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By C Xu, B Li

Multi-sensor fusion visual perception technology significantly enhances visual perception capabilities in complex and harsh environments by combining visual data from different sensors. This technology is especially useful for scenarios where traditional single visible light sensors, such as standard cameras, struggle due to poor lighting, adverse visual conditions, or other visual obstructions. In environments with low light, smoke, or dust, the performance of conventional visible light sensors degrades, limiting their application. By integrating data from various sensors like radar, infrared thermal imaging, and others, multi-sensor fusion technology provides a more comprehensive and complementary visual solution. This fusion not only enhances the robustness of visual systems but also greatly expands their potential applications in fields like visual surveillance. In recent years, deep learning methods have become the mainstream technology for addressing multi-sensor fusion issues, demonstrating significant advantages in adaptive feature selection compared to traditional machine learning methods. However, deep learning-based multi-sensor fusion algorithms still face several challenges. The design of network structures is often redundant, lacking effective screening of useful components within multimodal information. Mainstream fusion algorithms focus excessively on improving display effects without adequately considering the needs of downstream applications and tasks. Existing fusion perception algorithms are generally designed for open visual scenes and lack targeted design for complex visual degradation factors present in tunnel environments of mines. Current deep learning methods for designing multi-sensor fusion networks heavily rely on manual experience to create fusion modules. This reliance increases the complexity of network design and can lead to redundancy. Redundant network modules are difficult to identify in end-to-end learning tasks at this stage, significantly slowing down network inference and potentially interfering with the output of fusion perception. Additionally, training these fusion modules typically requires large amounts of well-annotated multimodal data, further increasing implementation difficulty and cost. For instance, the fusion network design proposed by Li and Wu (2018) focuses on extracting multimodal fusion features, while Zhao et al (2020) introduced a feature decomposition mechanism in the feature fusion and extraction modules. Researchers like Zhang and Ma (2021), Xu et al (2020), and Liu et al (2017) have attempted to improve network structures through dense cascades and the introduction of residual connections. Although these methods have improved the handling of multimodal information to some extent, they still face limitations in distinguishing between useful and redundant information, with redundancy persisting in network designs. Ideally, the design of fusion networks should be more intelligent, guiding network structure design heuristically based on the importance of different modal information for the current task. This would reduce information redundancy and network weights, requiring the development of new algorithms or frameworks capable of automatically identifying and optimising fusion strategies, thereby reducing dependence on manual experience while improving fusion efficiency and network performance. Current deep learning designs for multi-sensor fusion algorithms often overlook the

Sep 1, 2024

By G N. Musafer, M H. Thompson, R C. Wolff, E Kozan

The most important aspect of modelling a geological variable, such as metal grade, is the spatial correlation. Spatial correlation describes the relationship between realisations of a geological variable sampled at different locations. Any method for spatially modelling such a variable should be capable of accurately estimating the true spatial correlation. Conventional kriged models are the most commonly used in mining for estimating grade or other variables at unsampled locations, and these models use the variogram or covariance function to model the spatial correlations in the process of estimation. However, this usage assumes the relationships of the observations of the variable of interest at nearby locations are only influenced by the vector distance between the locations. This means that these models assume linear spatial correlation of grade. In reality, the relationship with an observation of grade at a nearby location may be influenced by both distance between the locations and the value of the observations (ie non-linear spatial correlation, such as may exist for variables of interest in geometallurgy). Hence this may lead to inaccurate estimation of the ore reserve if a kriged model is used for estimating grade of unsampled locations when non-linear spatial correlation is present. Copula-based methods, which are widely used in financial and actuarial modelling to quantify the non-linear dependence structures, may offer a solution. This method was introduced by Bßrdossy and Li (2008) to geostatistical modelling to quantify the non-linear spatial dependence structure in a groundwater quality measurement network. Their copula-based spatial modelling is applied in this research paper to estimate the grade of 3D blocks. Furthermore, real-world mining data is used to validate this model. These copula-based grade estimates are compared with the results of conventional ordinary and lognormal kriging to present the reliability of this method.CITATION:Musafer, G N, Thompson, M H, Kozan, E and Wolff, R C, 2013. Copula-based spatial modelling of geometallurgical variables, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 239-246 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By J A Evans

A well-maintained and low cost mining operation today requires the use of condition monitoring techniques. At ERA - Ranger Mine oil analysis, vibration monitoring, infra-red themtography, and performance analysis have been successfully applied over the last four years to lower operating and maintenance costs. All condition monitoring is scheduled through a comprehensive in-house developed maintenance management system known as the Engineering Information System (EIS). (EIS) is used extensively at Ranger to ensure consistent frequency and recording .of all condition monitoring data. This paper provides an insight into the benefits of condition monitoring and explores the future direction for condition monitoring techniques at Ranger.

Jan 1, 1994

By Glasson K. R

The history of Ore Reserve classification and reporting in Australia is largely a history of the Australian mining industry itself. In the late 1800s, when the industry was dominated by small scale, high grade underground mines, Reserves (if estimated) were based on hand sampling and geological inference, and reporting of Reserves was either non-existent or of extremely variable quality. By the mid-20th century, the industry was dominated by large underground base metal and gold mines. Reserve estimation was coming under the control of skilled geologists and mining engineers, however Reserve classification and reporting standards continued to vary in type and quality from mine to mine. Today, large scale, low grade mining operations predominate, and Reserve estimates are generally based on extensive drilling results processed (with geological control) by computer. Through the efforts of a Joint Committee established by The Australasian Institute of Mining and Metallurgy and Australian Mining Industry Council, Reserve and Resource classification and public reporting has been standardised and controlled by adoption of the ""Australasian Code for the Reporting of Identified Mineral Resources and Ore Reserves"" released in 1989. The Code, which will continue to be modified and improved by the Joint Committee, has also been used as a basis for guides to reporting in other countries, and there is now some hope that this difficult and vexing topic may finally become subject to international standards and controls.

Jan 1, 1992

At Bulong, the mineralised (>0.8 per cent nickel) ore exists within numerous lithologies, which can and do occur coincidently. It is very important to discriminate between the different host units for estimation purposes, stockpiling and scheduling. This allocation of host unit has relied upon visual interpretation by the geologist recording the information, which can be very subjective by its nature. By using quantified constraints, such as assayed magnesium oxide and iron, the subjectivity of describing the host material of nickel laterite ore is reduced. The constraints permit consistent identification of the various host units, which improves the confidence in the overall estimation and scheduling of the ore.

Jan 1, 2000

By S A. Prevec

"An EXTENDED ABSTRACT is available for download. A full-length paper was not prepared for this presentation. Traditional models employed to genetically link associated chromite and platinum-group elements have favoured crustal contamination as the primary process for inducing sulfide and oxide saturation. The association of chromite ores with crustal contamination is well established in the literature. However, in the 1970s (cf Irvine, 1973, 1975), Irvine demonstrated that crustal contamination was in fact problematic as a tool for inducing chromite precipitation in mafic to ultramafic magmas. His results are re-examined here, along with some attempts to reconcile them with the effects of adding Fe to the models. It is concluded that crustal contamination is indeed problematic based on phase relationships alone. However, the role of fluids as a compensating agent is evaluated, which may explain why crustal contamination may indeed facilitate oxide and sulfide precipitation.CITATION:Prevec, S A, 2015. Chromite and platinum-group elements coprecipitation by crustal contamination or magma mixing revisited – genetic and exploration implications, in Proceedings PACRIM 2015 Congress, pp 311–316 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Mar 18, 2015

By J E. Everett

Ore selection is generally a highly subjective task, with emphasis on producing product with a consistent target grade matching a perceived market requirement. A composite selection criterion ‘Comp’ (a linear function of the grade vector) can be used to maximise ore tonnage at a specified target grade (in Fe and contaminants) extractable from a block model. The blocks correspond to the selective mining units, with grades interpolated from diamond drill hole data. An iterative procedure has been developed to identify the coefficients and cut-off value for the composite function ‘Comp’, maximising potential ore tonnage at target grade.Commonly, target grade is predetermined by marketing; however, there is no guarantee that maximising ore tonnage at a specified target grade maximises the potential value of the mine. If the value of ore could be expressed as a linear function of the grade components, then these coefficients would be appropriate for the selection criterion ‘Comp’. The cut-off value for ‘Comp’ would correspond to the marginal cost of production; all blocks with a ‘Comp’ score above the cut-off would be classified as ore; and the cumulative grade of these ore blocks would be the average product grade over the life of the mine.Unfortunately, no such value function for iron ore is generally available. Methods for estimating the value function are discussed, using the spot price and sensitivities based either upon operational tolerances for Fe and the contaminants or upon published price differentials. This approach has been applied to an anonymous Pilbara deposit with about one million blocks averaging 1.1 kt. It is shown how the optimal target grade, cut-off grade and maximum tonnage can all be explored as a function of marginal cost and iron ore price. Alternatively, given marginal cost and an iron ore price, the effects of changing the tolerances (and therefore the relative costs of the contaminants) can also be explored.It is suggested this procedure can enable informed decision-making for an iron ore mine. Rather than setting final policy for the entire life-of-mine, the method enables target grades and tonnage estimates to be revised by re-computation as the mine life proceeds to recognise changes in market conditions and knowledge about the resource.It should be emphasised that this paper is concerned with ore selection rather than ore sequencing. Appropriate ore selection is a necessary but not sufficient condition for maximising value. Having identified the set of blocks that potentially maximise value, the sequence in which they are mined will present a trade-off between producing at constant grade and extracting high value ore early. The sequencing issue is not considered here. It is shown that optimum ore selection for an open pit mine is independent of any consideration of discount rates, although ore sequencing will depend upon the time value of money if the product grade is to vary across time.CITATION:Everett, J E, 2015. Ore selection to maximise value, in Proceedings Iron Ore 2015, pp 443–448 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 13, 2015

By S Prakash, M Danish, D Kumar

The green energy transition is speculated to cause an increase in the demand for raw materials while recycling rates are improving; discoveries are required to maintain supply. Geological remote sensing offers a cost-effective way of exploring mineral resources. While satellite and airborne imaging are routinely used for exploration targeting, a gap remains at the outcrop scale. Hyperspectral imaging (HSI) is advantageous for large-scale regional mapping, typically performed from satellite or airborne platforms. Accurately mapping geological lithologies remains challenging in inaccessible areas, such as rough terrain or mine sites. Proximal sensing techniques can boost geological activities’ reliability, safety, and efficiency in exploration or during mining activities. It also generates objective, reproducible results and valuable raw data that can be archived, further processed, and re-interpreted. Ground-based hyperspectral imaging of outcrops has steadily increased during the last decade due to sensor development and better processing routines. There is often a big technology gap related to mineral mapping, integration of images and spectra (reduction in manual identification of minerals and types) and hyperspectral data visualisation through remote sensing. TEXMiN Foundation, in collaboration with its industry partner Dassault Systemes Foundation, carried out the project, ‘Developing Hyperspectral Image Processing and Analysis, build a rich library of Hyperspectral images of a vast range of minerals from India which will help in increasing prediction accuracy of mineral deposit for mining exploration’.

Sep 1, 2024

By King MN, Travis IC

PISCES - the Planning and Information System for Coal Export Services has been developed by Port Waratah Coal Services (PWCS) to ensure the efficient and economic operation of the Hunter Valley Coal Chain. PISCES is a computer based integrated information and management system to coordinate operations commencing at minesites through the coal chain to shiploading at the Port. The PISCES system comprises a central database around which a number of planning modules interact. These include interactive programs for train planning and timetabling, stockpile management, berthing analysis and a coal shippers enquiry system. PWCS has previously developed - number of computer based systems for planning, controlling and monitoring complex coal handling operations and the extension of the PISCES system to the Hunter Valley Coal Chain is another initiative to assist the Hunter Valley Coal Export Industry in looking confidently towards the Future.

Jan 1, 1987

By S Hapugoda, H Han, L Lu

Sinter quality depends heavily on the mineral phases, textures and physical structure of the sinter, which are formed upon solidification of the sinter melt. The interaction of sinter initial melt with its surrounding materials will impact the properties of the sinter melt and consequently the solidified structure and quality of resultant sinter. Spreading and penetration are two important phenomena dictating the interaction of the initial melt with its surrounding materials, such as nuclei and adhering fines. In the present study, the penetration behaviour of an initial melt into various natural iron ore substrates of different ore types during sintering was studied using a laboratory scale penetration testing procedure. The penetration depth and width were found to increase with temperature and vary with the type of iron ore due to their unique pore structure and mineralogical texture.

Nov 8, 2021

Late Cretaceous and early Tertiary coals in the Greymouth, Ohai and Kaitangata coalfields represent a major national energy resource comprising-up to 2500 x 106 tonnes of coal in ground. The three coalfields are closely related in terms of age and sedimentary and structural character. The most striking feature common to all is the complexity of the geology which has severely restricted exploration and mining of these coal resources. This complexity is manifest in the irregular lensoid nature of coal seams and by the incidence of unpredictable medium and small scale faulting and folding. There are many geological similarities between these coalfields which are considered to arise from a common origin which can be related to the plate tectonic evolution of the southwest Pacific region. In order to efficiently assess and exploit these coal resources it will be necessary to know their geology in great detail. This can he achieved by increasing the understanding of the sedimentary and structural evolution of the coal bearing basins. This knowledge may be gained by the application of traditional (ie drilling, field mapping) and innovative techniques (ie high resolution seismic, in seam seismic) with on going research-oriented studies (ie basin analysis, coal petrography).

Jan 1, 1987

An efficient energy management programme can often significantly reduce a company's energy costs. The programme consists of a series of steps, namely a statement of support from senior management, assigning the management team, the energy audit, defining the goals and comwnicating with and training employees. The energy audit provides the information base from which the company can start to control and improve upon energy Usage.

Jan 1, 1983

By X Yang

White phosphorus (P4) is a critical element in industry, but its current production from phosphate rock using the carbothermal reduction method raises significant environmental and health concerns. This pressing issue necessitates the development of new technologies that facilitate the sustainable production of P4. In response, my team is actively developing an innovative, coke-free approach for white phosphorus production. This method involves the dissolution of phosphate rock in molten CaCl2, followed by the extraction of P4 through electrochemistry at relatively low temperatures. Moreover, this approach has the potential to utilise phosphate-bearing solid wastes as raw materials, further enhancing its environmental benefits. By employing this cutting-edge technique, we aim to revolutionise the phosphorus chemical industry towards greener and more sustainable practices. This paper provides evidence of the feasibility and effectiveness of our proposed approach.

Aug 21, 2024

By B D. Sowerby, N G. Cutmore

Improved process control can be achieved through the accurate and reliable on-line measurement of critical process parameters in real time. Over the past few decades on-line analysis techniques have been developed and applied in the mineral and energy industries for the measurement of process stream composition, mineralogy, particle size and flow rates. This has led to increased throughput and product quality while reducing processing costs and minimising emissions. The support of a sustainable mineral processing industry will require the further development of on-line analysis and control technologies. The application of developments in instrumentation combined with improved control strategies will lead to improvements in process efficiency resulting in large economic benefits to the industry, reduced energy usage and lower process emissions. There is a clear convergence of interests in being able to better measure and control minerals processing in terms of the triple bottom line of economic, social and environmental impacts. Technology is increasingly becoming available enabling measurement across the entire spectrum of minerals processing from mining to hydrometallurgy and pyrometallurgy. CSIRO is developing a range of these new technologies. A number of these developments are discussed in the present paper, namely: on-line particle size analysis and acoustic emission methods to enhance the control and optimisation of grinding circuits; on-line mineralogy of dry powders and slurries; on-line bulk elemental analysers for application on conveyor belts and in smelters; and the development of new sensing technologies for sorting base metal ores.

Jan 1, 2002

By A Melkumyan, M Balamurali

Geological domains describe the mineralisation state and stratigraphic location of different geological regions of mines. They play a very important role at the geological modelling stage as weight percentage of chemical elements information within each geological domain is assumed to be correlated while information coming from different geological domains is considered uncorrelated. Having correct domain assignments to both the chemical assays and the 3D space of modelling is critical for building a reliable geological model of the mine.As standard practice significant effort is invested into the interpretation of the deposit and identification of the geological domains for the chemical assays and the 3D geological space. Despite these efforts, there are some chemical assays with ambiguous geological domain where the exploration assay domain and the block domain do not match. Incorrectly including a sample in the wrong domain can cause a large change in the predicted weight percentage of chemical elements estimation for that block. Therefore, the major challenge of grouping the spatially correlating weight percentage of chemical elements is how to determine which of these domains best fits these misclassified samples.This work proposes machine learning based autonomous technique for identifying to which geological domain (the exploration assay domain or the block domain) the misclassified sample belongs. The proposed methodology is based on Gaussian Process (GP) classification. The GP machine learning approach consists of training and inference stages. At the training stage the GP automatically learns the properties of different geological domains using the chemical assays with unambiguous domain assignment. Then, at the inference stage the GP makes predictions for the geological domains of the assays with ambiguous initial domain assignment.We tested the proposed technique by conducting a case study for an iron ore deposit located in Western Australia. The results demonstrate that the application of the proposed GP technique removes statistically significant deviations in the assay values in many regions: mineralised ore region, unmineralised region, highly weathered region and intrusive material that is highly variable. In this case study the proposed methodology autonomously identified the geological domains for more than 50 per cent of samples which are initially ambiguously classified. For the majority of the assays the GP predicted domain agrees with the geological domain assigned by the exploration assay.CITATION:Balamurali, M and Melkumyan, A, 2014. Automated identification of geological domains for exploration assays with ambiguous initial domain assignment in an iron ore deposit, in Proceedings Ninth International Mining Geology Conference 2014 , pp 99–106 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 18, 2014

By Pollard D. M

The removal of nickel from lead by sulfur drossing was investigated using a technique which models industrial batch decoppering practice. Successful denickeling to 0.005% (Ni)pb was consistently obtained in the absence of silver, well below the experimental equilibrium value of 0.015% but still above levels obtained in industrial practice. Silver appeared to have little effect upon denickeling. The final nickel content was dependent upon the initial nickel content, the total sulfur addition and the stirring time. The presence of nickel was found to give no assistance to successful decoppering. Simul- taneous removal of nickel and copper was observed at silver contents in excess of 0.02%. Extended stirring resulted in a further lowering of the nickel content but reversion of copper to the bullion was observed. Experimental work has shown that nickel can be removed by a standard decoppering operation; a procedure which is used in secondary lead re- fining. Alternatively nickel can be removed during initial decoppering in primary lead re- fining operations.

Jan 1, 1987

By Wingham DW

Based upon the previous work of Norman and Loeb, a method, was used, to assess-the comparative wearing properties of ten different alloys being used, or being considered for use, as mill liners and grinding media at Mount Isa. The materials to be tested were obtained in the form of large diameter balls and were run, along with the regular ball charge, in a large ball mill operating-under normal grinding conditions. At selected. intervals, the test balls were removed from the mill, weighed and identified, and their relative wear rates determined.

Jan 1, 1963

The paper outlines the development and objectives of the slim core (50 mm dia.) testing procedures employed at the Company's Central Research Laboratories with particular reference to washability studies required for coalfield evaluation. It shows that when the routine test procedures, that have been developed for cores recovered form the Sydney Coal Basin, were applied to Bowen Basin Coals some of the washing data obtained appeared to be suspect. Though yield indications appeared to be of the right order, product ash, required separating gravity and percentage of near gravity material were found to be at variance with bulk washability data. The results of a correlation study made on an underground seam from the Bowen Basin comparing slim core, hulk sample, and pilot plant washing data are given. The results of a series of studies including washability studies made on slim cores, strip samples, large diameter cores and bulk samples, pilot washing trials and computer simulations of washery circuits treating Lilyvale seam coal from the Bowen Basin are shown.

Jan 1, 1977

By M Robotham

In April 2013, Rio Tinto Kennecott Copper’s (RTKC) Bingham Canyon mine experienced what is arguably the world’s largest ever in-pit slope failure. The failure initiated along a major, continuous, low-strength bedding fault, named the Manefay bed, and comprised approximately 145Mtonnes of rock and waste dump material. Initial East wall slope deformations were detected some months prior to catastrophic slope collapse by RTKC’s slope monitoring systems. However, the failure mechanism was different to that which had historically been experienced in the prior more-than-100 years of open pit mining. Failure occurred as two separate events, the initial one likely consisting of a frictional failure of rock materials along the Manefay bed itself, before a second phase of failure occurred, which included a large mass of old rail dump material. The combination of these events led to a much larger failure run-out than had been anticipated by the RTKC team which led to loss of, and damage to, mining equipment and infrastructure as well as significant interruption to mining activities. Due to the diligent monitoring of the slopes no safety impacts were experienced at the mine, and as such the details and learnings from this failure can readily be shared with the broader mining and geotechnical industries to avoid similar recurrences elsewhere. This is an extended abstract only. CITATION:Robotham, M, 2018. Bingham Canyon Mine – from adversity to excellence, in Proceedings The Fourth Australasian Ground Control in Mining Conference (AusRock), pp 3–4 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 30, 2018

Integrated chemical thermodynamic software and computerised databank systems have evolved over the last 15 years. The extent to which these systems have automated thermodynamic calculations relevant to extractive metallurgy is examined. The scope for further automation and the role of the expert consultant is discussed.

Jan 1, 1991