Technology for Sustainable Operations and Risk-Mitigation in the Mining Sector

mining sector

The mining sector produces raw materials for almost every other industry, making it an influential player in the global economy. The advancement of the mining sector is bound to create a ripple effect for all the industries it influences.

A 2020 global mining survey report by KPMG reveals that mining companies are now adopting IoT and Big Data technologies, embracing the consequent digital disruption as an opportunity for growth.

In this article, we will explore what mines of the future will look like and how technology will reduce the risk of mining disasters.

We endeavor to make these blogs relevant and informative to you, and we welcome your feedback. If these blogs inspire you to join us in solving the mentioned challenges, check out our opportunities, to connect with us.

Recent Operational Disasters in the Mining Industry
Brazil Dam Collapse of 2019

The Brumadinho dam was a tailings dam that stored waste from an iron ore mine. On January 25th, 2019, the dam snapped, causing a mudflow of particles into the dam office, flooding the cafeteria during lunchtime, along with many houses, farms, inns, and roads.

In the devastating aftermath, around 270 casualties were recorded. It was predicted that the metals in the tailings would mix with a nearby river’s soil, severely polluting that region’s entire ecosystem.

According to reports, the disaster occurred as a result of design flaws, high water levels, and weak fine tailings. For starters, the dam was constructed using the upstream method on a relatively steeper slope. This itself made it susceptible to disasters, say experts. Water accumulation behind the dam caused finer tailings to continually deposit around the crest as no proper drainage system was put in place. This further weakened the dam’s structural integrity, leading to the disaster.

Several executives from Vale, the mining firm responsible, were accused of intentional homicide and arrested by the police.

Chinese Mining Disasters

In China, mining disasters are quite common and are usually a result of lax safety measures, poor ventilation systems, and illegal mine shafts. In December 2020, a gas leak occurred in a coal mine in the Chongqing municipality while workers were dismantling equipment.

In September 2020, 16 workers lost their lives in another Chongqing mine when a conveyor belt caught fire and produced fatal levels of Carbon Monoxide. This year, the roof of a coal mine in northern China collapsed, killing 21 coal miners and trapping several others underground.

In 2020 itself, around 9 mining accidents were reported across the country, all occurring due to roof collapses, gas leaks, or fire explosions.

Mining 4.0: The Smart Future of Mining

Industry 4.0 introduces smarter ways of doing business, turning incumbents into proactive decision-makers – and mining is no exception.

Automation, AI, data-driven decisions, and smart equipment are set to improve operational efficiencies, reduce unplanned downtimes, and prevent mining accidents.

  1. Predictive Maintenance

The key to reducing process interruptions and downtimes lies in predictive maintenance – a technique that detects irregularities or defects in your operational procedures and equipment at early stages so that you can intervene before they can cause harm.

By regularly checking their equipment’s functioning, operators can identify failures before they occur or cause significant damage. Early identification will allow you to schedule low-cost repairs well in advance and avoid the disruptions caused by sudden breakdowns.

An active condition monitoring system will also help you identify redundant maintenance procedures. You can optimize your operational workflows and eliminate mundane tasks that leave scope for human errors.

Recently, an African gold mine used data from the sensors in its equipment to identify oxygen level problems during leaching. On fixing the problem, they increased their yields by 3.7% and saved $20 million annually.

  1. Autonomous Vehicles and Drillers 

Rio Tinto, an Anglo Australian mining giant, uses autonomous trucks that can carry materials weighing up to 350 tonnes using a GPS without needing human intervention. These trucks have reduced fuel use by 13 percent and are much safer to operate.

Rio Tinto has also experienced success with its Automated Drilling System (ADS)- an automated blast hole drill system employed during production drilling. Under this, operators can manage multiple drills from a remote location using a single console. It enables operators to judge rock structures accurately and determine the number of explosives that need to be deployed.

According to Kellie Parker, an MD at Rio, the autonomous drilling fleet enables Rio operators to drill more safely, accurately, and consistently.

  1. Digital Twinning

Digital simulations replicating the physical structure of an actual working mine can bring unlimited operational intelligence to mining.

Workers can plan out operation schedules and make accurate calculations required in drilling, mineral exploration, extraction, etc. The virtual copy or ‘twin’ of a mine will also allow teams to test new and innovative ways of executing operations without involving any actual resources or equipment.

Moreover, operators can alter the conditions in a virtual simulation to play out different real-life scenarios. They can change the weather, for example, or create emergency-like circumstances to better prepare frontline workers for mining disasters and equip them with situational awareness.

  1. AI-Powered Mineral Exploration 

In recent times, there has been a rise in startups that use explainable AI for mineral exploration. Earth AI, an Australian startup, creates AI-based mineral exploration technologies, which include drone exploration and mineral targeting.

Earth AI trains its AI algorithms by feeding them pre-acquired global mineral exploration data, significantly increasing mineral deposit targeting. Its drone magnetic surveys are much cheaper than manned excursions and can be carried out 10 times faster. Thus, Earth AI reduces the risk of accidents during mineral exploration and enables companies to execute these at a low cost.

An Indian startup, Aganitha Cognitive Solutions, utilizes Machine Learning to identify deposit points and potential hotspots for exploration. They combine geological, geographical, and geophysical data – using AI to construct mining exploration models.

OreFox, another Australian startup, offers mining exploration services based on two AI systems. Prospector AI specializes in greenfield exploration. Using deep learning algorithms, it compares unexplored areas with the places where mineral deposit data exists. Hunter AI then uses Machine Learning to further refine the targets identified by Prospector AI.

In the end, OreFox consolidates findings from the two systems to give you a spatial dataset of the target site along with its visual representation.

The Era of Smart Mines

The future of mining is digital. With the increasing adaption of Industry 4.0 technologies, mining companies will be able to address challenges plaguing the industry for a long time.

Mining operations are dangerous, which is why the safety of frontline workers is every company’s priority. If a mine collapses or internal equipment goes haywire, it can give rise to serious ecological setbacks.

Moreover, with the changing times, companies must embrace that several mining operations will have to be performed remotely in the future. Technologies like smart equipment, AI, Machine Learning, and Virtual Reality can help transform old mining workplaces into agile ones.

Discover how Eugenie.ai brings forth innovations in the mining industry that consistently improve disaster prevention efforts, remove operational redundancies, and create safer working conditions for operators.

To learn more about how Eugenie has helped international mining companies ensure a profitable transition to operational sustainability, talk to us today or feel free to reach out to us at support@eugenie.ai.

 

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