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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

International tenders for contract mining opportunities require the Contractor to make an assessment of risk for an operation with a life span of up to twenty-five years usually based on data made available by the Client. This paper seeks to address how the Contractor assesses the data and quantifies the risks associated with the project. The areas of risk are common to most projects, but in the--Lontractor's case, risk assessment is carried out on the Client's data base and within a compressed time frame of usually less than six months. Incorporation of that risk assessment into the contract documentation is also discussed. Practical examples are given in a number of areas.

Jan 1, 1986

By R deMoraes, A Bobet

The last decade has seen a resurgence of interest in lunar exploration and the emergence of countries like China and India as space fairing nations. In 2004, the US announced a new Vision for Space Exploration, whose objectives focused on human missions to the Moon and then Mars. The near future is likely to see the emergence of a worldwide drive to revisit the Moon as the first step in investigating the Solar System. To date, the Apollo missions provide our only experience of human operations on the Moon or anywhere else beyond Low Earth Orbit. Much was learned from these missions. However, their short duration means that many of the environmental effects that will be important for longer-duration missions could not be quantified. In addition, long-duration missions and infrastructure on the Moon require new technologies and capabilities, which must operate successfully and reliably in this lunar environment. Developing these technologies poses significant challenges for the exploration program. The paper discusses the risks associated with the construction of lunar surface and underground structures and the challenges to adopting the standards and rock mass classification systems developed on Earth and their applicability to the Moon. Moreover, an exchange of views to explore near-surface geologic and geotechnical profiles of the Moon is emphasized and implications of the lack of knowledge on rock mass characterisation of the Moonrock mass, the lack of theoretical/empirical experience on the use of rock mass systems outside Earth, and uncertainties.

Nov 29, 2022

By C M. Sennitt, M J. Spadafora

Systematic field evaluation of known gold and clay occurrences in South Korea has led to the recognition and discovery of both high and low sulphidation gold-silver epithermal systems. These are associated with Cretaceous acid-intermediate volcanic centres in the southern parts of the peninsula. The high sulphidation deposits such as Seongsan and Kohung are characterised by pervasive silica-alunite-dickite-pyrophyllite alteration with anomalous arsenic geochemical signatures. Gold mineralisation is associated with fine grained marcasite and pyrite. A low sulphidation quartz vein system observed at the Tongyoung mine comprises multistage colloform banded quartz-carbonate assemblages, with minor manganese and base metals. Gold and silver occur as electrum and tellurium minerals. Some deposits such as Gasado and Dogcheon display both high and low sulphidation alteration assemblages. Early pervasive silica-alunite-pyrophyllite alteration caps formed by acid fluids with a significant magmatic component, occur above cryptocrystalline colloform banded vein systems. The quartz vein stage hosts the precious metal mineralisation and appears to have been deposited by later neutral fluids generated in part through mixing with meteroric water.

Jan 1, 1999

By G Pizarro, O Miranda, D Rosales

In panel/block caving mines, large amounts of information are collected at the operations. This information includes grades and extracted tonnage, fragmentation, lithology, design of stopes and main infrastructure, geotechnical information, overbreak progress, seismicity, numerical modelling and many other mine variables. To improve the reliability of the production plan it is required to integrate the mine data into one platform. In this paper, the logic of a new technological platform is presented for analysis, modelling and design in panel/block caving mines. The platform includes an acquisition system for mine data and geotechnical information, a back-analysis module for calibrating statistical models and a module of visualisation of the results. The application and benefits of the platform is illustrated for mining induced seismic risk for panel/block caving mining operation. The platform can be used in the mine design and planning processes and is useful for reducing uncertainty in the mining business of panel/block caving operations.CITATION:Miranda, O, Rosales, D, Pizarro, G and Vallejos, J, 2016. Development of an integrated platform for the analysis, modelling and design in panel caving mines, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 237–240 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2016

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

Currently all engineering schools face a common problem, an ever expanding technological base which must be presented to students within a fixed time period. Traditionally the programmes of study taken by engineering students destined for employment within the mineral industry include formal instruction in aspects of management to an extent greater than that presented in other engineering disciplines. Providing an adequate coverage of an expanding technological base within a fixed time span can lead to a reduction in the time available for 'non-technical' subjects, such as 'management'. This situation calls for periodic review of course contents and allocations of time. Following consultation with an Advisory Committee representing the mineral industry, mining research establishments, and kindred teaching institutions, the University of Melbourne has revised its mining programme. This programme is offered as an example of how aspects of management such as communications, economics, labor

Jan 1, 1980

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 I Canbulat, J Emery, L Yao and C Zhang

This paper documents a process of data analytics and machine learning applied to coalmine roof convergence data. It follows a publication documenting the formation of the database. For each set of analyses Python scripts were utilised to extract data from an SQL database, with subsequent analyses also executed in Python, utilising open-source tools such as scikit-learn. Preliminary analyses with classic machine learning algorithms included multiple logistic regression, K-nearest neighbours, decision tree, random forest, support vector machine, kernel support vector machine, artificial neural network (ANN), naïve Bayes and multiple linear regression. Results indicated that relationships were complex and non-linear, with the artificial neural network most suitable. An ANN model was then refined through a standard process of hyperparameter optimisation and data augmentation to arrive at a final model. Due to the black box nature of the ANN further insight into how the parameters interacted was sought and found with Shapley additive explanations (SHAP values). SHAP values utilise Game Theory, where each input parameter or feature is a ‘player’ while the data set is the ‘team’. The SHAP value is the impact of each player on the target value, essentially establishing the contribution of each input parameter. The results from all analyses are delivered in the paper, together with insights, applications, strengths, and limitations of the methodology.

Nov 29, 2022

By E Jamieson

For a number of years, Alcoa World Alumina Australia (Alcoa) has been investigating opportunities to produce economically viable products from bauxite residues. Three of these potential products are described here, being Red Sand, ALKALOAM« and REDLIMETM.Red Sand has a nominal particle size of +100 micron and is physically little different to crushed bauxite from which it is derived. In AlcoaÆs Western Australian refineries, Red Sand constitutes up to half of the residue going to impoundment areas. Initial testing indicates that this sand could be processed to provide an acceptable general landfill material, a construction back fill or a material suitable for road base construction. ALKALOAM« is the fine-grained residue often referred to as red mud. A great deal of work has been done to show the benefits of adding this material to sandy soils, which are common in coastal regions of WA. ALKALOAM« has properties that can elevate the pH of the acidic soils and also retain phosphorous, reducing overall fertiliser use and protecting sensitive waterways. REDLIMETM is a by-product of the refining process. This material has a high acid neutralising value. REDLIMETM could be more effective at pH control than most available products because of the fine particle size and sodium carbonate that remains with the product.

Jan 1, 2004

In spite of the efforts done in the past to implement in Codelco best practice guidelines and standards on several important sampling, assaying and variability issues, many people, particularly top managers and operators, are still not aware of the relevance of the use of the principles and practice of geostatistics, sampling theory, analytical chemistry and the understanding of processes variability from rock to cathodes. As a result, an important challenge for the sampling community is to find out the causes of this lack of awareness and to develop communication skills to influence the organisation, particularly the top management level. This situation is not affecting only Codelco; it is a common characteristic of the copper mining industry. The main cause is cultural. Judeo-Christian and occidental sciences are deterministic. This implies a disregard of process variation and the assumption that homogeneity is omnipresent. Another important aspect of the cultural challenge is education. Education is the most efficient way to transfer a system of ideas, traditions and practices. To implement proper courses of sampling theory and geostatistics and support to multidisciplinary education at universities, mining companies will improve teamwork between geologists, mining engineers, metallurgists and chemists. The improvements will imply a more sustainable and efficient mining business.

Mar 1, 2010

By J Wood

Steady depletion of the world’s primary copper reserves coupled with a meteoric rise in electronic waste (e-waste) generation has led to secondary copper processing assuming an ever-increasing importance within the global copper industry. Treatment of secondary copper feeds at existing smelting operations has traditionally been performed in the Blast furnace, Peirce-Smith converter and/or anode furnace. In the past decade or more, bath smelting technologies have been preferred for the processing of these materials due to their superior environmental performance and flexibility to operate under a wide range of conditions. This paper focuses on the pyrometallurgical processing of copper secondaries in the Outotec Ausmelt TSL Furnace and some of the unique issues facing secondary copper smelters and downstream gas handling/cleaning equipment.

Aug 8, 2011

By M Balamurali

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

Jun 22, 2022

By S Pradhan, A Kuras, R Mahajan

This paper describes the design of the precast concrete segmental lining for the 13.1 m internal diameter and 450 mm thick, 2.4 km long mainline tunnels for the Waterview Connection project in Auckland for the New Zealand Transport Agency.The design and detailing of this segmental lining presented some noteworthy challenges, including an alignment that has sections of low cover in soft ground. Key points of interest are that this precast segmental lining is the largest to ever include steel fibres as the primary method of reinforcement and the lining includes cams on circumferential joints to control ring deformation.The paper describes some of the key design decisions that were made in a collaborative manner with the construction team, tunnel boring machine manufacturer, segment manufacturer and the designers.CITATION:Mahajan, R, Kuras, A and Pradhan, S, 2014. Design of segmental lining for Waterview Project, Auckland, New?Zealand, in Proceedings 15th Australasian Tunnelling Conference 2014 , pp 289–294 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 17, 2014

By S Bouzalakos, W Timms, H Malli

Combining ultramafic mine tailings and acidic mine drainage via mineral carbonation to sequester carbon dioxide, presents an opportunity to minimise waste on mine sites. Low pH mine water with trace element concentrations can be effectively treated by high magnesium mine tailings, a useful source of finely ground reactive matrix. By using milled tailings for mineral carbonation, there are negligible energy requirements for communition of ore rocks to reduce particle size. This study aimed to assess the technical applicability of reacting ultramafic tailings and acidic mine water through mineral carbonation technology in the mining industry. Dissolution has been identified as the rate limiting stage in mineral carbonation; hence the study focused on identifying optimal dissolution conditions. Ultramafic tailings (particle size <100 µm) derived from serpentinite ore, were reacted with a typical mine water that was synthesised in the laboratory. Through variances in the solid to liquid ratios and temperature of dissolution, a maximum five per cent magnesium dissolution efficiency was reached at 50 g/L and 100°C. The acidic mine water was neutralised (from pH <2 to pH ~6) and iron, copper and zinc ion concentrations reduced significantly during the formation of solid precipitates. Carbonation was undertaken by injecting carbon dioxide into the magnesium-rich solution to form stable carbonates. The experimental work demonstrated proof of concept; however, dissolution efficiency and carbon dioxide sequestration was low, indicating that further research would be required before larger scale implementation.CITATION:Malli, H, Bouzalakos, S and Timms, W, 2015. Ultramafic mine tailings to treat acidic mine water and lock away carbon through mineral carbonation, in Proceedings Tailings and Mine Waste Management for the 21st Century , pp 133–136 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 27, 2015

By R Bradford, W Gift-Agbey, T Vorster, P Morgan, E Grobler

The Asanko Gold Mine in Ghana started processing ore from the Nkran pit in January 2016 at a production nameplate of 3 Mt/a. The flowsheet is a conventional SAG, ball mill and pebble crusher (SABC) with gravity-gold and carbon-in-leach (CIL) recovery. The nameplate production was comfortably achieved within 3 months, so much so, that it was clear the SABC circuit was capable of processing more than the downstream section of the plant. Consequently, a program of de-bottlenecking was initiated to rectify volumetric capacity constraints. This program was later referred to as ‘Project 5 Million’ (t/a) or P5M. Having completed the de-bottlenecking exercise, it became evident that the SABC circuit could not reach 5 Mt/a solely on hard rock from Nkran. The new challenge was therefore to increase the capacity of the milling circuit through a focused comminution initiative that would address the following: geometallurgical treatment plan; upfront fragmentation requirements; installation of feed and mill monitoring equipment; and integration of a knowledge-based control strategy. This paper takes the reader through the various measures implemented to increase the milling rates and to create a stable/ sustainable control philosophy. As a result of the implementation of advanced mill performance monitoring technology (MillSlicer) and the collaboration between a team of experts in comminution, control and operations, the Asanko Gold Mine process plant feed tonnage has successfully increased from 3 Mt/a to 5 Mt/a.CITATION:Bradford, R, Gift-Agbey, W, Grobler, E, Morgan, P and Vorster, T, 2018. Optimisation of Asanko’s Obotan SABC circuit to achieve ‘Project 5 Million’, in Proceedings 14th AusIMM Mill Operators' Conference 2018, pp 477–484 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Aug 29, 2018

By R Berry, A Escolme

"Predictive geometallurgical models are being developed for the Productora Cu-Au-Mo deposit based on geochemistry to reflect variability in Cu species based on Cu sequential leach data (ie oxide, transitional-oxide, transitional-sulfide, sulfide and non-recoverable Cu).Productora, located in the coastal range of northern Chile, is a magmatic-hydrothermal tourmaline breccia-hosted, structurally controlled Cu-Au-Mo deposit hosted by a thick sequence of Jurassic rhyodacitic volcanic rocks. North and north-west striking fault sets control the distribution of mineralised breccias. Alteration is widespread, complex and pervasive. Hypogene alteration assemblages range in composition from distal magnetite-amphibole, to sodic-calcic, to sodic, to phyllic, to proximal potassic. Low-temperature advanced argillic assemblages are locally juxtaposed next to proximal high-temperature K-feldspar-tourmaline assemblages. The dominant hypogene Cu phase is chalcopyrite.A simple Cu species classification scheme based on sequential leach data and S per cent was devised to account for non-recoverable Cu (ie non-sulfide Cu), which is insoluble in weak acids. Through machine learning, a proxy for this Cu species classification scheme was developed based on: depth, Ca per cent, Cu per cent, Fe per cent, K per cent, Mn ppm, S per cent and Ln(Cu/S) plus logged regolith class, thus allowing classification to be extended to areas of the deposit where no sequential leach data are available. The modelled data has been used to generate deposit-wide 3D models for Cu species classification.The validity of the proxy Cu species classification model is being tested against the results of flotation analysis. Initial results indicate that geochemical proxies can be used to successfully predict Cu species class and provide a high density of classification data.CITATION:Escolme, A, Berry, R and Hunt, J, 2016. Predicting Cu Species Classification Using Geochemistry at the Productora Cu-Au-Mo Deposit, Chile, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 113–118 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Jun 15, 2016

By Mine F. J, Woods P. H

Waste waters from mine sites may be screened for all constituents of significance as a first step in establishing water quality criteria for protection of aquatic ecosystems. Inductively-coupled plasma mass spectrometry (ICP-MS) offers the capability to screen water samples and digest solutions for virtually all elements in the periodic table. This technique is therefore able to identify elements whose concentrations are elevated in waste waters and may be rarely present in background surface or ground waters. Such elements may be present in waters at only a few ¦g/L. ICP-MS has sufficient sensitivity to detect sub-¦g/L or even ng/L levels of many elements. Waste waters from most mining activities in the Northern Territory have been examined by ICP-MS. The concentration data provide the basis of a contaminant data base. The data can show specific tracers in the ore or mineral itself, subsequently found in waste water. An example is the presence of rhenium in uranium ore. Such trace elements may be present in soluble or insoluble chemical forms and are more useful as indicators when soluble and exemplifying conservative chemical behaviour. The approach described, based on ICP-MS screening of mine site waste waters, together with general physico-chemical variables, provides the most comprehensive means yet available of selecting variables for monitoring programs and ensures that key elements and/or their chemical species are not excluded from monitoring programs.

Jan 1, 1994

By J Wen, P Hou, W Yu, M Hu, Z Jiang, C Bai

Vanadium titano-magnetite (VTM) is one of the important iron ore resources in the world. High viscosity and low fluidity are the basic characteristics of hot metal containing vanadium and titanium during the ironmaking process. Clarifying the influencing mechanism of the melt viscosity would be a significant issue for the high efficiency smelting of VTM. In this paper, a self-developed melt viscometer based on the principle of torsional vibration theory was employed to measure the viscosity of hot metal accurately. Simultaneously, the thermodynamic calculations and some modern detection methods were also used to investigate the precipitation behaviour of high-melting-point phases in hot metal containing vanadium and titanium. The measuring results show that the viscosity of molten iron gradually increases with the increase of titanium content when the mass fraction of titanium is between 0.09 wt per cent – 0.50 wt per cent. Meanwhile, the viscosity of the hot metal was affected significantly by the vanadium content. When the vanadium mass fraction was higher than 0.30 wt per cent, the viscosity of hot metal increased sharply, which will have a serious impact on the fluidity of the hot metal. Furthermore, it was revealed by thermodynamic calculations that an increase in titanium and vanadium content would result in the precipitation of high melting point phases such as TiC, TiN, VC, and VN in the molten iron, ultimately resulting in a decrease in its fluidity. Meanwhile, it was confirmed by Scanning Electron Microscope (SEM) observation that the precipitates in the hot metal were compounds consisting of V, Ti, C, and N elements, which agrees well with the thermodynamic analysis results.

Aug 21, 2024

By A Purton, C Taylor, E Conroy

The spatial modelling of geometallurgical domains is an important step in the characterisation of an ore deposit allowing for the quantification of various rock types, their differing rock properties and the overall value of a project. The traditional approach to modelling such features is through the use of deterministic models, where a single estimate of the extents and structure of the ore deposit is mapped out by geologists based on available data and their interpretation of the geological processes. Any uncertainty in the layout of different domains is not accounted for and heterogeneities of chemical and physical properties of the orebody are likely underestimated. Spatial machine learning techniques can be used to derive geometallurgical categories, or classes, from multiscale, multiresolution and high dimensional measured rock properties. Subsequently, geostatistical simulations of these classes can be applied to obtain multiple equiprobable realisations that define the layout of the geometallurgical classes at unknown locations between boreholes, or domains. In-turn these realisations can be used quantify the uncertainties in the location of boundaries between different domains. The development of a workflow for defining geometallurgical domains and later geostatistically simulating them across the Orebody H; a complex stratabound Bedded Iron Ore deposit located in Western Australia’s Pilbara region is demonstrated. This could be used to identify zones of high uncertainty where collection of additional data might help mitigate or minimise risks and in turn improve forecast production performances.

Mar 22, 2022