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THE LEADER VOLUME 18 • NUMBER 1
The intelligent mine concept
T
he case for digitalisation is clear , 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 . 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 .
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 .
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 .
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 multidimensional 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 .
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 , making 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 number 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 .”
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 VP and GM of Metals & Mining , AspenTech
JANUARY 2023 | International Mining 3