Abstract—The operating modes in the smelting process of fused magnesium are cyclically shifted, resulting in severe fluctuations in electricity load. Accurate prediction of its operating mode shifting can optimize the power supply curve of an electric furnace, to improve electric energy efficiency and reduce electricity expenses. In this paper, we propose a prediction model of fused magnesium operating mode based on ADASYN-XGBoost. Four supervised machine learning algorithms including a eXtreme Gradient Boosting (XGB), ADASYN-LGB and ADASYN-RF, ADASYN-SVM were compared with the proposed ADASYN-XGB method. The results indicate that the ADASYN-XGB has the best prediction accuracy (92.5%), high average precision (>0.8), low hamming loss (0.03) and low ranking loss (0.075). Based on these results for classification performance and prediction accuracy, the ADASYN-XGB is a solid candidate for a correct classification of operating modes. These findings suggest that ADASYN-XGB systems trained with real data may serve as a new tool to assist in fused magnesium smelting process.
Index Terms—Machine learning, operating mode prediction, eXtreme gradient boosting, multi-label classification, adaptive sampling.
The authors are with the College of Information Science and Technology, Donghua University, Shanghai 20160, China (e-mail: kongweijian@dhu.edu.cn, 2201768@mail.dhu.edu.cn).
Cite: Weijian Kong, Zhiyong Su, and Yinpeng Ding, "Prediction of Fused Magnesium Operating Mode Based on ADASYN-XGBoost," International Journal of Machine Learning and Computing vol. 12, no. 5, pp. 215-220, 2022.
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