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Leveraging the intelligent mine concept for improved profitability, sustainability

Automation, Equipment maintenance, IoT, Mining equipment, Mining maintenance, Mining services, , , , , , , ,

The case for digitalisation is clear, according to AspenTech’s Jeannette McGill*, with digitalisation being critical for the metals and mining industry to achieve sustainability and operational excellence in the years ahead.

This is why the more forward-looking decision makers in the mining industry are embedding digital capabilities in their operations so they remain agile, competitive and profitable over the long term, while realising immediate and measurable benefits, she says. Technology providers have responded to the industry’s needs with solutions designed for the mining sector that directly address the dual imperatives of greater business efficiency and enhanced sustainability.

The intelligent mine

In their digitalisation initiatives, today’s operators also know that managing data more efficiently and effectively will be crucial in helping them to meet the challenges they face. Multiple difficulties remain in the way that organisations across the sector manage their data.

Senior mining company executives frequently make tough decisions but, in doing so, they must aggregate isolated pockets of data to generate insights that are relevant and actionable. For data to be available is no longer sufficient. The top priority for effective decision making is for appropriate management of diverse and disparate data sets in a range of locations.

The key is to integrate data and conduct high-level analysis with an understanding of the domain work requirements. Mining companies achieving this will establish what has become known as the intelligent mine. This is a concept that focuses on centralising information from multiple locations and business processes to reveal useful insights. It supports senior level decision making with designed-for-purpose analytical platforms.

Data held in 50 separate systems will not in itself drive operational efficiencies or support sustainable operations. By addressing several issues simultaneously, an organisation is more likely to move towards the intelligent mine.

First, though, businesses must implement automated data gathering systems to capture relevant data from various parts of the mining process and facilities. Second, organisations must have tools that detect bad data because only good data enables good decisions. They must ensure all changes are consistent, correct and improve data processing. Third, the business should assist the mine personnel by providing the capability to integrate data with built-in relevant analytics, so they process and act on insights in a meaningful time frame.

The predictive dimension

To create an intelligent mine, good data is important but only one part of the equation. Organisations must also analyse data in different ways within a context of the problem to be solved with appropriate predictive outcomes.

Companies need to predict the degradation of equipment that, if unattended, will lead to equipment breakdowns and unplanned maintenance, thereby adversely affecting both operational efficiency, reliability, sustainability and safety. Mining is equipment- and infrastructure-intensive with expensive machinery. It demands operational continuity for profitability and sustainability.

Bringing in prescriptive maintenance

Traditional preventive maintenance methods generally fail on the benchmark of equipment availability and performance. Earlier preventive maintenance efforts were unable to deliver sufficient time-to-failure warnings to deliver a significant impact on profitability.

That is where modern prescriptive maintenance plays a vital role. The technology monitors data from sensors on and around the machine to develop intense multi-dimensional and temporal patterns of normal operation, abnormal operation and explicit degradation patterns that precede breakdown. This provides early warnings, using artificial intelligence (AI)/machine learning digital technology to spot patterns that humans will never pick up.

Also surpassing human capability, the technology can assess the health of numerous machines every few minutes. It also delivers early warnings to maintenance teams, often with prescriptive advice on resolution. Facing tough challenges and spread thinly over large sites, workers benefit from warnings. Much of the intense repetitive analytics and engineering help them prioritise what matters most. Maintenance teams with such prescriptive maintenance tools ensure an intelligent mine makes significant progress in eliminating unplanned breakdowns.

Finding a solution

An asset performance management (APM) approach – with integrated prescriptive maintenance capability – ensures mines improve reliability, availability and uptime, simultaneously reducing the considerable cost of redundant equipment.

Operations teams often work on the assumption of lower availability by, for example, installing three machines when they only need two, or purchasing 10 trucks to ensure they always have eight up and running. These practices are now deemed too wasteful and have become unsustainable.

By embracing the most effective technology, mines can achieve benchmark reliability without the need for more people, equipment, or expenditure. Companies can operate at the required production levels and either mothball or switch off redundant equipment. Being able to do this with full confidence it will actually enhance overall outcomes, makes a significant contribution to the bottom line. It reduces emissions and increases sustainability.

Yet to develop an efficient digitalisation strategy, certain components must be in place. All too often, mines try to invest minimally in digital solutions to save money. Without domain-centric AI/machine learning analytics, this limits the reach and value technology can deliver. Successful digital strategies deploy solutions that draw on data from sensors and other sources. Enterprise resource planning systems, manufacturing execution systems, laboratory information management systems and advanced process control systems are all part of the mix, as well as general mine planning and design systems. Machine learning and other data science techniques require timely delivery of available data, so historian technology plays a vital role.

Across the industry, a growing numbers of mines are pursuing an APM approach. Australia-based gold miner, Evolution Mining, for example, has deployed Aspen Mtell software at the company’s Mungari Gold Operations, in Western Australia, to help reduce unplanned downtime and provide information to support productivity improvements.

Greg Walker, previously Evolution Mining Mungari General Manager, said: “Evolution’s Data Enabled Business Improvement program has achieved excellent results in recent years. With this new technology, Mungari Gold Operations can achieve further productivity improvements via increased asset availability.”

Looking to the future

Today’s mining industry is now sufficiently mature that it should fully embrace digital optimisation technologies. Operators that fail to adapt and build a strategy to utilise such technology are destined to struggle against competitors that do. Prescriptive maintenance delivers quick results by improving the use of existing capital assets and eliminating the surprise of unplanned downtime, which directly affects productivity, safety and sustainability.

The industry should understand how scalable prescriptive maintenance solutions add value to assets. This works as well with a single asset, conveyer system, a processing plant, a large mill, as it does with equipment across a worldwide enterprise. The truly intelligent mine empowers mining companies across a vast array of contemporary challenges. From reducing unplanned downtime and decreasing safety risks, to greater operational efficiency, sustainability and increased profitability, this approach will be essential for mining companies to surmount all their challenges in short and longer terms.

*Jeannette McGill is VP and GM of Metals & Mining at AspenTech

Weir preparing to trial proprietary ore sorting tech by the end of 2022

Automation, Comminution of minerals, Explosives and blasting, IoT, Mineral processing, Mining equipment, Mining services, Mining software, Sustainable mining, Water management, , , , , , , , , , , , , ,

In the Weir Group Capital Markets Event presentation last week, Chris Carpenter revealed that the company was collaborating within its divisions on trials of ore sorting technology in an effort to move less rock at mine sites and optimise processing within the plant.

Carpenter, Vice President of Technology at Weir ESCO, said the company was combining Motion Metrics’ particle size distribution (PSD) capability with ore characterisation technology to explore “in-pit sorting” opportunities for its clients.

“Looking further out, we believe ore characterisation and in-pit ore sorting has the potential to transform mining by moving less rock, using less energy and creating less waste,” he said during his presentation. “Ore characterisation technology, which is underpinned by sophisticated sensing systems, captures critical data on properties and composition of rock, including rock hardness and mineral and moisture content.

“When coupled with Motion Metrics fragmentation analysis technology, it has the potential to be a game changer, giving miners a full picture of the size and characteristics of rocks.”

Motion Metrics, a developer of artificial intelligence (AI) and 3D rugged machine vision technology, was acquired by Weir almost a year ago, with the business incorporated into the Weir ESCO division. Its smart, rugged cameras monitor and provide data on equipment performance, faults, payloads and rock fragmentation. This data is then analysed using embedded and cloud-based AI to provide real-time feedback to the mining operation.

These technologies were initially developed for ground engaging tool applications but have recently been extended into a suite of products and solutions that can be applied from drill and blast through to primary processing.

Carpenter said the added PSD capability from Motion Metrics was expanding the company’s value presence across the mine to the processing plant, where Weir Minerals operates.

“Results from early adoption of Motion Metrics PSD solutions have been extremely encouraging,” he said. “Feedback from customers is positive; data sharing and collaboration have increased.

“Given this early progress, we are really excited about the opportunity and expect fragmentation analysis to be a key growth driver for Motion Metrics in the years to come.”

On the in-pit sorting potential, Carpenter said Weir ESCO had laboratory-validated equipment and field trials of its proprietary solution that were due to start at customer sites before the end of the year tied to these developments.

“If successful, this technology opens the door to in-pit sorting, where miners complete the first stage of crushing in the pit and analyse the outputs to make real-time decisions about which rocks have sufficient mineral content to be moved,” he said. “This is a step change from the current process, where energy is expended in transporting and processing all of the rocks, regardless of mineral content, and with significant waste generated from zero- and low-grade material.”

He concluded: “Our vision is to move less rock, moving only the rocks with sufficient mineral content and using the data that is captured on size and hardness to optimise processing. The natural evolution thereafter will be towards real-time automation control of processing equipment, ensuring the right rocks are processed in the most efficient way, using less energy and creating less waste.”

Gradiant concentrating its mining proposition

Automation, Base metals, Environmental, Mineral processing, Mining services, Mining software, Water management, , , , , , , , , , , ,

There are plenty of mining applications one can see Boston, Massachusetts-based Gradiant’s end-to-end water technology solutions serving.

A spinout of the Massachusetts Institute of Technology, the company calls itself the “experts” of industrial water, water reuse, minimum liquid discharge (MLD) and zero liquid discharge (ZLD), and resource recovery of metals and minerals.

That is a big remit, hence the reason why it caters to at least nine industries on a global basis in mission-critical water operations, with over 70% of its clients being Fortune 100 companies in the world’s essential industries.

Mining companies have historically been wary of suppliers that serve a variety of industries, believing their needs rarely cross over with the requirements of other industries. Gradiant believes it is different in that its solutions incorporate not only the hardware and software to fine-tune water technologies, but also the artificial intelligence (AI) to ensure the tools being used are effective regardless of the inputs.

This includes the RO Infinity™ (ROI™) platform of membrane-based solutions for complex water and wastewater challenges, which combine Gradiant’s patented counterflow reverse osmosis (CFRO) technology with reverse osmosis and low-pressure membrane processes. ROI solutions enable customers around the world to achieve sustainability goals to reduce their water and carbon footprint, the company says.

This platform is complemented with AI-backed SmartOps™, an integrated digital platform for asset performance management to optimise and predict plant operations using historical and real-time process data, resulting in performance and cost efficiencies.

Prakash Govindan, Co-Founder and COO of the company, says most water solutions on the market are built for consistent liquid/solid feeds and work effectively when the input is in accordance with these specifications. When the feed changes, they often become ineffective, needing to be updated or changed out, which costs money and impacts the various processes on either side of the water treatment section.

“The machine-learning algorithms we use – neural networks and time-series algorithms – ensure we consistently optimise the operation of our solutions,” Govindan told IM. “These tools make sure we always use the right performance metrics and don’t lose efficiency in the face of variability.”

The algorithms cannot change the hardware built into the water treatment plant, but it can, for instance, change the speed of the pumps or blowers. “We call it balancing, which is all part of our IP portfolio,” Govindan said.

SmartOps is an integrated digital platform for asset performance management to optimise and predict plant operations using historical and real-time process data

For mining companies looking to employ water treatment tools at their operations, this results in Gradiant’s technology being able to concentrate metals to a higher degree than any other solution on the market, according to Govindan.

“We can concentrate an aqueous solution to the point where you can produce a solid material that miners can then process,” he said.

Considering desalination applications represent a significant portion of the company’s work to this point – through its CFRO process – the mining sector has already provided some wins.

The CFRO process enables remote inland desalination and water reuse that was not previously possible due to a lack of viable brine management solutions, according to Gradiant, concentrating brines to saturation for disposal or crystallisation while producing a purified product water stream for beneficial reuse.

One significant nickel miner in Australia with a brine stream is using this solution to recover large amounts of concentrate it can feed through to its captive processing plant to produce an end-use product.

“Gradiant’s technologies enable clients to recover more than 50% of the nickel and cobalt from leached brine – this stream would have otherwise been wasted without our solutions,” Gradiant said. “Overall, this was a client benefit of about 20% increase in nickel and cobalt production across the entire operation.”

When considered together with the energy savings (75%), freshwater savings (25%) and environmental benefits, Gradiant continues to see high interest from miners around the world to adopt its solutions, it says.

That is before even factoring in the other complementary benefits that come with using SmartOps.

“All our products benefit from in-built sensors that not only allow us to update the operating parameters based on the detected materials, but also carry out scheduled maintenance on the hardware using these algorithms,” Govindan said. “This allows us to carry out 30-40% less service intervals than many conventional suppliers as we only take the solution out of operation based on what the data is telling us.

“Not only this, but we also have complete oversight of these parameters from remote locations, meaning you can monitor the systems from remote operating centres and not remain on site after installation.”

With mines getting more remote and hiring local employees getting even harder with the well-documented skills shortages, Gradiant feels its solutions will continue to win miners over.

SensOre expands AI-based geophysics capacity with Intrepid Geophysics acquistion

Mineral exploration, Mining mergers and acquisitions, Mining software, , , , , , , , , , , ,

SensOre Ltd says it has reached an agreement to acquire Intrepid Geophysics, a provider of geophysics software and services headquartered in Melbourne, Australia.

The deal, valued at around A$5 million ($3.4 million) will be primarily funded through the issue of new fully paid ordinary SensOre shares.

Intrepid Geophysics’ advanced automated geophysical software and geoscience expertise complement SensOre’s existing suite of machine learning and artificial intelligence-based mineral exploration software products and technology offerings, it said, adding that Intrepid Geophysics’ large client base and strong cashflows were integral to its strategic assessment of the transaction.

SensOre Chief Executive Officer, Richard Taylor, said: “Acquiring Intrepid Geophysics is a major opportunity for us. Intrepid Geophysics’ deep geoscience and machine-learning expertise in geophysics complements SensOre’s geochemistry and economic geology focus for targeting in mineral exploration. Demand for advanced geophysics software is strong and deployable globally.

“Intrepid Geophysics’ years of product leadership, data collation and collaboration with government geological surveys will benefit SensOre’s data platform development and client service offerings. We are looking forward to integrating Intrepid Geophysics’ exceptional talent with our team of innovators.”

Intrepid Geophysics Managing Director, Dr Desmond Fitzgerald, added: “The combination of SensOre and Intrepid Geophysics will unlock growth opportunities in a strong market for high-level exploration targeting. We look forward to being part of a growing and exciting geoscience group.”

SensOre and Intrepid Geophysics completed a successful pilot project in Victoria in the June quarter, confirming the technological synergies and product complementarity between the two companies, it said. The results of the pilot are expected to be deployed with clients in the September quarter, focused on the Stawell and Ballarat gold corridors. There is strong interest from prospective clients within these corridors, according to SensOre.

SensOre says its strategy has been to organise all of Australia’s geoscience data within its proprietary data cube technology, with the acquisition of Intrepid Geophysics significantly advancing that capability.

The deal also expands SensOre’s mineral exploration technology sector presence and exposure, with the deal seeing the company acquire a second team of dedicated geoscience professionals with cross-over expertise in the oil and gas, groundwater, geothermal and the emerging hydrogen storage sectors.

“In acquiring Intrepid Geophysics, SensOre gains access to multiple new targeting and decision-based proprietary technologies and strategic decision-based services using 2.5 dimension Airborne Electro-Magnetic inversion technology, tensor gradient technology, geology from geophysics feature extraction, and service automated workflows,” the company added.

These technologies, SensOre says, fill a gap in its product suite by incorporating geophysics products that take exploration targeting from the macro-focused Prospectivity Modelling and Discriminant Predictive Targeting® approach into drill target delineation in three dimensions.

Intrepid Geophysics’ software portfolio includes:

  • Intrepid 3D – an airborne and ground geophysical data processing and interpretation package;
  • Moksha-EM – an airborne electromagnetic full waveform inversion data processing and interpretation package;
  • Argus – a 3D geological modelling package with a tightly integrated geophysical forward and inverse modelling capability;
  • JetStream II – a web-based, spatially searchable data catalogue that enables geoscientists to quickly assess the coverage, type and vintage of georeferenced spatial data held over any given area; and
  • Sea-g Marine Gravity – a fully featured marine gravity processing application powered by Intrepid Geophysics technology for on-cruise and post-cruise use.

Maptek machine learning trial points to future of mineral deposit modelling

Base metals, Energy minerals, Mineral exploration, Mining services, Mining software, , , , , , , ,

A trial of Maptek DomainMCF at an underground metals mine has concluded that machine learning will most likely become the preferred modelling method for mineral deposits, according to the software company.

DomainMCF is a platform that Maptek says will ‘put the geology back into geologists’, applying deep learning and big data computing methods to generate domain boundaries directly from drill hole sample data. Such rapid generation of resource models is a game changer for operations, according to Maptek.

In the trial of DomainMCF, geologists at the IGO Limited Nova-Bollinger underground mine in Western Australia trialled the solution’s machine-learning tools for modelling its resource.

Nova-Bollinger is 700 km due east of Perth, with the operation mining and processing nickel-copper-cobalt sulphide ores.

Traditional resource modelling is based on a drill hole database containing 99 lithological and 11 sulphide mineralisation logging codes. From this database the mine geologists use implicit modelling to interpret 22 different domains – 21 sulphide domains and one all-encompassing waste halo domain.

The block modelling process is undertaken annually by a team of geologists on site and in the Perth corporate office, taking several months to complete. The team trialled DomainMCF in parallel to the standard workflow as part of the 2020 resource update.

The required inputs for DomainMCF are a csv file comprising drill hole database or composite data, and an optional upper and lower surface to define the spatial extents of the region to be modelled. A block model parameter file details the origin of the block model, the 3D spatial extents and the block/sub-block dimensions.

Grade estimates are done on a 6 m x 6 m x 2 m block size and sub-blocks are permitted down to a quarter of the parent block size, according to Maptek.

During the trial, three primary tests were run using different versions of the drill hole file to explore the capabilities of the application and see how they compared to the existing workflow.

Test 1 provided DomainMCF with the drill hole composite file for the 22 different domains. Six chemical elements (Ni, Cu, Co, Fe, Mg and S) were provided to assist with the training phase of the machine-learning algorithm.

For Test 2, the data used in the first test was augmented with lithology coded data from the drill hole information outside the estimation boundary limits. The chemical variables were again used to help train the algorithm.

The purpose of this test was to determine if a combined sulphide and lithological model could be produced, and to see if giving DomainMCF additional information would impact the prediction of sulphide domains.

A hands-off approach was used in Test 3 to see how DomainMCF modelled a file containing only mineralisation codes and the grouped lithology used for Test 2. None of the domain codes from Test 1 were used.

Test 3 examined if the DomainMCF model was comparable with a manually-coded domain model and whether it was useful in the mineral resource estimate process.

IGO Senior Mine Geologist, Fletcher Pym, presented the trial results in a paper to the AusIMM International Mining Geology Conference 2022 in March.

“We were able to run Test 3, which was a relatively complicated model, in 45 minutes,” Pym said.

For Pym, Test 3 also showed that machine learning can produce very comprehensive models without the strong influence of a geologist.

Because machine learning made resource modelling much faster, senior staff had more time to focus on training less experienced core loggers. Improving the processes resulted in better quality drill hole logging, according to Maptek.

Pym added: “Machine learning will become particularly attractive if the process can not only model geological domains, but also return reliable grade estimates for mine planning across the full range of mineralisation styles.

DomainMCF model section

“Providing a well-understood confidence measure can assist in risk quantification of both geology and grade.”

The study, Maptek says, highlighted several advantages of machine learning:

  • The inputs required for machine-learning processing can be readily prepared in most resource modelling software;
    Machine learning modelling times are relatively short;
  • The pay-by-use business model is more cost-effective than maintaining implicit modelling software systems;
  • The machine learning model returns an objective measure of uncertainty in the geological model, which is likely to be useful in mineral resource classification and mining reconciliation work; and
  • Multiple different geological models can be prepared in parallel, meeting the JORC requirement to investigate ‘the effect, if any, of alternative interpretations on mineral resource estimation’.

Maptek Technical Lead for DomainMCF, Steve Sullivan, says he is excited at the potential of machine learning for revolutionising resource modelling.

“I’m amazed at the response – we are already seeing companies subscribe to DomainMCF for use in domain modelling for their 2022 resource reports,” he said.

“Machine learning works best when all the available data is presented, as shown in Test 3. The more data the better.

“The industry is struggling to find experienced personnel during the current mining boom, so embedding years of experience into smart systems helps get the job done on time and under budget.”

Maptek continues to work on proposed enhancements following feedback from industry trials of DomainMCF, with grade trend prediction added in the March 2022 release.

This is an edited version of an article that appeared in Maptek’s Forge newsletter.

Unico to help further commercialise SensOre’s mining exploration technology platform

Mineral exploration, Mining services, , , , , , ,

SensOre Ltd has executed an agreement with Unico, now part of global IT and business consulting firm CGI, to collaborate on the commercialisation of SensOre’s mining exploration technology platform.

The project represents an opportunity to expand SensOre’s current client services to a cloud-based SaaS (software as a service) product, it said.

SensOre’s technology was created to improve exploration success rates and performance, leveraging artificial intelligence and machine-learning technologies, specifically its Discriminant Predictive Targeting® (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 contract with Unico is an important step in SensOre’s technology development roadmap, it said. It is envisaged that development and deployment of the proprietary platform will open the door to scale the company’s products and expand SensOre’s capacity to service mineral exploration companies in Australia and overseas, while reducing the company’s unit cost per engagement.

The project will involve using SensOre’s AI-driven technology to create a digital twin of the Earth’s surface, enhancing the way exploration companies identify and analyse mineral exploration targets, SensOre says.

SensOre Chief Executive Officer, Richard Taylor, said: “Our background is creating and deploying technology and software that generates exploration targets using AI. While we have previously focused on Western Australia, a key objective of the Unico partnership is to enhance the pace of our data expansion across the globe.

“This project is a massive opportunity to use our technology to build a model that we believe will be in demand from mining exploration companies around the world. We are excited about the prospect of a global relationship with CGI to help expand our technology offerings into new markets.”

Unico’s Director of Innovation, Evan Harridge, said: “Imagine being able to analyse an MRI scan of the Earth. SensOre’s AI-driven analysis tools will be able to see what is underground in great detail, similar to how medical imaging technology can accurately see inside the human body. This technology would enable exploration to be more targeted and limit the overall environmental impact.”

METS Ignited-backed program to fund OreFox’s AI exploration ambitions

Base metals, IoT, Mineral exploration, Mining finance, Mining services, Mining software, , , , , , , , , , , ,

Artificial intelligence-based geological data analytics company, OreFox, has successfully secured funding via the Queensland METS Collaborative Projects Fund to further its geological mapping efforts at the Mount Chalmers mine site in Queensland, Australia.

The Queensland METS Collaborative Projects Fund is delivered by METS Ignited with funding support from the Queensland Government as part of its Queensland Mining Equipment, Technology and Services 10 Year Roadmap and Action Plan.

OreFox is working with QMines Ltd and Ironbark Marketing to further commercialise its technology that, it says, solves a pressing challenge facing the Queensland mining sector of how to accelerate critical mineral deposit discovery and mine more efficiently.

The consortium led by OreFox will use cutting-edge proprietary AI technology to gain further knowledge of the Mount Chalmers deposit, located near Rockhampton. As a historical producing mine, there is significant potential to increase the known mineral endowment and add new economy mineral opportunities.

QMines has commenced exploration activities across the Mount Chalmers project, including an aggressive drilling program and is planning an airborne EM survey. QMines has reported an initial JORC 2012 inferred resource equal to 73,000 t of contained copper. Historical drilling at the site shows the mineralisation is open in multiple directions.

The consortium will carry out a multi-element geochemical sampling program focused on critical minerals at the mine site and other notable prospective sites around the historic mine site.

Data collected from this program, including assays from the current drilling program, will be processed by OreFox, using its machine and deep learning systems to gain further insights. QMines believes the discovery of further economic minerals will enable the company to move to development faster, thus bringing economic benefits to the region and the state.

QMines has recently acquired Traprock Resources and Rocky Copper, which both held significant tenements in the Mount Chalmers region. QMines has extended their tenement and landholding within the area since these acquisitions.

OreFox Chief Executive Officer, Warwick Anderson, said: “This project has the potential to increase exploration activity in Queensland, particularly for new economy minerals and could be applied to numerous other historical mines and deposits.

“The partnership between OreFox, QMines and Ironbark Marketing is anticipated to bring more regional jobs to Queensland and aid Queensland exploration frontiers.

“If we can prove the value of this project, then that opens the door to a significant export market for the processes we are developing.”

The OreFox project is one of five recipients of the Queensland METS Collaborative Projects Fund receiving a share in A$1 million ($733,978) to accelerate the commercialisation of technology into industry.

METS Ignited CEO, Adrian Beer, said the growth centre is backing the collaborative projects to fast-track the commercialisation of innovative technologies and provide value to both the local and global resources sector.

“METS Ignited is driving collaborative projects to accelerate commercial outcomes for the Australian economy and promote collaboration opportunities as part of a long-term strategy for growth,” he said.

“We are backing projects using technologies such as sensors, data analytics, machine learning, optimised x-rays, and solar energy that result in improvements in productivity, efficiency, safety and sustainability.

“The OreFox project is a great example of how AI and data science technologies can be harnessed to improve exploration and unlock the economic benefits of a historical mine site.”

Seequent adds to cloud-based geoscience software base with Imago acquisition

IoT, Mineral exploration, Mining services, Mining software, , , , , , , , , , , , , , , ,

Bentley Systems’ Seequent business unit has acquired Imago Inc, a developer of cloud-based software for the capture and management of geoscientific imagery.

The acquisition, which comes only a month after announcing the purchase of Aarhus GeoSoftware, will expand Seequent’s technology solutions portfolio while boosting cloud capabilities to help geoscientists and engineers solve earth, environment and energy challenges, it said.

Imago’s cloud-based platform enables the capture, cataloguing and review of drilling core and chip images from any source, to support every aspect of the geological process from exploration to grade control. Continued development of Imago’s machine learning will lead to a step function in the interpretation of geological data, according to Seequent.

Seequent said: “Mining companies around the world apply Imago’s solution in conjunction with geology data management and modelling tools to enable teams to make more confident, profitable decisions using instantly available, high-quality images. Seequent already integrates its Leapfrog, Oasis montaj, Target, and Minalytix MX Deposit with Imago’s solution, making it easy for geologists, engineers and other stakeholders to extract knowledge and learn from geoscientific imagery. The goal is to unlock significant potential for mining and other industries, transforming image data into meaningful insights for geological activities.”

Imago establishes a consistent process for capturing high-quality images, which integrate with existing workflows and allow the application of machine learning

Graham Grant, CEO of Seequent, said: “It’s an exciting step to welcome the Imago team on board to help advance Seequent’s progression into the cloud. We’re continually exploring ways to provide new technologies and solutions to solve workflow challenges, improve operational efficiency and deliver greater value for our users who are working to solve some of the world’s major civil, environmental, and energy challenges. This acquisition demonstrates Seequent’s continued growth and our commitment to make a positive contribution to the industries we serve globally.”

Imago’s Co-Founder, Federico Arboleda, said: “As a small team in Phoenix and Perth, we’re excited to join forces with Seequent, as this will now allow us to substantially scale Imago’s solutions in mining and other markets. We founded Imago to help mining companies manage the high volume and size of geological images and unlock the great value in this geoscience imagery. Image data is an increasingly important source of data across the geosciences – and can come from potentially any source, including core photos, hyperspectral, aerial photos, drones, and handheld devices. It will become even more important to transform image data into knowledge as automation needs increase.”

LKAB to trial AI-backed XRF drill core logging with help of Minalyze and Sentian

IoT, Mineral exploration, Mining services, Steel and iron ore, , , , , , , , , , , , , ,

LKAB, Minalyze AB and Sentian say they have joined forces in a consortium to develop the latest technology for scanning drill core.

In March 2020, LKAB started a test with the Minalyzer CS drill core scanner where the goal was to improve the workflow for core logging – ie how the results of exploration drilling are analysed. The test led to a permanent installation in Kiruna (Sweden) and expansion to Malmberget where data from the Minalyzer CS is used to help geological logging of the drill core.

The consortium of LKAB, Minalyze and Sentian are now set to take the use of data to the next level when boreholes in LKAB’s deposits are to be investigated. The new artificial intelligence application being developed by the trio will make the analysis much faster, with the time to evaluate a drill core reduced from weeks to minutes, with increased accuracy.

This could see Minalyze’s X-ray Fluorescence-backed CS scanner analyse LKAB drill core while leveraging Sentain’s industrial artificial intelligence solutions to make real-time decisions relating to drilling and exploration activities.

The technology development driven by the consortium will be a world first, changing the entire industry, the companies say.

Jan-Anders Perdahl, Specialist at LKAB’s Exploration Department, said: “With the collaboration, the core logging takes a big step through machine learning and artificial intelligence. The geologist can, at an early stage, place greater focus on the parts of the core that show chemical or other changes. Opportunities are opened up to gain increased knowledge about ore formation processes and alterations in a completely different way than before. One can also get indications that you are close to mineralisation and where it may be located, and thereby streamline exploration.”

The technological leap will give LKAB’s staff increased competence, increased quality in and efficiency of the work, as well as reduced need for other analysis methods, according to the companies.

Annelie Lundström, CEO of Minalyze AB, said: “We are at an interesting time when the hardware to extract consistently high-resolution data from drill cores is available and we can now take the next step and generate value from data together with our customers. In this collaboration, we will develop algorithms that can map rock layers in so-called lithological logs with very high confidence. This can only be done by combining expertise from all three parties.

“The results from our collaboration will forever change how drill core logging takes place everywhere and will result in a more efficient, non-subjective and consistent process.”

Martin Rugfelt, Sentian CEO, added: “We see great power in the application of modern artificial intelligence to data from the mining industry and there is major potential in further combining our machine learning technology with Minalyze’s unique capabilities in data collection and analysis.”

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

Base metals, IoT, Mining services, Mining software, , , , , , , ,

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.