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Deep Learning Models for SO2 Distribution in a 30 MW Boiler via Computational Fluid Dynamics Simulation Data.

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This study uses a hybrid deep learning model to analyze sulfur dioxide (SO2) distribution in boilers. Oxygen (O2) concentration was found to be the most significant factor influencing SO2 distribution, impacting furnace tube corrosion.

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Area of Science:

  • Combustion Engineering
  • Chemical Engineering
  • Artificial Intelligence in Engineering

Background:

  • Sulfur dioxide (SO2) distribution in boilers is critical for managing furnace tube corrosion.
  • Understanding the correlation between SO2 and other combustion variables is essential for process optimization.

Purpose of the Study:

  • To develop a hybrid deep learning model for analyzing SO2 distribution in boilers.
  • To identify key variables influencing SO2 distribution using computational fluid dynamics (CFD) data.
  • To establish the relationship between SO2 and other combustion products.

Main Methods:

  • Computational fluid dynamics (CFD) simulations were performed to generate training data.
  • A LASSO algorithm was employed for input variable selection based on correlation with SO2 distribution.
  • A deep belief network (DBN) integrated with a restricted Boltzmann machine (RBM) and a fully connected layer was utilized.

Main Results:

  • The hybrid deep learning model successfully described the nonlinear relationship between SO2 distribution and other combustion variables.
  • Oxygen (O2) concentration was identified as the most influential variable affecting SO2 distribution.
  • This represents the first application of deep learning to correlate SO2 distribution with combustion products.

Conclusions:

  • The developed deep learning model provides a novel approach to understanding SO2 distribution in boilers.
  • Optimizing O2 concentration can effectively manage SO2 distribution and mitigate furnace tube corrosion.
  • Further research can explore advanced deep learning architectures for combustion process analysis.