Optimization of thermal efficiency and unburned carbon in fly ash of coal-fired utility boiler via grey wolf optimizer algorithm

Yiding Zhao, Qinghe Wu, Heng Li, Shuhua Ma, Ping He, Jun Zhao, Yangmin Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


This paper focuses on improving thermal efficiency and reducing unburned carbon in fl ash by optimizing operating parameters via a novel high-efficient swarm intelligence optimization algorithm (grey wolf optimizer algorithm,GWO) for coal-fired boiler. Mathematical models for thermal efficiency and unburned carbon in fl ash of the discussed boiler are established by artificial neural network (ANN). Based on the ANN models, the grey wolf optimizer algorithm is used to obtain higher thermal efficiency and lower unburned carbon by optimizing the operating parameters. Meanwhile, the comparisons between GWO and particle swarm optimization (PSO) and genetic algorithm (GA) show thatGWO has superior performance to GA and PSO regarding the boiler combustion optimization. The proposed method can accurately optimize the boiler combustion performance, and its validity and feasibility have been experimentally validated. Additionally, a run of optimization takes a less time period, which is suitable for the real-time optimization.

Original languageEnglish
Article number2935300
Pages (from-to)114414-114425
Number of pages12
JournalIEEE Access
Publication statusPublished - 2019


  • Coal-fired utility boiler
  • Grey wolf optimizer
  • Thermal efficiency
  • Unburned carbon in fl ash

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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