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.