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Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR

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A model combining kernel principal component analysis(KPCA)and Xtreme Gradient Boosting(XGBoost)was intro-duced for forecasting the final oxygen content of electroslag remelting.KPCA was employed to reduce the dimensionality of the factors influencing the endpoint oxygen content and to eliminate any existing correlations among these factors.The resulting principal components were then utilized as input variables for the XGBoost prediction model.The KPCA-XGBoost model was trained and proven using data obtained from companies.The model structure was adapted,and hyperparameters were optimized using grid search cross-validation.The model performance of the KPCA-XGBoost model is compared with five machine learning models,including the support vector regression model.The findings demonstrated that the KPCA-XGBoost model exhibited the highest level of prediction accuracy,indicating that the incorporation of KPCA significantly enhanced the regression prediction performance of the model.The accuracy of the KPCA-XGBoost model was 82.4％,97.1％,and 100％at errors of±1.5 × 10-6,±2.0×10-6,and±3 × 10-6 for oxygen content,respectively.

Electroslag remeltingOxygen contentMachine learningKernel principal component analysisXGBoost

Yu-xiao Liu、Yan-wu Dong、Zhou-hua Jiang、Qi Wang、Yu-shuo Li

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School of Metallurgy,Northeastern University,Shenyang 110819,Liaoning,China

National Frontiers Science Center for Industrial Intelligence and Systems Optimization,Northeastern University,Shenyang 110819,Liaoning,China

Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University),Ministry of Education,Shenyang 110000,Liaoning,China

Key Laboratory of Ecological Metallurgy of Multimetallic Mineral,Northeastern University,Shenyang 110819,Liaoning,China

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2024

10.1007/s42243-024-01205-6

钢铁研究学报(英文版)
钢铁研究总院

钢铁研究学报(英文版)

CSTPCD
影响因子：0.584
ISSN：1006-706X
年,卷(期)：2024.31(12)