Tag Archives: Aspen Mtell

AspenTech out to advance levels of operational excellence with aspenONE updates

Automation, IoT, Mining services, Mining software, Sustainable mining, , , , , ,

Aspen Technology has introduced new performance and sustainability capabilities available in aspenONE®, the company’s portfolio of asset optimisation solutions, spanning design, operations and maintenance.

Designed to accelerate customer digitalisation strategies and help customers achieve advanced levels of operational excellence, the new capabilities progress net-zero initiatives, empower engineers with enhanced artificial intelligence, improve asset health and performance, enable customers to achieve an integrated enterprise data management system and more, the company says.

Rasha Hasaneen, Chief Product and Sustainability Officer at AspenTech, explained: “AspenTech is committed to innovating with our customers and developing solutions to close the gap between available technology today and what is needed to achieve net zero. Optimising for efficiency and sustainability across operations is critical and these new capabilities make it easier for customers to overcome this challenge.”

New aspenONE updates help customers more quickly develop and implement reliable, impactful sustainability solutions, the company says. Updates also enable customers to extract insights based on operational data from across the enterprise, resulting in reduced inefficiencies and improved operational performance. Key among the new updates are an expanded set of models incorporating AI, as well as a number of integrations providing comprehensive access to operational technology data.

AspenTech said: “As customers progress toward their net-zero goals, the ability to ensure accuracy in their sustainability solutions and tackle complex problems is critical”

AspenTech’s modelling capabilities address both these challenges with new and expanded updates, including:

  • AspenTech’s library of sustainability sample models now contains more than 140 options to drive faster time-to-market for new processes and technologies. Incorporating industrial AI, the models help overcome execution barriers for sustainability projects, improve economics and support investments;
  • New high-fidelity models increase hydrogen and carbon capture model accuracy, extend from design into operations, and recalibrate based on changing process conditions; and
  • Support for green hydrogen initiatives is now available with out-of-the-box electrolyser models that help ensure safe operations of key equipment.

aspenONE also comes with new integrations to help customers centrally manage enterprise OT data aggregation, including:

  • AspenTech Inmation™, the company’s centralised data management system, now seamlessly connects to its historian, Aspen InfoPlus.21®, and its data visualisation solution, aspenONE Process Explorer™, to more easily identify inefficiencies, bottlenecks and production challenges from across the organisation;
  • AspenTech’s asset performance management solution, Aspen Mtell®, now includes out-of-the-box integration with AspenTech Operational Insights™ that provides configurable dashboards to inform strategy, optimise resource allocation and improve performance; and
  • In addition, AspenTech Inmation for Aspen Mtell provides native connectivity for quick integration with multiple, high volume data sources to enhance asset performance management and drive customers’ reliability programs.

Peter Reynolds, Principal Analyst, ARC Advisory Group, said: “AspenTech’s new release of aspenONE demonstrates breakthrough innovation with multiple AI-powered software enhancements designed to help industrial customers achieve operational excellence. AspenTech customers can now use their reconciliation and accounting software to track plant wide GHG emissions accurately and consistently to lower their carbon intensity. Moreover, the capabilities in this release make it easier to progress sustainability initiatives with sample models for carbon capture, hydrogen, battery recycling and more.”

Leveraging the intelligent mine concept for improved profitability, sustainability

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

The case for digitalisation is clear, according to AspenTech’s Jeannette McGill*, with digitalisation being critical for the metals and mining industry to achieve sustainability and operational excellence in the years ahead.

This is why the more forward-looking decision makers in the mining industry are embedding digital capabilities in their operations so they remain agile, competitive and profitable over the long term, while realising immediate and measurable benefits, she says. Technology providers have responded to the industry’s needs with solutions designed for the mining sector that directly address the dual imperatives of greater business efficiency and enhanced sustainability.

The intelligent mine

In their digitalisation initiatives, today’s operators also know that managing data more efficiently and effectively will be crucial in helping them to meet the challenges they face. Multiple difficulties remain in the way that organisations across the sector manage their data.

Senior mining company executives frequently make tough decisions but, in doing so, they must aggregate isolated pockets of data to generate insights that are relevant and actionable. For data to be available is no longer sufficient. The top priority for effective decision making is for appropriate management of diverse and disparate data sets in a range of locations.

The key is to integrate data and conduct high-level analysis with an understanding of the domain work requirements. Mining companies achieving this will establish what has become known as the intelligent mine. This is a concept that focuses on centralising information from multiple locations and business processes to reveal useful insights. It supports senior level decision making with designed-for-purpose analytical platforms.

Data held in 50 separate systems will not in itself drive operational efficiencies or support sustainable operations. By addressing several issues simultaneously, an organisation is more likely to move towards the intelligent mine.

First, though, businesses must implement automated data gathering systems to capture relevant data from various parts of the mining process and facilities. Second, organisations must have tools that detect bad data because only good data enables good decisions. They must ensure all changes are consistent, correct and improve data processing. Third, the business should assist the mine personnel by providing the capability to integrate data with built-in relevant analytics, so they process and act on insights in a meaningful time frame.

The predictive dimension

To create an intelligent mine, good data is important but only one part of the equation. Organisations must also analyse data in different ways within a context of the problem to be solved with appropriate predictive outcomes.

Companies need to predict the degradation of equipment that, if unattended, will lead to equipment breakdowns and unplanned maintenance, thereby adversely affecting both operational efficiency, reliability, sustainability and safety. Mining is equipment- and infrastructure-intensive with expensive machinery. It demands operational continuity for profitability and sustainability.

Bringing in prescriptive maintenance

Traditional preventive maintenance methods generally fail on the benchmark of equipment availability and performance. Earlier preventive maintenance efforts were unable to deliver sufficient time-to-failure warnings to deliver a significant impact on profitability.

That is where modern prescriptive maintenance plays a vital role. The technology monitors data from sensors on and around the machine to develop intense multi-dimensional and temporal patterns of normal operation, abnormal operation and explicit degradation patterns that precede breakdown. This provides early warnings, using artificial intelligence (AI)/machine learning digital technology to spot patterns that humans will never pick up.

Also surpassing human capability, the technology can assess the health of numerous machines every few minutes. It also delivers early warnings to maintenance teams, often with prescriptive advice on resolution. Facing tough challenges and spread thinly over large sites, workers benefit from warnings. Much of the intense repetitive analytics and engineering help them prioritise what matters most. Maintenance teams with such prescriptive maintenance tools ensure an intelligent mine makes significant progress in eliminating unplanned breakdowns.

Finding a solution

An asset performance management (APM) approach – with integrated prescriptive maintenance capability – ensures mines improve reliability, availability and uptime, simultaneously reducing the considerable cost of redundant equipment.

Operations teams often work on the assumption of lower availability by, for example, installing three machines when they only need two, or purchasing 10 trucks to ensure they always have eight up and running. These practices are now deemed too wasteful and have become unsustainable.

By embracing the most effective technology, mines can achieve benchmark reliability without the need for more people, equipment, or expenditure. Companies can operate at the required production levels and either mothball or switch off redundant equipment. Being able to do this with full confidence it will actually enhance overall outcomes, makes a significant contribution to the bottom line. It reduces emissions and increases sustainability.

Yet to develop an efficient digitalisation strategy, certain components must be in place. All too often, mines try to invest minimally in digital solutions to save money. Without domain-centric AI/machine learning analytics, this limits the reach and value technology can deliver. Successful digital strategies deploy solutions that draw on data from sensors and other sources. Enterprise resource planning systems, manufacturing execution systems, laboratory information management systems and advanced process control systems are all part of the mix, as well as general mine planning and design systems. Machine learning and other data science techniques require timely delivery of available data, so historian technology plays a vital role.

Across the industry, a growing numbers of mines are pursuing an APM approach. Australia-based gold miner, Evolution Mining, for example, has deployed Aspen Mtell software at the company’s Mungari Gold Operations, in Western Australia, to help reduce unplanned downtime and provide information to support productivity improvements.

Greg Walker, previously Evolution Mining Mungari General Manager, said: “Evolution’s Data Enabled Business Improvement program has achieved excellent results in recent years. With this new technology, Mungari Gold Operations can achieve further productivity improvements via increased asset availability.”

Looking to the future

Today’s mining industry is now sufficiently mature that it should fully embrace digital optimisation technologies. Operators that fail to adapt and build a strategy to utilise such technology are destined to struggle against competitors that do. Prescriptive maintenance delivers quick results by improving the use of existing capital assets and eliminating the surprise of unplanned downtime, which directly affects productivity, safety and sustainability.

The industry should understand how scalable prescriptive maintenance solutions add value to assets. This works as well with a single asset, conveyer system, a processing plant, a large mill, as it does with equipment across a worldwide enterprise. The truly intelligent mine empowers mining companies across a vast array of contemporary challenges. From reducing unplanned downtime and decreasing safety risks, to greater operational efficiency, sustainability and increased profitability, this approach will be essential for mining companies to surmount all their challenges in short and longer terms.

*Jeannette McGill is VP and GM of Metals & Mining at AspenTech

AspenTech Mtell Agents getting ahead of the mine maintenance game

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

AspenTech is looking to turn condition monitoring procedures in the minerals processing plant on their head by providing prescriptive maintenance tools powered by machine learning that offer the earliest possible issue detection along with the required context to allow operators to act.

“After more than a decade of working on Mtell, we understand how to slot into an operation to make sure our data is clear, prescriptive and acted on,” Mike Brooks, Global Director of APM Solutions at AspenTech told IM recently.

Aspen Mtell® has been a gamechanger for industries such as metals and mining, according to the company, performing prescriptive maintenance by forecasting degradation and equipment failures, alerting staff in advance of when a failure could occur, identifying potential causes and the scope of any failure, and providing advice on the corrective action to avoid or mitigate the impending failure.

This is leading to increased operational efficiency, resulting in improved energy efficiency and reduced emissions, according to the company.

Unlike other mining-related predictive maintenance proponents, AspenTech and Aspen Mtell have been using machine learning for over a decade, using the benefits of this technology to improve on the condition monitoring and firefighting maintenance procedures in place at industrial sites.

“By obtaining sufficient domain knowledge and packaging it into a solution, we have created a product that is able to detect patterns in the data, track any anomalies and contextualise these anomalies on the basis of past performance and previous incidents,” Brooks explained.

This process involves detecting failures, “hidden failures” (spikes or changes in behaviour not associated with an event) and when an asset is offline from past operating data and contextualising this within what is considered ‘normal’ operating conditions. From this, data analysts create “Failure Agents” and “Anomaly Agents” to spot potential failures and watch for changes in normal operating behaviour.

Once these Agents have been trained from historical data, they are deployed to monitor live equipment feeds with all deviations labelled as anomalies and detected by the appropriate Agent.

If an anomaly does not match the signature of a deployed Failure Agent, the anomaly triggers an alert requesting an inspection to determine the cause. The results of the inspection will categorise the anomaly as either a new variation of “normal” or a new never-before-seen failure pattern.

If it is the former, the Anomaly Agent will be updated with this new information to make sure no future false alerts with the same signature occur. If categorised as a new failure, a new Failure Agent will be deployed to allow for earlier detection in the future.

The more operating data the Aspen Mtell platform ingests, the more accurate the alert system becomes and the more context the solution can provide operators. Brooks said around a year’s worth of data often proves enough to know what ‘normal’ looks like while ensuring false alerts are kept to a minimum.

In some instances, Aspen Mtell has managed to get ahead of a potential failure on certain components by 4-6 months, allowing maintenance personnel to strategically schedule maintenance procedures and reduce unplanned downtime, according to Brooks.

“Not only are we able to identify the root cause and failure mode with alerts, but we can also often provide details of exactly what is needed to fix it based on past experience,” he said. Such information is particularly useful in an industry like mining, which has an ageing employee demographic that will, in the future, need to be replaced with a new generation of personnel.

“This is all part of our vision of the ‘Self-Optimizing Plant’,” Brooks said.

The Self-Optimizing Plant, as AspenTech puts it, is a self-adapting, self-learning and self-sustaining set of software technologies that work together to anticipate future conditions and act accordingly, adjusting operations within the context of the enterprise. The plant does this through pervasive real-time access to data and information, combining engineering fundamentals and artificial intelligence, and capturing and using knowledge to optimise across multiple levels, provide recommendations and automate actions securely in a closed feedback loop.

While the mining industry is still some way off adopting such a vision, AspenTech is getting nearer to convincing the sector of its potential future worth.

Brooks provided an example from a mining company with a worldwide presence that was having difficulty with frequent production interruptions caused by unexpected equipment failures as a case in point.

This company decided to deploy Aspen Mtell across a whole site to improve the reliability and availability of equipment, boost production yields and reduce maintenance costs.

On the secondary cone crusher at the operation in question, the Aspen Mtell application gave an extreme early warning and exposed a multi-dimensional pattern showing fast incremental changes, according to AspenTech. This provided the technicians with the required insights to detect the degradation issue and take the appropriate action, avoiding operational complications that can result in production and maintenance costs in the order of $100,000-500,000 per incident.

Similarly, Aspen Mtell was able to deliver a very early lead time and warnings of a bearing issues on the cone crusher, well in advance of the vibration detection system, allowing early action to service a minor issue before a catastrophic failure. This resulted in savings of around $75,000, according to AspenTech.

Equally, monitoring and catching potential bearing problems on conveyors allowed early replacement without the extended shutdowns associated with unplanned maintenance. Such avoidance is generally worth around $1-$1.5 million in operational costs, AspenTech says.

“The net results were that the company was able to better plan and schedule service and repairs on the mobile heavy haul trucks and the static ore processing, improving operators’ safety, extending component lifetimes, and increasing equipment availability besides improving on spare part/resource planning,” it said.

“The positive results encouraged the company to expand the Aspen Mtell application to other mining sites.”

Brooks says this specific company is one of a handful of miners realising the benefits of Aspen Mtell, with the mining sector fast becoming one of AspenTech’s key growth markets behind oil & gas.

And, with AspenTech having just completed the acquisition of Emerson’s OSI Inc and Geological Simulation Software business, there could be many more mining-related opportunities on the horizon.

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

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

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

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

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

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

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

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

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

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

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