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

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 heat loss model 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 cumulative heat loss 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.