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By M Yahyaei, P Knights, M S. Kizil, J Qu

Digital Twins (DTs) are transforming business operations across many industries through their accurate representations of physical entities enabled by IoT technologies.

Sep 1, 2024

By G McCullough, J Bassan, d P. Knights, C T. Farrelly

By 2045, carbon-neutral mines will be economically producing products that are vital for the world’s transition to renewable energy. After overcoming significant technical challenges (and the investment of significant capital), leaders have successfully eliminated the use of diesel and other fossil fuels by introducing a wide range of new decarbonisation technologies. These leaders will be reaping the benefits of lower production costs (resulting from elimination of diesel), stable and predictable operations, and easier access to capital for their future projects. This paper describes a future scenario of how a net zero carbon and zero-entry mine could operate by taking maximum advantage of technologies in development or already proven in other industries. Some miners have been unsuccessful in their efforts to reach decarbonisation targets. Instead, they have been forced to close or offload their assets to be acquired by operators with better sustainability credentials, or, in some cases, to indigenous or community-based enterprises with vested interest in making operations viable and sustainable. Regardless of ownership structure, all successful mining operations will have fostered strong and positive community relations that bring education, health, security and economic development opportunities to the communities while respecting their traditions, land-uses and culture. As shallow, easy to mine deposits have become scarce, the majority of new mines are underground. Advancements in electric underground technology and the feasibility of ‘zero entry mining’ (with no personnel in active mining zones) has made it increasingly possible to economically operate underground mines, even at shallow depths. The newest mines have a near zero footprint on the surface, produce no harmful by-products, and are employing in-place or in situ methods of extraction that are efficient and low impact. Surface land-uses, such as agriculture and renewable energy generation, continue in the immediate vicinity of the mine, with no significant ground disturbance, community impact or environmental footprint.

Sep 1, 2024

By B Y. Luan, Y Tian, W Zhou, X Lu, Q X. Cai

The unmanned driving technology in open pit coalmines is an important technology that relies on innovation to drive the development of the open pit coal mining industry in China’s transformation of growth drivers and industrial structure upgrading. It is an important path to solve the shortage of mine truck drivers in the future mining, an important way to improve the working environment of open pit mining personnel, and an important foundation for achieving flexible production capacity in open pit mining to cooperate with the coordinated development of new energy. The communication rules at intersections are key to ensuring the safety and efficiency of unmanned driving in open pit coalmines. This paper proposes a collaborative traffic strategy between unmanned trucks for the single bucket unmanned truck intermittent process system in open pit coalmines. Based on the gap theory, this paper analyses the conflict characteristics and establishes a coordinated traffic strategy for the common intersection and merging conflicts of unmanned truck trucks in open pit coalmines, A multi-agent system simulation platform was built to evaluate the coordinated traffic efficiency under different initial average gap sizes of vehicles, with intersection traffic density, vehicle speed, and gap changes as key indicators. The results show that under the simulation conditions of this article, when the initial average gap of vehicles reaches three (four) times or more of the safe distance, the coordinated traffic performance of intersection conflicts (merging conflicts) is better, and the traffic capacity of intersections is close to the traffic capacity without conflicts.

Sep 1, 2024

By J Q. Shi, M Karakus, Y Liu

Predicting the spatial distribution of rock mass properties in cave mining is a critical challenge. In this study, we introduce a novel machine learning-based model to advance our comprehension of rock behaviour. This model specifically focuses on the spatial distribution of rock mass and mechanical properties, a crucial aspect of our understanding of rock mechanics. Our model is inspired by the recognition that neighbouring information serves as crucial prior knowledge for predicting the spatial distribution of these properties, wherein closer neighbours are likely to exhibit similar characteristics. To effectively capture and model these neighbour relationships, we design a neural network-based model to map the spatial locations within the data set into a new highdimensional space. Within this transformed space, we utilise Euclidean distance to identify N neighbours. Leveraging these neighbours, we introduce a straightforward yet highly effective approach—label propagation—to construct a graph that illustrates neighbour relationships. This graph not only facilitates the prediction of the spatial distribution of rock mass properties but also offers a measure of uncertainty for these predictions. We validate the proposed method using a real rock data set and showcase its efficacy in capturing intricate relationships and distributions within rock properties, enabling precise predictions across diverse spatial scales. Our results underscore the potential of machine learning-based approaches to revolutionise the field of rock mechanics, providing valuable insights applicable to geotechnical engineering, mining, and other sectors where an accurate understanding of rock properties is paramount. This model has direct practical implications, offering a new tool for geotechnical engineers and mining professionals to enhance their understanding and prediction of rock properties, thereby improving safety and efficiency in their operations.

Sep 1, 2024

By 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 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 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, 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 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 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

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 Padekar, C Tapia, D Likhachev, S Harlikar, D Sapkale

In the quest to access valuable critical minerals, humankind is expanding the frontier into extreme environments like space and deep-sea mining. These ambitious enterprises involve considerable costs and lengthy development cycles to ensure the robustness of mining systems in the face of the harshest conditions and unforeseen challenges. Unfortunately, obtaining information from these unexplored territories is complicated. Data is often scarce, incomplete or impossible to obtain until equipment is deployed, leaving engineers facing many uncertainties throughout the design, testing, production and preparation of such missions. Despite these challenges, these extreme environments operate under consistent physical and chemical principles that can be faithfully digitally replicated. However, unexplored areas and dynamic systems like weather and geology continue to carry uncertainties. While eliminating uncertainties remains elusive, Digital Twins offers a means of robust risk management tool. These virtual replicas can excel in simulating extreme conditions and unexpected events, helping optimise and validate mining system designs tailored for such hostile environments. This study takes inspiration from the Mars Science Laboratory (MSL) Curiosity Rover’s wheel damage rate to develop a Multiphysics Modelling and Virtual Motion Simulation (MMVMS) system. Using advanced Generative Design, this MMVMS system refines the MSL’s design, simulates component and system performance, and assesses the risk of premature damage while operating on Mars. Additionally, MMVMS integrates cutting-edge CAD design technologies from aerospace and manufacturing to realistically recreate the Martian environment and a detailed representation of MSL, allowing mobility verification and validation simulation to test different components in terms of materials, design, weight, power and, therefore, cost. The research highlights the precision of MMVMS in replicating extreme conditions, simulating and evaluating internal and external variables influencing the entire system (Rover) in real-time, and predicting movements, failures, and premature damage. This sophisticated Digital Twin technology, showcased in this research, holds promise not only for space missions but also to be adapted for terrestrial and deep-sea mining operations.

Sep 1, 2024

By B Bradák, D Asahina, S Saydam, S Akdag

Human exploration of Mars has been on the horizon of scientific goals for decades now, and as part of the objectives of human missions to various planets, one of the possible goals is opening new economic frontiers by the potential extraction and utilisation of resources, eg mining minerals and using surface regolith (Neukart, 2024). A comprehensive understanding of surface morphology, lithostratigraphy, basic structural geology, and civil engineering technology is essential to conduct mining operations for resource extraction on extraterrestrial planets. For instance, proper assessment of the risks associated with excavation and infrastructure construction under unique environments such as low gravity, surface irregularities, and the presence of regolith is fundamental. Such knowledge has been accumulated through satellite imagery and numerical topographic data using remote sensing technology, as well as seismic surveys by the on-site lander (Knapmeyer- Edrun et al, 2021; Banerdt et al, 2020; Giardini et al, 2020). Recent studies show that Marsquakes could be caused by the release of strain energy generated by the contraction of Mars. In particular, Marsquakes occur around the graben where many faults exist and form regularly spaced patterns. This implies that Marsquakes occur when the stress field of the Martian crust is an extension condition, and normal faults appear on the surface. In this study, we focus on so-called graben structures, one of Mars’s common tectonic structures (Plescia, 1991; Banerdt et al, 1992; Hoogenboom Hagen, 2014). The study aims to provide additional information about the tectonic environment during the formation of such features and, in addition, the internal mechanical-rheological structure of Mars, revealed by the geometrical pattern of faults modelled and simulated in a laboratory environment.

Sep 1, 2024

By O J. Restrepo Baena, S Nowosad, R Webber-Youngman, N Mojtabai, P Foster, A Binder, S Hazuria, erson, S Raval, L Liu

As the mining industry faces times of change, new challenges arise to ensure raw material supply for the younger and future generations. Different scenarios are currently shaping the way we mine and teach mining engineering. Led by an increasing demand for raw materials, net zero requirements for improving long-term operation performance, technological advancements such as automation and digitalisation, safety improvements, environmental considerations and a need for social acceptance, mining is pursuing to change and adapt to these requirements at a faster pace. Such changing times raise new concerns, one of the most relevant is the lower number of students interested or enrolling in mining engineering and related fields leaving mining engineering as one of the less preferred career options between the younger generations. Nonetheless, it is essential to continue addressing and supplying the skill sets that is suited to contemporary and future of the mining industry. To this end, globally, academia is currently undergoing several initiatives such as curricula updates and adaptation, up skilling, improving teaching practices and techniques, including e-learning techniques, enhancing industry-academia and inter-university collaboration, and much more

Sep 1, 2024