摘要
真空自耗熔炼是生产高品质特殊钢的重要方法之一,但熔炼过程中易挥发元素容易烧损并使铸锭成分产生波动,影响最终铸锭的质量。为准确控制锰元素熔炼前后的成分波动,采用皮尔逊相关系数法和互信息法对生产数据进行降维处理,选取12个特征作为模型输入,数据标准化和超参数寻优后建立真空自耗铸锭梯度提升决策树(GBDT)终点锰含量预测模型,并与决策树(DT)、随机森林(RF)和适应性提升(Adaboost)预测模型进行对比。研究发现,工艺参数中熔炼电流、补缩电流及真空度对终点锰含量的影响程度最大。经过降维处理后各模型的预测误差都有降低,其中GBDT模型的降低幅度最大,并且其均方根误差为0.03146,平均绝对误差为0.02551,模型误差最低,在±0.06%、±0.04%、±0.02%误差范围内的炉次所占比例分别为96%、78%、44%,整体误差范围内预测效果较好,对实际生产有一定的指导意义。
Vacuum consumable melting is one of the important methods to produce high-quality special steel.However,volatile elements are easy to burn and cause fluctuations in ingot composition during the melting process,which affects the quality of the final ingot.In order to accurately control the composition fluctuation of Mn before and after smelting,the Pearson correlation coefficient method and mutual information method were used to reduce the dimension of production data,and 12 features were selected as model input.After data standardization and super-parameter optimization,a prediction model for the end point Mn content of vacuum consumable ingot Gradient Boosting Decision Tree(GBDT)was established,and compared with the Decision Tree(DT),Random Forest(RF)and adaptive improvement(Adaboost)prediction model.The study has found that the melting current,feeding current,and vacuum degree among the process parameters have the greatest impact on the end point Mn content.After dimensionality reduction,the prediction error of each model has decreased,and the GBDT model has the largest reduction,with its root mean square error 0.03146,and average absolute error 0.02551.The error of GBDT model is the lowest,with the percentage of heats within the error range of±0.06%,±0.04%,and±0.02%being 96%,78%,and 44%,respectively.The prediction effect within the overall error range is good,which has certain guiding significance for actual production.
作者
查伟
董艳伍
姜周华
刘玉潇
ZHA Wei;DONG Yanwu;JIANG Zhouhua;LIU Yuxiao(School of Metallurgy,Northeastern University,Shenyang 110819,Liaoning,China)
出处
《中国冶金》
CAS
CSCD
北大核心
2023年第7期107-114,共8页
China Metallurgy
基金
国家自然科学基金资助项目(52174303,51874084)
中央高校基本科研业务费资助项目(N2125026)。
关键词
真空自耗炉
元素烧损
终点锰含量
特征降维
GBDT预测模型
vacuum consumable furnace
element burning
Mn content at end point
feature dimensionality reduction
GBDT prediction model
