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By Murray G

This paper outlines the processes involved in acid generation from sulphides and the key factors in identifying and predicting time dependent changes in the nature of acid mine drainage (AMD). The initial characteristics of mine water; whether drainage from exposed mining faces, runoff and seepage from waste dumps or seepage from process tailings; do not necessarily reflect the long term potential for AMD. The ultimate quality of mine drainage is a function of the interaction of many chemical, physical, biological and engineering (operational) factors. Techniques are available that can predict the qualitative nature of acid generation and mine drainage quality but accurate quantitative predictions are essentially impossible at the present time. As more and more mining operations exploit sulphide ore, the AMD and acid waste issue will become a. far greater concern. The significance of time dependent factors in the production of acid mine drainage-.is not just restricted to particuL.ar mine sites but needs to be recognised by the mining industry as a whole.

Jan 1, 1988

By A H. Bagherieh, S Mesroghli, H Jorjani, S Chehreh Chelgani

The rare earth elements (REEs) assay and recovery in leaching processes is being determined using expensive analytical methods, ICP-AES, and ICP-MS. This paper presents a neural network model to predict the effects of operational variables on the lanthanum, cerium, yttrium, neodymium recovery in the leaching of apatite concentrate. The effects of leaching times: ten to 40 min, pulp densities: 30 to 50 per cent, acid concentrations: 20 to 60 per cent and agitation rates: 100 to 200 rpm were investigated and optimised on REEs recovery in laboratory at a leaching temperature of 60¦C. The obtained data, on the laboratory optimisation process were used for training and testing of a neural network. A feed-forward artificial neural network with 4-5-5-1 arrangement, was capable of estimating the REEs leaching recovery. Neural network predicted values were in good agreement with the experimental results. The correlations of R = 1 in training stages, and R =  0.971, 0.952, 0.985 and 0.98 in testing stages were results for Ce, Nd, La and Y recovery predictions respectively and that theses values are considerably acceptable. It was shown that the proposed neural network model accurately reproduces all the effects of operation variables, and can be used in the simulation of a REEs leaching plant.

Jan 1, 2008

By A J. Moore

Predictive assessment of surface blast vibration requires the identification and quantification of significant factors, and the summation of their combined effect. Methods available for the prediction of significant factors are described, and examples are given of the combined use of these methods to evaluate alternative proposals.

Jan 1, 1999

By D T. Feinstein, M B. Gotkowitz

In Wisconsin, state laws and regulations governing metallic mine permitting stipulate that protection of groundwater quality be assured indefinitely through all phases of the mining project, including development, active mining, reclamation, and long-term closure. By rule, the Wisconsin Department of Natural Resources (WDNR) requires that permit applicants use a numerical model to assist in the assessment of compliance with this requirement for all proposed mine-waste facilities. As a part of the compliance assessment, model calculations are used to predict groundwater solute concentrations through time at designated boundaries for comparison to current groundwater quality standards. In practice, this modelling requirement has involved the application of numerical solute-transport models. Our review of a solute-transport model submitted by the developers of a proposed mine near Crandon, Wisconsin, provides us with an opportunity to assess the usefulness of transport modelling as a permitting requirement. Recent evaluations of solute-transport models by other workers suggest limitations in predictive accuracy. Our review of the model submitted for this project also identified important limitations due primarily to uncertainty in calibration of model parameters at the undeveloped site. The utility of any model to predict project compliance or non-compliance with numerical standards is limited by the range of uncertainty of key model parameters, such as dispersivity and porosity. Uncertainty in the future behaviour of the solute source(s) also reduces the predictive power. Moreover, the site characterisation provided by the model may have limited power to describe the site condition post-development. Although these inherent shortcomings accompany the use of solute transport models at undeveloped sites, the requirement for model development and the ensuing technical review has substantially assisted in our evaluation of site conditions and project design. Through this process we have identified elements of the waste facility design that are crucial to the environmental viability of the project, resulting in modifications to original project proposals. While results from this model review will be used to evaluate compliance, additional benefits come from the identification of critical design features that enhance groundwater protection.

Jan 1, 2003

By Y Xie, D Willingham

Predictive maintenance (also known as ‘condition/health monitoring’) refers to the intelligent monitoring of equipment to avoid future failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:- reduces equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetime- determines the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis- avoids the unnecessary costs of maintenance and unexpected failures- improves safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data is first preprocessed using advanced statistical and signal processing techniques; it is then combined with predictive modelling techniques, such as machine learning, to estimate equipment health.Once tested, predictive maintenance algorithms may be used in an operational setting within an IT environment, such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.CITATION:Willingham, D and Xie, Y, 2017. Predictive maintenance modelling for bearing-based equipment, in Proceedings Iron Ore 2017, pp 627–630 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 24, 2017

By R Marchant, D Willingham

"Predictive maintenance (also known as PHM or equipment ‘health monitoring’) refers to the intelligent monitoring of equipment to avoid future equipment failures. In contrast to conventional preventive maintenance, the maintenance schedule is not determined by a prescribed timeline; instead, it is determined by analytic algorithms, such as machine learning, using data collected from equipment sensors and/or via simulation.Predictive maintenance offers the following benefits for operators and equipment manufacturers:reduction of equipment downtime by identifying issues before failure, thereby enabling convenient scheduling of equipment service and extending equipment lifetimeautomatic determination of the root cause of the failure, enabling appropriate service to be performed without utilising resources to determine a diagnosis avoidance of costs of unnecessary maintenance and unexpected failures improved safety and product quality.Algorithms and data are critical to predictive maintenance success. Sensor data preprocessing is performed using advanced statistical and signal processing techniques. In the case that data isn’t available for the full range of operating modes, operating scenarios can be simulated. Machine learning techniques are then used to estimate equipment health. Once tested, predictive maintenance algorithms may be operationalised in an IT environment such as a server or cloud. Alternatively, algorithms may be implemented in an embedded system directly on the equipment, allowing for faster response times and significantly reducing the amount of data sent over the network.In this paper, we use both historical and simulated data of the plant under various modes of operation including run to failure, and we then leverage machine learning techniques in MATLAB to estimate degradation and remaining useful life. This technique, of combining both simulation models and machine learning, in the areas of predictive maintenance and equipment health monitoring has been adopted by Safran, makers of aircraft jet engines, since 2007. They now have over 160 modules developed (Lacaille and Aurelie, 2015).Citation:Willingham, D and Marchant, R, 2016. Predictive Maintenance Using Simulation and Machine Learning, in Proceedings 13th AusIMM Mill Operators’ Conference 2016, pp 279–284 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Oct 10, 2016

By A Fitzpatrick

An integrated and dynamic water balance and water quality model for a proposed base metal mine has been created using the GoldSimÖ software package. The model was initially developed to address the water quality of the Tailings Management Facility pond and how pond discharge would potentially affect downstream water quality. However, due to changing project objectives, the modelling has expanded over time through an iterative process to encompass the full mine site and all receiving environment water quality. The model results have been used as part of the mine planning/design process but are also intended to be used for the environmental impact assessment and permitting process. The model has been built to allow for changes to input terms to be readily made as additional data becomes available. The model results serve as a prediction of water quality and the potential project impacts to the receiving environment water quality. This paper presents the process by which data from a wide variety of sources was used to construct the model and how the model results were used to guide development of a conceptual water management plan. In addition, the role of the model in establishing proposed discharge criteria will be highlighted. The model has been delivered as a fully functional application with the intent that the end-user can be other than the modeller, the development of this aspect shall also be covered. CITATION:Fitzpatrick, A, 2012. Predictive site-wide water quality modelling as a tool for mine planning and permitting - A recent Canadian example, in Proceedings Life-of-Mine 2012 , pp 71-78 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 10, 2012

By J Q. Shi, M Karakus, Y Liu

Predicting the spatial distribution of rock mass properties in cave mining is a critical challenge. In this study, we introduce a novel machine learning-based model to advance our comprehension of rock behaviour. This model specifically focuses on the spatial distribution of rock mass and mechanical properties, a crucial aspect of our understanding of rock mechanics. Our model is inspired by the recognition that neighbouring information serves as crucial prior knowledge for predicting the spatial distribution of these properties, wherein closer neighbours are likely to exhibit similar characteristics. To effectively capture and model these neighbour relationships, we design a neural network-based model to map the spatial locations within the data set into a new highdimensional space. Within this transformed space, we utilise Euclidean distance to identify N neighbours. Leveraging these neighbours, we introduce a straightforward yet highly effective approach—label propagation—to construct a graph that illustrates neighbour relationships. This graph not only facilitates the prediction of the spatial distribution of rock mass properties but also offers a measure of uncertainty for these predictions. We validate the proposed method using a real rock data set and showcase its efficacy in capturing intricate relationships and distributions within rock properties, enabling precise predictions across diverse spatial scales. Our results underscore the potential of machine learning-based approaches to revolutionise the field of rock mechanics, providing valuable insights applicable to geotechnical engineering, mining, and other sectors where an accurate understanding of rock properties is paramount. This model has direct practical implications, offering a new tool for geotechnical engineers and mining professionals to enhance their understanding and prediction of rock properties, thereby improving safety and efficiency in their operations.

Sep 1, 2024

By M J. Nugus, F Clark, E J. Hill, J T. Carswell, J S. Cleverley, N H. S Oliver

High-nugget gold orebodies are difficult to model in 3D because samples from diamond drill core are generally too small to provide reliable estimates of the gold content of the larger body of rock, and therefore interpolation of gold assay values between drill holes is also unreliable. As a result, gold resources are generally underestimated in areas of high nugget. In practice, proxies for gold mineralisation, such as presence of alteration, specific structures or high vein density, have been used by geologists to predict rocks which are favourable for mineralisation. However, there has been no automated method for combining the predictive capabilities of several proxies, except by human judgement.We have been developing a method to combine several different types of geological features or geochemical analyses into a single measure using conditional probability. Conditional probability provides a meaningful numerical measure for the favourability of rocks for containing high-grade gold mineralisation. This measure can be interpolated in the exploration space, providing a sound basis for a 3D model of the orebody. We have developed this technique using data from the Sunrise Dam Gold Mine, which is a high-nugget orebody hosted in a structurally complex Archaean greenstone belt and which has historically proved to be a very challenging orebody to model.CITATION:Hill, E J, Nugus, M J, Clark, F, Oliver, N H S, Cleverley, J S and Carswell, J T, 2013. Predictive three-dimensional modelling of nuggety gold orebodies, Sunrise Dam, Western Australia, in Proceedings World Gold 2013 , pp 463-468 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 26, 2013

There is considerable variability in the level of detail incorporated in prefeasibility studies relating to mining developments. Hence, when undertaking a review or audit, it is critical to attempt to ascertain at an early juncture precisely who has compiled the various facets of the study, for what purpose it was intended and the terms of reference given to the authors. Likewise, when commissioned to undertake an audit it is preferable to have proper terms of reference and to be aware of the purpose to which the audit will be put.These factors will influence the degree of detail an auditor would expect to find in the study and consequently, the degree of effort/time required for the audit process. In most instances audits do not imply recalculation of the entire study or modifying it in anyway, but involve an overall assessment of the methodology used, combined with spot checking of detail. One of the most difficult aspects is to determine a realistic order of accuracy for the overall study when the accuracy of the individual components may differ markedly.

Jan 1, 1990

By K B. Quast

During the prefeasibility stage of the mining cycle for a gold deposit, an evaluation of the ore leaching characteristics from various defined areas is essential prior to plant design. There are a number of simple laboratory tests which have proven successful in establishing gold and silver leaching kinetics, carbon adsorption and fouling, as well as elution characteristics. A number of case studies of carbon-in-pulp (CIP) simulation are presented which show excellent agreement between calculated and assayed gold and silver grades in feed and adsorbed on carbon which validate the methods used.

Jan 1, 1998

By D McB Martin, A Piechocka, M Jessell, M Lindsay, E Ramanaidou

The Hamersley Basin of Western Australia, approximately 500 km long by 200 km wide, is recognised globally for its rich endowment of iron ore resources. Two types of bedded iron ore mineralisation are recognised: the Proterozoic hypogene martite-microplaty hematite ores; and the more recent Cenozoic supergene martite-goethite ores. Exploration strategies depend on the style of mineralisation being targeted; however, stratigraphy and structure are fundamental controls on both ore types. Therefore, the ability to visualise the 3D geology of the entire basin is critical for the understanding of iron ore occurrences and their relationship to ore genesis of bedded iron ore mineralisation. However, due to the large size of the basin, generating a 3D geological model of the regional stratigraphy and structure has not been possible, or prioritised, primarily due to software limitations. Recent advances in implicit automated geological modelling presents an ideal opportunity to develop a 3D model of the entire Hamersley Basin. The innovative Loop software was used to model open source geological data (ie geological mapping, outcrop orientation data, and fault data) sourced from the Geological Survey of Western Australia. In addition to generating a 3D geological model, information such as stratigraphic thickness and fault offset information was extracted using the map2loop module and demonstrated its application to exploration targeting. This study demonstrates how a basin-wide 3D geological model was used to conduct a regional structural analysis of the Hamersley Basin. The results of this study provide opportunities for other prospective regions to be rapidly modelled in order to enhance the understanding of the geology and improve exploration targeting.

Nov 8, 2021

At Hilton Mine, 18 kilometres north of Mount Isa, the silver-lead-zinc sulphide bodies have undergone strong deformation in the form of folding and later faulting and brecciation in the hangingwall region of the mine. Variation in grades and widths over compara- tively short distances causes problems regarding accurate sampling of orebody exposures in underground development, and in defining confidence limits for the extrapolation of ore intersections in ore reserve calculations. The paper gives a preliminary insight into the problem of obtaining representative samples for exposed orebodies and discusses the sampling techniques employed. Results gained from these sampling methods could not be verified due to lack of individual bulk samples for comparison but strong evidence for precision was provided. INTRODUCTION In a developing mine like Hilton, accurate grade information is of extreme importance. Original grade information for a potentially viable ore deposit is gained from surface drilling, and initial ore estimates are calculated from the resultant ore widths and grades. After underground development has proceeded, the opportunity arises for detailed examination of orebody exposures, and the resultant assay grades and widths from sampling of these exposures may, in the long term,

Jan 1, 1976

By H Asche

The proposed Rockwater Power Project is a hydropower development on the Waitaki River in the South Island of New Zealand. The proposed development will have an installed capacity of 250 MW with a peak flow capacity of 260 m3/s. The project commences at Lake Waitaki and extends some 35 km downstream on the north bank to a surface power station at Stonewall. Flow is returned to the Waitaki River via an outfall channel.The tunnelling for the project is through Greywacke, which comprises Permian and Triassic age marine sediments that are well indurated and weakly metamorphosed. The rock mass is jointed and sheared and there are frequent faults, which range up to hundreds of metres in width. Three 12 m diameter tunnel boring machines (TBMs) are proposed to excavate the 35 km long tunnel.The paper describes the development of the tunnel alignment, rock mass classification, design of the support, rock and hydraulic loads, support installation options and construction sequencing.The alternatives of rock bolts and shotcrete and segmental lining were both investigated and are discussed. Support using rock bolts and shotcrete offers the potential of rapid construction with minimum support cost when the ground conditions are good, but delays due to support installation increases with deteriorating ground conditions. In contrast, segmentally lined tunnels offer greater certainty in cycle time with reduced program risk. Both options are described and discussed.The horizontal alignment is affected by the presence of major fault zones, which are either avoided, or pretreated by driving conventional tunnels through in advance of the TBM delivery, topographical constraints with the rugged topography and operational hydraulic constraints. Construction requirements, future power station operation and the minimisation of leakage from the tunnel have all impacted on the vertical alignment.The number and sequencing of TBMs to be used is discussed in terms of program time and risk minimisation.

Mar 8, 2011

In a previous study, various pyrite depressants were examined to improve the flotation performance of a copper-sulfide ore. Optimal results from this study were used to examine the effect of elevation on recovery and grade. Tests were conducted at elevations of 3350, 1735, 610 and -760 metres, consisted of five repetitive experiments for statistical analysis, and were performed both with and without airflow control. Tests were also performed in a glove box at Montana Tech to mimic the pressure conditions. Results indicate that both recovery and grade are dependent on pressure via bubble size and airflow suggesting that pressurised flotation cells should be considered for operations, particularly those at high elevation. Economics are extremely favourable for implementation because ensuing capital expenses are inconsequential with minimal time for return-on-investment.

Jan 1, 2005

By D M. Weedon

This paper introduces a novel roll crusher design, which incorporates non-cylindrical crushing rolls. The use of non-cylindrical rolls enables the crusher to achieve a greater reduction ratio than is possible with conventional roll crushers. Referred to as the NCRC (non-cylindrical roll crusher), the new crusher incorporates two rolls comprised of an alternating arrangement of plane and convex or concave surfaces. The two rolls are complementary and arranged so that when they rotate the convex and concave surfaces of each roll are substantially opposed. With the rolls rotating in opposite directions at the same speed, this arrangement produces a gap between the rolls that remains approximately constant. Discontinuities on the roll surfaces provide improved angles of nip, which increase the probability of larger particles being drawn into the crusher. Initial tests on a scale prototype of the NCRC have indicated that reduction ratios exceeding 10:1 are achievable.

Jan 1, 2000

The Ngakuru zeolites are located about 20 km south of Rotorua and comprise a number of deposits dispersed along several fault lines. Relatively recent volcanic activity (250,000 years ago) resulted in ash depositions that vary in depth and mineralogy. Subsequent variable thermal alteration intensities generated predominantly clinoptilolite and mordenite zeolites with a range of physical characteristics. Typically the zeolites have low density (650 kg/m3), have high liquid (60% of dry weight) and odour absorption characteristics, and a high affinity for soluble cations (CEC =: 100 meq/100 g). Traditional kitty litter and oil absorbency markets are being expanded. New opportunities in waste and potable water treatment, organic and synthetic slow release fertilisers, use as a light weight aggregate for speciality concretes, and pozzalome activity are currently being researched.

Jan 1, 2000

By Winter W, Steele DA, Trudu AG, Gray CM

The Luzon arc extends for about 900 km from Taiwan to the island of Mindoro (central Philippines); in this area late Tertiary to Recent volcanism is considered to be the result of eastward subduction along the Manila Trench. The arc is composed of five segments, all of which have individual characteristics with respect to tectonic setting and magma chemistry (Defant et al., 1989). From the north to the south these are (Fig. 1): (1) the eastern Coast Ranges of Taiwan and the islands Lan-Hsu and Lu-Tao (Taiwan Segment), (2) the islands off the north coast of Luzon (Babuyan Segment), (3) the Northern Luzon Segment, (4) the Bataan Segment, and (5) the Mindoro Segment. Defant et al. (1988) suggest that the Bataan and Mindoro segments of the arc, both of which are characterized by different isotopic compositions and levels of large ion lithophile element (LILE) enrichment in the lavas, are separated by a 'pull-apart' zone, the Macolod Corridor.We are currently studying the mineralogy and petrochemistry of Late-Miocene to Pliocene volcanism in the Northern Luzon Segment (NLS), which comprises the geographic area of the Cordillera Central. This area has so far received limited attention, inspite of its wealth of porphyry copper, skarn and epithermal gold deposits. In this contribution we report the first results of our study of igneous rocks from the Mankayan Mineral District (MMD) which is located in the centre of the arc segment under consideration.

Jan 1, 1990

The Greymouth Coal field is situated on the West Coast of New Zealand's South Island and has been mined for coal for more than 120 years. The area covered in this paper is within the Rapahoe Sector of this field and contains >200 million tonnes of low ash, low sulphur, bituminous rank coal. Paper No 6, presented to the 19th Annual Conference of the New Zealand Branch of The Australasian Institute of Mining and Metallurgy in Christchurch in August 1985 by R G Bowman, explained 'A Geologist's View' of the 'Economic Potential of the Greymouth Coalfield', and describes previous geological assessments of the Rapahoe Sector.

Jan 1, 1989

QEMSCAN« technology is a widely accepted automated e-beam technique providing quantitative and statistically robust mineralogical data to the mining industry. It uses X-ray mapping combined with backscattered electron (BSE) brightness to identify minerals in almost any type of geological material (Gottlieb et al, 2000). Besides the modal mineralogy also the liberation, associations, average grain size and other textural information can be obtained. The aim of this study is to test reproducibility of this technique by setting up and running standard tests on different QEMSCAN« systems. Multiple sample blocks were prepared by our standard operating procedure as epoxy-mounted, carbon-coated, polished blocks. The replicates were measured under the same conditions and measurement parameter settings on different QEMSCAN« instruments. The relative standard deviation (RSD) for the set of measurements was calculated. Generally the reproducibility of the systems themselves was found to be excellent, ranging between 0.4 and 1.5 per cent RSD. The more significant change in the reproducibility was found to be caused potentially by the sample preparation and variation in the mineralogy of the replicates. The measurement parameter settings together with these variations can account for almost 15 per cent RSD for minerals of low abundance. For analysed samples good results for the main phases present in quantities higher than 2 - 5 wt per cent of the total mass of the sample were confirmed with RSD generally below six per cent. Following the experiments, the parameters having the biggest influence on the performance of the QEMSCAN« and its precision are discussed in detail. The importance of sample preparation procedures and measurement parameter settings is emphasised. It is shown that experience and excellence is necessary for getting the most from the automatic mineralogical methods like QEMSCAN«.

Jan 1, 2008