Mining, a vital industry, is in need of upgrades to enhance its productivity and sustainability. While human labor has been the backbone of the industry for years, there is a growing integration of AI and other digital technologies to optimize efficiency.
The demand for resources is constantly increasing, and implementing AI in mining is essential to address this demand.
McKinsey estimates that smart mining achieved through data analysis and AI will save mineral raw materials producers between $290 billion and $390 billion annually by 2035.
However, significant capital investment is required to meet the growing resource demand.
This article will explore the current state and trends of AI in mining, along with its benefits and drawbacks.
How AI is Used in Mining Industry
The mining industry has significantly contributed to the global economy, providing essential minerals for various products and industries.
However, mining operations are often associated with significant environmental risks and workplace hazards.
This is where AI comes into play, as it can help minimize these risks while optimizing the efficiency and productivity of mining operations.
Autonomous Vehicles and Machinery
Self-driving trucks, drills, and excavators are safer, more efficient, and cost-effective and can work around the clock without the need for breaks.
Autonomous mining vehicles and machinery are specifically designed to work in challenging and demanding environments where safety is a top priority.
With the help of AI, these machines can navigate the mining site and perform tasks that are often too dangerous for human workers. AI-powered vehicles can also optimize their routes to increase efficiency, reducing fuel consumption and lowering emissions.
For example, the Canadian mining company Goldcorp has deployed an autonomous haulage system that uses AI to optimize haulage routes, cut down fuel consumption, and improve safety.
Machine learning algorithms can predict equipment failures, allowing companies to conduct preventive maintenance before costly breakdowns occur.
This reduces equipment downtime and unexpected shutdowns and increases productivity, ultimately saving time and money.
For example, Rio Tinto, one of the world’s largest mining companies, uses AI to analyze data from its heavy equipment fleet, reducing downtime by up to 20%.
Moreover, predictive maintenance ensures that mining equipment always operates at peak efficiency, increasing productivity and reducing operational costs.
This helps prevent downtime and unexpected shutdowns, ultimately saving time and money. Predictive maintenance also ensures mining equipment operates at peak efficiency, increasing productivity and reducing operational costs.
AI can also help mining companies optimize their operations by analyzing data from various sources. This data can include:
- Geological data,
- Operational data,
- Environmental data.
This can help mining companies make more informed decisions on where to focus their exploration efforts, reducing the time and costs associated with traditional exploration methods.
For instance, the mining company BHP Billiton uses AI to optimize its drilling process, reducing costs and increasing efficiency.
Environmental Monitoring and Management
Finally, AI can be used for environmental monitoring and management to minimize mining companies’ environmental impact.
AI algorithms can analyze data from various sources, including air and water quality sensors, to identify potential risks and suggest ways to mitigate them.
Environmental monitoring and management help mining companies comply with environmental regulations and minimize their environmental impact, improving their reputation and sustainability.
AI Technologies Used in Mining
The mining industry has adopted various AI technologies to extract valuable insights from vast amounts of data, which enabled it to optimize operations and reduce costs.
Here are some of the most commonly used AI technologies in mining:
Machine Learning (ML)
The mining industry heavily relies on data from various sources, such as soil samples, drill results, and chemical assays, to identify areas with promising mineral deposits.
With the help of ML, this data collected during previous explorations and analyses can be used to identify new areas with similar patterns in terms of ground chemistry and mineral distribution. This approach ensures that the right amount of material is extracted while reducing waste.
One company that is at the forefront of this approach is EarthAI. The company can precisely locate ore bodies, hard rocks, and regolith by collecting geophysical data from multiple remote sensors and leveraging ML algorithms.
EarthAI creates prospectivity maps by utilizing lithological, topographical, and radiometric data to showcase the likelihood of new mining locations.
Computer Vision (CV)
Computer vision is used to analyze images and videos from drones, satellites, and cameras mounted on mining equipment. It enables remote monitoring of operations, detecting anomalies, and improving safety.
For example, a mining company based in South Africa, Anglo American, is an example of how computer vision can be used to analyze images of its mines to detect safety hazards and reduce accidents.
Autonomous Haulage Systems (AHS)
AHS utilizes a combination of GPS, radar, and other sensors to enable self-driving trucks to transport materials from the mining site to the processing facility.
The sensors gather data from the environment, such as the distance between the truck and other objects, and feed it into the system’s AI algorithms.
The AI algorithms then use this data to navigate the vehicle autonomously, avoiding obstacles and following a pre-determined route.
Robotic Process Automation (RPA)
Robotics technologies are being increasingly used in mining for tasks such as drilling, blasting, and loading.
These technologies improve safety by reducing human exposure to hazardous environments, and they can operate in challenging conditions such as deep underground mines.
For instance, Sandvik has developed autonomous drilling systems that reduce human error and improve productivity.
Predictive analytics utilizes historical data to make projections about future events.
It enables mining companies to optimize operations by predicting the amount of material that can be extracted from a particular location, thus reducing waste and increasing productivity.
Digital Twin Technology
Mining companies use digital twin technology to create digital twins of mines, equipment, and processes to simulate and optimize operations.
This technology enables businesses to effectively track and evaluate various scenarios, enabling them to make well-informed decisions regarding investments in equipment and infrastructure.
For example, Gold Fields has implemented a digital twin of its Tarkwa mine in Ghana, enabling the optimization of the mine’s performance.
Augmented and Virtual Reality (AR/VR)
AR technology provides workers with real-time information and guidance, enabling them to work more efficiently and safely. It also simulates mining scenarios, allowing workers to practice procedures in a virtual environment before implementing them in the real world.
On the other hand, VR creates immersive experiences that simulate mining environments and operations, allowing workers to train in a safe and controlled environment. This way, workers can develop the necessary skills and experience without the risk of accidents.
VR training is also more effective than traditional training programs, providing a more engaging and interactive learning experience.
Current State and Trends
The mining industry is poised to experience a major transformation in 2023 as it embraces new technologies to enhance automation and safety.
As per MarketWatch, the use of AI in the mining market is expected to soar from $520 million in 2020 to a whopping $2.85 billion by 2031, underscoring the critical role of AI in the mining sector.
Here are the top eight trends that are anticipated to impact the mining industry:
- Process Automation: With the help of robotics and associated AI innovations, autonomous or remote mine operations are becoming increasingly common. This reduces the need for human presence in hazardous environments, improving worker safety and increasing mining operations’ efficiency and productivity.
- Industrial Internet of Things (IIoT): Leveraging IIoT provides global access to operational data to stakeholders, from mine operators to top management. Mining companies increasingly utilize real-time condition equipment monitoring, smart measurements, and remote operations to improve operational efficiency and optimize resource utilization.
- Sustainability: Mining companies are expected to achieve various sustainability goals set by governments. Startups develop solutions that reduce the environmental impact due to mining activities. Incorporating these solutions can enable mining companies to meet the increasing demands of growing economies while minimizing their environmental impact.
- Artificial Intelligence (AI): Startups leverage machine learning (ML), deep learning (DL), and natural language processing (NLP) to provide insights into mining operations. AI-driven solutions can detect anomalies, provide predictive maintenance, and create digital twins of mining operations to facilitate decision-making and increase efficiency.
- Wearable Technology: Wearable devices, such as IoT-enabled helmets, vests, and wristbands, can track operator movements, monitor vital signs, and detect safety incidents, among other functions. These devices allow mining companies to monitor employee expertise and ensure their well-being.
- Virtual Modeling: Virtual modeling improves asset and mine monitoring, ensuring safety and high throughput.
- Additive Manufacturing: Advances in additive manufacturing enable custom manufacturing of parts with sudden requirements with minimal lead time. 3D printing allows for the on-site production of mining components, eliminating the need for costly transportation of parts to remote locations in the event of equipment failure.
- Cloud Computing: Cloud computing allows mining companies to leverage data processing and handling capabilities without maintaining expensive IT infrastructure. It also allows for remote access to mining data for authorized users, promoting smooth communication and reducing the risk of data silos.
What are the Key Benefits of AI in the Mining Industry?
The mining industry has already integrated AI technologies to improve its operations and is poised to reap even more benefits in the future.
These applications of AI in mining have brought about several advantages, including heightened efficiency, enhanced safety, and increased profitability.
Below are some of the key benefits that AI brings to the mining industry:
Prospecting and Exploration
In the early stages of mining operations, like prospecting and exploration, AI is making a big impact. These stages require collecting and analyzing data to determine if a mineral deposit is economically feasible.
Thanks to AI-enabled applications, prospecting, and exploration tasks have become more accurate, and high-precision data analysis is now possible.
By incorporating AI into prospecting and exploration processes, mining companies can save time and money while also increasing their returns on investment (ROI).
Mining operations are often associated with high-risk working conditions. Workers face various hazards, such as exposure to toxic waste and gases, inhalation of particles, inadequate lighting, and small workspace.
With AI-enabled tools, companies can significantly limit the workers’ exposure to such conditions through the use of machines that:
- Monitor the atmosphere,
- Detect problematic areas,
- Send signals and warnings.
Such tools can also work continuously, even in dangerous situations, improving the overall safety of mining operations.
The use of autonomous mining haulage trucks that operate without breaks or changes in shifts has led to a 15% reduction in operating costs and increased worker/driver safety.
AI-enabled tools such as robots or sensors can investigate areas of concern, collect data on dangerous gases, toxic dust, and radiation, and trigger alarms or signals when unsafe conditions occur.
As a result, companies can improve working conditions, increase productivity, and reduce accidents and related costs by using such tools.
AI-enabled tools can be employed in mineral processing to use color-sorting, X-ray transmission, or near-infrared sensors to eliminate waste and identify physical, mineralogical, and chemical properties.
This would significantly increase the efficiency of the communication process and reduce energy costs, as crushing and grinding are the most energy-consuming and least energy-efficient parts of the mineral processing cycle.
Sustainability and Decision-Making
AI has the potential to improve decision-making in mining operations by making it more comprehensive and sustainable.
This can be achieved by incorporating large amounts of economic data and significant amounts of data on environmental, land use, communities, and governance factors. With machine learning processes, multi-objective optimization of operations is possible.
Another way AI can help promote sustainability in mining is through AI-enabled image detection for earth observation. This technology can facilitate more sustainable mining practices by identifying the type of ground cover.
Environmental Safety Violations
AI technology can also be used to monitor mining sites for potential environmental or worker safety violations by companies or government agencies.
For instance, facial recognition and image detection systems can be used to monitor and respond to trespassers or other perimeter/entry aspects of mining site operations.
Furthermore, earth observation techniques that employ machine learning can help identify illegal mining, speed up verifications or administration for land management, and plan for effective land reclamation.
These AI-enabled technologies also assist governments in mining communities detect air or water pollution violations, monitor biodiversity, and prevent breaches.
What are the Challenges of AI in the Mining Industry?
The introduction of AI-enabled tools in the mining industry can potentially optimize practices at different points of production and operations.
However, using these tools comes with tradeoffs and potentially significant ethical challenges.
In this section, we will discuss four main ethical considerations for using AI in the mining industry.
Autonomy and Observation
Using AI tools in mining could negatively impact job availability, social systems, and relations between workers. In addition, automation and increased surveillance could lead to a loss of privacy and decision-making ability.
While automated systems have safety benefits, it is important to consider their impact on job costs.
To avoid adverse effects, systems should enhance rather than replace human workers or use human-in-the-loop methods. Surveillance and facial monitoring tools must be designed with privacy and human rights in mind.
Balance of Rewards
In the mining industry, the use of AI can result in winners and losers, leading to potential discrepancies in the balance of rewards between companies or countries. Therefore, it is essential to ensure that smaller firms benefit from technological advancements.
However, making mining more efficient and profitable through AI may lead to the over-extraction of minerals or excessive growth in mining dirty resources, delaying the transition away from fossil fuels.
Thus, it is crucial to consider where and how these tools contribute to broader sustainability goals instead of slowing them down.
Bias and Prioritization
The mining sector relies on large amounts and varieties of data to enable AI applications, which can lead to biases and distortions.
This is particularly problematic in the Global South, where the available data may be inappropriate or inadequate for the tool’s context, potentially leading to biased decisions or outputs.
Furthermore, commonly identified AI ethics concepts may not be relevant to specific contexts, particularly those outside the Global North.
Therefore, involving local or regional stakeholders at all levels is vital to reducing potential data bias issues.
Explainability and Understanding
Transparency and stakeholder engagement are crucial ethical considerations when it comes to the use of AI in the mining industry.
To manage worries and concerns about the impact of AI, companies should use explainable and transparent systems and work with employees and communities to gain acceptance of how these tools will be used.
This links clearly to the responsible use of AI-enabled tools and can improve accuracy by ensuring that workers and communities understand the tradeoffs of new technologies and systems built with human-in-the-loop designs in mind.
Wrapping Up on AI in Mining
The implementation of AI-powered technologies in the mining sector holds the potential to enhance and streamline various aspects of production and operational processes.
The benefits of smart mining are evident, including significant cost savings and increased efficiency.
However, the introduction of AI-enabled tools comes with tradeoffs and significant ethical challenges, including the following:
- Autonomy and observation,
- Balance of rewards,
- Bias and prioritization,
- Explainability and acceptance.
As the industry continues to evolve and the demand for resources grows, it will be essential to balance using AI-enabled tools to optimize production while preserving worker autonomy, community privacy, and environmental sustainability.