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Publicly Available Case
How academic teams lowered ventilation energy through AI-based process control
Controllers predict gas levels and pre-emptively adjust fans and doors. Energy drops while safety margins stay intact.
Context
Static ventilation settings wasted energy because airflow did not adjust to changing underground activity. This approach was costly and sometimes insufficient for emerging safety needs.
Solution
AI controllers were developed to modulate ventilation dynamically based on sensor and location data. The system ensures airflow is delivered where activity is occurring.
Results
Sites achieved material energy savings while maintaining stronger safety margins. Ventilation efficiency improved as airflow better matched operational demand.
Data Inputs
Gas sensors (CH4/CO2), airflow/pressure, equipment tracking