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By H Utama, R Heryadi

PT Indo Muro Kencana (PT IMK) is a surface gold and silver mine located in Mt. Muro area, Central Kalimantan province, Indonesia. The mining operations of multiple open pits using combination of excavator and haul truck is operated. This operation is performed in two shifts in a day. The articulated dump truck (ADT) with capacity of 40 t and 60 t deliver ore to the mineral processing plant and discard waste material to the waste dump area. Overall, PT IMK’s mining operation department is consisting of two sections, namely mining operation section and drill and blast section. Firstly, mine operation section is responsible to manage loading, hauling, pit service (mine dewatering, earthwork, road maintenance) and mine operation engineering. Secondly, drill and blast section is in charge to manage drilling and blasting (blast engineering, blasting operation, explosive permit and magazine or explosive storage). Additionally, conventional blasting method (ore-waste blasting) with non-electric initiation system is used and sleep blasting is undertaken as bulk explosive is buried for maximum two days. The first purpose of this paper is to elucidate about role of mining engineer in mining operation department (mining operation engineer and D&B engineer). At this point, mining operation engineering practice (fleet management system, production control and mining manpower management) and also blasting daily practice will be elucidated. Daily blasting practice is consisting of surface blast design, explosive engineering, sleep blast practice and the concept of scaled depth of burial to ensure a safe work environment in blasting activity. Next, the second purpose of this paper is to explore workforce evolution and future skills for mining engineer in surface mining operation. Furthermore, strategy to advance future skills such as mining continuous improvement program, training and skills development for mining engineer (mining operation engineer and D&B engineer) in order to develop their skills is also discussed. It is expected that in the future, these future mining leaders will become new mining leader, for instance mining superintendents, D&B superintendents and mining managers. In conclusion, it is clear that awareness about workforce evolution, professional development, training, skills development, mining economics awareness and mining continuous improvement program are essentially required as professional development for mining engineer (mining operation engineer and D&B engineer), in order to be commercially competitive in their future career and in order to be adapted with future workforce evolution in mining.

Sep 1, 2024

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 S Sullivan

The key skills for a competent geologist are observation, documentation, analytical thought and the ability to communicate. These attributes have served the geological world for the past two centuries since the field of geology was separated out from natural philosophy. The understanding of the structural setting, lithologies, alteration and 3D distribution of the target mineral species are essential precursors to resource evaluation and potential economic extraction. The advent of artificial intelligence and machine learning has matured to the point where observation by dedicated sensors and analysis by customised AI algorithms can replace manual processes and deliver credible results. In combination with analytical data such as assays, various sensors now collect physical properties of rocks which are used as proxies for mineral identification and rock mass classification. These sensors include X-ray and infra-red spectra, magnetic susceptibility, gravity/density, EH/pH measurements, conductivity, colourimetric readings, porosity, fracturing and fracture intensity. Analysis using machine learning techniques trained using known data sets can determine rock type classification, rock density, quantification of grade/quality attributes, mineral alteration, weathering and geotechnical attributes such as rock quality. This type of data relies on calibration and quality control of a variety of different types of electronic hardware. The resulting data is non-additive, has error ranges and will be suitable for consumption by fast AI methods providing probabilistic analysis. A second stage could include geometallurgical constraints to provide an ‘extractability index’, which is of more interest to miners than pure geotyping. Using resultant interpreted geological criteria, 3D domain models with spatial attributes will be built.

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 L Mackinlay

The Apeketon Business Hub in Lihir, Papua New Guinea, emerged in 2019 through a partnership between Australian Business Volunteers (ABV) and Lihir Gold Limited (LGL), a subsidiary of Newmont Corporation. This development was driven by a shared vision for the Lihir gold mining operation to further enhance opportunities for the local community and strengthen the economy’s resilience post-mine. At its core, the business hub serves as the epicentre of ABV’s operations in the region. It includes a drop-in resource centre which provides the community with mentoring and consulting services, and access to a range of impactful workshops facilitated by ABV’s local staff and skilled volunteers. These workshops focus on local Lihirians but are open to the entire community, including Newmont employees. We design and continually refine Apeketon Business Hub support programs and offerings driven by community priorities, building trust through long-term relationships, and working side-by-side on real-time challenges. We offer three core programs to different levels of the community, from individuals and families to start-ups, large corporations, and landowner groups. There is significant demand for the support services provided by ABV, with over 350 Lihirians participating in our workshops over the 2023 calendar year. One of the hub’s core offerings is the Family Money Management Program, which addresses crucial challenges faced by Lihir households, particularly in terms of financial resilience. The program takes a comprehensive approach, assisting families in long-term planning and developing skills to effectively manage the income they derive from their employment with LGL and other enterprises. Significantly, the program also addresses financial stress, a factor often associated with domestic violence (Fox et al, 2002). By empowering women and promoting joint financial decision-making, the Family Money Management Program fosters a culture of savings and improved financial management, ultimately enhancing economic resilience within the community.

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 M Rea, E O’Connell

Decarbonising explosives manufacturing and catalysing low-carbon mining supply is a significant global challenge. Low-carbon manufacturing precincts – clusters of geographically close businesses across multiple industries that share a goal of adopting firmed renewable energy and decarbonising process emissions – offer one potential solution. In an Australian first, Orica commissioned tertiary catalyst abatement technology in 2023 to mitigate greenhouse gas (GHG) emissions at its Kooragang Island (KI) facility in New South Wales (NSW), Australia. The EnviNOx® technology (ThyssenKrupp Industrial Solutions AG, Dortmund, Germany) used reduces nitrous oxide (N2O), a potent greenhouse gas arising as a by-product of nitric acid manufacture. As a result, mining customers in NSW are now benefitting from ammonium nitrate (AN) products that are almost 50 per cent less emissions-intensive than those Orica previously offered. The $37 million project is a private-public partnership that has involved partners across Federal and State government, global engineering and technology vendors, local community groups and local carbon advisors to overcome financial barriers.

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 L Abrahamsson

The aim with this paper is to discuss experiences from the ongoing research project ‘Attract: A roadmap to attractive, inclusive, and safe mining workplaces’. The project’s aim is to analyse possibilities, effects, and consequences of the green and digital transformation in the Swedish mining industry from workplace, socio-technical and gender equality perspectives. The research project (2022–2025) at Human Work Science at Luleå University of Technology is financed by the strategic innovation program Swedish Mining Innovation (a joint venture by the national research funds Vinnova, Formas and the Swedish Energy Agency). The project includes a multidisciplinary research group of five senior researchers and five PhDstudents and a broad repertoire of methods, eg literature reviews, qualitative interviews with both managers and workers, workplace observations, workshops, surveys, and project seminars where the researchers and the company representatives together analyse the results. One important part of the project is the applied approach where the researchers work together with two mining companies and one subcontractor company. The project has two parallel main subprojects.

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 S Nowosad, O Langefeld

The mining industry has undergone a profound and transformative shift in recent years, driven by the rapid advancement of emerging technologies such as automation, digitalisation, and electrification. These technological innovations have not only revolutionised operational efficiency and productivity but have also introduced an imperative need for new and innovative approaches to conducting mining activities. Specifically, the mines of the future face a new series of complex challenges when planning and developing greenfield or brownfield mining projects in the 21st century. These challenges include greater operational depths, higher seismicity levels, higher rock temperatures, increased environmental constraints and sustainability requirements, a higher degree of interoperability, lack of skilled workforce, and the need for sustainable and eco-friendly mining practices. Consequently, there is an essential need to formulate novel strategies and robust planning methodologies that re-evaluate how mines are planned, executed, and managed, from the early exploration stages to the post-mining era and the consequent uses of mine sites. This research focuses on identifying the factors influencing the implementation of modern technologies, such as autonomous machinery in mining operations, and their impact on mining method selection and longterm mine planning. The findings underscore a crucial need for a paradigm shift in how mines are planned and developed, going beyond mining method selection, which primarily focuses on geological and economic factors, and must evolve to incorporate the multifaceted implications of modern mining operations.

Sep 1, 2024

By D Brown, K Markovic

The world is becoming an increasingly complex environment to operate within. Adoption and evolution of technology is occurring at a rapid pace and operating spaces are growing in interconnectedness and interdependency. In mining this is evidenced by the large-scale adoption of autonomous machinery, intelligent systems, Internet of Things (IoT) devices, and robotics to realise benefits in optimisation, productivity, and safety. To enable continual delivery and growth in operational performance, and to realise the aforementioned benefits, a new approach for managing mining and metals is required (Mining Review Africa, 2023). This means that an already talent constrained workforce must learn to manage the complexity inherent, evolving, and growing in the environment (Abenov et al, 2023). A key skill that needs to be further developed and deployed to combat these challenges and to enable robust problem solving is that of systems thinking, which is already practiced in pockets within the mining industry. Experience and learning can be leveraged from other industries and their application of systems thinking to ensure that focus is maintained on areas where the benefits from which will be optimised. With targeted application, a workforce capable of effective systems thinking can be the catalyst to optimisation across mining organisations.

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