Ventilation is the unsung hero of underground mining. Without it, nothing works. Operators can’t breathe. Diesel equipment can’t run. Explosive gases can’t be cleared. But pushing millions of cubic metres of fresh air through kilometres of tunnels every day is enormously expensive. At many underground operations, ventilation accounts for 30-40% of total electricity consumption.

For years, mines have run ventilation systems at or near full capacity around the clock, regardless of how many people or machines are actually working in different areas. It’s the “leave all the lights on” approach to mine safety. Effective, but wasteful.

AI is changing that, and the energy savings are substantial.

Ventilation on Demand Gets Smarter

The concept of ventilation on demand (VOD) isn’t new. It’s been around for over a decade. The basic idea is straightforward: instead of running all fans at full speed all the time, adjust airflow based on where activity is happening.

What’s new is the AI layer on top of VOD systems. Traditional VOD uses simple rules: if a vehicle enters Zone 3, increase airflow to Zone 3 by 30%. AI-enhanced VOD learns from patterns and optimises dynamically across the entire mine network.

ABB’s Ability Ventilation Optimizer is one of the more mature offerings. It uses machine learning to predict ventilation demand based on shift schedules, vehicle movement patterns, blasting schedules, and real-time gas sensor readings. The system adjusts fan speeds, damper positions, and airflow routing across the entire ventilation network simultaneously.

The difference between rules-based VOD and AI-optimised VOD is significant. Rules-based systems typically achieve 15-20% energy savings over traditional fixed-speed ventilation. AI-optimised systems are consistently delivering 25-35%.

Real-World Results

Let me share some numbers from actual deployments:

A gold mine in Western Australia with approximately 35 kilometres of development installed an AI ventilation optimisation system in early 2025. Their annual ventilation electricity cost dropped from $4.8 million to $3.1 million, a 35% reduction. Air quality metrics remained within all regulatory limits throughout the transition.

A copper-gold operation in Queensland reported 28% energy savings after six months on an AI-optimised system. Their site engineer noted that the AI was particularly effective at managing the transition periods between shifts, when ventilation demand drops but traditional systems keep running at full capacity.

A coal mine in NSW achieved 24% savings, lower than the hard rock examples because coal mine ventilation has stricter regulatory minimums due to methane management. Even so, the AI found significant optimisation opportunities during non-production periods and in areas of the mine that were temporarily inactive.

These numbers are attracting attention from executives and sustainability teams alike. When you’re talking about million-dollar annual savings at a single site, the business case writes itself.

The technical results have also impressed external observers. team400.ai recently published analysis noting that AI-optimised mine ventilation represents one of the highest-ROI applications of AI in heavy industry, with typical payback periods under twelve months.

How the AI Makes Decisions

The AI ventilation controller processes several data streams simultaneously:

Personnel and vehicle tracking. Using the mine’s tracking system (usually a combination of RFID tags and WiFi positioning), the AI knows where every person and vehicle is at any given moment. Zones with no activity get minimum ventilation. Zones with heavy diesel equipment activity get maximum airflow.

Gas monitoring. Real-time readings from gas sensors across the mine feed directly into the AI model. If CO, NO2, or methane levels start rising in any zone, the system increases airflow to that area before levels approach regulatory limits.

Environmental conditions. Surface temperature and humidity affect the cooling requirements for incoming air. The AI adjusts for seasonal and daily variations in ambient conditions.

Production schedule. By integrating with the mine planning system, the AI anticipates where activity will happen in the next hour and pre-positions ventilation airflow. This eliminates the lag that occurs with reactive systems, where airflow only increases after the activity has already started.

Energy pricing. In mines connected to the grid (or with variable power generation costs), the AI can time-shift ventilation during non-critical periods to take advantage of cheaper electricity rates. This is particularly effective for mines with on-site solar that generates cheap power during daylight hours.

Installation Challenges

It’s not plug-and-play. Here are the honest challenges:

Sensor density. AI ventilation optimisation requires significantly more sensors than traditional VOD. You need gas sensors, airflow sensors, and temperature sensors at frequent intervals throughout the mine. Retrofitting an existing mine with adequate sensor coverage typically costs $200,000-$500,000 depending on the size of the operation.

Reliable communications. The AI needs real-time data from every sensor, which means reliable underground communications. Mines with patchy WiFi or ageing leaky feeder systems may need to upgrade their communications infrastructure first.

Integration with existing SCADA. The AI controller needs to interface with the mine’s existing SCADA (Supervisory Control and Data Acquisition) system that controls fans and dampers. This integration is technically straightforward but can be complicated by legacy equipment and proprietary control protocols.

Regulatory approval. In most Australian jurisdictions, any change to a mine’s ventilation management plan requires regulatory notification or approval. Safety regulators are generally supportive of AI optimisation, but they want to see failsafe mechanisms that revert to full ventilation if the AI system fails.

The Safety Question

Let me address the elephant in the room: is it safe to let AI reduce ventilation in an underground mine?

The short answer is yes, when properly implemented. Every system I’ve reviewed maintains hard minimums that cannot be overridden by the AI. These minimums are set based on regulatory requirements and mine-specific safety parameters. The AI optimises above those minimums, not below them.

Additionally, the systems fail safe. If the AI controller loses communication with sensors or the control system, ventilation reverts to full capacity. You lose the energy savings, but you don’t compromise safety.

That said, human oversight remains essential. The best implementations have a ventilation engineer reviewing AI decisions daily and approving any significant changes to the ventilation strategy. The AI recommends. The human approves. At least for now.

Getting Started

If you’re running an underground mine and spending more than $2 million per year on ventilation electricity, an AI optimisation assessment is worth pursuing. Start with a ventilation energy audit to establish your baseline, then evaluate vendors against your specific mine conditions (depth, development extent, gas profile, and existing control infrastructure).

The payback period for most mines is six to eighteen months. The energy savings alone make it worthwhile. The additional benefits, better air quality control, reduced carbon emissions, and improved equipment life from optimised fan operation, are the bonus.

Ventilation isn’t glamorous. But it’s where some of the most practical, proven AI applications in mining are delivering real results right now.

MinerMundo covers technology and innovation in the mining sector.