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By P W. Wypych, D Curry, A P. Grima

The market has become highly competitive for engineering and equipment vendors, who are challenged to reduce costs while improving efficiency and productivity of mine operations through plant improvements and expansions. Although conveyor transfer systems account for a small percentage of the infrastructure cost of a mine, they form an integral part of the process supply chain. Malfunctions or suboptimal performance can result in substantial financial cost from delayed start-up, unscheduled downtime, reduced throughput and increased maintenance costs.Discrete element modelling (DEM) is a powerful simulation method used to virtually prototype and test the performance of transfer equipment throughout a design cycle. When developed and applied correctly, DEM can provide insight into performance that exceeds the traditional analytical methods, especially when dealing with complex ore properties, behaviour and/or situations.Using case studies sourced from the authors’ recent research and industrial project work, this paper will address the industry goal to increase efficiency and productivity as well as the methods available to the engineers responsible for delivering designs that meet the aims of this strategic approach.The main focus of the paper will be the new calibration technology, which has been developed by the authors to generate DEM material models that are linked to real-world properties and flow behaviours via unique dynamic testing and innovative parameter calibration methods. These calibrated DEM material models can be deployed by engineers into their existing workflows, whether it be for feasibility studies, concept or final design, plant troubleshooting or optimisation.This new technology allows engineers to gain greater insight than that provided by traditional approaches to the design of transfer chutes and other process equipment and also allows informed design decisions to be made that meet industry efficiency and productivity requirements.CITATION:Grima, A P, Curry, D and Wypych, P W, 2015. New discrete element modelling calibration technology to facilitate the increased efficiency of ore handling and processing operations, in Proceedings Iron Ore 2015, pp 449–464 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By A Scott, I Onederra

Most iron ores occur in mine benches as complex mixtures of lithologies and ore types. This significantly complicates their characterisation for both blast design and fragmentation modelling. A great deal of technical and research effort has been applied to the development of models to predict fragmentation from rock blasting. A number of quite useful engineering models are currently available to blasting engineers and consultants, and more sophisticated approaches using mechanistic descriptions of rock breakage continue to be developed and applied by researchers and explosives companies.The engineering or empirical models that are in more general use within the industry, such as the Kuz-Ram model, require appropriate data if they are to generate useful fragmentation predictions. Some of these models have reached significant sophistication and have made very useful contributions to both routine blasting operations and the exploration of new blasting paradigms. However, these models struggle to adequately deal with rock that is as complex as iron ore. This paper reviews the characteristics of the most successful blast fragmentation models available to the industry and discusses approaches to the derivation of suitable rock mass properties to adequately describe blast fragmentation in a range of iron ores.CITATION:Scott, A and Onederra, I, 2015. Characterising the blasting properties of iron ore, in Proceedings Iron Ore 2015, pp 481–490 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By C Taylor, E Retz

Sequential Gaussian conditional simulation of wide-spaced exploration data has been completed in order to optimise a drilling grid at Fortescue’s Eliwana deposit. The aim of this drill grid is to improve the confidence in the Inferred Mineral Resource. A mineralised domain within the Joffre Member of the Brockman Iron Formation was selected to be tested as it makes up a large proportion of the Inferred Mineral Resource and is drilled at a reasonable spacing.One hundred realisations of Fe, SiO2 and Al2O3 grades were completed in the domain. Three realisations were selected for each analyte to represent the range of equiprobable values and a further realisation was selected to represent the truth data set. These realisations were used to flag theoretical drill holes on varying spacings and patterns. The theoretical drill holes were used to estimate into block models using ordinary kriging.These estimates were analysed and the accuracy and precision of each drill spacing was assessed. It was concluded that the greatest improvement in estimation results was noted at either a 100 m × 50 m or 50 m × 50 m spacing.CITATION:Taylor, C and Retz, E, 2015. How much do we need to drill?, in Proceedings Iron Ore 2015, pp 491–500 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By T Tan, C P. Holmes, C E. Hartford

Transportation of iron ore from the mine to the plant is critical to business success. Ore from the mine may have to be transported across states, countries and even continents to get to the processing plant or export facilities, and ultimately to the client. Numerous transportation steps may result in potential logistical difficulties. Companies are continually seeking ways to increase production rates, decrease delivery times and reduce operating costs. Transportation cost is a major component of the total cost of mining iron ore and therefore requires effective use of transport systems.Bottlenecks in iron ore transport systems, due to material handling issues, can result in loss in profit in the order of millions of dollars per hour. Bottlenecks can be seen anywhere in the transport system where the desired flow rate cannot be achieved. These bottlenecks occur for various reasons such as increasing throughput in a transport system originally designed for a lower throughput resulting in plugged transfer chutes, changing material characteristics (eg finer material, increased moisture content), changing to a new type of material or blend that again alters handleability, or changes in operational philosophy due to equipment availability or process utilisation.Stockpiles may have limited live capacity such that an adequate amount of ore may not be readily reclaimed when required (eg to fill a complete train’s railcars). In bins and hoppers, material may discharge through a central flow channel resulting in the formation of a stable rathole, which can then collapse causing material to flood the downstream system and cause significant downtime for clean-up (eg railcars beneath train load outs). In transfer chutes, bottlenecks can be caused by pluggages or downtime required for the change-out of liners due to excessive wear. In ship loading, long settling or de-aeration time, or material exceeding its transportable moisture limit (TML) may result in further bottlenecks. These bottlenecks all have associated costs because of the need to address their causes and because of the lost opportunities for throughput.This paper will discuss how bulk material technology and proper understanding of material flow characteristics can be applied to mitigate bottlenecks. It will discuss how to predict potential bottleneck locations through a scientific method of analysis and evaluation to determine if an increase in throughput of an existing transport system is possible, as well as whether or not a new material will flow reliably through an existing system.CITATION:Hartford, C E, Holmes, C P and Tan, T, 2015. De-bottlenecking iron ore transportation from mine to plant, in Proceedings Iron Ore 2015, pp 393–398 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By A Dunne, R Kruger, H Coleman, P G. Laurens

External and internal challenges in mining are increasing the difficulty and complexity of operating mines. The external mining environment has a big impact on mining operations and is driving the focus on safety, sustainable mining, green issues, profitability and productivity. There is a very high emphasis on ‘reducing cost to improve’, lower mining and beneficiation costs by eliminating waste/inefficiencies (reduce people, use less consumables, multiskilling, etc). The internal mining environment is getting tougher to operate in, with the deposits being more difficult to mine, more difficult to manage due to external pressure along with issues associated with maintaining quality, etc.Technological and environmental developments too numerous to mention, mean that cost control models, reduction in human resource numbers, as well as other drastic measures to curtail expenditure and increase profitability have become part of everyday business. The capability to manage the mining environment in a systemic manner has been lacking and this increases the pressure for performance on those individuals operating and managing mines.Working smarter has become crucial to ensure that businesses optimise the use of their resources, become more safety conscious, more productive and remain cost-effective, while recognising the importance of sustainable mining now and in the future. The logic of determining and managing to the capability of the mining value chain will be examined and the underlying assumptions or beliefs guiding behaviour and decision-making will be highlighted.In order to identify the obstacles in the mining value chain that hinder achievement of its full potential, the following questions have to be asked:What are the factors that influence the performance of a mining value chain?What is the design capacity of the mine?How can the inherent flow and value capacity of a mine be determined?What types of solutions will allow a mine to operate at its inherent flow and value capacity?This article will discuss the way we plan, measure, assess and manage the mine value chain, by addressing the following factors:balanced capacity versus flow and value capacity designthe use of averagingoptimising individual components in a value chain versus optimising the whole value chain.Optimising flow and value through the mining value chain is one of the three pillars of Mineral Resource Throughput Management (MR™). MR™ is an entrenched and academically supported philosophy that has been successfully applied in the past decade in various mines throughout the world by EXXPLEO, a mining and beneficiation consulting company. EXXPLEO has partnered with HCL (a global IT company) to embed the MR™ philosophy, methodology and toolsets into a formal software product called Synchronised Digital Mining (SDM). The SDM system will provide mining companies with a sustainable platform to effectively apply the MR™ principles and toolsets effectively across all operations targeting increased productivity, efficiency and profitability without having to train operations and management staff into expert MR™ practitioners to successfully achieve the desired results.CITATION:Dunne, A, Laurens, P G, Kruger, R and Coleman, H, 2015. Optimising flow in the mining value chain, in Proceedings Iron Ore 2015, pp 437–442 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By R C. Morris

Two major types of economic secondary iron ores occur in the banded iron formation (BIF)-rich Hamersley Province of the Pilbara, Western Australia:dominant BIF-hosted bedded iron deposits (BID)detrital ore deposits (DID sensu lato), which are mainly hosted in the three province-wide Cenozoic sequences that include coeval non-ore sediments and comprise:a. the major Miocene, fluvial channel iron deposits (CID)b. the lesser Eocene and Pliocene detrital iron deposits (DID sensu stricto).Striking differences exist between the massive CID resources and the much smaller underlying and overlying DID. CID are essentially riverine alluvial ooidal rocks with abundant small fossil wood fragments and variable iron peloids, but with only extremely rare recognisable lithic remnants. The original matrix is typically ramifying layered goethite. Eocene DID are mainly alluvial with minor pisoids, whereas Pliocene–Quaternary DID are dominantly coarse colluvial gravels, both derived from and largely retaining the original textures of BID, hard cap or variably ferruginised surface BIF. The colluvial DID matrix is typically ferroan-aluminous soil, resulting in canga where replaced by goethite, which may be dehydrated to hematite by exposure. The Cenozoic deposits occur in two dominant geomorphological environments: the southern Marra Mamba to Brockman Iron Formation strike valleys containing all three Cenozoic sequences and the much later northern Brockman Iron Formation plateau valleys that include only the Miocene and Pliocene sequences. The Cenozoic detritals formed in different climatic regimes, with an extended dry period forming a prominent province-wide dehydrated carapace on the Eocene DID. The Miocene ‘optimum’ followed with its thick scrub-covered deep regolith that produced the fossil wood-rich CID and was succeeded by the arid cool period of the Oakover limestone/calcrete. A major renewal of exposure and erosion in the Pliocene resulted in the extensive iron gravels of the Pliocene–Quaternary.CITATION:Morris, R C, 2015. The Cenozoic detrital iron deposits of the Hamersley Province of Western Australia – a tribute to Mal Kneeshaw, in Proceedings Iron Ore 2015, pp 319–326 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By E A. G Marques, P M. Dight, T A. V Costa, K Mercer

Brazilian banded iron formation (BIF), known as ‘itabirite’, is the main host rock for iron ore in the Iron Quadrangle mines, Brazil. Their geneses are controversial and it is agreed that diagenetic, metamorphic and tectonic events, as well as weathering processes, were responsible for the iron concentration. In addition, it is understood that weathering processes are mainly responsible for reducing the original high intact rock strength, and for generating deep weathered profiles with low strength rocks commonly observed in Vale’s mines.A considerable amount of work has been done to correlate the macro and micro aspects of the itabirites as physical, chemical and geotechnical characteristics that directly or indirectly affect the intact rock strength; however, it is not yet clearly understood what effects the weathering processes have on the strength reduction of these rocks and its associated impact on pit slope stability. This forms a key focus of the lead author’s current research work.This paper presents a review of the geological, geotechnical and weathering characteristics of the itabirites, and early results for the evaluations of fresh itabirites from the PhD thesis research being undertaken by the authors at the Australian Centre for Geomechanics, School of Civil, Environmental and Mining Engineering, The University of Western Australia, sponsored by Vale SA.The results showed that to evaluate the density and unconfined compressive strength (UCS) correlation, not only the porosity but also the geological features should be considered, as they are responsible for data dispersion; however, the presented data suggested a trend that provides the confidence to develop advanced studies and a data population increment to establish proper correlations to support a reliable UCS method.CITATION:Costa, T A V, Dight, P M, Mercer, K and Marques, E A G, 2015. An overview of the weathering process, preliminary density and unconfined compressive strength correlations for fresh itabirites in vale mines on the western side of the Iron Quadrangle, Brazil, in Proceedings Iron Ore 2015, pp 241–254 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By A Manfrino

The Iron Bridge magnetite mine and infrastructure project is currently in development as a joint venture between Fortescue Metals Group, Formosa and Baosteel. It is supported by an extensive database of reverse circulation drill hole samples analysed by X-ray fluorescence (XRF), with a large subset tested for concentrate mass recovery, concentrate and tail grades by Davis tube recovery (DTR) testing. A limited number of rock physical testing results, including unconfined/uniaxial compressive strength and crushing work index, completes the database.By combining all available information – assays, DTR results, geology, stratigraphy, mineralogy and rock physical results – and using multivariate statistical analysis techniques, the impact of the critical factors that control concentrate quality, mass recovery and grindability characteristics have been outlined. A set of geochemical groups was created by using a cluster analysis technique from the combined XRF-DTR data. These groups allow optimal separation of material with consistently high concentrate Fe grades and mass recovery. This approach is superior to domaining based on stratigraphic logging or mass recovery as it is commonly used in standard magnetite estimation models. Using the results of a normative mineralogy study, the main geochemical groups were given a mineralogical profile fitting their geochemical characteristics. These findings further refined the understanding of the link between the process characteristics of the samples and their geochemical signature and mineralogy.The geochemical grouping defined on head grades and DTR concentrate mass recovery was generalised into a ‘geomet’ code based solely on the routinely assayed head grades, thus bypassing the need for systematic DTR testing. The geomet code identifies spatial domains with homogeneous geochemistry, mineralogy and process characteristics in both oxide and fresh material that form the basis of the current Iron Bridge resource model. The allocation of geometallurgical characteristics in terms of concentration amenability and concentrate characteristics on a block-by-block basis over the whole project allows for an optimum mine schedule and plant feed. This ensures that the desired concentrate characteristics and mass recovery can be better predicted over the life-of-mine, thereby improving the mine’s economics and stability by derisking the mine schedule and optimising the performance of the concentrators. A key benefit of the geomet code devised for the Iron Bridge mine is that it can be attributed in an automated manner to all samples analysed for a routine XRF analytical suite, from exploration to blasthole drilling. This allows each sample to be allocated to a geometallurgical material type with predictable process response in terms of concentrate mass recovery and concentrate quality without the need to complete systematic DTR testing.A standard multivariate analysis coupled with a cluster analysis has allowed the linking of interdisciplinary results between geology, mineralogy, geochemistry, process and metallurgy to better understand, predict and ultimately mine and process the Iron Bridge deposits. This allows plant operations and product quality to be optimised through each stage thanks to the predictable ore characteristics embedded in the mining model.CITATION:Manfrino, A, 2015. Unravelling the factors impacting on concentrate quality by geometallurgical data analysis at Fortescue Metals Group’s Iron Bridge magnetite mine, in Proceedings Iron Ore 2015, pp 567–578 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By C Jenkins, M Mativenga, C Stanton, J Gerber

In 2014, Arrium completed an optimisation program on its magnetite concentrator, enabling a reduction in the overall cost base of producing concentrate through an increased ability to treat a wider range of ore types, previously considered waste. The development was the culmination of four years of work, conducted in house by Arrium’s mining development group. The overall intent of the plant design focused on improving the concentrator’s ability to treat a wider range of magnetite ore types. Metallurgical analysis of these feed ore groups outlined a need for more grinding power if production quality was to be maintained and throughput increased. As such, a tertiary grinding circuit was designed which, alongside the rectification of key operational bottlenecks, would allow the plant to continue to produce concentrate at the required specification and rate. The technology chosen for the tertiary grinding application was the XT IsaMillTM, which is known for efficient fine grinding when compared to conventional ball milling. The transfer sizes between the existing comminution circuits were coarsened, allowed for the treatment of different ores at the required rates. The optimisation project was successfully completed in February 2014 after a three month commissioning phase. The initial metallurgical testing and engineering of new equipment proved highly successful with all equipment meeting or exceeding design parameters.CITATION:Stanton, C, Mativenga, M, Gerber, J and Jenkins, C, 2015. Unlocking plant capability through targeted debottlenecking of Arrium’s magnetite concentrator, in Proceedings Iron Ore 2015, pp 185–190 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By T Nassis, P Louw, M R. Fry, T du Toit

Business intelligence (BI) and business analytics (BA) are established IT technologies that are used extensively in the business environment. This paper discusses the use of these technologies in production environments and beneficiation plants.Assmang’s Khumani mine is an iron ore operation located in the Northern Cape, South Africa. Assmang Limited is jointly owned by African Rainbow Minerals Limited and Assore Limited. This paper presents a case study of a BI solution applied to this plant, which is used by metallurgists, supervisors and all levels of operations and management to extract and analyse data to control and optimise the operation of the plant.The main advantage of adopting an established BI solution is the ease with which production personnel can extract and analyse data. The data is easily presented in familiar spreadsheets and no IT staff or specific training is required to achieve this.The solution adopted at Khumani mine uses a data mart with a multidimensional model (called an online analytical processing (OLAP) cube) acting as the main repository and source of data. The OLAP cube simplifies the access and analysis of plant data.The OLAP cube stores data in a specifically-designed way, which allows extraction and analysis to be performed quickly. It is designed to hold large amounts of data on a long-term basis, which could be for the life of the plant. It also performs automatic aggregations over many dimensions on a background process.A major benefit in adopting such a BI solution is that it allows common BA methods to be applied. This is due to both the efficient long-term storage of data and the availability of standard BA tools. BA tools are now able to perform data mining, predictive analytics, forecasting and other machine-learning algorithms. This paper presents the possible use of such advanced data analysis methods in a beneficiation plant.CITATION:Fry, M R, Nassis, T, Louw, P and du Toit, T, 2015. The use of common business intelligence and analytics tools in the operation and optimisation of iron ore process plants, in Proceedings Iron Ore 2015, pp 129–138 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By P J. Howard, P Darvall, J H. Mainwaring

In less than a year, Atlas Iron’s Corunna Downs Project has moved from a new discovery to a substantial resource of high quality in an underexplored greenstone belt. Located approximately 170 km south-east of Port Hedland and 40 km south-west of Marble Bar, in the Pilbara Region of Western Australia, Atlas Iron Ltd (Atlas) has conducted extensive exploration work at Split Rock, Razorback, Runway and Shark Gully, consisting of geophysical surveying, field mapping, 464 reverse circulation and 16 diamond drill holes resulting in a current resource of 51 Mt at 57.3 per cent Fe (Atlas, 2014b).The Corunna Downs Project is interpreted to be hosted within the Cleaverville Formation of the Archaean Gorge Creek Group. Within the project area these rocks outcrop as subvertically dipping banded iron-formations (BIFs) and jaspilites, cherts, minor shales and meta-sediments forming a north–south trending plateau.Primary hematite-martite iron mineralisation is overprinted in the near surface environment by supergene goethite, and frequently capped with a thin laterite cover. The strongest enrichment areas have been discovered within topographic lows, proximal to fault zones, which coincide with a geophysical anolmaly. These characteristics were targeted, and the subsequent areas mapped in greater detail, allowing for efficient resource definition.Although first explored for iron in the late 1960s, it wasn’t until Atlas’ acquisition of the project in October 2012, that major strides were made to defining an economic resource. At present, Corunna Downs is Atlas’ best greenfields exploration discovery, both in terms of the potential scale of the resource and its blending capacity.The Corunna Downs prospect is emerging as a major new iron ore province in the Pilbara and includes 80 km of potential host stratigraphy. Corunna Downs sits strategically between Atlas’ Mt Webber and McPhee Creek tenements, and can play an important part in Atlas’ growth strategy as iron ore market conditions become more volatile and challenging.CITATION:Mainwaring, J H, Howard, P J and Darvall, P, 2015. Discovery of the Corunna Downs deposits, in Proceedings Iron Ore 2015, pp 305–312 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By M T. Grigoleit, M S. Schneider

The iron ore industry in Australia has, in recent years, experienced an unprecedented growth phase. But now that major expansions are moving into the operation phase, new challenges have emerged. Whereas initial planning may involve a few modelling studies, ongoing performance monitoring and improvement requires a different, more intense approach. This is especially important in the face of changing factors such as commodity prices, exchange rates and labour markets. Response time is key. As a mining operation proceeds from initial planning to daily operations, the time window for analysis shortens, going from months to weeks to days as the business matures.Simulation models and analysis tools are typically built and run by specialists in the planning phase, but then what? The investment has been made, but realised only once. What if further analysis could be carried out in-house, re-using existing models using up-to-the-minute data? Can we create virtual mines that run in parallel to live operations that allow computers to identify areas of a value creation or operational improvement? These questions can be answered by the innovative application of big data, simulation and predictive analytics. The future will be having this all integrated into one platform that offers, for the first time, an opportunity for non-experts to apply existing modelling and analysis assets – combined with live data – to deliver support for continuous improvement, and the ability to respond quickly to external changes in the best possible way, before they become big problems. These techniques will represent a step change in the ability for operations to maximise efficiency and will underpin the intelligent operations of the future. These issues are addressed in this paper.CITATION:Schneider, M S and Grigoleit, M T, 2015. Maximising productivity through big data, simulation and predictive analytics, in Proceedings Iron Ore 2015, pp 475–480 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By X L. Liu, W Huang, J M. Zhu, S L. Wu

The shape, position and thickness of the cohesive zone have a great influence on the operational performance of the blast furnace. Many researchers have found that the high temperature properties of iron ores determine the formation of the cohesive zone. The results of this work show that sinter of high basicity interacts with lump ores and acid pellets due to the difference in chemical compositions. The softening and melting properties and the primary slag formation behaviours in the cohesive zone are improved by the high temperature interactions. The high temperature interaction is mainly influenced by the chemical compositions, porosity, interfaces and so on. Although the softening and melting properties and primary slag formation behaviours of lump ore A (a typical limonite lump ore from Australia) is the poorest of the iron ores studied, the high temperature properties of integrated burden A (consisting of sinter and lump ore A) is no worse than the other integrated burdens due to the high interaction between the sinter and lump ore A. It is useful to apply the results in the optimisation of blast furnace burden.CITATION:Liu, X L, Wu, S L, Huang, W and Zhu, J M, 2015. High temperature interactivity of iron ores in the cohesive zone of the blast furnace, in Proceedings Iron Ore 2015, pp 363–366 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By L Köresaar, J Moilanen

With lower and increasingly variable Fe grades in iron ore resources, the monitoring of Si, Al, Mg and other light elements to meet quality requirements is gaining importance in the iron ore beneficiation industry. The high cost per manual assay and associated manpower requirements often limit the frequency of shift composite samples and laboratory assays. Furthermore, the shift composite assays represent past performance of a plant but are not useful for immediate reactions to feed variations, detection of equipment malfunctions or process control. Automated online analysis of light elements radically reduces the cost per assay and provides repeatable, timely data for process analysis, control and optimisation.A light element online slurry analyser, based on laser-induced breakdown spectroscopy (LIBS) optical emission technique, will be presented. This paper discusses the analyser design for the iron ore industry and also reviews the performance of the analyser against the manual laboratory. Additionally operator safety, user friendliness and maintenance requirements are discussed.A case study will also be discussed in this paper. The sophisticated measurement technique has been implemented in an industrial automated analyser and sampling system to allow for slurry measurement for up to 12 sampling points. However, careful selection of the sampling points and implementation of the sampling system is required for smooth operation.CITATION:Köresaar, L, Timperi, J and Moilanen, J, 2015. Optimised process control in iron ore via online analysis of light elements, in Proceedings Iron Ore 2015, pp 511–518 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By D Connelly

Magnetite orebodies generally means mining lower grade iron ores, crushing and grinding with significant energy costs in processing and the capital costs being significantly higher than for direct shipping ores (DSO). Overseas, the mining and processing of magnetite ores has a long history of production due to the lack of DSO deposits. This raises the question as to why the new magnetite projects have been so problematic and also whether there is a future for the long list of potential magnetite projects in Australia.The quality of the magnetite concentrate product is superior to most DSO products and the magnetite contains energy benefits when converted to hematite in sintering or pelletising. The industry’s hopes for new projects have been dashed and questions are being raised as to the financial viability of such projects.A comparison of Grange Resources Savage River, Arrium’s Project Magnet, the Sino Iron Project and Gindalbie’s Karara Iron Project is used to understand the differences between the projects and why some are more successful than others.CITATION:Connelly, D, 2015. What is the future for magnetite projects in Australia and why have some of the new projects been so problematic?, in Proceedings Iron Ore 2015, pp 107–114 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By J Foo, T Kleinschmidt, B Reynolds, K Kennewell

The consideration of complex supply chains often highlights multiple system bottlenecks that are localised and dynamic – they shift depending on what variability last influenced the system. Using the understanding of a dynamic system model, synergies between items that tackle localised bottlenecks can be understood to generate a suite of solutions. These can be simple solutions, such as a couple of new conveyors placed in series, or more complex ones, including buffer dynamics (capacity, location, behaviour, etc).Each solution in the suite will have an associated uplift in system performance and an associated cost of implementation. Dynamic simulations can address each situation in turn and estimate the projected improvement it would offer. However, these solutions will have different benefits when implemented together, and each solution will give different uplift depending on what other solutions have already been assumed. If the number of solutions is numerous, traditional dynamic simulation analysis becomes the bottleneck, limiting options analysis to the processing power available.This paper will illustrate the use of a new technique applied as part of a capital growth optimisation project with a major iron ore producer. Metamodelling is the creation of a model of a model; in this case study, the focus is on developing a response surface representing the suite of solutions. This can be achieved through a combination of dynamic modelling and an appropriate mathematical process called design of experiments. The result is an approximation surface of the outcomes of the dynamic model, encapsulating interactions between key elements of the value chain.This modelling approach provides a number of advantages compared to traditional dynamic modelling approaches, including but not limited to:early and rapid evaluation of a large range of potential solutionsaccommodation of multiple initial states of the value chainoptimisation of the overall suite of upgrades (down to route-level upgrades) based on business objectiveseasy re-evaluation of outcomes when key inputs such as costs change.CITATION:Kleinschmidt, T, Foo, J, Reynolds, B and Kennewell, K, 2015. Case study of iron ore value chain optimisation, in Proceedings Iron Ore 2015, pp 605–612 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

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 D Andrew, G Stanway, P Mahoney, C Griebel

The iron ore mining industry is currently going through an adjustment from high to more moderate growth as China’s economy transitions from capital-intensive investment to a more services-oriented economy. We are now witnessing a renewed focus on productivity improvements and cost savings as planned expansions are completed. Despite the scale and importance that the mining industry plays in the global economy, innovation literature is generally more targeted at faster-moving industries with shorter innovation timeframes and lower capital requirements. To fill this gap, VCI set out to understand the state of innovation in the mining industry in 2014. This State of Play report aims to give an objective appraisal of the strengths and weaknesses of innovation in mining. The report has three sections covering both the structure and delivery of innovation in mining:Ecosystem – focused on understanding how the iron ore mining innovation ecosystem system works, including the roles of large and small miners, service companies, researchers and governments. Our recommendations focus on what is required for the system to work well and improve the level and quality of innovation in the industry.Strategy – how do companies define their innovation strategy in relation to their corporate strategy? How are these related to prevailing global and industry trends? How do they communicate this with their business? How successful are they? These are among the questions that this section attempts to answer. Our recommendations are pitched at the executive leadership of iron ore mining and service companies.Delivery – focused on how companies develop and deliver innovation, looking at the success of different structural options, leadership and culture, partnerships and the best approaches to change. Recommendations are aimed at improving outcomes within the iron ore mining value chain, including the role that suppliers play.CITATION:Stanway, G, Mahoney, P, Andrew, D and Griebel, C, 2015. Understanding the state of innovation in the iron ore industry, in Proceedings Iron Ore 2015, pp 631–646 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By D Kepert

It is the role of the exploration geologist to create wealth through the discovery, delineation and characterisation of economic mineralisation. This requires a critical depth of geological understanding.Unfortunately, the resource industry is well known for its cyclic nature. Many of the current generation of workers have only ever experienced the boom of the ‘super-cycle’ and are only just discovering the negative side to the business. For the case of the geologist and especially the exploration geologist, the magnitude of peaks and troughs are usually amplified and may become destructive to all parties. During the uptime a healthy stream of exploration good news becomes the staple diet of company releases, in particular those of junior companies attempting to bolster the share price. The geological knowledge and experience gained thus forms a significant and integral part of the value and therefore, future of a company. On the downturn it is often viewed that geologists are a luxury, an optional extra and a burden on the bottom line with companies entering a period of memory loss and myopia.Retention of geological knowledge however remains critical as projects, companies and cycles mature. Beyond exploration per se, this knowledge becomes both fundamental in de-risking and commercialising an asset (through development or divestment) and in positioning the company for cyclic or counter-cyclic opportunities.The growth and retention of this knowledge base is difficult to quantify in terms of direct value but becomes one of a successful resource companies greatest assets. At worst, its loss can lead to millions of investors’ funds wasted on missed opportunities, drilling, or poorly designed plant and mine.But the rocks are around for the longer haul and pay little notice to the vagaries of the scurrying of Man.This paper reflects on some of the authors own observations through 25 years of iron ore exploration, in why it is critical to always rigorously question the prevailing geological (mis)understanding and the technical and commercial benefits which that may bring, and looks forward to the future.CITATION:Kepert, D, 2015. Geology – the affirmative case, in Proceedings Iron Ore 2015, pp 285–298 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By R. G. Jones

Many novel projects suffer delays, cost overruns or failure because of inadequately qualified technologies. A new qualification to measure the quality of innovation is discussed.

Jul 1, 2015