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By B P. Voutharoj, R A. Dutch, M Paknezhad, T Ostersen

With the proliferation of new sensor technologies, acquiring multiple data sets over the same ground is becoming cheaper and easier than ever. This new, higher resolution multivariate data provides a significant resource for exploration and resource geologists but comes with the added complexity of effectively integrating the various data sets in useful and meaningful ways to elucidate new geological understanding. Both geophysical data sets and hyperspectral data are extensively used for exploration targeting, providing different information at different resolutions and crustal scales. One of the biggest challenges comes from trying to effectively integrate data sets that record very different physical properties, across the different scales these data are captured at, in a way which can allow for a data-driven analysis of these combined data sets.

Sep 1, 2024

By J Leem, S Mehrishal, J Kim, A Fereshtenejad, J Song

Rock mass discontinuity mapping plays a crucial role in developing a reliable Discrete Fracture Network (DFN), providing insights into the distribution of geometrical and mechanical properties of discontinuities. This enables an accurate assessment of geotechnical infrastructure and mining excavations. Data obtained through conventional mapping approaches are substantially limited by factors such as accessibility and subjectivity. Moreover, these conventional methods may pose inherent risks due to manual techniques, exposing practitioners to potential dangers. Recent digital developments in remote surveying techniques offer opportunities for safer and more reliable rock mass discontinuity surveys. These technologies allow extensive three-dimensional surveying and the production of high-resolution 3D surface models, applicable at any time. This paper presents a workflow for digital discontinuity mapping employing drone technology and photogrammetry. Additionally, the capabilities and challenges of applying artificial intelligence (AI) algorithms in the rock mass discontinuity characterisation process are discussed.

Sep 1, 2024

By A Murphy, I Samsonova, N Eastwood, D Whittle

Part XI of the United Nations Convention on the Law of the Sea (UNCLOS or the Convention) (United Nations (UN) Division for Ocean Affairs and the Law of the Sea, 1982) establishes a comprehensive regulatory framework for the conduct of deep seabed mining (DSM) activities of mineral resources in the Area (defined in Article 1(1) of UNCLOS as ‘the seabed and ocean floor and subsoil thereof, beyond the limits of national jurisdiction’). Under that framework, DSM activities can only take place in the Area pursuant to an approved Plan of Work in the form of a contract with the International Seabed Authority (ISA) (the Authority), a 168-member international body created under UNCLOS to administer the Area. Private contractors can undertake DSM activities if they are sponsored by Member States of UNCLOS, providing a dual system of international and domestic regulatory control and oversight for activities in the Area.

Sep 1, 2024

By M Pyle, R Ch, G Lane, ramhan

Data is the most valuable commodity in the information age. The use of data enables productivity, opportunity, and safety in various sectors and industries. In the late 1950s, Alan Turing coined the term artificial intelligence (AI) to describe the ability of computing machines to think and behave like humans through simplified reasoning and logic (Turing, 1950). This concept of mimicking human behaviour is the building block of machine learning algorithms used in most AI software. The ‘learning’ aspect in machine learning requires data, that is, information on the environment or the objective, to be modelled or predicted using AI. An AI-enabled system aims to predict the unknown faster and intuitively. Intuition in software algorithms can be described as probabilistic, learned through the data that the AI system is trained on; indeed, the AI software cannot predict the future, but it can provide the likelihood of an event/action occurring, thereby enabling decisions or responses to prevent it or take action. Just as various industries have adopted AI as the tool of choice to seek new opportunities (examples: medical research, self-driving cars, financial modelling), the mining industry, too, has been receptive to embracing AI for problem-solving and maximising the value of its business.

Sep 1, 2024

By B P. Banerjee, D Patra, S Raval

Roof bolts are an essential component of roof support systems in underground mines. They are used to provide structural support to the roof of the mine and prevent it from collapsing. Therefore, it is imperative to perform regular assessments of these roof bolts to avert any hazards. Manual investigations of roof bolts are done by surveyors in underground mines, which is extremely timeconsuming and challenging due to the low-light conditions in underground mines accompanied by stringent mine access rules. To this end, automated recognition of roof bolts in 3D point cloud data obtained through laser scanning in underground mines serves as a potential solution to aid roof bolt monitoring. With the continuous advancements in laser scanning-based 3D data acquisition systems, the feasibility of making such surface estimations from 3D point cloud data has increased severalfold.

Sep 1, 2024

By D Connelly

High purity alumina (HPA) is a processed, premium non-metallurgical alumina product characterised by its purity level ie 99.99 per cent (4N) or 99.999 per cent (5N). The market price, application, and performance of HPA varies widely according to its degree of purity. High purity alumina is a form of aluminium oxide (Al2O3) with growing use in LED lighting and coating on the ceramic cathode separator sheets in lithium ion batteries. This paper describes a study on producing high purity alumina from feldspar to produce HPA and potash for use as a fertiliser. The project was positive for a number of reasons. High potassium extraction (>99 per cent) was achieved in the alkali leach using sodium hydroxide as the leaching agent and full dissolution of the alkali leach residue was achieved using hydrochloric and sulfuric acid. The potential to produce a high purity alumina product via a complex but promising emerging process was assessed. The potential to extract potassium as potassium sulfate to add value to the project was also demonstrated with the test work.

Sep 1, 2024

By K Clode, J Al-Shdifat, S Daykin

Planning for a circular economy allows for previously untapped market opportunities to be explored, resulting in new, innovative revenue generating opportunities, while also reducing environmental impacts. In particular, rare earth elements (REE) are in rising demand as they are key components of over 200 products across a wide range of applications (Xie et al, 2023) including renewable energy technologies. There is a geographical disparity in deposits, with China being the top producer, generating 70 per cent of global production in 2022 (Laboure, 2023). This single dominating source has created supply chain concentration and hence supply risks for other nations, providing a driver for finding alternative sources, such as mine-influenced water (MIW) and tailings. In this study, Isle presents a robust due diligence methodology for undertaking a global horizon scan into REE recovery technology, for a large mining company, from tailings decant water on a specific site. Previous studies of the tailings decant water at this site had shown potentially significant concentrations of both heavy and light REE, but challenges such as high flow, very low pH and high concentrations of iron had previously made recovery unfeasible. Due to these challenges, the horizon scan focused on technologies that had the potential for selective recovery, despite the challenges with water quality, and considered a range of Technology Readiness Levels (TRLs). This included an adsorption-based process that has the potential to selectively separate individual REE, and a polymer based selective flocculation technology.

Sep 1, 2024

By T Hicks

Designers of mining facilities play a significant role in defining the overall mine greenhouse gas emissions performance. This demands an appreciation of all the greenhouse gas emissions sources, including Scope 3 emissions, which will form an increasing fraction of the emissions as we transition our generation sources to renewable energy. Traditionally overland conveyors are designed to operate as fast as practical, and through this approach, achieve relative minimisation of: conveyor belt tensions; belt width; mechanical equipment masses; structural steel quantities; and concrete quantities. It is well understood that higher belt speeds reduce capital cost and improve project economics. Operating at higher speeds introduces operating costs through system energy losses and increased wear rates. This paper assesses the life cycle greenhouse gas emissions for a typical iron ore overland conveyor to demonstrate how the design criteria and engineering decisions impact greenhouse gas emissions and project economics. An analysis of the decarbonisation potential and project economics using a range of operational parameters has been completed, developing a marginal abatement cost for each system configuration, creating a basis for integration of low emission overland conveyor designs as part of the broader facility decarbonisation strategy.

Sep 1, 2024

By B Murphy

Seeking to reduce the likelihood of uncontrolled personnel entry into the operating footprint of a dragline, Coronado Global Resources initiated a review of proximity detection systems currently available and equipment interactions in and around dragline operational areas. At the time of the assessment, there were no established personnel proximity detection systems in compliance with functional safety standards for large machinery in Australia. Our primary objective was to guarantee a functionally safe outcome, particularly by implementing a performance-level-rated system in accordance with AS4024 (Safety of Machinery series of Australian Standards). Applying appropriate detection technologies, the goal was to reduce the residual operating risk for machine-to-machine and machine-to-personnel interactions. A comprehensive analysis of available or in-development technologies was conducted, and a layered approach (demonstrated in Figure 1) was adopted to achieve the desired risk reduction by segregating the sensing zones into distinct areas using diverse and disparate technologies consisting of radar (short); light detection and ranging (LiDAR) (medium); and optical (long). To address the potential limitations of each technology, overlapping layered sensing zones were designed. With LIDAR and optical using two distinct artificial intelligence (AI) systems to classify detections, these were specifically trained to handle the unique environments of a mining operation.

Sep 1, 2024

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 S Raval

The estimation of greenhouse gas emissions from coalmines is increasingly becoming a priority for both operators and regulators. At the same time, involved scientific challenges in the methane measurements is generating significant interest from global research community. Various technology providers are also actively engaged in improving their sensors and platforms to address the measurement challenges and capitalise the market opportunities. This presentation highlights rapidly evolving multifaceted landscape of coalmine greenhouse gas emissions. It covers the advances in sensors for the detections, including ground-based, airborne, and satellite-based systems as well as models for the emission estimations, including attempts to reduce the involved uncertainties.

Sep 1, 2024

By I Mason, M van de Werken, B Zhou, J Hargreaves, W Stasinowsky

A critical step in open cut coal mining is clean removal of the overburden. This is usually achieved by drilling, blasting and digging. A key part of this process being successful is to stop drilling at the correct blast ‘stand-off distance’. The stand-off distance is the distance between the base of the drilled blasthole and the top of the coal horizon to be mined. Having this correct provides a protective cap of rock above the coal horizon so that the blast will fracture all the overburden without damaging the coal horizon or mixing it with the overburden. Drilling beyond the correct stand-off distance causes the blast to continue into the coal horizon, damaging it and mixing it with the overburden, leading to significant losses of coal product. Conversely, drilling short of the correct stand-off distance results in additional work to remove the intact overburden.

Sep 1, 2024

By S Gulati, N Sarkar, T Vink, S Battersby, L Okada

This paper examines a successful project implemented by BHP Mitsubishi Alliance (BMA) and the Polymathian team at Deswik, aimed at enhancing short-term scheduling processes to reduce vessel turn times in the BMA Central Queensland coal value chain. The project’s objective was to digitalise the planning process to enable planners to make data-driven insights to improve the profitability of the value chain. The strategy involved developing a digital mining system that incorporates data storage, cloud computing, and the ability to meet the integration needs of both internal and external business systems. This was complemented by a sophisticated, optimisation-based, decision-support tool to enhance the planning teams’ ability to make timely and effective decisions.

Sep 1, 2024

By K Sherry, N Shahbazi

Scope 3 emissions are a set of often overlooked indirect greenhouse gas emissions within a company’s value chain and represent the majority of emissions for many sectors. These emissions arise from activities conducted by entities beyond the reporting organisation’s ownership or direct control and are often challenging to calculate. Scope 3 emissions typically encompass purchasing goods and services, transportation and distribution, managing operational waste, downstream processing and utilising sold products. Within the mining sector, scope 3 emissions often constitute a significant portion of the overall carbon footprint. Therefore, these emissions are a significant aspect of the future of sustainable mining. As the journey towards a world committed to achieving Net Zero Emissions has started, the time to spark a transformative conversation in the mining industry has arrived. Consequently, a better knowledge of scope 3 footprints allows organisations to assess their emission mitigation projects against the total carbon impact of their operations and products.

Sep 1, 2024

By E. Matinde

Human beings have been dependent on metals and alloys for livelihoods since time immemorial. In fact, there is irrefutable evidence that successful ancient civilizations achieved supremacy over their environments through the mastery of metals and alloys (Miller, 1995; Mtetwa, 2022). Over time, metals and alloys have evolved to become ubiquitous engineering materials indispensable to mankind, with applications in industrial and residential infrastructure, agriculture, energy generation and storage, transportation and communication systems, and manufacturing, among others (Elshkaki et al., 2018; Watari et al., 2021). Population growth, technological developments, and increase in urbanization and industrialization are all intricately linked to the intensity of use of metals and alloys. With up to 5 billion people estimated to live in urban areas by 2030 (Armstrong, 2009), the demand for metals and alloys will inevitably increase in order to satisfy the increased demand for infrastructure, machinery, and other human needs (Elshkaki et al., 2018; Watari et al., 2021). According to LePan (2022), urbanization and growth of mega-cities will increase the demand for metals and alloys, such as nickel, steel, aluminium, and copper by 116%, 50%, 57%, and 26%, respectively, between 2019 and 2035. Naturally, the production of metals and alloys from natural ores, secondary resources, and/or a combination of both, to meet this increased demand will invariably lead to the increase in generation of deleterious emissions in the form of wastes. In fact, mining and metallurgical wastes currently constitute the world’s largest anthropogenic streams by mass, often containing a high concentration of elements and compounds that can cause potential damage to natural ecosystems and humans (Hudson-Edwards et al., 2011; Lottermoser, 2011; Matinde et al., 2018; Seetharaman et al., 2022). Obviously, society’s heavy reliance on new mineral resources to drive the emerging energy transition entails that the legacy of these waste streams will continue unabated for the near-foreseeable future. The scarcity of high-grade mineral reserves, coupled with deliberate efforts to solve the anthropogenic circularity conundrum, therefore introduces a high level of urgency in the metal extraction industry to develop nascent and more eco-friendly processes and technologies. Integrating circular economy principles based on intentional configurations of circular industrial ecosystems in order to maintain the value of materials and resources in the economy for as long as possible is widely accepted as a practical approach to mitigating the negative externalities imposed by

Sep 1, 2024

By Mustafa Kumral, Mehmet Balci

Strategic mine planning focuses on cutoff grade selection, capacity planning and block sequencing. Despite significant knowledge accumulated, potential value can be added to mineral sourcing by addressing various aspects. A case study on a metallic deposit with a lognormal grade distribution examines if grade/metal distribution influences cutoff grade selection. Six data sets with different shape and scale factors, but the same ore tonnage and metal quantity, are analyzed. Results indicate deposits with lower shape and scale factors are more sensitive to metal price and discount rate changes. The study also investigates the effect of economies of scale (EoS) on project value, focusing on the ratio of variable cost to total cost (capacity factor). Two regression analyses are conducted for optimal capacity planning and cutoff grade selection. The first shows capacity planning is highly sensitive to the EoS of mining and mineral-processing costs, with grade variability and metal distribution being negligible. In the second analysis, grade variability and EoS of mineral processing costs are vital for cutoff grade selection.

Sep 1, 2024

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 H Aoki, A Nakayama, K Morita, S Yamada

The thermal conductivity of mold flux is crucial for optimising the productivity and quality of steels. This study investigates the thermal conductivity of molten oxides, including CaO-BO1.5-AlO1.5 (CBA), CaO-BO1.5-AlO1.5-SiO2 (CBAS), and CaO-MgO-BO1.5-AlO1.5 (CMBA) melts, using a hot wire method. Additionally, their structure is examined using Raman spectrometry and magic angle spinning nuclear magnetic resonance (MAS-NMR) to explore their relationship. Furthermore, first-principles calculations are employed to assess the covalency of each bond in the melts for a more quantitative analysis of their relationship. The study confirms that an increase in the CaO concentration of the CaO-BO1.5-AlO1.5 melts reduces the amount of bridging oxygen and consequently decreases thermal conductivity. The covalency of the B[4]-O bond is highest, especially when the second-nearestneighbour Al atoms transition from Al[6] to Al[4], forming the AlB3O7 structure with B[4]. The relationship between thermal conductivity and the structure of CaO-BO1.5-AlO1.5-SiO2 melts can be quantitatively assessed using the degree of polymerisation of the network structure and the variation of covalency as indicators. Furthermore, replacing CaO with MgO in the CaO-MgO-BO1.5-AlO1.5 melts reveals a mixed alkaline earth effect on thermal conductivity.

Aug 21, 2024

By M A. Rhamdhani, R Z. Mukhlis

Removing the ledge formation in the Hall-Heroult cell during the electrolysis process may significantly reduce the energy requirement of the aluminium production process. In the absence of the ledge, however, the sidewall-material types become heavily restricted since the cryolite is very corrosive at the electrolysis temperatures. This paper study the corrosion behaviour of sintered nickel ferrite, nickel aluminate, and magnesium aluminate refractories (as possible candidates for sidewall material) in molten aluminium and cryolite melts with various alumina content at 980°C. Immersion tests of refractories with various porosity were conducted to assess the suitability of the refractories in the corrosive environment of the electrolysis cells. Microstructural change of the refractory after immersion were analysed using secondary scanning electron microscopy (SEM). Sampling of the melts were taken at 0, 10 min, 30 min, and each hour up to 6 hrs, and subsequently analysed by Inductively Coupled Plasma (ICP) spectroscopy. Based on the projected concentration of the refractories constituents in the melts, it was predicted that the corrosion rate of NiFe2O4, NiAl2O4 and MgAl2O4 in cryolite with 11 wt per cent Al2O3 were 461 × 103, 1.03 × 103, and 798 × 103 cm/a, respectively. Much lower corrosion rate of 2.3 cm/a for NiFe2O4, 2.6 cm/a for NiAl2O4 and 0.3 cm/a for MgAl2O4 were predicted if the refractories in contact with aluminium melts. Erosion played an important role in the corrosion as the concentration of the refractory constituents in the melts were beyond its solubility limit. It is therefore crucial to increase the densities of the refractory and improve its wetting characteristics.

Aug 21, 2024

By K Saito, M Hasegawa

In steelmaking processes, it is known that P2O5 in dephosphorisation slags often exists in solid solutions between Ca2SiO4 and Ca3P2O8, <C2S-C3P>SS, and thus the solid solutions play an important role in the dephosphorisation reaction. Hence, the knowledge of thermochemical properties, ie phase relations and activities of components, in slags containing <C2S-C3P>SS are necessary for the effective removal of phosphorus from hot metal. In this study, the P2O5 activities in <C2S-C3P>SS were measured through the gas equilibrium method. In the present experiment, oxide samples were equilibrated with Cu-P liquid alloys at 1573 K under a stream of Ar + H2 + H2O or Ar + H2 + CO2 gas mixture in which oxygen potential was fixed and then equilibrium phosphorus concentrations in the Cu-P alloys, [mass%P]Cu, were analysed with ICP-OES.

Aug 21, 2024