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Optimizing boiler combustion parameters based on evolution teaching-learning-based optimization algorithm for

Yunpeng Ma1, Shilin Liu1, Shan Gao1

  • 1School of Information Engineering, Tianjin University of Commerce, Tianjin, China.

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|December 5, 2023
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Summary
This summary is machine-generated.

This study introduces an Evolution Teaching-Learning-Based Optimization algorithm (ETLBO) to reduce boiler nitrogen oxide (NOx) emissions. ETLBO effectively optimizes combustion parameters, demonstrating superior accuracy in reducing NOx concentrations for thermal power plants.

Keywords:
boiler combustion optimizationevolution computationextreme learning machinemodeloptimizationteaching-learning-based optimization algorithm

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Area of Science:

  • Environmental Engineering
  • Combustion Science
  • Artificial Intelligence

Background:

  • High nitrogen oxide (NOx) emissions from boilers pose a significant environmental challenge for thermal power plants.
  • Optimizing boiler combustion parameters is crucial for mitigating NOx pollution.

Purpose of the Study:

  • To develop and validate an advanced optimization algorithm for reducing boiler NOx emissions.
  • To enhance the efficiency and accuracy of combustion parameter optimization.

Main Methods:

  • An Evolution Teaching-Learning-Based Optimization algorithm (ETLBO) was developed, incorporating chaotic mapping and genetic evolution principles.
  • The ETLBO algorithm was benchmarked against 20 IEEE congress on Evolutionary Computation test functions for convergence speed and accuracy.
  • ETLBO was applied to optimize boiler combustion parameters, including coal supply and air valve settings.

Main Results:

  • ETLBO demonstrated superior convergence accuracy on most benchmark test functions compared to existing algorithms.
  • The application of ETLBO successfully reduced NOx emission concentrations in boilers.
  • The algorithm proved effective in optimizing critical combustion parameters.

Conclusions:

  • ETLBO is a highly effective and accurate optimization tool for reducing NOx emissions in thermal power plant boilers.
  • The developed algorithm offers a promising solution for environmental compliance and operational efficiency in combustion processes.