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
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
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
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