Mining

As mining operations move deeper and become increasingly complex, the industry faces growing challenges related to safety, productivity, ore recovery, dilution, energy use, and production stability. Addressing these challenges requires a strong scientific understanding of mining systems and their interacting components.

Within Work Package 2 (WP2) – Mining, CAMM develops knowledge, methods, and technologies that enable safer, more efficient, and more resilient mining operations. The work supports the transition towards data-driven, automated, and environmentally responsible mining systems.

Digitalisation, Automation, and Mine Planning

A central focus of WP2 is the development of digital mining solutions and autonomous mining equipment. The long-term vision is fully autonomous mining operations with no human exposure at the production face. Research addresses obstacles to automation by replacing human-dependent observations with sensor-based, data-driven systems that improve productivity and resource knowledge.

WP2 also advances short- and medium-term mine planning through the use of discrete event simulation models. These models function as digital twins of underground mines, supporting improved scheduling, lean mining operations, and optimised use of resources.

Data-Driven Production and Mine Infrastructure

To improve production reliability, WP2 develops data-driven mine capacity assurance frameworks that identify equipment- and infrastructure-related uncertainties affecting planned production volumes. This systems-based approach considers both front-end and back-end processes within the mine production system.

Research in ventilation and air conditioning focuses on strategies for deep underground mines, including the assessment of Ventilation on Demand (VOD) and Controlled Partial Recirculation (CPR). These solutions aim to improve working conditions, energy efficiency, and operational resilience.

Rock Mechanics, Seismicity, and Blasting Research

WP2 includes fundamental research on energy transmission from non-ideal detonations to rock, supporting the development of improved explosive models tailored to different rock types and mining conditions.

The work package also addresses mining-induced seismicity through the development of algorithms for automatic seismic waveform processing and source parameter determination. Automated processing enables efficient analysis of large seismic datasets, reducing time and cost while improving safety.

In parallel, research on the response of underground openings, ground surfaces, and rock support systems under deep mining conditions improves understanding of seismic and static loading effects. This knowledge supports the design of safer and more effective ground control measures.

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