GA-BP渣系活度预测模型及不锈钢脱氧机理  

Prediction model of GA-BP molten slag activity and deoxidation mechanism of stainless steel

作　　者：庄迎[1] 李吉东[1] 

机构地区：[1]山西太钢不锈钢股份有限公司技术中心,山西太原030003

出　　处：《中国冶金》2017年第1期7-11,18,共6页China Metallurgy

摘　　要：采用GA-BP神经网络模型对熔渣组元活度进行预测,通过对不同温度条件下不同组元渣系活度值的验证,证明了GA-BP渣系活度预测模型有较好的预测精度。在此基础上建立了奥氏体不锈钢、铁素体不锈钢冶炼过程中钢液脱氧热力学模型。热力学模型表明,钢液中铬质量分数越高,脱氧越困难;奥氏体不锈钢铝脱氧条件下,镍质量分数越高,脱氧能力越差;任何情况下降低熔渣中脱氧产物的活度都有利于降低平衡条件下钢液中溶解氧质量分数。Prediction model of molten slag activity was developed based on GA-BP neural network,which had good prediction precision proved by verifying the activity value of different slag system in the condition of different tem- peratures. On this basis,thermodynamic model of deoxidation of austenite and ferrite stainless steel was also built up. And the model was showed that the higher the Cr mass fraction,the more difficult the deoxidation. And the a- bility decreased with the increase of the Ni mass fraction on the condition of the deoxidation of austenite stainless steel. The decrease of the activity of deoxidation product in molten slag was beneficial to the reduction of dissolved oxygen mass fraction in steel in any case.

关 键 词：GA-BP神经网络 熔渣活度预测模型 不锈钢钢液脱氧 

分 类 号：TF764.1[冶金工程—钢铁冶金]