铸造技术2025，Vol.46Issue(10)：948-953,6.DOI:10.16410/j.issn1000-8365.2025.5146
基于贝叶斯优化机器学习的转炉耗氧量预测研究
Research on the Prediction of Oxygen Consumption in Converters via Bayesian-optimized Machine Learning
摘要
Abstract
The converter smelting process is characterized by multivariable,nonlinear,and strongly coupled dynamics,where oxygen blowing control significantly influences the composition and temperature of molten steel.To achieve precise forecasting of the oxygen-blowing volume,actual production data were first preprocessed via the boxplot method.Subsequently,prediction models for converter oxygen consumption were constructed on the basis of the back propagation neural network(BP)algorithm and the extreme learning machine(ELM)algorithm.The Bayesian optimization(BO)algorithm was employed to optimize the hyperparameters of the BP neural network algorithm and ELM algorithm.Finally,model performance was evaluated via multiple metrics.The results demonstrate that the BO-ELM prediction model outperforms the BO-BP model,achieving R2,RMSE,and MAE values of 0.721,137.176,and 113.622,respectively.The hit ratio within the error range of±300 m3 of oxygen consumption was 98.10％.关键词
转炉/耗氧量预测/BP神经网络算法/极限学习机/贝叶斯优化Key words
converter/oxygen consumption prediction/BP neural network algorithm/extreme learning machine/Bayesian optimization分类
冶金工业引用本文复制引用
丁志豪,信自成,张江山,刘青..基于贝叶斯优化机器学习的转炉耗氧量预测研究[J].铸造技术,2025,46(10):948-953,6.基金项目
国家重点研发计划(2024YFB3713602) （2024YFB3713602）
国家自然科学基金(52374321) （52374321）
绿色低碳钢铁冶金全国重点实验室自主课题(41625030) （41625030）
