Adaptive phase shift control of thermoacoustic combustion instabilities using model-free reinforcement learning

Khalid Alhazmi, Mani Sarathy

Research output: Contribution to journalArticlepeer-review

Abstract

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 languageEnglish (US)
Pages (from-to)113040
JournalCombustion and Flame
Volume257
DOIs
StatePublished - Sep 8 2023

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • General Physics and Astronomy
  • General Chemical Engineering
  • General Chemistry
  • Fuel Technology

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