A data-based soft-sensor approach to estimating raceway depth in ironmaking blast furnaces

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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

A soft sensor approach is proposed to estimate the raceway depth in ironmaking blast furnace from thermal images. The method includes generating representative thermal images through a raceway CFD model, using PCA to extract key features, and developing a model-learning tool (SVM) to learn the underlying relationship between thermal images and raceway depth. The soft-sensor model shows promising results for real-time estimation of raceway depth.