Cement, Energy and Environment

objectives - • he mining industry in developing countries is key to the global energy transition. • AI when used responsibly has the potential to increase the efficiency of this transition. • For AI to contribute to sustainability, ethical considerations will have to be addressed. • Multi-objective optimization, using environmental and social data, offers a solution. Key Areas Where AI Is Being Applied in Mining 1. Exploration and Resource Discovery AI-driven prospecting mapping models are emerging that analyse geological, geochemical and geophysical data sets to pinpoint promising areas for mineral exploration. By amalgamating diverse data sources, these AI models can enhance the successful discovery of promising resource deposits. Traditional methods of mineral exploration rely heavily on geological surveys, manual drilling, and extensive sampling. AI, however, accelerates this process by using advanced algorithms to analyze vast amounts of geological data. Machine learning models can detect patterns and anomalies that humans might overlook, leading to more accurate predictions about the location and quantity of mineral deposits. AI systems can analyze satellite imagery, seismic data, and historical exploration data to pinpoint high-potential areas for mining. These predictive models can drastically reduce the time and cost associated with exploration, improving the efficiency of resource discovery. 2. Geotechnical monitoring Geotechnical monitoring and analysis are crucial to ensure ground and infrastructure stability at mining operations. Here, AI can be integrated with sensor networks to detect early signs of instability or failure, while predictive models can forecast ground behaviour and assess potential hazards. AI can also be applied to create detailed simulations of rock masses that can help guide the design of tunnels, underground layouts, and slope stability. 3. Autonomous Mining Operations One of the most notable applications of AI in mining is the use of autonomous vehicles and equipment. Autonomous trucks, drones, and excavators are increasingly becoming a common sight in mines. These machines are powered by AI and machine learning algorithms that allow them to operate without human intervention. Autonomous haul trucks, for example, can transport ore more efficiently, with AI ensuring optimal routes, fuel efficiency, and safe operation in potentially hazardous environments. The use of AI in this area helps improve safety by removing human workers from high-risk zones, while also driving down operational costs and increasing productivity. 4. Mine planning and optimisation AI technologies enable dynamic, datadriven decision-making to optimise mine plans and production schedules. Mining operations can leverage AI to predict performance under various conditions, helping decision-makers identify optimal productivity strategies while minimising costs. Predictive maintenance systems can also optimise the performance of mining equipment, reducing downtime and improving overall operational efficiency. 5. Predictive Maintenance and Equipment Monitoring Mining operations rely heavily on machinery and equipment, which often operate in extreme conditions. Breakdowns or failures can lead to costly downtime and safety incidents. AI can predict when equipment is likely to fail by analyzing data from sensors embedded in the machines. By using machine learning algorithms to detect early signs of wear and tear, such as unusual vibrations or temperature fluctuations, AI can trigger maintenance actions before a failure occurs. This predictive maintenance approach not only increases the lifespan of mining equipment but also ensures that operations are not disrupted by unexpected breakdowns. 6. Ore Sorting and Processing Optimization AI is also playing a pivotal role in improving ore sorting and mineral processing. Traditional methods of ore sorting are often inefficient and can result in the 44