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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.

Sensors (Basel, Switzerland)

|February 13, 2026

Summary

This summary is machine-generated.

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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.

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:

Keywords:

artificial intelligence cyber–physical systems ventilation on demand

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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.