Tag Archives: Artificial Intelligence

Plotlogic’s precision mining pursuit bolstered with new investor funds

Plotlogic’s mission to deliver precision mining across the world has been given a boost with A$7.5 million ($5.5 million) of funding from international investors.

Plotlogic’s primary focus is on providing accurate real-time orebody knowledge to enable greater operational efficiency and resource utilisation. Its OreSense® technology has demonstrated its ability to improve health and safety, enhance overall mining operations and deliver tangible productivity gains, according to the company.

Andrew Job, former mining manager and company Founder, recently completed his PhD in artificial intelligence-based sensing at University of Queensland, and has said “Plotlogic’s technology is an exciting development in resource knowledge and forms an important part of our growth as we digitally transform our mining businesses”.

Plotlogic’s most recent international deployment of its OreSense system was to an Anglo American project in South Africa. This comes on top of deployments at BHP and South32’s operations.

The company says it continues to increase its global footprint with imminent deployments to South America, North America, Asia and Russia.

Precision mining with the help of technologies like OreSense have the potential to increase worldwide industry value by $370 billion/y, according to Plotlogic, while reducing carbon emissions and improving the sustainability of mines over their life cycle.

To support commercial expansion, Plotlogic’s team has grown substantially, with the team currently sitting at 30 people, up from only six a year ago. It also has plans to double in size before the end of this year.

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

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

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

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

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

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

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

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

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

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

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

Rio Tinto investigates Heliogen’s AI-backed solar technology to decarbonise Boron ops

Rio Tinto and renewable energy technology company, Heliogen, have announced an agreement to explore the deployment of Heliogen’s solar technology at Rio Tinto’s borates mine in Boron, California.

Under a memorandum of understanding, Heliogen will deploy its proprietary, artificial intelligence (AI)-powered technology at the Boron operation, where it will use heat from the sun to generate and store carbon-free energy to power the mine’s industrial processes.

The two companies will begin detailed planning and securing government permits for the project, with the aim of starting operations from 2022. They will also use the Boron installation to begin exploring the potential for deployments of Heliogen’s technology at Rio Tinto’s other operations around the world to supply process heat, which accounted for 14% of Scope 1 & 2 emissions from the group’s managed operations in 2020.

Heliogen’s high-temperature solar technology is designed to cost-effectively replace fossil fuels with sunlight for a range of industrial processes, including those used in mining. At Rio Tinto’s Boron mine, the company’s proprietary technology will use AI to control a network of mirrors that concentrate sunlight to capture energy used to make steam, the companies said. Heliogen’s system will also store the captured energy in the form of heat, allowing it to power night-time operations and provide the same uninterrupted energy stream offered by legacy fuels.

The Boron operation mines and refines borates into products ranging from fertilisers to construction materials and is producing lithium carbonate from a demonstration plant. The site currently generates steam using a natural gas co-generation plant and natural gas fired boilers. Heliogen’s installation will supplement these energy sources by generating up to 35,000 pounds per hour (15.9 t/h) of steam to power operations, with the potential to reduce carbon emissions at the Boron site by around 7% – equivalent to taking more than 5,000 cars off the road. Rio Tinto will also be assessing the potential for larger scale use of the Heliogen technology at Boron to reduce the site’s carbon footprint by up to 24%.

Heliogen’s mission of slashing global carbon emissions by replacing fossil fuels with sunlight, as well as its focus on industrial sectors, made it an ideal partner for Rio Tinto, which is committed to decarbonising its global operations, it said.

Rio Tinto Chief Executive, Jakob Stausholm, said: “This partnership with Heliogen has the potential to significantly reduce our emissions at Boron by using this ground-breaking solar technology, and we look forward to exploring opportunities across our global portfolio.

“Addressing climate change effectively will require businesses, governments and society to work together through partnerships like this one, to explore innovative new solutions throughout the entire value chain. Our work with Heliogen is part of Rio Tinto’s commitment to spend approximately $1 billion on emissions reduction initiatives through to 2025 and our commitment to work with world-leading technology providers to achieve this goal.”

Heliogen CEO and Founder, Bill Gross, said: “Since its founding, Heliogen has been laser-focused on decarbonising industrial sectors, including mining. As a result, this agreement with Rio Tinto is incredibly gratifying.

“We’re pleased to find a partner committed to cutting its contributions to climate change. We’re also pleased that Rio Tinto is exploring our technology to play an important role in helping reach its sustainability goals while dramatically reducing its energy costs. More broadly, we’re excited to take this important step as we pursue Heliogen’s goal of avoiding more than 1 gigaton of CO2 emissions – 5% of the world’s annual total – from the global economy by turning sunlight into an industrial energy source.”

Metso Outotec on ore sorting’s potential ‘revolutionary change’

Metso Outotec stands out among the mining original equipment manufacturers for having publicly acknowledged ore sorting is on its radar.

The Outotec business had a relationship with TOMRA Sorting Solutions dating back to 2014 when the two companies signed an agreement that would see the particle sorting company supply Outotec-branded sorting solutions to the mining and metallurgical industry. Metso, meanwhile, has previously disclosed it was developing “breakthrough proprietary technology to address the demand of high throughput accurate sorting”.

Close to eight months after the two companies merged to become Metso Outotec, IM put some questions to Erwin Huber, Vice President, Crushing and Conveying Systems; David Di Sandro, Business Development Manager – Optimisation and Test Labs; and Rashmi Kasat, VP, Digital Technologies, Minerals, to find out the current state of play with ore sorting at the mineral processing major.

IM: Back in November at your Capital Markets Day, there was mention of ‘AI-powered Ore Sorting Solutions’ during a presentation. Can you expand on what this offering might include? What stage is it at in terms of commercialisation?

DDS: Ore sorting is one of the most exciting recent developments in our industry. With improvements in sensor capabilities and adoption of artificial intelligence (AI), this may well become the revolutionary change this industry needs to sustain itself in the face of diminishing grades and orebody quality.

EH: With our ore sorting solution development, we are targeting the ability to deliver complete offerings of hardware and sensor-fusion platforms as it relates to both bulk and particle ore sorting. These platforms would utilise AI to optimise the feed material for the downstream process. Metso Outotec is uniquely positioned to understand and optimise that plant feed stream with deep knowledge and almost complete technology coverage in both the concentrator and tailings processing areas.

We plan to bring new solutions to the market in the short term and continuously launch new technologies to increase capabilities and capacities when the developments are mature enough.

IM: Will these solutions leverage existing tools within the Metso Outotec product offering? Will they make use of existing agreements with other companies (for instance, the agreement with TOMRA that Outotec previously had in place)?

EH: Metso Outotec carries out its own development of these solutions, and some partnerships are part of it once sensoring and analysing different minerals and elements are not possible with a single or only a few technologies. Mining and concentration are becoming more and more a digital world where breakthrough innovation is finding its space towards efficiency and sustainable possibilities. Smart systems will enable improved equipment uptime, efficiency and remote diagnosis of process and maintenance, and will be the bonding element between our traditional offering portfolio and new technologies.

IM: Previously Metso has talked about the development of a bulk sorting solution: do these ‘AI-powered Ore Sorting Solutions’ fit into that category, or are they more particle sorting solutions?

EH: Bulk ore sorting enables material selection at high throughput flows and particle technology is limited by capacity while bringing the benefit of high accuracy on selectivity.

RK: Bulk sorting is in its early stages in industry and no single sensor can determine minerals content across all ore types and mine sites. This is where AI algorithms play a significant role in ‘self-learning’ ore characteristics, mine site by mine site. It also provides great opportunities to do sensor fusion and more accurately determine the minerals content based on outputs from various sensors and sensor types. AI augments our expert’s tacit knowledge and provides a more reliable way over time to analyse big data generated from online mineral analysis.

IM: Where in the flowsheet do you envisage these solutions going?

EH: The earlier we can remove the gangue from the flow stream, the better our energy efficiency will be by reducing the volume of waste material that is processed by downstream equipment. Deposits in advanced development allow for in-pit backfill bulk ore sorters that may be deployed behind mobile in-pit crushers, or before the coarse ore stockpile where backfilling is not an option. There are several pre-concentration technologies that can be applied at each stage of mineral processing and the ideal operation should combine those tools to remove the liberated gangue at multiple stages of the processing plant in order to achieve the most sustainable process (ie bulk/particle ore sorting, selective breakage, coarse flotation).

IM: Will the benefits of your solution be felt beyond the crushing and grinding stage? Do you intend to use the data generated from the ore sorting solutions to benefit the whole downstream flowsheet?

DDS: One of the benefits of ore sorting is more efficient removal of waste from the process feed. Under certain circumstances, this also means removal of deleterious material which otherwise would adversely affect downstream process performance such as flotation recoveries. In these cases, the downstream benefits are intrinsic. The key would be understanding the geometallurgical mapping of all rock types and their mineralogy, so a philosophy of ‘include or reject’ can be applied on a metallurgical response basis. This mapping can be improved with SmartTag™ and GeoMetso™ technologies from Metso Outotec.

EH: The ability to sort, the geometallurgical mapping and metallurgical response obviously feed back into the block model and allow for more options in the mine plan and life of mine resource recovery, for example with the deployment of low-grade stockpiles. This further enhances the sustainability of the mining operation.

IM: Is the market ready for and receptive to such a powerful ore sorting solution?

DDS: As we all know, for good reason, our industry is full of early adopters rather than innovators. Most operations will need to see the technology succeed elsewhere before increasing their uptake of the technology. The initial implementation will likely occur in partnership with customers whose operations need this technology to be economically viable.

EH: The key is to understand the ore variability through the deposit and through the life of mine. Adopting ore sorting as an integrated processing step does not differ that much from testing and sizing flotation circuits, where small changes in ore properties can affect the overall recovery. It is important to understand these changes and how to react to them during operations.

The confidence level in sensor-based ore sorting testing will grow over time. We already see real-life examples where customers report on ore reserves based on lower cutoff grades due to ore sorting.

IM: Anything else to add?

EH: Despite the fact that the concept of ore sorting, and the sensors required to detect the valuable ore from the waste, have existed for several years, if not decades, the implementation of these systems in full-scale operations have been relatively restricted to particular cases with the right kind of orebody to make the process viable. Implementing ore sorting more broadly remains the challenge and requires the dual application of the right sensors working effectively with the right mechanical handling systems to detect and remove the waste stream efficiently and accurately. The skills required to solve these challenges are not just for the traditional mining and mineral processing engineers, but need to include a cross-disciplinary team addressing the issues from all angles.

This Q&A interview was carried out as part of the IM March 2021 annual ore sorting feature, to be published early next month

Robotics on its way to the exploration industry, QR’s Scott says

Mining has entered a robotics boom as developers take substantial strides in artificial intelligence (AI), use of drones, and data capture and analysis technology that will deliver safety improvements and better managed mines, Queensland Robotics Executive Chairman, Andrew Scott, says.

Speaking at the IMDEX Xploration Technology Symposium, he said that with the development of autonomous haulage and drilling technology, the mining industry had moved through a “trough of disillusionment” around robotics and was rapidly accelerating towards the “plateau of productivity”.

The two-day online conference brought together experts in mining innovation and exploration industries to discuss the latest in new technologies, tools and advanced analytics.

Scott said acceptance of new technologies had been aided by restrictions caused by the COVID-19 pandemic, with the development of some digital transformation projects planned for the next three to five years being executed in three months.

“COVID is a significant accelerator and robotics is no exception,” he said.

Capital was available to fund new and emerging projects and was another clear indicator of a robotics boom, which Scott said would undoubtedly mean more jobs not less.

“There’s a lot of work that’s underway right now to really bring to the forefront a lot of automation and robotics to deal with enhanced data capture and execution of exploration programs and also within the mining environment,” he said in an interview ahead of the symposium.

“In the mining environment, we’ve seen the proliferation of automation in the form of autonomous haulage and autonomous driving, but now we’re seeing all the other ancillary services that are requiring automation and robotic solutions to take people out of danger but also to enable a highly efficient and productive system.

“We’re starting to see some of those capabilities move across into exploration, including the ability to deploy smart sensors in the field robotically, the collection of samples, and the analytical processing of those samples.”

He told the symposium the increase in robotics was aided by a reduction in sensor and computational costs, and, with more tools and technology available, there was increased adoption and acceptance.

“Robots are helping with the dirty, dull and dangerous, and distance challenges,” he said. “Applying robotics can definitely remove people from harm’s way. It can also augment what they are able to achieve by being able to explore in environments where until now we’ve been limited.”

This included in Australia, with areas subject to extreme heat, the high altitudes of the Andes, and subsea exploration.

“Robotics is surfing the wave of AI,” he said. “There’s a huge amount of development and growth in this area. We’ve gone past the AI winter, as they call it, and the acceleration of tools, and the ease of use of those tools is becoming a critical enabler.

“My prediction is that we’re going to see more and more solutions where they’re highly engineered highly capable, robust, highly configurable and easy to use.”

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

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

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

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

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

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.”