首页|基于递归特征消除-梯度提升决策树的高炉悬料智能诊断模型

基于递归特征消除-梯度提升决策树的高炉悬料智能诊断模型

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高炉悬料是高炉冶炼期间极易发生的炉况失常现象之一.由于实际生产中悬料诊断主要依赖于操作人员的经验,个人主观性较强且传继性较差,容易出现误判而导致炉况的大幅度波动.随着计算机技术发展,数据驱动的过程监测理论逐渐成熟,基于机器学习的故障诊断被广泛应用于复杂工业生产中并取得了良好的诊断效果,因此,本研究通过分析悬料现象及形成原因,基于专家经验对初始特征集进行数据清洗;利用递归特征消除算法(recursive feature elimination,RFE)筛选有效变量并确定其重要性;依据选择的关键指标建立梯度提升决策树(gradient boosting decision tree,GBDT)进行实时悬料诊断.结果表明,采用集成学习投票机制融合GBDT分类结果准确率超过90％,呈现出较强的鲁棒性,诊断模型在精度和计算效率方面均表现出色.基于诊断模型进行智能诊断系统功能应用开发,能辅助高炉操作人员对悬料发生进行实时监测,从而保证高炉的稳定顺行.
Intelligent diagnosis model of BF hanging based on RFE-GBDT
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.

blast furnace conditionhangingrecursive feature elimination(RFE)algorithmgradient boosting decision tree(GBDT)intelligent diagnostic system

李福民、杨柳、刘小杰、孟令茹、李宏扬、吕庆

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华北理工大学冶金与能源学院,河北唐山 063210

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

国家自然科学基金青年科学基金项目唐山市科技局项目现代冶金技术教育部重点实验室开放基金河北省科技研发平台建设专项

5200409623130202E2024YJKF0123560301D

2024

10.3969/j.issn.1000-7059.2024.04.010

冶金自动化
冶金自动化研究设计院

冶金自动化

影响因子：0.685
ISSN：1000-7059
年,卷(期)：2024.48(4)