The mining industry is under more pressure than ever to increase efficiencies. This comes as declining ore grades and more disparate and remote deposits create greater challenges in securing new resources, and rising mining costs drive the need for greater productivity at the mine site.
At the same time, there is a strong focus on ensuring safety and sustainability within mines. Artificial intelligence (AI) in the mining industry can address many of these challenges and inefficiencies through several key technologies in the value chain, including computer vision, smart robots, data science, and machine learning.
Leading miners in artificial intelligence
Exploration costs can be reduced by using AI to identify the most likely locations of mineral deposits. Predictive maintenance can ensure that equipment defects are solved before they become extremely costly and ensure that equipment downtime is kept to a minimum, increasing productivity. Smart sensors and cameras aid automated equipment while also monitoring the safety of workers in mines.
Leading adopters of AI for mining are Goldcorp, BHP, Rio Tinto, Freeport-McMoRan, Fortescue, Newcrest, Barrick Gold, and Dundee Precious Metals.
Discover the leading AI companies in the mining industry
Using its experience in the sector, Mining Technology has listed some of the leading companies providing products and services related to artificial intelligence.
The information provided in the download document is drafted for mining executives and technology leaders involved in AI mining solutions.
The download contains detailed information on suppliers and their product offerings, alongside contact details to aid purchase or hiring decisions.
Amongst the leading artificial intelligence suppliers are: Goldspot Discoveries, Earth AI, Minerva Intelligence, DroneDeploy, Hikvision, Imago, Caterpillar, Komatsu and Microsoft.
Future of artificial intelligence in mining
Mining firms will spend $218m on AI platforms worldwide by 2024. This is up from $76m in 2019, representing a compound annual growth rate (CAGR) of 23.4%.
Total spending on AI mining solutions is difficult to estimate. There are two main reasons for this. Firstly, AI is an intrinsic part of many applications and functions, making it almost impossible to identify revenue explicitly generated by AI. Secondly, the range of sub-sets and technologies that make up AI can be challenging to locate and track.
With falling yields and increasingly hostile locations, AI is more important than ever in the mining industry. AI has reached a point where it can effectively impact every section of the mining value chain, from prospecting to extraction, processing, and even marketing.
FAQs
How is AI used in mining operations?
AI is used to optimise mining operations by improving predictive maintenance, enhancing safety, and increasing resource discovery efficiency. AI algorithms analyse large datasets, predict machinery failures, and aid in reducing operational costs through automation.
What are the benefits of AI for mineral exploration?
AI significantly reduces exploration costs by identifying potential mineral deposit locations using predictive models. This allows mining companies to focus resources on high-probability areas, improving discovery rates and operational efficiency.
What types of AI technologies are applied in mining?
AI technologies such as machine learning, computer vision, and robotics are used in the mining industry. These include smart sensors for equipment monitoring, automated drones for site inspections, and AI-based models for real-time data analysis.
How does AI improve safety in mining?
AI improves safety by integrating smart sensors and cameras that monitor hazardous environments, detect equipment malfunctions, and track worker safety. These systems provide real-time alerts to prevent accidents and ensure swift responses to emergencies.
Which companies are leading artificial intelligence innovation in the mining industry?
Leading companies include Goldspot Discoveries, Earth AI, and Minerva Intelligence, which provide AI-driven solutions for resource identification, operational efficiency, and safety management within the mining sector.
(See below for more FAQs).
For full details (including contact details) on the leading companies within this space, download the free Buyer’s Guide below:
Frequently asked questions
-
What role does AI play in mineral exploration within the mining industry?
AI is revolutionising mineral exploration by analysing geological data to identify potential mineral deposits more accurately and efficiently than traditional methods. Start-ups like KoBold Metals utilise AI algorithms to process vast amounts of geological, geochemical, and geophysical data to predict the locations of critical minerals essential for technologies such as electric vehicle batteries. This approach not only accelerates the discovery process but also reduces the environmental impact by focusing exploration efforts on high-potential areas, thereby minimising unnecessary drilling and land disturbance.
-
How is artificial intelligence enhancing safety in mining operations?
Artificial intelligence (AI) is significantly improving safety in mining by enabling real-time monitoring and predictive analytics. AI systems can process data from various sensors installed on equipment and within mines to detect anomalies that may indicate potential hazards, such as equipment failures or structural weaknesses. For instance, companies like Rio Tinto have implemented autonomous haulage systems that reduce the need for human operators in hazardous environments, thereby minimising the risk of accidents. Additionally, AI-powered predictive maintenance schedules ensure machinery operates optimally, reducing unexpected breakdowns that could pose safety risks to workers.
-
How does AI contribute to environmental sustainability in mining practices?
AI contributes to environmental sustainability in mining by optimising resource utilisation and reducing waste. For example, AI-driven predictive maintenance ensures equipment operates efficiently, leading to lower energy consumption and reduced greenhouse gas emissions. Companies like Fleet Space Technologies are developing AI-powered exploration tools that minimise the environmental footprint by accurately targeting mineral deposits, thus reducing the need for extensive drilling and land disruption. Furthermore, AI facilitates real-time monitoring of environmental parameters, enabling prompt responses to any ecological disturbances caused by mining activities.
-
In what ways is AI improving operational efficiency in mining?
AI enhances operational efficiency in mining by automating processes and optimising decision-making. Autonomous vehicles and drilling systems, guided by AI, can operate continuously without fatigue, increasing productivity and reducing operational costs. For instance, Sandvik has developed battery-electric mining trucks equipped with AI systems that improve haulage efficiency while reducing emissions. Additionally, AI algorithms analyse data from various sources to optimise mine planning and scheduling, ensuring that resources are allocated effectively and operations run smoothly.
-
What are the challenges of integrating AI into the mining industry?
Integrating AI into the mining industry presents challenges such as data quality and infrastructure limitations. Mining operations often generate vast amounts of data, but this data can be unstructured or incomplete, making it difficult for AI systems to process effectively. Additionally, the remote locations of many mining sites may lack the necessary infrastructure to support advanced AI technologies, such as reliable internet connectivity and robust power supplies. There is also a need for workforce training to ensure that personnel can effectively operate and maintain AI-driven systems. Addressing these challenges requires investment in infrastructure, data management practices, and employee education to fully realise the benefits of AI in mining.