AI is shaping the future across domains and influencing our day-to-day lives. The mining industry, which has been pivotal in spearheading global economic development, is not untouched by it. However, mining operations confront many challenges, including safety issues, operational inefficiencies, lack of transparency, environmental repercussions, and more. It is the introduction of cutting-edge technologies that has brought a breakthrough revolution here. 

In this blog, we take you through an exploration into the transformative potential of artificial intelligence in the mining industry. 

1. Exploration and discovery: The first step in mining is to find a mine. The process of searching mines is resource, time, and capital intensive. Additionally, it is a high-risk endeavour as digging in the wrong location can result in losses worth million dollars. AI-powered computer vision, predictive analytics, forecasting, and machine learning algorithms can help prevent such disastrous errors. Such systems can process geological data to predict the most accurate and economically feasible location for extraction. ML models can be leveraged to analyze historical mining data, mineral composition, geological characteristics, and other crucial parameters to ultimately derive optimal mining strategies. 

2. Autonomous technology: The use of autonomous technology such as drones for exploring resource sites is time-saving as compared to manual approaches. The precision of identifying locations for drilling mines is higher, resulting in a higher return on investment. Drones operate on advanced electromagnetic technology for collecting magnetic, electromagnetic, and radiometric data. The data gathered can be used to drill spots, which minimizes cost and time. Another advantage of using drones is that they reduce risks in areas that are dangerous for human inspection. 

3. Raw material extraction: Artificial intelligence can regulate the dimension and weight of the rocks that are on the way to the crusher. In this system, rocks that are too large for the crusher are prevented from entering and disrupting the crusher, ultimately avoiding any delay or downtime. 

4. Real-time monitoring and decision making: Generative AI can be used to power real-time monitoring and enhance the decision-making process in the mining industry. By integrating cutting edge-technology such as sensors and comprehensive data collection systems, AI algorithms can proficiently analyze bulk data streams. Such robust analytics enables identification of potential safety hazards, optimize blast and drilling methodologies, and streamline mining excavation operations. This integrated approach also yields improved overall productivity and reduces environmental impact by minimizing redundant excavation activities. 

5. Predictive Equipment Maintenance: An additional application of AI in the mining industry is equipment maintenance. Machinery used in the mining industry is subjected to deterioration frequently, eventually leading to costly repairs and operational halts. By leveraging AI, stakeholders can anticipate maintenance requirements and timelines. This empowers mining companies to proactively schedule repairs before the breakdown transpires. Such an approach not only saves time and resources for mining companies but also reduces safety hazards countered by the workforce. 

6. Environmental Impact: Undoubtedly, mining impacts the environment, and it is impossible to reverse the damage completely. Drilling as a mining activity usually generates a bulk of dangerous debris. Environmental issues resulting from mining activities include erosion, formation of sinkholes, contamination of soil from chemical leakage, reduction in biodiversity, and so on. By nature, mining is a highly destructive process. However, AI has the potential to reduce and mitigate negative environmental impacts. With AI, satellite imagery can be analyzed to monitor land degradation, deforestation, and changes in vegetation cover. The information derived can be used to identify areas that need conservation measures, and monitor the effectiveness of reforestation and land restoration initiatives. With AI systems, mining companies can identify patterns, forecast outcomes, and assess the effectiveness of various mitigation strategies.

7. Safety: The potential of AI to improve safety in mines is immense. Predictive technologies can be used to deliver real-time data insights for avoiding hazardous situations and establishing quick alerts for risk mitigation. The most efficient approach to eliminating risk is removing it entirely and enabling automation. AI can replace and automate many manual activities requiring human involvement. Robots can be deployed for underground operations, preventing humans from entering hazardous areas. Remotely operated land rovers and drones can perform inspections without overloading the structure. Autonomous borers and rock cutters can perform ore extraction and excavation. With AI, miners can also use sensors and leverage data to gain understanding of various factors. Parameters such as vibrations and temperature, which can be indicative of danger, can be analyzed and send early warnings to machine operators to reduce accidents. AI can monitor the health of mining equipment and ensure predictive maintenance based on critical aspects. 

Conclusion
Mining, essentially, is about commodity production and transition in the most efficient and productive way. Thus, staying competitive in this industry means you need to produce cheaper and faster. While tons of minerals and resources are processed every hour, even a minor improvement in processing, labor, or downtime can result in significant cost and effort savings. It is not surprising that some of the mining companies have already identified such potential and proactively incorporated AI. With smart mining already here, other players are to undertake the AI endeavour eventually.