Tag Archives: Artificial Intelligence

SentianAI

Weir expands digital capability with acquisition of AI-focused SentianAI

Automation, Comminution of minerals, IoT, Mineral processing, Mining mergers and acquisitions, Mining software, Sustainable mining, , , , ,

Weir has acquired Sweden-based SentianAI in a move that, it says, will accelerate its technology roadmap and expand its digital capability to provide enhanced productivity and sustainability offerings to customers. SentianAI is a developer of artificial intelligence-based solutions that optimise performance in minerals processing. Founded in 2016, it is based in Malmö and has a team of software developers and data scientists.

The software that SentianAI develops uses advanced AI algorithms that continuously learn and adapt to the dynamic processes within a mine, providing continuous improvement and optimisation over time, Weir says. Jon Stanton, CEO of The Weir Group, said: “Digital technology has an important role in helping address the challenges of declining ore grades, production efficiency and CO2 emissions for our customers. SentianAI’s advanced software solutions complement and will bridge our Synertrex® and Motion Metrics™ technologies well. Together, these will enable us to provide holistic performance monitoring and optimisation for smart, efficient and sustainable mining.”

Earlier this year, SentianAI and Xore Analyzers formed a strategic cooperation to combine XORE’s XRF analysers, which provide real-time data on metal content, with SentianAI’s machine-learning technology, which adapts to variations in ore properties. The pact, SentianAI says, could allow mining operations to improve their recovery rates and overall efficiency.

IM interviewed SentianAI Founder and CEO Martin Rugfelt last year on its technology and approach. He said it already had a flotation project targeting increase of recovery rate but also the stabilisation of the circuit performance. It was also working on optimising a crushing and grinding circuit with the primary goal of an increase in throughput.

Martin Rugfelt, SentianAI Founder and CEO

When asked how its approach differed to other AI approaches in industry he commented: “A lot of the ‘traditional’ AI systems we see are actually AI toolkits/platforms that are sold on the basis that the customer can create AI logic that they need to solve specific problems without having to understand the detailed data science. Unfortunately, without knowledge of the underlying AI and data science, creating AI for complex systems eg control processes is very difficult. As a result, many ‘traditional’ AI systems are abandoned after purchase. Sentian has taken a different approach. Our SentianController is explicitly designed to optimise control of industrial processes, so when customers buy it, they already have the AI algorithms developed and tested for the complexity of control system optimisation. It is effectively a point solution that means you do not need a large data science team to build and run the AI solution.”

He added on the underlying technology: “SentianAI has worked for many years to select the best algorithms and refine how these algorithms work to deliver a unique AI system that has been designed to control and optimise industrial processes. This is very complex and requires some of the latest technologies in AI to be able to achieve the necessary control. We have also developed a system that can uniquely be applied in stages as data quality improves and operator confidence increases – going from making recommendations to fully autonomous control at the speed our customers want. This allows customers to build confidence before committing to fully autonomous control. We have chosen not to patent our solution as it would have exposed the technology, however, we would argue it is very unique.”

He said the system is also capable of self-adaptation, which can be achieved when you have both the right data and the right AI models. “The AI creates a dynamics model that is made from both historical and ‘live’ operational data. It can then choose the set of control parameters that deliver optimum performance towards a specific goal, eg maximum production for minimum energy usage. In comparison to traditional supervisory control systems it adapts to changes in the process. For example, if the process changes for some reason, leading to new data points, the AI incorporates these into its model, new predictions are made, and new control parameter settings are used. New goals can also be set, resulting in SentianController choosing the best control parameters to achieve those goals.”

Humyn.ai and Dundee Precious Metals put out call to accelerate mineral discoveries

Mineral exploration, Mining services, , , , ,

Humyn.ai has partnered with Dundee Precious Metals to launch a crowd-sourced open data competition looking to fast track the discovery of mineral deposits.

The competition, which will run until May 2024, is a worldwide call for geologists and data scientists to collaborate with the DPM team and develop innovative ways to accelerate mineral discoveries.

Humyn.ai says: “Mineral exploration currently has a less than 1% success rate globally, in terms of discovering new economic orebodies. One of the challenges, especially on the near-mine data rich exploration environment, is the increasingly complex and multi-layered datasets that geologists need to collect and further integrate with already available heterogenous historical data.”

Dundee Precious Metals sees this challenge as an opportunity to work closely with geologists and data scientists, to share industry, technical and site-specific knowledge in order to collaboratively develop an innovative targeting approach applicable in such an environment, Humyn said.

The Future Explorer Challenge comes with the chance to win up to $250,000 in prizes, as well as the prospect of future closer collaboration with the DPM team, Hymun.ai says.

David Rae, President and Chief Executive Officer of Dundee Precious Metals, said: “We are excited to be launching this innovative competition. While the overall objective is the potential discovery of significant new deposits, we are also targeting the opportunity to work with top innovators – both geoscientists and data professionals – to develop new ways of unlocking the value in our data.”

Humyn.ai Founder and Director, Holly Bridgwater, said: “This crowd-sourced data competition will engage a global community of innovators, many of whom have no experience with the mining industry. We can’t wait to see how people with diverse skills from around the world will contribute to solving a hard problem faced by industry.”

Micromine introduces cloud-based AI capabilities to software suite

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

Micromine has revealed its 2024 release, saying the update comes with new features and enhancements across the company’s entire product suite, further underscoring its mission to deliver state-of-the-art technology to reshape the industry.

Kiril Alampieski, Micromine’s Chief Strategy and Product Officer, said: “Addressing the ever-evolving needs and requirements of our clients is the driving force behind our relentless focus on innovation to ensure the industry can achieve more by integrating technology within the client operational workflows and reducing data errors and productivity bottlenecks.”

Powered by the company’s cloud-native data sharing and collaboration tool, Micromine Nexus, the 2024 release introduced two significant features: Micromine Origin Copilot and Micromine Geobank Panorama, enhancing the company’s exploration solutions.

Alampieski said: “The ground-breaking feature, Micromine Origin Copilot, is poised to revolutionise geological and resource modelling. The cloud-based AI companion can process data categorised or quantified and employs advanced machine-learning techniques to craft thorough and robust models autonomously.

“Micromine Origin Copilot plays the role of a skilled ally, offering a supplementary perspective to support and authenticate conventional resource estimation methods, thereby empowering geologists with greater confidence and peace of mind in their models. This AI journey begins with grade modelling and will be implemented to other features throughout Micromine Origin and the wider Micromine ecosystem.”

Micromine Geobank’s Panorama feature also benefits from AI and cloud-computing assistance, able to automate the labour-intensive task of creating a seamless down-hole image from drill core imagery, the company says.

The company’s three mine planning solutions received updates for the 2024 release.

Micromine Alastri, the company says, expands on its industry-leading battery-electric haulage modelling capabilities. The functionality allows mine planners to analyse, validate and implement robust decarbonisation strategies that describe what the mine of the future looks like in practice.

Micromine Spry evolved with a broader set of tools and industry-leading visualisation to tackle the demands of modern coal and soft-rock mine planning head-on. The updates are designed to provide a better understanding of mine data and more straightforward methods to communicate results.

Micromine Beyond improves strategic scheduling with new pit optimisation and materials management capabilities. The new functionality builds more confidence and certainty when developing life-of-mine plans.

Alampieski said: “Micromine’s mine planning and scheduling tools are engineered to meet the needs of mine planners at each planning horizon and primary mining method. The 2024 release is future-focused, delivering precise and dependable outcomes for today’s mine planners.”

Lastly, Micromine’s mine production, control and fleet management solution, Micromine Pitram, adds significant improvements, making it easier to track shift progress and gain valuable insights, as well as reducing time spent on data extraction and manipulation.

MaxMine talks up data-led emission reductions for open-pit mines

Environmental, Mining equipment, Mining services, Mining software, Sustainable mining, , , , , , , , ,

MaxMine has been talking up the potential of data in the quest for reducing emissions and boosting productivity at mine sites, with Tom Cawley, Executive Chair and Interim CEO, arguing that there is still plenty of low hanging fruit for mining companies to leverage on their way to achieving longer-term net zero mining targets.

MaxMine, the company says, is an automated, high-resolution data-based business reporting tool that combines advanced data acquisition technology with AI analysis to fully optimise mobile equipment and operator performance within mining and other mobile equipment-based operations, measuring performance differently and using gamification to change behaviours.

According to Cawley, the average open-pit mine can reduce Scope 1 emissions by up to 10-15% by leveraging data, with MaxMine insights enabling this average open-pit mine to improve productivity while also reducing its carbon footprint by around 15,000-20,000 t of CO2.

IM put some questions to Cawley to find out more.

IM: In terms of your expanding product portfolio, where are clients receiving the biggest and potentially quickest return on investments (ROIs) from your solutions?

Tom Cawley, Executive Chair and Interim CEO of MaxMine

TC: MaxMine has the most extensive dataset in the open-cut load and haul mobile mining equipment sector. We continuously collect data from all sensors across 10 original equipment manufacturers or equipment manufacturers. We have around eight million hours of data, enabling MaxMine to provide unique levels of measurement in the mining sector and equip us with an unparalleled range of potential applications. This enables us to cover a broad scope across all mines while delivering tailored solutions to our clients, depending on their requirements from site to site.

The areas of the highest value proposition for our clients include improved TMM (total material moved) by increased payload, reduced cycle times via driver feedback, improved haul road conditions, enhanced safety and faster incident investigations. Cost reductions are delivered by reduced queueing, off-haul travel, idle time, and improved tyre life. High-resolution data is used to measure the performance of and diagnose mining trucks delivering greater efficiency via engine performance improvements and increased availability via predictive analytics and faster fault resolution.

IM: Although you have a specific product focused on decarbonisation (MaxMine Carbon), would you say the majority of your solutions are providing emission reduction benefits? In terms of adding new clients, is this where a lot of the emphasis is?

TC: Yes, there is a strong correlation between productivity and fuel intensity reduction. The better productivity, the better the carbon intensity.

MaxMine Carbon enables mine sites to measure fuel burn on every asset, every second of the day. MaxMine’s high resolution data allows the difference between business as usual and improved operations to be measured, allowing the fuel saved to be included in the productivity benefits.

MaxMine’s high resolution data is used to:

  • Measure actual truck performance, identify trucks operating below design efficiency and provide diagnostic information; and
  • Provide granular feedback to drivers, which can be used to reduce fuel burn.

MaxMine Carbon isn’t only a separate fuel-saving feature; its capability allows open-cut mining companies to measure, manage and reduce their carbon footprint associated with Scope 1 diesel emissions and reduce operating costs related to diesel consumption.

IM: Where is the development of MaxMine Carbon for underground mining? What timelines do you have around the development and rollout of this solution?

TC: Open-cut mining is a large sector and offers significant opportunities for growth. It is much more energy-intensive at the equipment level compared with underground mining.

MaxMine is growing robustly in this area, and we’ll continue focusing on further penetration in the open-cut mining space in the near and medium term.

IM: Is your market proposition stronger at bulk mining operations than others?

TC: Our market is focused on open cut-mining and larger-size trucks, with a size class greater than 100 t.

IM: Outside of your existing portfolio, where do you see room for growth with different solutions? Are you actively engaged in pursuing such opportunities?

TC: We continue to focus and remain disciplined on our core markets, and our unique selling proposition delivers huge opportunities for MaxMine and the open-cut mining industry.

Orica and Caterpillar set for mine to mill collaboration

Automation, Explosives and blasting, Mineral processing, Mining services, Mining software, Sustainable mining, , , , , , , , , , , ,

Orica’s Digital Solutions segment continues to make major inroads across the mining value chain, with its latest mine to mill initiative set to involve a collaboration with Caterpillar.

Speaking during the company’s FY23 financial results webcast, Sanjeev Gandhi, Orica Managing Director and Chief Executive Officer, said demand for software, sensors and data science continued to increase as orebodies become increasingly hard to find and extract against a backdrop of high commodity prices and increasing ESG obligations and commitments.

“Customers are continuing to seek operational efficiencies across the mining value chain and unlocking the value of digitisation and automated workflows is key to achieving these efficiencies,” he said.

Orica was reporting Digital Solutions’ first full year result, with Gandhi highlighting a doubling of earnings alongside a significant improvement in margins.

“This was driven by growth across all three sub-verticals, namely Orebody intelligence, Blast design and Execution solutions, GroundProbe,” he said.

The Digital Solutions business has been identified as one of Orica’s key growth verticals as it continues to build and invest in the next generation of digital technologies and solutions, beyond its blasting core.

This was witnessed during the company’s most recent financial year, when, among other developments, it acquired Axis Mining Technology, a leader in the design, development and manufacture of specialised geospatial tools and instruments for the mining industry; as well as released what it said was its most innovative fragmentation monitoring solution yet, FRAGTrack Gantry.

Gandhi said: “Innovation continues to be a focus, and this year we have released 15 new digital features, with a focus on artificial intelligence-based solutions to support our customers.”

And, as the industry and Orica’s customers look to solve their biggest challenges through partnership, Gandhi announced its new collaboration with Caterpillar, saying the two companies had signed a memorandum of understanding (MoU) to explore opportunities to integrate key elements of their respective domains.

He explained: “The initial focus will be on the potential integration between Orica’s Rhino™, BlastIQ™ and FRAGTrack™ technologies with Cat® MineStar™ Terrain technologies.”

Rhino (graphic pictured above) is an autonomous drill string-mounted geophysical sensor that measures unconfined compressive strength while drilling. It enhances orebody knowledge in real-time, enabling miners to make better blast planning, improve fragmentation profiles and increase throughput, according to Orica. The technologies in the BlastIQ platform, meanwhile, are, Orica says, designed to deliver economic and operational value individually, with the benefits maximised when integrated in a systemised process. And finally, FRAGTrack is Orica’s state-of-the-art fragmentation measurement tool designed to provide rapid insights into the outcome of blasting processes.

Caterpillar says of MineStar Terrain: “Cat MineStar Terrain uses high-precision guidance technologies, material tracking and more to help your machines work according to plan – increasing efficiency, reducing variability and helping you get the most out of your drilling, digging, loading and grading operations…The solution helps you increase drill capacity, crusher throughput and material accuracy while driving consistency in payloads and bench heights.”

Gandhi said on this MoU: “The goal of this integrated workflow is to provide customers with high-fidelity rock property information enabling significant improvements to on-bench safety, drilling and blasting program accuracy and productivity, along with higher quality blast outcomes that generate enhanced mill performance.”

In the future, the two companies intend to extend their collaboration to optimisation of the entire value chain, from mine to mill, according to Gandhi, who said the approach aligned with both organisations’ ambitions to create sustainable solutions and services that will build the momentum for more intelligent and solution-driven mining ecosystem.

Volvo Construction Equipment leveraging AI to optimise fluid conditions

Environmental, Mining consumables, Mining safety, Mining services, Sustainable mining, , , ,

Volvo Construction Equipment has unveiled its new Fluid Analysis program, tapping into the artificial intelligence realm to identify wear metals and contaminants on equipment or changes in fluid conditions.

The solution expands on its existing Oil Analysis program and encompasses lubricants, diesel fuels, AdBlue and coolants, and comes alongside a 250% increase in global testing capacity, which is equal to 20 labs globally.

The new digitised process – including a cloud-hosted customer portal and Fluid Analysis mobile app – uses AI data analysis to provide customers with easy-to-understand reports and insights with the highest levels of accuracy which can help them make better informed decisions, it says. This allows customers to take preventive actions against contamination and wear, leading to improved uptime and contributing to a lower total cost of ownership (TCO).

Volvo CE says: “AI accelerates the testing process, making analysis quicker and easier. This allows lab technicians to focus on more sensitive testing issues – such as analysing abnormal or critical samples, those of greatest concern to clients – and, thereby, offer more useful insights and recommendations.”

Alongside the expansion of the program, Volvo is partnering with an industry-leading testing provider to, it says, ensure consistency and efficiency on a global level. Besides leading to a significant increase in testing capacity, this partnership will also reduce lead time and simplify the sampling and analysis process.

With 75% of repair costs and equipment downtime traceable to the use of contaminated lubricants and fuels, and up to 65-75% of all bearing failures traced back to to lubrication issues, the importance of effective fluid analysis cannot be overstated, the company says. Through early identification of possible contamination or wear, customers can take proactive action before any unplanned downtime occurs, helping them to maintain productivity and avoid any potential repair costs.

Volvo CE says the expanded program has taken the potential of fluid analysis to the next level, providing customers with efficient and easy-to-understand reporting. The digitised process is quick, straightforward and provides reports to the highest levels of accuracy.

Once a sample is taken from the machine it is sent to Volvo’s newly expanded global laboratory network. Here it is analysed with a diagnosis performed as required based on any trace elements found.

Once testing is complete, reports – along with recommended actions – are made immediately available via the Volvo Fluid Analysis customer portal. This cloud-based platform features a user-friendly interface which presents reports in a highly visual and easy-to-analyse format, the company claims. It is also the first fluid analysis solution on the market which uses AI data analysis, providing an intuitive solution to deliver consistent and easy to understand recommendations.

The process is made easier still with the Fluid Analysis mobile app. This enables users to register their samples from anywhere in the world, quickly and easily access sample reports, and receive notifications should anything require urgent attention.

Routine fluid analysis is proven to reduce downtime by 15%, Volvo CE says.

“Through a more efficient testing process, increased testing capacity and intuitive reporting, Fluid Analysis from Volvo has made it significantly easier for customers to gain the insight needed to take corrective actions which can help avoid expensive repairs and unexpected downtime.”

How artificial intelligence is revolutionising the mining industry

Automation, IoT, Mineral exploration, Mining events, Mining safety, Mining services, Mining software, Sustainable mining, , , , , , , , , , ,

The mining industry has always been at the forefront of technological progress. From the steam engine enabling coal mining to be profitable, to advanced drilling techniques, innovation has played a pivotal role in improving productivity and efficiency, the organisers of IMARC 2023 say.

In recent years, the adoption of artificial intelligence (AI) has emerged as a gamechanger for mining, allowing for more efficient exploration, taking automation to new levels, generating greater yields, dramatically improving safety, and maximising extraction, maintenance and operational performance.

Improving mine site efficiency

AI-powered systems are being rolled out across mining operations to enhance resource estimation accuracy. By examining geological data patterns and incorporating historical mining data, AI algorithms can provide more precise estimates of mineral reserves. This helps mining companies make informed decisions regarding investment, production planning and resource allocation, ultimately maximising the economic potential of mining projects.

Mark O’Brien, General Manager for Digital Technology & Innovation at CITIC Pacific Mining, notes that AI is already having a sizeable impact within the mining industry.

“In South Australia, mining companies already have access to a massive library of core samples, which are literally centuries of data,” he said. “Using AI-enabled algorithms, we’re now finding resources that were originally missed. The process is relatively similar to the advancement in DNA technology that has allowed criminologists to review and solve old cases.”

Farzi Yusufali, co-Founder of Stratum AI, a company which provides bespoke, AI-driven solutions to help mine sites increase their yields with lower risk, says: “One of our clients noted that their yield predictions in terms of what was pulled out would swing 30% each way on quarter-on-quarter. If you are processing millions of tonnes of copper, that’s a problem. Now with the application of our AI system we found there has been 58% accuracy increase in predictions quarter-on-quarter for the last couple of years.”

AI also offers immense potential in streamlining mining operations and optimising asset management. Through the use of Internet of Things (IoT) devices and sensors, real-time data collection becomes feasible, enabling mining companies to monitor equipment performance, evaluate operational metrics and identify potential bottlenecks. AI algorithms can then process this data, generating valuable insights and predictive models that enhance decision making and prevent unplanned downtime.

Furthermore, AI-powered automation systems can significantly improve efficiency and safety in mining operations. Autonomous vehicles and machinery equipped with AI algorithms can navigate complex terrains, optimise routes and execute tasks with precision. This minimises human error, reduces the risk of accidents and enhances worker safety. Additionally, AI-driven predictive maintenance systems can monitor equipment health, detect anomalies and schedule maintenance activities proactively, maximising uptime and extending the lifespan of mining assets.

AI technology also holds promise in promoting environmental stewardship and sustainability in the mining industry. For instance, AI algorithms can optimise the mine planning process, considering environmental factors such as land reclamation and habitat preservation.

Alex de Jager, the Managing Director of Conundrum Australia, says: “Our technologies focus specifically on creating efficiencies in operations and production, the absolute benefit of all that is not only in the profitability, but it’s in making the mines greener. If you’ve been able to extract more material from what you have mined, you can dramatically lower your electricity and water usage thanks to AI systems.”

de Jager says sensors and remote sensing technologies, combined with AI algorithms, enable continuous monitoring of air and water quality, allowing rapid identification and response to any environmental disturbances. Such monitoring systems help ensure compliance with environmental regulations and promote sustainable resource extraction practices.

Bridging the skills gap

Gavin Lind, CEO of the Australian Minerals & Energy Skills Alliance (AUSMESA), says the rapid advancement of AI presents unparalleled opportunities for the nation’s industries, including mining. For example, he says AI presents opportunities to transform how the most basic tasks are undertaken to make them quicker, safer and more efficient.

“The core function of a mechanic, which is to fix and repair a vehicle, does not change with the adoption of AI,” he said. “With AI, however, a mechanic may not be needed in a garage on a mine site, but can be stationed at a remote operations centre thousands of kilometres away.”

Encouragingly, the mining industry in Australia has already taken some steps to develop digital skills and Lind says AI is also helping address one of the industry’s biggest challenges: attracting the workforce needed to meet global demand. He says mining is at a disadvantage compared with typical city-based employers, as potential recruits can’t just go down to their local mine to see what it’s like firsthand.

He said: “In countries where mining operations are remote, AI can allow us to tell that story from capital cities with the assistance of virtual reality to attract a new generation of workers to the industry.”

Is excessive risk aversion stalling progress?

There is no doubt the industry is inherently hazardous, and risk management is a priority to safeguard both people and the environment.

However, de Jager believes being excessively risk-averse can hinder progress and impede opportunities for growth. In this context, he says, countries with more progressive attitudes towards embracing innovation have surged ahead in AI adoption, gaining a competitive edge in efficiency and cost-effectiveness.

Conundrum Australia creates machine-learning software for the digital transformation of the metals and mining industry and de Jager believes Australia’s reputation for rigorous regulation means it can be a big challenge for a new innovator coming into the Australian market. Conundrum will be presenting at the 2023 International Mining and Resources Conference (IMARC) in Sydney later this year.

de Jager says: “The upside to this risk averseness is that no matter what industry you point to in Australia, the laws that govern that industry are incredibly tight and well thought out, well designed and well developed. Australia is a very innovative country, but it is also incredibly risk averse, and this can be a hurdle to market.”

He believes that by maintaining a low threshold to risk, Australia risks falling behind in the global race towards AI-driven mining solutions. Without embracing new technologies, mining companies in the country may face challenges in accurately estimating resources, optimising extraction processes and mitigating environmental impacts. Moreover, they may fail to capitalise on AI’s potential to create safer and more sustainable mining practices.

Trends in AI

When it comes to trends in AI, one of the most significant applications in mining lies in exploration and resource estimation. Traditional exploration methods can be time-consuming and costly, often yielding limited results. However, as Yusufali says, AI-driven technologies such as machine-learning algorithms and data analytics have transformed the exploration process.

She says there is a range of tools now available to analyse vast volumes of geological data, including historical drilling records, satellite imagery and sensor data, to identify promising areas for mineral deposits.

“With AI, geologists can optimise their decision-making by rapidly identifying potential mining sites, reducing the risk of exploratory failures, and saving valuable time and resources,” she says.

“AI in the exploration process can help mining companies find minerals and resources faster and more efficiently, by identifying patterns and anomalies in the data that might otherwise be missed by human geologists.”

International Mining is a media sponsor of IMARC 2023

Gradiant’s process water solutions to be used at SLB, Rio Tinto operations

Energy minerals, Environmental, Mineral processing, Mining services, Precious metals, Water management, , , , , , , , , , , , , ,

Gradiant, a global solutions provider and developer for advanced water and wastewater treatment, has announced partnerships with SLB (formerly Schlumberger), Rio Tinto and an Australia-based global mining company to, it says, improve productivity and sustainability in the mining industry with a focus on reducing carbon and water footprints.

The projects are in the US and Western Australia for resource recovery of critical minerals and industrial process water.

Gradiant’s collaborations with SLB and the Australia-based global mining company target the recovery of valuable metals such as lithium, nickel and cobalt. The mining of these materials is highly complex and water intensive. Moreover, with increased market demand and environmental regulations, businesses must identify cost-effective and sustainable technologies. Gradiant’s technologies enable sustainable, efficient and economical water governance through end-to-end customised solutions, it says.

Gradiant’s work with SLB integrates Gradiant’s technologies to concentrate lithium solution with SLB’s direct lithium extraction (DLE) and production technology process – allowing reduced time-to-market and environmental footprint for lithium extraction. The solution enhances the impact of the sustainable lithium extraction process by enabling high levels of lithium concentration in a fraction of the time required by conventional methods while reducing carbon emissions, energy consumption and capital costs compared with thermal-based methods, the company says.

Back in October, Gradiant and Schlumberger entered into a partnership to introduce a key sustainable technology into the production process for battery-grade lithium compounds.

For Rio Tinto, Gradiant will deliver a new facility in Western Australia to replace ageing facilities by employing the company’s proprietary RO Infinity membrane technologies and SmartOps Digital AI into existing mining operations. Gradiant has introduced two chemical-free technologies into operations to minimise chemical consumption and waste discharge, it said.

Lastly, Gradiant’s RO Infinity and SmartOps technologies will concentrate complex wastewater from nickel and cobalt production at a new facility in Western Australia for a global mining company, resulting in up to 75% cost savings with lower carbon and water footprints compared with conventional technologies, it says.

Prakash Govindan, COO of Gradiant, said: “Mining is a uniquely complex industrial sector with challenges of remote locations, large volumes of waste, wide fluctuations in water quality and the high-value end-product that demands relentless design and operations efficiencies. The real opportunity for water technology in the mining industry is resource recovery in wastewater coupled with machine learning AI. We are excited to work with the world’s leading mining operators to enter a new era of sustainable resource recovery. This is made possible by Gradiant’s deep understanding of the complex chemistry that underlies the production processes, which is then operationalised by machine learning digital technology.”

Weir Motion Metrics to expand into new state-of-the-art facility in Vancouver

Comminution of minerals, IoT, Mining equipment, Mining services, Mining software, , , , ,

Weir Motion Metrics recently cut the ribbon on a new state-of-the-art facility in Vancouver, British Columbia, that, the company says, will allow the business to better serve customers and provide employees with an innovative workspace for collaboration and development.

On March 13, Weir Motion Metrics opened the site, which is a combined manufacturing, R&D and demonstration space. At 19,725 sq.ft (1,833 sq.m), the new facility is an exciting next step for Motion Metrics, which was born in a business incubator at the University of British Columbia and has grown to a leader in innovative artificial intelligence (AI) and 3D rugged machine vision technology for miners worldwide. Weir acquired Motion Metrics in late 2021.

Mike Funke, Vice President of Weir ESCO, which oversees Weir Motion Metrics, said: “In recent years, there has beena very strong pull from mining leaders for AI-enabled digital solutions. This space will be the new home for Weir’s global centre of excellence for AI and digital solutions. The grand opening reflects our continued commitment to the Vancouver technology and business communities.”

Miners are increasingly focused on improving the safety, efficiency, productivity and sustainability of their operations. Weir Motion Metrics specialises in developing advanced monitoring solutions designed to attain these critical objectives. The facility will continue to support demand and customer needs for years to come, it said.

The company has recently embarked on an ore characterisation trial at a copper mine, leveraging its AI and 3D rugged machine vision technology alongside spectral sensors.

TALPA looking to democratise the data dynamic in mining

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

TALPA Solutions is a software platform provider that aims to democratise the data dynamic in mining, building platforms that, it says, integrate data, decisions and operations effectively, making it easier for mining companies to bring together multiple datasets into one interface with actionable insights.

The ability to do this can set solution providers apart in the mining industry.

Another differentiator for TALPA in this space is its independence. As an entity backed by venture capitalists, it is not tied to one specific OEM or equipment provider. This allows it to look not only at maintenance-related data but also at information that impacts productivity, safety and other on-site considerations. It gives TALPA the freedom to work with raw data and apply various data models directly on its cloud platform.

TALPA’s approach to obtaining on-board machine data is also unique, it claims. Instead of using APIs for already processed signals, it connects directly to the machine’s ECU data via a data logging device and multiple CAN bus interfaces. This approach ensures integration is both quick and easy, and that no potential data points are left behind.

According to Alexey Shalashinski, Head of Business Development at TALPA, such an approach is starting to be recognised by both the mining company community and the OEMs themselves.

TALPA has already partnered with several companies, including GHH and SMAG, to create digital solutions for their end users. For example, the GHH inSiTE Digital analytics solution offered by GHH to various underground mine sites has allowed significant reductions in MTTR (mean time to repair) at several mines owned by GHH’s biggest client in Germany and is now being populated across further sites internationally.

Referencing a project in North America with one of the industry’s leading tyre manufacturers, TALPA’s industrial AI platform has identified productivity improvement opportunities at an open-pit mine site by analysing the carrying capacity of haul trucks in operation, merging it with the tyre diagnostics on these vehicles as well as spatial and other contextual data. On this particular project, TALPA identified there was a potential 5-10% opportunity to increase the average payload on these trucks based on what the sensor data from various systems was saying.

Many mining OEMs claim to provide software solutions with insights on maintenance and productivity, but these normally cover only the product range of particular manufacturers, according to TALPA.

Shalashinski says mine sites find it difficult to get “buy-in” for several systems in the case of running mixed fleets from respective companies and integrating them into the workflow. TALPA is looking to provide that connection to clients so they can leverage all the major benefits that come from pulling data off hundreds of sensors on multiple pieces of equipment.

He concluded: “TALPA Solutions’ unique approach to obtaining on-board machine data, and its partnerships with various companies, make it a valuable partner for the mining industry.”