Collaborative Extraction of Intervariable Coupling Relationships and Dynamics for Prediction of Silicon Content in Blast Furnaces

Soft sensor of silicon content (SI) in hot metal is challenging due to the nonlinearity, dynamics, and non-Euclidean coupling relationship between process variables. The intervariable coupling relationship widely exists in the ironmaking process. Nevertheless, they are generally overlooked in modeling. To solve this issue, this article proposes a novel temporal graph convolutional network with supervised graphs (TGCN-S). Different from traditional soft sensor methods, TGCN-S explicitly models the inherent non-Euclidean and irregular coupling relationships in the ironmaking process. First, a graph structure learning module is designed, which can adaptively learn potential intervariable relationships from data. This module is embedded in TGCN-S to learn the supervised graph structures in an end-to-end manner. Second, a novel methodological framework is proposed based on the supervised graph structures, which can collaboratively extract the intervariable coupling relationships and intravariable dynamics. In this structure, information between process variables is selectively aggregated while considering the dynamics within variables. Finally, experiments based on the real ironmaking process demonstrate the effectiveness of the proposed method.