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Improving the Heat Transfer Efficiency of Economizers: A Comprehensive Strategy Based on Machine Learning and Quantile Ideas

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  • Nan Wang

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Yuanhao Shi

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Fangshu Cui

    (School of Computer Science and Technology, North University of China, Taiyuan 030051, China)

  • Jie Wen

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Jianfang Jia

    (School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China)

  • Bohui Wang

    (School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Ash deposition on economizer heating surfaces degrades convective heat transfer efficiency and compromises boiler operational stability in coal-fired power plants. Conventional time-scheduled soot blowing strategies partially mitigate this issue but often cause excessive steam/energy consumption, conflicting with enterprise cost-saving and efficiency-enhancement goals. This study introduces an integrated framework combining real-time ash monitoring, dynamic process modeling, and predictive optimization to address these challenges. A modified soot blowing protocol was developed using combustion process parameters to quantify heating surface cleanliness via a cleanliness factor (CF) dataset. A comprehensive model of the attenuation of heat transfer efficiency was constructed by analyzing the full-cycle interaction between ash accumulation, blowing operations, and post-blowing refouling, incorporating steam consumption during blowing phases. An optimized subtraction-based mean value algorithm was applied to minimize the cumulative attenuation of heat transfer efficiency by determining optimal blowing initiation/cessation thresholds. Furthermore, a bidirectional gated recurrent unit network with quantile regression (BiGRU-QR) was implemented for probabilistic blowing time prediction, capturing data distribution characteristics and prediction uncertainties. Validation on a 300 MW supercritical boiler in Guizhou demonstrated a 3.96% energy efficiency improvement, providing a practical solution for sustainable coal-fired power generation operations.

Suggested Citation

  • Nan Wang & Yuanhao Shi & Fangshu Cui & Jie Wen & Jianfang Jia & Bohui Wang, 2025. "Improving the Heat Transfer Efficiency of Economizers: A Comprehensive Strategy Based on Machine Learning and Quantile Ideas," Energies, MDPI, vol. 18(16), pages 1-31, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4227-:d:1720622
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