LAUSR

Abstract Raceway is a key region in ironmaking blast furnace (BF). While the raceway depth is extremely difficult to measure, thermal images near tuyeres may be available. In this study, inspired by the concept of digital-twin, a soft-sensor approach is proposed to estimate the raceway depth from thermal images. This approach includes (1) The representative thermal images are generated through a raceway CFD model under industry-scale conditions of a specific BF; (2) A principal component analysis (PCA) method is used to reduce data dimension and extract key features from the thermal images of high dimension; (3) A model-learning tool, support vector machine (SVM) is developed to learn the underlying data-driven soft-sensor model between extracted features from PCA and raceway depth from CFD simulations. The result shows that the soft-sensor model can effectively capture the latent relationship between thermal images and the raceway depth, which can be used to estimate raceway depth in real-time in practice.