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Improving the Combustion Performance of a Hybrid Rocket Engine using a Novel Fuel Grain with a Nested Helical Structure
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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.
Mathematical Biosciences and Engineering : MBE
|December 5, 2023
Summary
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.
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.
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