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.

对于时间延迟大的复杂高炉冶炼系统，准确预测铁水质量指标对高炉控制具有重要的指导作用。最近，一些数据驱动的多输入多输出（MIMO）建模方法被提出来模拟多个铁水质量指标，包括铁水温度（MIT）、硅含量（[Si]）、磷含量（[P]）和硫含量 ([S])。然而，这些数据驱动的 MIMO 模型没有考虑指标间的相关性，这导致了用于估计多个铁水质量指标的次优模型。本文提出了一种新的 MIMO Takagi-Sugeno (TS) 模糊模型，充分考虑了指标间的相关性。在新颖的方法中，指标间相关性通过潜在空间中的低秩学习显式建模，克服了联合确定 MIMO TS 模型和指标间相关性的模糊规则的巨大挑战。针对相应的优化问题，提出了一种有效的交替优化算法。通过对真实高炉数据的仿真和与一些相关方法的对比，验证了所提方法的有效性。