2018 Volume 58 Issue 11 Pages 1999-2008
Radar detection is an advanced method for monitoring a blast furnace’s inner burden surface shape, which is an important factor that largely affects the production efficiency of the iron-making process. In this paper, a radar detection-based model for the prediction of burden surface shape was developed for assisting operators in developing a charging strategy. The data used are composed of both the detection and controlling records of a real, working-state blast furnace obtained by mechanical swing radar and a furnace database system, respectively. By defining and analyzing the stacking density function, the physical meanings of the modeling principles were revealed. Combined with the classical force charging trajectory sub-model and detection-driven burden descent calculation, the proposed model adopts Gaussian radius basis functions to approximate the stacking mechanism of the burden charging process. The parameter identification results show that the model can approximate the burden surface radius profile well. Compared with the results obtained for coke layers, the parameters’ ranges for the ore layers are narrower. Performance comparison shows that the proposed model has the advantages of higher prediction accuracy for both local details and global shape over the classical polygonal line model.