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By A C. Edwards

The Mesozoides of North-Eastern Asia represent a collage of exotic terranes which were accreted to the margins of the Siberian continent. In the Late Precambrian and Phanerozoic, the passive margin of the Siberian continent (the current north-east) broke up into a number of discrete fragments as a result of rifting events in the Riphean, Early and Middle Paleozoic. Rift related mineralisation includes stratiform and stratabound copper and base metal mineralisation in the structures of the Omulevka, Okhotsk and Prikolyma basement terranes, as well as along the edge of the Siberian continent in the Paleozoic sediments of the Verkhoyansk passive margin. During the Late Mesozoic the terranes were reassembled in a series of accretionary events. At the end of the Jurassic, the velocity of the Kula plate increased, and the opening of the Canadian Basin in the Arctic commenced, changing the vectors along which the Siberian and North American plates moved. Island arcs terranes were pushed back onto the Omolon super-terrane which was in turn pushed onto the Siberian Craton in a series of massive collisional events that resulted in the restructuring of the whole region. Numerous granitoids characterised by collisional and island arc chemistries with Au and accompanying Sn, and W mineralisation were emplaced. Mesothermal gold-quartz mineralisation was emplaced in a series of narrow belts that adjoin major crustal sutures and that spatially overlap belts of granitoids and zones of subgreenschist metamorphism. Each of these gold-quartz vein belts corresponds with a specific collisional phase. In the middle Cretaceous, the Okhotsk-Chukotka volcanic belt developed along the paleo-Pacific margin of the Siberian continent. Epithermal Au-Ag developed in the volcanics and shallow mesothermal Au-Ag-quartz and Au-Sb mineralisation developed in the Mesozoides. Porphyry and skarn style mineralisation developed in both the island arcs and in the continental Okhotsk-Chukotka Volcanic Belt.

Jan 1, 1999

By M Akbarpour, M Osanloo

In mining industries, materials handling costs represent a significant component of the operational cost. A variety of methods have been applied in surface mine equipment selection in order to select the suitable loading system with the lowest loading operation cost. These are queuing theory, bunching theory, linear programming and genetic algorithm. Among them, genetic algorithm is a simple method and applied for preliminary selection of the loading system. In this study, gathering information from anomaly No 2 of Gol-e-Gohar iron mine of Iran, loading system (number and its capacity) has been optimised by genetic algorithm. The study results showed that two shovels 8.25 m3 are required for the removal of 60 million tonnes of overburden. After removal overburden, these two shovels contribute to the extraction of 42 million tonnes of ore with a stripping ratio of 1:1.

Jan 1, 2007

In both exploration and mining, the laboratory is often seen as a cost rather than a profit centre. The aim in most mining companies is to increase profits by reducing costs, so the budget for the laboratory is under constant pressure.There is another option; to see the laboratory as the brain of the operation. By lifting the status of the laboratory and treating it as a profit centre, mine costs may be reduced in other ways, e.g. reduction in milling costs. This change of status will require investing more capital in better equipment, more training for employees and more employees to carry out project research.There are many examples of major costs being incurred because of inadequate laboratory work. Conversely, by spending more money on laboratories and their activities, overall costs can be reduced or profits enhanced.

Jan 1, 2002

By I Neilsen, A Walker, A Hooper

Final landform planning in Deswik software was used to determine if integration into the mine plans could achieve better closure outcomes. Using the dump surface outputs from the Deswik Landform and Haulage Simulation (LHS) and the Reshape Landform tool, the project intended to:1.Review costs to achieve the final landform for several grade criteria scenarios;2.Quantify reshape volumes at several stages of the mines development with the objective to contribute to margin ranking inputs; and3.Identify rehabilitation cost reduction measures such as backfilling ramps and pits.CITATION:Neilsen, I, Walker, A and Hooper, A, 2018. Case studies for integrating rehabilitation planning with mine planning in strip mining, in Proceedings Life-of-Mine 2018, pp 93–95 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 25, 2018

By A L. Wills

Cliffs Natural Resources Pty Ltd (Cliffs) operates iron ore mines in the Koolyanobbing region of Western Australia. Ore is mined from three separate hubs and trucked to Koolyanobbing. There it is crushed and screened to lump and fines finished products and railed to Esperance port for export to Asian customers. Cliffs is renowned in the marketplace for its consistent product quality. In 2010, 9 Mt of ore was exported to Asia.Over the last three years, Cliffs has systematically refurbished its product quality system (PQS), to improve efficiency of production while maintaining its high standard of product quality. Particular attention has been given to reducing inventory, which has required significant modifications to the production process design.In redesigning the production process, emphasis was placed on maximising the application of the inherent blending in each of the production process steps and dedicated blending was only introduced where essential. Simulation modelling and statistical analysis of process capabilities were utilised to develop a system that optimises inventory, minimises rehandle, eliminates several decision-making steps and maintains a pull focus throughout the value chain.The new planning functions are now fully integrated and effectively control long-term grade variability rather than relying on long-term stockpiling. The production process design and the daily crusher blend plan are used to control short-term grade variability. With the upgrading of the planning processes the accuracy and consistency of algorithms for lump per cent and lump and fi nes grades became more important and an increased geometallurgical function has been added to the company.The continuous stockpile management system was upgraded to increase the effectiveness of the daily crusher blend planning process and is now the sole intervention point in the production process for grade-based decisions. Grade control systems were modified to utilise advances in software and geostatistics to deliver accurate short range planning models. The reconciliation and sampling systems have also been refurbished with an emphasis on practical application and feedback loops.Accountability and transparency are provided by management reports that are generated from a centralised data warehouse and the introduction of a corrective action system. Standardised reports are available to all stakeholders and a quarterly KPI scorecard assists the management review process.The successful rejuvenation of the PQS has culminated in operational efficiency through the implementation of an integrated end-to-end process flow. Shareholder value is maximised through maximising recovery of the resource and minimising costs. Customer focus is maintained by reliably delivering product with acceptable and low grade variability.

Jul 11, 2011

By L MacDonald, D Molver, D Pepper

Increasing contamination/impurity levels (including arsenic, antimony, mercury etc.) of concentrates is an issue of global commercial and health / environmental significance. With increasing environmental pressures at smelters, penalties for contaminants in concentrates continue to rise, accompanied by lower reject limits. There is a strong incentive for miners to investigate alternative means to remove contaminants in order to increase the value of complex ores and concentrates. This paper discusses the global problem of contamination in complex ores and concentrates, how the Toowong Process technology is a potential solution to this problem, and the opportunities this technology brings to the business of dealing with deleterious elements. CITATION: MacDonald, L, Molver, D and Pepper, D, 2018. The Toowong process for treatment of complex ores, in Proceedings Complex Orebodies Conference 2018, pp 119–121 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 21, 2018

By M Andrew, H Reynolds, R Mills

As the global mining industry continues to grapple with ‘black swans’ (high consequence / low frequency events) for health, safety and environmental losses, the focus is shifting from risk assessment of these events to risk management using selected purposeful controls that reduce the risk.Fundamental to critical control management (CCM) is a soundly based rationale for selecting the right controls for each and every risk. The approach should be based on a thorough understanding of the dynamics of the event: What causes it? How frequently will it occur? How likely is the consequence to be realised? This is typically done through a bowtie and methodical numerical risk analysis. Core to this step, then, is the structure and content of the bowtie; specifically, the analytic architecture that is used to construct the bowtie. Different architectures yield different constructions, and bowties are no different.Although bowties for operational health and safety (OHS) hazards appear to have a common architecture, they are not generic. It is important to stress that they contain site specific information, are prepared by site based operational teams, and vary from site to site to reflect each site’s operational conditions. They are often a product of the knowledge and understanding of the operational teams involved, may be prone to human error, and potentially miss latent hazards that may have been embedded early in the planning, design or construction phase of a mine or facility, or within the mining equipment. Overcoming these deficiencies through constant re-evaluation of the risks, and challenging the content on the bowtie will help to minimise the chance that something will be missed.This paper will cover key themes in bowtie architecture, introduce the concept of barrier models for high consequence event (HCE) bowties and present a relevant case study to illustrate the issues that determine quality in bowtie construction and critical control (barrier) selection and management.CITATION:Mills, R, Andrew, M and Reynolds, H, 2016. Can we stop a black swan breeding? Advances in bowties and critical control management, in Proceedings International Mine Management Conference, pp 105-114 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jun 22, 2016

By C Simpson, J M. F Clout, G Plummer

Detrital iron deposits (‘DID’) in the Hamersley Province of Western Australia have been successfully mined and processed as small quantities of saleable standalone or blended lump and fine ore products over many decades. Despite their past, present, and potential future exploitation, they have received far less geological attention than the economically more significant higher grade bedded iron deposits (BID) or channel iron deposits (CID). DID are composed of Pliocene to Quaternary unconsolidated colluvial/alluvial hematite gravels with minor sand, silt, and ferruginous clay, as well as minor iron rich hematite clast conglomerates. DID mineralisation at Solomon has been subdivided into six distinct stratigraphic units, DID-5 (lowermost) to DID-0 (uppermost) based on differences in clast and matrix textures and mineralogy, degree of consolidation/lithification, particle size distribution and replacement textures. The most significant accumulations of DID-1 to DID-5 are in tributary valleys that have been cut through the Joffre Member of the Brockman Iron Formation on either side of the main Kings-Queens valley which contains the CID. DID-5 and DID-4 are ferruginous conglomerate, referred to as canga, with four subtypes recognised. DID-4 is composed of BID hematite clasts cemented by hematite or goethite. DID-5 is characterised by partial to complete replacement of BID hematite clasts and matrix by the yellow ochreous and brown to black vitreous forms of goethite. DID-3 to DID-0 are unconsolidated hematite detritals composed of largely (>80 weight per cent) fine to coarse gravel, minor sand and little silt or clay. The gravel clasts are largely composed of hematite and hydrohematite that replace BID, micro-nanoplaty hematite-martite-ochreous goethite (m-nplH-M-oG) BID, reworked canga, hematite pisoids, siliceous hematite BID (micro-nanoplaty hematite-martite-quartz, m-nplH-M-quartz), chert (~10–20 per cent) and minor (<5 per cent) Banded Iron Formation (BIF). Hematite abundance decreases from DID-3 to DID-0 whilst siliceous hematite BID is highest in abundance in DID-0 and lowest in DID-3. A key finding of the study is that >90 per cent of mineralised detrital clasts are derived from hypogene altered BID micro-nanoplaty hematite-martite with subsequent later supergene ochreous goethite mineralisation hosted by the Joffre Member. The DID genesis model entails interpreted Palaeoproterozoic siliceous hematite hypogene microplaty BID mineralisation of the Joffre Member undergoing Cenozoic supergene leaching of quartz and replacement by ochreous goethite to form low phosphorous BID micro-nanoplaty hematite-martite-ochreous goethite clasts. BID martitenanoplaty hematite-martite-ochreous goethite clasts and detritus eroded from hill slopes were deposited in the valleys as an initial single DID-5-DID-4 unit over a weathered pre-CID basal conglomerate. DID-5 and DID-4 underwent goethite cementation of the matrix followed by replacement by hydrohematite, hematite, and maghemite. DID-3 formed during subsequent periods of erosion of largely DID-4 hematite clasts and pisoids. Groundwater flow-through hematite canga resulted in partial to complete hydration replacement of DID-4 hematite clasts and matrix by ochreous and vitreous goethite to form DID-5. Deposition of DID-2 to DID-0 was via reworking and deposition of DID-5 to DID-4 canga and DID-3 as clasts and pisoids/ooids, as well as siliceous hematite and BIF sourced from the Joffre Member in the hill slopes.

Nov 8, 2021

By R Berry, J Hunt, T Kojovic

Geometallurgy aids the understanding of orebody variability through the integration of geological and processing information, thereby enabling spatial mapping of processing (eg comminution or separation) performance across a deposit. In order to do this, estimates of quantitative geometallurgical attributes are needed so that continuous downhole profiles can be generated for each attribute, providing vital data for the development of processing domains through geostatistical modelling. However, as traditional comminution tests are high cost and/or require large samples, current practice typically results in insufficient data density and distribution of comminution indices making it difficult to model and map deposit scale variability. The aim of this work was to find a method to estimate comminution indices based on data that is widely available at mine sites. This data includes mineralogy and chemistry of drill core samples, plus logging information such as lithology and alteration. Work to date indicates that comminution parameters such as semi-autogenous grinding (SAG) Power Index (SPI), Bond Work Index (BWI) and A*b (Julius Kruttschnitt Mineral Research Centre (JKMRC) drop weight test (DWT) Index) can be modelled using chemical and/or mineralogical data. The estimated hardness indices can then be propagated into the mine site database and used in the definition of processing domains that map inherent variability across a deposit. The estimates can thus provide geometallurgical inputs for spatial modelling, mine planning and optimisation initiatives.CITATION:Hunt, J, Kojovic, T and Berry, R, 2013. Estimating comminution indices from ore mineralogy, chemistry and drill core logging, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 173-176 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By G Anderson, V Lawson, P Voigt

Glencore operates zinc concentrators at both Mount Isa and McArthur River. Both orebodies have a history of complexity and required the development of new technologies to maximise the value from these long-life assets. The Mount Isa orebodies were plagued since operations started in 1931 with complex mineral textures making separation challenging and many areas uneconomic. Examples are given in Figure 1 with different grain sizes from the same orebody in different areas. The McArthur River deposit, located in the Northern Territory, was discovered by Mount Isa Mines geologists in 1955 (Logan, Murray and Williams 1990). The extremely fine-grained nature of the ore was quickly recognised and the difficult separation of the minerals was soon obvious. The geographical, metallurgical and economic factors resulted in the orebody not being developed until the technologies for the mineral separation had been developed. The development of the IsaMill allowed for an economic mineral separation flowsheet to be developed and ore treatment commenced in May 1995 (Nihill, Stewart and Bowen 1998). CITATION: Lawson, V, Anderson, G and Voigt, P, 2018. Developing technologies for a complex orebody, in Proceedings Complex Orebodies Conference 2018, pp 116–118 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 21, 2018

By S Grimsey, E Janetzki, J Emery

Floor failure on the longwall face is a complex problem due to the dynamic interaction between the surrounding strata and powered roof supports involving multiple variables. It can also be highly problematic and costly when not well understood as there are significantly less remedies available post event. This was the early experience at the case study mine where significant lost time was incurred due to unplanned delays in the first two panels. To detect and prevent such occurrences is clearly the best approach, hence a tool to predict both floor failure occurrence and magnitude prior to mining was developed. Several parameters were determined as the key drivers for the failure events that had been experienced at this site. These parameters were, in list of influence from highest to lowest, material strength, retreat rate and the presence of water. The parameters were evaluated using a semi quantitative risk rating system ‘Heave Risk’ and given a weighting per their apparent influence. This ‘expert system’ methodology was selected as the use of risk rating systems in underground coal mining is well established. The heave risk model was first applied to previously mined panels. It accurately identified all areas of known major floor heave, with clearly defined areas of low and high risk apparent. The heave risk model was then used prior to mining and evaluated post mining based on observed conditions. It was found to also be highly accurate for prediction. The variability of each parameter and its influence on the overall risk rating is illustrated and discussed.

Nov 29, 2022

By A Krishnan, C Garcia, I Luna-Berbesi

The discovery and exploration of deposits are progressively shifting to extremely remote regions and greater depths. As a result, exploration costs and the economic risk of a bad investment increase. Current megatrends in the exploration and mining sector indicate a growing demand for the implementation of automation, digitalisation, and machine learning techniques. The implementation of a multi-sensor-based core logging device (DMT, 2024) was done on 400 m of drill cores to optimise interdisciplinary processes in terms of time and quality, by using interlinked and consistent data in real-time, to obtain reliable predictions that support and speed up decision-making. The results are consistent with the laboratory data previously obtained by the mining company, highlighting the benefits of introducing ANCORELOG into their core logging procedures.

Sep 1, 2024

By A Winchester, D Grant

The massive 1100 Orebody in Mount IsaÆs X41 Copper Mine has been extracted over the last 40 years form north to south utilising sublevel open stoping methods.De velopment has continually stepped ahead of production to provide access to new primary stoping blocks. The remaining secondary pillars contain stopes with one, two or three fill mass exposures and were constrained by access requirements and stope extraction turnaround time.The remaining southern mining areas consist of large 40 m wide by 300 to 400 m long east-west transverse pillars that have been left to provide access for ventilation and service requirements. Three large transverse cross-cuts or transverse pillars û 4180 x/c, 4010 x/c and the Panel pillar now remain. The 4180 x/c was the last major transverse pillar to be successfully broken.This transverse pillar is being extracted in a retreating sequence, creating 3SLOS stopes due to the surrounding backfilled stopes.In recent years the extraction of the first, and most critical, stopes in the transverse pillars have resulted in large changes to the induced stress field. This paper describes the extraction of the 1.1 million tonne stope P418 in the 4180 x/c pillar.T echnical challenges that were faced and how they were overcome to allow for the successful extraction of the stope are described.Critical to the success of the P418 stope extraction were the rehabilitation methodology used; extraction strategy modifications to address deteriorating ground; mass firings with multiple directional changes; managing the exposure of backfill of variable strengths; and geotechnical monitoring prior to, during and after extraction.

Jan 1, 2005

By S H. Algie, D P. Taplin, S G. Doig

"Suboptimal performance of clusters of hydrocyclones (cyclones), relative to the performance of the individual cyclones that comprise the cluster, is a common phenomenon. The primary cause is well known: uneven distribution of the slurry feed into the individual cyclones. The direct result of uneven distribution is that each cyclone in a cluster performs somewhat differently. Hence, some, if not all, of them do not work at their intended operating points, and the classification is not as sharp as intended. The impact on grade, recovery and milling efficiency will vary depending on the duty of the processing circuit, but the most immediately apparent consequence of uneven distribution is a difference in wear rates in the individual cyclones; this exacerbates the problems of unequal performance of the individual cyclones. In severe cases, and as differential wear proceeds, flows from the individual cyclones may become visibly different. Uneven distribution, once the phenomenon was identified, was seen to be a significant issue in the high-solids hydrocyclone cluster in the ball mill recycle section of Evolution Mining’s Edna May Operation (EMO, Westonia, Western Australia) gold processing plant. In 2012, a FloCEP&reg; hydrocyclone feed distributor system was installed to alleviate the problem. Over the next four years of operation, this innovation has proven its value in the metallurgical and maintenance areas. Individual cyclones now wear at the same rate, greatly reducing the need for operator intervention, as well as increasing overall plant availability through minimising unplanned maintenance. The costs of the installation can be justified by savings in maintenance alone, though the greatest value, as is described in this paper, is in allowing the plant to produce consistently closer to its operational design limits. The FloCEP&reg; system is a low maintenance, readily installed retrofit, based on two patented devices that are combined with complementary proprietary design methodology allowing customisation for specific flow rates and slurry particle properties. Together, these devices produce and distribute a pseudo-homogenous slurry flow without moving parts.Citation:Taplin, D P, Doig, S G and Algie, S H, 2016. Experience with FloCEP® Slurry Flow Conditioning at Evolution Mining's Edna May Operation, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 251&ndash;260 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Oct 10, 2016

By J B Tucker, A T. McGrath

Situated in north-west Queensland, Australia, the Ernest Henry Mine (EHM) has been producing copper and gold since 1996. Owned and operated by Glencore Xstrata, ore is currently sourced from a newly developed underground sublevel cave (SLC) mine below the old EHM open pit (the pit). Ore feed to the concentrator is supplemented by satellite open pits comprising the Mount Margaret operation.The transition from open pit to a producing underground mine involved a complex extraction sequence for the first two production sublevels on RL1650 and RL1625. Underground mining during cave initiation was further complicated when south wall instability prevented completion of the final open pit shell. The transition mining phase included mass firing of an inclined crown pillar between the initial SLC void and the pit followed by a combination of transverse and longitudinal SLC mining.The inclined crown pillar mass firing was the first significant milestone in the extraction of the transition ore block. Comprising approximately 890 000 t of in situ material; 70 000 m of drilling was charged with 351 t of emulsion and 6026 electronic detonators to initiate the single mass firing. The firing contained five direction changes over 19 seconds making it one of the largest and most complex underground blasts ever conducted in Australia.Risk management was integrated into every facet of the technical and operational planning and execution of the transition mining phase, whilst maintaining daily mine construction and production commitments. This paper provides a detailed overview of the transition objectives and constraints, mining method selection, post mass blast outcomes and the journey ahead.CITATION:McGrath, A T, Campbell, A D and Tucker, J B, 2014. Transition of the Ernest Henry Mine from open pit to an underground sublevel cave, in Proceedings 12th AusIMM Underground Operators’ Conference 2014 , pp 297–308 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Mar 24, 2014

By D Kentwell

IGO Ltd’s (IGO) Nova Operation (Nova) is 160 km east-north-east of the town of Norseman in Western Australia (WA). Discovered by Sirius Resources NL (Sirius) in 2012 and acquired by IGO in 2015, the Nova-Bollinger Deposit (Nova-Bollinger) occurs within the Fraser Zone of the AlbanyFraser Orogen. Nova-Bollinger is a complex nickel-copper-cobalt (Ni-Cu-Co) magmatic intrusive sulfide deposit, which is characterised by high-grade folded and undulating breccia lodes that host the bulk of the payable metals. Significant sulfide mineralisation is also found in several magmatic intrusive styles including net-textured, disseminated and blebby styles. A smaller but material volume of sulfides has also been remobilised into the deposit’s footwall rocks as ‘stringer’ bands in metasediments, or as discrete massive sulfide lenses. When interpolating Mineral Resource estimate (MRE) block grades in the presence of locally varying grade connectivity trends, such as folds or undulations, the sample search neighbourhood, which is used to identify the composites subset that is used for each block estimate, should be oriented to match the local trends. In theory, the most robust method of estimation where locally varying angles (LVAs) are applied to search neighbourhoods, are techniques where the composites and the blocks to be estimated are transformed to an unfolded coordinate system (UCS). However, UCS methods can be tedious to implement and require subjective decisions to be made that cannot always be fully defended. An alternative LVA method commonly used in the industry is dynamic anisotropy (DA), which is much simpler to implement and is often assumed (but not that often demonstrated) to result in similar results to UCS-style estimation. In 2018, Nova’s Mine Geology team (MGT) estimated the grades and density of the Nova-Bollinger MRE using the UCS approach. For the 2020 MRE update, Nova’s MGT tested and compared the USC grade interpolation method with the DA approach. As applied to the Nova-Bollinger’s 2020 MRE, the comparison, advantages and disadvantages of the two estimation methods are the subject of this paper

Mar 22, 2022

By A Woodley, A N. Danoucaras

In 2012 the Bureau of Meteorology, under the banner of the Water Accounting Standards Board, released the Australian Water Accounting Standard 1 (AWAS 1). This standard has been in development since 2007 with key milestones being the release of the Preliminary Australian Water Accounting Standard in 2009 and the exposure draft of the Australian Water Accounting Standard in 2010. Throughout this period, the Minerals Council of AustraliaÆs Water Accounting Framework has developed concurrently with the Australian standards and the standards have informed elements of the framework. However, the framework is not identical to the standard as the objectives between the two are different. The objective of the Water Accounting Framework is to create consistency in water reporting of the minerals industry and to assist companies reporting to corporate sustainability initiatives. The objective of AWAS 1 is to provide information to water management bodies to facilitate decisions about the allocation of water resources. Companies are to report on an annual basis - not only physical flows of water but contractual requirements to supply and obtain water - regardless of whether the transaction has been fulfilled in the reporting period. In contrast, the Water Accounting Framework only reports on flows that have physically happened. The paper will provide summary information on aspects of AWAS 1 that are most relevant to the minerals industry, show the alignment and differences between AWAS 1 and the Water Accounting Framework and explain how to obtain the information for the AWAS 1 reporting statements.CITATION:Danoucaras, A N and Woodley, A, 2013. Alignment and differences between the Australian Water Accounting Standard and the Water Accounting Framework for the minerals industry, in Proceedings Water in Mining 2013 , pp 85-90 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 26, 2013

By R Thompson, D Tulloch

"Wet-weather or 24×7 wet-trafficability is a commendable design objective for unpaved mine haul roads, but practically often difficult to achieve, due both to the road sheeting material selected, and also its breakdown under the action of traffic and environment. Accidents allied to wet-trafficability issues, either due to rain or often more commonly, over-watering of mine roads, is increasing and especially the loss of control or uncontrolled vehicle movements categorisation. The dominant system failure is that once vehicle control is lost, control cannot be regained.The friction supply, or ‘skid resistance’ of a mine haul road sheeting is critical to the safe operation of the road and data has to be collated concerning the variation of friction supply with sheeting material engineering or indicator properties. In the light of the paucity of data existing in this field, this paper presents the contributory research that informs the development of improved and validated unpaved mine road friction supply estimates, based on a number of mine site evaluations across a range of sheeting material types and characteristic parameters, leading to improved predictive models of friction supply.Results from this preliminary review will enable operators to assess their road sheeting materials, improve and validate unpaved mine road friction supply estimates. This will contribute to improved road design, management and 24×7 operability decisions and contribute to a reduction in heavy vehicle loss of control incidents on gravel-surfaced mine haulage roads.Citation:Thompson, R and Tulloch, D, 2016. What's Stopping You? The Role of Sheeting Material Selection in Safe Haulage Operations, in Proceedings Ninth AusIMM Open Pit Operators' Conference 2016, pp 253–264 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 15, 2016

By N Madani, S Abulkhair, N Morales

Conventional geostatistical algorithms cannot reproduce bivariate complexities such as inequality constraint, nonlinearity and heteroscedasticity. Poor reproduction of these features may decrease the accuracy and reliability of mine planning results. For example, it is not unusual to have an inequality constraint between primary and disturbing elements in a metalliferous deposit. Implementation of traditional methodologies for such complex data sets can lead to the incorrect reproduction of a bivariate relationship, which will affect the validity of NPV results. In this paper, an iron data set containing iron and silica grades with an inequality constraint between variables is introduced as a case study. This study proposes an algorithm based on a hierarchical sequential Gaussian cosimulation integrated with inverse transform sampling. The proposed methodology considers the linear inequation between two variables in the hierarchical cosimulation process to reproduce an inequality constraint. As a comparison, conventional sequential Gaussian cosimulation is also applied to the same data set to demonstrate the difference in bivariate relationships from both models. Unlike the proposed algorithm, the conventional cosimulation cannot reproduce an inequality constraint and slightly overestimates silica grades. The modelled realisations are then used to assess the uncertainty of a plan and generate a stochastic strategy that adapts the destination of the blocks depending on the scenario. Two-stage stochastic long-term production scheduling takes extraction decisions using average information (ie e-type model) and ore/waste destinations based on geostatistical realisations. As a result, the proposed strategy is closer to the upper bound, highest possible NPV for each realisation, than to the lower bound, deterministic strategy that does not manage the risk of sending extracted material to wrong destinations. Furthermore, Comparing production schedules resulting from proposed and conventional geostatistical methodologies shows the importance of inequality constraint reproduction and more accurate long-term mine planning.

Nov 8, 2021

By R Shaw, R Williamson

Global iron ore demand, sparked by ChinaÆs rapid industrialisation and emergence as the worldÆs newest steel market, has seen iron ore groups investing heavily in production capacity as well as infrastructure. The success of these new projects depends heavily on reliability, competitiveness and quality control in an increasingly demanding trading environment. Furthermore, the iron ore industry continues to improve its competitiveness in mining practices, technology innovation and management processes. The continued fine tuning of these sectors will be critical to the success of the industry in the years ahead, and this mining sector will continue to demand process improvements from their service suppliers to maintain competitiveness. Automation offers a real solution to these demands. A crucial quality control element in mining, beneficiation, stockpiling and shipping stages is ensuring representative samples of ore are taken along each step of the process. When these samples reach the laboratory it is imperative that the sample is representative of the entire stream and that no induced errors occur. Examples of automation start with mechanical primary and secondary samplers in the run-of-mine processes through to sample tower and laboratory crushers, dividers and collectors. Automation is widely used in many forms to provide reliable and repeatable samples for analysis whilst providing a safer working environment. Laboratory automation systems in use today vary from simple linear systems to sophisticated robotic cells. Sample automation in the iron ore industry has resulted in many benefits for both producers and their customers. The most significant benefits have come in the form of higher safety standards whilst reducing sample error, sample handling and sample contamination during collection. These benefits have been further augmented by reliability and repeatability of automating the final sample preparation and analysis stage. Today these benefits are reflected in reduced costs of sampling and improvement in both turnaround time and quality of results, whilst complying with Australian safety and ISO sampling standards.

Jan 1, 2007