基于烧结状态与集成学习的烧结矿转鼓强度预测  

Sinter tumbler strength prediction based on sintering state and ensemble learning

作　　者：王茗玉 唐珏 储满生 冯艳平 侯健[2] 黄小波 WANG Mingyu;TANG Jue;CHU Mansheng;FENG Yanping;HOU Jian;HUANG Xiaobo(School of Metallurgy,Northeastern University,Shenyang 110819,Liaoning,China;Produce and Manufacture Department,Handan Iron&Steel Group Co.,Ltd.,Handan 056015,Hebei,China)

机构地区：[1]东北大学冶金学院,辽宁沈阳110819 [2]邯郸钢铁集团有限责任公司生产制造部,河北邯郸056015

出　　处：《烧结球团》2023年第6期75-82,共8页Sintering and Pelletizing

基　　金：国家自然科学基金资助项目(52274326);中央高校基本科研业务专项资金资助项目(N2125018)。

摘　　要：鉴于影响烧结转鼓因素较多,难精准预测的问题,将数据分析与现场经验结合,提出了一种基于烧结状态与集成学习的烧结矿转鼓强度自适应预测模型。该模型首先通过数据收集整合、异常数据识别与缺失数据处理等数据预处理过程,获取高质量烧结矿转鼓强度预测数据集合;再采用Boruta算法结合Pearson线性、XGBoost非线性相关性分析选择转鼓强度预测模型输入特征,避免了模型输入特征的不完整性;最后采用5种不同机器学习算法构建转鼓强度集成学习预测模型,各子模型预测结果权重可根据烧结生产实际情况进行自适应更新。结合现场生产实践与烧结历史数据分析,通过烧结终点温度与主管负压对烧结状态进行区分,在误差范围为0.5%时,转鼓强度的预测命中率在90%以上。In view of the problems that there are many factors affecting the sintering drum and it is difficult to predict accurately,data analysis and field experience are combined to propose an adaptive prediction model of sinter tumbler strength based on sintering state and ensemble learning.Firstly,a prediction data collection of high-quality sinter tumbler strength is obtained through such data preprocessing processes as data collection and integration,abnormal data identification and missing data processing.Then,the Boruta algorithm combined with Pearson linear and XGBoost nonlinear correlation analysis is used to select the input features of the tumbler strength prediction model to avoid from the incompleteness of the input features of the model.Finally,five different machine learning algorithms are used to construct the ensemble learning prediction model of the tumbler strength,and the weight of the prediction results of each sub-model could be updated adaptively according to the actual situation of sintering production.Combined with the on-site production practice and the analysis of the historical data of sintering,the sintering state is distinguished by the temperature of the sintering end point and the negative pressure of the main pipe,and the predicted hit rate of the tumbler strength is more than 90%when the error range is 0.5%.

关 键 词：烧结矿 转鼓强度 烧结状态 集成学习 预测 

分 类 号：TF046.4[冶金工程—冶金物理化学] TP183[自动化与计算机技术—控制理论与控制工程]