Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization
The phosphorus (P) content of molten steel is of great importance for the quality of steel products in the electric arc furnace (EAF) steelmaking process. At present, the initial conditions of smelting process in the prediction of end-point P content are still the core part. However, few studies foc...
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MDPI AG
2022-09-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/9/1519 |
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| author | Yuchi Zou Lingzhi Yang Bo Li Zefan Yan Zhihui Li Shuai Wang Yufeng Guo |
| author_facet | Yuchi Zou Lingzhi Yang Bo Li Zefan Yan Zhihui Li Shuai Wang Yufeng Guo |
| author_sort | Yuchi Zou |
| collection | DOAJ |
| description | The phosphorus (P) content of molten steel is of great importance for the quality of steel products in the electric arc furnace (EAF) steelmaking process. At present, the initial conditions of smelting process in the prediction of end-point P content are still the core part. However, few studies focus on the influence between process data and end-point P content. In this research, the relationships between process data and end-point P content are explored by a BP neural network. Based on the theoretical analysis, influencing factors with high correlation were selected. The prediction model of P content coupled with process data and end-point P content is established. On this basis, the model is optimized with process data of oxygen supply and the time of the first addition of lime. Compared with the practical production data, the results indicate that the hit rate of the model optimized is 87.78% and 75.56% when prediction errors are within <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>0.004</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>0.003</mn></mrow></semantics></math></inline-formula> of P content. The model established has achieved the effective prediction for end-point P content, and provided a reference for the control of P content in practical production. |
| first_indexed | 2024-03-09T23:10:02Z |
| format | Article |
| id | doaj.art-a91ed0dfeb1a489ca99050e2d986c39b |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T23:10:02Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-a91ed0dfeb1a489ca99050e2d986c39b2023-11-23T17:47:25ZengMDPI AGMetals2075-47012022-09-01129151910.3390/met12091519Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data OptimizationYuchi Zou0Lingzhi Yang1Bo Li2Zefan Yan3Zhihui Li4Shuai Wang5Yufeng Guo6School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaSchool of Minerals Processing and Bioengineering, Central South University, Changsha 410083, ChinaThe phosphorus (P) content of molten steel is of great importance for the quality of steel products in the electric arc furnace (EAF) steelmaking process. At present, the initial conditions of smelting process in the prediction of end-point P content are still the core part. However, few studies focus on the influence between process data and end-point P content. In this research, the relationships between process data and end-point P content are explored by a BP neural network. Based on the theoretical analysis, influencing factors with high correlation were selected. The prediction model of P content coupled with process data and end-point P content is established. On this basis, the model is optimized with process data of oxygen supply and the time of the first addition of lime. Compared with the practical production data, the results indicate that the hit rate of the model optimized is 87.78% and 75.56% when prediction errors are within <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>0.004</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mn>0.003</mn></mrow></semantics></math></inline-formula> of P content. The model established has achieved the effective prediction for end-point P content, and provided a reference for the control of P content in practical production.https://www.mdpi.com/2075-4701/12/9/1519electric arc furnace steelmakingend-point phosphorus contentmodeling predictionartificial neural network |
| spellingShingle | Yuchi Zou Lingzhi Yang Bo Li Zefan Yan Zhihui Li Shuai Wang Yufeng Guo Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization Metals electric arc furnace steelmaking end-point phosphorus content modeling prediction artificial neural network |
| title | Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization |
| title_full | Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization |
| title_fullStr | Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization |
| title_full_unstemmed | Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization |
| title_short | Prediction Model of End-Point Phosphorus Content in EAF Steelmaking Based on BP Neural Network with Periodical Data Optimization |
| title_sort | prediction model of end point phosphorus content in eaf steelmaking based on bp neural network with periodical data optimization |
| topic | electric arc furnace steelmaking end-point phosphorus content modeling prediction artificial neural network |
| url | https://www.mdpi.com/2075-4701/12/9/1519 |
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