Digging the Mining Industry with AI and ML tools

By CIOReview | Wednesday, July 3, 2019

Artificial intelligence and machine learning have a massive potential in the exploration and extraction of minerals, which is critical to a smart mining operation.

FREMONT, CA: The mining industry relies on large equipment and vehicles for transportation and operations. Due to the involvement of heavy machines and resources, productivity and efficiency are crucial to business revenue and growth. As such, even slight improvements in yields, speed, and processes can have a significant impact. However, the current advancements are not enough and are impacting other industries too, as it provides raw materials for nearly every aspect of the economy.

AI and ML for Exploration and Extraction

Artificial intelligence (AI) and machine learning (ML) have a massive potential in the exploration and extraction of minerals, which is critical to a smart mining operation. Though AI and ML are new to mining, some companies have already started transforming processes such as gold exploration using the technology. Few organizations are collaborating to incorporate AI to analyze the entire geological data and discover better drilling locations for gold.

Autonomous Drillers and Vehicles

Some organizations have already been using autonomous haul trucks to bear heavy loads; such trucks require lesser fuel. Also, autonomous drilling systems are being leveraged, which requires a remote operator with a single console to manage multiple autonomous drill rigs.

Segregation of Minerals

In most of the mining operations, a chunk of mined extracts contains unwanted volumes of materials that have to be removed, which is an expensive and time-consuming process. To counter it, several companies have deployed smart sorting machines that use technologies like AI and ML to sort the mined debris as per the requirements of the company.

Digital Twin

Digital twin involves developing a virtual model that uses real-time data from the field. Conclusions can be drawn by simulating the mining conditions, and the results can be employed for optimizing the processes. The advantage of testing the decisions before their actual deployment results in better outcomes and savings.