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Integrating Artificial Intelligence into Ventilation on Demand: Current Practice and Future Promises.

Chengetai Reality Chinyadza1, Nathalie Risso1, Angel Aramayo1

  • 1School of Mining Engineering and Mineral Resources, University of Arizona, Tucson, AZ 85721, USA.

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|February 13, 2026
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) enhances Ventilation on Demand (VOD) systems for safer, more energy-efficient underground mining. Future AI integration promises greater adaptability to complex, deep-earth environments.

Keywords:
artificial intelligencecyber–physical systemsventilation on demand

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Environmental Science

Background:

  • Increasing depth and complexity in underground metal mining escalate ventilation energy demands and safety risks.
  • Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time data to enhance energy efficiency and safety.
  • Critical minerals extraction in deeper, extreme environments presents new challenges for existing VOD systems.

Purpose of the Study:

  • To conduct a comprehensive review of Artificial Intelligence (AI) integration in Ventilation on Demand (VOD) technologies.
  • To identify advancements in AI for sensing, prediction, control, and optimization within VOD systems.
  • To highlight opportunities for AI to improve energy efficiency, safety, and performance in evolving mining operations.

Main Methods:

  • Review of current literature on AI applications in VOD systems.
  • Analysis of AI techniques for data sensing, predictive modeling, and control strategies.
  • Examination of AI-driven optimization frameworks for VOD system performance.

Main Results:

  • Hybrid deep learning models (CNN-LSTM, Bi-LSTM) are increasingly used for forecasting in VOD.
  • AI-enabled optimization methods are being developed for sensor and actuator placement.
  • Current AI integration often relies on narrow AI, with limited long-term predictive capabilities and a focus on simulation.

Conclusions:

  • AI offers significant potential to enhance VOD systems for deep underground mining.
  • Research gaps include the need for generalized AI, long-term predictive models, and real-world validation.
  • Future directions involve integrating generative AI and Human-Cyber-Physical Systems (Human-CPS) for robust VOD.