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7 Cutting-Edge Applications of AI in Mining for 2024


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Anyone involved in the mining industry knows it’s a complex field of high stakes and significant challenges. According to a recent Deloitte report, mines today face immense resistance to change because they rely on legacy technology.

This hampers mining operations’ ability to face more evermore challenging conditions as they are forced to go deeper and further in search of resources.

It should come as no surprise, then, according to the same study, that mining productivity continues to decline across the board. To make matters worse, the industry as a whole is facing volatile commodity prices, changing workforce demographics, and the maturation of existing sites.

The good news is that Artificial intelligence and machine learning algorithms can potentially change the industry’s trajectory. In combination with IoT devices and big data, intelligent systems can greatly increase mining organizations’ ability to process, manipulate, handle, and analyze information to:

  • Discover mining opportunities
  • Plan mining operations
  • Improve efficiency and productivity
  • Enforce stricter quality control
  • Improve worker safety
  • Minimize the risk of human error
  • Make operations more resilient and agile

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Application 1: Exploration and Targeting

As mentioned, one of the key challenges for the mining industry today is finding potentially lucrative sites. This requires collecting, managing, and analyzing vast amounts of data, something that AI and ML technologies excel at. Mines already use this approach to increase the chance of discovering new deposits and proactively reduce the environmental impact.


Satellite imagery using computer vision technology to illustrate the impact of mining deforestation in the Southern Amazon. The map shows the presence of mining operations both in and outside the established mining corridor.
Example of satellite and computer vision imagery used by the MAAP project to monitor the impact of mining operations in the Amazon. (Source)


AI models can process vast amounts of geological data much faster than human surveyors. As the technology evolves, the gap in terms of speed and accuracy also continues to grow.

There are four main ways in which these systems can aid mining operations:

  1. Identify and suggest potential exploration targets
  2. Estimate a target’s potential value
  3. Identify potential environmental concerns
  4. Generate optimized strategies for deeper exploration

At the same time, AI techniques can help rule out non-viable leads, which can result in unnecessary high testing costs and make exploration more efficient.

The natural evolution of AI exploration is that of automated exploratory drilling. These systems use AI to pinpoint the location of mineable resources within target sites, conduct initial exploratory drilling, and analyze the data to gauge its potential with greater accuracy and estimate successive drilling locations.

While still mostly in an experimental stage, this may help mines transition to fully autonomous exploration by targeting new sites and conducting on-site tests using remote, AI-powered equipment.


Application 2: Automated Drillers and Intelligent Drilling Systems

For any mining operation, drilling and blasting are two indispensable processes that take place daily. Unfortunately, they also happen to be some of the most dangerous.

It typically involves an iterative process of drillers creating holes in hard surfaces. These holes are filled with explosives to blast open even larger holes or create cracks in the inner geology in preparation for a mining site.

Drilling patterns are carefully planned based on the characteristics of the site and its purpose. AI and ML systems can help in the planning of optimal drilling patterns as well as automate the process. This information can be used to configure and deploy automated drillers that follow a predetermined drilling pattern

Built-in sensors can provide the system with real-time data on the environment. AI and computer vision-powered systems can use this input to continuously identify possible hazards and check the pattern and dimension of drill holes. Or, to inform the actions of a remote human operator.

Based on real-time data feedback, the system can also dynamically modify drilling behavior if needed.

These intelligent systems typically use fuzzy logic-based controllers. Artificial Neural Networks (ANNs) for predicting emulsion-based drilling fluid properties and the rate of penetration.

Another benefit is that these machines can be operated remotely, minimizing the risk and improving safety in incidents and serious injuries.


Application 3: Predictive Maintenance

⁤Mines face a continuous battle to try and make their maintenance processes as efficient, cost-effective, and non-disruptive as possible. ⁤⁤A typical mining operation oversees such a vast portfolio of infrastructure and equipment that it’s almost impossible to accurately and efficiently track maintenance schedules using conventional methods, like spreadsheets. ⁤

As in other industries, AI and ML algorithms can use model specifications and real-time equipment performance to try and predict when maintenance will be needed.


Example architecture for an intelligent predictive maintenance model based on deep learning and AI.
Example architecture for an intelligent predictive maintenance model based on deep learning and AI. (Source)

ML algorithms can analyze equipment specifications, service contracts, and real-time performance data to predict failure rates or identify assets that may require maintenance. ⁤⁤This allows mines to carry out proactive maintenance or prepare auxiliary equipment in advance. ⁤

⁤Automating maintenance also helps in improving productivity long-term as timely maintenance interventions extend the lifespan of both fixed and mobile assets. ⁤⁤And, as a bonus, better-functioning equipment is safer for workers to operate. ⁤

For example, Vale, a Brazilian multinational metal and mines corporation, uses predictive analysis to help save capital costs. They were able to expand the lifespan of haul trucks by 30% and predict up to 85% of rail breakdowns ahead of time. Reportedly, predictive analysis helps the company save up to $7 million per year.


Application 4: Worker Safety and Risk Assessment

AI and ML technologies are already being deployed to help ensure worker safety and the conditions within mines. Not only does this help mining operations respond quickly to immediate threats, but it also helps plan infrastructure to be more resilient and secure.

One current solution is using AI-powered wearable IoT devices to monitor the vitals of miners on shift, using health markers such as fatigue or physical stress in real-time. Sensors installed on the site can also quickly detect potential dangers in the immediate environment, such as the presence of hazardous substances, high humidity, or extreme temperatures.

Other monitoring systems can scan for environmental risk factors, such as weather patterns, landslides, or other geological disasters.

Lastly, intelligent computing systems can help plan and execute mine operations more safely. For example, it’s possible to model and predict roof or support collapse, flyrock, and blasting pattern analysis, water rush-in, subsidence risk, or goaf stability using integrated CAD or 3D modeling AI software.


Application 5: Ore Sorting and Grade Control

As the most attractive and easy-to-reach resources are exhausted, mines are struggling with declining quality levels. For example, the ore quality in copper mining decreased by 25% in just 10 years. AI and ML-based systems can help them optimize the quality of their existing output, mitigating some of the impact of this decline.

Using AI technology for dig-line optimization is nothing new, helping mines get the most out of a specific site. More advanced systems also help miners analyze the potential value of a site or ore deposit to plan or prioritize extraction.

Further down the pipeline, AI-based sorting systems are used to analyze vast amounts of waste rock and identify valuable minerals in real-time. This not only improves the recovery rate of the primary material but also that of secondary raw materials. These systems are more accurate and efficient than human sorters, without the need for rest or shifts.


Application 6: Robotics and Autonomous Vehicles

Drilling and blasting are not the only processes that may be automated with the help of intelligent computing. From drones to hauling trucks to “robot miners,” the mining industry is increasingly employing autonomous technologies to perform both mundane and dangerous operations.

For example, drones fitted with sensors and computer vision technologies are already being deployed to survey sites from the air or provide real-time monitoring. Some drones even use Light Detection and Ranging (LiDAR) to create 3D maps of the environment, which can be useful in identifying transport corridors or stockpiling locations.


An image of the CAT 749 autonomous mining haulage truck. It appears much like a conventional haulage truck, with the exception of having no cabin for a human operator.
The CAT 749 AC Mining Truck is an autonomous hauling solution featuring various proprietary AI technologies. (Source)


Automated haulage trucks are especially valuable, especially in open pit or surface mines with limited haulage routes. These trucks use AI algorithms to follow pre-determined, optimal routes, often over difficult or dangerous terrain. The same intelligent systems also formulate and follow strategies that optimize fuel usage and wait times.

Innovators are developing swarm robotic mining systems in the cutting-edge field to take care of excavation activities within mines. These land-based “drones” use sensors and articulating robotic arms fitted with different equipment to replace the need for human miners effectively.

Offworld is one company looking to debut their robotic mining systems in 2024. They envision a fleet of automated mining drones that work in unison, consisting of surveyors, excavators, collectors, haulers, and more, using the latest edge technology. Working within the mines themselves, these AI-enabled excavators and collectors will replace some of the most labor-intensive, potentially hazardous, and life-threatening work within mining operations.


Application 7: Digital Twinning

A digital twin is a virtual model of a physical system that uses real-world data to run simulations and make predictions. Many industries already use digital twinning to simulate complex real-world scenarios for testing, integration, monitoring, and maintenance.

However, due to the sheer scale and complexity of mining operations, the adoption of digital twins has yet to pick up speed. This will evolve with massive advancements in AI, ML, and deep learning technologies from the last several years.

Mines will soon be able to create and run full digital twins of individual mining assets or their entire worldwide portfolio. This will open unprecedented opportunities to optimize mining operations at every scale and across every facet, from exploration to operating mining sites to managing the entire supply chain.

A reference framework showing the flow of data between components in a digital twin model.
A reference framework showing the flow of data between components in a digital twin model. (Source)


In short, digital twins will enhance mining companies’ ability in regards to:

  • Modeling and simulating the behavior and performance of mining assets
  • Stochastic mine planning and scheduling
  • Optimize operations and predict failures
  • Short and long-term strategic decision-making

Numerous mining organizations are already planning and launching these so-called “smart mines,” enabled through the use of comprehensive digital twinning technology.


The Future of AI in Mining

Growing environmental concerns, resource depletion, and the growing demand for precious minerals mean that the challenges facing the mining sector are likely to persist. At the same time, AI and ML continue to evolve and prove their worth in a growing variety of applications.

For example, McKinsey estimates that the use of AI will lead to between $290-$390 billion in annual savings for those mining copper, iron ore, natural gas, coal, and crude oil by 2035. With these kinds of results, the marriage between mining and intelligent computing technologies will likely become permanent. It will reach a market size of $18.96 billion by 2030, growing at a CAGR of 21.8% from just $3.96 billion in 2022.

Read more about how Computer Vision can be applied in various industries in the blogs below:

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