Tag Archives: Artificial Intelligence

ANDRITZ hopes to bring autonomous plant operation to mineral processing

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After being crowned the winner of #DisruptMining 2019, ANDRITZ is now ready to negotiate a contract or investment of up to C$1 million ($750,480) with Goldcorp.

The live finale of the 2019 #DisruptMining, the innovation accelerator that offers entrepreneurs a platform to bring disruptive and exponential technologies to the sector, took place last night on the sidelines of the annual Prospectors and Developers Association of Canada event in Toronto, Canada.

Sohail Nazari (second from right), Business Development Manager, ANDRITZ, and Arthur Gooch (second from left), Director of Innovation, ANDRITZ, said: “We thank Goldcorp and KPMG for their tremendous leadership driving innovation and digitalisation forward in mining. We are excited to be part of Goldcorp’s success to bring autonomous plant operation to mineral processing and we look forward to getting to work.”

David Garofalo (left), President and CEO, Goldcorp, said: “Innovation doesn’t stop or start with one idea, one technology, or one company. For the mining industry to reach the demands and potential of the 21st century, every company must step up and innovate. We must all be safer, more efficient, and responsible and we’ll get results faster through collaboration and the kind of break-through thinking the #DisruptMining platform is meant to uncover for our industry.”

ANDRITZ, a supplier of machines and automation solutions worldwide, developed a unique and continuous way of training artificial intelligence to operate a mineral processing facility using ANDRITZ’s digital twin as part of its award winning concept, Goldcorp said. “The AI is trained to respond to a variety of situations, making it capable of adapting to changing inputs and improving recovery time. The trained AI’s ability to quickly process information and recommend data-driven solutions will allow for the improvement of the operation, such as start-up and shutdown, and assist operators to achieve plant-wide optimisation.”

Deciding the fate of the three finalists was a panel of industry judges including Ian Telfer (Chair of Goldcorp), Katie Valentine (Partner at KPMG Australia and Global Head of Mining Consulting), Sue Paish (CEO of Canada’s Digital Technology Supercluster), Jacob Yeung (University of British Columbia student and #DisruptMining UBC Captain) and Wal van Lierop (President and CEO, Chrysalix Venture Capital).

Net proceeds of C$200,000 from the #DisruptMining live finale will be granted toward mining, innovation and technology scholarships to the University of British Columbia, Garofalo announced.

Anglo readying predictive maintenance solutions following Barro Alto implementation

Automation, Base metals, Equipment maintenance, IoT, Mine operation news, Mineral processing, Mining equipment, Mining maintenance, Mining services, Mining software, , , , , , ,

Anglo American has highlighted its predictive maintenance efforts on equipment at its Barro Alto nickel mine in Brazil in its recently-published annual report.

The company said it is developing predictive models so it can make better informed operational decisions. These models, built by data scientists and often powered by artificial intelligence and machine learning, contain advanced algorithms that leverage the power of data to generate predictions, according to the company.

“At the operational level, we are using customised learning algorithms across a range of different applications,” Anglo said. “In one such instance, we monitor equipment health at a number of our operating sites, with the aim of improving operational performance through predictive maintenance.”

The company said at Barro Alto, which has two rotary kilns and two electric furnaces that smelt nickel ore, it is focusing its predictive maintenance efforts on key pieces of high-power equipment.

Anglo said: “By building a comprehensive data platform that monitors 38 major elements of the Barro Alto operation, we are increasing our knowledge of the performance of the equipment and we are using data to accurately forecast failures before they happen.”

Soon, the company will be able to “dynamically manage” maintenance intervals – only replacing parts when required – Anglo said. This ensures greater operational uptime and product throughput, according to the company. “The implementation is expected to improve furnace reliability, as well as realise cost savings for the nickel business,” Anglo said.

The learnings from Barro Alto are also being applied to fixed-plant assets in other operations, Anglo said. “This nascent project is on track to deliver considerable value from just one data analytics application.”

On the technology in general, Anglo said: “Data analytics augments the intelligence in our people by helping them make better, confident data-driven decisions. Remote monitoring of assets takes people away from physical equipment and helps avoid high-energy failures, which leads to a safer working environment. Reducing unplanned equipment failures can also bring significant environmental benefits owing to the reduced likelihood of spillages.”

Anglo plans to extend the reach of its data analytics platforms to all aspects of its value chain and extend operational decision support to the mining and processing phases of its assets, it said.

Robotics and automation projects among latest METS Ignited funding recipients

Automation, Bulk handling, Careers in mining, Coal technology, Environmental, Equipment maintenance, IoT, Mineral exploration, Mineral processing, Mining equipment, Mining finance, Mining industry recognition, Mining maintenance, Mining safety, Mining services, Mining software, Mining techniques, Precious metals, Steel and iron ore, Sustainable mining, Water management, , , , , , , , , , , , , , , , , , , ,

Australia’s Minister for Industry, Science and Technology, Karen Andrews, has announced seven mining supply businesses as the recipients of A$4.1 million ($2.9 million) in innovation funding under the METS Ignited Collaborative Project Funds.

The recipients of the funding will now be able to launch eight collaborative industry projects delivering highly-advanced solutions to a variety of mining challenges and contribute to the growth and capability of the METS sector, according to METS Ignited.

This funding is part of a four-year, A$15.6 million commitment made by the Australian Government to incentivise collaboration and address METS sector priorities. The funding established the METS Ignited Collaborative Project Funds, which support industry-led projects to improve the productivity, competitiveness and innovative capacity in the METS sector.

Today’s announcement at Mineral Technologies, on the Gold Coast of Australia, is the third tranche of funding. METS Ignited received 26 grant applications and has awarded the funds to businesses specialising largely in robotics and automation, data analytics, data platforms, Internet of Things and business and professional services. The recipients are: Mineral Technologies, Premron, Austmine, Roobuck, Process IQ, AMOG (x2) and Magotteaux.

Acting CEO of METS Ignited, Ian Dover, said: “Active collaboration across the ecosystem is core to accelerating commercialisation of innovation and has been lacking in the METS and mining sector, where historically relationships have been in the main transactional.”

“Facilitating such innovation is part of the mandate for METS Ignited. It’s vital we support the application of influential future technologies across the METS sector and maintain Australia’s competitiveness.”

Recipients of the Collaborative Project Funds are required to secure equal or greater investment from an industry partner. As a result, the total value of the eight projects is A$11 million.

The largest fund recipients were Queensland-based Mineral Technologies and Premron, awarded A$1 million each. Mineral Technologies’ automation of the Roy Hill Iron Ore beneficiation plant project automates the gravity separation spiral process used in the mine to optimise the concentration of lower-grade ore into higher value ore for export, METS Ignited said.

Roy Hill CEO, Barry Fitzgerald, said: “I am delighted the government is supporting our partnership with Mineral Technologies – a project that seeks to enhance the operational efficiency of our mine, delivering more high-grade product while reducing waste for the same operational cost.”

The automation of spiral control in the Roy Hill beneficiation plant will materially improve the concentration of ore into high value product for export, according to Roy Hill. More high-grade product and less waste will be produced for the same feed and processing cost, delivering value to both the environment and Roy Hill’s bottom line, the company said. Once proven effective at Roy Hill, the technology can be commercialised and rolled out at similar operations across the world.

“This innovation project will deliver a step-change improvement through real time control of our 720 spirals, enabling our processing plant to dynamically respond to the natural variability of the material it is treating,” Fitzgerald said.

Premron’s Continuous Haulage System (CHS) project, meanwhile, will revolutionise coal mining in underground mines, according to METS Ignited. It eliminates the use of shuttle cars, used to take the coal cut from the wall of the mine to a transfer point further away in the mine (dead time). CHS will see the coal go straight to a conveyor belt and out of the mine.

Other projects that received funding in this round include: sensor technology to monitor the location of people and equipment underground; artificial intelligence technology to emulate the role of a grinding expert; automated sensor detection for oversized rocks; a predictive analytics tool that pinpoints the best time for equipment descaling; a METS career pathway programme; and a device to give more detailed information on the chemistry inside the grinding mill while it is operating.

METS Ignited said: “Collectively, the projects will benefit the mining sector by optimising the value chain, increasing productivity for mining and mineral processing, supporting and enhancing environmental management, and improving operational safety.”

The project fund recipients include:

Automation of the Roy Hill Iron Ore beneficiation plant

  • Recipient: Mineral Technologies
  • Partners: Roy Hill
  • Collaborative project funds: A$1 million
  • Industry investment: A$1 million
  • This project automates the gravity separation spiral process used in the mine to optimise the concentration of lower-grade ore into higher value ore for export.

CHS

  • Recipient: Premron
  • Partners: Gauley Robertson Australia, Kestrel coal mine
  • Collaborative project funds: A$1 million
  • Industry investment: A$1.13 million
  • Continuous haulage will revolutionise coal mining in underground mines. It eliminates the use of shuttle cars, which are used to take the coal cut from the wall of the mine to a transfer point further away in the mine. CHS will see the coal go straight onto a conveyor belt and out of the mine.

Austmine METS career Pathway Program

  • Recipient: Austmine
  • Collaborative Project Funds: A$240,000
  • Industry investment: A$1.76 million
  • This project places university students as interns in METS companies around Australia, increasing the interest level and uptake of graduates into the METS sector

The OVERwatch Platform

  • Recipient: Roobuck
  • Partners: Redpine Signals, Northparkes Mines, University of Wollongong
  • Collaborative project funds: A$600,000
  • Industry investment: A$1.5 million
  • This project develops sensors and software to track the location of people and machinery working in underground mines and ensure that collisions are avoided. This is a complex project as there is limited communication options underground (eg no Wi-Fi).

Remote grinding optimisation and support centre

  • Recipient: ProcessIQ
  • Partners: Orway Mineral Consultants, Jamieson Consulting, Curtin University
  • Collaborative Project Funds: A$620,000
  • Industry investment: A$780,000
  • This project enables grinding experts to interact directly and in real time with grinding circuits on remote mine sites to ensure they are operating at their most productive levels. The project will develop automated artificial intelligence software to emulate the experts as there is very limited supply of this specialist expertise, leading to increased processing efficiency globally.

Automated Oversize Detection

  • Recipient: AMOG
  • Partners: Omniflex
  • Collaborative Project Funds: A$150,000
  • Industry investment: A$220,000
  • This project involves developing sensor equipment that alerts the mine when rocks are too big to process through the crushing and grinding equipment. Blockages in the crushing and grinding circuits are costly and time consuming. Haulage trucks with oversized rocks will be diverted to a separate location in the mine, which avoids stoppages.

Smooth Operator leach circuit process optimisation

  • Recipient: AMOG
  • Partners: Lithium Consultants
  • Collaborative Project Funds: A$220,000
  • Industry investment: A$220,000
  • This project involves developing a predictive analytics tool that allows copper and nickel mines to pinpoint when they should close equipment for descaling. Closing equipment too late or early is very costly. There is a very large global market for this product.

Commercialisation of pulp chemistry monitor for the mining industry

  • Recipient: Magotteaux
  • Partners: Hydrix, Manta Controls, Newcrest Mining
  • Collaborative Project Funds: A$250,000
  • Industry investment: A$310,000
  • This project involves developing a device to give more detailed information on the chemistry inside the grinding mill while it is operating. Grinding and flotation circuits use many chemical inputs in order to extract minerals from the ore. Getting the chemical balance right in the mill and the next stage of floatation is critical to removing as much of the valuable mineral as possible. The percentages of the yield vary between 85% and 95% and a 1% improvement in yield will deliver a very large financial benefit to the mine.

ANDRITZ’s AI mineral processing concept named #DisruptMining 2019 winner

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ANDRITZ, and its artificial intelligence-backed mineral processing facility operation concept, has been named #DisruptMining 2019 winner at the event in Toronto, Canada.

The company was one of three finalists selected to pitch to a panel of judges at the live finale, which took place last night.

ANDRITZ, which is a leading supplier of machines and automation solutions worldwide, has developed a unique and continuous way of training artificial intelligence to operate a mineral processing facility using ANDRITZ’s digital twin, Goldcorp, which hosts the event on the sidelines of the Prospectors and Developers Association of Canada’s annual gathering, said.

“The AI is trained to respond to a variety of situations, making it capable of adapting to changing inputs and improving upset recovery time,” Goldcorp said. The trained AI’s ability to quickly process information and recommend data-driven solutions will allow for the improvement of the operation, such as start-up and shutdown, and assist operators to achieve plant-wide optimisation, the company added.

Todd White, Goldcorp Chief Operating Officer and Executive Vice President of Operations, previously said: “#DisruptMining continues to represent the best of innovation in the mining industry. These finalists demonstrate break-through thinking and help build digital momentum in mining. The industry needs to help accelerate the development of these kinds of technologies.”

ANDRITZ beat off stiff competition from Anaconda Mining, which has developed a two-stage drilling method to enable economic mining of narrow-vein deposits, and Voith Turbo, a division of Voith GmbH & Co KGaA, whose Internet of Things application, BeltGenius, creates a digital twin of belt conveyors providing real-time insight into the behaviour of the operation.

Micromine, Geobank and Pitram to come under PDAC 2019 spotlight

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MICROMINE says attendees at the upcoming Prospectors & Developers Association of Canada Convention (PDAC) in Toronto, Ontario, will be able to witness software demonstrations for Micromine 2018 and Geobank 2018, while also hearing about its artificial intelligence and machine learning initiatives for Pitram 2019.

All three solutions have been developed on the back of extensive consultation with MICROMINE’s key clients from across the globe, the company said.

The mining software provider has exhibited at PDAC for eight years and says it has experienced, first-hand, the growth, stature and influence of the conference over the years.

Amelie St-Onge, Regional Manager MICROMINE Canada, said: “Many exciting things happened for the company since last year’s conference, and we are proud and excited to share these news as well as information on our upcoming releases with our clients and with the mining community.”

Specialists attending the conference from March 3-6 include Technical Product Manager for Micromine, Frank Bilki; Regional Manager for Canada, Amelie St-Onge; Technical Pre-Sales for Pitram, Chris Hunt; Training & Support Consultant for Micromine, Liam Murphy; Technical & Support Consultant for Micromine/Geobank, Caleb Birchard; Business Development Manager, Jeremy Pestun; Business Development Manager, Joel Jeangrand, and; Regional Marketing Coordinator, Maryam Abbaszadeh.

Geobank is a data management solution that helps mining and exploration companies maintain the quality, integrity and usability of their essential data, according to MICROMINE. Geobank 2018 includes a range of features and enhancements including a new and improved user interface, Global Substitution Parameters and increased functionality when designing or editing Graphic Reports.

Micromine, the company’s 3D modelling and mine design solution, is due a new release in the December quarter of 2019. This is set to include a range of new features and enhancements that increase the overall usability and performance of the software, according to MICROMINE.

MICROMINE said: “While the initial look and feel of Micromine 2020 will be the same, the new version will come with some new features, these include:

  • “New charting tools for Geostaticians; swath plots, boundary analysis, QKNA, top cut analysis, multiple charts, and ternary charts;
  • “New unfolding tool for model interpolation – Micromine has long been considered the #1 product for un-folding complex orebodies for interpolation and our new unfolding tool takes this to the next level allowing us to model more complex orebodies, more rapidly;
  • “New Stope Optimiser which will enable engineers to design optimal stope shapes based on economic and design constraints from a block model;
  • “Improved scheduler; the existing Scheduler module has had significant improvements made to it for MM2020. A new Gantt chart and the ability to schedule auxiliary tasks are important but the biggest change will be the ability to use Gurobi to solve the schedule. Gurobi is the world leader in schedule optimisation solving and its integration with Micromine Scheduler will enable engineers to schedule larger, more complex problems, and;
  • “Enhancements to Implicit Modelling and Pit Optimiser modules.”

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

“This new offering will see the introduction of Artificial intelligence to take loading and haulage automation in underground mines to a new level,” MICROMINE said. “Utilising the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time. The video feed is processed on the Pitram vehicle computer edge device, the extracted information is then transferred to Pitram servers for processing and analyses.”

Teck’s Babaei joins GMG Artificial Intelligence Working Group

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The Global Mining Guidelines Group (GMG) has announced that its Artificial Intelligence (AI) Working Group, launched last November, now has two leaders.

Mohammad Babaei (pictured speaking on the left), Digital Mining Innovation Lead in Digital Operations at Teck, has come on board as a co-leader, joining joins Mark O’Brien, Manager, Digital Transformation at CITIC Pacific Mining, who has been leading the group so far.

GMG said: “The AI Working Group has already seen an incredible level of engagement. With this new team, Babaei in Canada and O’Brien in Australia, the group has leadership in both hemispheres.”

Early on, O’Brien said, the group knew it needed good global representation to reflect both how globally relevant AI is and how rapidly the field is changing. “Part of that meant trying to build good coverage with our leadership to make sure we could keep up and share the load,” he said.

Babaei has worked in a variety of contexts including open-pit mines, consultancies and universities. In his current role at Teck, he guides and supports digital innovations that improve safety, sustainability and productivity, according to GMG. “For example, he led projects applying machine learning in mining and maintenance and developing a unique real-time diggability solution.”

Babaei said: “AI can bring revolution in many streams of mining like mineral exploration, haulage, planning and logistics, safety and maintenance.

“I hope we will be able to promote better understanding of AI within the industry and open doors for collaboration between operators, subject matter experts, academia and other innovators.”

O’Brien said this partnership, and the working group’s collaborative approach, reflects the broader importance of collaboration and openness across the industry.

He said: “When you take a close look at the most exciting things happening in the realm of AI over the past few years, one thing that becomes quickly apparent is the most impactful advances are coming out of collaboration.

“Two leaders will, I hope, be far better than one.”

The AI Working Group has recently launched its first project. “This Foundation for AI in Mining project will provide a unified understanding of the basics of AI in mining that cuts through the hype and clarifies what methods are useful, and for what circumstances they can be applied,” GMG said.

IntelliSense.io creates AI algorithm to predict thickener performance

Base metals, Environmental, Mineral processing, Mining equipment, Mining services, Mining software, Precious metals, Water management, , , , , , , ,

A UK-based startup says it has devised a machine learning-algorithm that can help mining companies predict how thickeners will operate an hour into the future.

IntelliSense.io, which has been helped along the way by Digital Catapult (an agency for the early adoption of advanced digital technologies) and the UK’s Department for International Trade, said it wanted to help the mining industry become more efficient and sustainable by harnessing the power of artificial intelligence.

“Traditional operations technology cannot handle dynamic conditions, so IntelliSense.io is focused on using advanced digital technologies to create a platform that can predict varying conditions and is, therefore, far more responsive to change,” it said.

This led the company to develop an application to control thickeners in mining operations, which, IntelliSense.io says, would provide three key benefits:

  • Less water would be needed to complete the thickening process;
  • More water could be recycled, resulting in less wastewater;
  • Reduced power would be consumed as less water would be pumped into the thickener.

To create an algorithm, IntelliSense.io needed to analyse three years’ worth of data from six thickeners, each measuring roughly 800 different metrics collected every minute.

“This represents a volume of data that would only be possible with a significant amount of computer power and specialist expertise,” the company said. This led to IntelliSense.io applying to join Machine Intelligence Garage, Digital Catapult’s AI programme that helps businesses access the computation power and expertise they need to develop and build machine learning and artificial intelligence solutions.

Thanks to this assistance, IntelliSense.io has devised an AI tool that ingests these 800 different metrics every minute and can, according to the company, “predict how thickeners will operate an hour in the future”.

“This invaluable knowledge will make mining more efficient and sustainable, and provides optimum operating condition recommendations to maximise output,” the company said.

The thickener algorithm has since been applied in an optimisation stability project at a gold-copper mine in Chile where the miner in question had seen low underflow percentage solids and water recovery, and high flocculant consumption.

The implementation of the IntelliSense.io Thickener Circuit Optimisation application at the mine, which integrated data from SCADA and other control systems with advanced statistical data modelling and machine learning algorithms and first principle models, came up with a solution.

This has seen, among other benefits, decreased variability in the thickener circuit operation, enhanced water recovery at the thickener circuit and reduced equipment downtime due to stricter torque constraints.

The payback period has been less than 12 months with projected direct savings calculated at $400,000 in the first year alone, according to IntelliSense.io.

The company has also signed a memorandum of cooperation with JSC AK Altynalmas, a gold producer in Kazakhstan. This involves the development of an AI system for predictive analysis and optimisation of the grinding process, according to IntelliSense.io.

This agreement is part of a wider pact around the implementation of industry 4.0, IntelliSense.io says.

Micromine to release AI solution for underground loading and hauling

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New underground mining precision performance software, using machine learning to refine and enhance loading and haulage processes, is set to be launched by global mining software company, Micromine.

The solution is to be released in early 2019 as part of the company’s fleet management and mine control solution, Pitram.

Using the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time. The video feed is processed on the Pitram vehicle computer edge device. The extracted information is then transferred to Pitram servers for processing and analyses.

Micromine Chief Technology Officer, Ivan Zelina, said the solution considered the information gathered to pinpoint areas of potential improvement that could bolster machinery efficiency and safety.

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

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

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

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

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

Micromine trialled the new technology in Australia, Mongolia and Russia as part of a research and development pilot programme.

The initial concept was on the back of a trial project in partnership with the University of Western Australia. One of the master’s students from the university was subsequently employed by Micromine to help drive the company’s development of machine-learning projects across its global business.

“This advance is another demonstration of how Micromine is operating differently to other software providers by extending our products well beyond simple built-in machinery automation to artificial intelligence,” Zelina added.

“The ability for mining companies to increase their knowledge of mining processes through automated data collection and analysis is endless and this is just the start of the work Micromine is doing with our mining software solutions.

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

Vale after more cost savings with development of artificial intelligence centre

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Vale’s is today inaugurating its Artificial Intelligence Center in Vitória (Espírito Santo state, Brazil), a centre aimed at improving maintenance, processes, and environmental, health and safety compliance controls.

The facility, which will develop and monitor AI initiatives from the company’s units across several countries, has already saved the company more than $20 million/y, and another $37 million is expected to come from initiatives already underway.

“The benefits derive from improving maintenance of assets (from off-highway trucks to railroad tracks), improving management of processes in pelletising and ore processing plants, as well as enhancing environmental, health and safety and compliance controls,” Vale said.

Hélio Mosquim, the IT Innovation executive manager, said the new centre will “intensify the integration and collaboration” among those people responsible for different projects.

“Also, this initiative will promote the exchange of experiences and knowledge, increasing synergy among teams and generating results on a global scale. Most features developed for one project can be applied to others,” he said.

The centre is located at the Tubarão unit, in Vitória, which comprises eight pelletising plants, the operational centre of Vitória Minas railroad, and four port terminals distributed across 14 km². Some 50 professionals – including data scientists and engineers as well as business experts – are exclusively dedicated to Vale’s AI projects. Vale has 15 of them working at the new centre to support thousands of assets (such as trucks, excavators, trains, conveyor belts, etc), among other tasks.

“Broadly speaking, AI is the ability of machines to simulate the human decision-making process and perform complex tasks normally requiring human intelligence,” Vale said. “It is part of a variety of systems – from those used for recommendations on shopping sites to stand-alone cars. Vale uses AI systems to collect and analyse millions of data from its projects, generating insights that will help predict problems and influence decision making.”

Vale’s teams are currently working on 13 lines of projects carried out alongside some of the company’s corporate and business areas covering ferrous metals, base metals, and coal.

Vale’s Digital Transformation Director, Afzal Jessa, said: “artificial intelligence has the potential to generate value for all business areas of the company. We’re taking another important step towards digital transformation to increase productivity and operational efficiency, achieve the highest levels of health and safety, improve our financial performance and drive innovation.”

Vale’s digital transformation programme is expected to generate gains in all business areas. In iron ore, in particular, it shall reduce the cost of production by $0.50/t until 2023.

The programme is based on improving asset performance, optimising maintenance, increasing workforce efficiency, and integrating the value chain. Technological innovations developed by the company include the Internet of Things, AI, mobile applications, robotisation and autonomous equipment (such as trucks and drills).

Vale outlined six examples of projects being developed in the new centre:

  1. Rail fracture prevention – One of the high-impact projects being developed at Carajás railroad (Estrada de Ferro de Carajás) is focused on predicting rail fracture, which is one of the most common occurrences and most serious for the operation. Data generated by the railroads uncovered a solution that identifies the occurrence of one or more fractures in a specific rail branch. In addition to increased operating security, the solution decreases railroad shutdowns to repair fractured rails.
  2. Train wheel-set maintenance – A set of sensors installed by the railroad called waysides monitor the wear as well as impact of wheel-sets, temperature and noise of bearings, as well as displacements of the bogie (an important part of the railroad car). By cross-checking the data generated by these sensors with information from other systems, mathematical models were created to allow the maintenance team to predict the behaviour of wheel-sets in the following 30 days. Based on this information, the team can plan the purchase and maintenance of assets, thus extending their useful life. In the first year, the programme generated savings of BRL2.3 million ($624,019) – about 10 times the amount invested in its implementation.
  3. Maintenance of mine assets – Collection of data generated by mine equipment, such as off-highway trucks, excavators and loaders, as well as use of AI techniques. A project implemented at Salobo mine, in Pará, increased the useful life of off-highway truck tires by approximately 30% in one year. These projects have produced $8 million in savings.
    Another project addresses the increase of useful life and prevention of premature failure of powertrains on off-highway trucks and other mobile assets of the mine, such as loaders and excavators. This is one of Vale’s major projects, involving 15 operations in Brazil, Canada and Mozambique. Sixty per cent of the company’s off-highway trucks already use this system. The approved potential of savings amounts to more than BRL2 million.
  4. Reduction of fuel consumption – A partnership between Vale’s operational areas and researchers of the University of Queensland, Australia, helped the company’s data scientists develop a system to reduce the fuel used by off-highway trucks. Tests conducted in the state of Minas Gerais showed the project can potentially reduce the diesel consumption.
  5. Pelletising process optimization – Data generated during production of pellets were analysed using AI techniques and generated several insights as well as recommendations on ideal operational conditions for the pelletising plants. These process improvements generated $3 million in savings per year in one of the plants, in Vitória. The gains came from a 7% reduction in variable costs by improving the balance between the coal and natural gas used in the process and reducing the use of electric power, among other factors.
  6. Data analysis to avoid safety incidents – Started in 2017 in partnership with the health and safety area, the project analyses the demographic profile of employees to evaluate which ones are more exposed to accidents. The data generated is combined with historical accidents, near misses, and unsafe conditions of the localities. The system uses this information to calculate the probability of incidents occurring in each area in a specific period of time – in a week, for example – as well as its current risk, enabling to evaluate whether the employees’ risk exposure in the work environment has increased or decreased. Then, it is possible to prioritise the activities of health and safety professionals.

Goldcorp hopes AI technology can improve gold exploration

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Goldcorp and IBM Canada have co-authored what they term “an innovative first of a kind technology product” to improve predictability for gold mineralisation.

IBM Exploration with Watson applies artificial intelligence to predict the potential for gold mineralisation and uses powerful search and query capabilities across a range of exploration datasets, according to Goldcorp.

“The potential to radically accelerate exploration target identification combined with significantly improved hit rates on economic mineralisation has the potential to drive a step-change in the pace of value growth in the industry,” said Todd White, Executive Vice President and Chief Operating Officer, Goldcorp.

Developed using data from Goldcorp’s Red Lake Gold Mines in northern Ontario, IBM Exploration with Watson leverages spatial analytics, machine learning and predictive models, helping explorers locate key information and develop geological extrapolations in a fraction of the time and cost of traditional methods, Goldcorp says.

Mark Fawcett, Partner with IBM Canada, said: “Applying the power of IBM Watson to these unique challenges differentiates us in the natural resources industry. We are using accelerated computing power for complex geospatial queries that can harmonise geological data from an entire site on a single platform. This is the first time this solution has been ever used, which makes this project all the more significant.”

At Goldcorp’s Red Lake operations, IBM Exploration with Watson provided independent support to drill targets planned by geologists via traditional methods and proposed new targets which were subsequently verified. Drilling of some of these new targets is ongoing, with the first target yielding the predicted mineralisation at the expected depth.

Maura Kolb, Goldcorp’s Exploration Manager at Red Lake Gold Mines, said: “Timelines are short in mining and exploration. I am excited to see the improvements we can make with the data platform and gold mineralisation predictions. These tools can help us view data in totally new ways. We have already begun to test the Watson targets from the predictive model through drilling, and results have been impressive so far.”

The IBM Watson initiative recently earned Goldcorp a prestigious Ingenious Award in the large private sector category from the Information Technology Association of Canada (ITAC). The ITAC award for Goldcorp’s Cognitive Journey recognises excellence in the use of information and communications technology by organisations to solve problems, improve performance, introduce new services, and grow business.

Goldcorp says it will put the new technology to work on additional targets in 2019.

Back in September, fellow Canada-based gold company Falco Resources said Albert Mining, a leader in the use of AI, had used its pattern recognition algorithms, CARDS, to locate some 50 gold anomalies in addition to a number of other copper, zinc and silver signatures.