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By C Zhang, I Canbulat, S Saydam, H Yu, H Chen

Natural rocks are frequently exposed to groundwater and complex geological conditions. Environments with water presence are more susceptible to chemical corrosion, which notably affects rock’s micro properties. Generally, the pH of acid mine drainage in hard rock mines can be low to 5, and the pH of quarrying waste deposits can be below 3. With the expanding scope of mining engineering in aquifers and salty areas, chemical corrosion can also caused by different types and concentrations of anions. The anions can significantly affect rock’s mechanical properties. Therefore, it is critical to emphasise the impact of anion and acid on the mechanical and micro characteristics of rock. This paper commences with an introduction to representative corrosive environments. Then the UCS tests are conducted on granite samples corroded by chloride ions and sulfate ions. Anion has negative effects on the strength of granite. The reason is that certain minerals can react and be dissolved by anions and hydrogen ions, such as Al2O3. Furthermore, the scanning electron microscope (SEM) shows that the roughness of the fracture surface increases with the anion concentration. The effect of acid is notably more pronounced than anion on the micro properties of granite. The initiated fissures and pores also increase with the concentration of SO42- and Cl- ions, which is the micro explanation for the impact of anion on rock strength and stability. Subsequently, the permeability of the granite specimens is tested. The reason that granite performs insensitivity to acid and anion is attributed to its low permeability and high content of inactive oxides, such as quartz. To summarise, anionic and acidic conditions can slightly deteriorate the mechanical and micro properties of granite. While SO42- demonstrates a higher level of corrosiveness on granite than Cl-.

Sep 1, 2024

By K Zhao, H Gong, B Li

Health and safety are the first priority for Australian minerals sector. Australia has a global reputation in the mining industry. However, accidents occur every year. Legislation and regulation are important to prevent the accident from occurring, so do the new technologies. It is sad to witness the collision incident persisting and the long called collision avoidance system (CAS) is still not widely used in underground mines. The main reason is that the existing systems are still not advanced enough. The current CASs utilise GPS, infrared, ultrasonic, RFID, Radar, electromagnetic etc have their limitations and disadvantages. For instance, GPS is widely used in a surface mine, but it cannot be applied in underground mine; Infrared and electromagnetic have the false positive problem.

Sep 1, 2024

By S Prakash, M Danish, D Kumar

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

Sep 1, 2024

By i, A Shirbazo, S Saydam, S Ata, H L. Ram, B Li

The integration of internet technology into industrial sectors has significantly accelerated the adoption of Internet of Things (IoT) devices, transforming communication, data aggregation, and information distribution. The mining industry has embraced this evolution by deploying IoT devices and advanced technologies like autonomous robots and sensors to enhance operational safety. These sensors enable real-time data transmission and continuous monitoring of key parameters, providing decision-makers with timely insights. However, the interconnected nature of these devices has introduced complexities, resulting in inconsistencies across operations. Addressing these challenges, this research explores the application of modern digital principles to the mining industry through the Reference Architecture Model Industry 4.0 (RAMI 4.0). RAMI 4.0 offers a robust framework that integrates digital and physical systems, promoting data transparency, interoperability, and decentralised decision-making. This study aims to briefly introduced framework designed for the unique challenges of mining operations, leveraging the latest digital technologies. The lack of standardisation in the mining sector highlights the need for a comprehensive approach to tackle issues such as interoperability, scalability, security, safety, and repeatability. Establishing standard procedures can ensure smooth interaction between connected devices, ultimately enhancing operational efficiency in the growing IoT ecosystem within the mining industry. Introducing a well-defined reference architecture like RAMI 4.0 can streamline management processes and improve operational efficiency in mining. By adopting this framework, the mining industry can effectively navigate the challenges of digital transformation, leading to a more connected and resilient future. The study concludes that RAMI 4.0, with its structured approach, can significantly enhance the efficiency and productivity of mineral processing operations, fostering sustainable growth in a digitally driven industrial landscape.

Sep 1, 2024

By M Yadav, K Clode, S Daykin

The mining industry faces numerous challenges related to sustainability and circular economy principles, necessitating strategic approaches to innovation and resource management. As demands for mineral resources continue to rise, mining companies are under increasing pressure to address environmental impacts, minimise waste generation, and optimise resource utilisation. These challenges underscore the importance of Research and Development (R&D) roadmaps as strategic tools for guiding innovation and sustainability efforts within the sector (Jochen Berbner, 2022). A design-led methodology offers a structured approach to R&D roadmap development, emphasising stakeholder engagement, collaboration, and alignment with industry objectives. By leveraging design principles, mining companies can ensure that their R&D initiatives are responsive to evolving challenges and opportunities, fostering a culture of innovation and continuous improvement. Against this backdrop, the project aims to address the pressing challenges associated with sustainability and circular economy in the mining industry. Through the development of R&D roadmaps and the application of a design-led methodology, the project seeks to identify innovative solutions for enhancing resource efficiency, minimising environmental impacts, and promoting sustainable practices within mining operations. This paper highlights the collaborative efforts among Isle, the mining industry, and water utilities, which have yielded tangible benefits. The study demonstrates how the outcomes from the roadmaps outlined below effectively align research with strategic objectives, fostering innovation to address pressing challenges.

Sep 1, 2024

By A G. Dempster, N Barnett, J Oh, S Saydam

NASA’s Artemis III mission is expected to return astronauts to the surface to explore and sample the lunar volatiles by 2026. These volatiles are anticipated to provide scientific insights as to how the Moon was created and economic value to off-Earth operations. These same volatiles can pose health implications to astronauts and Earth based personnel handling lunar regolith samples. Using Australian, USA and NASA airborne contaminant exposure limits and the Lunar CRater Observation and Sensing Satellite (LCROSS) measurements of lunar volatiles in the Cabeus crater ejecta, the minimum mass of lunar regolith that can be safely handled by humans on Earth and in spacecraft can be calculated. This analysis determined a 0.15 grams pure sample of Cabeus crater regolith within a cubic metre of air has enough volatiles to exceed safe working conditions, and 14.9 grams of pure Cabeus crater regolith per cubic metre of air can impact human life. The intent of this analysis is to assist safe handling and exposure calculations for the safe handling procedures of lunar regolith containing volatiles.

Sep 1, 2024

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 J Johansson, J Lööw

There are many reports about how the new technology will affect our work, sometimes they are utopian but often dystopian. Most of the visions describe the industrial work, while the work of the managers does not seem to be affected. In this article, the authors will discuss how the new technology within the concept of Mining 4.0 changes the conditions for leadership and the work of managers in a modern mining industry.

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. S Raval, P M. Ranasinghe

Simultaneous Localisation and Mapping (SLAM) is an important technology that enables autonomous robots to navigate independently and create detailed, real-time maps of their surroundings. This capability is crucial in underground mining settings, where it enhances operational efficiency and significantly strengthens safety in these subterranean environments. SLAM algorithms typically rely on identifying and using distinct geometric features, such as plane and edge features, to understand the spatial layout of the environment and to track how it evolves as the sensor moves through it. In underground roadways, the scarcity of noticeable edge features, coupled with the repetitive nature of walls and corridors, poses a significant challenge, making it difficult for these systems to distinguish one area from another. Furthermore, the absence of global navigation satellite system (GNSS) signals in underground adds more challenges, leading to the accumulation of errors in both the mapping and localisation.

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 S J. Schafrik, M Long

There are several factors that are increasing the need for new graduates to be ready to design and manage more autonomous operations at mines. Autonomous controls and systems range from the start-up, control, and shutdown of processing plants to the autonomous operation of vehicles. These operations require the workers, engineers, and management to have additional skills for programming, debugging, tasking, and maintaining the equipment. The future survivability of many mining operations will be influenced by a company’s willingness and ability to understand, accept, and utilise this technology for safe and productive operations. This is an educational opportunity and challenge that needs to be addressed throughout the student’s experience, from first year to graduate level. This paper discusses the lessons learned from a decade of multidepartment effort at all student levels and many courses that are able to incorporate data science, controls, and autonomous design into the already topic-rich national curriculum. This approach also exposes other engineering disciplines to the mining industry which is a valuable recruiting tool for talent outside of the mining engineering departments.

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 H S. Shin, H Jin, B H. Ryu, J Lee

Prospecting ice on the Moon necessitates drilling systems to obtain subsurface samples and measure the composition of ice deposits. Landers and rovers equipped with drilling equipment are essential for analysing ice and subsurface resources located at the Moon’s poles. These devices must be small, lightweight, low-power, highly efficient, and high-performance units to function properly in the extreme conditions of the lunar environment. Researchers have developed a prototype drilling apparatus capable of operating in atmospheric and cold environments. A newly developed drilling system in Korea, capable of performing not only sampling but also subsurface investigation, is introduced.

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