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Machine learning-assisted synergetic optimization of carbon emission and thermal efficiency for a 660 MW pulverized coal-fired boiler

Author

Listed:
  • Yin, Yue
  • Lu, Jiahui
  • Jiang, Yushuang
  • Sun, Zhenkun
  • Liu, Yudong
  • Tang, Hongjian
  • Duan, Lunbo

Abstract

Coal-fired power generation is the largest anthropogenic source of carbon emission in China. However, the variable load operation of power generation units, which is necessary to meet peak demand, can significantly reduce boiler efficiency and increase carbon emission intensity. This study involved on-site sampling of feeding coal, unburned carbon, and operation parameters of a 660 MW pulverized coal-fired boiler over a continuous operation period of 25 days. Machine learning (ML) and reverse balance models were integrated to rapidly predict the carbon emission and thermal efficiency for this boiler. Our models demonstrated high accuracy both on the prediction of carbon emission intensity and boiler thermal efficiency. To optimize the carbon emission and thermal efficiency simultaneously, multi-objective optimization was then carried out by employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II). For three fixed-load conditions (420 MW, 550 MW, and 660 MW), optimal operation parameters were identified by solving the respective Pareto fronts. Compared to the actual operating conditions, the optimized solutions manifest significant potential for achieving high boiler efficiency and low carbon emission concurrently under all three load scenarios. This study offers valuable decision-making assistance to guiding high-efficiency and low-carbon operation of existing coal-fired power plants.

Suggested Citation

  • Yin, Yue & Lu, Jiahui & Jiang, Yushuang & Sun, Zhenkun & Liu, Yudong & Tang, Hongjian & Duan, Lunbo, 2025. "Machine learning-assisted synergetic optimization of carbon emission and thermal efficiency for a 660 MW pulverized coal-fired boiler," Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:energy:v:341:y:2025:i:c:s0360544225049333
    DOI: 10.1016/j.energy.2025.139291
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    References listed on IDEAS

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