Unlocking future copper resources
An Adelaide-based consortium of universities, METS partners, mining companies and research support agencies has unlocked significant economic potential worth millions of dollars through innovative projects. These initiatives aim to enhance copper recovery, throughput and production while ensuring operations are conducted safely, sustainably and with reduced water and energy consumption.
The Integrated Mining Consortium was established in 2017 and funded by the South Australian Premier’s Research and Industry Fund (PRIF) to run under a banner of ‘Unlocking Complex Resources through Lean Processing’ for end-user partners BHP and Oz Minerals (now part of BHP).
During the initial phase, research progressed on two programs targeting the challenges defined by the end-user partners. An 18-month extension was granted in early 2023, allowing for Consortium activities dedicated to commercialising the research.
At this point, translation partners such as technology providers Maptek, Eka, and Magotteaux were crucial in defining the steps to turn the research into reality. While the door remains open for multiple commodity sectors to benefit from the applied research, copper was the initial beneficiary.
The overall economic gain is predicated on a 2% increase in recovery, 15% increase in throughput and 17% increase in production.
Please refer to the infographics created by our industry partner, Maptek.
Ahead of the wrap-up in August 2024, Director of the Consortium Professor Nigel Cook reports on the overwhelming success of the program.
‘Our vision was to maximise value and lower costs for mineral production using machine learning, sensors and data analytics,’ said Cook. ‘We wanted to increase the value of complex resources, which we defined as being those that are increasingly lower grade, display variable mineralogy, or harder to mine or process, and considered the entire mining process from in-place resources to final products.’
Given that 'easier' resources have already been exploited, the PRIF was established to tackle more diverse heterogeneous resources that inherently impact feed variability, making mining and processing more costly.
‘Resources that are more diverse in character and content have a flow-on effect through stockpiling, crushing, grinding and milling,’ Cook said. ‘High-cost processing may end up being applied to the entire Run of Mine ore in complex resource scenarios, even when lower cost alternative processing could be more efficient for some batches if timely knowledge about them was available via new technology.’
Hence the overarching goal to integrate smart technologies across the mining value chain.
In addition to significant research outcomes, Cook counts combining research and industry knowledge, and fostering collaboration between partners as a resounding success.
‘We wouldn’t have a Consortium without the exploration, mining and minerals processing companies that will ultimately consume the products and systems we create,’ said Cook. ‘We also rely heavily on our expert partners to translate research into industry-ready products and services.’
The two research partners, the University of Adelaide and University of South Australia brought expertise in resource modelling, mining, geomechanics, sensors, data analytics, computer optimisation, process modelling and mineral processing.
A pool of supporting partners was called on to provide assistance in evaluating PRIF technologies and identifying commercialisation opportunities and pathways alongside dissemination and marketing of research outputs.
Numbers tell part of the success story
In all, 14 research projects and 11 translation projects were established to break down and tackle multiple elements of the upstream and downstream mining processes.
The SA Government invested $4 million with an injection of an additional $1 million provided by industry. Impressively, the Consortium has catalysed total funding and support amounting to $49 million across its 17 industry partners since 2017.
In terms of delivering on the goals, the numbers only tell part of the story, according to Director of the Integrated Mining Consortium, Professor Nigel Cook.
Gone are the days when researchers focused solely on challenges they decided to be important. Today’s industry demands relevant, real-world applications that make a technological, environmental and economic difference. Our programs have successfully delivered on all fronts.
‘Our goal was to harness sensors, machine learning, data analytics and their various technologies to provide tools that integrate and optimise the mining value chain.’
‘Aligning the research with data-driven deliverables, future proofs the outcomes for any miner of any commodity in any operating environment.’
Four consortium projects are now at the commercialisation stage, each targeting different, yet interlinked parts of the mining–processing value chain. The first leverages rapid advances in computation and machine learning to achieve more accurate modelling of geological domains essential for resource estimation.
Building on this, a second project focuses on optimised reclamation of ore stockpiles in near real-time, offering direct cost savings and also providing a competitive advantage to miners exploiting complex heterogeneous ores.
A third project aims at maximisation of mill throughput and increased copper recovery through improved knowledge of pulp chemistry, thus reducing energy/water consumption and operational cost, while the fourth offers a revolutionary, cost-effective new approach to online measurements of particle size based on force measurement.
The last of these, as well as another project developing new sensors designed to detect ferric iron, both of which have wide-ranging commercial potential beyond mining.
Impact and achievements
Equally significant as delivering on research outcomes, the Consortium played an important role in training the next generation of scientists, engineers and data analysts.
Please refer to the infographics created by our industry partner, Maptek.
Looking towards a sustainable future
Professor Cook said that Australia desperately needs to strengthen our workforce with scientists skilled in the technologies of the future and programs like this help mining to be one of the industries to benefit.
Consider the impact of 18 young researchers receiving a boost in skills, 17 scholarships awarded to Women in STEM, 15 experienced mining and computer science researchers joined by 10 higher-degree-by-research students and 8 postdoctoral researchers.
Collaboration was the key to keeping on top of the multiple streams, activities and topics.
‘Offering a single point of focus definitely helped lower the barriers between METS and mining companies to foster cooperation and innovation,’ said Cook. ‘Moreover, by combining complementary research groups at The University of Adelaide and UniSA, we have formed a national powerhouse in leading high-level integrated research addressing optimisation of mining and mineral processing.'
Cook is justly proud of what has been achieved under the banner of 'Unlocking complex resources through lean processing' and believes that the Consortium has placed Adelaide and its two universities at the forefront of researching complex mining and mineral processing.
‘The eyes of the mining industry were on Adelaide during ‘Copper to the World’ in June. I like to think that our contribution has been a small part of ensuring a sustainable, bright future for the industry and for our State.’
Feedback
Feedback from the people behind the research provides evidence of our success. The projects below are a snapshot of achievements arising from the PRIF.
Postdoctoral Researcher Shi Zhao (Robotics Researcher) sees that expanding the use of robotics and AI across various stages of the mining process can increase efficiency, accuracy, 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 uncertainty and therefore the 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.
‘Building 3D stockpile models in almost real-time enabled assay results to be incorporated as available, thus improving the accuracy of quality assessments,’ Zhao said.
Zhao is most excited that this PRIF project extends his PhD research into an area that enhances business performance for mining companies. He believes that robotics and AI play a pivotal role and will revolutionise current operations and processes.
Postdoctoral Researcher Hirad Assimi (Computer Scientist) hopes that mining companies significantly boost their support for innovation and research in electrification and digitalisation to meet the growing demand for minerals essential for green technologies within the next five years. His specialist topic was resource heterogeneity modelling from trucking to multiple stockpiles to mill feed.
‘My solution fine-tunes stockpile management, resulting in smarter decision making, while undesirable properties stay within acceptable bounds,’ Assimi said.
'Tailoring my message for audiences from diverse backgrounds—conveying complex concepts to both my tech-savvy supervisors and hands-on mining professionals—was unexpectedly enlightening.'
The most satisfying part of Assimi’s PRIF experience was the journey from presenting preliminary, less polished ideas to developing concepts ready for commercialisation.
Postdoctoral Research Fellow Difan Tang (Mechanical Engineer) helped to develop a new sensor that can measure and report, online and in real-time, the particle sizes of mineral slurries conveyed in the plant.
Optimising particle size to match downstream pulp chemical conditions and flotation response enhances mineral liberation, leading to improved recovery rates and operational efficiency in mineral processing. Minimising energy consumption and chemical usage while maximising valuable mineral extraction ultimately boosts profitability in the mining industry.
Tang relishes that his work contributes to freeing operators from tedious manual sampling and examination of mineral slurries, some of which are also radioactive. He sees that mining companies could automate mineral grinding and size-classification by integrating the new sensor into their control systems, to enable more energy-efficient processes.
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.
‘Using mathematics to understand the behaviour between a sensor and the metal it is sensing allowed me to make recommendations to the chemists on how to change sensor properties to optimise sensor readings,’ Louw said.
‘Maths is great! Some nonbelievers thought the very theoretical subject area of mathematics would not be useful to the group, but we won them over.’
Luow appreciated how cross-pollination of ideas from different areas can drive innovation. She enjoyed working with people outside of her field and on a project that could potentially also improve outcomes in medical and agricultural industries.
Dr Yerniyaz Abildin completed his PhD on a project comparing explicit, implicit and geostatistical modelling with the machine learning approach, and saw the main advantages of AI as the ‘stunning’ speed of cloud computing and the user-friendly domaining tools. Although not a geologist, Abildin discovered useful features in geological modelling software. Yerniyaz has continued his postgraduate research in collaboration with Maptek.
‘Access to Maptek software for my research proved to be an advantage. DomainMCF produced the models extremely fast – the run time exceeded my expectations by around ten times,’ Abildin said.
Abildin notes that companies could be better at exploiting the unused data that has been painstakingly collected. Thinking ahead, he hopes that mining companies will increasingly leverage machine learning to optimise operations, reduce environmental impact, and enhance safety standards.
14 research projects and 11 translation projects tackled multiple elements of the upstream and downstream mining processes.