| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Characterization of Minerals, Metals and Materials 2025: In-Situ Characterization Techniques
|
| Presentation Title |
Association Rules Mining for Oxygen Consumption Reduction in Basic Oxygen Furnace Steelmaking Process |
| Author(s) |
Nanlv Liu, Lingzhi Yang, Guofu Zhu, Yuchi Zou, Zeng Feng, Yufeng Guo |
| On-Site Speaker (Planned) |
Yuchi Zou |
| Abstract Scope |
Oxygen consumption reduction (OCR) is benefit for the reduction of off gas and saving energy in basic oxygen furnace (BOF) steelmaking process. Traditional OCR method relies on manual experience, which has repeated blowing adjustment and increases the oxygen consumption. In this study, a method based on association rules mining (ARM) has been proposed to obtain standard operation guidance for OCR. Practical 686 heats data were collected and divided into 3 groups according to steel target composition: low carbon and low phosphorus steel (LCLPS), high carbon and low phosphorus steel (HCLPS), and normal steel (NS). ARM algorithm is used in each dataset to obtain the operation guidance. The results indicate that the oxygen consumption in LCLPS, HCLPS, and NS is averaged reduced by 9.5 Nm3/heat, 2.9 Nm3/heat, and 39.3 Nm3/heat, respectively when the generated operations guidance is used in real production. This study is contributed for OCR in BOF steelmaking process. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Machine Learning, Modeling and Simulation |