BOF steelmaking endpoint carbon content and temperature soft sensor based on supervised dual-branch DBN

Abstract

Abstract Accurate prediction of endpoint carbon content and temperature is critical in the basic oxygen furnace (BOF) steelmaking process. Although deep learning soft sensor approaches have the capacity to extract abstract features from high-dimensional nonlinear steelmaking data, they confront the challenge of a low correlation between acquired features and labels. This work presents a BOF steelmaking soft sensor model based on supervised dual-branch deep belief network (SD-DBN) to address this issue. The SD-DBN model incorporates label information into the feature extraction process and fuses crucial feature information to complete the feature extraction in order to extract features that are closely connected to the target variables. First, the supervised Restricted Boltzmann Machine (RBM) is improved by using a pruning strategy to extract features that are highly correlated with quality information, and then the autocorrelation key feature extraction module is spliced and fused to form a dual-branch feature extraction module to improve key information extraction. Second, stacking the supervised dual-branch RBM modules to build a deep feature extraction network enhances the deep extraction capabilities of data features. This deep network stacking not only increases the impact of essential target data in hierarchical training, but it also acquires characteristics associated with the target variables.