Artificial Intelligence (AI) simulates human intelligence processes using machines, especially computer systems. These processes include learning (acquiring information and rules for using information), reasoning (using rules to reach approximate or definitive conclusions), and self-correction. AI has made significant advances in several sectors, and its integration into the mining industry is a promising development that is revolutionizing how we extract and process minerals.
During 2021, La Comision Minera organized a workshop on AI applied to resource estimation; there, we reviewed topics such as the use of Machine Learning in the analysis of mineral resources as an alternative to kriging, the application of AI in mineral exploration in three-dimensional modelling of mineral deposits, resource modelling and metal recovery, optimization and automation of the parameterization of the fleet management system. Therefore, using AI in the mining industry is not just a futuristic concept; It has been happening for a few years and will continue to evolve.
From autonomous vehicles to predictive maintenance systems, AI is already improving mining operations’ operational efficiency, safety and decision-making processes. Adopting artificial intelligence technologies in the mining industry brings numerous benefits, including increasing productivity, reducing operating costs, improving safety, and minimizing environmental impact.
However, implementing AI in mining also presents several challenges. These include the high cost of technology adoption, the need for trained personnel to manage and operate AI systems, concerns about data privacy, and the integration of AI with existing infrastructure. Addressing these challenges requires continued research, training and development investment, and industry stakeholder collaboration.
Current uses of AI in the mining industry
AI is currently used in various aspects of the mining industry to improve efficiency and safety. A notable application is the automation of vehicles and machinery. Autonomous drilling trucks and rigs perform highly precise tasks, reducing the risk of human error and increasing productivity. These automated systems can run continuously, leading to more consistent and efficient mining operations. In this case, we have the example of the autonomous mining transport trucks of the Caterpillar company, which has generated a 15% reduction in operating costs since these trucks can operate continuously without breaks or shift changes.
Predictive maintenance is another area where AI is having a significant impact. By analyzing data from equipment sensors, AI systems can predict when a machine is likely to fail and schedule maintenance before a breakdown occurs. This proactive approach reduces downtime and maintenance costs, ensuring equipment operates optimally. In this area, we have the example of the Chilean company Coddi, which provides conditions monitoring and predictive maintenance solutions driven by artificial intelligence. These solutions optimize equipment reliability, minimize downtime, and improve operational efficiency without using additional sensors to mining assets.
AI is also used in mining exploration. Machine learning algorithms analyze geological data to identify potential mineral deposits, reducing the time and cost associated with traditional exploration methods. This technology allows mining companies to make more informed decisions about where to focus their exploration efforts.
Examples in this area are the Maptek Company, an Australian company, and its Vulcan GeologyCore technology, which allows interactive visualization of drill holes, recoding lithologies and modelling vein-type, disseminated or stratigraphic deposits. Maptek uses machine learning to accelerate resource modelling, grade estimation, fragmentation analysis and production monitoring tasks.
Start-up companies are working in the area of mining exploration. We can mention the company Earth AI, which discovers untapped deposits of critical metals at lower costs and in less time. Minerva Intelligence, Aganitha Cognitive Solutions, DataRock and Orefox are other companies dedicated to mining exploration. In Chile, Octodots Analytics specialises in fleet optimization of the dispatch process, and AI Bruna predicts the characteristics of ores from deposits, optimizing subsequent metallurgical processes. In the research area, the Institute of Advanced Mining Technology Center (AMTC) of the University of Chile is the leading research centre in technology applied to mining. They cover studies and projects that include advanced tools using machine learning and AI for mining explorations.
Without a doubt, all these initiatives, research and AI applications will substantially impact the mining industry.
Potential uses of AI in the mining industry
AI has the potential to transform the mining industry in several ways. One exciting possibility is the development of intelligent drones and robots that can perform tasks in dangerous environments. These AI-powered devices could be used to inspect, sample, and even perform repairs in dangerous or inaccessible areas to human workers.
AI can also improve the efficiency of mineral processing. By optimizing processing plant operations through real-time data analysis and machine learning algorithms, mining companies can improve the recovery rate of valuable minerals, reduce waste, and reduce energy consumption. All these improvements are also possible due to the numerous developments of online instruments for grade measurement (|Courier), particle sizes (PSI), phase measurement (Smartdiver), and online rheology measurement in thickeners (Paterson & Cooke).
AI could not only increase profitability but also support sustainable mining practices.
Additionally, companies can use AI to leverage and improve environmental monitoring and management. Advanced AI systems can analyze data from various sources to detect and predict ecological impacts like air and water pollution. This allows mining companies to mitigate these impacts and comply with environmental regulations proactively.
Figure 1 below shows the areas where AI can be used in the mining industry.
CONCLUSION
Currently, mining faces significant challenges, mainly due to the depletion of natural resource laws. In addition, the industry must adapt to higher sustainability and environmental protection standards. For these reasons, mining must seek new technologies to be more efficient in its operations, treat large volumes of minerals, and increase the precision and speed of its estimates.
Integrating AI in the mining industry has enormous potential to improve operational efficiency, safety and sustainability. While AI is already used in various applications, there are still many untapped opportunities that can further revolutionize the industry. However, successful implementation of AI requires addressing challenges such as high costs, the need for trained personnel, and data privacy concerns. By investing in research, training and collaboration, the mining industry can harness the power of AI to build a more efficient, safer and sustainable future.
If your mining operations could benefit from the transformative power of AI, our consulting services are here to help you navigate this technology and implement effective strategies for optimization. While we specialize in enhancing operational efficiency and managing tailings, we can collaborate with AI experts to tailor solutions that meet your specific needs. Reach out to us today to explore how we can assist you in leveraging AI for improved productivity and sustainability. Contact us at info@processminerals.cl to schedule a consultation. Let’s work together to drive innovation in your mining operations.
