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Background

The purpose of this Training Centre is to deliver the vital enabling tools – advanced sensors, data analytics and artificial intelligence – for automated, integrated and optimised mining. Automating a mine requires the integration of all stages of the mining and processing system so that intelligence across the value chain can be automatically generated, delivered and exploited. The Training Centre will train the next generation of engineers and scientists in the development and application of these enabling tools, a current knowledge priority for the mining industry.

The Training Centre comprises three university partners (University of Adelaide, University of South Australia and Curtin University), two mining companies (BHP and OZ Minerals) and 20 mining equipment, technology and services companies and organisations.

ENQUIRIES

Further general information on postgraduate research degrees can be found at the hosting universities’ websites: The University of Adelaide and UniSA

Queries regarding specific projects should be directed to the relevant contact shown in each project.

CONTACT

General queries regarding the Training Centre should be directed to:

Name: Professor Peter Dowd, Training Centre Director
Ph: +61 08 8313 4543
Email: iocr@adelaide.edu.au

Projects

PROJECT: HDR1 (PhD)

Cross-borehole seismic interferometry to interpolate rock mass and geometallurgical variables.

PROJECT: HDR2 (PhD)

Draw-point and cave operations and fragmentation sensing

PROJECT: HDR3 (PhD)

Sensed core data to predict process responses for mining projects

PROJECT: HDR4 (PhD)

Gold sensing

PROJECT: HDR5 (PhD)

Vibration and accelerometer sensing for early stage roping detection in hydrocyclones.

PROJECT: HDR6 (PhD)

Pulp chemistry monitoring for mineral processing applications.

PROJECT: HDR7 (PhD)

Integration and analytics of drill sensor information to derive geometallurgical attributes.

PROJECT: HDR8 (PhD)

Fingerprinting ore types and blends by fusing hyper-spectral and other sensors using assisted machine learning.

PROJECT: HDR9 (PhD)

Ore tracking model from uncertain resource model to belt sensors and run-of-mine stockpiles

PROJECT: HDR10 (PhD)

Maximising AG/SAG mill grinding efficiency through intelligent online sensing and health monitoring.

PROJECT: HDR11 (PhD)

Maximising mill throughput using machine learning techniques and evolutionary algorithms.

PROJECT: HDR12 (PhD)

Integration and analytics of pulp chemistry sensor information with in-stream analysis for flotation plant optimisation.

PROJECT: HDR13 (PhD)

Integration of in-stream and particle size measurements in flotation

PROJECT: HDR14 (PhD)

Rapid updating of resource knowledge with sensor information including structures

PROJECT: HDR15 (PhD)

Measuring and monitoring particle size distributions and grade so as to divert low value waste.

PROJECT: HDR16 (PhD)

Linking the resource to down-stream products.