The mining industry, one of humanity’s oldest endeavours, traces its origins back to ancient civilisations that extracted minerals and metals for tools, construction, and trade. Despite its age, the sector is at a cornerstone of transformation where a technological revolution is essential to meet the growing demand for commodities critical to the global energy transition.

This article explores how Artificial Intelligence (AI) technology is becoming an important factor in the global mining sector. 

AI spans the entire mining value chain

Integrating AI technology paves a new pathway for the mining sector, signifying a move from traditional, labour-intensive methods to a more technologically advanced approach. For an industry rich in data, AI has the potential to optimise processes and improve performance across the mining value chain - from mineral extraction to customer delivery. 

^{Figure 1: Example of AI technology utilised for improvements across the mining value chain.}

AI systems analyse vast amounts of mining data gathered by on-site sensors and other monitoring systems to identify patterns and make informed decisions, leading to increased efficiency, reduced costs, improved safety, and minimised environmental impact.

Over the years, AI has helped BHP unlock potential value through innovations such as predictive maintenance, energy optimisation, autonomous vehicle and machinery operation, data-driven decision making and real-time monitoring and reporting. AI is helping keep our workers safe. It is building efficiency in what we do. It is helping to solve operational problems and realise new business opportunities. It is helping increase our speed of resource discovery. It is optimising our ore quality and improving customer management. It is helping reduce our power and water consumption.

Precision in exploration and extraction

The race is on to find the mineral deposits required to support the world’s growing population and energy transition. Those who utilise AI technology for exploration and extraction are already in the lead. 

AI supports the enhancement of mining precision in exploration and extraction by using advanced algorithms to identify mineral deposits and accurately optimise operational processes. By analysing extensive geological data, such as surveys, soil compositions, and historical extraction records, AI algorithms can assist with accurately predicting the location of mineral deposits, reducing unnecessary drilling and excavation. In real-time, these algorithms assess changing geological conditions and adjust extraction techniques, accordingly, ensuring that resources are extracted efficiently, minimising waste, and maximising yield.

For BHP, machine learning, coupled with human ingenuity, has recently allowed us to discover new copper deposits in Australia and the United States. Cutting-edge work continues within our business as teams shift their focus to muon tomography. Muons are a type of cosmic radiation that allows us to scan and map underground deposits faster and more accurately than before.

Ivanhoe Electric, a BHP’s alliance partner, is utilising machine learning and data analysis to accurately detect the presence of sulphide minerals containing copper, nickel, gold, and silver at depths of over 1.5km. Ivanhoe Electric’s proprietary electrical geophysical surveying transmitter, Typhoon™, uses switches and capacitance systems that generate a very pure and stable transmitted signal, resulting in an extremely high signal-to-noise ratio. The data collected is then interpreted via CGI, Ivanhoe Electric’s machine learning algorithmic and data inversion software. This precision minimises land disturbance and preserves surrounding ecosystems.  

Predictive maintenance

Maintenance is one of the biggest drivers of operating costs globally. Predictive maintenance minimises unexpected equipment failures and costly downtime, making our operations more reliable and resilient. When we can accurately predict when equipment will require maintenance, we can schedule repairs during planned downtime, minimising production disruptions and costs.

AI technology, such as automation and machine learning, enables predictive maintenance of mining equipment, reducing the likelihood of equipment failures. AI algorithms analyse data from mining equipment to predict when maintenance is needed, preventing unexpected breakdowns and reducing downtime. This proactive approach may reduce the risk of accidents caused by equipment breakdowns, ensuring machinery operates safely and efficiently.

At BHP, we have predictive analytic models running across most of our load and haul fleets globally and our materials handling systems. These models are developed and maintained by a small footprint of people in our maintenance centre of excellence, which provides real-time and long-range indications of machine health and potential failure or degradation. 

At our West Australia Iron Ore (WAIO) operations, one of the material handling facilities was challenged by ongoing vibration and material handling impacts that threatened to shorten the structures’ lifespan. We developed a scalable framework through our technical centres where hundreds of gigabytes of sensor data were processed to diagnose and solve the challenge. It enabled us to identify other locations in the fixed plant structures where we could make changes to prevent the same risk from occurring.