A Method for Reconstruction of Boiler Combustion Temperature Field Based on Acoustic Tomography

Abstract

Acoustic tomography (AT), as a noninvasive temperature measurement method, can achieve temperature field measurement in harsh environments. In order to achieve the measurement of the temperature distribution in the furnace and improve the accuracy of AT reconstruction, a temperature field reconstruction algorithm based on the radial basis function (RBF) interpolation method optimized by the evaluation function (EF-RBFI for short) is proposed. Based on a small amount of temperature data obtained by the least square method (LSM), the RBF is used for interpolation. And, the functional relationship between the parameter of RBF and the root-mean-square (RMS) error of the reconstruction results is established in this paper, which serves as the objective function for the effect evaluation, so as to determine the optimal parameter of RBF. The detailed temperature description of the entire measured temperature field is finally established. Through the reconstruction of three different types of temperature fields provided by Dongfang Boiler Works, the results and error analysis show that the EF-RBFI algorithm can describe the temperature distribution information of the measured combustion area globally and is able to reconstruct the temperature field with high precision.

Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning

“…The parameters in CS-IMOMP are α = 0.6, p = 3, and the column selection strategy change condition is . The reconstruction formula of LSA and SIRT for Equation (4) is shown in Equations (20) [ 2 ] and (21) [ 12 ], respectively, and the meaning of each symbol is the same as Equations (3) and (4). Relaxation factor α SIRT in Equation (21) is 0.3, and k is the iteration count of SIRT.…”

“…The combustion condition monitoring of the power station boiler is highly critical to ensure the stable operation of the system and improve the combustion efficiency, where the change in temperature distribution in the furnace directly reflects the actual combustion situation of the furnace. Therefore, in order to achieve optimal combustion, as well as safe and efficient operation of the boiler, it is necessary to conduct real-time and accurate monitoring of the temperature distribution inside the boiler [ 1 , 2 , 3 , 4 , 5 ]. The change of atmosphere and ocean has an important impact on the climate and human survival activities, and these changes are also reflected in the change of temperature distribution to a certain extent [ 6 , 7 , 8 , 9 ].…”

“…Most reconstruction algorithms assume uniform temperature distribution in the grid, and the mathematical model is established based on the length of sound paths in the grid. Typical reconstruction algorithms include least squares algorithm (LSA) [ 2 , 11 ], synchronous iterative reconstruction technology (SIRT) [ 12 ], etc. These approaches require M > N to avoid under-determination of the inverse problem.…”

“…Jia et al [ 11 ] used the LSA method to reconstruct the temperature field on the coarse grid, and then used the radial basis function approximation model to obtain the temperature distribution on the fine grid. Wu et al adopted a similar method [ 11 ], and used a population-based heuristic random search algorithm (differential evolution algorithm) to optimize radial basis function (RBF) and shape parameters to minimize root mean square error [ 2 ]. Fundamentally, the two-stage method is not a fixed algorithm, but a framework for solving inverse problems.…”