[1]刘 晶,蒋文杰,冯海领,等.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),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.013]
 LIU Jing,JIANG Wenjie,FENG Hailing,et al.Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.013]
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基于知识与数据融合驱动的转炉炼钢供氧量预测方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-8
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven
作者:
刘 晶1,2, 蒋文杰1, 冯海领3, 张海滨4, 季海鹏2,3,5
1. 河北工业大学 人工智能与数据科学学院,天津 300401;2. 高性能轧辊材料与复合成形全国重点实验室,天津300400;3. 天津开发区精诺瀚海数据科技有限公司,天津 300400;4. 河钢数字技术股份有限公司,河北 石家庄050000;5. 河北工业大学 材料科学与工程学院,天津 300401
Author(s):
LIU Jing1,2, JIANG Wenjie1, FENG Hailing3, ZHANG Haibin4, JI Haipeng2,3,5
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; 2. State Key Laboratory of High Performance Roll Materials and Composite Forming, Tianjin 300400, China; 3. Tianjin Development Zone Jingnuo Hanhai Data Technology Co. , Ltd. , Tianjin 300400, China; 4. Hegang Digital Technology Co. , Ltd. , Shijiazhuang 050000, China;5. School of Materials Science and Engineering, Hebei University of Technology, Tianjin 300401, China
关键词:
融合驱动协同建模双分支转炉炼钢供氧量预测
Keywords:
fusion driven dual-branch knowledge fusion converter steelmaking oxygen supply prediction
分类号:
TP301.6;TF713
DOI:
10.13705/j.issn.1671-6833.2026.04.013
文献标志码:
A
摘要:
针对转炉炼钢过程中传统供氧量预测方法存在领域知识与数据驱动模型割裂的问题,提出一种基于知识与数据融合驱动的转炉炼钢供氧量预测方法。首先,构建三级知识融合模块,将冶金机理嵌入深度学习模型;其次,设计双分支架构以多维度挖掘工艺特性特征与跨炉次时序规律;最后,采用某钢厂的实际生产数据进行实验。实验结果表明:相比GBRBM-DBN、HyGPR、Stacking和BOA-LGBM等主流方法,在SPHC钢种下供氧量的MAE和RMSE最高分别降低7.59%和6.80%,准确率(相对误差±5%)达到85.29%;在HRB400E钢种下MAE和RMSE最高分别降低15.24%和15.13%,准确率(相对误差±5%)达到87.91%,验证了所提方法的供氧量预测能力。
Abstract:
Aiming at the problem of the disconnection between domain knowledge and data-driven models in traditional oxygen supply prediction methods in converter steelmaking process, a knowledge and data fusion driven oxygen 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 steel plant was used for the experiment. The experiment results showed that compared with mainstream methods such as GBRBM-DBN, HyGPR, Stacking, and BOA-LGBM, the MAE and RMSE of oxygen supply under SPHC steel grade decreased by a maximum of 7.59% and 6.80%, respectively, and the accuracy (relative error ±5%) reached 85.29%. Under 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.

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备注/Memo

备注/Memo:
收稿日期:2026-03-07;修订日期:2026-03-23
基金项目:国家重点研发计划(2024YFB3311901) ;河北省重大科技支撑计划( 252G0301D) ;天津市制造业高质量发展专项资金(20241047)
作者简介:刘晶(1979— ) ,女,内蒙古包头人,河北工业大学研究员,博士,博士生导师,主要从事工业人工智能研究,E-mail:liujing@ scse. hebut. edu. cn。
更新日期/Last Update: 2026-04-08