Tag Archives: machine learning

MICROMINE’s Whitehouse to explore machine learning in exploration at APCOM 2019

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

MICROMINE says machine learning has the potential to transform mineral exploration, and the company’s Ian Whitehouse intends to discuss just how at the upcoming APCOM 2019 conference, in Poland.

More than 500 delegates from across the globe are expected to travel to Wroclaw, in June, to discover the latest developments in the application of technology in the mineral industry at the 39th Application of Computers and Operations Research in the Mineral Industry (APCOM) conference.

Whitehouse, MICROMINE’s Geobank Product Strategy Manager, will be a keynote speaker at the symposium, which has the theme “Mining Goes Digital”.

Whitehouse said the application of machine learning to the process of collecting and analysing geological data in mineral exploration has the “potential to transform the way explorers operate”. He will delve into just how during his “Transforming Exploration Data Through Machine Learning” presentation on June 6.

“By adding machine learning to the process of collecting and analysing geological data, we vastly reduce the time a geologist spends doing administration work, enabling more time to concentrate on the quality and analysis of the data collected,” he said.

“This type of offering creates opportunity to lower exploration costs and increase the amount of data that can be collected, which are key drivers of the mining industry and will contribute to more exploration projects being approved.”

The traditional process of plan – drill – observe – measure – analyse, can be inefficient, and the application of technology and machine learning can address common issues such as inconsistent data collection and categorisation, Whitehouse said.

“In the exploration industry it is very common to find that one geologist has classified a rock and the next has classified it as something different. This has huge complications when trying to model the data. However, machine-learning algorithms can be used to fix these inconsistencies and errors in the databases prior to the resource geologist working with the data.”

Machine learning can be tapped by the resources industry to streamline geological processes, such as cleansing and validating data prior to starting the modelling process, according to MICROMINE.

Whitehouse said high quality DSLR cameras can provide a tool for exploration companies to collect high-quality imagery of core and chip trays, with machine-learning algorithms able to recognise features in the images.

“It is feasible for this data to be automatically collected and stored in a database,” he said.

To illustrate the power of machine learning, MICROMINE has built an algorithm to determine and map the spatial extents of core imagery in a core tray photo. The application of this technology will result in the reduction of man-hours required to manually review and analyse core tray photography, the company said.

MICROMINE is incorporating machine learning into its solutions, with the results of the research project leading to the functionality being incorporated into the Geobank data management solution, enabling core tray images to be transferred into the database and displayed in Geobank drill-hole trace along with other downhole data, the company said.

MICROMINE’s presentation is part of APCOM’s technical program, which is presented within six streams: Geostatistics and Resource Estimation; Mine Planning; Scheduling and Dispatch; Mine Operation in Digital Transformation; Emerging Technologies and Robotics in Mining; and Synergies from Other Industries.

Whitehouse will be joined by around 100 international presenters from science and industry at the three-day APCOM conference (June 4-6).

You can read more about the event here.

International Mining is a media partner for APCOM 2019.

Dingo improves Trakka predictive maintenance capabilities with AI

Communications in mining, Equipment maintenance, IoT, Mining equipment, Mining maintenance, Mining services, Mining software, , , , , , , , ,

Dingo says its new Trakka Predictive Analytics solution uses artificial intelligence and machine learning to predict impending equipment failures with confidence, allowing customers to proactively perform corrective maintenance actions to minimise downtime and optimise asset life.

The release comes around five months since the company laid the groundwork for the new solution with an announcement that it would introduce practical machine learning models built using real customer data and targeted at specific industry problems from January.

The new Trakka solution includes a series of sophisticated predictive analytics models to provide anomaly detection and failure prediction for asset intensive industries, the company said. These models are built by uniting failure data from actual equipment, “Dingo’s industry expertise and data science to address common component-specific failure modes, such as final drive gear teeth wear”.

Powered by a proprietary machine-learning library, the Trakka Predictive Analytics solution can, Dingo says, predict the time until asset/component failure with a high degree of accuracy. The company said its customers will reap the benefits of these remaining useful life (RUL) models (pictured) as they:

  • Reduce unexpected failures and downtime;
  • Reduce repair cost as scheduling is optimised;
  • Reduce loss of wasted potential in capital;
  • Reduce unnecessary maintenance activities;
  • Reduce personnel and process risk by creating a safer and more controlled environment;
  • Improve component life by acting earlier;
  • Improve confidence in planning component replacements;
  • Improve equipment availability and reliability;
  • Improve budgeting and the bottom line, and;
  • Improve business related processes such as procurement, logistics and management.

Dingo said: “Before any predictions can be made, Dingo’s domain experts and data science team work with a customer’s historical failure and condition monitoring data to deploy or adapt existing models or create new machine learning models to correctly identify failures within the customer’s fleet.

“This process typically involves data collecting, cleansing and validation to ensure model outputs are as accurate as possible. The transition to online predictive analytics is complete once the data ingestion pipeline is ready and the models are fully trained and tested.”

The predictive models are designed with scalability in mind, Dingo said, meaning they can be easily re-trained to work with a broad range of asset and failure mode problems experienced by real mining operations, making them highly reusable.

“The models are continuously optimised through ongoing validation and the input of new data and equipment performance information,” Dingo said.

And, the platform connects a broad range of systems and software to provide data surrounding asset health, including enterprise resource planning & enterprise asset management systems, computerised maintenance management systems, fleet management systems and all forms of condition monitoring data, including oil analysis, visual inspections, sensor data, vibration and thermography.

Newtrax AI helps out Agnico Eagles’s Goldex mine maintenance team

Communications in mining, Equipment maintenance, IoT, Mine operation news, Mining equipment, Mining maintenance, Mining safety, Mining services, Mining software, Precious metals, , , , , , ,

Newtrax Technologies says it has applied machine-learning algorithms to help Agnico Eagle Mines’ Goldex mine predict mobile equipment maintenance issues up to two weeks in advance.

With the two companies already having an existing relationship at the mine, in Quebec, Canada, Newtrax was approached in the fall to discuss the data Agnico had collected from sensors over the past six years. This amounted to 10 billion data points, according to Newtrax.

“This data was exactly what was needed to apply machine-learning algorithms in order to predict mobile equipment maintenance issues at least two weeks before they were supposed to happen,” Newtrax said.

Daniel Pinard, Team Lead, Special Projects with Agnico Eagle, said this predictive Newtrax AI solution allowed the company to intervene before incurring serious problems that could potentially break vehicle engines.

“Through the use of machine-learning algorithms with Newtrax, we were recently able to analyse an engine that had a potential problem and we saved it from failing. This helped Goldex mine avoid serious damage on that engine which saved them C$85,000 ($63,610).”

The Newtrax AI solution is unique in three ways, according to Michel Dubois, VP QA & Artificial Intelligence at Newtrax, “first, Newtrax has years of unique data that is extremely well suited for machine learning (ML)”.

This creates a source of training data for ML that is unique in the world, with the data growing every time a mining company decides to join in, he said.

“Second, we have a unique AI team who knows how to generate actionable results using existing AI algorithms. And, third, we have a unique approach where our AI specialists go underground and focus on quick wins, and they leverage those existing algorithms to solve high-value problems.”

This is the first ever applied case study for ML in the underground hard-rock mining industry with a defined return on investment, according to Newtrax.

Newtrax said it worked with artificial intelligence and ML researchers such as IVADO to apply existing algorithms to the data collected in mine sites.

SGS to demo real-time field data acquisition solution at PDAC convention

IoT, Mineral exploration, Mining equipment, Mining events, Mining services, Mining software, , , , , ,

SGS, a leading certification, inspection and testing service provider for the global metals and mining industry, says it will announce the launch of a new real-time field data acquisition solution at the upcoming PDAC 2019 convention in Toronto, Canada.

The solution pairs portable instrumentation and machine learning to significantly speed up turnaround time for field data, enabling clients with enhanced decision-making capabilities and quicker speed-to-market, SGS said.

The solution, being launched in North America and Australia, will be on show at a demonstration at the Metro Toronto Convention Centre on March 5 in a demonstration involving Peta Hughes, Project Manager, Global Geochemistry, SGS Canada, and Matthew Rees, Chief Geologist, IAMGold.

Micromine, Geobank and Pitram to come under PDAC 2019 spotlight

Automation, Communications in mining, Equipment maintenance, Mineral exploration, Mining equipment, Mining events, Mining maintenance, Mining services, Mining software, , , , , , ,

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.”

Miners need to spell out future value drivers to survive, Deloitte says

Automation, Environmental, High tech minerals, Mineral commodities, Mineral project development, Mining services, Mining software, Mining techniques, Power supply for mines, Sustainable mining, Water management, , , , , , , , , , , ,

In Deloitte’s Tracking the Trends 2019 report, the company has urged mining companies to clarify how they plan to drive value into the future how they intend to respond when prices inevitably drop again.

The report highlighted disruption and volatility as two key issues the mining sector is facing that made long-term planning and decision making more important.

“In this new world order, miners must go beyond communicating the value that they currently bring to communities and will need to articulate what they stand for by developing differentiated business models designed to drive long-term value,” Deloitte said in the report.

Deloitte’s ten trends to watch for 2019 included:

  • Rethinking mining strategy;
  • The frontier of analytics and artificial intelligence;
  • Managing risk in the digital era;
  • Digitising the supply chain;
  • Driving sustainable shared social outcomes;
  • Exploring the water-energy nexus;
  • Decoding capital projects;
  • Reimagining work, workers, and the workplace;
  • Operationalising diversity and inclusion programmes, and;
  • Demanding provenance.

On rethinking mining strategy, Deloitte said: “Mining companies have typically anchored their strategic planning on producing the highest volumes of ore at the lowest possible cost. However, in today’s environment, companies must take an ever-expanding range of issues into account when setting corporate strategy.

“Consumers, governments, and communities are becoming more vocal and irrevocably altering industry dynamics. As a result, corporate social responsibility initiatives are now morphing into stakeholder engagement programmes, and social license to operate is becoming a pivotal strategic issue that will either differentiate mining companies or derail them.

“Looking at these factors alone – consumer awareness, social license to operate, geographic risk, and access to input commodities – it becomes clear that mining companies must take an ever-expanding range of issues into account when setting corporate strategy if they hope to create competitive portfolios robust enough to generate value across multiple scenarios. This is especially critical as the industry shifts into a new stage of growth.”

On the frontier of analytics and artificial intelligence, Deloitte said: “Although mining companies are exploring and investing in analytics and AI, there is still a long way to go. Three horizons in AI are emerging and, to date, most organisations are working in Horizon One, where machine intelligence requires human assistance and interpretation.

“To move up the analytics maturity curve into Horizons Two and Three, organisations must answer progressively complex questions. The first is ‘what happened?’ The second is ‘why did those things happen?’, this allows organisations to identify the root causes.

“Only with this foundation in place can organisations answer the third question: “what will happen?” This is the key that empowers organisations to predict variability, mitigate emerging risks, and manage stakeholder expectations.”

On managing risk in the digital era, Deloitte said: “The current risk landscape is characterised by a host of issues such as mounting tariffs and sanctions, potential trade wars, cyber threats, uncertain tax and royalty regimes, rising input costs, heightened scrutiny from the investment community, environmental disasters, and infrastructure breakdowns.

“To stem this tide, mining companies must take their cue from organisations that take a more holistic view of risk. Increasingly, these leaders are moving towards the next generation of internal audit, Internal Audit 3.0.

“This approach should help mining companies address risk at an enterprise-wide level, rather than assessing isolated risks at the functional or mine site level and develop appropriate controls to both mitigate and manage the expanding array of risks they face.”

On digitising the supply chain, Deloitte said: “The mining supply chain is ripe for transformation, as supply chain improvements remain incremental instead of delivering innovations designed to optimise mining operations.

“To create a more interconnected and responsive supply chain, mining companies need to stop thinking in linear terms and imagine instead a circular system that we call the digital supply network.

“The ultimate goal is to leverage advanced algorithms, AI, and machine learning to turn data into insights that allow companies to reduce their capital expenditures, respond to changing project requirements quickly, and optimise mine planning to integrate real-time changes.”

On driving sustainable shared social outcomes, Deloitte said: “Until recently, mining companies’ social spend has been seen as a cost of compliance, rather than a way to deliver measurable and sustainable benefits to host countries and communities. If mining companies hope to drive different social outcomes, that dynamic has to change. A social enterprise is an organisation whose mission combines revenue growth and profit making with the need to respect and support its environment and stakeholder network.

“Finding value beyond compliance is no easy task. It requires miners to listen more closely to their constituents to determine what stakeholders truly want, and then to shift their operational processes in response.

“To deliver on the social breadth of these programmes, mining companies cannot work in isolation. Instead, they should look for opportunities to collaborate with other companies working in the region.”

On exploring the water-energy nexus, Deloitte said: “True value from energy management can only be derived by addressing the triple bottom line of social, environmental, and financial performance. This requires companies to approach energy management as an integrated corporate initiative.

“Yet energy isn’t the only input at risk. Mining companies must now contend with water scarcity as well as risks associated with excess rainfall, which can result in flooding.

“With a constant knowledge of how every drop of water is being used, and an understanding of all the parameters associated with its use, mining companies can manage water in the way they have begun to manage electricity, as a valuable resource.”

On decoding capital projects, Deloitte said: “Burdened by years of sub-par returns, cost overruns, and impairment charges, many mining companies opted to concentrate on maximising output from their existing operations rather than investing in new mine supply and exploration.

“This resulted in supply shortages for commodities such as copper, zinc, cobalt, lithium, and gold. But with the cycle turning, mining companies will need to engage in a wave of new capital projects to offset production declines and meet demand.

“To overcome these challenges, mining companies must build their maturity in five key areas: delivery models, data and technology, project controls, license to operate and collaboration.”

On reimagining work, workers, and the workplace, Deloitte said: “The mining industry is facing a changing talent landscape, with digitisation necessitating new skillsets, a massive generational shift when considering C-suite succession planning and a younger generation of workers who measure loyalty to an employer in months instead of years.

“To prepare for this imminent future, organisations need to clarify not only their business goals and aspirations, but also the role that their talent strategy should play to deliver on them.

“They will also need to identify the workers of the future by considering what the employee experience will look like, and the role that innovation will play in that experience. Finally, they must reconceive how employees will interact with each other and conduct their work, be it in a physical location or remotely.”

On operationalising diversity and inclusion programmes, Deloitte said: “The mining industry is not attracting sufficient numbers of diverse candidates and, to shift this balance, companies will not only need to change their talent attraction and retention policies, they will also need to change historical perceptions about the mining industry.

“Instead of approaching the issue by adopting point initiatives, they must design integrated programmes to tackle the challenge holistically. This extends into the area of talent retention, because when companies do attract women, they often struggle to retain them.

“In tandem with shifting the way they operate, mining companies must take steps to amend their public image. This starts with the image they portray on their reports and in their advertisements.”

And, finally, on demanding provenance, Deloitte said: “Rising demand for electric vehicles (EVs) is increasing demand for EV battery materials such as cobalt, lithium, graphite, and copper.

“However, socially-conscious consumers are now questioning the provenance of raw materials. As a result, downstream customers, such as automotive manufacturers and technology giants, are demanding ethically-sourced minerals.

“This is putting unprecedented pressure on mining companies to create a more transparent interface with their customers and driving the adoption of technologies such as blockchain to enhance the traceability of commodities.”

To download the full report, go to deloitte.com/trackingthetrends

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

Automation, Communications in mining, IoT, Mining equipment, Mining services, Mining software, , , , , , ,

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.”