Blast furnace temperature monitoring and early warning system based on big data
LIU Xiao-jie1, ZHANG Yu-jie1, LI Xin1, LIU Ran1, ZHANG Zhi-feng1, CHEN Shu-jun2
1. School of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, Hebei, China; 2. Chengde Branch, HBIS Group Co., Ltd., Chengde 067000, Hebei, China
Abstract：With the rise of a new round for industrial and technical revolution, iron and steel manufacturing is transforming and upgrading from high-carbon to low-carbon and from lower end to higher end. In order to achieve the goal of high-efficiency, low-energy, long-life and low pollution, modern ironmaking process gradually leads to arenization and intelligentially. Blast furnace is nonlinear and long time-delay black-boxed system, its high temperature and pressure environment makes that measurement and control of furnace temperature is not easy to achieve. Based on the positive correlation between hot metal silicon content, hot metal temperature and blast furnace temperature, prediction models of hot metal silicon content and hot metal temperature based on big data analysis were established, which indirectly realized the prediction of furnace temperature. Firstly, the input variables of the models were selected using the blast furnace standard data set after the processing of outliers, missing values and normalization, and through multi angle correlation analysis. And then from contrasting different models comprehensively, prediction models of the silicon content and temperature of hot metal were set up based on Adaboost model. Finally, blast furnace temperature monitoring and early warning system based on big data system was established with computer technology. This system not only solves the malpractice of traditional ironmaking processing, but also plays the role of prolonging the device life cycle and predicting the trend of furnace condition in advance, which promots the intelligent transformation effectively.
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