How Mining Companies Are Using AI, Machine Learning And Robots To Get Ready For The 4th Industrial Revolution

In an industry such as mining where improving efficiency and productivity is crucial to profitability, even small improvements in yields, speed and efficiency can make an extraordinary impact. Mining companies basically produce interchangeable commodities. The mining industry employs a modest amount of individuals—just 670,000 Americans are employed in the quarrying, mining and extraction sector—but it indirectly impacts nearly every other industry since it provides the raw materials for virtually every other aspect of the economy. It's already been 10 years since the British/Australian mining company Rio Tinto began to use fully autonomous haul trucks, but they haven’t stopped there. Here are just a few ways Rio Tinto and other mining companies are preparing for the 4th industrial revolutions by creating intelligent mining operations.

Rio Tinto’s operations include 16 mines, 1,500km of rail, three ports and more, and it creates 2.4 terabytes of data every minute from all of its mobile equipment and sensors that collect and transmit data in real-time to help monitor equipment. Rio Tinto’s former CEO Sam Walsh, when speaking at the Gartner Data & Analytics Summit, explained how the company has successfully integrated these multiple mines into an integrated processing and logistics system controlled by operators not located at the physical site.

Mineral exploration

Artificial intelligence and machine learning can help mining companies find minerals to extract, a critical component of any smart mining operation. Although this is a fairly new application of AI and machine learning, many mining companies are excited about the prospect. Goldspot Discoveries Inc. is a company that aims to make finding gold more of a science than art by using machine learning. Similarly, Goldcorp and IBM Watson are collaborating to use artificial intelligence to review all the geological info available to find better drilling locations for gold in Canada. These efforts to be more precise when finding areas to mine by using machine learning can help the mining industry be more profitable.

Autonomous vehicles and drillers

While many of us have been focused on the progress Uber, Google and Tesla have made with autonomous vehicles many people don’t realize that Rio Tinto had already been using autonomous haul trucks that can carry 350 tons and operate totally independently since 2008. These trucks have impacted the company’s bottom line by reducing fuel use by 13 percent and are safer to operate. While arguably the challenges of autonomous driving in a quarry aren’t as daunting—the trucks move slow, they don’t have to worry about pedestrians—it’s still a notable accomplishment. This year, the company’s long-haul autonomous rail system will go live and is the next step in developing the Mine of the Future. With 244 cars, the autonomous train has been in development for five years, but will make its debut by the end of the year after some software and communication glitches have been worked out.

In addition, Rio Tinto has used autonomous loaders and drilling systems for several years. Just as with other autonomous applications, the company asserts the innovation has improved productivity by 10 percent.

Sorting minerals

In the majority of mining operations, a much larger volume of materials needs to be removed to find the valuable materials they are mining for. Inevitably, separating the useless rocks and debris to get to what you're mining for tends to be an expensive endeavor. Some companies have begun to use smart sorting machines that can sort the mined material based on whatever criteria a company wants. This work can lead to savings in fuel and energy during processing.

Digital twinning

As part of making the pit-to-port operations as intelligent as possible, Rio Tinto is creating an intelligent mine that should deliver its first ore by 2021. There are more than 100 innovations the company is evaluating, but one initiative called digital twinning, first created by NASA, is now being adopted by many in the industrial sector. By creating a virtual model that is fed real-time data from the field, scenarios can be quickly tested, and operations and production can be optimized. This ability to test out decisions before they are implemented in a replica system leads to better outcomes and savings.

Safety and maintenance

Thanks to Internet of Things technology and sensors, mining equipment can be monitored and maintained before breakdowns occur. Sensors can monitor temperature, speed, and vibration on machines to take action transforming preventative maintenance into predictive maintenance. By assessing real-time data and analytics, mining operations can be safer for all involved.

This adoption of this new tech requires reskilling the mine workers, and Rio Tinto is already taking steps by partnering with the Australian government and a vocational training provider to help fill the gap. Collectively, they will spend $2 million to up-skill potential and existing workers to handle tasks in analytics, IT and robotics.

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About Bernard Marr

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things.

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