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The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, its modeling is crucial and representative. In order to facilitate the realization of control, this paper proposes a Bayesian block structure sparse based Takagi–Sugeno (T–S) fuzzy modeling method, with which the main important fuzzy rules and the corresponding pivotal consequent parameters can be selected automatically to obtain a compact fuzzy model with good generalization performance. For being conjugate to the Gaussian likelihood that would lead to the associated Bayesian inference to be performed in closed form, a hierarchy of block structure sparse priori is adopted, and the variational Bayesian inference is used to solve it. The screening of model inputs and data processing appropriately consider the characteristics of the blast furnace process. The applicability and performance of the proposed method are demonstrated on no. 2 blast furnace of Liuzhou Steel in China. [ABSTRACT FROM PUBLISHER]