MICROMINE to optimise underground mines with machine learning

MICROMINE is releasing new underground mining precision software to refine and enhance loading and haulage processes as part of its Pitram solution in early 2019.

Using the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time.

The video feed is processed on the Pitram vehicle computer edge device, and then transferred to Pitram servers for processing and analyses.

This gathered information will ultimately pinpoint areas of potential improvement to bolster machinery efficiency and safety.

“Pitram’s new offering takes loading and haulage automation in underground mines to a new level,” MICROMINE chief technology officer Ivan Zelina said.

“By capturing images and information via video cameras and analysing that information via comprehensive data models, mine managers can make adjustments to optimise performance and efficiency.

“It also provides underground mine managers with increased business knowledge, so they have more control over loading and hauling processes and can make more informed decisions which, in turn, improves safety in underground mining environments.”

The technology has gone through trials in Australia, Mongolia and Russia this year as part of MICROMINE’s pilot program.

Its initial concept was developed in partnership with the University of Western Australia, with one of its Master’s students subsequently recruited by MICROMINE.

“We’re striving to help companies optimise their mining value chain and we believe enhancing one of the most fundamental and critical underground mining assets – loaders – is a great place to start,” Zelina said.

“This can contribute significantly to the overall optimisation of underground mines, which we believe have a lot of room for improvement.”

Pitram is a fleet management solution that records, manages and processes mine site data in real-time.

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