The University of Queensland has developed an artificial intelligence (AI)-based scanner, which can identify valuable minerals and waste rock in a mine face and unlock further potential for autonomous mining vehicles.
The current practice of ore-grade classification does not allow for the use of precise ore grade maps in mine planning, which reduces productivity and efficiency gains.
The AI scanner uses visible and infrared light to classify minerals in the mine face and real-time mapping to allow the mining process to be planned before digging starts.
University of Queensland head of school of mechanical and mining engineering, professor Ross McAree said the scanner might be used in future autonomous mine systems.
“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,” McAree said.
According to McAree, the AI scanner will also enable autonomous machines to identify ore grades during excavation.
“Linked to artificial intelligence, this could allow automated machinery to operate in the mine environment, removing workers from hazardous parts of the mining process,” he said.
The technology was developed in partnership with Plotlogic, with the research supported by the Minerals Research Institute of Western Australia (MRIWA) with a $250,000 contribution.
MRIWA chief executive Nicole Roocke said the research could further the foothold of Australia’s mineral industry on the latest 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,” she said.