Wiener Model Identification of Blast Furnace Ironmaking Process

Abstract

To account for the nonlinearity of blast furnace ironmaking process, a nonlinear Wiener model identification algorithm is presented. The system consists of a linear time invariant (LTI) subsystem followed by a static nonlinearity. The inverse of the nonlinearity is assumed to be a linear combination of known nonlinear basis functions and the linear subspace algorithm is used to identify the model. The inputs to the model are parameters regarded to be most responsible for the fluctuation of thermal state in blast furnace while the output to the model is silicon content in hot metal. The identified Wiener model is then tested on datasets obtained from No. 6 Blast Furnace from Baotou Steel. It is found that the blast furnace of concern is a short memory system, so that for each prediction the Wiener method is retrained. It is shown that the retrained model well improves the predictive accuracy.KEY WORDS: blast furnace iron-making; silicon content; Wiener model; subspace algorithm. the chamber, so that chemical reactions take place throughout the furnace as the material moves downward. The end products are usually molten iron and slag phases tapped from the bottom, and flue gases exiting from the top of the furnace.The critical operating parameters are the temperature and silicon content in hot metal. It has been shown that silicon content has a linear approximation relation with temperature. Furthermore, the silicon content is a good measure of the heat content and the course of the blast furnace process. Therefore, only silicon content is chosen as an output variable for the process model. In the meantime, a large number of variables are monitored and they become the basis for the blast furnace operators to judge the status of blast furnace. Since not all monitored process variables have an influence on the fluctuation of silicon content in hot metal, selections must be made. According to engineering knowledge and controllability of variables, we select out 7 process variables 20) as the model inputs and silicon content in hot metal as the model output to construct the predictive model. These process variables include: the quantity of blast, the temperature of blast, the pressure of blast, the quantity of coal powder, the pressure of top gas, the coke ratio and the quantity of oxygen. Delay time for each input variable for this specific blast furnace is given in Table 1. Subspace MethodsSubspace methods are a relatively recent development in the field of system identification which is most suitable for multi-variable identification. There are 3 main subspace methods, i.e., N4SID developed by van Overschee and De Moor, 15) CVA developed by Larimore 16) and MOESP developed by Verhaegen and Dewilde. 17) For the 3 algorithms, N4SID and CVA have been seen much more applications. The CVA algorithm was based on mathematical statistics and time series analysis methods while the N4SID algorithm is more closely related to linear system theory. For the present work, we use the N4SID algorithm to identify the linear block of...

Nonlinear Modeling Method Applied to Prediction of Hot Metal Silicon in the Ironmaking Blast Furnace

“…The first attempts 21À24 applied linear time series analysis to the problem and were followed by VARMAX and state space modeling. 25,26 During the last two decades, nonlinear methods have been used and found partial success: These include neural networks, 27,28,17 support vector machines, 29 Wiener models, 30 and techniques based on detecting and characterizing chaotic behavior. 31À34 However, a general challenge is to find suitable input variables for the model among the large number (typically several thousands) of available measurements and calculated quantities at a modern blast furnace.…”

Soft Sensing of a Nonlinear Multimode Process Using a Self Organizing Model and Conditional Probability Density Analysis

“…According to ref , five process variables are selected as model inputs, that is, coal injection ( u 1 ), blast quantity ( u 2 ), air permeability ( u 3 ), descending speed of feed ( u 4 ) and difference between top and bottom pressure ( u 5 ). In addition, as the blast furnace is a strong dynamic system, the silicon content at the last two time instances are also considered to be the model inputs.…”

Toward Budgeted Online Kernel Ridge Regression on Streaming Data

“…Silicon content in hot metal (abbr. [Si]) is a chief indicator of the internal thermal state of blast furnace which determines the quality of hot metal and fuel consumption in hot metal production [26], [27]. The measurement, modeling and control of [Si] have always been of importance in metallurgic engineering and automation.…”