首页|基于AdaBoost模型和SVM模型的铁水温度预测

基于AdaBoost模型和SVM模型的铁水温度预测

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以某炼铁厂3号高炉实际生产数据为基础,针对原始数据存在的重复值、缺失值和异常值等问题进行数据处理,选用AdaBoost模型和SVM模型对铁水温度进行预测.结果表明,AdaBoost模型相较于SVM模型取得更好的预测效果,R2达到0.878,±5℃预测准确率为85.21%,可满足高炉实际生产需要.基于高炉炼铁数据仓库系统,建立FineBI前端工具与铁水温度预测应用的数据连接,构建由特征参数、相关性分析、预测结果、模型评估和预测曲线等模块组成的前端界面,实现了高炉铁水温度预测应用的可视化展示.
Hot Metal Temperature Prediction based on AdaBoost Model and SVM Model
Based on the actual production data of No.3 BF in an ironmaking plant,the primary data of the furnace is pro-cessed because of its problems of value duplicating,missing or abnormality,and the AdaBoost model and SVM model are used to predict the hot metal temperature.The processing results prove that the prediction accuracy of AdaBoost model is better than that of SVM model with R2 reaching 0.878 and prediction accuracy reaching±5℃ for 85.21%of predictions,demonstrating that AdaBoost model can satisfy the actual needs of the furnace production.Based on the data warehouse sys-tem of BF ironmaking,the data connection is built up between the FineBI front-end tool and the application of hot metal temperature prediction technology to form a front-end interface that is composed of the modules of characteristic data,cor-relation analysis,prediction result,model evaluation and prediction curve.This interface providesa visualized display for the application of hot metal temperature prediction technology.

blast furnacehot metal temperature predictionAdaBoost modelSVM modeldata warehouse

李欣、李宏扬、刘然、刘小杰、唐文文、吕庆、陈树军

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华北理工大学冶金与能源学院,河北唐山 063210

河钢集团有限公司承德分公司,河北承德 067000

高炉 铁水温度预测 AdaBoost模型 SVM模型 数据仓库

国家自然科学基金青年基金资助项目

52004096

2024

炼铁
中冶南方工程技术有限公司

炼铁

北大核心
影响因子:0.28
ISSN:1001-1471
年,卷(期):2024.43(3)