Artificial Intelligence and Digitalization as a Response to New Challenges in Underground Mining

Abstract

Underground mining faces unprecedented challenges requiring real-time information access at operational and strategic levels. Digitalization of mining processes and systematic implementation of artificial intelligence are fundamentally reshaping the industry's operational paradigm. Modern AI-driven solutions enable comprehensive real-time monitoring of equipment utilization and technical condition, transforming both tactical decision-making and long-term strategic planning. The integration of artificial intelligence with digital twins opens unprecedented opportunities for optimizing resource management and predicting technological outcomes with 85-95% accuracy. Contemporary mining demands intelligent tools that transcend historical data analysis, supporting dynamic management through automated reporting, predictive equipment reliability assessment, and continuous workplace condition monitoring. This paper synthesizes a decade of implementation projects and scientific research, presenting practical examples of AI applications across the mining value chain - from exploration and planning through production, transport, mineral processing, and metallurgical operations. The paper address key challenges in implementing AI across diverse operational scales, identify success factors, and provide recommendations for mining enterprises seeking to realize the transformative potential of artificial intelligence in underground operations.