Tag Archives: artificial intellgence

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

SensOre says it is to advance its Joint Targeting Agreement (JTA) with BHP to “Phase 3” after receiving approval from the major miner.

Under the JTA, SensOre was required to meet certain hurdle rates and technical thresholds through deployment of its Discriminant Predictive Targeting® (DPT®) technology and related auxiliary systems. SensOre says it has met or exceeded the requirements set for Phases 1 and 2.

Richard Taylor, CEO of SensOre, said: “The SensOre team has been excited by the performance of its systems in targeting new commodity and deposit types. The relationship with BHP and its support for innovation in exploration has been incredibly valuable. The results derive from the truly joint nature of the project and shared view that better use of geoscience data will lead to improvements in discovery rates. We are really thrilled with the results.”

SensOre and BHP reached agreement on a letter of intent in May 2020, confirmed via execution of the JTA on September 18, 2020. The JTA envisages a phased process, training the DPT technology on commodity-specific deposit types and applying the knowledge gained to a predetermined search space. SensOre stands to benefit from fees for the targeting exercise and potential success-based payments on certain discoveries arising from the technology, it said.

SensOre aims to become the top performing minerals targeting company in the world through the deployment of artificial intelligence and machine-learning technologies, specifically its DPT workflow. SensOre collects all available geological information in a terrane and places it in a multi-dimensional hypercube or Data Cube, with its big data approach allowing DPT predictive analytics to accurately predict known endowment and generate targets for further discovery, it says.

The Axora take on crushing and comminution

As we are continually told, comminution is one of the most energy intensive single steps in the resource extraction business.

One estimate is that it accounts for 36% of all the energy used in the extraction of copper and gold, which is only a shade over the 30% proposed as an average by another industry expert for all mining and mineral processing industries.

It also accounts for an estimated 3% of the global energy requirement for metal production.

These energy requirements are shocking from a sustainability and greenhouse gas emission perspective; they are also extremely costly regarding operating expenses on site.

It is with this in mind that IM touched base with Joe Carr, Industry Innovation Director of Mining at Axora.

A spinoff from the Boston Consulting Group, Axora has emerged as a business-to-business digital solutions marketplace and community for industrial innovators. It says it allows industrial companies to discover, buy and sell digital innovations and share knowledge in its community, powered by an advanced marketplace.

“We exist to transform industries to be digital, safer, more sustainable and efficient,” the company states on its website.

Having recently gone to press with the annual crushing and comminution feature (to be published in the IM April 2021 issue), IM spoke with Carr to find out what the Axora marketplace has to offer on the comminution and crushing front.

IM: What are the main issues/concerns you continuously hear from your mining clients when it comes to designing and maintaining comminution circuits? How many of these problems/issues can already be solved with existing technology/solutions?

JC: One of key issues in this area we hear from our customers at Axora is the blending quality of the input ores.

Joe Carr, Industry Innovation Director of Mining at Axora

This could be particularly relevant in the sulphide space, for instance.

I did some work years ago on Pueblo Viejo for Barrick. When I was there, one of the things we were working on was blending the sulphides as we were feeding the mill from numerous satellite pits with very different sulphide grades. Because we were processing the ore with an autoclave, high-grade sulphides would cause a temperature spike and the low-grade sulphides would lower the temperature. This constant yo-yoing of the feed into the autoclave was terrible for the recovery of metals against the plan.

Generally, the old school way of blending is setting up stockpiles of ore based on whatever variable you want to manage at your operation. You would put a defined amount of each into the primary crusher on the understanding this would create a ‘blended’ feed for the processing plant.

With the information we have at our fingertips today, this process seems outdated.

You could, for example, use HoloLens or another VR system in tandem with the shovel operator to be able to see exactly what material he or she is excavating. That can then be linked back to the geological block model, with this material then tracked in the trucks and onto the run of mine stockpile, before heading to the plant.

This is where something like Machine Max comes in. Machine Max is a bolt-on IoT sensor that tracks where your trucks are in real time – where they have been and where they are going. The processing piece requires block model integration into a mine plan system. If you have the building blocks in place – the networking, sensors, additional infrastructure, etc – Machine Max could, when integrated with this model, allow you to attempt real-time ore tracking.

“If you have the building blocks in place…Machine Max could, when integrated with this geological block model, allow you to attempt real-time ore tracking,” Joe Carr says

The issue is not that the technology doesn’t exist, but that the mining industry hasn’t yet cracked putting all of this together at an industry-wide scale, available to all miners.

You can carry out a project like this or go totally the other way and have a machine-learning or artificial intelligence algorithm in the plant that is constantly reading the incoming feed. These could be based around the block model inputs, or a digital XRF solution, which is able to constantly tweak or adjust the plant settings to the feed specifications. Process plants are generally setup to handle one type of feed. This is usually only tweaked in retrospect or for short periods of time when the mine plan moves into a different mining horizon.

We also have a comminution solution that understands the feed coming in and optimises the mill and power settings to get the optimal grind for flotation, maximising recovery at the back end. While the input is typically set up to be grind quality and hardness for optimal flotation, there is no reason why you couldn’t configure it for, say, sulphides going into an autoclave, tweaking the autoclave heat settings dependent on the feed.

Once that system is set up, it becomes a self-learning algorithm.

Saving operational costs is another pain point for mining companies we always hear about.

We have a solution on our marketplace from Opex Group, which is looking to optimise production while reducing power. Coming from the oil & gas space, this AI algorithm, X-PAS™, offers the operator an opportunity to adjust the settings while still achieving the same required outputs. This is tied to CO2 reduction, as well as power cost reductions.

Opex Group’s AI algorithm, X-PAS, offers the operator an opportunity to adjust the plant settings while still achieving the same required outputs

In mining, the plant is your largest drawer of power, hands down. Generally, if it is not powered on the grid, it is powered by diesel. Opex Group’s solution can save up to 10% of power, which is a significant amount of fuel and CO2.

The solution reads information from your pumps and motors, analyses the planned output of your plant using all the sensor feeds, and tweaks the variables while sustaining the required output. The algorithm slowly learns how you can change configurations to reduce power, while sustaining throughput. This results in lower power costs, without impacting the output.

Importantly, instead of automating the process, it offers the saving to the operator sat in the control room. Operators, in general, are incredibly reluctant to pass over control to an AI algorithm, but when faced with such power saving opportunities, they will often elect to accept such a change.

And, of course, plant maintenance is always on the agenda.

This is where Senseye, which has been used in the car industry by Nissan and the aluminium sector by Alcoa, is useful.

Essentially, this provides predictive maintenance analytics. It is also a no-risk solution with Senseye backed by an insurance guarantee. It is sold on the basis that if you do not earn your money back within the first 12 months, you get an insurance-backed refund.

There could also be openings in the plant for Razor Labs’ predictive maintenance solution, which is currently increasing the uptime of stackers, reclaimers and car dumpers for iron ore miners in the Pilbara.

IM: When it comes to future comminution equipment design, do you expect digitalisation, wear liner innovations, or equipment design to have more of a bearing on operational improvements at mine sites? Phrased another way; is more emphasis being given to refining and extending the life of existing products with digital technologies and wear solutions, than the design of brand-new equipment?

JC: We believe there is always going to be a focus on retrofit and extensions. Once a mill is built, changing the equipment, upgrading, etc is very hard and time consuming. The logistics of getting a new SAG mill to site, for example, are mind boggling. New technology will always come for new sites, but most of the world’s mining capacity is already in place. I would expect most digitalisation to focus on two areas:

  1. Getting more and longer life from all the assets. For example, extending liner life, reducing operating costs and shortening downtime between refits; and
  2. Drawing insights from the existing asset with a view to sweating it. No mill ever stays at nameplate; there is always an increase in production. One or two percent more throughput can put millions onto the bottom line of a company. No mill wants to be a bottleneck in the cycle. In a mine there are always two goals: the mine wants to produce as much ore as possible to put the pressure on the mill, and the mill wants to run as fast as possible to put pressure on the mine.

When it comes to extending liner life, we have a solution worth looking at.

One of the companies we work with out of Australia has an IIoT sensor all tied to wear and liner plates. It is a sensor that is embedded into a wear plate and wears at the same time as the wear plate itself wears. It provides this feedback in real time.

So, instead of the standard routine changeout, it gives you real-time knowledge of what it is happening to these wear parts.

We have a great case study from Glencore where they installed the sensors for around A$200,000 ($152,220) and it saved several million dollars. The payback period was just weeks.

Where I want to take it to the next level is pairing the wear plate monitoring technology on chutes and ore bins and looking into SAG mills and crushers. Relining your SAG mill or primary gyratory crusher is a massive job, which takes a lot of time and cuts your productivity and output by a huge amount. Wear plates are made as consumables, so if you can use 5% less over the space of a year, for instance, there are huge cost and sustainability benefits. You can also more accurately schedule in maintenance, as opposed to reacting to problems or sticking to a set routine.

IM: When compared with the rest of the mine site, how well ‘connected’ is the comminution line? For instance, are gyratory crushers regularly receiving particle size distribution info for the material about to be fed into it so they can ‘tailor’ their operations to the properties of the incoming feed?

JC: Generally, not really. The newer, better financed operations tend to have this. Taking the example above, when designing a plant flowsheet, the close side settings are used. But are they updated on the fly to optimise the plant? Not really. Most processes are designed with a set number of conditions to operate at their maximum.

Most plants dislike, and are not set up to handle, variation in their system, according to Carr

Most plants dislike, and are not set up to handle, variation in their system. They like consistent feed quality and grade to achieve maximum recoveries. Over the next few years, the companies that develop the best machine learning or AI models to run plants in a more real time, reactive way will see the biggest growth. A mill will always say it’s the mine that needs to be consistent, but the nature of geology means that you can never rely on this. As one geologist I knew said, “geology, she is a fickle mistress”.

IM: Where within the comminution section of the process flowsheet do you see most opportunity to achieve mining company sustainability and emission goals related to energy reductions, water use and emissions?

JC: In terms of emissions, at Axora we are actively looking at technology that can help across the entire plant. There was a great paper published in 2016 around this specific topic ‘Energy Consumption in Mining Comminution’ (J Jeswiet & A Szekeres). The authors found that the average mine used 21 kWh per tonne of ore processed. Given diesel produces 270 g per kWh, this means a plant produces 5.6 kg of CO2 per tonne of ore processed, on average. For a 90,000 t/day site, this might represent 510 t of CO2 per day (186,000 t/y), just for processing. To put that into context, you would need 9.3 million trees to offset that level of carbon.

If the industry is serious about lowering its carbon footprint, especially Scope 1 and 2 emissions, then the focus has to come into the process. There are easy wins available from proven solutions in other sectors for companies that want to take them.

Nordgold taps Swift Geospatial for tailings and community monitoring at Lefa

Nord Gold has implemented a new state-of-the-art monitoring system at its Lefa mine in Guinea that, it says, will help it keep track of the condition of the operation’s tailings storage facilities to proactively identify potential risks.

The move, in line with the company’s commitment to environmental stewardship, has been facilitated by Swift Geospatial Solutions, a service provider experienced in change detection algorithms that developed the solution.

Lefa’s new monitoring system uses satellite imagery processing algorithms to both analyse the condition of the mine’s tailings storage facilities, as well as monitor community welfare by tracking community dynamics, including house building, it said.

Additionally, the technology can help to monitor artisanal mining activities around Lefa’s current mining permit, allowing local management to better assess and prevent potential safety and security hazards

The company explained: “The Lefa mine’s licence area currently spans more than 1,100 sq.km. The latest satellite technology enables cost-effective monitoring of this expanse with regular updates and is backed by a robust AI package.

“As part of the new system, Planet and SkySAT satellite platforms will be used to perform different tasks, both integrated within the Swift Geospatial Solutions online platform. All outputs are delivered through a custom-built web-application and dashboard environment.”

Evgeny Tulubensky, Nordgold’s Chief Legal Officer and Director of ESG at Nordgold, added: “We are very pleased to test this innovative tool, enabled by satellites, at our Lefa mine. It will help us to receive timely and relevant information about the condition of Lefa’s critical environmental infrastructure and the mine’s immediate surroundings.

“Using this rich data, our aim is to continue reducing our impact on the natural environment and ensuring the sustainable development of local communities. Depending on the outcomes of this pilot, we will assess its viability for other Nordgold operations.”

Hyperspectral imaging technology tested at Western Australia gold, iron ore mines

The University of Queensland and research partners Plotlogic Pty Ltd have developed new automated mining technology that, they say, will facilitate automation of the mining process while improving operating efficiency.

The research has shown how artificial intelligence can use scans of the mine face to almost instantly identify valuable minerals and waste rock, allowing each stage of the mining process to be planned more effectively in advance, UQ said.

Professor Ross McAree, Head of School of Mechanical and Mining Engineering from UQ, said the new technology used visible and infrared light to automatically classify materials.

“Each mineral has its own characteristic response to different wavelengths of light, so by scanning the mine face with our system we can map out the minerals present in the rock and their concentration (ore grade) almost instantaneously,” Professor McAree said.

This real-time mapping allows the mining process to be planned out before digging even starts, according to the researchers.

“Beyond this immediate efficiency gain, the enhanced ability to recognise ore grade could also underpin future autonomous mine systems,” Professor McAree said. “Machines equipped with this imaging system would be able to recognise ore grade as they were excavating it. Linked to artificial intelligence, this could allow automated machinery to operate in the mine environment, removing workers from hazardous parts of the mining process.”

Real-time ore grade classification at the mine face could also enhance mine scheduling and improve resource recovery and minimise processing waste, the researchers claim.

The project was supported by the Minerals Research Institute of Western Australia (MRIWA), with MRIWA CEO, Nicole Roocke, saying investment into research like this helped position Australia’s minerals industry at the leading edge of technology development.

“This imaging approach could prove particularly valuable where rapid extraction and consistency of ore grades could provide a competitive advantage to those leading the way,” Roocke said.

The project, which was conducted in 2018-2019, had a total grant value of A$850,850 ($653,322). In addition to MRIWA, UQ and Plotlogic, CITIC Pacific Mining and AngloGold Ashanti were also involved, hosting trials at the Sino iron ore and Tropicana gold mines, in Western Australia, respectively.

It was based off the OreSense® prototype system, developed to meet the needs of the research project, as well as offering a commercial pathway for early industry adoption of the technology.

“The prototype delivers a system capable of acquiring, processing and classifying hyperspectral data in the field and in real time, mapped to terrain and geo-referenced for integration with mine maps,” the project partners said. “In order to be the most general and applicable to all minerals, the hyperspectral imaging capabilities cover the visible to short wave infrared spectrum (400-2,500 nm).

“The surveying capabilities of the system rotate in more than one axis to perform face scans and build a 3D data-cube from two individual line-scanning hyperspectral sensors. The system spatially and spectrally fuses the data cubes from the two sensors to provide a single data-cube for an entire scene. The system also performs on-board corrections and post-processing of the hyperspectral data to support real-time ore grade classification.”

The prototype used on site during the trials consisted of a sensor head with LiDAR and hyperspectral cameras, a pan-tilt unit and a GNSS receiver among other elements (see photo above).

Giga Metals taps Minerva’s AI prospect generator software for Brazil exploration

Minerva Intelligence says its Cognitive artificial intelligence-powered prospect generation software, TARGET, has helped Giga Metals identify and evaluate new prospective exploration targets at the Parnaiba Basin project in Nordeste, Brazil.

Giga, after validating the results produced by Minerva’s TARGET software, made the decision to acquire exploration permits covering significant new regional sediment-hosted copper anomalies along the southern perimeter of the Parnaíba Sedimentary Basin in southern Piauí State, Northeast Region, Brazil, it said.

This amounted to the staking of 24 exploration permits totalling 40,722 ha in four properties along 80 km of strike length in an area with known “Kupferschiefer-style” sediment-hosted copper mineralisation.

Scott Tillman, CEO of Minerva Intelligence, said: “The commercial validation of our TARGET software is yet another indication of the power of Minerva’s Cognitive AI-powered software.

“The successful deployment of our TARGET software highlights the value we are able to provide to companies that are managing large datasets and seeking to incorporate an artificial intelligence element into the decision-making process. Our success with Giga in Brazil, in conjunction with our recent success in Mexico, points to even greater success in the future in delivering results for mining and exploration companies around the world.”

Using Minerva’s TARGET software, Giga was able to sift through, organise and evaluate large datasets that were subsequently used to analyse the validity of the prospective exploration region, Minerva explained. TARGET’s mapping technology was able to determine, based on existing comprehensive datasets, that the project in Brazil had a high likelihood of success and, as a result, Giga should pursue investment in the region.

The final result of the analysis was a list of AI-produced target areas throughout Brazil that are completely auditable and explainable, and, most importantly, actionable by Giga, Minerva said.

Giga Metals CEO, Mark Jarvis, said: “TARGET enabled us to work our way through an immense volume of regional geological data to focus on areas prospective for the deposit types of interest to us. This is a type of regional survey that was previously possible only for a major mining company with a large team of geologists. It is exciting to experience at first-hand how artificial intelligence is now making this type of survey accessible to smaller companies.”

Jake McGregor, Minerva’s COO, added: “In 2019, Minerva was contracted by Giga to build a set of prospectivity maps for the country of Brazil. In this capacity, the company compiled various datasets from across the country, both from public and private sources, and significant work was undertaken by Minerva to standardise and translate the data from Portuguese to English, and then into the standard terminologies that we use in our mineral deposit models. It is extremely rewarding to see our clients getting value out of that hard work.”

Orica leverages MWD data, AI to create new blast loading design benchmark

Orica is looking to set a new benchmark for blast loading designs in Latin America after deploying its Design for Outcome solution in the region.

The company, focused on integrating its digital blasting tools to improve outcomes, is leveraging its BlastIQ digital blast optimisation platform within this new solution, Angus Melbourne, Chief Commercial and Technology Officer of Orica, told delegates at Massmin 2020 last week.

In a presentation titled, ‘Blasting’s Critical Role in Extracting Ore’, Melbourne mentioned Design for Outcome as an example of where the company was delivering integrated digital solutions in Latin America.

“Design for Outcome is an automated continual optimisation solution that sets a new benchmark for blast loading designs,” he said. “It utilises data science to process both upstream and downstream data to automate blast designs. This produces tailored and optimised blast designs by reducing blast variability and explosive consumption while increasing productivity.”

Using machine-learning algorithms, Design for Outcome processes measured-while-drilling data to classify ground hardness throughout each blast hole and then match explosives energy to hardness domains to automatically generate tailored blast loading designs, Melbourne explained.

Through artificial intelligence, these algorithms are trained with the data received from the fleet control systems (FMS) and previous blast results. This enables final automation of the blasting design process and its execution in the field with Orica’s smart control systems and programming interfaces, loading the blast accurately according to the generated design. These elements combine to ensure the desired outcomes are achieved, Melbourne said.

“Digitally-enabled blasting solutions such as Design for Outcome are allowing us to work with customers in different ways, to think and act differently and expand our role in the mining value chain,” he said.

Such a solution is part of the company’s plans to automate its segment of the mining process. This goal was strengthened last month with the launch of the Orica and Epiroc jointly developed Avatel™ semi-automated explosives delivery system.

A key enabling technology of Avatel, which is built on the foundation of Epiroc’s Boomer M2 carrier, and Orica’s automation vision is WebGen™, the company’s fully wireless initiation system. When combined with Orica’s LOADPlus™ smart control system, specifically designed on-board storage, assembly, digital encoding capability and Subtek™ Control bulk emulsion, Avatel provides customers with complete and repeatable control over blast energy from design through to execution, Orica says.

While referencing the second key pillar in Orica’s digital strategy, Melbourne highlighted the use of the company’s Bulkmaster™ 7 smart, connected explosives delivery system in Latin America during the virtual event.

The new delivery systems not only improve productivity but begin to digitise critical workflows between design and execution in drill and blast operations, according to Melbourne.

The Antamina copper mine in Peru, a joint venture between BHP, Glencore, Teck and Mitsubishi, will soon be leveraging such a system, with Melbourne confirming seven Bulkmaster 7 units had been shipped to the mine and were undergoing commissioning.

Orica’s third digitalisation pillar is the measurement of downstream impacts of the drill and blast process, which is where FRAGTrack™, the company’s automated rock fragmentation measurement device comes into play.

This device captures, analyses and reports real-time data for optimising blast operations, improving downstream productivity and tracking overall operational performance in mining and quarrying, Melbourne explained.

This system is active across several key customer sites in Latin America, with Teck’s Carmen de Andacollo operation in Chile being one of the first to adopt the technology in the world, according to Melbourne. He said the copper operation is using the insights to deliver efficiencies across the value chain through digitally enabled optimised blasting.

Mobilaris’ new devices to leverage latest communication, machine-learning tools

Intent on “mastering the latest technologies” in its domain, Mobilaris says it will focus on the use of next-generation communication technologies such as 5G and Wi-Fi 6, and artificial intelligence, to build out its new safety solutions in 2021.

Mobilaris says it is building a device using 5G technologies that will be used in a new offering for Mobilaris Industrial Solutions.

By leveraging these new technologies, it will bring Industry 4.0 digital workforce safety to all its customers, it said.

To ensure this new device is “truly world-class in terms of safety, performance and resilience”, Mobilaris has partnered with Sigma Connectivity and Ericsson to leverage their expertise in this domain. It says it is the first company to use the new reference cellular IoT design from Ericsson called Ardesco.

The company said: “5G and cellular IoT are technologies that will open up new possibilities, but they need connection to existing public mobile networks, or private networks. Therefore, Mobilaris has partnered with Telia to bring our new solution to the market.”

Earlier this year, the company joined Telia’s 5G program as a new member and, after that, secured a commercial partnership to bring solutions to the market while at the same time tailor its use of the Telia network to maximise performance and efficiency.

Another key technology for next generation communication solutions is Wi-Fi 6.

Mobilaris has been deploying Wi-Fi-based solutions for many years, with 2021 representing no change to the status quo.

“Many of our customers have Wi-Fi networks, and we are continuing to invest in this technology to secure our capability to meet all customer demands and to innovate, leveraging the new additions coming in Wi-Fi 6, 6E and beyond,” it said.

This is where a partnership with Aruba will bring best-in-class, real-time situational awareness to industry customers around the globe, Mobilaris said.

The use of artificial intelligence is also nothing new for the Sweden and US-based company. It has already deployed its Mobilaris Onboard product in several mines across the globe and, at its core, machine learning is creating “value for our customers” that would not have been possible just a few years ago, it says.

It concluded: “Moving ahead, we are continuing to invest in AI to further accelerate our products and solutions and we expect to announce several new research partnerships here within the near future.”

RPMGlobal futureproofs inventory management for miners

RPMGlobal has completed the acquisition of IMAFS, adding a cloud delivered, inventory management and forecasting software solution to a suite of technology solutions that use proprietary artificial intelligence algorithms to greatly improve inventory management.

IMAFS analyses inventory data from corporate Enterprise Resource Planning (ERP) systems to deduce the optimal timeframe for inventory orders, costs, and order frequency. It supports not only several forecasting methods, but also takes advantage of AI and machine-learning capabilities to optimise the calculation of the key variables in inventory management, RPMGlobal says.

The ‘forward looking’ predictive algorithms become particularly powerful when used in conjunction with RPMGlobal’s asset management system, AMT, the company added. AMT uses a Dynamic Life Cycle Costing engine that forecasts, in real time, every maintenance event for a piece of equipment, at a component level, until the end of its economic life.

The future demand of an organisation’s assets, combined with the optimisation algorithms within IMAFS, are the critical pieces of the puzzle to optimise procurement and management of critical parts and components, according to RPMGlobal.

“The delivery of an integrated AMT and IMAFS offering delivers an industry-first, predictive solution with more certainty of the future and less reliance on historical data,” it said.

ERP systems typically use historical consumption to predict future requirements, which, according to RPMGlobal, has its limitations in a volatile maintenance and repair focused industry such as mining.

“This future demand knowledge will further improve the accuracy of parts availability, reducing inventories, decreasing stockouts and reducing equipment downtimes,” it said.

IMAFS has been proven in industry, according to RPMGlobal, with one operation reducing 78% of stockouts for items with high usage, while concurrently reducing stock levels by 14% for the items controlled by IMAFS. Another operation was able to reduce global inventory by 15% within the first 10 months without any diminution of service levels.

RPMGlobal Chief Executive Officer, Richard Mathews, said: “The company is leading the way in the practical application of innovative technology that increased miners’ operational competitiveness. With our growing suite of optimisation products, RPMGlobal has built a capacity to help global operations extract more value where it counts. We are excited to be utilising AI technologies to improve the efficiency of mining operations across the asset management function.”

He added: “We intend to build on this AI capability by including additional variables in the models that may impact availability for our customers operating in remote locations. This experience will also assist us as we look to use these same advanced techniques to further optimise mine planning and scheduling.”

RPMGlobal adds predictive element to mine maintenance solutions with IMAFS buy

RPMGlobal has entered into a share purchase agreement to acquire Canada-headquartered, inventory optimisation management software company, IMAFS.

As a Software-as-a-Service and cloud-delivered provider of inventory optimisation software, IMAFS has more than 20 years experience developing and selling its flagship IMAFS product, RPMGlobal says.

The IMAFS solution is an inventory management and forecasting software solution that connects to an organisation’s Enterprise Resource Planning (ERP) system and uses proprietary artificial intelligence (AI) algorithms to greatly improve inventory management, according to the company. The product has been designed and built for the sole purpose of optimising the inventory holdings of large asset-intensive companies.

RPM CEO and Managing Director, Richard Mathews (left, pictured with David Batkin, Executive General Manager – Technology Consulting), said: “We are very pleased to have concluded negotiations to acquire IMAFS and are really looking forward to welcoming the Quebec-based IMAFS team into the wider RPM family. The product is a great fit with the existing RPM product suite and further builds on our cloud and optimisation offerings.”

RPM explained: “In the mining industry, management and optimisation, specifically the maintenance, repair and operational (MRO) inventory is critical to ensuring operational continuity and attainment of production targets.

“The key to accurately forecasting any type of inventory is understanding future demand. Mining MRO inventory optimisation is often a unique challenge to solve due to low volume and/or erratic turnover with long lead times, high component costs and the complex logistics associated with operating in remote locations leading to companies over-stocking parts inventory and tying up capital unnecessarily.”

When it comes to mining, properly managing MRO inventory is vital, RPM says. If the plant, or key pieces of equipment (loaders, trucks, conveyors, etc) stop operating because spare parts are not available, a costly operational problem develops. A poor inventory optimisation process can result in a company ordering inventory urgently due to reactive inventory processes rather than predictive inventory processes.

IMAFS has developed a hosted subscription service that, RPM says, allows inventory data to be extracted from a company’s ERP product or Computer Maintenance Management system and analysed programmatically.

IMAFS’ proprietary and cutting-edge algorithms also include AI logic incorporating parameters such as transport mode, carrier, weather, customs, seasonality, holidays, availability, and many other data points. IMAFS will also identify excess or obsolete stock that can be returned or disposed of, according to RPM.

Mathews added: “Four years ago, we acquired iSolutions because we understood the importance of planning maintenance in parallel with production. AMT stands alone when it comes to forecasting the lifecycle cost of an asset using its dynamic lifecycle costing engine. This real-time engine accurately predicts when customers will require major parts and components.

“In other words, by going back to first principles (as AMT does), we can predict the future demand that can be factored into IMAFS’ advanced AI algorithms. That future demand is the critical piece of the puzzle so that IMAFS can optimise procurement and management of critical parts and components.”

Mathews says the AMT solution is used by the major OEM’s and their dealer network. These organisations can take forecasts from their customers into the IMAFS product, thereby assisting them in optimising their spare parts inventory.

“While we haven’t had a product to do this in the past, we have been involved in a number of discussions with dealers and miners to do exactly this,” he said.

Robert Lamarre, IMAFS Founder, said: “It is immensely pleasing to see the passion emanating from the team at RPM to championing inventory optimisation and cloud-driven enterprise integration. We are convinced that the IMAFS product suite will benefit from increased investment and the sales and marketing support that RPM can offer these products right around the world.”

Following completion, Lamarre will continue his involvement with promoting IMAFS through a third-party business partner authorised to market and distribute IMAFS products to customers in North America outside of mining and resources.

The acquisition is expected to close on November 25, 2020 subject to several conditions precedent and customary completion events.

ABB and IBM collaborate on industrial cybersecurity solution

ABB and IBM have announced a collaboration focused on connecting cybersecurity and operational technology (OT) for industrial operations.

As a first result of this collaboration, ABB has developed a new OT Security Event Monitoring Service that combines ABB’s process control system domain expertise with IBM’s security event monitoring portfolio to, ABB says, help improve security for industrial operators.

Industrial control system environments are increasingly targeted in cyberattacks, with IBM’s latest X-Force Threat Intelligence Index finding that attacks on industrial and manufacturing facilities have increased by over 2,000% since 2018.

To better connect OT data with the broader IT security ecosystem, ABB has developed a new offering that allows security events from ABB to be sent to IBM’s security information and event management platform, QRadar.

The ABB solution was designed according to a reference architecture jointly developed by ABB and IBM. It provides the domain knowledge needed to swiftly react to security incidents related to process control, and is especially suited for complex industrial processes in industries such as oil, gas, chemicals and mining, ABB says.

The new event collection and forwarding software, which enables this integration, is currently being used by early adopter customers and will be made broadly available by ABB in the coming months.

This collaboration marks the first time that operational technology (OT) data and process industry domain expertise is being brought directly into a Security Information and Event Monitoring system, allowing threats to be managed as part of an organisation’s broader cybersecurity operations and strategy, ABB says.

Robert Putman, Global Manager of Cyber Security Service for Industrial Automation at ABB, said: “ABB’s collaboration with IBM makes it possible to analyse process control events in the context of security and impact to the operational environment, delivering strong improvement in our OT cyber threat visibility across the board.”

ABB explained: “Disruption of production due to a cyberattack or technical glitches can be costly in terms of lost production and damage to physical assets. Most mature operational monitoring is focused on the performance of the asset, whether it be a gas turbine for electricity, a drive system used to crush ore, or simple monitoring of pollution output from a chemical facility.”

The new ABB offering allows ABB’s process control system data collection and forwarding technology to harvest event log details from ABB process control systems, and share that information with IBM Security QRadar, which uses automation and artificial intelligence to help identify security anomalies and potential threats.

Dr Andreas Kühmichel, CTO, Chemicals, Petroleum & Industrial Products, IBM, said: “We see the integration of these solutions as bringing market-leading capabilities together for a singular view of OT security. With more comprehensive OT and IT security visibility, clients can help reduce the risk of production being suddenly interrupted due to a security event, resulting in costly downtime and broader risk to the company.”

The ABB and IBM technologies involved in this solution are designed on open platforms allowing them to operate on the edge and deploy across hybrid cloud environments spanning on-premise, private or public clouds, ABB says. The joint solution is designed so that security processes operate via automation and do not disturb industrial workflows. The security analysis in QRadar operates through a use case library, which automatically flags incidents and triggers corresponding alarms.

The two companies plan continued collaboration in the realm of OT security to develop new capabilities and offerings that address customer challenges in this space.