Tag Archives: Artificial Intelligence

Freeport senses PNG exploration opportunity with Minerva’s DRIVER AI solution

Base metals, IoT, Mineral exploration, Mining services, Precious metals, , , , , , , , , , , , , ,

Freeport Resources has signed a contract with Minerva Intelligence Inc that will see the artificial intelligence focused company deploy its DRIVER AI solution at the Star Mountains project in Papua New Guinea.

Freeport recently acquired Quidum Resources, which, through its wholly owned subsidiary, Highlands Pacific Resources Ltd, controls the Star Mountains project. The company thinks applying Minerva’s technology to the project will enhance its next phases of surface and sub-surface exploration of the extensive property, which is located close to the Ok Tedi mine.

“Freeport is committed to embracing new technologies to enhance the understanding of our portfolio of projects beginning with Star Mountains,” Nate Chutas, Senior VP of Operations at Freeport, said. “We believe that the advances in technology that DRIVER brings will provide deep insights into our project data and provide a better understanding for the development and prioritisation of high-quality exploration targets.”

DRIVER delivers these insights by evaluating all elements typically returned by modern laboratories, not simply the elements of direct economic interest, Freeport says. The work this requires is too time-consuming and complicated to be carried out by project geologists, according to the company.

Minerva’s cognitive reasoning platform is able to compare identified geochemical exploration vectors with its database of hundreds of past and present mines around the world, identifying those most similar to the explored target using the company’s proprietary AI technology.

The resulting similarity rankings can then provide reliable, explainable models upon which geologists can build their exploration strategies, Freeport says.

Gord Friesen, President and CEO of Freeport, said: “Despite having identified a very significant resource already, it is our assertion that Star Mountains is still vastly under-explored. We believe that utilising AI-based, deep-thinking tools such as DRIVER will exponentially hasten our understanding of Star Mountains’ true potential.”

The data analysis will involve three studies, the first two being 3D studies focused on the drilling results from the Olgal deposit where there is a current inferred resource, using a 0.3% copper cutoff grade, of 210 Mt grading 0.4% Cu and 0.4 g/t Au, for 2.9 Moz of contained gold and 840,000 t of contained copper.

The third study will be a combined 2D and 3D analysis of data collected from the remainder of the Star Mountains claims. All these studies will be integrated with interpretation of available airborne geophysics data, according to Freeport.

The first 3D study will be a geochemical cluster analysis to identify the lithogeochemical characteristics of the logged drill holes to use as a comparison against the interpreted logging, and for comparison with the lithogeochemistry of drilling results for other Star targets.

This will be followed by a second 3D study involving the use of Minerva’s DRIVER software to examine multi-element zonation patterns throughout the Olgal dataset.

The third study will apply Minerva’s SOLACE workflow to a combination of the surface and drilling data available for the rest of the Star Mountains claims for incorporation into Minerva’s Target target generation system.

Aspen Tech and Wood to offer clients predictive, prescriptive maintenance solutions

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

Aspen Technology and Wood have announced a new partnership that will offer Wood’s clients Aspen Mtell® asset performance management (APM) technology for predictive and prescriptive maintenance.

The partnership will enable global enterprises to improve the performance of their manufacturing and facility assets through a maintenance solution built upon industrial artificial intelligence (AI) and machine learning, the companies said.

Aspen Mtell analyses historical and real-time operational and maintenance data to discover the precise failure signatures that precede asset degradation and breakdowns, predict future failures, and prescribe detailed actions to mitigate problems, they explained.

Wood has decades of experience providing solution-independent asset performance consulting, as well as integrating and deploying specialty engineering services and real-time performance monitoring systems, some of which has been mining-related.

“The combination of this deep domain expertise of asset and operator challenges, with AspenTech’s extensive knowledge of the process manufacturing industry and proven AI-driven predictive and prescriptive maintenance solutions, provides a unique customised asset performance management solution for operators’ needs,” the two companies said.

Prabu Parthasarathy, Vice President of Intelligent Operations at Wood, said: “Wood has an extensive understanding of the performance optimisation needs of our clients and realised a unique opportunity to provide a solution to help enhance asset productivity and identify potential issues well ahead of time.”

Darren Martin, CTO at Wood, added: “We are excited to bring AspenTech into our strategic partnership ecosystem to unlock innovative technology solutions to solve our clients’ challenges. Aspen Mtell is part of our connected operations and maintenance programs that will allow our clients to detect patterns in operating data, allowing them to take prescriptive action and avoid unplanned downtime. Together, our vision is to drive value through digital twins across the full asset lifecycle, working to optimise asset performance, monitoring, and control across any environment.”

Greg Mason, Senior Vice President and General Manager of APM, Aspen Technology, said the value of predictive and prescriptive maintenance represents much more than simply predicting failures on large rotating assets.

“Companies that are truly focused on eliminating safety and environmental incidents tied to machine failure, in addition to avoiding production losses, understand the need to have a comprehensive predictive maintenance culture throughout the entire plant,” he said. “This requires an analytics technology that is scalable, resources needed to deploy to scale, and the expertise to lead change management. I’m pleased to say that the partnership of AspenTech and Wood around the Aspen Mtell solution provide these three unique capabilities needed to bring contextualised AI for the process industries to scale.”

Retenua’s RefleX machine vision tech set to go underground in EU-backed project

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

An EU-backed project looking to tap into the full potential of the ‘digital mine’ goes live this month, with Retenua’s AI-driven RefleX™ machine vision technology set to be further optimised, adapted and tested as part of the scope.

The illuMINEation project under the European Union-backed Horizon 2020 has a budget of €8.9 million ($10.5 million) and is looking to embed digital thinking into the heart of the mining sector by improving digital skills of mining personnel and enhancing the cooperation along the entire digital mining value chain, according to Retenua.

“Europe urgently needs to reduce its import dependency in respect to a multitude of raw materials,” it said. “In order to do so, Europe’s mining industry must completely redesign the process of traditional mining via the adoption of pioneering innovations and extensive use of data analytics.”

The illuMINEation project will highlight significant aspects of digitalisation in underground mining activities with the core objective of improving the efficiency as well as health and safety of European mining operations and its personnel, Retenua said, with RefleX set to be one technology to undergo testing.

In the scope of IlluMINEation research project, RefleX will be employed in demanding underground mining environments. The core technology of Retenua’s product line emitrace®, RefleX includes both embedded infrared stereo vision hardware and smart algorithms for detecting and tracking workers and equipment from mobile heavy machinery.

The ability to reliably detect worksite personnel and selected infrastructure in the vicinity of vehicles not only in good daylight conditions but also in poorly illuminated environments makes Retenua’s solution highly suitable for use both above and below ground, the company says.

The technology evaluation and customisation will be primarily carried out in collaboration with project partner Epiroc Rock Drills AB and represent an important step towards improved safety standards in mining operations, Retenua said.

The multidisciplinary project consortium within illuMINEation consists of 19 partners from six European countries, constituting a well-balanced assembly of world leading industrial and academic players from a multitude of technical fields and applications, it added.

This includes Montanuniversitaet Leoben, Joanneum Research Forschungsgesellschaft MBH, Epiroc Rock Drills AB, ams AG, KGHM Cuprum sp zoo, DMT GmbH & CO KG, GEOTEKO Serwis Sp zoo, Lulea Tekniska University, Universidad Politécnica de Madrid, KGHM Polska Miedz SA, Minera de Orgiva SL, RHI Magnesita GmbH, DSI Underground Austria GmbH, Retenua AB, IMA Engineering Ltd Oy, Fundacion Tecnalia Research & Innovation, Worldsensing SL, Instytut Chemii Bioorganiczney Polskiej Akademii Nauk and Boliden Mineral AB.

DataCloud bridging the mining industry’s data divide

Automation, Base metals, Bulk handling, Comminution of minerals, IoT, Mineral processing, Mining services, Mining software, , , , , , , , , , , ,

DataCloud is looking to collect and merge the mining industry’s datasets through a cleaning, processing, integration, and predictive analytics platform that can help different stages of an operation prepare and plan for the ore and waste heading their way.

While the coarse ore stockpile may be the section of the flowsheet currently in DataCloud’s crosshairs – thanks to a well-attended webinar a few months back – any part of the mining process that is “between departments” could benefit from the MinePortal solution, according to Steven Putt, Director of Software Solutions for the company.

“The value case is inherent anywhere between departments – ie the stockpile is after crushing, but before the mill,” he told IM.

“The reason that stockpile is there – it tends to only be half a day or a day’s material – is it is a buffer for the mill,” Putt said. “Within this pile, one truck might have been hauling very hard material that the mill is exclusively treating for a week or so. Then, in accordance with the mine plan, this can switch to another truck and a new area of the mine, meaning the mill is going to have to adapt to a completely different material.”

The distinction between material in the coarse ore stockpile is often not this apparent; it tends to represent the mine site’s ‘melting pot’, taking in material from all over the operation.

Yet, to operate effectively, the mill needs to know the origins of the material coming its way ahead of time. The mill would then, ideally, be re-configured to treat the material.

“The mill operator would need to change the speeds of operation, the water balance, potentially the grinding media, etc,” Putt said. “Operators would typically prefer not to make those changes though, having the mill running at some ‘optimal’ speed based on the idea that the material is relatively consistent.”

The reality of the situation is different, as DataCloud and its MinePortal platform have been proving.

“The last client we worked with could end up saving around $20 million a year by carrying out our recommended processes as part of a wider mine to mill tracking solution,” Putt said of a copper-gold operation the company worked at. “Basically a specific rock type (skarn) was being fed into the mix too often and the mill was not prepared to handle this in the blend.”

This client turned out to be spending more money than necessary on its blasting process – using too much energy blasting the material to create a ‘uniform’ blend. But, in upping the amount of explosive used, it created sub-optimal crusher feed.

This saw the primary crusher assigned to treat material around 5 in (127 mm) in size attempting to ‘crush’ material that was averaging around 1 in in size, according to Putt.

The primary crushing process was ineffective to say the least.

By adapting the blasting process to target the designed-for primary crush size, reorienting the mine plan so not as much skarn material was being fed into the coarse ore stockpile at once, and adding steel ball media to the mill to deal with skarn that was fed into it, the headline savings were made, according to Putt.

Such savings come with quite a bit of due diligence work, he explains.

“It is not just about connecting disparate datasets; a tremendous amount of work goes into cleaning and contextualising the data – knowing which information is right for the project at hand and which data is not applicable,” Putt said of the MinePortal data gathering and analysis procedure.

Where other data-focused companies can clean datasets and put them into algorithms to form various predictions, DataCloud’s mining knowledge and deep collaboration with customers enables the company to create fit-for-purpose solutions that work in a practical sense on the mine site.

This process sees at least six months of relevant data required up front. Then, a four-week deep dive of this data is needed to find out if the existing dataset can solve production bottleneck issues. The US-based company normally then allocates another three months to kick off the solution, on-board all teams and see improvements come through, according to Putt.

“I wouldn’t say it is a complete customisation, but there does tend to be differences in place at every mine site we visit that means the MinePortal solutions are somewhat unique,” Putt said.

Coming back to the coarse ore stockpile example, Putt recommends hard-rock miners add another filter to their existing blending process to help improve results.

“It is about adding a mill risk factor to an existing grade control program; getting the engineers to plan the mining regime in a certain way to effectively prepare the mill for the material being fed into the coarse ore stockpile,” he said.

Miners can do this by obtaining a good idea of the time window in which the material delivered to the stockpile is entering the mill, enabling engineers to trace it back into the pit and analyse the properties that were observed – and captured – during the drill and blast process.

“This can be a tricky thing to do as the size of the stockpile is changing so often,” Putt says.

Some miners use RFID tags embedded in truck loads to get a rough idea on a weekly or monthly basis when the delivered material is finding its way into the mill, but few do this on a consistent basis.

MinePortal uses machine-learning algorithms the company has augmented for geology and mining needs to automate the process.

Using features such as dynamic time warping – which measures the similarity between two temporal sequences that may vary in speed timing differences – the platform is able to reconcile timing differences from dumping ore into a primary crusher, to sitting in a stockpile, and to when the ore goes through the rest of the mill.

Putt expands on this: “There is enough robust data within a mill’s database to run dynamic time warping, a machine-learning method, to compute the delays (of the material coming into the mill) as they change.

“We don’t need the timing of the delay to be consistent; we need the data to be recorded consistently so we can find the patterns of the delays from stage to stage. Running the data through machine learning will learn the rhythms of the stockpile and filter out inconsistencies.”

At the reconciliation stage, mining companies can pair the material signatures (rock hardness, for instance) with the results from the mill (energy draw, grind size, etc).

“Typically, we find there might be one or two specific blend types that are causing the issues,” Putt said. “From there, we can carry out real-time planning to improve the operation. We then have a feedback loop where you identify the problem feeds, change the blending over the next three months and then keep running through the process for continued improvements.”

But it all comes back to ore blending.

“The best way to handle the problem is from the ore blending point of view,” Putt said. “If you can get your ore blending to be spot on where it comes with the lowest risk of impacting the mill’s performance or availability, then the mill won’t have to do anything different (change speeds, adopt new grinding media, etc).

“You still have to dig, haul and send the material to the mill, but you are sending this material to the mill in different proportions.

“It comes with the same input costs; it just requires a bit of extra planning ahead of time to save a tonne of money in the mill.”

Eclipse Mining’s SourceOne can help miners prepare for the unexpected

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

While all businesses should have a risk plan, the very nature of mining presents a unique set of problems and opportunities for consideration, according to Abinash Moharana of Eclipse Mining Technologies.

Whether an operation is contained in one location or spread throughout the world, strategic plans impact physical activity, which, again, impacts the strategic plan. The optimisation of this continuing cycle is imperative to success, Moharana says.

Even when physical operations are forced to shut down, data analysis and other planning must continue to meet revenue projections, and to prepare for physical operations to resume. This is increasingly relevant during today’s COVID-19-related lockdowns.

“In the event of a physical shut down, all existing plans become irrelevant,” Moharana, Technical Product Manager at Eclipse, says. “New plans are needed quickly. And the most significant risk (or opportunity) at this point, is the quality and timeliness of your data, and access to that data for employees working in remote locations.”

Eclipse’s SourceOne solution, which features a collaborative platform to connect data from different sources, and a datahub to store historical and contextual data, can help here. SourceOne renders this data serviceable for analytics and for adoption of tools, such as artificial intelligence and machine learning.

The ability to create new reactive plans and multiple scenarios is imperative to surviving shutdown events, Moharana says. “And, doing so accurately may provide an opportunity to emerge on the other side even stronger than before so that when physical operations resume, you can jump right into it without wasting time or resources.”

Moharana added: “To achieve this, you must turn your biggest risk into opportunity using high-quality data that is easily accessible with real-time updates. And SourceOne can make this happen for you.”

As a mine platform designed with multi-users in mind, SourceOne is made to host multiple remote users.

It accommodates concurrent on and off-site users, while also handling off-line users with automated merges to a clean state, according to Moharana. “A geographically disparate team can work seamlessly together, transfer data and messages as part of a workflow, always be able to work with the latest data, and know the genesis of each data.”

Moharana explained: “With all the existing mine plans rendered redundant, the mine planning engineers can start working on new mine plans with updated assumptions and requirements. The requirements may have changed, such as volume becoming an immediate priority, rather than profitability, to be able to supply the end user. While doing this, the goal is also to not deviate too far from the strategic plan.

“SourceOne maintains the complete history of each project and data. Historical plans can be used to generate the differentials between the existing plans and the strategic plans. These can be weighed against the new goals, and a sub-optimal plan with smaller recovered metal may be considered as it does not stray too far from the strategic plan.”

With so many plans being made, the chances of errors magnify, as does the need to be able to audit the results with an internal or external auditor.

SourceOne maintains a complete audit trail of the entire project, so every plan can be traced back to the assumptions that were used at each step, according to Moharana. This speeds up the process to validate the suitability of the plan to be implemented in the field.

Mine planners must realise that for their corporate management, there is a big difference between risk and uncertainty, Moharana says. Risk is something that can be measured, while uncertainty cannot. The difference lies in measurable information.

“A good Business Continuity Plan (BCP) for mine plans allows the enterprise to convert some of these inherent uncertainties into calculated risks, which then can be properly weighted by the management,” Moharana said.

Mining personnel may think that the COVID-19 pandemic is an unprecedented event, but they need to be prepared for such a unique occurrence, according to Moharana.

“This preparedness allows mines to better manage the risk by being able to make mine planning a part of your BCP and ensuring that the mine is well prepared for any disruption, rare or not.

“De-risking the mine planning process is one of the many ways SourceOne can help your organisation become more resilient and be prepared for ordinary (and extraordinary) events.”

Augmentir AI solution helps HOLT CAT optimise maintenance, repair and service ops

IoT, Mining equipment, Mining services, Mining software, , , , , , ,

Augmentir Inc is to work with HOLT CAT, the largest Caterpillar machine and engine dealer in the US, to create, it says, an artificial intelligence-led platform for its maintenance, repair and service operations.

Augmentir calls itself a leading provider of AI-based connected worker software for industrial companies, while HOLT CAT sells, services and rents Cat equipment, engines and generators for construction, mining, industrial, petroleum and agricultural applications.

“With the selection and rollout of Augmentir’s connected worker software platform, HOLT CAT continues its commitment to delivering innovation in heavy equipment and engine service and repair,” Augmentir said.

Augmentir’s software platform will allow HOLT CAT to move from paper-based to digital, augmented work instructions for service, maintenance, and repair procedures; accelerate onboarding and training times for new technicians; provide instant training for novice technicians; and improve overall efficiency and tracking using Augmentir’s AI-based operational insights, it said.

Brandon Acosta, Vice President of Enterprise Operations for HOLT CAT, said the company needed a software platform that could help it reduce on-boarding time for new technicians and help to reduce the variability in its standard job times.

“The Augmentir platform provides us with an easy-to-use set of tools to deliver rich guided procedures to our technicians helping them perform at their peak,” he said.

“Furthermore, as we continue along our journey with Salesforce Field Service Lightning, we truly believe that the seamless connectivity of Augmentir with that platform will empower our technical staff within one end-to-end digital environment; not just what to do, but how to do it.”

Augmentir’s Connected Worker Platform is a suite of AI-powered tools designed to help manufacturing and service teams improve operations, close skills gaps, capture “tribal knowledge”, and drive continuous improvement efforts, according to the company.

“The platform provides tools to help teams author and publish digital work instructions and workflows and also provides an industrial collaboration solution to support remote work scenarios,” Augmentir says. “In addition, the platform delivers AI-based organisation-wide insights and recommendations that focus on improving the quality and productivity of frontline workers.”

Russ Fadel, CEO and Co-Founder of Augmentir, said: “Our AI-based Connected Worker platform helps industrial companies to intelligently close skills gaps so that the entire workforce can perform at its peak. Additionally, our AI-based True Opportunity™ system helps companies identify the areas of largest capturable opportunity and make recommendations on how to capture them.”

With this selection, HOLT CAT believes it will be able to utilise the Augmentir platform in other areas of its remanufacturing and rebuild operations, and also implement a more seamless integration across its business systems and workflows, according to Augmentir.

Windfall Geotek adds drones to AI-driven exploration tech offering

IoT, Mine surveying, Mineral exploration, Mining equipment, Mining services, Mining software, , , , , , ,

Mining technology services company, Windfall Geotek, says it has launched a new drone-based solution for artificial intelligence (AI) driven digital exploration in mining.

A services company using AI with a portfolio of gold, copper and zinc properties in Quebec, Canada, Windfall Geotek has been using AI and advanced knowledge-extraction techniques since 2005 in the mining sector. EagleEyeTM leverages this experience, it said.

Michel Fontaine, President and CEO of Windfall Geotek, said: “Our new services have allowed us to bring to market the survey, sensor, and AI-driven software for digital exploration. Our ability, in the mining sector, to find targets is directly tied to the quality of the source data we receive from our customers.

“EagleEye will allow us to work more closely with our customers, generating a better return for their investors with our CARDSTM AI-generated targets.”

Windfall’s CARDS (Computer Aided Resources Detection System) solution consumes open data from around the world to identify a high statistical probability of target identification within known areas of interest, the company said.

Don Moore, CEO of Playfair Exploration, a previous user of Windfall Geotek’s technology, said: “Windfall Geotek’s experience in collecting and analysing data has been proven over the past 15 years. We recently worked closely with Michel and his team on a great project in Finland.”

EagleEye will begin tests in mining sector with the acquisition and analysis of survey data. The company plans to partner with operators of leading surveying companies to obtain geophysical data and generate potential drill targets using drones, modified sensors, and the CARDS AI software system, it said.

Exyn drones help Rupert Resources map Pahtavaara gold mine

IoT, Mine surveying, Mining equipment, Mining services, Mining software, Precious metals, , , , , , , , , ,

Exyn Technologies says it has completed a successful mission for Rupert Resources at its historic Pahtavaara gold mine in northern Finland.

By harnessing Exyn’s autonomous drones, Rupert Resources was able to produce highly detailed 3D models of the mine, which is otherwise completely inaccessible to traditional CMS tools or even manually piloted drones, Exyn said.

“Rupert Resources needed to plan for a potential restart of operations by estimating tonnage previously removed from the mine, as well as calculating the remaining ore in heavily restricted areas,” the company said.

Exyn’s fully autonomous aerial robots mapped 30 stopes in three days with a single drone. In addition, Exyn mounted a version of its robot to a car to scan all access drifts which, together with the stope maps, provided a complete mine map in under four days.

Jukka Nieminen, Managing Director of Rupert Finland, said: “Rupert is actively seeking new technologies where we think big gains can be made in terms of safety, productivity and accuracy.

“Exyn achieved accurate assessment of the volume of remaining stopes at Pahtavaara with an unprecedented level of detail, and obviously the use of remote technologies means that this was achieved with a greatly reduced degree of risk. We have no hesitation in recommending this technology.”

Exyn’s autonomous drones are built on the exynAI™ platform, enabling aerial robots to fly intelligently without a human pilot using a multitude of high-tech sensors and AI-based software, the company says. The system operates without the need for GPS or external communications, and is deployed as an all-in-one software and hardware package.

Raffi Jabrayan, Director of Markets & Industries, Exyn Technologies, said: “Our mission with Rupert presented some of the most difficult and seemingly impossible challenges to navigating, analysing, and assessing a mine – which therefore makes it exemplary in demonstrating the heights of Exyn’s capabilities.

“Our AI-based software and state-of-the-art sensors were able to get the job done quickly and safely, proving once again that no exploration task is impossible for Exyn drones.”

Zyfra leveraging AI for bucket tooth, fragmentation detection and analysis

Automation, Comminution of minerals, Equipment maintenance, IoT, Mineral processing, Mining consumables, Mining equipment, Mining services, Mining software, , , ,

Zyfra says it has developed an automated system using artificial intelligence (AI) to monitor the condition of excavator bucket teeth based on its machine vision BucketControl system.

The system is designed to detect the presence or absence of excavator bucket crowns quickly and features functions to alert the excavator operator if a crown is lost or ceases to work.

The application, developed jointly by the AI and Mining divisions of Zyfra, uses an on-board controller to acquire images from the camera, process and analyse them using internal software and sends a signal to the operator if a crown is lost or ceases to work. The wear of the tooth is also assessed, and when a critical value is reached, a notification is sent to the dispatcher, according to the company. This data is transmitted to the server in real time, Zyfra added.

Alexander Smolensky, Business Development Director of Zyfra, said: “With the help of machine vision, you can locate a broken tooth immediately and prevent it from getting into the crushing compartment, whose breakdown threatens the company with a loss of up to $200,000.”

He added: “When mining ore, a broken tooth may cause damage to the bucket, which would entail additional damage worth several million rubles. Our BucketControl system will ensure a cost reduction of 90% when finding a broken tooth.”

The automated system to monitor excavator bucket teeth has been further developed to look at fragmentation. This application measures continuously the size of the pieces of rock in the excavator bucket. “Correlating that size with the location coordinates yields a performance map of rock which measures the efficiency of rock blasting to balance the cost of blasting against the quarry output,” the company said.

Smolensky explained: “In contrast to images taken after the blast, the entire depth of exposed rock is analysed. That enables us to increase excavator productivity by up to 3%, minimises the chances of oversized pieces hitting the crushing compartment, makes it possible to track the quality of blasting operations and ultimately increase rock removal by up to 10%.

“Using the system to analyse previous blasting operations will also help determine the amount of explosives required for future blast works.”

GMG expands AI and automation focus with new projects

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

The Global Mining Guidelines Group (GMG) has launched new projects in the fields of artificial intelligence and autonomous equipment to ensure mining companies can best leverage these technologies.

The ‘Open Data sets for AI in Mining’ project will be used for building open data sets to advance AI research and development, while the ‘Autonomous System Safety’ sub-project (under the Functional Safety for Autonomous Equipment project) looks to deliver valuable context and education on system safety, GMG said.

As GMG states: “Open and curated data sets can enhance the ability to build meaningful solutions for the industry by providing typical data relating to assets or operations for training and testing models and improving benchmarking and research by offering an alternative to proprietary data.”

The open data sets project will seek to leverage what the wider AI community has learned over time and ensure the approaches used in the mining domain are consistent with best practices, it added.

In terms of deliverables, the GMG is hoping for three core outcomes. Namely, a register of suitable candidate data sets, a set of guidelines for the collection and curation of these data sets and a set of repositories of gathered data.

“AI research and progress in many spheres has benefited hugely from having a set of public and curated datasets,” GMG said. “This has allowed for developers and researchers to have suitable data to test and train their models on for a variety of applications. Even more importantly, it has provided data which can be used to benchmark various solutions and allow for effective and fair comparison, as well as allowing for research to be repeated and validated.”

The ‘Autonomous Safety System’ sub-project, meanwhile, covers overall system safety. It will be a white paper to “provide valuable context and education on system safety, its history in other industries and how to deliver safe systems that can be operated effectively”, according to GMG.

The GMG said: “An outlook that expands the focus from functional safety to system safety will enable improved outcomes to the delivery of autonomous mining systems because:

  • To ensure functional safety, autonomous systems need to perform their functions correctly;
  • A technological system and its design within the operating environment can influence human performance;
  • Delivering and benefiting from complicated and complex systems requires addressing the behaviour of their interactions;
  • Cybersecurity risks affect all aspects of autonomous system safety; and
  • A full picture of system safety is needed to achieve a balance of operations, reliability and other associated disciplines.