AI And The Digital Mine Posted on : May 29 - 2020

When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.

Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.

On a recent AI Today podcast episode, Antoine Desmet of mining technology and equipment company Komatsu shared how they’re using advanced forms of AI, automation, and robotics to make an impact on the organization's operations. Antoine has an interesting background, starting his career as a telecom engineer and receiving a Ph.D in neural network engineering. After getting his Ph.D, he returned to Komatsu and started working in surface analytics. He states that at the time there was a lot of data to work with, but very few analytics in place. He decided to start implementing machine learning and in the last few years his company has seen significant growth through this approach, with his data science team growing from just one person to ten people.

The role of machine learning in mining

The mining industry uses a lot of big, expensive machinery to perform a wide range of operations at the mine site as well as farther away when the materials need to be processed. Much of this machinery has many sensors that provide large volumes of data that give insights into how the very expensive machines are operating, the conditions in which they operate, and also insights into their performance on specific tasks. Keeping machines up and running is essential to making sure that the mining operation can continue. Any downtime or unnecessary maintenance will result in significant cost and complications for the mining operation.

Prior to the use of machine learning to help give greater insights into operations, the information coming in from the sensors was just feeding into the control loop and wasn't really being used to provide any sort of pattern identification or predictive analytics value. The use of machine learning has resulted in a shift in how that information is used. By storing and continuously evaluating the huge volumes of sensor and other operational data, the organization can get significantly better insight into what problems are potentially occurring, the evolution of how those problems are occurring, and patterns that can lead to problems down the road. View More