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A Multi-Objective Optimization Framework That Incorporates Interpretable CatBoost and Modified Slime Mould Algorithm

Shan Gao1, Yunpeng Ma1

  • 1School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China.

Biomimetics (Basel, Switzerland)
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI framework for optimizing circulation fluidized bed boilers, enhancing thermal efficiency by 0.68% and reducing NOx emissions by 37.55%. The method effectively models and optimizes complex combustion systems.

Keywords:
boiler combustion optimizationinterpretable CatBoostmulti-objective optimization frameworkslime mould algorithm

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Area of Science:

  • Combustion Engineering
  • Artificial Intelligence
  • Environmental Science

Background:

  • Circulation fluidized bed (CFB) boiler combustion presents a complex multi-objective optimization challenge.
  • Simultaneously improving thermal efficiency and reducing nitrogen oxides (NOx) emissions is critical for CFB boilers.

Purpose of the Study:

  • To develop a novel multi-objective optimization framework for CFB boiler combustion.
  • To enhance boiler thermal efficiency and decrease NOx emissions concentration.

Main Methods:

  • An interpretable CatBoost model coupled with TreeSHAP was employed for modeling thermal efficiency and NOx emissions.
  • Data correlation analysis was performed using the established models.
  • A modified slime mould algorithm was utilized to optimize adjustable operational parameters of a 330 MW CFB boiler.

Main Results:

  • The proposed framework achieved an average thermal efficiency improvement of +0.68%.
  • A significant average reduction of -37.55% in NOx emission concentration was observed.
  • The optimization process demonstrated an average execution time of 6.40 seconds.

Conclusions:

  • The developed framework effectively models and optimizes complex CFB boiler combustion systems.
  • The approach offers a viable artificial intelligence solution for enhancing efficiency and reducing emissions in industrial boilers.
  • The method's superiority was further validated through benchmark testing functions and constrained optimization problems.