Mining Companies And Current Advancements


In the Escondida copper mine in Chile, the company trialed smart caps which analyzed driver brain waves to measure and act on fatigue. This was integrated into over one-hundred-and-fifty trucks to boost productivity and increase safety.

They are also using AI in automating decision making. For example, Mining Area C in Western Australia Iron Ore is using a system that selects which crusher trucks they should use. This cuts down a lot of time spent on making decisions and also increases efficiency since decisions are generally better than humans.

BHP is also deploying autonomous hauling vehicles at their Jimblebar Iron Ore mine and through this change has reduced costs by roughly 20%.

Data Mining Vs. Machine Learning: What Is the Difference?


Vale is using AI in several areas with Advanced Analytics and is saving big as a result. At Salobo copper mine in Para, Brazil, there was a 30% increase in the lifespan of haul truck tires in one year which saved the company $5 million. This same technique is being applied in other mines and other truck parts including engines and fuel consumption.

Vale also uses AI to predict rail fractures which is helping to reduce the occurrence of fractures by up to 85%. This can save Vale $7 million per year. In total, the company expects to save around $26 million in 2018 from these changes alone.


Goldcorp recently partnered with IBM to put their smart technology towards exploration. IBM Watson services are being used to analyze drilling reports, geological survey data and more in determining which areas to explore and to quickly locate high-value targets.

In addition to the other beenfits mentioned before, this will also lead to a smaller impact on the environment.

Current Innovations

  • Goldspot Discoveries is applying AI in mineral exploration as well. Recently they predicted 86% of existing gold deposits in the Canadian Abitibi gold belt, which was achieved using geological, topographical and mineral data from only 4% of the surface area.
  • Motion Metrics’ Fragmentation Analysis is using AI to more accurately measure rock fragmentations. This method of data collection can provide valuable feedback to the engineers, increase productivity, and even optimize teeth changeouts by monitoring teeth wear.
  • Tomra has developed mineral and ore sorting equipment which uses sensors to separate valuable mineral ores from waste rock. From things like fast-paced laser sorting and product recognition.
  • PETRA Data Science offers technology that uses machine learning AI to enable automated ore fragmentation assessment. Their algorithms use data collected from 3D mapping to assess ore fragmentation within one minute as opposed to manual processing which can take more than an hour to complete.

Looking Forward

It is clear that automation and use of Ai and Machine Learning can significantly help save costs, increase efficiency and have tons of other benefits for companies. What’s holding us back is data and having really good, and a large amount of it. However, companies are working on scaling the use of AI in Machine Learning in Mining and with big data becoming a huge industry we can start seeing more use of AI in the mining industry. One that is completely changing from what we’ve traditionally known it to be.