基于数据驱动的转炉耗氧量模型研究

Study on oxygen consumption model based on data drive in a converter

基于45 t转炉炼钢实际生产数据,通过数据预处理和互信息(MI)法进行特征选择,采用贝叶斯算法(BOA)优化BP神经网络模型、支持向量回归机(SVR)模型和LGBM模型的参数,预测转炉吹炼的耗氧量。用1176炉的实际生产数据对模型进行训练,504炉的数据用于验证模型的预测效果。结果表明,在预测的氧气体积偏差分别为±50、±40、±30 m^(3)的范围下,LGBM模型的预测命中率分别为94.04%、85.91%、76.58%。与SVR模型和BP神经网络模型相比较,LGBM模型有着更高的预测精度和稳定性以及更强的泛化能力。

Models of oxygen consumption in a 45 t converter were set up based on actual production data with BP neural network,support vector regression machine(SVR)and LGBM(Light Gradient Boosting Machine)algorithms where their parameters were optimized with Bayesian optimization algorithm and the models′features were selected by data preprocessing and mutual information methods.The actual production data of 1176 heats were used to train these models,and the data of another 504 heats were used to verify the prediction results of the models.The results showed that the hit rate with LGBM model within±50 m^(3),±40 m^(3)and±30 m^(3)was 94.04%,85.91%and 76.58%,respectively.Compared with the support vector machine and BP neural network models,the LGBM model had higher prediction accuracy and stability and better generalization ability.