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Editorial comment

In 2020, the World Bank noted that the production of minerals such as graphite, lithium, and cobalt was on track to grow nearly 500% by 2050 to meet rising demand for clean energy technologies.1 Despite a shaky and unpredictable economic climate these past two years, that demand has not abated.

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In fact, despite ongoing global supply chain woes, new batteries, electric vehicles, infrastructure, and connectivity in electronics are poised to continue driving this massive consumption of metals. We’re not talking years down the road either – it starts right now, in 2022.

This being said, the increased consumption of metals presents a dual challenge for digital mines, which will need to balance rising market demand from a globalised economy with the need for sustainability. How do mines maintain a level of operational effectiveness that can support this demand, while facing growing pressures for decarbonisation and greater impacts from climate change?

The dual challenge of accommodating increased market demand and meeting sustainability targets is driving demand for digital mines in 2022. Tackling and supporting digital sustainability in mining operations will require a few key factors.

Firstly, asset-intensive digital mines will need to stay ahead of maintenance requirements to support business imperatives, such as lowering costs and increasing productivity. This will rely on predictive maintenance analytics for precisely determining when equipment is expected to fail and empowering the business to make more informed, real-time decisions accordingly. This insight isn’t just about preventing equipment failures and downtime that disrupt operations, it’s also about reducing the environmental impacts caused by those failures and achieving greater long-run sustainability in operations.

For example, consider the case of Evolution Mining, a leading Australian gold miner, which is using industrial artificial intelligence (AI) for predictive maintenance, mitigating unplanned downtime and generating new insights into how to support productivity improvements. Another mining company I spoke with recently used this same software to avert what would have been a major equipment failure at its mining site. Had the equipment failed, they would’ve been facing four days of downtime. By catching the failure ahead of time, they saved themselves from a (literal) million-dollar loss in productivity.

Asset optimisation technologies like these help companies to make significant improvements to their operations’ workflows by deploying digitised elements necessary for supporting operational effectiveness.

Secondly, the knock-on effect that industrial AI, predictive maintenance, and other asset optimisation technologies have on mining sustainability must also be taken into account. Deploying these technologies and empowering mining sites to begin producing in more effective and optimised ways creates new opportunities for carbon footprint reductions.

If you’re using a predictive maintenance schedule on your truck fleet, for instance, you have more insight into, and proactive control over, things such as tyre wear on the trucks, diesel usage, and emissions control – all of which can be fine-tuned as needed. These digital assets allow companies to report direct improvements in their carbon footprint, driving down energy consumption, water usage, and other wasteful areas.

Predictive maintenance and industrial AI tackle the dual challenge of sustainability and operational effectiveness by reducing environmental footprints and saving businesses money through more optimised production. This combination of analytics and machine learning is a powerful business weapon for creating a more sustainable future, without undercutting business operations at the same time. Ultimately, they allow digital mines to improve business operations, productivity, profitability, and sustainability all at once.

  1. ‘Mineral Production to Soar as Demand for Clean Energy Increases’, The World Bank, (11 May 2020),

Comment written by:

   Jeannette McGill, (VP and GM, Metals and Mining), AspenTech


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