基于变量选择的转炉炼钢终点预报模型

转炉炼钢的终点预报模型对于钢水终点碳含量和温度的命中非常重要.针对高维输入不利于建立精确模型的问题,使用互信息方法对预报模型输入变量进行选择.为了区分各输入变量对输出的不同重要程度,对各输入变量进行加权处理,并采用微粒群算法对权值进行优化.最后,使用支持向量机方法建立转炉炼钢终点碳含量和温度预报模型.对一座180t转炉实际生产数据进行仿真,结果表明,合理的变量选择和加权处理能有效提高模型的预报精度.

Variable Selection Based BOF Endpoint Prediction Model

Basic oxygen furnace(BOF) steelmaking endpoint prediction model is very important for endpoint hit of endpoint carbon content and temperature. Mutual information calculation is used to select input variables. To distinguish the importance of input variables to output variables, the input variables are weighted and the values are established by using particle swarm optimization algorithm. Finally, two support vector machine models are built to predict BOF endpoint carbon content and temperature. Simulations are implemented by using practical production data from a 180t BOF. The results show that proper variable selection and weighted pretreatment can improve the precision of prediction models effectively.