The hanging of blast furnace is one of the abnormal conditions which can easily occur dur-ing blast furnace smelting.Because the hanging diagnosis in actual production mainly depends on the experience of the operator,the personal subjectivity is large and the transmission is poor,and it is easy to misjudge and lead to a large fluctuation of the furnace condition.With the development of computer technology,data-driven process monitoring theory has gradually matured,and fault diagnosis based on machine learning has been widely used in complex industrial production and has achieved good diagnostic results.Therefore,this study analyzed the hanging phenomenon and its formation cau-ses,and conducted data cleaning on the initial feature set based on expert experience.Recursive fea-ture elimination(RFE)algorithm was used to screen valid variables and determine their importance.The gradient boosting decision tree(GBDT)was established according to the selected key indexes for real-time hanging diagnosis.The results show that the accuracy rate of GBDT classification by in-tegrated learning voting mechanism is more than 90％,showing strong robustness,that is,the diagno-sis model has excellent performance in both accuracy and computational efficiency.Based on the di-agnosis model,the intelligent diagnosis system can help the BF operators to monitor the hangings in real time,so as to ensure the stable running of the BF.
关键词
高炉炉况/悬料/递归特征消除算法/梯度提升决策树/智能诊断系统
Key words
blast furnace condition/hanging/recursive feature elimination(RFE)algorithm/gradient boosting decision tree(GBDT)/intelligent diagnostic system
维普
万方数据