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

Nan Wang, Yuanhao Shi (), Fangshu Cui, Jie Wen, Jianfang Jia and Bohui Wang ()
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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

Energies, 2025, vol. 18, issue 16, 1-31

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.

Keywords: ash fouling; economizer; cleanliness factor; subtraction-average-based optimizer; quantile regression; bi-directional gated recurrent units (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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