摘要
转炉脱氧合金化是转炉冶炼工艺的最后一步,钢液内合金成分含量控制是否精确直接影响着精炼工序的冶炼难度与冶炼周期。合金收得率是转炉合金操作人员配加合金时的重要参考标准。合金元素收得率判断的准确性直接影响着钢水成分稳定性与生产成本。通过理论分析和实际数据验证,选取了9项影响锰收得率的可观测指标,并借助因子分析法对数据进行降维处理,得到6个公因子矩阵作为模型的输入,以锰收得率为模型的输出,建立基于GA-BP神经网络的锰收得率预测模型。结果表明,模型的回归系数R^(2)=0.71478,平均误差为0.01,预测精度98%以上的炉次占总炉次的75%,预测的精度较高,对实际生产具有一定的指导意义。
Deoxidization alloying is the last step of converter smelting process.The accuracy of alloy content control in molten steel directly affects the smelting difficulty and smelting cycle of refining process.The yield of alloy is an important reference standard for converter alloying workers.The accuracy of determining the yield of alloying elements directly affects the stability of molten steel composition and production cost.Through theoretical analysis and actual data verification,9 observable indicators that affecting manganese yield were selected.The data was then reduced by means of the factor analysis,and the 6 public factor matrices were obtained as the input of the model,the manganese yield was the output of the model,and the manganese yield prediction model based on GA-BP neural network was established.The results show that the regression coefficient of the model R^(2)=0.71478,the average error is 0.01.The number of more than 98%of the predicted accuracy accounts for 75%of the total,and the accuracy of the prediction is high,and the actual production has certain reference significance.
作者
何孝雨
王敏
冀建立
包燕平
杨俊峰
王仲亮
HE Xiaoyu;WANG Min;JI Jianli;BAO Yanping;YANG Junfeng;WANG Zhongliang(State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,Beijing 100083,China;Shougang Qian’an Iron and Steel Co.,Ltd.,Qian’an 064400,China)
出处
《炼钢》
CAS
北大核心
2022年第4期14-20,共7页
Steelmaking
基金
中央高校基本科研业务费资助项目(FRF-BD-19-022A)
钢铁冶金新技术国家重点实验室基金资助项目(41619018)。
关键词
锰收得率
因子分析
预测模型
影响因素
BP神经网络
manganese yield
factor analysis
predictive model
influencing factors
BP neural network
