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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]

It is very difficult to control a blast furnace efficiently because of the characteristics of the closed smelting and the system's large delay. It is very helpful for blast furnace control to establish a model that can accurately predict the tendency of silicon content in molten iron. In this article, a nonparallel hyperplane–based fuzzy classifier is proposed reconciling the model accuracy with the model interpretability. Each hyperplane is as close as possible to one class of samples and as far as possible from the other class of samples. This leads to two small quadratic programming problems instead of a large one as a support vector machine–based fuzzy classifier. This makes our classifier faster than a support vector machine–based fuzzy classifier. We evaluate the proposed method on blast furnace data and synthetic data, which verifies the effectiveness. [ABSTRACT FROM AUTHOR]

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. [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]

The molten iron quality (MIQ) in the blast furnace (BF) needs on-site sampling and laboratory tests, which is very unfavorable to the control. To estimate multiple molten iron quality indicators (MIQIs) such as molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]) in real-time, some data-driven multi-input multi-output (MIMO) modeling methods have lately been developed. However, those modeling methods ignore the inter-indicator correlation. Moreover, the above modeling methods do not consider the processing strategy for the samples with missing indicators. To conquer the above two problems in the process of modeling, this paper proposed an output space transfer based MIMO Takagi–Sugeno (T–S) fuzzy modeling method for the four MIQIs. Through sparse learning of the correlation matrix, the inter-indicator correlation is specifically modeled. Due to the full use of inter-indicator correlation, the impact of missing indicators on modeling accuracy is greatly reduced. An effective alternative optimization algorithm is also provided for the corresponding intractable optimization problem. The actual data test shows that the proposed method can greatly improve the accuracy of multiple MIQIs estimations. [ABSTRACT FROM AUTHOR]

Silicon content prediction is quite significant for supervising the state of blast furnace and is usually selected as the indicator to represent the thermal state. In practical industry, the fluctuation exists in the operation of blast furnace all the time. What's worse, it is inaccurate to build the predictive model with many outliers. To solve these problems, this paper has developed a model to predict the silicon content using support vector regression (SVR) combined with clustering algorithms, including hard C-means (HCM) clustering and fuzzy C-means (FCM) clustering. Through data processing, the data points are clustered based on the similarity, and then different SVR models are established. In order to make full use of FCM, a new method using multiple SVRs and FCM based on membership degree (MFCM-SVRs) is proposed where the membership degree is applied to eliminate the outliers. Simulation results verify that the multiple SVRs based on HCM (HCM-SVRs) and MFCM-SVRs possess superiority in terms of accuracy and speed, which makes the method serve better for practical production. [ABSTRACT FROM AUTHOR]