An improved sequential quadratic programming method for identifying the total heat exchange factor of reheating furnace

Abstract

Accurate identification of the total heat exchange factor (THEF), which serves as the foundation for online control data in the reheating furnace, holds significant importance for enhancing the digitization and intelligence level of the furnace, mitigating environmental pollution, and improving the economic performance of enterprises. For this purpose, based on the theory of inverse heat transfer problem, a solver combining Sequential Quadratic Programming and Broyden Combined Method (SQPBC) was developed to accurately identify high-dimensional and strongly nonlinear THEF in the reheating furnace. Integrating the efficient Broyden Combined Method (BCM) into Sequential Quadratic Programming (SQP) enables the rapid and accurate estimation of the Jacobian matrix to effectively enhance computational speed without compromising accuracy. Experimental data has been employed to validate the accuracy of the inversion results. The performance of SQPBC has been comprehensively analyzed through a series of numerical experiments, with a particular focus on investigating the impact of sampling frequency and sensor location on the inversion results. The findings reveal that after reducing the sampling period to 2 min, further decreasing the sampling period has a minor effect on the average relative error of the identification results; the closer the measurement points are to the surface of the slab, the more accurate the identification of THEF on the slab's surface.