A Temperature Field Reconstruction Method Based on Acoustic Thermometry

In power plant boiler, the temperature field information reflects its internal combustion conditions directly. In order to ensure the proper running of the whole system and reduce environmental pollution, it is important to monitor the temperature field inside the boiler quickly and accurately. In this paper, the fast iterative shrinkage-thresholding algorithm (FISTA) is introduced into the temperature field reconstruction for the first time, and a new temperature field reconstruction method based on acoustic thermometry is proposed. First, in order to obtain the temperature of the discrete coarse grid in measurement area, the temperature field reconstruction problem is transformed into an optimization problem, which is solved by the improved monotone FISTA (IMFISTA). Then, based on the obtained temperature of the coarse grid, the temperature field of the whole measurement area is reconstructed by kernel extreme learning machine (KELM). Compared with existing algorithms, simulation and experimental results show that the proposed method can acquire complete reconstruction results with shorter reconstruction time, better reconstruction accuracy and robustness, and it is feasible for engineering applications