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Mine sites testing out CSIRO, Mining3’s precision mining concept

CSIRO and Mining3’s wide-ranging precision mining concept looks to be gaining momentum with multiple mining companies testing out aspects of this innovative notion to reduce the footprint of future mine sites.

Among the headlines from the organisations’ latest report on this technology was its ore sorting technology, NextOre, has three trials underway at mine sites, with up to three more systems to be delivered this year.

A Chilean copper mine is testing up to 10 types of sensors, complementing other recent trials in Australia and CSIRO desktop studies. Another study found that a mining company could make the same profit as it is now, but with a 30% reduction in capital and operating costs.

In this pursuit, the mining industry can learn a lot from medical science, according to CSIRO Research Director in Precision Mining and Mining3 Research Leader, Ewan Sellers.

As the CSIRO rock mechanics specialist says, modern medicine has used technology to better understand and treat illnesses and injuries while reducing the impact on people. Sellers is now working towards creating low impact “zero entry mines”.

CSIRO explains: “Precision mining is the industry’s version of keyhole surgery. Once a deposit is discovered, precision mining aims to target the ore and extract the deposit as economically and sustainably as possible.”

CSIRO and Mining3’s shared vision is for mines of the future to be mostly underground, remotely operated by robotics, with minimal or remote offices and a very small environmental footprint. All waste would be used to make other products.

Sellers believes this vision could become a reality for most mines within 20 years, as vast mining operations that leave large scars are consigned to history.

Minerals 4D

Key to enabling precision mining is a concept CSIRO is leading called Minerals 4D.

Minerals 4D ‘intelligence’ aims to image minerals in the subsurface and predict their distribution. By integrating sensors and specialised imaging techniques tied with data analysis and machine learning, miners can better understand the orebody and quantify the rock mass at multiple scales.

Precise cutting, blasting and in-mine processing techniques can then accurately target the ore and leave the waste behind. Miners can focus on the most economic part of the deposit, reducing the need to move, crush and process massive amounts of rock, saving significant amounts of energy, water and waste.

CSIRO said: “Although information about the grade of the material and type of rock may currently be known over a block or at mine scale, Minerals 4D aims to add information about the mineralogy at a much smaller scale. This will enable companies to target the orebody and characterise the rock mass more accurately to increase efficiency at the processing plant.”

Rob Hough, the Science Director for CSIRO Mineral Resources, says Minerals 4D is about adding a time series to three-dimensional (3D) data. Essentially, it’s about tracking mineralogy over time.

The mining industry is now capable, through its geophysical sensing technology, to create extremely accurate 3D spatial models of orebodies, but 4D adds in the critical time element – tracking that mineralogy through the metal production line as if it were a barcode in a manufacturing circuit.

The concept involves linking modular mining operations to sensors – including fibre optics and systems attached to robots – to precisely characterise material in the subsurface before mining, through to a mine face, bench, conveyor, stockpile, truck, train or a ship.

Then you can measure the chemistry, mineralogy and rock structures at a range of scales, and provide unprecedented detail and volumes of data that capture ore and waste variability. Measuring the mineralogy is critical to understanding the quality, so where the value is created and lost.

This is like the artificial intelligence algorithms that companies such as Petra Data Science are developing to track ore from the pit to the processing plant.

A focus on value, rather than volume, means less waste and emissions in this context.

“If you have the knowledge of what you’re dealing with in a 3D picture you can then start to make predictions as to how minerals will perform when you go to mine, through to process and beneficiation,” Hough says.

“Operators can choose one set of mining or processing systems over another, knowing the texture and hardness of a material. We need to understand what is in the rock mass in terms of the minerals, but also how hard it is, its strength and how it breaks up to best separate the ore from the waste rock.”

Drone-deployed sensors

It is now possible to produce a detailed face map of a mine, fly a drone with spectral sensors to image surface mineralogy and use data analytics to identify correlations between ore types and rock strength. X-ray diffraction is also being used for analysis. These instruments are applied to samples in the field, drill holes and at bespoke laboratories that run thousands of samples at a low cost in order to build a 3D mineralogy model.

“We have a range of sensors available, but we don’t yet have a fully ‘sensed’ mine,” Hough adds.

“What we’re missing is all sensors in place, in a given operation. We’re also missing the assembling of data to inform decision making throughout the process as it happens – we need that information conveyed in real time and viewed in our remote operations centres.”

Advanced sensor-based ore-sorting

CSIRO partnered with RFC Ambrian and Advisian Digital to launch joint venture, NextOre, to deliver a sensor that intelligently directs a conveyor – sorting valuable ore from waste. CSIRO said NextOre has three trials of the sensor system underway at mine sites, with up to three more systems to be delivered this year.

“On the back of better data, we should be able to take advantage of applied mathematics that will then allow us to move to artificial intelligence and machine learning,” Hough says. “I can see a real-time conveyor belt start making automatic decisions about what is coming down the line. It’s the ultimate sensing and sorting solution.”

Reducing energy and water use

Sellers believes a move to precision mining can improve the conditions for communities living nearby mines, and even improve the social acceptance of mining.

He said several companies are testing out the value cases of sensors and data integration, and he understands they need to see proof that precision mining works on the ground. The economic benefits of sensing were demonstrated recently at a Western Australia iron ore mine, where A$25 million ($17 million) of additional resources were discovered using data provided by a relatively inexpensive hyperspectral sensor, according to CSIRO.

A Chilean copper mine is testing up to 10 types of sensors, complementing other recent trials in Australia and CSIRO desktop studies, it said. Another study found a mining company could make the same profit as it is now, but with a 30% reduction in capital and operating costs.

“Once miners gain confidence that we can actually do this, I think it will take off very quickly,” he says.

Precision mineral exploration and discovery

Beyond the mine itself, tracking minerals over time – in 4D – will also benefit greenfields exploration upstream.

According to CSIRO Digital Expert, Ryan Fraser, implementing the Minerals 4D concept is at its most challenging at the exploration and discovery stage – the point where data are sparse, and little is known about a potential target orebody.

“For example, we know a lot about a deposit such as Mount Isa, including how it forms. So, can we use the intelligence we have of that mineral system to foresee where the next Mount Isa will be?” he asks.

Fraser says if we understand how mineralogy evolves over time and the overall geological process, we can then look for signatures across the Australian landscape that help to identify similar things.

“Normally you drill in these spots, take back samples, check data and then in about two years you might have some idea of what’s under the surface and have some idea of mineral boundaries.”

The new sampling techniques will be far quicker and more efficient, he says.

“Instead of sampling a sparse, evenly spaced grid, we use machine learning to reduce uncertainties and guide where to sample and that will enable us to do much smarter edge detection of mineral boundaries,” Fraser explains.

Already this kind of predictive work has been tested in a project for the South Australian (SA) government at Coompana in SA with surprisingly accurate results and significant cost savings over traditional methods, according to CSIRO.

Other key challenges that researchers and the industry are working to address to make this a reality, include designing and developing sensors robust enough to work effectively in the mining environment (for example, in robotic cutting machines) and across rock types, and understanding which sites in the mine process are most suitable for sensors.

CSIRO concluded: “These sensors will be linked to precise and automated drilling, cutting and blasting technologies under development through Mining3 to transform the way that mining is performed.”