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By M Edgar, G Pattemore, M Auad, K Job, A Job, S Gale

In recent decades, significant advances have improved the modelling techniques for predicting slope failure. Predicting failures is vital in open pit mining due to the significant risk posed to personnel and equipment from unstable pit walls. Failures not only cause harm and damage to people and equipment but also cause delays to production and subsequently increase operational costs. Coupled with improvements in modelling techniques, utilisation of advanced scanning systems, such as high-definition imagery from UAVs has enabled faster and more precise mapping and modelling. However, one key element required for accurate slope stability modelling and monitoring is the ability for mine geotechnical engineers to have an accurate understanding of the mineralogy and moisture content of slopes, in real-time. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum, combined with high-definition imagery, and LiDAR to scan slopes for minerals and moisture content. In addition to accurately mapping mineralogy and moisture, the data must be collected autonomously and remotely to ensure ongoing safety. The results show that there are many direct and indirect benefits from this new technology, including the ability to monitor slopes at a safe distance and in real-time, improved accuracy of modelling, detection of risks, and improving the automation workflow for geotechnical engineers. The paper presents recommendations for the implementation of the technology in open cut mining environments.

Jun 22, 2022

By H Lemos, E Nunes, D Jacobson, R Costa, L Ono, A Alves, H Delboni Jr

Oversize rock breaking and metal tramp removal are key issues for reducing primary crusher downtime, as well as improving the safety associated with its operation. Remote operation of such tasks would thus not only increase productivity, but also reduce the risks related to work in confined spaces, fall risks, and overhead hazards. These issues are particularly relevant in high-capacity operations such as iron ore mining, where large machines are in continuous operation. This article describes the architecture, construction and advanced testing of a novel remotely operated robotic mechanism for assessment and removal of metal tramp from primary crusher chambers. The system essentially includes a robotic-arm equipped with a hydraulic clamp tool for handling oversize material, as well as an oxy-flame module for cutting metal pieces entrapped within the primary crusher cavity. The first industrial unit was delivered to a large iron ore mine in Brazil, where the installation and testing is scheduled within the coming months.

Nov 8, 2021

By H Indirawati, R Ayres, A Nur Kasnanto, S Konopa

As a project moves through the mine life cycle, the quantity of data available increases and the understanding of the orebody matures. Incorporating new information quickly and efficiently in current 3D models, is essential for adding value across the life-of-mine value chain. At the Martabe Gold Mine, the resource development group manages 14 drill rigs on six resource projects, including three operating mines. The Martabe deposits are geologically complex and the volume of new information that becomes available each day is substantial. It is imperative that new geological and assay information is integrated into the 3D models as soon as possible, so that drill planning and targeting can be modified accordingly. Martabe uses Leapfrog<sup>®</sup> 3D software to update and interrogate the 3D models on a dynamic basis as data becomes available. This results in faster and more effective changes to drill planning and schedules. The impact of this is better drill productivity and cost savings due to optimal drill targeting, and availability of up-to-date models incorporating the most current data. CITATION: Konopa, S, Ayres, R, Nur Kasnanto, A and Indirawati, H, 2018. Optimised drill targeting using dynamic 3D modelling, Martabe Gold Mine, Indonesia, in Proceedings Complex Orebodies Conference 2018, pp 99–101 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 21, 2018

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 E Rubio, N Morales

Currently, mine plans are optimised by using several criteria as objective functions, like profit, life-of-mine, concentration of some pollutants, mining costs, confidence level or ore resources, with consideration of constraints related to production rates, plant capacities and grades. Whilst this approach is successful in terms of producing high value production schedules, it uses a static opening sequence of the drawpoints and therefore the optimisation is made within the level of freedom left by the original opening schedule, and as a result it is far from the true optimal value of the project. This paper presents a model to optimise the sequence of drawpoint opening over a given time horizon. The emphasis is in the precedence constraints that are required to produce meaningful operational sequences considering the exploitation method (panel or block caving), physics considerations and logical rules. Further on, while it applies the standard approach of maximising net present value (NPV), it considers other targets for optimisation, like the robustness and constructability of the plans. Finally, it applies the model to real data to obtain feasible plans by means of this model. The first tests were done varying the production capacity of the mine.

Sep 26, 2011

By X Hu, Z Pokrajcic, R Embry, P C. Stewart, J Carpenter, E Jones

Whilst the value of Pit to Plant and Mine to Mill optimisation has been proven since the 1990s, sustaining the productivity and efficiency gains typically requires ongoing test work, blast trials, plant surveys, and model calibration services. In contrast, the input to digital twin Pit to Plant or Mine to Mill optimisation is tens to hundreds of millions of tonnes of standard mine operating data. This paper presents a series of digital twin case studies from across the mine value chain, including machine learning testing methodology, model accuracy statistics and the application of Shapley Additive Explanations (SHAP) for model interpretation. These case studies demonstrate how machine learning and data fusion ore tracking connects hundreds of millions of tonnes of downstream performance data to produce digital twin models (operating models that are continuously updated and integrated into the wider Mine to Mill/Pit to Plant ore tracking system). Digital twin modelling software has been used to add block model variables for dig-rate, crusher and mill throughput, recovery (tailings grade), lump yield, product grade and product quality, fines yield and fines in lump. Block-by-block predictions are added to the geological resource and grade control block models as new block model variables. These new block model variables are used across all mine planning time horizons from life-of-mine planning to daily schedules. In addition to the materials handling and geometallurgical prediction case studies, the paper also presents digital twin case studies for drill and blast simulation and plant set point optimisation. The case studies show how digital twin simulation enables drill and blast engineers to simulate the effect of different blast designs in different blast domains using the mines operating data. Plant set point optimisation applies mathematical optimisation algorithms to the machine learning model to provide live operational decision support to metallurgists and plant operators. The digital twin machine learning case studies demonstrate a digital and sustainable paradigm for Mine to Mill and Pit to Plant optimisation.

Jun 22, 2022

By S Khadhraoui, T Yusuf, C Kirmse, E Hecker, R Degel, A Akouch, N Borowski, M Reuter

For an optimal design and control of metallurgical processes, a deep understanding of the thermodynamics of the governing reactions is required. Modelling the thermochemical behaviour of those reactions is complex, as it involves multi-component and multi-phase systems. For this purpose, thermodynamic computing systems such as FactSage™ (Bale et al, 2016), which combine Gibbs energy minimisation routines with large databases of optimised thermodynamic data of solutions and compounds, have been used within the SMS group GmbH. Recently, the successful integration of those thermodynamic computing systems into the digital platforms of SMS has been achieved through ChemApp, which made running complex thermochemical calculations in Python-integrated development environments possible. ChemApp is a programmer’s package for thermochemistry and incorporates the Gibbs energy minimiser of FactSage™ as reported by Petersen and Hack (2007) as well as by Zietsman and Petersen (2018). The digital platforms link the fundamental and the industrial aspects together and can be applied as a process advisor, or for the evaluation of the environmental footprint and the economic implications of different process routes, as discussed by Reuter (2023). Two examples illustrating the progress of the developments in this regard will be introduced: 1. The Tilting Refining Furnace (TRF) for copper scrap recycling and refining to anode copper (the BlueControlApp). 2. Dephosphorisation control for the oxygen steelmaking process (Basic Oxygen Furnace (BOF)).

Jun 19, 2024

By J Herrmann, J Richards, B Hutchison

An extensive trial of laser-based survey and monitoring systems at Hillgrove Resources Kanmantoo Copper Mine in the Adelaide Hills, South Australia, has proved the capabilities of the system in monitoring rock movements on open pit walls. Initially, the trial involved deploying a Maptek I-Site 8820 laser scanner and the proof-of-concept Maptek Sentry software in the Nugent Pit to monitor ductile rock movements located up to 260 m away. The system was proven to be capable of monitoring various rock movements from 1.5 tonne rockfalls up to a 40 m high toppling wall failure. Data collected from smaller rockfalls was used to help build a database for Hillgrove’s rockfall management program. As part of the trial, the laser scanner was deployed to monitor a ramp movement event, as well as the toppling failure, adjacent to an IBIS slope stability radar. Both devices monitored the slump and the slowly developing toppling movements, with similar results. Although the radar was used as the successful ‘critical alarm’ device for the toppling failure, post-failure analysis showed the laser-based system to be capable of performing a similar role in the ductile rock regime; albeit over much shorter distances than the radar capability. A second laser scanner was deployed to monitor Kanmantoo’s Emily Pit in early December 2014, where it successfully provided critical alarm warnings for a block topple event. Sentry software, which takes advantage of Maptek I-Site 3D spatial imaging technology, was first released in November 2014 to meet industry demand for a cost effective, flexible system to detect movement. Sentry has now been re-launched as a fully transportable monitoring system. Deployed in a custom trailer, the system offers a power and communications module, cellular and Wi-Fi networking and a stable bollard for the I-Site laser scanner and Sentry software. This paper will present rock wall movement data from the Sentry trial and a subsequent two years of monitoring of the open pit walls at the Kanmantoo Copper Mine, at distances out to 750m. In addition, examples of remote geological mapping and small rockfall detection, using the laser scans, will be described. The Sentry system has proven to be beneficial for the continual improvement of slope monitoring and risk management at hard rock mines. CITATION:Hutchison, B, Richards, J, Richards, J and Herrmann, J, 2018. Mine rockfall and rock wall failures – proven digital laser-based approach for monitoring slopes at the Kanmantoo Copper Mine, in Proceedings The Fourth Australasian Ground Control in Mining Conference (AusRock), pp 463–472 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 30, 2018

By S Khosrowshahi, A Weeks, W J. Shaw

"To provide an indication of expected performance of the mine and mill, feasibility studies should include an assessment of the expected day-to-day variability in process plant feed. Attempts to do this based on conventional resource models will be flawed as such studies fail to consider and account for the in-built smoothing (averaging) of input data.This can lead to one of two situations: an expensive, over-engineered process plant that can cope with every possible situation, or operational strategies that fail to manage the natural variability within the orebody and the impact of day-to-day operational decision-making.In both cases the value of the project diminishes through excessive capital costs or reduced throughput or recovery during operations.Unlike conventional estimation techniques, the geostatistical method of conditional simulation can reproduce the inherent variability of the deposit. Once a valid model has been developed, it is possible to analyse the model using a transfer function that simulates the proposed mining processes and provides a representation of the likely variation of mill feed characteristics at any time scale.The case study demonstrates how the proposed mining strategy would result in mill feed being considerably more variable than expected, resulting in poor recovery, low throughput and potential breaches of environmental conditions. Mitigation measures included budgeting for additional mining equipment to enable greater flexibility during mining and revised stockpiling and crusher feed strategies.Awareness of the benefits of modelling the variability of geometallurgical parameters will increase the use of this approach in producing more realistic financial models of expected mine performance at the design stage.CITATION:Shaw, W J, Khosrowshahi, S and Weeks, A, 2013. Modelling geometallurgical variability - a case study in managing risk, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 247-252 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By L H. G McClean

Environmental, social and governance (ESG) factors have always been important to the minerals industry and is now considered one of our most important opportunities (and risks). Getting it right from the beginning is critical for the minerals industry, and that starts with mineral exploration. Exploration, or the process of investigation of an economic mineral deposit, is generally low impact and short-term. Exploration operations vary in size and scale and often shift priority, focus and location relatively quickly. Australia is a highly regulated jurisdiction with different regulatory systems and requirements in every state and territory. Most mineral exploration is undertaken by junior exploration companies or ‘explorers’. Exploration companies are lean operations with limited staffing and resources, traditionally with no income apart from funds raised or work through partnerships. Funds are tight and administrative investment is minimised so that investment into activity on the ground is maximised. All these variables mean that understanding and implementation of ESG frameworks for an exploration company can be challenging. ESG is a fast-moving sector with currently multiple frameworks and reporting standards. Interest from community and government stakeholders as well as investors and off-take partners has been growing, with mandatory reporting soon to be implemented. The upcoming changes to the JORC Code will ensure that ESG considerations will be a key component of all public reporting. Resources have been developed to support explorers implement, manage and report on ESG considerations. Due to their scale, agility and direct importance of land access and social licence, many explorers are not the ‘cowboys’ (or ‘cowpersons’) of the industry, but industry leaders in ESG. Strong interest and leadership have led to innovative practices and strongly embedded vertical frameworks that directly contribute to social licence to operate.

Sep 1, 2024

By E Widzyk-Capehart, P Vallejos, R Asenjo, J Ruiz-del-Solar

Throughout the minerals industry, it is evident that a new wave of technological innovation is needed as the mining sector looks for ways to achieve higher security and safety as well as higher production rates with less investment. Innovation has always been one of the key factors in achieving success within the mining industry. However, its importance will be especially relevant in the coming years if negative trends in commodity prices and new challenges facing the industry (lower grades, deeper mines and economy of scales) are to be overcome and the future sustainability and profitability of the sector ensured.The drive towards fully autonomous operations has begun, with the implementation of automated systems in both surface and underground operations. However, these automated systems are currently planned and realised with well-defined automated tasks within structured environments and isolated spaces. Currently, there is no interaction between automated systems, manned systems or humans, which creates physically isolated environments.For fully autonomous and continuous operations, the automated systems need to operate in environments that enable flexible interactions between automated machines, tele- and manually operated machines and humans.In this paper, we address the automation paradigm considering the synergistic collaboration between humans and automated systems. In this paradigm, humans and machines would interact in a flexible, collaborative and synergistic way without the need for isolation. The implementation of this concept within mining operations will have to be underpinned by science and technological developments and will require adaptation from many fields, including robotics, automation, distributed systems, communication, human factors, pattern recognition and mine design.The paper also presents the research and development work being undertaken at the University of Chile’s Advanced Mining Technology Center, in collaboration with the Chilean mining industry. This will provide the basis for flexible interactions between humans and technology-based systems towards a future of autonomous and continuous mining systems.CITATION:Ruiz-del-Solar, J, Widzyk-Capehart, E, Vallejos, P and Asenjo, R, 2015. Synergistic collaboration of humans and automated systems – innovative steps towards fully autonomous and continuous mining systems, in Proceedings Third International Future Mining Conference, pp 83–94 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 4, 2015

By A Boucher, M Baudin, P Androszczuk, I Minniakhmetov, A Rigby, P Machado, M Wright, B Van Dijken

IGO Ltd’s (IGO’s) Nova Operation (Nova), which is 160 km east-north-east of the town of Norseman in Western Australia (WA), mines and processes nickel-copper-cobalt (Ni-Cu-Co) sulfide ore from the magmatic Nova-Bollinger Deposit (Nova-Bollinger), which is hosted by the Mesoproterozoic age high-grade metamorphic rocks of the Albany Fraser Orogen. Over its 56-month life-of-mine (LOM) operations to 31 December 2020, Nova has processed 5.63 Mt of ore grading 2.04 per cent Ni, 0.86 per cent Cu and 0.07 per cent Co from its underground mine. At the end of 2020, both the Nova-Bollinger JORC Code reportable Mineral Resource estimate (MRE) and Ore Reserve estimate (ORE) were >90 per cent classified in the highest JORC Code confidence categories of Measured Mineral Resource and Proved Ore Reserve respectively. The high JORC Code classification confidences assigned to the Nova-Bollinger MRE and ORE results from the 386 km of surface and underground diamond drill testing of the deposit to a nominal 12.5 by 12.5 m pierce point spacing in mineralisation. The close spaced high-quality drilling and bulk mining method employed at Nova has effectively removed the need for any grade control modelling outside of the MRE process. Importantly, having only the one fully drilled out deposit being mined and only the one process plant, Nova’s reconciliation process is in the fortunate situation of not being confounded by issues such as external ore sources and constant updates as new grade control information becomes available. IGO’s Nova reconciliation processes are focused on reconciliation ratios that quantify operational period differences between the MRE and ORE head grade and tonnage forecasts and the process plant head actuals. These metrics have been used to identify key reconciliation issues and have initiated several studies into the causes for specific reconciliation results, which are discussed in the paper. In late 2020, a period of sustained poor reconciliation results led to the recognition that increasing the understanding of risk between the MRE and ORE should be investigated. To assist with modelling stope level risk within the mine plan and subsequent reconciliation, an attempt was made to develop a formula to quantify ‘stope risk’ from MRE metrics such as sample spacing, kriging efficiency and so on

Mar 22, 2022

By G Hill, D Grant, M Tyler

Located approximately 570km NNW of Adelaide in South Australia, the Olympic Dam mine (OD) is the world’s fourth largest remaining copper and gold resource and the largest uranium resource. Discovered in 1975 and going into production in 1988, the operation consists of a fully vertically integrated underground mine, mineral processing plant, copper smelter and refinery, as well as associated infrastructure. BHP achieved production of 203,000 tonnes of copper cathode plus associated by-products in financial year 2016. The immense size and high-grades of the Olympic Dam resource offer a range of growth pathways, making closure timing estimation and planning a challenge. Current estimates put the OD resource at approximately 79 million tonnes of copper. Major factors influencing the closure timing estimation, as a result of the large resource and grade tonnage profile, include the mining method that may be used and the downstream processing method, ultimately influencing such things as the total disturbance area and the extent of infrastructure to be demolished at the end of mine life. In addition, factors such as the mining and processing method and production rate have a direct impact on larger rehabilitation items such as the total number of tailings cells required.CITATION:Grant, D, Tyler, M and Hill, G, 2018. Olympic Dam mine closure planning process, in Proceedings Life-of-Mine 2018, pp 43–45 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 25, 2018

By R Dimitrakopoulos, I Minniakhmetov

Geostatistical simulation techniques are used to quantify uncertainty of spatial attributes of interest describing mineral deposits, petroleum reservoirs, hydrogeological horizons, environmental contaminants and so on. The majority of existing methods consider second-order spatial statistics and Gaussian processes, while the more advanced multiple point-based simulation approaches are algorithmic and do not consistently account for the high-order spatial relations in data. Recently, simulation techniques for complex and non-Gaussian spatially distributed variables have been developed, based on high-order spatial cumulants, and make no assumptions on data distribution or require data transformations. In this paper, the previous developments are extended and a new approach for the joint simulation of multiple correlated variables using high-order spatial statistics is proposed. The technique is based on a new algorithm for the decorrelation of correlated variables into factors, using the so-termed diagonal domination condition of high-order cumulants. The decorrelated factors are then simulated using high-order simulation and back-transformed into the initial correlated variables. The decorrelation using diagonal domination of high-order statistics is tested with a data set from a multielement iron ore deposit, and then a fully known two-dimensional data set with two correlated variables is used to demonstrate the practical intricacies of the proposed method.CITATION:Minniakhmetov, I and Dimitrakopoulos, R, 2014. Joint simulation of correlated variables using high-order spatial statistics for orebody modelling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 53–60 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By P Mitchell, S Pushpan, G Ferres, K Bobbermien, L Hudson

"Falling commodity prices and a rising supply surplus have ushered in a period of restraint in capital project investment across the global mining and metals sector. Competition for capital project funding has seen fewer projects progress through investment gates and a stronger focus on projects that deliver gains in capital productivity and strategic outcomes.Over the course of the 2015 financial year, EY conducted a study of 108 projects in the global mining and metals sector (EY analysis; Larsson and Ericsson, 2014; SNL Financial, 2015). The projects studied were at various life cycle stages and related to the development of copper, iron ore, gold, coal, nickel and other commodities. Each individual project investment exceeded US$1 B, and the studied group represented a cumulative investment value of US$367 B.The research found that the majority of projects were delayed and/or over budget when measured against the initial estimates made during the early stages of the project life cycle. Of the surveyed projects, 69 per cent were identified as facing cost overruns with an average overrun of 62 per cent above the initial estimate and 50 per cent reported schedule delays, even after remedial acceleration initiatives.The analysis found that budget and schedule overruns could be attributed to five key causes:project management factorsstakeholder conflictsresource constraintsregulatory, and policy-related challengesunfavourable external environment.Improving project delivery performance starts with successful planning, followed by rigorous management during delivery. From global experience on large and complex capital programs, EY has observed a consistent theme of underinvestment and a lack of focus in three often-overlooked but critical areas:implementing effective governance and reporting frameworks with lead indicators that reliably flag emerging risks while they can still be mitigatedallocating adequate cost and time contingency to account for risks across the life cycleenhancing the value of contingency by aligning contingency plans with scenario plans.This paper explores the surprising findings of the study, proposes a root cause model and examines key considerations in applying these critical enabling techniques.CITATION:Hudson, L, Mitchell, P, Ferres, G, Bobbermien, K and Pushpan, S, 2016. Are your megaproject schedules and costs under – or out of – control?, in Proceedings Project Evaluation 2016, pp 75–85 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Mar 8, 2016

By S Davidge, J Pile, R Schumann, E Kepe, W Stewart, M Ridd, B Rathi

Geographical constraints, including steep terrain and high rainfall, at the Ok Tedi copper-gold mine in PNG’s Western Province present Ok Tedi Mining Limited (OTML) with significant challenges in mine waste management. Waste management includes discharge of tailings into the local river system and storage of waste rock from the mine pit in waste rock dumps which erode into the river system. Copper bearing skarn mineralisation is associated with a massive sulfide (mainly pyrite) deposit. Consequently, waste rock and tailings can contain significant amounts of pyrite. Without appropriate management, these waste streams represent a risk of producing acid and metalliferous drainage (AMD) when discharged to the river system. Throughout the life of the project, OTML have developed evolving strategies to manage mine wastes and mitigate AMD.

Aug 2, 2023

By A Fern, A Hawkes, o, W Smith, K Mee

Serious Injury and Fatality (SIF) prevention remains a priority focus within high-risk industries sector. A major mining operation sought to strengthen their approach to the management of high-risk activities through an in-depth review of all historical incidents that had serious injury or fatality potential (SIFp) events (sometimes known as hi-potential events). The analysis found that the organisation invested a disproportionate level of resources, time and effort on low consequence incidents with a high proportion of SIFp incidents going under the radar. It was found that 76 per cent of SIFp incidents were rated in moderate or lower risk categories and conversely, almost half of those incidents with a ‘serious risk’ rating had little or no potential for a serious outcome. As a result of this project, the operation engaged in a renewed focus on critical risk management and enhanced its operating cadence in critical risk for the workforce. This included bow tie analysis, an update to critical risk standards and increased workforce engagement in the use of critical control verifications in the field. Data analytics with lead and lag indicators continue to provide senior leadership team and risk owners with an understanding of exposure for SIFp and control effectiveness. The audience will take away learnings from a real-world case study that provides data-driven insights of the human, local and organisational factors contributing to incidents with SIF potential. In addition, participants will understand the steps taken to categorise and analyse this incident data and the initiatives currently being implemented by the organisation as a result.

Apr 16, 2024

By P Marianelli, J Theiler, B Beamish, A Spillman

The presence of reactive pyrite in waste rock contributes to the hazard of spontaneous combustion in emplacement facilities as it acts as an initiator of self-heating at low ambient temperatures. Organic carbon also present in the waste rocks acts as a fuel at elevated temperatures above 120°C and can lead to thermal runaway resulting in eventual ignition. A new laboratory test method (adiabatic incubation testing) is now available that can identify the presence of reactive pyrite and quantify the self-heating behaviour of the waste rock. McArthur River Mine has a waste rock profile that contains pyritic shale units, which have created spontaneous combustion events in the past. A systematic sampling and testing program is underway to characterise the spontaneous combustion hazard likelihood of the McArthur River Mine waste rocks to provide a sound database for future mine planning of the waste removal and emplacement. This paper presents some initial outcomes of this program and shows the leading practice in spontaneous combustion hazard assessment and management that has been adopted at McArthur River.CITATION:Beamish, B, Marianelli, P, Theiler, J and Spillman, A, 2018. Spontaneous combustion hazard assessment of McArthur River Mine waste rock, in Proceedings Mine Waste and Tailings Stewardship Conference 2018, pp 422–426 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 23, 2018

By R Berry, S G. Walters, J A. Hunt, D Collett

The texture of an ore type is fundamental in understanding the metallurgical behaviour of an ore. This study will show examples of how quantified textural attributes (mineral shape, grain size and mineral association) can be obtained from automated and scanning electron microscope (SEM)-based microscopy and utilised using advanced image processing techniques. Examples will be shown of integrating quantified and semi-quantified meso- and macro-scale textural attributes as well as how they relate to texture-based visual logging systems. The implications of texture for processing behaviours such as comminution, liberation and recovery will be discussed. This research is part of a major collaborative geometallurgical project undertaken at the Centre of Excellence in Ore Deposits (CODES, University of Tasmania) and the Julius Kruttschnitt Mineral Research Centre (JKMRC, University of Queensland). The authors wish to acknowledge financial support and permission to publish from the sponsors of the AMIRA P843 GEMIII project.

Jan 1, 2008

By A Calderon, J Harris, M Shi, D Viswanathan, C Ribeiro Duthie, P A. Kirsch

Mineral resource development activities impact the health and well-being of many people, from workers, to families and surrounding communities. In the current environment of corporate social responsibility life-of-mine planning considers that mining activities should be conducted in the best environmentally, socially and economically acceptable way for the entire life of the mine (ie from exploration to decommissioning and closure). In this context, the health impacts of mining extend throughout the life of the community from worker injury and chronic exposure to communicable and non-communicable causes of illness, through to family and community health and well-being. At the inaugural Life-of-Mine conference in July 2012, the authors proposed the life-of-mine in the life-of-community (LOMLOC) matrix as a framework for understanding issues at the interface between resource extraction operations and the surrounding environment.This paper reviews and compares health activities extracted from social and environmental reports published by selected major mining companies with American operations in Canada, Colombia and Brazil. These corporate approaches to public health management are mapped to the LOMLOC matrix to identify and compare trends and behaviour in corporate health investments. This analysis of how companies approach and prioritise health issues in the context of different regions, cultures and environmental drivers will serve as a resource for minerals industry managers, civil society and regulators regarding current health management approaches in this sector.CITATION:Ribeiro Duthie, C, Calderon, A, Viswanathan, D, ?Shi, M, Harris, J and Kirsch, P A, ?2014. Resource company investments in health – a life-of-mine / life-of-community perspective across America, in Proceedings Life-of-Mine 2014 , pp 349–364 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Jul 16, 2014