A Machine Learning driven flow modelling of fragmented rocks in cave mining. The project aims to develop an integrated method that uses micro scale and macro scale information to predict block scale behaviour so that a better cave mining design can be established. The role of various mineral composition on the energy storage and fracture properties of rocks will be investigated to examine rock fragmentation for block cave mining. Later Machine Learning based models will be developed to establis ....A Machine Learning driven flow modelling of fragmented rocks in cave mining. The project aims to develop an integrated method that uses micro scale and macro scale information to predict block scale behaviour so that a better cave mining design can be established. The role of various mineral composition on the energy storage and fracture properties of rocks will be investigated to examine rock fragmentation for block cave mining. Later Machine Learning based models will be developed to establish various predictive models for Block Scale rock mass behaviour and caveability of ore deposit. Finally, we will develop a new constitutive model based on a dual damage concept that will capture the rock fragmentation and simulate the cave propagation in a large scale mine layout using Smoothed-particle hydrodynamics.Read moreRead less
Multi-phase modelling and characterisation of mudrush hazard in cave mining. A mudrush is a sudden, uncontrolled flow of wet fine particles (mud) into an underground mine that damages equipment, infrastructure, and can even cause fatalities. This project aims to develop cost-effective management and monitoring of mudrush hazards within the at-risk Carrapateena cave mine operated by OZ Minerals. Building on recent technological and numerical advances, a novel experimental–theoretical–numerical ap ....Multi-phase modelling and characterisation of mudrush hazard in cave mining. A mudrush is a sudden, uncontrolled flow of wet fine particles (mud) into an underground mine that damages equipment, infrastructure, and can even cause fatalities. This project aims to develop cost-effective management and monitoring of mudrush hazards within the at-risk Carrapateena cave mine operated by OZ Minerals. Building on recent technological and numerical advances, a novel experimental–theoretical–numerical approach will be used to simulate mudrush risk based on moisture content, particle sizes, compaction, geological conditions, and seismic energy. Outputs will include a practical framework to boost the safety, productivity, and profitability of caving operations to benefit miners and the broader resources industry.Read moreRead less
A multi-scale theory for solid-granular transition due to fragmentation. The prediction of rock fragmentation and fragment sizes during its phase transition from solid (rock mass) to granular (ore fragments) is the most crucial problem in a cave mining operation. Current practice relies on empirical tools without fundamentals of fracture, and hence cannot reliably predict the fragmentation process and fragment sizes. This can lead to huge economic loss due to damage to extraction points, hold-up ....A multi-scale theory for solid-granular transition due to fragmentation. The prediction of rock fragmentation and fragment sizes during its phase transition from solid (rock mass) to granular (ore fragments) is the most crucial problem in a cave mining operation. Current practice relies on empirical tools without fundamentals of fracture, and hence cannot reliably predict the fragmentation process and fragment sizes. This can lead to huge economic loss due to damage to extraction points, hold-ups for safety precautions, and mine closures. The project will develop a new theory and models to describe this solid-granular transition, and computational tools for simulations of cave mining operations. The expected benefits and outcomes include safer operations, and better control of production schedule and budgeting.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC220100028
Funder
Australian Research Council
Funding Amount
$4,969,602.00
Summary
ARC Training Centre for Innovative Composites for the Future of Sustainable Mining Equipment. The Centre aims to train industry-focused researchers in advanced manufacturing of new-generation mining equipment and sustainable mining technology, through close collaborations among key universities and mining and manufacturing companies. The Centre will cultivate a team of world-class academic researchers and industry leaders to deliver an innovative program on research of innovative composites coup ....ARC Training Centre for Innovative Composites for the Future of Sustainable Mining Equipment. The Centre aims to train industry-focused researchers in advanced manufacturing of new-generation mining equipment and sustainable mining technology, through close collaborations among key universities and mining and manufacturing companies. The Centre will cultivate a team of world-class academic researchers and industry leaders to deliver an innovative program on research of innovative composites coupled with work-integrated learning, to not only produce a workforce that meets future skills demand but also develop sustainable and cost-effective mining equipment and high-efficiency mining technologies, benefiting the nation's manufacturing and mining sectors and significantly enhancing the competitiveness of the Australian mining industry.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC180100030
Funder
Australian Research Council
Funding Amount
$3,925,357.00
Summary
ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching ....ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching objectives are to enable development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets.Read moreRead less
Controlling arsenic to unlock value in gold and copper resources. This project aims to characterise the transformation of arsenic between oxidation states during mineral processing. Up to one third of the world’s gold reserves are locked up in arsenic rich minerals and 5000 tonnes of arsenic is released annually from mine waste. The project will enable the development of process technology that immobilises and removes arsenic at the earliest possible stage. The use of novel time-resolved in-situ ....Controlling arsenic to unlock value in gold and copper resources. This project aims to characterise the transformation of arsenic between oxidation states during mineral processing. Up to one third of the world’s gold reserves are locked up in arsenic rich minerals and 5000 tonnes of arsenic is released annually from mine waste. The project will enable the development of process technology that immobilises and removes arsenic at the earliest possible stage. The use of novel time-resolved in-situ techniques proposed in this research will give vital information of the complex chemical pathways involved during processing which current characterization methods on pre- and post-processed species do not achieve.Read moreRead less
Sewer Monitoring and Management in the Digital Era. Overflow, flooding, corrosion, and odorous emissions are persistent issues for utilities managing sewers. Current sewer maintenance is reactive, and focuses on solving problems in local networks, despite that optimal solutions require a system-wide approach. Capitalising on recent development in IoT sensors, wireless transmission, and machine learning, this multidisciplinary project aims to develop digital-twin supported data analytics for proa ....Sewer Monitoring and Management in the Digital Era. Overflow, flooding, corrosion, and odorous emissions are persistent issues for utilities managing sewers. Current sewer maintenance is reactive, and focuses on solving problems in local networks, despite that optimal solutions require a system-wide approach. Capitalising on recent development in IoT sensors, wireless transmission, and machine learning, this multidisciplinary project aims to develop digital-twin supported data analytics for proactive sewer management including network-wide real-time control. The project aims to generate significant social, environmental and economic benefits by enabling utilities to better protect public and environmental health, reduce sewer odour and greenhouse gas emissions, and extend sewer asset life.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC190100017
Funder
Australian Research Council
Funding Amount
$3,703,664.00
Summary
ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource ....ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource attributes, maximising value ‘on the fly’. Benefits will include increasing certainty on product quality and maximising throughput and recovery. Outcomes will include new tools to rapidly model geological and geometallurgical uncertainty with sensor inputs, to track the resource to product and enhance interpretation.Read moreRead less
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.Read moreRead less
Critical metals from complex copper ores. The aims of this project address the critical mineral resource potential of complex copper ores. The research will generate new knowledge on the concentration, distribution, physical form and chemical speciation of critical minerals, including tellurium, cobalt and rare earth elements, in ores and processing streams using innovative approaches and utilising state-of-the-art analytical techniques. Expected outcomes include integrated models for critical e ....Critical metals from complex copper ores. The aims of this project address the critical mineral resource potential of complex copper ores. The research will generate new knowledge on the concentration, distribution, physical form and chemical speciation of critical minerals, including tellurium, cobalt and rare earth elements, in ores and processing streams using innovative approaches and utilising state-of-the-art analytical techniques. Expected outcomes include integrated models for critical element endowments in Australia's largest copper resource, Olympic Dam (S.A.). Future recovery of these elements would add significant value to existing operations, providing long-term economic and commercial benefits and would also contribute to Australia's transition to a low-carbon future.Read moreRead less