郑州大学学报（工学版）2026，Vol.47Issue(4)：66-73,8.DOI:10.13705/j.issn.1671-6833.2026.04.013
基于知识与数据融合驱动的转炉炼钢供氧量预测方法
Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven
摘要
Abstract
Aiming at the problem of the disconnection between domain knowledge and data-driven models in tradi-tional oxygen supply prediction methods in converter steelmaking process,a knowledge and data fusion driven oxy-gen supply prediction method for converter steelmaking was proposed.A three-level knowledge fusion module was constructed,embedding metallurgical mechanisms into deep learning models.Secondly,a dual-branch architecture was designed to collaboratively mine process characteristics and cross furnace temporal patterns.Finally,actual production data from a domestic steel plant was used for the experiment.The experiment results showed that com-pared with mainstream methods such as GBRBM-DBN,HyGPR,Stacking,and BOA-LGBM,the MAE and RMSE of oxygen supply with SPHC steel grade decreased by a maximum of 7.59%and 6.80%respectively,and the accu-racy(relative error±5%)reached 85.29%.With the HRB400E steel grade,the MAE and RMSE decreased by a maximum of 15.24%and 15.13%respectively,with an accuracy(relative error±5%)of 87.91%,verifying the oxygen supply prediction ability of the proposed method.关键词
融合驱动/协同建模/双分支/转炉炼钢/供氧量预测Key words
fusion driven/collaborative modeling/dual-branch/converter steelmaking/oxygen supply prediction分类
信息技术与安全科学引用本文复制引用
刘晶,蒋文杰,冯海领,张海滨,季海鹏..基于知识与数据融合驱动的转炉炼钢供氧量预测方法[J].郑州大学学报（工学版）,2026,47(4):66-73,8.基金项目
国家重点研发计划(2024YFB3311901) （2024YFB3311901）
河北省重大科技支撑计划(252G0301D) （252G0301D）
天津市制造业高质量发展专项资金(20241047) （20241047）
