information for all levels of the organisation.Queensland Nickel use a combination of Microsoft and SAP business intelligence tools to collect, cleanse, aggregate and present plant information to the appropriate people for supporting decision making in the business. SAP Business Objects, for visualisation, and SAP MII, for integration, allows complex and accurate metrics to be delivered as real-time dashboards. Designing a rigid data management architecture to segregate the data collection, integration and logic and visualisation into distinct logical layers, allows for rapid report development and greatly assists with IT maintenance, while also minimising the complexity of any customised programming. This type of architecture ensures a common data source for all business reports, ensuring one version of the truth. The past few years at Queensland Nickel have seen process and maintenance engineers spending considerably less time collecting and integrating data and spending more time analysing what is happening in the plant. Real-time reports and dashboards have been developed around the business needs rather than around the data. Two years ago Queensland Nickel introduced an employee bonus scheme based upon meeting monthly performance targets; an easy to use dashboard that allows everyone, from operators through to management, to monitor these metrics day-to-day.

Sep 26, 2011

By J Lombardi

OZ Minerals' Prominent Hill copper-gold concentrator is located 130 kilometres south east of the town of Coober Pedy in the Gawler Craton of South Australia. The concentrator was built in 2008 and commenced commercial production in early 2009. The Prominent Hill concentrator is comprised of a conventional grinding and flotation processing plant with a 9.6Mtpa ore throughput capacity. The flotation circuit includes six rougher cells, an IsaMill for regrinding the rougher concentrate and a Jameson cell heading up the three stage conventional cell cleaner circuit. In total there are four level controllers in the rougher train and ten level controllers in the cleaning circuit for eighteen cells.Generic proportional – integral and derivative (PID) control used on the level controllers alone propagated any disturbances downstream in the circuit that were generated from the grinding circuit, hoppers, between cells and interconnected banks of cells, having a negative impact on plant performance. To better control such disturbances, FloatStar level stabiliser was selected for installation on the flotation circuit to account for the interaction between the cells. Multivariable control was also installed on the five concentrate hoppers to maintain consistent feed to the cells and to the IsaMill. An additional area identified for optimisation in the flotation circuit was the mass pull rate from the rougher cells. FloatStar flow optimiser was selected to be installed subsequent to the FloatStar level stabiliser. This allowed for a unified, consistent and optimal approach to running the rougher circuit. This paper describes the improvement in the stabilisation of the circuit achieved by the FloatStar level stabiliser by using the interaction matrix between cell level controllers and the results and benefits of implementing the FloatStar flow optimiser on the rougher train.

Aug 8, 2011

By S Nie, I Bergqvist, F Ouchterlony

In blasting, a few or many cracks are driven from the borehole into the rock. But what causes the cracks? The most common theory of breakage consists of two stages; first the shock wave causes radial cracks to form around the hole then the gases penetrate into the cracks, and widen them and make them longer. Another theory presented by Brinkmann suggests that the back damage is primarily controlled by shock and that the gas penetration is the mechanism controlling breakout of the burden. He did his experimental work using blasthole liners. Recent research at SveBeFo has examined this matter further. In a quarry a number of benching holes have been blasted simultaneously. In some of these holes tubular Swellex bolts were inflated and decoupled charges put inside the tubes without stemming. Other holes were identically charged but without the lining. Finally some holes were also stemmed. After blasting the cracks in the remaining rock were studied. There was no difference in crack lengths between holes charged normally (no stemming) and holes where the charges were inside the bolts. On the other hand when stemming was used, the crack lengths increased for some explosives but remained the same for an emulsion explosive. In another set up blasted granite blocks were charged in the same way as above. Then we could also measure the bore hole pressure. The pressure gauge consists of a small carbon resistor inside a steel cylinder. It is called LHM (Location-fixed Hydrodynamic Measuring Cup) and is placed at the bottom of the hole. A smaller exit hole from the bottom is drilled for the cables. The paper presents the technique and the results obtained from both the quarry blasting and the blasting of the blocks.

Jan 1, 2001

By B P. Banerjee. S Raval, P M. Ranasinghe

Simultaneous Localisation and Mapping (SLAM) is an important technology that enables autonomous robots to navigate independently and create detailed, real-time maps of their surroundings. This capability is crucial in underground mining settings, where it enhances operational efficiency and significantly strengthens safety in these subterranean environments. SLAM algorithms typically rely on identifying and using distinct geometric features, such as plane and edge features, to understand the spatial layout of the environment and to track how it evolves as the sensor moves through it. In underground roadways, the scarcity of noticeable edge features, coupled with the repetitive nature of walls and corridors, poses a significant challenge, making it difficult for these systems to distinguish one area from another. Furthermore, the absence of global navigation satellite system (GNSS) signals in underground adds more challenges, leading to the accumulation of errors in both the mapping and localisation.

Sep 1, 2024

By H S. Pescini

A material aim when feeding ore blends into a processing facility is to reduce the variability of the properties of the feed material and to maximise the output, or throughput. At the Mining Geology conference of 2019, we presented a paper on optimal blending of coking coal given geological variability. The paper introduced integer-programming techniques to both maximise value and minimise feed or product variability. We discussed an application where the geological uncertainty is characterised using a normal distribution and exact solutions are readily obtainable using second order conic constraints in the mathematical model. Geological uncertainty is not always normally distributed, many properties will be characterised as log normal or some other skewed distribution. For example, copper can be some form of lognormal and Fe in Iron ore has a left skewed distribution. In this paper, we discuss some distribution free techniques to optimise the blends of plant feed or output given a quantified geological uncertainty. We discuss the sample average approximation method and how to set-up and apply the method to an ore blending problem. Evaluating a case study demonstrates that the explicit use of non-normally distributed geological uncertainty can better optimise feed into an ore processing facility and potentially add significant value. The approach is equally applicable to all mining and processing operations that blend material into a processing facility or directly into a saleable product.

Mar 22, 2022

By Y Li, D J. Williams, E Topal

A mine waste rock dump does not have any intrinsic value, yet is an inevitable structure in surface mining operations. Since the purpose of mining is to maximise profit, possible expenditure reduction in hauling waste rock to the dump, which can represent up to 50 per cent of the total operating cost, is highly desirable. Apart from cost, potential acid mine drainage (AMD), due to seepage from surface waste rock dumps containing reactive waste rock, could threaten the ecological balance, drinking water supplies and irrigation systems downstream from a mine site. In order to minimise these potential risks, selective placement of benign and reactive waste rock, and the encapsulation of reactive waste rock by benign waste rock, need to be carried out during the construction of the rock dump.In this paper, a new methodology, based on mixed integer programming (MIP) is developed to manage waste rock dumping. The model aims to minimise waste rock haulage costs by locating the optimum destination(s) for each waste rock block, while at the same time controlling the placement of reactive waste rock to minimise the potential for AMD. Tests on small-scale virtual data demonstrated that the model is capable of dealing with selective placement of different waste rock types, encapsulating reactive, potentially acid forming (PAF) waste rock, and stockpiling benign waste rock for encapsulation and rehabilitation purposes. The solution time for the MIP model is far less than the traditional manual approach, and the resulting schedule is up to 46 per cent more cost effective.CITATION:Li, Y, Topal, E and Williams, D J, 2012. Mathematical approach for better mine waste rock dumping management, in Proceedings Life-of-Mine 2012 , pp 207-214 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 10, 2012

By P Brown, M Nagarajan, A Green, P Dare-Bryan

Coal loss is an important issue at many coalmines, with losses of five per cent being common. The type of loss varies from mine to mine but is dependent on mine strata, the drilling and blasting processes applied and the mining method used. Typical coal loss mechanisms from the drill and blast process are coal roof damage and edge loss. Usually a reduction in coal loss is in direct competition with the need to provide material that is well-fragmented and a muck pile that has the required cast percentage. These competing issues were addressed over a two-year period at a mine in the New South Wales Coalfields by implementing a five-step approach. This was centred around building an understanding of the coal loss mechanisms utilising proprietary heave- modelling software, measuring coal losses by comparing survey to drill and/or gamma logs and implementing appropriate coal loss reduction processes. The end result exceeded expectations with a 95 per cent reduction in coal loss. Mine excavation productivity was maintained and coal loss minimised to less than one per cent of the in situ coal block.CITATION:Nagarajan, M, Green, A, Brown, P and Dare-Bryan, P, 2015. Managing coal loss using blast models and field measurement, in Proceedings 11th International Symposium on Rock Fragmentation by Blasting, pp 429–436 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 24, 2015

By i, A Shirbazo, S Saydam, S Ata, H L. Ram, B Li

The integration of internet technology into industrial sectors has significantly accelerated the adoption of Internet of Things (IoT) devices, transforming communication, data aggregation, and information distribution. The mining industry has embraced this evolution by deploying IoT devices and advanced technologies like autonomous robots and sensors to enhance operational safety. These sensors enable real-time data transmission and continuous monitoring of key parameters, providing decision-makers with timely insights. However, the interconnected nature of these devices has introduced complexities, resulting in inconsistencies across operations. Addressing these challenges, this research explores the application of modern digital principles to the mining industry through the Reference Architecture Model Industry 4.0 (RAMI 4.0). RAMI 4.0 offers a robust framework that integrates digital and physical systems, promoting data transparency, interoperability, and decentralised decision-making. This study aims to briefly introduced framework designed for the unique challenges of mining operations, leveraging the latest digital technologies. The lack of standardisation in the mining sector highlights the need for a comprehensive approach to tackle issues such as interoperability, scalability, security, safety, and repeatability. Establishing standard procedures can ensure smooth interaction between connected devices, ultimately enhancing operational efficiency in the growing IoT ecosystem within the mining industry. Introducing a well-defined reference architecture like RAMI 4.0 can streamline management processes and improve operational efficiency in mining. By adopting this framework, the mining industry can effectively navigate the challenges of digital transformation, leading to a more connected and resilient future. The study concludes that RAMI 4.0, with its structured approach, can significantly enhance the efficiency and productivity of mineral processing operations, fostering sustainable growth in a digitally driven industrial landscape.

Sep 1, 2024

By P J. Handley

"Predicting how coal seams will be beneficiated through the coal handling processing plant (CHPP) has long been challenging within BHP Coal. Various coal seams from different areas are mined and stockpiled separately, often depending upon a deleterious coal quality parameter; high/low trace elements for example. These stockpiles are then fed in different ratios into the CHPP to produce the desired product/s. In understanding the washability characteristics of the feed source, mine geologists and process engineers work together to improve the beneficiation of different coal materials through the CHPP.For the majority of the BHP Coal deposits, the washability data is available for the coal seams across the deposits. However, no previous standardised modelling and prediction technique existed that could ensure the correct information was available for forecasting and refining CHPP processes. This paper will discuss the steps undertaken to implement a geometallurgical prediction tool that simplified and streamlined the washability modelling and yield estimation process. The value realised to the BHP Coal assets resulting from this process was: improved compliance to plan increased throughput increased CHPP recovery through improved efficiencies and increased revenues.CITATION:Handley, P J, 2017. Washability modelling and estimation techniques used to improve coal handling preparation plant beneficiation, in Proceedings Tenth International Mining Geology Conference 2017, pp 327–336 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 20, 2017

The steps for the beneficiation of iron ores differ from ore to ore, depending on the mineralogical phases of the particular iron ore and the nature of the association of the gangue minerals with iron oxide in that ore. The extent to which the beneficiation can be undertaken economically depends both on the amount and the nature of the gangue. The analysis of the phases present in an orebody optimises the mining and process operations (separation, beneficiation, sintering, etc).X-ray diffraction (XRD) provides qualitative and quantitative information about the main ore phases as well as the gangue mineralogy and the amorphous content present in a sample. In addition, modern quantification methods such as Rietveld refinement allow the calculation of the distribution of elements in solid solutions such the substitution of Fe with Al in goethite or hematite.The use of high speed detectors has made XRD an important tool for grade and process control. Finger printing of XRD data can facilitate multi-dimensional mapping of ore deposits and drill cores, identifying regions of favourable mineral compositions. Grade control using XRD can be completely automated and controlled remotely.The two case studies in this paper demonstrate how XRD and finger printing can be used for grade control, process optimisation and quality control of a goethitic and a magnetitic iron ore. The enormous potential of XRD as an inexpensive, reliable tool, useful in the characterisation of iron ores will be demonstrated.CITATION:K÷nig, U, 2013. Application of x-ray diffraction to iron ores - potential implications for grade control and downstream processing, in Proceedings Iron Ore 2013 , pp 275-280 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 12, 2013

By Y Xie, L O’Connor, S C. Dominy

"Sheeted vein gold deposits are characterised by multiple subparallel veins and free milling gold. In situ heterogeneity of mineralisation results in grade and process parameter variability, which will increase project risk. Measured variability is often enhanced by poorly designed sampling and test work protocols. Protocols that are optimised within the framework of the theory of sampling to suit the ore type, together with quality assurance/quality control systems, will reduce variability and provide fit-for-purpose results. The geometallurgical approach requires spatially distributed samples within a deposit to support variability modelling. Diverse attributes from core logging, mineralogical/textural determination and small-scale tests are used to measure variability. This contribution emphasises early stage geometallurgical intervention during project development. It presents a discussion of representative sampling in the context of the theory of sampling. A case study presents a protocol that exemplifies how data can be gained from a well-designed and planned program to support a resource estimate and prefeasibility study.CITATION:Dominy, S C, O’Connor, L and Xie, Y, 2016. Sampling and Test Work Protocol Development for Geometallurgical Characterisation of a Sheeted Vein Gold Deposit, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 97–112 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By A Fyans, B Ghahreman-Nejad

This paper discusses the method and results of an options study into selection of the optimal form for a 130 m high tailings/water retaining dam, located in a region of high seismicity in central Iran. The proposed dam is part of a planned expansion project for Sarcheshmeh Copper Mine (SCM), which after construction will provide 550 million tonnes of additional tailings storage over the next 27 years. Geotechnical investigation at the proposed site indicated that adequate suitable low permeability materials (i.e. clay) were not available in the immediate vicinity of the dam site and would need to be imported from a distance of well over 10 km (direct). Given the magnitude of the estimated quantity of material and construction cost it was considered prudent to compare the cost of alternative dam design options for the proposed storage. A total of five dam design options are considered including a conventional rockfill embankment dam with an upstream sloping earth core (ECRD), a geomembrane face rockfill embankment dam (GFRD), a sloping asphalt core rockfill embankment dam (ACRD), a roller compacted concrete dam (RCCD), and a filter rockfill embankment dam for tailings storage together with a separate smaller earthfill core rockfill dam (ECRD) located downstream for storage of decant water. The risks, costing and design of all options are discussed in detail and the outcome of the study is presented.CITATION:Ghahreman-Nejad, B and Fyans, A, 2018. Optimum embankment design for a tailings dam, in Proceedings Mine Waste and Tailings Stewardship Conference 2018, pp 427–438 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 23, 2018

By M Balamurali

In mining, grade control generally focuses on blasthole sampling and the estimation of ore control block models with little or no attention given to how the materials are being excavated from the ground. In the process of loading trucks, the underlying variability of the individual bucket load will determine the variability of truck payload. Hence, accurate material movement demands a good knowledge of the excavation process and the buckets’ interaction with the environment. However, equipment frequently goes into off-nominal states due to unexpected delays, disturbances or faults. The large amount of such disturbances causes information loss that reduces the statistical power and biases estimates, leading to increased uncertainty in the production. A reliable method that inferences the missing knowledge about the interaction between the machine and the environment from the available data sources, is vital to accurately model the material movement. In this study, a twostep method was implemented that performed unsupervised clustering and then predicted the missing information. The first method is Density-based spatial clustering of applications with noise (DBSCAN) based spatial clustering which divides the diggers’ and buckets’ positional data into connected loading segments. Clear patterns of segmented bucket dig positions were observed. The second model utilised Gaussian process regression which was trained with the clustered data and the model was then used to infer the mean locations of the test clusters. Bucket dig locations were then simulated at the inferred mean locations for different durations and compared against the known bucket dig locations. This method was tested at an open pit mine in the Pilbara region of Western Australia. The experimental results demonstrate the advantage of the proposed method in inferencing the missing information of bucket-environment interactions and therefore enables miners to continuously track the material movement.

Jun 22, 2022

By M Zlatev, S Silge

Producers of primary resources such as iron ore face the challenge of processing crude materials with increasing levels of impurities, which may result in the requirement for wet processing. This results in a significant increase in water usage, power consumption and operating costs. Given the remote locations of most operations in Australia (Stewart et al, 2013; Prosser, Wolf and Littleboy, 2011), the required volumes of power and fresh water for wet processing can be very difficult to achieve.HAVER&TYLER is renowned across the world for their screening technology in wet and dry processes. Its innovative Hydro-Clean® system represents a cost-effective and eco-sensitive way to clean any crude materials and material blends with a grain size distribution of 0–150 mm that are contaminated with adhesive clay, slit and other impurities.CITATION:Silge, S and Zlatev, M, 2015. Iron ore washing – achieve more with fewer resources, in Proceedings Iron Ore 2015, pp 519–528 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015