Tag Archives: sensors

CRC ORE simplifies complexity for value

“There are a lot more variables to bulk ore sorting than just the technology,” Jon Rutter says.

The Principal Geologist of the Cooperative Research Centre for Optimising Resource Extraction (CRC ORE), Rutter knows his stuff. He has worked underground in both narrow-vein and mass-mining operations, as well as at large scale open-pit mines; in the base and precious metal arena.

During a presentation at International Mining Events’ IPCC Virtual event in early-February, he shared a slice of this knowledge while reviewing a recent installation project CRC ORE had been involved in at a platinum group element (PGE) operation.

“The intrinsic value of bulk ore sorting comes from the delivered heterogeneity,” Rutter said. “We have got to be able to sense and divert a higher-value pod of material versus an adjacent pod of lower-grade material on a conveyor.

“You essentially want to put more material into the mill that adds value – and not what destroys value.”

Looking at the wider bulk sorting opportunity in mining, Rutter explained the sensor diversion units (SDU) in bulk ore sorting were smaller than what the mine itself can typically offer in the form of a selective mining unit (SMU), which may be comprised of a dig block totalling around 15,000 t.

A truck offers a 100-300 t opportunity, while a shovel typically comes with a 50-100 t opportunity.

Even with a modest conveyor running at a 2,000 t/h rate, an on-board sensor (eg PGNAA or PFTNA) running at a 30 second integration time (the time to analyse one grade) would provide an SDU of 16.7 t. A sensor with lower integration time (eg XRF at 10 seconds) comes in at 5.6 t.

The ability to provide analysis down to this level has enticed several major companies into testing bulk ore sorting solutions.

Anglo American has trialled bulk ore sorting solutions at copper and platinum group metal mines, while BHP recently engaged CRC ORE to examine deployment of cutting-edge preconcentration techniques under its Grade Engineering® platform at the Olympic Dam mine, in South Australia.

The SDU with bulk sorting may be that much smaller than the SMU of a typical mine plan, but lab-level precision is not required for these solutions to work, according to Rutter.

“What I need is the ability to measure the metal content adequately,” he said. “When I say adequate, this incorporates the entire error bar of the system. That system includes the inherent geology, the mineralisation style and heterogeneity. We also need to consider the precision, accuracy and integration time – which is the technology constraint; but we also need to include the weightometers, the flop gates, the diversion gates, as well as that entire mining and materials handling process right from the start – from blasting, loading, hauling and dumping to the plant.

“But for bulk ore sorting what I end up requiring from this combined data is usually a binary decision: am I above or below a certain threshold?”

He expands on the bulk ore sorting (BOS) assessment process: “The other way of looking at this is simply considering it as planned ore loss and dilution. If we go back into that dig block, in that 15,000 t of material, I’ve already incorporated planned ore loss and dilution decisions or parameters into that SMU decision. So, if we look at bulk ore sorting, I am just talking about those different attributes – the error bars of a BOS system – as the inputs or parameters for BOS planned ore loss and dilution – it’s now just at a smaller and more precise opportunity.”

The company took a two-phase approach to the BOS opportunity at the PGE operation in question.

The first phase involved carrying out heterogeneity analysis of the orebody; correlation analysis of PGEs to base metals; selection of sensor technologies (XRF and PGNAA were selected in this case), design, layout and equipment selection for the bulk ore sorting plant; natural deportment analysis of the orebody; development of a preliminary business case; the ore type selection and sampling strategy; and project planning and management.

CRC ORE and the company in question settled on a solution where a Caterpillar 992 wheel loader dropped material off to a system using a combination of grizzly, feeder, sizer, conveyors, diverter, stackers and associated equipment from MMD, used in conjunction with an ore sensing system equipped with both PGNAA and XRF sensors to continuously measure the elemental composition. The PGNAA sensor provided a “penetrative” analysis calculation whereas XRF provided a “surface” sensing calculation, Rutter explained.

An incline conveyor ahead of the diverter gate and the accept/reject stream provided the 30 second integration time the PGNAA analyser required.

Phase two of the project involved online and offline (pre-install) work; sensor calibration; proving the technology; and proving the technology can drive physical separation.

Rutter said the completion of static calibration of the sensors saw the PGNAA sensor 20-30% calibrated, and the XRF sensor 70-80% calibrated.

This outcome harked back to Rutter’s assertion that “bulk ore sorting implementation is not a plug and play opportunity”.

A dynamic calibration in online mode completed under normal conditions was required to get the PGNAA sensor up to speed. This process, meanwhile, solidified the operation of the XRF sensor.

While the two sensors were calibrated in different ways, Rutter showed data that confirmed both were in unison when it came to reading the ore/waste that came through the conveyor (see right-hand graph below).

“The two sensors are independent of each other and fundamentally very different, but they can work well together, or separately,” he said.

CRC ORE was able to prove the technology by running the same sample through the circuit a number of times, as Rutter explained: “We fed 15-20 t of run of mine material into the hopper and repeated the process 15 times, putting the same 15-20 t sample through the system. We could then start to determine the precision and accuracy of the sensors and the system.”

For further verification, the sample was crushed, sub sampled and assayed.

“We wanted a binary response to ore and waste to build confidence,” Rutter added.

Phase three involved the ramp up to production scale, going from, say, 500 t/h to 1,000 t/h; carrying out validation by campaign; and finally integrating with the operation.

There were several lessons all mining companies – and bulk sorting vendors – should keep in mind from such a project, Rutter said.

Operations need to assess the impact of mixing across the entire materials and mining handling process as soon as possible, for one.

“The earlier we can put this data into the system, the better,” Rutter said. “Without a heterogeneity signature, we cannot implement bulk ore sorting.”

He also stressed the importance of timely feedback. Sensor calibration, a secondary crushing/sampling plant and assaying were all required to build confidence in the solution.

Rutter added: “The proper calibration of sensors does require a considerable and ongoing effort…but that is no different from any other process plant or equipment.”

Operators also need to be wary of where they set these solutions up in mines, recognising this heterogeneity dynamic.

“Bulk ore sorting is quite unlikely to be universally suited to the entire deposit,” Rutter said. “The analogue for this is a flotation plant; there are ore types in the mine where you achieve better performance in the flotation plant and others where you get worse performance.”

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.”

Schenck Process filling screen performance data gaps with sensors

Schenck Process says performance data provided by extra sensors fitted to a prototype vibrating screen is substantially improving the understanding of operation of the equipment.

The data is also giving indicators about the overall performance of the processing cycle, according to the company.

Designed and developed in Australia by Schenck Process, the prototype screen is undergoing site trials, but the company already believes the new screen has the potential to change the way vibrating screens are developed and operated.

The standard condition monitoring system comprises two sensor nodes including six degrees of freedom MEMS accelerometers, a high-resolution accelerometer and a temperature probe. On the prototype screen, four additional sensors have been fitted, one on each corner.

Schenck Process Senior R&D Engineer, Doug Teyhan, said: “The measurement regime for the additional sensors includes spring amplitude and mean compression, allowing the estimation of tonnage and load bias (to determine if the feed is presented square to the screen or favouring a side) and the determination of spring operating characteristics and cumulative fatigue damage.

“We are also looking into the development of a predictive failure program to improve overall productivity and efficiency and significantly reduce the possibility of unplanned downtime.”

Historically, failure prediction has been determined by running components to the point of failure and assessing a mean time to this point based on a known operating history.

“The data generated by the prototype screen is utilised to estimate the operating stress of the screen at the most aggressive fatigue areas and assessing the cumulative damage of those areas based on the measurement of non-ideal operating characteristics,” Schenck Process said.

Using a Cumulative Damage System, which counts machine cycles and trend characteristics that have the potential to adversely affect vital component life expectation, the plan is to make the machine monitoring system a lead measure in predicting the potential for component failure, Schenck Process said.

“The expanded monitoring system will also provide input into machine development of the next generation of vibrating screens by filling in the unknowns in the design process with real-time field data,” the company said.

According to Teyhan, the benefits for the customer – including increased availability and improved screen performance – are substantial and have the potential to initiate improvements in the processing cycle.

“And, from a screen operation point of view, the additional data is bringing to light characteristics not previously known. It is highlighting transient feed characteristics – not visible using traditional condition monitoring techniques – that impact the loading of the screen and affect machine life expectation,” he said.

“We also believe there are potential industry-wide benefits, through new design parameters and possible changes to machine construction techniques and materials,” he added.

To optimise the greater range and scope of data the screen is generating, the company is collaboratively investigating and assessing other performance variables, it said. The potential is for control of the variability in the feed rate, more consistent performance and improved overall efficiency of the cycle.

Tailings monitoring could go autonomous, Mining3 says

Mining3 says it and The University of Queensland, in conjunction with the Australian Coal Association Research Program (ACARP), are currently in the process of building prototype autonomous sensors for the constant monitoring of tailings and spoil storage facilities.

The Australia-based company said: “Tailings impoundments are one of the largest man-made structures on earth and ensuring their integrity for the safety of human life, the environment and property are critical in today’s mining operations. Past and recent catastrophic tailings dam failures have placed an urgent need for improved waste disposal, storage processes and monitoring capabilities.”

Currently, the integrity of the tailings dam infrastructure is monitored by mining staff walking along the, potentially unstable, perimeter and visually inspecting the exterior. Piezometer-like devices are also placed throughout dams to measure changes in liquid pressure. “Combined, these methods provide subjective data that cannot deliver an ongoing and accurate assessment of the integrity of these waste storage facilities,” Mining3 said. “Without a reasonable assessment of these large structures, there is no way to identify if or when one might fail.”

With a web of small, interconnected sensors spread across a tailings dam or spoil dump, Mining3 says accurate measurements in the change of water pressure or movement in the soil can be delivered to the surface in real-time. “This provides up to date readings of environmental factors that can affect overall wall stability, limiting the need for staff on the ground,” the company added.

Mining 3 and the university’s research will also delve into identifying indicators and precursors to failures, in relation to data collected from these sensors. “This could revolutionise the understanding of these storage facilities. By understanding the causation, steps can then be taken to minimise risk in the future,” Mining3 said.

“The current project addresses key industry outcomes surrounding safety and the removal of personnel from hazardous situations such as those involved in ground stability, the investigation of material properties and their implications in the design and functionality of a dump site, and the investigation into aspects of effective mine closure and the long term impacts associated with tailings dams and spoil dumps.”