AI Gas Turbine Optimization

Machine learning for gas turbine performance and efficiency optimization

The Problem

“Reduce gas turbine heat rate and downtime”

Organizations face these key challenges:

Fuel cost dominates O&M, yet heat-rate degradation from fouling, wear, and off-design operation is hard to quantify continuously and is often discovered after material losses accrue

Operators must balance MW demand, emissions limits, and reliability using static OEM curves and heuristics that do not adapt to unit-specific aging, ambient swings, or fuel variability

Unplanned outages and start/stop cycling create high costs and revenue loss, but early warning signals are buried in noisy sensor data and inconsistent instrumentation quality

Impact When Solved

0.5–1.5% lower heat rate and 0.5–2.0% higher net output under comparable conditions via continuous setpoint optimization10–30% reduction in forced-outage hours through early detection of compressor fouling, combustor instability, cooling issues, and sensor/actuator drift20–50% fewer emissions exceedances (NOx/CO) and improved compliance reporting with automated root-cause insights and operating envelope recommendations