Tag Archives: artificial intellgence

Bis to provide tailored equipment solution for Anglo American Capcoal contract

Australia-based Bis has secured a new multi-year contract for Anglo American’s Capcoal operations near Middlemount, in the Bowen Basin of Queensland.

The off-road haulage, materials handling and site services contract is the latest in an ongoing relationship between Anglo and Bis that spans more than 20 years.

The contract will see Bis supply a tailored high payload equipment solution for the operation’s rejects haulage. Additionally, the company will deliver site services including road maintenance, dust mitigation and run of mine equipment feed, as well as haulage of topsoil, rock and run of mine coal as required.

Bis Chief Executive Officer, Brad Rogers, said the company’s ability to provide a tailored haulage and logistics solution, specific to this operation, was a key factor in securing the new contract.

“We have a long history of integrating customisable OEM innovations and existing solutions to deliver against specific customer objectives. This competency continues to drive operational efficiencies and reduce costs for our customers. It’s a formula that works.

“For instance, the specific higher payload capacity solution put forward for this project delivers significant advantages for Anglo American. It means a reduction in the total equipment required, vehicle movements and fuel consumption; all three of which directly contribute towards improved safety, sustainability and productivity outcomes for the customer.”

The range of tailored equipment incorporated to deliver the project includes double trailer configuration haulers, wheel loaders, graders, water trucks and a compaction roller.

The fleet is fitted with the latest Bis safety and productivity management systems, including Trifecta, which is a new in-cabin artificial intelligence driver and vehicle monitoring software developed with EDGE3 Technologies. The system collects and analyses data in real time to improve both safety and productivity. The system collects, analyses and reports driver behaviours such as drowsiness, mobile phone use, smartwatch use, smoking, seatbelt and other violations. Trifecta then ‘learns’ over time to pre-empt high risk incidents in real-time through alerts to drivers and supervisors. Bis says it has exclusive rights to use and sell the system across a range of markets.

On site mobilisation for this new contract is expected to commence from August.

Rio Tinto and Schneider Electric partner on decarbonisation initiatives

Rio Tinto and Schneider Electric have signed a memorandum of understanding (MoU) for a “first-of-its-kind” collaboration to develop a circular and sustainable market ecosystem for both companies and their customers.

This multi-product partnership will see Schneider Electric use responsibly-sourced materials produced by Rio Tinto. These include low-carbon aluminium and copper produced with renewable power, iron ore and borates. Rio Tinto will, in turn, use energy and industrial services from Schneider Electric, as the companies work together to develop digital platforms, technologies and solutions to be deployed across the metals and mining supply chain to drive further decarbonisation, they said.

Rio Tinto Chief Commercial Officer, Alf Barrios, said: “This unique partnership will help accelerate decarbonisation and renewable energy solutions by combining low-carbon materials with cutting-edge digital technology. Working together will allow Rio Tinto and Schneider Electric to pursue opportunities beyond what is possible for either company on its own.

“This collaboration also opens doors to consider strategic initiatives such as expanding the use of artificial intelligence and predictive analytics to reduce downtime in our plants, digitisation of our supply chains, and a host of other transformative technologies.”

Schneider Electric Executive Vice-President Industrial Automation, Barbara Frei, said: “We are excited to work with Rio Tinto to develop clean and pioneering solutions to meet industrial decarbonisation challenges. As the world’s most sustainable corporation and a manufacturer with a global network of smart factories and smart distribution centres, Schneider Electric is on a mission to make industries of the future eco-efficient, agile, and resilient through open, software-centric industrial automation and sustainable energy solutions. This new partnership demonstrates that Rio Tinto is as passionate as we are about bridging progress and sustainability for all.”

The partnership will draw on Schneider Electric’s Energy as a Service expertise to evaluate the use of innovative solutions, including microgrids, to supply energy from low-carbon sources, and artificial intelligence and advanced analytics to help meet sustainability goals at Rio Tinto sites and throughout its supply chain.

Rio Tinto’s START traceability and transparency initiative, the first sustainability label for aluminium using blockchain technology, will be deployed with Schneider Electric to unlock value for customers, suppliers and partners, it said. The companies will work to expand this transparency, offering START in combination with Schneider Electric’s EcoStruxure™ platform, an IoT system architecture that connects everything in an enterprise to deliver enhanced safety, reliability, efficiency and sustainability.

The companies will also partner to evaluate emerging innovation opportunities, such as the efficient production of critical materials for renewable technologies and advances in low-carbon, green steel manufacturing, both of which will play a significant long-term role in industrial decarbonisation.

TruckMetrics and the true costs of lost crusher production

The importance of optimising blast parameters to reduce the cost of comminution and cut back on energy use is often stressed across the industry, but effective blasting can also reduce the likelihood of crusher obstructions, Motion Metrics says.

Most unplanned plant downtime is crusher-related and primarily due to blockages caused by oversized feed. These events can cause mines to incur significant financial losses due to unplanned downtime, a decrease in throughput, or an increase in energy use, according to the company.

When boulders are larger than the opening of the primary jaw crusher, they can build up in – and eventually block or obstruct – the crusher. In this case, production must be temporarily stopped to break down or remove the boulder. But even boulders small enough to be processed by the primary jaw crusher can cause problems as breaking down large rocks requires a great deal of energy and can result in power spikes, slower production rates, and wear and tear of the crusher liner, Motion Metrics says.

Even brief crusher delays can have massive effects over time.

“For example, one of our customers is a large copper mine in Kazakhstan that experienced average crusher delays of approximately seven minutes per incident,” the company said. “Although these delays were short, they add up to an estimated total cost of $650,000 in lost production each year.”

Another Motion Metrics customer, a Peruvian mine that is one of the largest copper producers in the world, experiences an average loss of $5.73 million/y, Motion Metrics says, while, at an iron ore mine in Brazil, production interruptions cost roughly $3.65 million/y.

“Mines have traditionally taken a reactive approach to mitigating the problems associated with oversized material,” Motion Metrics says. “A boulder obstruction is typically identified by monitoring trends in crusher throughput – a falling trend indicates that material is not able to pass through the crusher. At this point, the blockage or obstruction has already occurred. Mine personnel must halt production to dig out the boulders, or use rock breakers to clear the obstruction, creating a bottleneck and further decreasing production.”

Motion Metrics says a common misconception is that a grizzly can eliminate the problem of oversized material.

“It is true that, with a grizzly in place, boulders are less likely to enter the primary crusher, however, a grizzly is still susceptible to blockages – mine personnel need to remove oversized material or schedule rock breaking,” it explained.

The best way to manage oversized material is to avoid the situation entirely but, failing that, mines should aim to mitigate problems caused by boulders as early in the process as possible.

Motion Metrics developed TruckMetrics to prevent oversized material from reaching the processing plant in the first place.

Mounted on a gantry above the mine road, TruckMetrics monitors each passing haul truck to detect boulders and analyse particle size in real time – without interrupting production. Using artificial intelligence and stereo imaging, the system automatically analyses the truck bed, segments each visible rock, and identifies any oversized material. If a boulder is detected, the system automatically alerts dispatch so that trucks can be diverted.

“TruckMetrics, therefore, provides a two-pronged approach to mitigating problems caused by oversized material,” Motion Metrics said. “First, it helps keep boulders out of the crusher by identifying trucks that contain oversized material and diverting them before they reach the plant. Secondly, the particle size data TruckMetrics captures can be used to optimise blasting parameters so that fewer boulders are produced in the first place.”

TruckMetrics is just one of several services within the Motion Metrics ecosystem that boost productivity and energy efficiency without compromising on safety, the company says.

BHP, SensOre progress artificial intelligence-backed exploration agreement to ‘Phase 3’

SensOre says it is to advance its Joint Targeting Agreement (JTA) with BHP to “Phase 3” after receiving approval from the major miner.

Under the JTA, SensOre was required to meet certain hurdle rates and technical thresholds through deployment of its Discriminant Predictive Targeting® (DPT®) technology and related auxiliary systems. SensOre says it has met or exceeded the requirements set for Phases 1 and 2.

Richard Taylor, CEO of SensOre, said: “The SensOre team has been excited by the performance of its systems in targeting new commodity and deposit types. The relationship with BHP and its support for innovation in exploration has been incredibly valuable. The results derive from the truly joint nature of the project and shared view that better use of geoscience data will lead to improvements in discovery rates. We are really thrilled with the results.”

SensOre and BHP reached agreement on a letter of intent in May 2020, confirmed via execution of the JTA on September 18, 2020. The JTA envisages a phased process, training the DPT technology on commodity-specific deposit types and applying the knowledge gained to a predetermined search space. SensOre stands to benefit from fees for the targeting exercise and potential success-based payments on certain discoveries arising from the technology, it said.

SensOre aims to become the top performing minerals targeting company in the world through the deployment of artificial intelligence and machine-learning technologies, specifically its DPT workflow. SensOre collects all available geological information in a terrane and places it in a multi-dimensional hypercube or Data Cube, with its big data approach allowing DPT predictive analytics to accurately predict known endowment and generate targets for further discovery, it says.

The Axora take on crushing and comminution

As we are continually told, comminution is one of the most energy intensive single steps in the resource extraction business.

One estimate is that it accounts for 36% of all the energy used in the extraction of copper and gold, which is only a shade over the 30% proposed as an average by another industry expert for all mining and mineral processing industries.

It also accounts for an estimated 3% of the global energy requirement for metal production.

These energy requirements are shocking from a sustainability and greenhouse gas emission perspective; they are also extremely costly regarding operating expenses on site.

It is with this in mind that IM touched base with Joe Carr, Industry Innovation Director of Mining at Axora.

A spinoff from the Boston Consulting Group, Axora has emerged as a business-to-business digital solutions marketplace and community for industrial innovators. It says it allows industrial companies to discover, buy and sell digital innovations and share knowledge in its community, powered by an advanced marketplace.

“We exist to transform industries to be digital, safer, more sustainable and efficient,” the company states on its website.

Having recently gone to press with the annual crushing and comminution feature (to be published in the IM April 2021 issue), IM spoke with Carr to find out what the Axora marketplace has to offer on the comminution and crushing front.

IM: What are the main issues/concerns you continuously hear from your mining clients when it comes to designing and maintaining comminution circuits? How many of these problems/issues can already be solved with existing technology/solutions?

JC: One of key issues in this area we hear from our customers at Axora is the blending quality of the input ores.

Joe Carr, Industry Innovation Director of Mining at Axora

This could be particularly relevant in the sulphide space, for instance.

I did some work years ago on Pueblo Viejo for Barrick. When I was there, one of the things we were working on was blending the sulphides as we were feeding the mill from numerous satellite pits with very different sulphide grades. Because we were processing the ore with an autoclave, high-grade sulphides would cause a temperature spike and the low-grade sulphides would lower the temperature. This constant yo-yoing of the feed into the autoclave was terrible for the recovery of metals against the plan.

Generally, the old school way of blending is setting up stockpiles of ore based on whatever variable you want to manage at your operation. You would put a defined amount of each into the primary crusher on the understanding this would create a ‘blended’ feed for the processing plant.

With the information we have at our fingertips today, this process seems outdated.

You could, for example, use HoloLens or another VR system in tandem with the shovel operator to be able to see exactly what material he or she is excavating. That can then be linked back to the geological block model, with this material then tracked in the trucks and onto the run of mine stockpile, before heading to the plant.

This is where something like Machine Max comes in. Machine Max is a bolt-on IoT sensor that tracks where your trucks are in real time – where they have been and where they are going. The processing piece requires block model integration into a mine plan system. If you have the building blocks in place – the networking, sensors, additional infrastructure, etc – Machine Max could, when integrated with this model, allow you to attempt real-time ore tracking.

“If you have the building blocks in place…Machine Max could, when integrated with this geological block model, allow you to attempt real-time ore tracking,” Joe Carr says

The issue is not that the technology doesn’t exist, but that the mining industry hasn’t yet cracked putting all of this together at an industry-wide scale, available to all miners.

You can carry out a project like this or go totally the other way and have a machine-learning or artificial intelligence algorithm in the plant that is constantly reading the incoming feed. These could be based around the block model inputs, or a digital XRF solution, which is able to constantly tweak or adjust the plant settings to the feed specifications. Process plants are generally setup to handle one type of feed. This is usually only tweaked in retrospect or for short periods of time when the mine plan moves into a different mining horizon.

We also have a comminution solution that understands the feed coming in and optimises the mill and power settings to get the optimal grind for flotation, maximising recovery at the back end. While the input is typically set up to be grind quality and hardness for optimal flotation, there is no reason why you couldn’t configure it for, say, sulphides going into an autoclave, tweaking the autoclave heat settings dependent on the feed.

Once that system is set up, it becomes a self-learning algorithm.

Saving operational costs is another pain point for mining companies we always hear about.

We have a solution on our marketplace from Opex Group, which is looking to optimise production while reducing power. Coming from the oil & gas space, this AI algorithm, X-PAS™, offers the operator an opportunity to adjust the settings while still achieving the same required outputs. This is tied to CO2 reduction, as well as power cost reductions.

Opex Group’s AI algorithm, X-PAS, offers the operator an opportunity to adjust the plant settings while still achieving the same required outputs

In mining, the plant is your largest drawer of power, hands down. Generally, if it is not powered on the grid, it is powered by diesel. Opex Group’s solution can save up to 10% of power, which is a significant amount of fuel and CO2.

The solution reads information from your pumps and motors, analyses the planned output of your plant using all the sensor feeds, and tweaks the variables while sustaining the required output. The algorithm slowly learns how you can change configurations to reduce power, while sustaining throughput. This results in lower power costs, without impacting the output.

Importantly, instead of automating the process, it offers the saving to the operator sat in the control room. Operators, in general, are incredibly reluctant to pass over control to an AI algorithm, but when faced with such power saving opportunities, they will often elect to accept such a change.

And, of course, plant maintenance is always on the agenda.

This is where Senseye, which has been used in the car industry by Nissan and the aluminium sector by Alcoa, is useful.

Essentially, this provides predictive maintenance analytics. It is also a no-risk solution with Senseye backed by an insurance guarantee. It is sold on the basis that if you do not earn your money back within the first 12 months, you get an insurance-backed refund.

There could also be openings in the plant for Razor Labs’ predictive maintenance solution, which is currently increasing the uptime of stackers, reclaimers and car dumpers for iron ore miners in the Pilbara.

IM: When it comes to future comminution equipment design, do you expect digitalisation, wear liner innovations, or equipment design to have more of a bearing on operational improvements at mine sites? Phrased another way; is more emphasis being given to refining and extending the life of existing products with digital technologies and wear solutions, than the design of brand-new equipment?

JC: We believe there is always going to be a focus on retrofit and extensions. Once a mill is built, changing the equipment, upgrading, etc is very hard and time consuming. The logistics of getting a new SAG mill to site, for example, are mind boggling. New technology will always come for new sites, but most of the world’s mining capacity is already in place. I would expect most digitalisation to focus on two areas:

  1. Getting more and longer life from all the assets. For example, extending liner life, reducing operating costs and shortening downtime between refits; and
  2. Drawing insights from the existing asset with a view to sweating it. No mill ever stays at nameplate; there is always an increase in production. One or two percent more throughput can put millions onto the bottom line of a company. No mill wants to be a bottleneck in the cycle. In a mine there are always two goals: the mine wants to produce as much ore as possible to put the pressure on the mill, and the mill wants to run as fast as possible to put pressure on the mine.

When it comes to extending liner life, we have a solution worth looking at.

One of the companies we work with out of Australia has an IIoT sensor all tied to wear and liner plates. It is a sensor that is embedded into a wear plate and wears at the same time as the wear plate itself wears. It provides this feedback in real time.

So, instead of the standard routine changeout, it gives you real-time knowledge of what it is happening to these wear parts.

We have a great case study from Glencore where they installed the sensors for around A$200,000 ($152,220) and it saved several million dollars. The payback period was just weeks.

Where I want to take it to the next level is pairing the wear plate monitoring technology on chutes and ore bins and looking into SAG mills and crushers. Relining your SAG mill or primary gyratory crusher is a massive job, which takes a lot of time and cuts your productivity and output by a huge amount. Wear plates are made as consumables, so if you can use 5% less over the space of a year, for instance, there are huge cost and sustainability benefits. You can also more accurately schedule in maintenance, as opposed to reacting to problems or sticking to a set routine.

IM: When compared with the rest of the mine site, how well ‘connected’ is the comminution line? For instance, are gyratory crushers regularly receiving particle size distribution info for the material about to be fed into it so they can ‘tailor’ their operations to the properties of the incoming feed?

JC: Generally, not really. The newer, better financed operations tend to have this. Taking the example above, when designing a plant flowsheet, the close side settings are used. But are they updated on the fly to optimise the plant? Not really. Most processes are designed with a set number of conditions to operate at their maximum.

Most plants dislike, and are not set up to handle, variation in their system, according to Carr

Most plants dislike, and are not set up to handle, variation in their system. They like consistent feed quality and grade to achieve maximum recoveries. Over the next few years, the companies that develop the best machine learning or AI models to run plants in a more real time, reactive way will see the biggest growth. A mill will always say it’s the mine that needs to be consistent, but the nature of geology means that you can never rely on this. As one geologist I knew said, “geology, she is a fickle mistress”.

IM: Where within the comminution section of the process flowsheet do you see most opportunity to achieve mining company sustainability and emission goals related to energy reductions, water use and emissions?

JC: In terms of emissions, at Axora we are actively looking at technology that can help across the entire plant. There was a great paper published in 2016 around this specific topic ‘Energy Consumption in Mining Comminution’ (J Jeswiet & A Szekeres). The authors found that the average mine used 21 kWh per tonne of ore processed. Given diesel produces 270 g per kWh, this means a plant produces 5.6 kg of CO2 per tonne of ore processed, on average. For a 90,000 t/day site, this might represent 510 t of CO2 per day (186,000 t/y), just for processing. To put that into context, you would need 9.3 million trees to offset that level of carbon.

If the industry is serious about lowering its carbon footprint, especially Scope 1 and 2 emissions, then the focus has to come into the process. There are easy wins available from proven solutions in other sectors for companies that want to take them.

Nordgold taps Swift Geospatial for tailings and community monitoring at Lefa

Nord Gold has implemented a new state-of-the-art monitoring system at its Lefa mine in Guinea that, it says, will help it keep track of the condition of the operation’s tailings storage facilities to proactively identify potential risks.

The move, in line with the company’s commitment to environmental stewardship, has been facilitated by Swift Geospatial Solutions, a service provider experienced in change detection algorithms that developed the solution.

Lefa’s new monitoring system uses satellite imagery processing algorithms to both analyse the condition of the mine’s tailings storage facilities, as well as monitor community welfare by tracking community dynamics, including house building, it said.

Additionally, the technology can help to monitor artisanal mining activities around Lefa’s current mining permit, allowing local management to better assess and prevent potential safety and security hazards

The company explained: “The Lefa mine’s licence area currently spans more than 1,100 sq.km. The latest satellite technology enables cost-effective monitoring of this expanse with regular updates and is backed by a robust AI package.

“As part of the new system, Planet and SkySAT satellite platforms will be used to perform different tasks, both integrated within the Swift Geospatial Solutions online platform. All outputs are delivered through a custom-built web-application and dashboard environment.”

Evgeny Tulubensky, Nordgold’s Chief Legal Officer and Director of ESG at Nordgold, added: “We are very pleased to test this innovative tool, enabled by satellites, at our Lefa mine. It will help us to receive timely and relevant information about the condition of Lefa’s critical environmental infrastructure and the mine’s immediate surroundings.

“Using this rich data, our aim is to continue reducing our impact on the natural environment and ensuring the sustainable development of local communities. Depending on the outcomes of this pilot, we will assess its viability for other Nordgold operations.”

Hyperspectral imaging technology tested at Western Australia gold, iron ore mines

The University of Queensland and research partners Plotlogic Pty Ltd have developed new automated mining technology that, they say, will facilitate automation of the mining process while improving operating efficiency.

The research has shown how artificial intelligence can use scans of the mine face to almost instantly identify valuable minerals and waste rock, allowing each stage of the mining process to be planned more effectively in advance, UQ said.

Professor Ross McAree, Head of School of Mechanical and Mining Engineering from UQ, said the new technology used visible and infrared light to automatically classify materials.

“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,” Professor McAree said.

This real-time mapping allows the mining process to be planned out before digging even starts, according to the researchers.

“Beyond this immediate efficiency gain, the enhanced ability to recognise ore grade could also underpin future autonomous mine systems,” Professor McAree 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.”

Real-time ore grade classification at the mine face could also enhance mine scheduling and improve resource recovery and minimise processing waste, the researchers claim.

The project was supported by the Minerals Research Institute of Western Australia (MRIWA), with MRIWA CEO, Nicole Roocke, saying investment into research like this helped position Australia’s minerals industry at the leading edge of technology development.

“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,” Roocke said.

The project, which was conducted in 2018-2019, had a total grant value of A$850,850 ($653,322). In addition to MRIWA, UQ and Plotlogic, CITIC Pacific Mining and AngloGold Ashanti were also involved, hosting trials at the Sino iron ore and Tropicana gold mines, in Western Australia, respectively.

It was based off the OreSense® prototype system, developed to meet the needs of the research project, as well as offering a commercial pathway for early industry adoption of the technology.

“The prototype delivers a system capable of acquiring, processing and classifying hyperspectral data in the field and in real time, mapped to terrain and geo-referenced for integration with mine maps,” the project partners said. “In order to be the most general and applicable to all minerals, the hyperspectral imaging capabilities cover the visible to short wave infrared spectrum (400-2,500 nm).

“The surveying capabilities of the system rotate in more than one axis to perform face scans and build a 3D data-cube from two individual line-scanning hyperspectral sensors. The system spatially and spectrally fuses the data cubes from the two sensors to provide a single data-cube for an entire scene. The system also performs on-board corrections and post-processing of the hyperspectral data to support real-time ore grade classification.”

The prototype used on site during the trials consisted of a sensor head with LiDAR and hyperspectral cameras, a pan-tilt unit and a GNSS receiver among other elements (see photo above).

Giga Metals taps Minerva’s AI prospect generator software for Brazil exploration

Minerva Intelligence says its Cognitive artificial intelligence-powered prospect generation software, TARGET, has helped Giga Metals identify and evaluate new prospective exploration targets at the Parnaiba Basin project in Nordeste, Brazil.

Giga, after validating the results produced by Minerva’s TARGET software, made the decision to acquire exploration permits covering significant new regional sediment-hosted copper anomalies along the southern perimeter of the Parnaíba Sedimentary Basin in southern Piauí State, Northeast Region, Brazil, it said.

This amounted to the staking of 24 exploration permits totalling 40,722 ha in four properties along 80 km of strike length in an area with known “Kupferschiefer-style” sediment-hosted copper mineralisation.

Scott Tillman, CEO of Minerva Intelligence, said: “The commercial validation of our TARGET software is yet another indication of the power of Minerva’s Cognitive AI-powered software.

“The successful deployment of our TARGET software highlights the value we are able to provide to companies that are managing large datasets and seeking to incorporate an artificial intelligence element into the decision-making process. Our success with Giga in Brazil, in conjunction with our recent success in Mexico, points to even greater success in the future in delivering results for mining and exploration companies around the world.”

Using Minerva’s TARGET software, Giga was able to sift through, organise and evaluate large datasets that were subsequently used to analyse the validity of the prospective exploration region, Minerva explained. TARGET’s mapping technology was able to determine, based on existing comprehensive datasets, that the project in Brazil had a high likelihood of success and, as a result, Giga should pursue investment in the region.

The final result of the analysis was a list of AI-produced target areas throughout Brazil that are completely auditable and explainable, and, most importantly, actionable by Giga, Minerva said.

Giga Metals CEO, Mark Jarvis, said: “TARGET enabled us to work our way through an immense volume of regional geological data to focus on areas prospective for the deposit types of interest to us. This is a type of regional survey that was previously possible only for a major mining company with a large team of geologists. It is exciting to experience at first-hand how artificial intelligence is now making this type of survey accessible to smaller companies.”

Jake McGregor, Minerva’s COO, added: “In 2019, Minerva was contracted by Giga to build a set of prospectivity maps for the country of Brazil. In this capacity, the company compiled various datasets from across the country, both from public and private sources, and significant work was undertaken by Minerva to standardise and translate the data from Portuguese to English, and then into the standard terminologies that we use in our mineral deposit models. It is extremely rewarding to see our clients getting value out of that hard work.”

Orica leverages MWD data, AI to create new blast loading design benchmark

Orica is looking to set a new benchmark for blast loading designs in Latin America after deploying its Design for Outcome solution in the region.

The company, focused on integrating its digital blasting tools to improve outcomes, is leveraging its BlastIQ digital blast optimisation platform within this new solution, Angus Melbourne, Chief Commercial and Technology Officer of Orica, told delegates at Massmin 2020 last week.

In a presentation titled, ‘Blasting’s Critical Role in Extracting Ore’, Melbourne mentioned Design for Outcome as an example of where the company was delivering integrated digital solutions in Latin America.

“Design for Outcome is an automated continual optimisation solution that sets a new benchmark for blast loading designs,” he said. “It utilises data science to process both upstream and downstream data to automate blast designs. This produces tailored and optimised blast designs by reducing blast variability and explosive consumption while increasing productivity.”

Using machine-learning algorithms, Design for Outcome processes measured-while-drilling data to classify ground hardness throughout each blast hole and then match explosives energy to hardness domains to automatically generate tailored blast loading designs, Melbourne explained.

Through artificial intelligence, these algorithms are trained with the data received from the fleet control systems (FMS) and previous blast results. This enables final automation of the blasting design process and its execution in the field with Orica’s smart control systems and programming interfaces, loading the blast accurately according to the generated design. These elements combine to ensure the desired outcomes are achieved, Melbourne said.

“Digitally-enabled blasting solutions such as Design for Outcome are allowing us to work with customers in different ways, to think and act differently and expand our role in the mining value chain,” he said.

Such a solution is part of the company’s plans to automate its segment of the mining process. This goal was strengthened last month with the launch of the Orica and Epiroc jointly developed Avatel™ semi-automated explosives delivery system.

A key enabling technology of Avatel, which is built on the foundation of Epiroc’s Boomer M2 carrier, and Orica’s automation vision is WebGen™, the company’s fully wireless initiation system. When combined with Orica’s LOADPlus™ smart control system, specifically designed on-board storage, assembly, digital encoding capability and Subtek™ Control bulk emulsion, Avatel provides customers with complete and repeatable control over blast energy from design through to execution, Orica says.

While referencing the second key pillar in Orica’s digital strategy, Melbourne highlighted the use of the company’s Bulkmaster™ 7 smart, connected explosives delivery system in Latin America during the virtual event.

The new delivery systems not only improve productivity but begin to digitise critical workflows between design and execution in drill and blast operations, according to Melbourne.

The Antamina copper mine in Peru, a joint venture between BHP, Glencore, Teck and Mitsubishi, will soon be leveraging such a system, with Melbourne confirming seven Bulkmaster 7 units had been shipped to the mine and were undergoing commissioning.

Orica’s third digitalisation pillar is the measurement of downstream impacts of the drill and blast process, which is where FRAGTrack™, the company’s automated rock fragmentation measurement device comes into play.

This device captures, analyses and reports real-time data for optimising blast operations, improving downstream productivity and tracking overall operational performance in mining and quarrying, Melbourne explained.

This system is active across several key customer sites in Latin America, with Teck’s Carmen de Andacollo operation in Chile being one of the first to adopt the technology in the world, according to Melbourne. He said the copper operation is using the insights to deliver efficiencies across the value chain through digitally enabled optimised blasting.

Mobilaris’ new devices to leverage latest communication, machine-learning tools

Intent on “mastering the latest technologies” in its domain, Mobilaris says it will focus on the use of next-generation communication technologies such as 5G and Wi-Fi 6, and artificial intelligence, to build out its new safety solutions in 2021.

Mobilaris says it is building a device using 5G technologies that will be used in a new offering for Mobilaris Industrial Solutions.

By leveraging these new technologies, it will bring Industry 4.0 digital workforce safety to all its customers, it said.

To ensure this new device is “truly world-class in terms of safety, performance and resilience”, Mobilaris has partnered with Sigma Connectivity and Ericsson to leverage their expertise in this domain. It says it is the first company to use the new reference cellular IoT design from Ericsson called Ardesco.

The company said: “5G and cellular IoT are technologies that will open up new possibilities, but they need connection to existing public mobile networks, or private networks. Therefore, Mobilaris has partnered with Telia to bring our new solution to the market.”

Earlier this year, the company joined Telia’s 5G program as a new member and, after that, secured a commercial partnership to bring solutions to the market while at the same time tailor its use of the Telia network to maximise performance and efficiency.

Another key technology for next generation communication solutions is Wi-Fi 6.

Mobilaris has been deploying Wi-Fi-based solutions for many years, with 2021 representing no change to the status quo.

“Many of our customers have Wi-Fi networks, and we are continuing to invest in this technology to secure our capability to meet all customer demands and to innovate, leveraging the new additions coming in Wi-Fi 6, 6E and beyond,” it said.

This is where a partnership with Aruba will bring best-in-class, real-time situational awareness to industry customers around the globe, Mobilaris said.

The use of artificial intelligence is also nothing new for the Sweden and US-based company. It has already deployed its Mobilaris Onboard product in several mines across the globe and, at its core, machine learning is creating “value for our customers” that would not have been possible just a few years ago, it says.

It concluded: “Moving ahead, we are continuing to invest in AI to further accelerate our products and solutions and we expect to announce several new research partnerships here within the near future.”