A novel online method incorporating computational fluid dynamics simulations and neural networks for reconstructing temperature field distributions in coal-fired boilers

Abstract

Three-dimensional (3D) reconstructions of temperature distributions can be used to effectively design power plants and ensure production safety. Typically, 3D temperature reconstruction based on the flame image processing technology and finite element calculation of furnace combustion using computational fluid dynamics (CFD) simulation are performed to obtain the furnace temperature field. In this study, a novel online method that overcomes the defects of image detection devices was proposed for reconstructing the temperature field with improved evaluation accuracy. Numerical simulations were used to perform numerous calculations. In this method, deep neural network (DNN) models were used for reconstructing the 3D temperature distribution. The training set was derived from offline CFD simulations that were set for a specific boiler and a series of typical working conditions. Based on established DNN models, the online calculation of 3D temperature distribution was realized for current operating conditions. The result revealed that the furnace temperature field could be accurately reconstructed online in a 350-MW tangentially fired boiler. Compared with the numerical simulation results, the mean absolute percent error under the tilt angles of 0°, 10°, and −10° were 3.61 %, 4.25 %, and 4.52 %. The proposed integrated method was applied to actual boilers with average error 3.448 % and achieved feasible solutions within 20 s.