Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms
This study proposes a phosphorus content prediction model for the endpoint of electroslag remelting （ESR） refining process based on Mutual Information （MI） method and XGBoost. The MI method is utilized for feature selection and assessment of factors affecting the endpoint phosphorus content. The dat...
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| Language: | zho |
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Editorial Office of Special Steel
2025-02-01
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| Series: | Teshugang |
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| Online Access: | https://www.specialsteeljournal.com/fileup/1003-8620/PDF/2024-00096.pdf |
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| author | Liu Yuxiao, Dong Yanwu, Jiang Zhouhua, Chen Xi |
| author_facet | Liu Yuxiao, Dong Yanwu, Jiang Zhouhua, Chen Xi |
| author_sort | Liu Yuxiao, Dong Yanwu, Jiang Zhouhua, Chen Xi |
| collection | DOAJ |
| description | This study proposes a phosphorus content prediction model for the endpoint of electroslag remelting （ESR） refining process based on Mutual Information （MI） method and XGBoost. The MI method is utilized for feature selection and assessment of factors affecting the endpoint phosphorus content. The dataset after feature selection serves as the input variables for the model.The MI-XGBoost model is trained and validated using production data. Grid search cross-validation is employed for model structure adjustment and hyperparameter optimization. It is compared horizontally with MI-RR， MI-RF， MI-GBDT， and MI-KNN models. The results demonstrate that the MI-XGBoost model exhibits the highest prediction accuracy. The incorporation of MI and GridSearchCV enhances the model's predictive performance and fitting ability.Validation of the test set shows that the MI-XGBoost model achieves R2， Mean Absolute Error， Explained Variance Score， and Maximum Error values of 0.889 4， 0.000 4， 0.897 2， and 0.004 1， respectively， all superior to MI-RR， MI-RF， MI-GBDT， and MI-KNN models. The MI-XGBoost model effectively predicts the endpoint phosphorus content， providing valuable reference for endpoint control and determination in the ESR refining process. It presents a new perspective for realizing the intelligence of the ESR refining process. |
| format | Article |
| id | doaj-art-a782f49c7c5846bc8cf15500eb62a06c |
| institution | Kabale University |
| issn | 1003-8620 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Editorial Office of Special Steel |
| record_format | Article |
| series | Teshugang |
| spelling | doaj-art-a782f49c7c5846bc8cf15500eb62a06c2025-01-20T02:21:39ZzhoEditorial Office of Special SteelTeshugang1003-86202025-02-0146111712510.20057/j.1003-8620.2024-00096Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost AlgorithmsLiu Yuxiao, Dong Yanwu, Jiang Zhouhua, Chen Xi01 School of Metallurgy， Northeastern University， Shenyang 110819，China； 2 State Key Laboratory of Rolling and Automation， Northeastern University， Shenyang 110819，China； 3 Key Laboratory of Ecological Metallurgy of Multimetallic Mineral， Northeastern University， Minist Educ， Shenyang 110819，ChinaThis study proposes a phosphorus content prediction model for the endpoint of electroslag remelting （ESR） refining process based on Mutual Information （MI） method and XGBoost. The MI method is utilized for feature selection and assessment of factors affecting the endpoint phosphorus content. The dataset after feature selection serves as the input variables for the model.The MI-XGBoost model is trained and validated using production data. Grid search cross-validation is employed for model structure adjustment and hyperparameter optimization. It is compared horizontally with MI-RR， MI-RF， MI-GBDT， and MI-KNN models. The results demonstrate that the MI-XGBoost model exhibits the highest prediction accuracy. The incorporation of MI and GridSearchCV enhances the model's predictive performance and fitting ability.Validation of the test set shows that the MI-XGBoost model achieves R2， Mean Absolute Error， Explained Variance Score， and Maximum Error values of 0.889 4， 0.000 4， 0.897 2， and 0.004 1， respectively， all superior to MI-RR， MI-RF， MI-GBDT， and MI-KNN models. The MI-XGBoost model effectively predicts the endpoint phosphorus content， providing valuable reference for endpoint control and determination in the ESR refining process. It presents a new perspective for realizing the intelligence of the ESR refining process.https://www.specialsteeljournal.com/fileup/1003-8620/PDF/2024-00096.pdfelectroslag remelting； mutual information method； xgboost algorithm； phosphorus content； machine learning |
| spellingShingle | Liu Yuxiao, Dong Yanwu, Jiang Zhouhua, Chen Xi Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms Teshugang electroslag remelting； mutual information method； xgboost algorithm； phosphorus content； machine learning |
| title | Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms |
| title_full | Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms |
| title_fullStr | Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms |
| title_full_unstemmed | Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms |
| title_short | Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms |
| title_sort | prediction model of phosphorus content at the end point of electroslag remelting based on mi and xgboost algorithms |
| topic | electroslag remelting； mutual information method； xgboost algorithm； phosphorus content； machine learning |
| url | https://www.specialsteeljournal.com/fileup/1003-8620/PDF/2024-00096.pdf |
| work_keys_str_mv | AT liuyuxiaodongyanwujiangzhouhuachenxi predictionmodelofphosphoruscontentattheendpointofelectroslagremeltingbasedonmiandxgboostalgorithms |