BP神经网络IF钢铝耗的预测模型

为了解决某钢厂IF钢冶炼RH精炼过程铝耗偏高问题,通过数理统计和BP神经网络相结合的方法建立了铝耗预测模型,并与多元线性回归模型进行比较,该模型具有更高准确度.该模型分析了不同冶炼工艺参数对铝耗的具体影响,并对相应工艺参数进行了优化.结果表明:脱碳结束氧活度或RH进站氧活度降低0.005%左右,每吨钢铝耗可降低0.07~0.08 kg,铝脱氧有效利用系数为70.31%~80.35%;RH进站钢液温度增加35~40℃,铝耗降低1 kg左右,铝热反应升温利用系数在97.4%左右;吹氧量小于100 m³和大于100 m³时,氧气与铝反应的比例分别为37.3%和74.6%左右,吹氧量每增加50 m³,铝耗分别增加0.1 kg和0.2 kg左右.工艺参数优化后平均铝耗由1.359 kg降低到1.113 kg,降幅达18.1%.

Prediction model of aluminum consumption with BP neural networks in IF steel production

To solve the high aluminum consumption problem in interstitial-free steel production in a steel plant, an aluminum consumption prediction model was established by mathematical statistics and BP neural networks. Compared with the multiple linear regression model, this model's result is more accurate. The influence of different smelting processes on aluminum consumption was analyzed, and the process parameters were optimized. The results show that the amount of aluminum consumption per ton of steel decreases 0.07 to 0.08 kg when the oxygen activity before RH or after decarbonization reduces by 0.005%. The effective utilization coefficient of aluminum-deoxidizing is from 70.31% to 80.35%; the aluminum consumption decreases about 0.1 kg when the temperature of steel before RH increases by 35 to 40℃. The heating utilization coefficient of aluminum thermal reaction is about 97.4%. When the blowing oxygen quantity is less than 100 m3 and greater than 100 m3, the ratio of oxygen reacting with aluminum is about 37.3% or about 74.6% respectively, and the aluminum consumption increases by 0.1 kg or 0.2 kg, respectively, with the blowing oxygen quantity increasing by 50 m3. After the process parameter optimization, the aluminum consumption decreases from 1.359 to 1.113 kg, which results in a decrease of 18.1%.