Comprehensive evaluation of the blast furnace status based on data mining and mechanism analysis

Abstract As an industry with high energy consumption and high emission, the iron and steel industry not only drives the economic development, but also brings serious environmental pollution problems. In order to achieve green and low-carbon steel manufacturing, reducing CO 2 emissions in the blast furnace ironmaking process has become the current mainstream, of which the accurate judgment of the blast furnace status is a key to achieve it. Firstly, combining theory with production experience, this research established 6 evaluation systems of the blast furnace and extracted 22 evaluation parameters from them through mathematical statistics. After completing the data preprocessing with the help of Python, the potential elements in the initial variables were excavated and a comprehensive evaluation model of the blast furnace status was developed by Factor Analysis. Based on this, the status of the blast furnace were divided into four degrees, i.e. good, normal, poor and warning and the rationality was verified by comparison to the production logs. By means of comparing the law of data distribution under different furnace status, the optimal range of operation parameters was summarized. This study is expected to provide guidance for realizing energy conservation and consumption reduction of the blast furnace.