Subspace Identification, Prediction And Control Of Blast Furnace Ironmaking Process

As the pillar industry of national economy, steel industry is among the most energy intensive, while for all the sub-processes in steel industry, blast furnace (BF) ironmaking consumes the largest part of energy. Thus every technological progress in BF ironmaking will bring enormous economic and social benefits. By investigation into the actual situation of BF ironmaking and BF operators? concerns, the current study discussed the identification and predictive control of blast furnace ironmaking with data collected from No. 6 BF at Baotou Steel. The study introduces a new idea for closed-loop control of BF ironmaking and is meaningful both in theory and practice.In the efforts to achieve closed loop control of BF ironmaking, the crucial problem is prediction and control of silicon content in hot metal, whose complexity is the result of interaction between chemical reaction dynamics and kinetics. An accurate predictive model for silicon content will greatly enhance BF ironmaking, while an effective controller will bring significant benefits. These two problems become the main research efforts in this dissertation. Chapter 2 gave a brief introduction to the blast furnace ironmaking process and some latest development on design of BF expert system. By integration of 4 kinds of models, e.g. mechanism models, inferential models, optimization models and predictive control models a closed loop controller for BF ironmaking is constructed. Chapter 3 dealt with the problem of missing values and outliers in the raw data. For missing values, a C4.5 decision tree based algorithm was adopted by discretizing the missing attribute and good results are achieved. While for the outliers, 4 multivariate outlier detection methods were used and the detected outliers are deleted from the original dataset. The fluctuation of data decreased significantly after processing of missing values and outliers. Chapter 4 introduced a novel method in the theory of system identification- subspace methods and tested the method on data before and after processing. Subspace methods identify system model from input and output data, its algorithm uses some simple and reliable linear algebra methods and is robust and efficient. Its good capacity to deal with multivariate system is very suitable for identification of blast furnace ironmaking process. Simulation experiments were carried out to test the method based on data before and after processing of missing values and outliers. It is shown that processing of missing values and outliers enhances the identification of blast furnace ironmaking process. To get more accurate models, Chapter 5 identifies the BF ironmaking process based on Wiener model and Hammerstein-Wiener model using subspace methods. For identification of Wiener model, a polynomial function was used to approximate inversion of the nonlinear part of Wiener model. By this technique the multivariate input single output (MISO) nonlinear problem was converted to a multivariate input multivariate output (MIMO) linear system and the identification became simpler. The identified model was then test on data from Baotou Steel and a hit-rate of 81% was achieved. As for the Hammerstein-Wiener model, radial basis functions (RBF) were used to approximate both the Hammerstein and Wiener nonlinearity, a BFGS quasi-Newton method was used to optimize the parameters. Four kinds of RBFs, e.g. Laplacian, Logistic, Gaussian and Thin-plate Spline RBF were tested. Simulation results shown identification using thin-plate spline RBF is the most accurate with the hit-rate of 85%.After discussion on the identification and prediction problem, Chapter 6 made further research on predictive control of BF ironmaking. Currently, control of BF ironmaking heavily relies on expert experience of operators. However, since different operators have different habits, control of BF ironmaking becomes inconsistent and unstable. To solve this problem, an adaptive predictive control method was constructed based on subspace method. The predictive control method adopts a simpler constraint handling method and priorities of input variables were also taken into consideration. The best level of silicon content was computed from practical data. Simulation results shown that the designed predictive control method can effectively handle the fluctuation of BF ironmaking problem, while the constraints of input variables were not violated. Thus the effectiveness and feasibility of the proposed method was proved. Besides, the adaptive method can overcome the problem of previous predictive models. In practice, the BF operators make control movements based on future information of silicon content obtained through predictive models, which results in deviation between actual and predicted silicon content. The adaptive predictive control method is a combination of static prediction, control and dynamic prediction so that it is capable of handling the overall condition of the ironmaking process. Finally, Chapter 7 gave the conclusion and theoretical innovations in the paper, issues for further research were also investigated.