Quick Combustion Optimization for Utility Boilers Using a Novel Adaptive Hybrid Case Library

Abstract

To achieve carbon neutrality, thermal power plants must undertake heavier peak shaving tasks; consequently, utility boilers will be required to operate under frequently changing operating conditions. In light of this new circumstance, a combustion optimization decision that is executed more rapidly is necessary. A novel online combustion optimization framework is proposed for the combustion system of utility boilers. First, a robust filter for extracting high-quality steady-state data samples is designed and executed. Then, the K-means algorithm is used to divide the cleaned sample space and construct the working condition case library. Based on the constructed library, the boiler combustion model is constructed using the XGBoost algorithm. Therefore, the corresponding optimization case library can be established using the multiobjective optimization algorithm and working condition case library. To further capture the phenomenon of data distribution migrating as the operating conditions change, an adaptive update strategy for the combustion system is proposed, including online querying and data and model updates. The findings of this study conducted on a 660 MW utility boiler show that the proposed online optimization method can effectively decrease NOx emissions and improve combustion efficiency in approximately 2 milliseconds.