基于Xgboost的高炉透气性指数预测模型

高炉透气性指数是高炉监控指标的一项重要参数,及时掌控高炉透气性指数的变化趋势并对其进行准确预测,对辅助操作者保持高炉的稳定顺行十分必要。基于某高炉现场实际生产数据,对原始数据存在异常值、缺失值等问题进行处理。利用Spearman、MIC(最大信息系数)和随机森林特征消除等特征选择方法对标准化后的数据选取特征变量,选用Xgboost模型进行预测。结果表明,Xgboost相较于随机森林和线性回归模型具有较大优势,模型在误差±1.5%范围内的准确率达到94.27%,能够准确预测下一小时透气性指数,及时指导高炉生产,保证高炉稳定顺行。

Prediction model of permeability index based on Xgboost

Blast furnace permeability index is an important parameter of blast furnace monitoring index. It is necessary to control the change trend of blast furnace permeability index in time and predict it accurately for the operator to keep the blast furnace running smoothly. Based on the actual production data of a blast furnace site, this paper deals with the problems of original data such as outliers and missing values, and standardizes the data. Spearman, MIC and random forest feature elimination method were used to select feature variables of standardized data, and Xgboost model was used for prediction. The results show that Xgboost has more advantages than random forest and linear regression model, the accuracy of the model is 94.27% within the error ±1.5%, Xgboost can accurately predict the next hour permeability index and guide the blast furnace production in time, keep the blast furnace running smoothly and stably.