Soft-Sensing in Batch Annealing Based on Finite Differential Method and Support Vector Regression

The temperature of annealed steel coils is a determining variable of the future steel sheets quality. This variable also determines the energy consumption in operation. Unfortunately, the monitoring of coil inner temperature is problematic due to the furnace environment with high temperature, coil structure, and annealing principle. Currently, there are no measuring principles that can measure the temperature inside the heat-treated product in a non-destructive manner. In this paper, the soft sensing of inner temperature based on the theory of non-stationary heat conduction and approach based on Support Vector Regression (SVR) was presented. The results showed that a black-box approach based on the SVR could replace an analytic approach, though with lesser performance. Several annealing experiments were performed to create a training data set and model performance improvement in the estimation of inner coil temperatures. The proposed software based on non-stationary heat conduction can calculate the behavior of inner coil temperature from the measured boundary temperatures that are measured by thermocouples. The soft-sensing principles presented in this paper were verified under laboratory conditions and on the data obtained from a real annealing plant.