Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace

Abstract

For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.