Search Documents

By S Warden, T Neville, P Leckie

At its simplest, the classification and characterisation of an orebody is a matter of Where, What and When. Where and with what spatial resolution are measurements taken, What properties are measured, and When are the data acquired, processed, and delivered to create the most value. This paper delves into how existing and near-term advances in measurement technologies, field automation, and whole-system-digitisation are combining to open new routes for mine and exploration productivity improvement. Commencing when the initial exploration hole is dug, the advancement in deployment of survey technology—from expert-operated, to driller-operated, to driller-integrated—is used to demonstrate a template for adoption of higher technology measurements. We then introduce and discuss the multi-use measurement as offering outsized returns from a single measurement run, highlighting two new technologies that acquire rock strength while drilling and downhole assay data as examples. For rock strength, we highlight a novel approach to determining Uniaxial Compressive Strength (UCS) while drilling, its relationship to and advantages over Mechanical Specific Energy (MSE), and how it offers utility to multiple downstream processes. For downhole assay, we detail how a back-tophysics approach to Pulsed Fast and Thermal Neutron Activation (PFTNA) analysis offers significant advantages over other measurement methods and earlier implementations of PFTNA, generating a rich data set that can significantly extend and improve the information on which mining decisions are made. Finally, we discuss the possibilities enabled via optimising data delivery, collection, and curation— noting a fundamental limitation of historical systems and exploring the possibilities that will be unlocked by soon-to-market solutions that better enable Orebody Characterisation data to (virtually) move in space and time with rock material post-blasting. Ultimately, the value of optimising the Where, What, and When from point of measurement to the moment it generates maximum value will create greater life-of-asset production for a lower cost and energy input.

Sep 1, 2024

By V Salamakha, K Tang, G Si, J Oh, X Wu, Z Yu

The development of machine learning (ML) algorithms has led to promising advances in lithology classification from geophysical logs, which is indispensable in various underground engineering applications. Due to the resolution and availability of logging data, ML models can recognise lithology change in a definitive form at a fine scale (0.01 m). While the results from previous models indicated promising performance in the classification, the robust capability of generalising lithology prediction to unseen data may not be true for those models due to: (1) data bias caused by borehole selections, and (2) logging data mismatches among boreholes. To this end, this paper aims to investigate the generalisability of ML models and seek potential improvements. Four ML models were selected to be trained on single reference boreholes to test the other (as unseen) in the same region, which includes Support Vector Classifier (SVC), Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and Residual Neural Network (ResNet10). The data set involves 11 boreholes from a coalmine in north-western Bowen Basin (Queensland, Australia): density, gamma ray, neutron, and sonic logs are selected as inputs. Additionally, a data adaptation method is applied for better generalisability. The results show that there is an accuracy trade-off between the same borehole and unseen boreholes across the models. It also indicates a 16–43 per cent reduction in model performance when generalising the predictions (in marco-F1 score), while the adapted data sets can contribute to a around 12 per cent improvement. This study provides a fundamental understanding of the model generalisability when using a single borehole, essential for further correlating boreholes for regional lithology classification; the adaptation method can improve the generalised accuracy, and reduces the labour to label lithology, which facilitates the identification of gas storage mechanisms, geological/geophysical modelling, and stratigraphic analysis.

Sep 1, 2024

By H Utama, R Heryadi

The demand for environmentally friendly commercial explosives has increased globally due to concerns regarding environmentally friendly mining activities. Ammonium nitrate fuel oil (ANFO) and bulk emulsion explosives (BEE) are two kinds of civil explosives primarily used in coal and mineral mining in Indonesia. Indonesia is one of the leading countries in coal and mineral mining production and export, with abundant resources; therefore, the consumption of civil explosives such as ANFO and BEE is enormous. The selection and evaluation of commercial explosives are mostly done on the commercial and technical side, and no assessment is made based on environmental aspects. This preliminary study evaluates the economic value and environmental burden of greenhouse gas emissions produced by ANFO and BEE and determines which type of explosives is more environmentally friendly. The combination of environmental and economic indicators, eco-efficiency (E/E), was selected to investigate BEE’s economic and environmental performance compared to ANFO. The life cycle of greenhouse gas emissions of BEE and AN was determined using previously available studies and simple calculations. The cost of the product or explosives was used to represent the economic value. The E/E results showed that environmental and economic sustainability could be enhanced using BEE.

Sep 1, 2024

By A Krishnan, C Garcia, I Luna-Berbesi

The discovery and exploration of deposits are progressively shifting to extremely remote regions and greater depths. As a result, exploration costs and the economic risk of a bad investment increase. Current megatrends in the exploration and mining sector indicate a growing demand for the implementation of automation, digitalisation, and machine learning techniques. The implementation of a multi-sensor-based core logging device (DMT, 2024) was done on 400 m of drill cores to optimise interdisciplinary processes in terms of time and quality, by using interlinked and consistent data in real-time, to obtain reliable predictions that support and speed up decision-making. The results are consistent with the laboratory data previously obtained by the mining company, highlighting the benefits of introducing ANCORELOG into their core logging procedures.

Sep 1, 2024

By A Innis, A Bye, B McCarthy, S Glover

Technology adoption in the mining industry has been steadily increasing over the past decade, driven by the need to improve operational efficiency, safety, sustainability, and profitability. As mining companies wish to implement solutions to improve mining safety and productivity, various technologies are being integrated into mining operations to enhance processes, reduce risks, and optimise resource utilisation. Adoption of new technologies such as autonomous trucks requires a strong focus on foundational operational technology (OT) and a systems-thinking approach. OT support is critical however, OT is traditionally not considered or integrated into standard maintenance activities. Consequently, sourcing and applying service support to OT systems in real-time has posed significant challenges to date. Neglecting the monitoring and upkeep of these operational technologies and equipment can lead to a host of adverse consequences. Operating instability becomes more prevalent, necessitating reactive measures, and the vulnerability to cybersecurity threats increases significantly. To address OT monitoring and maintenance challenges, a systems wide mindset and remote support discipline must be utilised. Remote operations support refers to real-time, 24/7 monitoring, control and support of site OT: network, production systems, maintenance systems, enterprise systems and mobile and fixed plant through a single interface remote from site. The presence of a disciplined team, grounded in a systems-oriented approach, is of the utmost importance to remote monitoring, given the intricate interdependencies of OT systems. The ability to understand the relationships among multiple systems, coupled with the capacity to distinguish between symptoms and root causes, assumes a pivotal role in operations. Through diligent monitoring and the astute understanding of system thresholds, remote operations support can actively identify and alleviate constraints, in turn, serving as the enabler for transformative advancements in technology output.

Sep 1, 2024

By D Brown, K Markovic, Z Tabor

From ancient times to the modern world, the mining industry has played a pivotal role in shaping the society that we live in today. It remains an integral part of everyday life and is a catalyst for both technological and economic growth. With finite resources on Earth, and growing pressures surrounding climate change and the environment, space mining opportunities have become of increasing interest. Recent discoveries have shown celestial bodies including the Moon and surrounding asteroids, contain an abundance of critical minerals, as well as resources necessary to sustain human life. These discoveries coupled with a transition into the digital and autonomous age, offer an innovative solution to solving some of the mining challenges currently being faced on Earth. Similarly, they also offer a pathway to enabling deep space exploration and off Earth exploratory missions. However, like with mining operations on Earth, space remains and immensely challenging and complex operational environment. The inhospitable nature of space poses new challenges, including communication barriers, lunar dust, cosmic radiation, and gravity. The challenges and risks are not just technological, as Environmental, Social, and Governance (ESG) factors must also be considered to prevent unintended consequences and hazards. Furthermore, future workforce, law, financial, and security systems must evolve to enable space mining to take place. These are all known aspects to begin with, but as the complexity of the space grows it becomes apparent that a holistic approach to assess and manage these factors will be required.

Sep 1, 2024

By J Herd, J Rowe, M Cutbush, d F. Aziz

WearApp provides expertise for assessing the degradation of removable screening media panels on vibrating screens right in a user’s pocket. Through the WearApp application, users can simply take a picture of a panel using their mobile, enter some basic information and have the application assess the wear to provide a recommendation for whether the panel needs to be replaced. Where traditional mechanisms for assessing panel wear only measure a few points across the panel face and rely on having personnel with specialised knowledge available to perform the assessment, WearApp uses deep learning models to analyse the entire panel face and support the user to make an informed decision. The application works through analysing a panel image and using a model to automatically detect the corners of the panel. Given the known dimensions of the panel, this enables the application to calibrate the pixel size against a known reference. A second set of models then detect the apertures and measure their dimensions. With the aperture dimensions defined the application assesses the wear of the panel through referencing a stored 3D model of the aperture. Finally, the data from each aperture is collated and the wear of the entire panel face defined. Since WearApp performs measurement of the entire panel face, it also allows the user to perform a more detailed analysis of the panel’s wear profile. Where typically, the panel wear is only measured in a few locations, WearApp’s analysis enables users to visualise the panel wear across the entire panel face, identifying areas of high wear that could be leading to premature panel replacement. Additionally, because the entire panel face is measured the change in screening efficiency can be calculated accurately for the worn panel, even taking into account the impact of aperture pegging by small rocks.

Sep 1, 2024

By L Muñoz, L Steffen, W Vilas Boas, G Gontijo, R Macedo, C Braga, d D Machado, G Stevenson

Mines operating close to communities face growing challenges, especially with their drilling and blasting activities. Proper management of environmental and community impacts is critical to provide sustainability to the mining business in the long-term, by enabling the licensee to operate. This paper will present new technology solutions to minimise the impacts to communities and the environment from blasting operations. A case study of the wireless initiation projects conducted at Kinross Morro do Ouro Mine (located in Paracatu, Minas Gerais – Brazil), will be presented. Focused on overcoming the complex challenges in the mine, Orica and Kinross searched for initiatives to optimise the drilling and blasting operations. The team evaluated the WebGen™ wireless initiation system for controlling environmental impacts as well as operational flexibility and productivity. Since January 2023, three planned wireless blasts to demonstrate the technology and applications were executed. In the tests, the vibration level was able to be maintained lower than the limit of 3.0 mm/s while dust and flyrock impacts were well controlled. The main benefits of using this technology for the Morro do Ouro Mine are: reducing the number of blasts while increasing the blasted mass per month; control of projection of fragments using additional cover; dust control by watering the bench easily; lightning initiation risk reduction due to the absence of wires or tubes on the surface; flexibility in changing the blast day to better control the noise and dust; flexibility in mine planning mining (potential application in the future). The use of the WebGen™ wireless initiation system, enables improved productivity while enhancing safety and reducing impacts to communities. Implementation of this technology has enabled Kinross Morro do Ouro Mine to build trust with its neighbours, maintain the license to operate, and helping to mobilise resources in a sustainable way. This has allowed Kinross to position Morro do Ouro Mine as a world reference operation, especially in relation to innovation, safety, and social responsibility.

Sep 1, 2024

By K Rau

The overall success of a mining operation is directly impacted by the reliability and availability of mining equipment. Heavy-duty vehicles such as excavators and haul trucks are used for conveying of bulk material and improving such assets’ availability helps reduce costs, increase throughputs and improve safety (Ravande, 2022). If a mining operation’s key performance indicators (KPIs) are tied to operational performance, then overall equipment effectiveness (OEE) is an essential metric to track progress and to identify areas of improvement.

Sep 1, 2024

By P Mugebe, M Yahyaei, M S. Kizil, R Low

This paper seeks to establish an empirical relationship between the impact of mining technological innovation implementation and a company’s market value. The company’s market value is represented by its stock price. In a bid to ascertain the relationship, a stock price prediction model is sought to be established using the firm-specific factors impacted by the implementation of innovative technology. This work is performed under the watch of the prevailing macroeconomic factors. The background anchors in the understanding that the difference between the economic status of a mineral deposit and its uneconomic status lies in the level of mining technological innovation most prevalent at a given time (Hartwick, Olewiler and Preuss, 1986; Wellmer and Scholz, 2017; Wright and Czelusta, 2003). This is derived from productivity improvement, resulting in sustainable profitability due to technological innovation. Unfortunately, there is not an adequately established relationship that links this technological innovation impact to the share price performance of the mineral commodity company.

Sep 1, 2024

By A Seneviratne, Y Li, N Udugampola, X Ai, B Li

LoRa (Long Range) is a low-power, long-range wireless communication technology that has gained significant research interest for its potential use in multi-hop linear wireless sensor networks (LWSNs) for underground mines. These networks deploy LoRa-enabled sensor nodes in underground mines, together with LoRa repeaters placed along mine tunnels to relay sensor data to a LoRa gateway, which connects to a backend network for application-level processing. Since the packets generated by sensor nodes farthest from the gateway have to hop through many repeaters, their probability of successfully reaching the gateway is significantly lower due to the higher chance of packet collisions compared to sensor nodes closer to the gateway, resulting in a lack of network fairness for the sensor nodes. In this paper, we first present an algorithm to improve the fairness of a LoRa LWSN by grouping neighbouring nodes and adjusting the number of packet re-transmissions and transmission intervals for each group. Then, we demonstrate that the fairness of a simulated LoRa LWSN can be improved by approximately 500 per cent by applying our algorithm. We verify our algorithm with network simulations under different network traffic levels and grouping strategies, obtaining fairness improvements in each scenario. Our algorithm and results will pave the way towards developing highly fair LWSNs for underground mining applications.

Sep 1, 2024

By Y Tang, G Si

The analysis of rock fragment sizes plays a pivotal role in various stages of geotechnical and mining engineering. Traditional case-based methods often falter in addressing the variability in rock fragment images due to fluctuating geological conditions and imaging environments. To counter this challenge, AI-based models necessitate training on expansive and diverse data sets, encompassing a wide range of conditions and fragment types. However, the creation of such data sets is often hampered by financial and environmental constraints. This paper introduces a novel approach for rock fragment recognition, which leverages the power of AI for data augmentation, automated annotation and prediction. The methodology integrates three key components: Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN) for data augmentation, the Segment Anything Model (SAM) for automated annotation, and the multiscale version of you only look once (YOLO-MS) real-time instance segmentation model. The proposed method begins by training a new StyleGAN model using the original data set. It then delves into exploring the latent vectors of different rock fragment images to generate specific ‘style representations’. These latent vectors form the foundational elements of each image in a high-dimensional space, encapsulating critical features such as size, shape, texture, and colour variations. The next step involves conducting quality checks on the generated images to ensure their realism and suitability as training data. Subsequently, the augmented image set is annotated using modified SAM module, followed by training the YOLO-MS real-time instance segmentation model. The performance of this model, trained on the augmented data set, is compared with a model trained on the original data set, particularly in terms of generalisation abilities in new scenarios. The results exhibit a significant enhancement in accuracy, robustness, and generalisability of the model trained on the augmented data set, underscoring the potential of StyleGAN as a powerful tool for data augmentation in geotechnical engineering image analysis.

Sep 1, 2024

By R Daiyan, G Burge, M Haider Ali Khan

Australia’s mineral and metal exports have a substantial carbon footprint, twice that of its domestic emissions, mainly due to fossil-fuel-dependent downstream processes like processing into secondary metals and their transport. The country’s vast renewable energy and low-carbon hydrogen potential offer a promising alternative to mitigate this impact. These resources can be leveraged to displace fossil fuel use and become embedded into the metals value chain. Ultimately, it will yield ‘Green Metals’ that can be scaled to reduce the environmental footprint and provide an avenue for Australia’s vast green resources to be embedded (incorporated during production and supply) in green metals that can be exported. One potential green metal is copper, which is in great demand and likely to drive global decarbonisation through renewable electrification due to its role as an energy conductor. Given that Australia is a large producer and exporter of copper, this paper provides a techno-economic model of the copper value and applies it to Queensland (a hub of Australia’s copper production) as a case study. The results show that under incumbent hydrogen (A$8/kg) and renewable energy costs, the cost of green copper would be A$9704/t (~10 per cent higher than current copper costs). Further analysis revealed that the cost of hydrogen is a major cost driver; therefore, if the cost of hydrogen can be reduced to A$2/kg (a 2030 target of the Australian government), the cost of green copper would become competitive. Furthermore, if a carbon pricing regime ($54–79/kgCO2) is introduced, green copper would become competitive at a hydrogen cost of A$3/kg. Altogether, these findings provide insights for a future green copper export opportunity in Australia. Additionally, as the next step, the model can be extended to include other metals and released as an open-source resource for assessing global green metal opportunities.

Sep 1, 2024

By M Jones

Legacy tailings storage facilities exist within operating mine sites across Australia. These tailings storage facilities are often viewed as rehabilitation liabilities but with considered assessment may prove to be a resource. Extraction of such a resource might add value to operations by providing cash flow during the development of new mines; providing supplementary mill feed for existing mines undergoing step changes in operations; and supporting mine closure activities. Hellyer Gold Mines is successfully extracting and reprocessing sub-aqueous material from legacy tailings storage facilities at their Hellyer operation in north-west Tasmania. The Hellyer tailings storage facilities contain poly-metallic material from the Hellyer and Fossey underground deposits dating back to the 1980s as well as material from tailings reprocessing activities by other operators of the Hellyer site. Hellyer Gold Mines is reclaiming sub-aqueous material using cutter-suction dredgers and reprocessing on-site. Hellyer is the only operation of its type in Australia and few such operations exist worldwide. Key learnings around the estimation of a resource for legacy tailings storage facilities have been acquired at Hellyer that can be applied to the assessment of other tailings storage facilities. The application of dredging as a mining method at Hellyer is delivered through collaboration with experts from outside the mining industry under the direction of a mining engineer. Key elements required for the planning and delivery of safe and efficient dredging inside tailings storage facilities have been identified at Hellyer and are presented to provide a road map to the development of other dredging operations.

Sep 1, 2024

By S Jeon, K Haga, A Shibayama, L Godirilwe, C B. Tabelin, I Park, A Buronov

Open pit/underground mining and mineral processing can generate large amounts of waste that contains sulfide-type minerals such as pyrite (FeS2), and it can produce acid mine drainage (AMD) when exposed to oxygen and water. This highly acidic wastewater contains heavy metals and this is one of the most serious environmental issues after mining operations taking several decades to completely remediate with high costs. For green mining, the present study focused on the development of a recovery technique for gold from an eco-friendly solvent, thiosulfate, by utilising mine wastes, tailings. First, gold recovery experiments in ammonium thiosulfate system were carried out using model samples representatively present in tailings (ie SiO2, Al2O3, FeS2, Fe2O3, and Fe3O4). The results showed that gold recoveries were negligible when using Al2O3, SiO2, Fe2O3, and Fe3O4 while pyrite shows a recovery efficiency three-fold as high as the maximum recovery efficiency when using others. Subsequently, the study also addressed the identification of the recovery mechanisms and post-recover process. The results indicate that tailings can be sufficiently utilised for gold recovery in the hydrometallurgy process and a further sustainable process can be established.

Sep 1, 2024

By H Bao, M Nehring, M Kizil, P Knights

The mining sector, a notably energy-intensive industry worldwide, is a major consumer of fossil fuels, leading to significant carbon emissions globally. The move towards electrification, coupled with advancements in battery technology, has redirected focus from traditional diesel-powered solutions to battery-powered alternatives. This paper introduces a novel mining haulage system, termed the Battery Trolley system, and examines its theoretical foundations, configurations, and operational processes. It offers a comprehensive analysis of the system’s characteristics and limitations. Through a hypothetical road simulation, the study analyses various battery mining trucks, calculating energy consumption based on force parameters and tailoring battery sizes to contemporary materials and technology. When assessing various battery truck alternatives—considering aspects such as battery size, effective payload, duty cycle, energy efficiency, productivity, and single-shift production—a range of performance outcomes and characters become apparent within the mining context. The findings indicate that Battery Trolley configurations, requiring smaller battery capacities, entail lesser compromises on effective payload. Consequently, these configurations achieve a higher duty cycle, reduced energy consumption per tonne, heightened productivity, and enhanced singleshift production capabilities compared to Battery-only alternatives. The comparative analysis of Battery Trolley with stationary charging (BT-S) and Battery Trolley – dynamic charging (BT-D) underlines that battery Trolley systems are capable of overcoming the challenges associated with battery size, thus successfully fulfilling the production demands of large truck applications in mining. This positions Battery Trolley systems as a more advantageous option for the decarbonisation of mining fleets, underscoring their potential in contributing to the sector’s sustainable evolution.

Sep 1, 2024

By C Zhang, R Liang, I Canbulat, S Saydam, B Li

The integration of digital twins in mining represents a significant advancement towards enhancing operational efficiency and decision-making. Central to this development is the role of visualisation, a crucial element in mining digitalisation. This paper aims to develop a data-driven visualisation solution for the construction of mining digital twins, highlighting how this innovation revolutionises the representation of physical mining environments. It spotlights the shortcomings of conventional visualisation methods, particularly their inability to accurately depict complex underground environments and their predominantly historical orientation. To address these challenges, this paper proposes a novel, data-driven approach. This approach extensively utilises CAD files, encompassing data from geological surveys to intricate mine plans, thereby significantly elevating the precision of digital twins. It supports real-time updates and enables flexible interaction with a variety of data types. Moreover, it introduces a comprehensive data diagram alongside the adaptation of a 3D tile index. This strategy ensures the efficient rendering of extensive mining environments, allowing for a more dynamic and interactive digital twin model. In conclusion, the implementation of data-driven visualisation in the construction of mining digital twins marks a pivotal advancement in the industry’s digital transformation. It promises a more detailed, accurate, and dynamic depiction of mining operations.

Sep 1, 2024

By J P. van Dijk, M Munro

Discrete vein modelling presents an innovative geology driven 3D modelling method for quantification of vein hosted mineral deposits, with far reaching potential for critical mineral supply and low impact resource extraction. By integrating key geological drivers with advanced discrete fracture network modelling, the DMX protocol (launched in 2019) converges to a more realistic representation of complex vein systems in three-dimensional space. The approach insights greater geologic reasoning in distributing mineral grades where vein boundaries are already defined. This presents a viable alternative to existing modelling and estimation methods and has important implications for mineral resource estimation and reporting. Further implications are discussed, where the enhanced understanding of complex orebody characteristics can potentially be used in optimising mining and mineral processing, supporting more sustainable and efficient mining practices.

Sep 1, 2024

By M Liang, C Putzmann, D Gokaydin

The mining industry faces numerous challenges in swiftly evaluating and exploiting new mineral deposits to meet the escalating demand for critical minerals and metals. These challenges necessitate the adoption of innovative techniques and technologies to expedite development cycles and facilitate the efficient exchange of knowledge and expertise. Traditional mineral exploration methods rely heavily on data governance, visualisation tools, and expert interpretation skills, often operating in isolation and limiting the integration of valuable data and knowledge (Lindsay and Perrouty, 2019). To address these integration challenges and bridge the industry’s knowledge gap, the concept of a digital twin offers a holistic solution (Ahn, 2021; Nagovitsyn and Stepacheva, 2021).

Sep 1, 2024

By A Karrech, G M. Hassan, A Varghese, E Lessing, S Martins

Pumps play a critical role in mining operations, and their energy consumption and environmental impact are significant. This paper addresses the need for a holistic approach to sustainable pump deployment by expanding on previous work presented at IMPC Asia Pacific 2022 and World Mining Congress 2023. The research takes a significant step forward by outlining a structured approach to estimate and demonstrate sustainability tailored for operational slurry pumps. It details the calculation methodologies used to quantify the carbon dioxide emissions from electricity, wear materials and transport, as well as sealing water usage. To enhance practicality and usability, a simplified visual representation—called sustainability metrics—has been developed to monitor overall efficiency. The sustainability metrics consist of the availability, pump efficiency, and water utilisation efficiency. The proposed methodology is integrated with Pumps Condition Monitoring Service to maximise benefits. Three case studies, from iron ore, copper, and nickel operations globally, were employed to evaluate the proposed methodology. The findings demonstrate the critical role of sustainability calculations in understanding pumps operation. These calculations improve awareness of the pump’s role in the sustainability journey and enable effective monitoring. The studies highlight that continuous monitoring paired with effective prescriptive maintenance strategy can significantly reduce power and water consumption while maximising pump availability. The case studies also reveal significant shortcomings in current operational practices and non-optimal maintenance regimes, emphasising the need for practical recommendations to maximise savings and optimise pump performance. In the context of increasing climate change awareness and the imperative for global decarbonisation, this research highlights the necessity of critically assessing methods and operations to drive sustainability in the mining industry. By adopting a holistic approach to sustainable pump deployment, mining operations can effectively reduce their environmental impact while enhancing operational efficiency and economic viability. The proposed methodology and case studies provide valuable insights and practical recommendations for mining companies seeking to achieve sustainable practices and contribute to a decarbonised future.

Sep 1, 2024