Applications Of Self-organizing Data Mining To Predict And Control Silicon Content In B.F Hot Metal

Steel is one of the most important and widely used materials in modern society, it is the basis of sustainable development of national economy. As the upper procedure in the iron and steel industry, Blast Furnace (BF) iron-making has great influence on the succeeding process of steel making. It is also a prime factor for the energy saving and consumption reduction. However, since BF iron-making process is highly complicated, whose operating mechanism is characteristic of nonlinearity, time lag, high dimension, big noise and distribution parameter etc, its mechanism is still unknown. The complexity of the whole process makes it difficult for automatic control. The most difficult part of automatic control for BF iron-making is to make accurate prediction of silicon content in hot metal under instable status and use the predicted information to control the iron-making process, which is also the frontier of research in the field of metallurgical automation.The current work uses data collected from Blast Furnace No. 6 in Baotou Steel (with an inner volume of 2500m~3) as sample space. A profound analysis of the existing predictive methods for silicon content is exercised. To overcome the deficiencies of existing methods, a self-organizing data mining method is proposed. This method combines the merits of neural networks, genetic algorithm and regression analysis and can choose the model automatically after giving the inputs of system and model selection criterion. And its biggest merit is that it takes interaction between the influencing factors into consideration. Through in-depth investigation, the input variables that carry most information of the system, as well as transfer function of middle network and model selection criterion are introduced. The GMDH algorithm is then used to construct a model for prediction of silicon content in hot metal and good results are obtained. The interaction between input variables is helpful for analysis of the system. To further explore the algorithm, a non-parameter self-organizing data mining method is used and the merits and shortcomings of it are gained. The self-organizing data mining algorithm offers an effective tool for the understanding of the mechanism of BF iron-making. Based on mathematical theories and the technical mechanism of iron-making process in BF, an in-depth analysis of blast furnace iron-making is exercised and the self-organizing algorithm is implemented to predict silicon content in hot metal. Simulation results show that the mathematical models in this paper are effective and that they are helpful for further research of BF system.