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Predicting NOx Distribution in a Micro Rich-Quench-Lean Combustor Using a Variational Autoencoder.

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  • 1School of Energy and Power Engineering, Beihang University, Beijing 100191, China.

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|May 16, 2023
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Researchers used a variational autoencoder to predict nitrogen oxides (NOx) in micro gas turbine combustors fueled by low-heat-value gas. This data-driven approach accurately mapped NO distribution, aiding cleaner energy technology development.

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Area of Science:

  • Combustion Science and Engineering
  • Environmental Science and Technology
  • Artificial Intelligence in Engineering

Background:

  • Micro gas turbines are crucial for distributed power generation but produce significant nitrogen oxides (NOx) emissions.
  • Environmental regulations necessitate the development of low-emission combustors, especially for low-heat-value gas fuels.
  • Data-driven methods, particularly deep learning, offer novel approaches for analyzing complex combustion phenomena.

Purpose of the Study:

  • To introduce and evaluate a variational autoencoder model for predicting internal nitrogen oxide (NO) distribution in a micro rich-quench-lean combustor.
  • To assess the feasibility of using deep learning for NOx emission prediction in micro gas turbines utilizing low-heat-value fuels.
  • To provide a new data-driven methodology for optimizing combustor design for reduced environmental impact.

Main Methods:

  • A micro rich-quench-lean combustor fueled by coal bed gas was simulated to generate a dataset of internal NO distribution contours (60 images).
  • A variational autoencoder model was employed for predicting the spatial distribution of NO within the combustor.
  • Hyperparameter optimization for the model architecture was performed using a grid search method.

Main Results:

  • The variational autoencoder model accurately predicted the internal NO distribution within the micro combustor.
  • The study demonstrated the effectiveness of deep learning in capturing complex NO formation patterns.
  • The simulation-based dataset, though limited in size, was sufficient for training an accurate predictive model.

Conclusions:

  • The data-driven variational autoencoder approach provides an accurate method for predicting NOx production in micro gas turbine combustors.
  • This methodology can be extended to predict a wider range of combustion parameters, facilitating improved combustor design.
  • The study offers a novel pathway for developing cleaner and more efficient power generation technologies in the energy sector.