Combustion instability is a significant risk in the development of new engines when using novel zero-carbon fuels such as ammonia and hydrogen. These instabilities can be difficult to predict and control, making them a major barrier to the adoption of carbon-free gas turbine technologies. In order to address this challenge, we propose the use of model-free reinforcement learning (RL) to adjust the parameters of a phase-shift controller in a time-varying combustion system. Our proposed algorithm was tested in a simulated time-varying combustion system, where it demonstrated excellent performance compared to other model-free and model-based methods, including extremum seeking controllers and self-tuning regulators. The ability of RL to effectively adjust the parameters of a phase-shift controller in a time-varying system, while also considering the safety implications of online system exploration, makes it a promising tool for mitigating combustion instabilities and enabling the development of safer, more efficient carbon-free gas turbine technologies.
|Original language||English (US)|
|Journal||Combustion and Flame|
|State||Published - Sep 8 2023|
Bibliographical noteKAUST Repository Item: Exported on 2023-09-11
Acknowledged KAUST grant number(s): URF/1/4051-01-01
Acknowledgements: The research reported in this publication was supported by the Competitive Research Grant funding from King Abdullah University of Science and Technology (KAUST) under grant number URF/1/4051-01-01.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Physics and Astronomy(all)
- Chemical Engineering(all)
- Fuel Technology