炼钢 ›› 2022, Vol. 38 ›› Issue (6): 1-5.

• 转炉及电炉冶炼 •    下一篇

电弧炉终点碳含量预测模型的构建与分析

张家磊,李占春,石晨敏,张锦鹏,胡  适   

  1. 江苏永钢集团有限公司 特钢事业部,江苏 苏州 215628
  • 出版日期:2022-12-05 发布日期:2022-12-02

Construction and analysis of end-point carbon content prediction model for electric arc furnace

  • Online:2022-12-05 Published:2022-12-02

摘要: 现有的电弧炉终点碳预测模型大多忽略了电弧炉内余钢量中剩余的碳质量对下一炉次终点碳含量的影响,且电弧炉冶炼时渣层较厚,冶炼时钢水翻滚剧烈致钢水成分不均匀,电弧炉终点取样检测出的碳含量与钢水中实际的碳含量会存在较大的偏差,若直接使用电弧炉取样的碳含量作为分析数据,模型的预测值往往与钢水中实际的碳含量存在偏差。针对以上问题,首先利用多元线性回归算法计算钢铁料的收得率,推出每炉的余钢量,然后利用BP神经网络提出了一种关于电弧炉终点碳含量的预测方法,试验分析表明模型终点碳质量分数预测值与实际值误差在±0.02 %内的命中率达到了92 %,具有较高的预测精度。

关键词: 碳含量, 多元线性回归, 收得率, BP神经网络, 预测

Abstract: Most of the existing EAF end-point carbon prediction models ignore the influence of the remaining carbon mass in the EAF on the carbon content at the end of the next heat, and the thickness of the slag layer during EAF smelting is thick, and the molten steel splashes violently during smelting. If the composition of molten steel is not uniform, there will be a large deviation between the carbon content detected by the end point sampling of the electric arc furnace and the actual carbon content in the molten steel.If the carbon mass fraction sampled by the electric arc furnace is directly used as the analysis data, the predicted value of the model often deviates from the actual carbon content in molten steel. Aiming at the above problems, the multiple linear regression was used to calculate the yield of iron and steel materials, and the remaining amount of steel in each furnace was calculated. Then, a BP neural network was used to propose a predicition method for the carbon content at the end of the electric arc furnace. Experimental analysis showed that the error between the predicted value of the carbon mass fraction at the end of the model and the actual value was within ±0.02 %, and the hit rate reached 92 %, which had a high prediction accuracy.

Key words: carbon content, multiple linear regression, yield, BP neural network, prediction