Computer Vision-Based Online Heterogeneity Assessment of the Sintering Transversal Thermal State

The heterogeneity of the sintering transversal thermal state is one of the decisive factors of sinter quality and productivity. However, it is difficult to quantify directly the heterogeneity. In the paper, a computer vision-based online assessment method is proposed for the heterogeneity of the sintering transversal thermal state. The method makes use of the flame front distribution in the real-time collecting image of the sintering machine end section. First, the flame front was extracted using image segmentation methods. Second, the heterogeneity of the sintering transversal thermal state was converted into the spatial heterogeneity of the flame front distribution, by introducing the spatial point patterns. Finally, the sintering transversal heterogeneity index was established to evaluate quantifiably the transversal heterogeneity, by integrating the conventional spatial heterogeneity assessment methods and the features of the flame front. The proposed method performs more accurately, robustly, and comprehensively, in comparison with other existing heterogeneity assessment methods, and the sintering expertise. It was integrated into a sintering condition assessment and quality prediction system in a steelmaking plant. The industrial application of it proves its efficiency and significance for guiding the sintering operation, and improving the sinter quality and productivity.