TECHNOLOGY

Australian university reveals new mapping AI tech

The University of Queensland and research partner Plotlogic have developed a new automated mining technology using artificial intelligence to scan the mine face to identify and classify minerals, a technology with the potential to boost efficiency and also keep workers safer.

 UQ gas introduced newly developed automated technology

UQ gas introduced newly developed automated technology

The Australian university said visible and infrared light is at the heart of the new development, which offers real-time mapping so that a mining plan can be determined before digging begins.

UQ School of Mechanical and Mining engineering head Ross McAree said the scans can be done at every stage of the mining process, and its ability to recognise ore grade could aid other future autonomous mining systems as well thanks to hyperspectral imaging.

"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," he 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 school's work was backed by the Minerals Research Institute of Western Australia (MRIWA), which said this technology and research investment is what will place the country's mineral industry in a leading position on the development of technology.

"This imaging approach could prove particularly valuable, where rapid extraction and consistency of ore grades could provide a competitive advantage to those leading the way," CEO Nicole Roocke said.

MRIWA said real-time ore grade classification at the mine face has the capability to offer a substantial benefit to large-scale efficient operations, including enhanced mine scheduling; improved resource recovery and minimised processing waste; and the support of autonomous mining systems and machinery.

MRIWA contributed A$250,000 to the research work, which had a total grant earmark of more than 4850,000. A full outline of the research is available here.

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