Protecting the most precious assets—your workforce

The use of AI and IoT in mining operations has brought about a major change in safety and efficiency, greatly reducing the risks associated with mobile equipment. Self-driving systems such as dozers, loaders, and haul trucks, equipped with sophisticated AI algorithms and IoT sensors, work without human intervention, lowering the chances of accidents caused by human error. Innovations like these have become essential as data from 2021 shows a sharp rise in fatalities related to powered haulage equipment, highlighting the urgent need for advanced solutions like self-driving haul trucks and AI-powered drilling processes.

In addition, the adoption of AI and IoT goes beyond self-driving machinery to include advanced safety features like collision avoidance systems and fatigue monitoring. These technologies can help enable dynamic changes in equipment operation to avoid accidents, while real-time monitoring systems inside vehicle cabins alert operators to signs of fatigue, significantly improving safety protocols. As a result, mining operations not only become safer but also more efficient, with self-driving vehicles capable of constant operation, minimizing downtime and maximizing productivity.

Case study: Autonomous haul trucks

At their Bagdad, Arizona, copper mine, Freeport-McMoRan announced in late 2023 that, in collaboration with Caterpillar (CAT), they will be deploying 33 Cat 793 haul trucks at the mine site. The trucks will operate using Caterpillar’s Cat MineStar Command systems and hardware. Implementation of this technology will take three years of collaboration between CAT’s teams and Freeport-McMoRan. Freeport-McMoRan acknowledges the safety improvements from this decision by stating, “This project is expected to optimize our fleet, improve operating efficiency and contribute to safety by removing our people from this area of the operation.” Training of operators and site teams will be extensive including train the trainer and increasing worker awareness of autonomous zones and red zones for equipment maintenance.

The integration of drones, IoT, and AI has particularly helped improve safety and operational efficiency. In underground environments, drones provide real-time data, facilitating informed decision-making and enhancing safety of search and rescue operations. For instance, emergency response drones (ERDs) equipped with thermal imaging and hazard detection sensors provide critical information to first responders, thereby significantly reducing risks. Furthermore, drones armed with high-resolution cameras and advanced sensors like LiDAR serve as invaluable tools for remote assessment and 3D mapping. These AI-enabled drones can make quick, independent decisions, increasing safety and efficiency of survey processes, as evidenced in the partnership between Exyn Technologies and Dundee Metals.

IoT has also been instrumental in enhancing safety processes and environmental monitoring. The creation of a “mesh network” through interconnected IoT sensors allows for continuous monitoring of underground conditions such as air quality, temperature, radiation, and gas levels, enabling timely warnings of potential hazards. In addition, drones bolster seismic monitoring capabilities, aiding early detection of potential ground instabilities and preventing accidents. Notably, Freeport-McMoRan utilized drones for mine surveying of blasting and highwall monitoring, reducing employees’ exposure to traditional risks. Lastly, the mining industry has embraced robotics, especially in hazardous operations. Equipped with IoT sensors and AI processing, robots can explore and map risky areas, assist in rescue operations, and deliver critical supplies, thereby enhancing safety and operational efficiency.

Case study: Advanced technology in underground mine areas

Metal miner Glencore, in its Canadian Kidd Mine, incorporated specialized drone autonomy technology by Emesent Pty. Ltd. to map inaccessible underground areas and thereby remove personnel from areas at risk of ground falls. Rather than depending on mining engineers and surveyors to map unsupported voids, Emesent now deploys a drone system that uses LiDAR data and advanced algorithms. The drone and mapping technology generate specific 3D point clouds of the scanned environment, capturing critical visuals in places such as stopes, drawpoints, orepasses, and drives, while keeping personnel away from unstable ground.

Emesent was an award recipient in 2022 of the NIOSH Mine Safety and Health Technology Innovations Award for its work related to removing people from highly unpredictable and therefore dangerous environments. Additionally, Glencore uses Boston Dynamics’ Spot robot to perform underground surveys and prevent workers from entering potentially hazardous areas.

AI-driven predictive maintenance serves as a cornerstone of mining safety, harnessing sensor data and historical maintenance records to predictively identify machinery health degradation. Current maintenance practices range across mining companies, and unplanned maintenance could heighten incident risks due to insufficient planning and oversight of critical controls. AI-based predictive maintenance, however, can anticipate specific maintenance needs for each equipment type, utilizing data from past maintenance records, mine operations, equipment telemetry, and overall equipment trends. This technique reduces both unplanned downtime and failures, which often correlate with increased injury rates.

Unplanned equipment malfunctions represent a dual challenge, interrupting production and potentially jeopardizing safety. AI-empowered predictive maintenance enables early fault detection, preventing unplanned downtime and ensuring continuous operations. By striving for optimal equipment performance, mining companies can prevent potential hazards, thereby protecting personnel and operations. Additionally, the integration of AI and data analytics allows mining companies to glean insights from historical maintenance-related incidents. Performing root cause analysis related to maintenance and downtime enables proactive implementation of preventive measures and facilitates meaningful shifts in maintenance behaviors, effectively reducing the recurrence of safety incidents.

Case study: Preventing equipment failures

Gold miner Newmont announced in 2020 a global collaboration with Australia-based Dingo, a global leader in advanced predictive maintenance software. In 2019, Dingo unveiled Trakka Predictive Maintenance, a new software using machine learning models to predict impending equipment failures. “Trakka will enable our people to tap into the power of data to drive continuous improvements,” Newmont’s Senior Director, Operations Support Hubs, Jason Hill said. “The common platform allows us to identify best practices and detect emerging issues. Taking the appropriate action, in either case, is critical to continuously improving our business.”

Newmont plans to integrate its global predictive maintenance platform with existing enterprise resource planning systems to modernize supply chain and maintenance programs while reducing inventory costs. This proactive maintenance strategy will reduce injuries related to equipment failures—because there will be fewer failures.

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The convergence of AI and IoT presents a major step in enhancing health and safety across mining operations. By leveraging autonomous haul trucks, AI-based drones, and predictive maintenance, mining companies are helping to minimize human risk in hazardous environments and predict incidents before they occur. This digital transformation, combined with a growing understanding of the correlation between safety and efficiency, is driving the industry toward safer, more sustainable, profitable practices.

If you’d like to talk about how your business can better utilize automation, AI, and IoT to help enhance the health and safety of its mining operations, let’s set up a conversation.