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Mold-Level Prediction for Continuous Casting Using VMD–SVR

School of Mechanical Engineering, Xi’an Jiaotong University, 28 West Xianning Road, Xi’an 710049, China
China National Heavy Machinery Research Institute Co., Ltd., 109 Dongyuan Road, Xi’an 710016, China
Author to whom correspondence should be addressed.
Metals 2019, 9(4), 458;
Received: 5 March 2019 / Revised: 17 April 2019 / Accepted: 17 April 2019 / Published: 18 April 2019
(This article belongs to the Special Issue Continuous Casting)
PDF [3625 KB, uploaded 18 April 2019]


In the continuous-casting process, mold-level control is one of the most important factors that ensures the quality of high-efficiency continuous casting slabs. In traditional mold-level prediction control, the mold-level prediction accuracy is low, and the calculation cost is high. In order to improve the prediction accuracy for mold-level prediction, an adaptive hybrid prediction algorithm is proposed. This new algorithm is the combination of empirical mode decomposition (EMD), variational mode decomposition (VMD), and support vector regression (SVR), and it effectively overcomes the impact of noise on the original signal. Firstly, the intrinsic mode functions (IMFs) of the mold-level signal are obtained by the adaptive EMD, and the key parameter of the VMD is obtained by the correlation analysis between the IMFs. VMD is performed based on the key parameter to obtain several IMFs, and the noise IMFs are denoised by wavelet threshold denoising (WTD). Then, SVR is used to predict each denoised component to obtain the predicted IMF. Finally, the predicted mold-level signal is reconstructed by the predicted IMFs. In addition, compared with WTD–SVR and EMD–SVR, VMD–SVR has a competitive advantage against the above three methods in terms of robustness. This new method provides a new idea for mold-level prediction. View Full-Text
Keywords: variational mode decomposition; empirical mode decomposition; support vector regression; mold level; continuous casting variational mode decomposition; empirical mode decomposition; support vector regression; mold level; continuous casting

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Su, W.; Lei, Z.; Yang, L.; Hu, Q. Mold-Level Prediction for Continuous Casting Using VMD–SVR. Metals 2019, 9, 458.

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