Complexity Mechanism And Predictive Research For BF Ironmaking Process

Blast Furnace(BF) ironmaking,which is the main working procedure of the metallurgical industry,is the pillar of the national economy,and is very important for the development of iron & steel industry and economizing energy consumption. Taking Blast Furnace smelting data obtained form No.6 BF at Baotou Steel as the foundation,the complexity of silicon content fluctuation and prediction of furnace temperature were studied in detail.The metallurgical figures pay extremely attention to the development of the mathematical prediction model of furnace temperature,because analyzing the behaviors of the furnace temperature fluctuation and accurately forecasting the temperature is the key,with which,BF operators can control ironmaking process well, the utilization coefficient of BF will increase and ratio of coke burden will decrease, Hot metal silicon content([Si]) is an important index in BF ironmaking process.Not only is silicon content a significant quality variable,it also reflects the thermal state of BF and can be used to represent the furnace temperature.As[Si]time series of No.6 BF at Baotou Steel to be sample space,the quantitative surrogate data technique and qualitative method based on information-theoretic functionals-redundancies(linear and nonlinear forms) are used to test nonlinearity in time series.The results show that there is intrinsic nonlinearity in[Si]time series and provide firm rationale for the nonlinear prediction and control of furnace temperature.To further explore the fluctuation characteristic of silicon content series,the stationarity test based on the number of reverse order and the DVV(Delay Vector Variance) predictability examination are implemented.The conclusion is:although the variance of[Si]time series is nonstationary,there still exists deterministic components in ironmaking process,the predictability of[Si]series is between time series generated from Lorenz chaotic system and deterministic system.On the basis of identification of nonlinear and nonstationary characteristics,Chapter 3 proposes two predictive models using empirical mode decomposition technique,support vector machine,Volterra theory and Hopfield network method.Recognizing that Blast Furnace ironmaking is a complex system consisting of multi-procedure,a range of factors is involved in influencing the silicon content in hot metal.The prediction models are insufficient if we take no account of the influences of some operational and controlling factors.In accordance to data acquisition capability of No.6 BF at Baotou Steel,fourteen variables,such as blast volume FL. pulverized coal injection PM and material velocity LS,were selected.Employing the grey incidence analysis and rough set method,Chapter 4 provides precious insight into the analysis of relationships among these variables and silicon content.Then six crucial factors were finalized.In view of the extant models' shortcomings,such as instability causing by noise pollution,uncertain functional relation between variables and silicon content,etc,Chapter 5 made multivariate time series predictions respectively by set membership theory and genetic programming method according to the selected factors.The hit rates of silicon content are 85%and 83%.These algorithms come up to the industry index and they are valuable for practical application.Finally,Chapter 6 gives the conclusion and theoretical innovations in this paper,issues for further research are also investigated.