A systematic improvement of the high-quality temperature field reconstruction for acoustic pyrometry

Obtaining an accurate temperature distribution in furnaces for industrial combustion devices is critical. Acoustic pyrometry (AP) is a promising methodology for high-quality temperature field reconstruction, which is widely used in the monitoring of atmosphere, room, and furnace. However, due to the harsh working environment and the engineering limitations, the number of installed acoustic transducers is restricted, which in turn results in sparse valid data and an ill-posed AP problem. We used multiple means of improvement to improve the reconstruction performance of AP in a gradual and systematic manner. The fast finite-difference shooting method, the adaptive grid evolution strategy (AGES) and the radial basis function approximation with polynomial reproduction (RBFPR) were proposed and integrated into the sequential process optimization approach we concluded to systematically improve the reconstruction performance over the initial algorithm. In this approach, we optimized the parameters used for the reconstruction sequentially, analyzed the effectiveness of various means of improvement, finalized and validated an algorithm that takes into account universality and precision. Qualitative and quantitative analyses of the simulation and experimental results show the validity of the finalized algorithm and the sequential process optimization approach, demonstrating its significance for furnace temperature field measurement, combustion control, and environmental protection.