Estimation of total heat exchange factor based on adaptive kernel density estimation theory

Total heat exchange factor plays an important role in determining the ideal temperature rise curve of the slab under steady-state conditions. The "Black Box" experiment and the furnace temperature data measured by the thermocouples in the reheating furnace contain uncertainties and big noise that will affect the identification accuracy. In order to solve the above problems, this paper proposes weighted least squares method (WLS) based on adaptive kernel density theory. In the calculation process, WLS can adaptively reduce the influence of noise on the identification results, and avoid the "residual pollution" and "residual flooding" that may occur in the traditional fixed bandwidth kernel density estimation method. In this paper, a numerical algorithm combining conjugate gradient method (CGM) and gradient projection method (GPM) is used to iteratively solve the WLS model, which ensures the stability of the iterative process and improves the identification accuracy. Finally, the CuPCrNi experimental slab data was used to verify the effectiveness of the adaptive kernel density estimation method and ensure robustness.