Output Space Transfer-Based MIMO RVFLNs Modeling for Estimation of Blast Furnace Molten Iron Quality With Missing Indexes

Abstract

The final product quality of blast furnace (BF) smelting, i.e., the molten iron quality (MIQ), cannot be measured in real-time, which has become one of the important factors restricting the automation of BF. Although some data-driven soft sensing methods have been presented recently, two issues still need to be solved to meet the demand for on-site applications. The first issue is that the data-driven modeling process cannot overlook the correlation among multiple MIQ indexes (MIQIs). The second issue is that the data-driven modeling method can tolerate the missing of MIQIs, which can appear due to some factors. To address the above two issues, this article proposed an output space transfer (OST)-based multiinput-multioutput (MIMO) random vector functional-link networks (RVFLNs) for simultaneously modeling multiple MIQIs. Through sparse learning of the OST matrix, the interindicator correlation is reasonably modeled. Due to the full use of interindicator correlation, the impact of missing indicators on modeling accuracy is greatly reduced. For the corresponding knotty optimization problem, an effective alternative optimization algorithm is presented. The actual data test shows that the proposed method can greatly improve the accuracy of multiple MIQIs estimation.