Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This paper proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this paper proposes a novel stacked dynamic target-driven denoising autoencoder (D-DAE) for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.