Technology & equipment Deep data dive
A huge amount of data is produced every day across modern mining operations, and this hasn’t escaped the industry’s attention. Indeed, mentions of big data within the filings of mining companies rose 30% between the first and second quarters of 2022, according to GlobalData – up 279% since 2016. However, much of this information is wasted – either unused or poorly implemented for providing insights to mine operators. With the right tools, however, this data has the potential to help reduce unplanned downtime, streamline processes, improve asset performance and boost a mine’s ESG credentials.
Predictive analytics solutions use data to state the probabilities of the possible outcomes in the future. Mining companies can then make use of these probabilities to plan many aspects of their operations. The technology is intended to help maintenance planners, systems engineers, controllers and other mine personnel to make real-time decisions that improve performance, reliability and profitability. With mining operators facing a future of dwindling resources and the challenges presented by deeper mines, rising energy costs and infrastructure shortages, there has never been more pressure to improve efficiency and cut costs. However, as many within the mining sector would be the first to admit, the industry is often slow to embrace change, its conservative instincts instead preferring to rely on tried and tested methods that have served it well for decades. For many holdouts against predictive analytics, one of the apparent downsides of the technology is that it can seem intimidating and overly complicated, and it’s return-on-investment (ROI) unclear. However, modern solutions don’t require a data scientist to be at hand to model and configure the application – instead, they’re designed to be easy to use and implement across daily operations, and aim to provide quick payback for mine operators. For example, predictive analytics can be used to examine raw data and successfully diagnose equipment issues days, weeks or even months before failure. This presents a virtual goldmine – metaphorically, of course – for mining operations, with initial cost reduction and productivity gains of an estimated 10–20%, according to AVEVA. Similarly, it can help ensure that operators don’t waste time maintaining equipment that doesn’t need the attention, reducing asset downtime and expenditure. Combined with a deep learning approach, predictive analytics can even forecast the remaining lifespan of an asset. IBM has implemented this technology for the best part of the past decade, providing data analytics for mining giants like Thiess, with the goal of saving its clients up to billions of dollars each year. Back when they first introduced predictive analytics into their portfolio, all the way in 2014, Matt Denesuk, then- manager of smarter planet modelling and analytics for IBM Research, claimed that the data analytics could
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transform the $5trn business of operating mining equipment. Beyond helping to minimise downtime and optimise maintenance schedules, predictive maintenance can help users spend less time searching for potential problems, providing early-warning indications of when an asset’s current operation deviates from the norm – even highlighting the main factors responsible for this deviation.
According to a Deloitte report, transitioning from a reactive, condition-based maintenance strategy – in other words, acting only when it becomes apparent that an asset is underperforming or in need of maintenance – to a more data-driven proactive approach can offer considerable financial savings. The company has estimated that the use of predictive maintenance in the mining industry could reduce maintenance planning time by 20–50% and overall maintenance costs by 5–10%.
“Further investment in predictive maintenance is critical for mines looking to improve productivity and reduce expensive downtime.”
David Kurtz, GlobalData
Deloitte also notes that moving from a reactive, condition-based maintenance strategy to a more data- driven proactive approach can offer big savings. It has estimated that predictive maintenance can reduce mining operations’ maintenance planning time by 20–50% and overall maintenance costs by 5–10%. Mining operators have already implemented predictive analytics to save millions in averted asset failures, according to Fernanda Martins, process industries expert, AVEVA, in a February 2022 interview with Mining Magazine. Syncrude Canada, for example, reportedly saved $20m in annual operating cost avoidance by using this technology. Similarly, Votorantim Cimentos, Brazil’s largest cement manufacturer, avoided $5.5m in corrective maintenance costs per site across six sites after introducing a predictive analytics solution to reduce the overall cost of maintenance, increase productivity, and enhance operational reliability. Furthermore, between 2019–21 the company saw a 10% reduction in maintenance costs and a 6% improvement in asset reliability. A 2021 GlobalData survey found that 75% of mining companies had made at least minor investments into predictive maintenance, and 48% expected to invest in the technology for the first time or invest further by 2023.
“Further investment in predictive maintenance is critical for mines looking to improve productivity and reduce expensive downtime,” said David Kurtz, director of analysis, mining and construction, at GlobalData, in the 2021 report. “The technology not only ensures continued productivity of critical
20-50% The estimated
percentage reduction in mining operations’ maintenance planning time through the use of predictive analytics.
5-10%
The estimated percentage
reduction in overall maintenance costs through the use of predictive maintenance.
Deloitte 25
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