Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model
Author:
Affiliation:
1. State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Shanghai 200444 China
2. State Key Laboratory of Rolling and Automation Northeastern University Shenyang 110819 Liaoning China
Funder
National Natural Science Foundation of China
Science and Technology Commission of Shanghai Municipality
Publisher
Wiley
Subject
Materials Chemistry,Metals and Alloys,Physical and Theoretical Chemistry,Condensed Matter Physics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/srin.202200682
Reference27 articles.
1. Development of High Efficiency Dephosphorization System in Decarburization Converter Utilizing FetO Dynamic Control
2. Dynamic Model of Basic Oxygen Steelmaking Process Based on Multi-zone Reaction Kinetics: Model Derivation and Validation
3. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network
4. An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking
5. Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking
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1. Dynamic Prediction with Statistical Uncertainty Evaluation of Phosphorus Content Based on Functional Relevance Vector Machine;steel research international;2024-01-10
2. TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods;Journal of Iron and Steel Research International;2024-01-04
3. State of the art in applications of machine learning in steelmaking process modeling;International Journal of Minerals, Metallurgy and Materials;2023-11
4. Prediction model of BOF end-point phosphorus content and sulfur content based on LWOA-TSVR;Ironmaking & Steelmaking;2023-04-16
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