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

Abstract

Soft sensor of silicon content in hot metal is challenging due to the nonlinearity, dynamics, and non-Euclidean coupling relationship between process variables. The inter-variable coupling relationship widely exists in the ironmaking process. Nevertheless, they are generally overlooked in modeling. To solve this issue, this paper 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 inter-variable 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 inter-variable coupling relationships and intra-variable dynamics. In this structure, information between process variables is selectively aggregated while taking into account the dynamics within variables. Finally, experiments based on the real ironmaking process demonstrate the effectiveness of the proposed method.