Digital Twin-Based Framework For Intelligent Monitoring And Automation In Underground Mines

Abstract

Underground mining operations face critical challenges including safety hazards, operational inefficiencies, and environmental monitoring complexities. Digital twin technology integrated with Internet of Things (IoT) sensors and artificial intelligence offers transformative solutions for real-time monitoring and automation in underground mines. This research explores the implementation of digital twin-based frameworks that create virtual replicas of physical mining environments, enabling continuous monitoring of critical parameters such as air quality, ground stability, equipment performance, and worker safety. The study employs a mixed-methods approach combining systematic literature review and case analysis of digital twin applications in underground mining. Results demonstrate that digital twin frameworks can reduce equipment downtime by 23-28%, improve safety incident response by 40%, and enhance productivity by 15-20%. The integration of IoT sensors with LoRaWAN connectivity enables real-time data transmission from depths of 1-4 kilometers. Advanced analytics and machine learning algorithms facilitate predictive maintenance and risk assessment. This research contributes a comprehensive framework for implementing intelligent monitoring systems in underground mining operations, addressing technological, operational, and safety dimensions.