基于集成学习的转炉吹炼终点磷锰预测模型  

Prediction models of phosphorus and manganese at the end point of converter blowing based on ensemble learning

作　　者：黄一鑫 赵自鑫[2] 钟良才 于海岐[2] 刘承军 HUANG Yixin;ZHAO Zixin;ZHONG Liangcai;YU Haiqi;LIU Chengjun(School of Metallurgy,Northeastern University,Shenyang 110819,China;Bayuquan Iron and Steel Company,Angang Steel Co.,Ltd.,Yingkou 115000,China)

机构地区：[1]东北大学冶金学院,辽宁沈阳110819 [2]鞍钢股份有限公司鲅鱼圈钢铁分公司,辽宁营口115000

出　　处：《炼钢》2023年第6期15-22,共8页Steelmaking

基　　金：国家自然科学基金资助项目(U21A20117,51574069);中央高校基本科研业务专项资金资助项目(N2125018);科技部国家重点研发计划资助项目(2017YFB0304100)。

摘　　要：基于260 t转炉炼钢实际生产数据,用RF(Random Forests,随机森林)、LGBM(Light Gradient Boosting Machine,轻量级梯度提升机)和Stacking集成三种不同机器学习算法建立了转炉炼钢终点磷锰预测模型。通过相关理论分析和皮尔逊相关系数法确定了模型输入变量,对比三种集成学习模型的终点命中率,表明Stacking集成模型的预测性能最好,在预测终点磷质量分数误差为±0.001%、±0.0015%时的终点命中率分别为86.3%、97.1%,在预测终点锰质量分数误差为±0.008%、±0.01%时的命中率分别为83.4%、94.4%。Based on the actual production data of steelmaking in a 260 t converter,the integrated learning algorithms of RF(Random Forests),LGBM(Light Gradient Boosting Machine)and Stacking integration were used to establish phosphorus and manganese prediction models for blowing endpoint in the converter.The model input variables were determined through the correlation theory analysis and Pearson correlation coefficient method.It was found by comparing the end-point hit rates of the three integrated learning models that the prediction performance of the Stacking integrated model is the best.With error tolerances of the phosphorus mass fraction at the endpoint of blowing being±0.001%and±0.0015%,the hit rates are 86.3%and 97.1%,respectively.With error tolerances of the end point manganese mass fraction being±0.008%,and±0.01%,the hit rates are 83.4%and 94.4%,respectively.

关 键 词：转炉吹炼 终点磷预测 终点锰预测 机器学习 集成算法 数据驱动模型 

分 类 号：TF711[冶金工程—钢铁冶金]