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The research has shown how artificial intelligence can use scans of the mine face to almost instantly identify valuable minerals and waste rock, allowing each stage of the mining process to be planned more effectively in advance, UQ said
“Each mineral has its own characteristic response to different wavelengths of light, so by scanning the mine face with our system we can map out the minerals present in the rock and their concentration (ore grade) almost instantaneously,” Professor McAree said
“Beyond this immediate efficiency gain, the enhanced ability to recognise ore grade could also underpin future autonomous mine systems,” Professor McAree said. “Machines equipped with this imaging system would be able to recognise ore grade as they were excavating it. Linked to artificial intelligence, this could allow automated machinery to operate in the mine environment, removing workers from hazardous parts of the mining process.”
The project was supported by the Minerals Research Institute of Western Australia (MRIWA), with MRIWA CEO, Nicole Roocke, saying investment into research like this helped position Australia’s minerals industry at the leading edge of technology development
The project, which was conducted in 2018-2019, had a total grant value of A$850,850 ($653,322). In addition to MRIWA, UQ and Plotlogic, CITIC Pacific Mining and AngloGold Ashanti were also involved, hosting trials at the Sino iron ore and Tropicana gold mines, in Western Australia, respectively
It’s common knowledge that mining is a critical industry for Australia. The land is teeming with resources that have been heavily sought after since the mid 1800’s. The sheer scale of the industry is tremendous for Australia. Mining draws in $250 billion and accounts for 8.5% of the GDP in 2018. Comparatively, the US mining industry only accounted for 54 billion, or 1.6% of their GDP in 2018. Additionally, the mining sector of Australia employs around 200,000 people directly and countless more indirectly. Undoubtedly, Australian Mining is serious business.
Australia’s mining sector has been molded for efficiency after decades of driving profits. However, solutions that once supported efficiency often become antiquated over time. This phenomenon took place around 1950 after the invention of the Nuclear Density Meter, which replaced manual sampling. Though the nuclear density meter provides great accuracy and convenience compared to manual sampling, about 70 years has passed since its introduction to the industry. Subsequently, this begs the question, is another wave of new engineering instrumentation on the horizon?
When the nuclear density meter was invented, factors such as paperwork, inspections, and safety costs were considered necessary trade offs. After all, the novel nuclear density meter enabled massive increases in efficiency and profits. The march towards the future forces us to evaluate current systems in place to find room for improvement. In other words, should we assume that these ‘necessary trade offs’ are unavoidable if we want accuracy and precision in measurement?
Firstly, the nuclear density meter requires constant monitoring and maintenance by a Radiation Safety Officer. RSO salary costs average 82,798 AUS dollars according to Indeed Australia. This salary is another price to factor into the cost of ownership
Another potential point of optimization stems from the nuclear density meters methodology. Because the physical design is to clamp on, installation is very simple. Once the nuclear density meter is securely placed, radiation is emitted from the nuclear core, through the pipeline, and to the receiver. Then, the difference between the quantity of radiation emitted at the source and the radiation accounted for at the receiver is equated for density
However, the nuclear density meter takes its measurements in ‘slices’. In other words, the meter doesn’t emit radiation constantly, but instead emits radiation a set amount of times in a quantity of time. In essence, this indicates that the nuclear density meter takes sample measurements – not an entire reading of the process in full. Subsequently, process controller engineers are left to extrapolate complete measurements from the sample, but this leaves room for error.
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