AI and Machine Learning in the mobile mining industry

AI and Machine Learning in the mobile mining industry

AI and Machine Learning in the mobile mining industry

…advantages and potential implications for mining’s future.

The mining sector has a history of being associated with negative environmental impact and safety concerns. In recent years however, the industry has experienced a significant shift, largely driven by technology that has the potential to address many of its most pressing challenges. With the adoption of artificial intelligence, sophisticated machine learning algorithms, and data analysis approaches, the industry is making strides towards becoming safer and more sustainable.

Drones, sensors, and autonomous vehicles driven by AI are now capable of carrying out risky operations that formerly required workers to be present in remote locations. By gathering and analysing large amounts of data quickly and effectively, AI can help reduce the impact of mining operations on the environment. With the use of real-time data, analytics and predictive maintenance solutions, mines are able to prevent accidents, streamline processes, boost efficiency and output, while lowering operational costs.

In this blog, we explore 5 ways in which AI is being used in the mining industry, the benefits it offers, and potential implications for mining’s future.

1. Miner safety

The mining industry has made great strides in increasing the health and safety standards of its operations, and the industry is arguably the safest it has ever been. This transformation is being furthered by the use of AI, which is able to predict failures and reduce accidents.

AI-powered tools like wearable sensors are able to continuously monitor worker behaviour to detect any sign of physical discomfort, driver drowsiness or fatigue. For example, in its Chilean copper mine, BHP implemented the use of SmartCaps, which analyse the brainwaves of the drivers, alerting them as soon as it detected drowsiness.

Real-time monitoring and data analysis using machine learning is another less obvious (but no less impactful) use of AI. By detecting failure symptoms, machine learning can help predict and eliminate catastrophic failures, which not only help reduce the cost of repair, but also eliminates any risk to the operator.

Rockmass Technologies has started combining AI with LiDAR to create a tool that allows the quick and detailed geotechnical mapping of underground tunnels. This technology uses LiDAR to create a point cloud of data within a tunnel which then uses AI to auto generate stereonets, Q-System and RMR values, allowing for speedy decisions on ground support to be made.

2. Autonomous vehicles

One of the most significant applications of AI in mining is through the use of autonomous vehicles, which can operate without human intervention in hazardous and remote environments. AI-powered trucks can traverse challenging terrain and detect obstacles, reducing the risk of accidents and improving safety. Autonomous vehicles, loaders, and drill rigs deployed in mines around the world are being used for drilling, blasting, hauling and loading parts of the mineral extraction and transportation process. This is resulting in an increase in productivity, while eliminating risk and removing people from potentially dangerous situations.

An example of this is being able to run mining equipment through shift changes or blasting activities. This is especially valuable in underground mines or operations where there is lengthy downtime owing to operators having to travel to the machine they will be working on. 

Mines that do not adopt this technology will soon find it hard to compete with the reduced cost and increased productivity of mines that leverage the advantages of AI.

For example, drones are increasingly being used to conduct site surveillance, inspect equipment and blast sites. Data collected from these drones can be used in conjunction with advances in image processing to monitor slope stability and survey dangerous locations without putting operators at risk.

3. Efficiency of mining exploration

In addition to autonomous vehicles, AI and ML are also being used to enhance the accuracy and efficiency of mineral exploration. AI algorithms, for instance, can quickly analyse geospatial data to pinpoint the most likely locations of mineral deposits and other resources. This, combined with other disruptive technologies in the industry, including the use of drones to replace costly and time-consuming fieldwork will greatly speed up the entire exploration process.

Another massive application for AI in exploration/mining is in the Image Processing space. The development of ML imaging software can allow the instant identification of different lithologies in images of drill core, face and pit wall photos. This drastically improves the time for information that is critical for mine planning can be gathered and utilised.

A noticeable example of the use of AI in exploration is KoBold Minerals (More info here). This company, focused on prospecting for critical battery metals, has developed extensively into the AI space, allowing them to build advanced predictive models that utilises satellite data combined with more traditional datasets, to identify potential mineral targets. Currently they are exploring over 60 projects around the globe and have entered into numerous JV partnerships with established miners to develop some of these projects further (e.g. Bluejay in Greenland).

Check out this case study of how AI has been used to generate exploration models for Revival Gold in Idaho USA from Mira Geoscience.

AI and Machine Learning in the mobile mining industry

3D targets generated by combining knowledge and a data-driven workflow for Mineral Prospectivity Index (MPI)

4. Predictive maintenance

The mobile mining sector is also leveraging AI and ML to enhance maintenance and repair. Machine learning algorithms can be used by predictive maintenance systems to assess sensor data from machinery and predict when maintenance is required, thus reducing downtime and increasing productivity (More info here.)

Additionally, these algorithms can examine data to find patterns and identify root cause of equipment failure, thereby predicting and preventing upcoming failures. With the help of these technologies, mines can make better judgments, lower the risk of accidents, and streamline operations.

5. Reduced environmental impact

Mining by its very nature, is a destructive process often associated with the formation of sinkholes, erosion, loss of biodiversity and soil contamination. The introduction of AI, however, is dramatically reducing mining’s negative impact on nature.

For example, sensors and cameras deployed in mines around the world are able to monitor excavation and extraction activities. By detecting anomalies such as temperature changes and tremors, or chemical leaks, these sensors are able to avoid accidents and environmental disasters.

Moreover, data from sensors and cameras can be analysed to help reduce waste and lower the power consumption of the mining industry. For example, AI-based automatic regulation of ventilation systems is a key factor in saving power.

Monitoring tailings dams is another powerful application of AI in the environmental space. By combining and analysing data from various sensors. Real time, continuous monitoring can be carried out ensuring any potential weaknesses in the dam can be addressed promptly before failure.

With mines across the globe becoming more and more environmentally conscious, the adoption of AI has the potential to greatly contribute towards sustainable mining practices.

What does the future of mining look like?

As the adoption of AI and ML continues to grow in the mobile mining industry, it’s likely that we’ll see even more innovative applications of these technologies in the future.

As technology evolves, AI-powered mining equipment and sensors are expected to become more sophisticated, with higher accuracy and better predictive capabilities. For instance, AI-powered virtual reality technologies could be used to simulate mining scenarios and test new technologies before they are deployed on-site. With the advent of 5G networks, AI-powered devices, and systems are likely to be even more connected, allowing for real-time data analysis and faster decision-making. 

In the mineral exploration space it is well known that only about 1 in every 1,000 projects is successful. AI provides a massive opportunity to increase this success rate and the speed at which new projects come on stream, going a long way to securing critical resource supplies for the future.

With a growing consciousness among mining companies to become more sustainable, more ethical and more efficient, the future of AI in mining looks promising.

While the use of AI in mining offers many benefits, the industry must also consider concerns surrounding AI, like potential job loss and data security. As companies reduce reliance on human workers and look towards autonomous equipment and vehicles, it is essential to consider retraining programs for those affected. Mines also need to have strong data protection practices in place. 

With active collaboration across the industry to develop best practices for the use of AI in mining, the industry can ensure that AI is used in a responsible and sustainable manner that benefits both the sector as well as the wider community.

Author: Subhrata Sinha