A Novel MIMO RVFLNs for Predicting Molten Iron Quality Indexes in Blast Furnace

The accurate prediction of molten iron quality in blast furnace (BF) can effectively improve the smelting efficiency and operation stability, which plays an important role in energy saving, emission reduction, and economic benefits improving. With the improvement of BF information acquisition technology and data storage technology, some data-driven modeling methods for simultaneously predicting multiple molten iron quality indexes have been presented. However, these modeling processes do not explore the use of the correlation information among multiple molten iron quality indexes, resulting in the suboptimal models. In addition, they do not pay attention to the molten iron quality indexes missing issue, which may lead to a great decline in modeling accuracy. To avoiding the above disadvantage, this article presents a novel multiple-input–multiple-output random vector functional-link networks. An output space transfer matrix is exploited, which not only makes full use of the correlation among all the molten iron quality indexes, but also greatly reduces the impact of index missing. For the corresponding tricky optimization problem, an alternative optimization algorithm is given, the convergence of which is guaranteed. A simulation of an actual BF data illustrates the proposed method.