Study On The Tomographic Algorithm Of Furnace Flame Temperature Field Based On Acoustic Method

The measurement of furnace flame temperature is of great significance for understanding boiler optimal control and judging combustion environment.As a new type of non-contact and nondestructive temperature measurement technology,acoustic temperature measurement method has the characteristics of real-time and continuous measurement of temperature,large measurement range of temperature field,non-contact non-interference and high accuracy,which can meet the actual demand of on-line measurement in industrial production.For optimizing combustion It is of great practical significance to reduce the pollution caused by combustion and improve the efficient use of energy in the combustion environment.At the same time,the acquisition of temperature parameters is conducive to understand the operation status of the equipment and the furnace structure design for optimizing the combustion environment.This paper introduces the basic theory of acoustic thermometry and the reconstruction algorithm of temperature field,and deduces two kinds of reconstruction algorithms in detail.Due to the limitation of the number of acoustic transceiver,one kind of temperature field reconstruction algorithm requires that the number of grid division of the layer to be measured should not be greater than the number of effective acoustic paths generated by the acoustic transceiver,and the other kind of temperature field reconstruction algorithm requires that the grid division of the layer to be measured There is no strict limit on the number of effective acoustic paths generated by the fraction item and the acoustic transceiver.The classical least square method(LSM)is introduced for the first kind of reconstruction algorithm,which has the advantages of simple principle and fast imaging speed.The second kind of reconstruction algorithm introduces the Tikhonov regularization algorithm(MTR)based on Markov radial basis function,which has the advantages of high imaging accuracy and strong anti noise ability.In order to solve the ill posed problem in the reconstruction of temperature field in furnace,based on the Tikhonov regularization algorithm of Markov radial basis function,a modified Tikhonov regularization algorithm(MCTR)based on Markov radial basis function is proposed.By constructing a new regularization matrix to replace the unit matrix of standard Tikhonov regularization,the modified effect of the new algorithm is achieved By determining the proportion of the sum of the standard deviation components corresponding to the small singular value after the singular value decomposition of the coefficient matrix to the sum of the standard deviation components corresponding to all the singular values,the boundary of the small singular value defined in this paper is determined,and a new regularization matrix is constructed based on the eigenvector corresponding to the small singular value after the boundary is determined.Compared with the MTR algorithm which uses the unit matrix as the regularization matrix,after the regularization parameters are determined,all parameters are modified without difference.The MCTR algorithm has the option of only modifying the parameters corresponding to the small singular value,which can improve the stability of the obtained parameter solution,and then improve the reconstruction accuracy of the temperature field.Three reconstruction algorithms are simulated in different temperature models in Matlab environment,and the reconstruction accuracy of different algorithms under different noise levels is discussed.According to the regularization reconstruction algorithm based on radial basis function,the influence of the mesh number of the area to be measured and the layout of the acoustic transceiver on the reconstruction temperature field is also analyzed.Through the quantitative analysis of reconstruction error data,it shows that the modified Tikhonov regularization algorithm(MCTR)based on Markov radial basis function has better reconstruction accuracy and anti noise ability under the same variable condition.