1. 燕山大学 电气工程学院, 河北 秦皇岛 066004；2. 上海交通大学 电子信息与电气工程学院, 上海 200240
作者简介:李军朋（1987-），男，河北省石家庄市人，副教授，主要从事复杂工业过程建模优化控制研究. E-MAIL: jpl@ysu.edu.cn基金资助:国家自然科学基金资助项目(61603330)，河北省自然科学基金钢铁联合研究基金(F2017203260)，中国博士后科学基金面上资助项目(2017M611185)，中国博士后科学基金特别资助项目(2018T110204)Modeling Research for Blast Furnace Smelting Process Based on Smelting Mechanism, Operation Data and Expert Knowledge
LI Junpeng,HUA Changchun,GUAN Xinping1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China; 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
摘要/Abstract
摘要： 高炉冶炼过程具有强非线性、大时滞和欠调节的特性，其内部为多相多场耦合的复杂动态系统，仅单一地从机理角度构建高炉模型或简单地利用高炉数据建模很难达到较好的效果.为此，利用新型高炉传感器所测数据，并结合高炉冶炼机理、运行数据和专家经验构建了几个高炉局部模型.几个模型都以高炉实际数据进行了测试，并且成果已运行于柳钢2号高炉之上.
关键词: 高炉冶炼, 建模, 数据, 铁水质量, 布料控制
Abstract: Blast furnace smelting process is characterized by strong nonlinearity, large time delay and under regulation, and its interior is a complex dynamic system with multiphase and multi field coupling. Therefore, it is difficult to achieve a reliable blast furnace model by only mechanism modeling or simply data driven modeling. Based on the measured data of a new type of blast furnace sensor, and combining the blast furnace smelting mechanism, operation data and expert experience, several local models of blast furnace have been built. The models have been tested by actual data of blast furnace and the results are running on the No. 2 BF of Liuzhou Iron and Steel Company.
Key words: blast furnace smelting, modeling, data, quality of molten iron, burden distribution control
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