Unlocking future copper resources

Postdoctoral Research Fellow Difan Tang helped to develop a new sensor that can measure and report, online and in real-time, the particle sizes of mineral slurries conveyed in pipes.

He was excited about minimising energy consumption and chemical usage while maximising valuable mineral extraction to ultimately boost profitability in the mining industry.

Tang relished that his work contributes to freeing operators from tedious manual sampling and examination of mineral slurries, which may be radioactive. He sees the potential to integrate the new sensor into control systems to automate mineral grinding and size classification.

Tang’s young daughter thinks of his sensing rod as magic, since it ‘makes money’ for people, and his research has inspired her to become a scientist. 

Applied Mathematician Kirsten Louw worked on a project that aimed to model diffusion of Fe3+ ions in metal organic frameworks and solve non-linear diffusion equations using symmetry analysis. 

She reported that some non-believers thought the highly theoretical nature of mathematics would not be useful to the group, but were won over.  

Applying mathematics to better understand the behaviour between a sensor and the metal ion it is sensing allowed Louw to recommend to chemists how changing sensor properties could optimise sensor readings.

Louw appreciated how cross-pollination of ideas from different areas can drive innovation. She enjoyed working on a project that could potentially also improve outcomes in non-mining industries.

Dr Yerniyaz Abildin completed his PhD on a project comparing explicit, implicit and geostatistical modelling with machine learning. He highlighted the stunning speed of AI models generated with Maptek DomainMCF that exceeded his expectations. 

Abildin notes that mining companies could do more to exploit the unused data that has been painstakingly collected. He hopes that they will increasingly leverage machine learning to optimise operations, reduce environmental impact, and enhance safety standards. 

Although not a geologist, Abildin discovered useful features in geological modelling software, and has continued his postgraduate research in collaboration with Maptek.

Postdoctoral Researcher Hirad Assimi hopes that mining companies significantly boost their support for innovation and research in electrification and digitalisation to meet the growing demand for carbon neutral production of minerals essential for green technologies. 

Computer scientist Assimi’s specialist topic was resource heterogeneity modelling from trucking to multiple stockpiles to mill feed. His solution fine tunes stockpile management, resulting in smarter decision making, while undesirable properties stay within acceptable bounds. 

He found describing outcomes and tailoring the message to audiences from diverse backgrounds as unexpectedly enlightening, especially when developing concepts ready for commercialisation.

Postdoctoral Robotics Researcher Shi Zhao sees that expanding the use of robotics and AI across various stages of the mining process can increase efficiency, accuracy and worker safety, and reduce the environmental footprint.

His project aimed to optimise Load-Haul-Dump operations and provide precise quality control for Run-Of-Mine stockyards using computer simulated 3D stockpile models. 

The proposed system alleviates stress for mine managers and promotes agile decision making for meeting the required quality and quantity within a small margin. The solution helps reduce greenhouse gas emissions by minimising reclaiming time.

He found that building 3D stockpile models in near real-time enabled assay results to be incorporated as they became available, improving the accuracy of quality assessments.

Read more about the Integrated Mining Consortium