Artificial Intelligence: Five Ways AI is Disrupting the Mining Industry
Written by James Monighan
Metals & Mining
The term artificial intelligence has been generating huge buzz for a number of years, promising to revolutionize and impact almost every area of business.
The mining industry is no different, but far from being mere hype, AI really does present great potential for disruption and change. There are a number of developments and applications of AI in mining, all with significant benefits ranging from reduced costs and increased productivity to improved safety and diminished environmental impacts. Here we look at five of the ways in which AI is disrupting mining as we know it. The mining companies that embrace these innovations are the companies that will dominate the industry in the future.
1) Introducing a range of autonomous technology
Autonomous technology is probably the area that has the biggest potential to disrupt the mining industry. Some autonomous tech is already widely used, and more will continue to be introduced, all of it with the ability to more effectively optimize each step of the value chain. The VIST Group has already been testing autonomous trucks operating for 24 hours at a time and in extreme temperatures, and Rio Tinto deployed the world’s first autonomous train at its iron ore mine. Mining companies that do not adopt this type of technology in the future will find it hard to compete with the reduced cost and increased productivity of the companies that introduce this technology.
Looking to the skies, autonomous drone technology has a large number of applications for the mining industry. Drones can use advanced electromagnetic technology to collect electromagnetic, magnetic and radiometric data from autonomous aircraft to map out terrain. The data collected can be analyzed to understand the geology of an area to determine if and where drilling should take place, reducing the time and money that can be wasted on unnecessary drilling. In addition to mapping out territory before launching a mining operation, drones can also be used to monitor operations once they are underway, helping track aspects such as wastage and environmental impacts.
The increased use of autonomous mining machinery presents some key advantages. There is the immediate and obvious advantage of machines being able to work around the clock without experiencing fatigue, but there is also the advantage of machines being able to access areas and environments that may be too dangerous for humans. In this way, AI will lead to an increase in productivity and improved safety.
2) Improving exploration and resource discovery
One of the first ways in which AI is disrupting mining is by better identifying potential areas to mine or drill in. AI can be used to gain more knowledge and understanding of terrain and to predict and map it out to a much more precise degree than is possible by human effort alone. Pattern matching and predictive analysis are combined with AI’s ability to analyze large amounts of geological data to reveal the most likely locations of mineral deposits and other resources.
This increase in accuracy helps to reduce the time and money spent on exploration which leads to a much better return on investment. Canadian company GoldSpot Discoveries have been using AI to analyze swathes of data and discover new zones of gold in Newfoundland, Canada, proving that companies who employ this new technology are the ones who will lead the industry.
3) Advancing raw material sorting
There are a number of technologies that are being employed to improve the sorting of materials in the mining industry. Infrared sensors, X-ray transmissions, laser scanning and electromagnetic sensing are all used to help with the sorting process. Now, machine learning is being added into the mix, allowing sorting machines enabled with learning software to self-optimize and continually improve the process.
AI can also be used to monitor the size and weight of rocks on their way to the crusher. When a rock is too big for the crusher, it can cause obstructions and lead to downtime. By using AI to detect these, expensive failures such as this can be limited. TOMRA Sorting Solutions is an example of a company using this technology to advance the sorting process and shows how AI can be used to advance many aspects of the mining industry.
4) Reducing the environmental impact of mining
It’s impossible to completely eliminate all of the environmental impact that mining has as there is always a level of destruction in the process. In order to extract resources, parts of the environment will inevitably undergo some form of change. However, AI has the potential to greatly mitigate and reduce the negative environmental effects. Sensors and cameras can be used to monitor mines and gather data, which is then analyzed to better understand how waste can be reduced and how to be more energy efficient. Shyft Inc. is currently doing just this, using AI, data and machine learning to forecast energy peaks. AI can analyze data much faster than humans and so better monitor and control the ventilation of mines, greatly increasing energy efficiency and reducing costs in the process.
5) Improving safety and security
As technology has increased in capabality and scope over time, so has the safety of the mining industry. Now AI is being used to further improve safety in a variety of ways. AI can use sensors, real-time data and analytics to understand when changes in factors such as temperature and vibrations can lead to danger. Machine operators and drivers can then be warned in advance, preventing accidents and injuries as well as machine damage. For example, during the excavation process it can often be the case that one of the teeth on the excavator bucket breaks off and ends up in the crusher. A broken tooth will not stop an excavator from working, but it can cause damage and huge downtime for the crusher. By using AI and sensors to monitor the loaders and excavators, broken teeth can be detected before entering the crusher. One company pioneering this exact technology is Motion Metrics and their LoaderMetrics system.
Other ways in which AI is improving mining safety is in the monitoring of drivers. Drowsiness in drivers is one of the biggest causes of mining accidents and it is something that is hard for drivers themselves to catch – you often do not notice you are becoming drowsy until it happens. A solution to this problem is to use drowsiness detection technology to alert drivers of when they are about to drop into sleep. BHP implemented the use of smart caps in one of their copper mines in Chile. The caps analyzed the driver’s brainwaves and alerted them if they were about to nod off. The tech was so successful that it is now being rolled out across more than 150 trucks.