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First system worldwide to leverage physics and reinforcement learning to optimize gas turbine operation

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GT Auto Tuner improves combustion control and further minimizes seasonal tuning by supplementing the Omnivise T3000 controls with a trained reinforcement learning control policy together with a direct engine temperature control method.

Changes in ambient conditions and exhaust back pressure can result in different combustion visible in modified dynamics and emission levels.

Patented reinforcement learning (RL) methods extract existent dependencies into a neural network based RL control policy that is continuously adjusting the combustion process.

Field experience demonstrated the potential to lower NOx emissions and minimize the need for seasonal tuning based on the combination of AI and improved turbine inlet temperature estimation.

Benefits

• Power degradation recovery
• Increased part load efficiency
• Further reduction of seasonal tuning
• Reduced combustion dynamics and emissions: Possible ammonia reduction through improved emissions control as well as ~ 10% lower NOâ‚“ outputÌý
• Close loop controlÌýutilizing an AI control policy

Volkmar Sterzing and Steffen Udluft of µÂÖÝÆË¿ËÔÚÏß Corporate Technology developed a process that uses just a few recorded data to teach machines to regulate complex processes on their own. The continuous fine-tuning of the combustion valves has optimized gas turbine operations in terms of emissions and wear by constantly searching for the best solution in real time.

Test successfully passed

Before it went into practical use, Volkmar Sterzing and the team at the Berlin gas turbine plant thoroughly tested the GT Auto Tuner.

The scope includes the control logic that is required for autonomous optimization. Depending on individual project requirements, it can also include controls hardware as well as additional instrumentation.Ìý