基于数据挖掘的高炉铁水温度建模与预报-控制理论与控制工程专业论文.docx

内蒙 内蒙古科技大学硕士学位论文 PAGE PAGE IV Abstract Blast furnace temperature guarantees anterograde run for the blast furnace, it also restricts a main factor of the blast furnace smelting cost. Reasonable blast furnace temperature is the important indexes that blast furnace running smoothly. At present, the blast furnace iron silicon content is mainly used to predict the furnace temperature. But it appears positively relation rather than a strict linear relationship between the blast furnace temperature and the iron silicon content in blast furnace, and then the application of blast furnace hot metal silicon content for the single index prediction of blast furnace temperature has certain defects. With the rapid development of the information and technology, the blast furnace smelting process accumulated a large amount of data, how to look for low carbon, high efficiency and high-quality production of blast furnace model is imminent. It is imminent that how to look for low carbon, high efficiency and high quality of the blast furnace production mode. At the same time, the further research of intelligent control theory, which provides the theoretical basis and method support for the research of the blast furnace temperature prediction model established based on data mining technology. Under the data mining technology, through the blast furnace temperature data feature extraction to establish implicit mathematical relationship among different variables, it has caused highly attention and becomes the frontier of metallurgy science development today. In view of the huge amounts of data in the blast furnace smelting process, the inaccuracy of blast furnace iron silicon content forecasting furnace temperature hot metal silicon content in blast furnace smelting exist certain disadvantages as to the object of study and the subjectivity of the smelting mechanism modeling, the paper uses the data mining theory to establish the statistical model based on the blast furnace iron temperat