热力发电2025，Vol.54Issue(4)：13-23,11.DOI:10.19666/j.rlfd.202408174
面向深度调峰和智能发电的炉膛温度场在线监测及预测综述
Review of furnace temperature field online monitoring and prediction for deep peaking and smart power generation
摘要
Abstract
When thermal power units participate in deep peak loading,real-time acquisition of furnace temperature field is helpful to power plant boiler control and research of combustion process in the furnace.With the promotion of intelligent power generation,machine learning provides an important means for real-time acquisition of furnace temperature field.The principle and application of the three most commonly used online monitoring technologies of furnace temperature field,namely acoustic method,absorption spectral tomography and thermal radiation imaging,are summarized at first,and the advantages and disadvantages in the application of boiler furnace temperature measurement are reviewed.Then,the principle of the coupled machine learning and CFD prediction method is described in detail,indicating that the method is less affected in the harsh furnace environment,and the application research of the method in the combustion flame structure and parameters and the furnace temperature field is reviewed,demonstrating the feasibility of applying the method to the furnace temperature field,indicating it can accurately predict the furnace temperature field.Finally,the future development trend of furnace temperature field online monitoring technology and coupled machine learning and CFD prediction method is analyzed,so as to provide ideas for obtaining more accurate furnace temperature field in real time under the continuous advancement of intelligent construction of power station.关键词
电站锅炉/炉膛温度场/在线监测/机器学习/预测Key words
utility boiler/furnace temperature field/online monitoring/machine learning/prediction引用本文复制引用
方顺利,梅晟东,刘凯,陈新建,娄春,邹莹,晋中华,杨云,李翔,任世鹏,马帅,姚斌,王浩帆,张中晖..面向深度调峰和智能发电的炉膛温度场在线监测及预测综述[J].热力发电,2025,54(4):13-23,11.基金项目
国家重点研发计划项目(2022YFB4100703) National Key Research and Development Program(2022YFB4100703) （2022YFB4100703）
