Abstract
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.
Highlights
In the modern steel industry, high-efficiency continuous casting technology has become the most internationally competitive key technology [1]
This paper proposes a prediction method based on variational mode decomposition (VMD)–support vector regression (SVR), which is suitable for mold-level
This paper proposes a prediction method based on VMD–SVR, which is suitable for mold-level prediction in continuous casting
Summary
In the modern steel industry, high-efficiency continuous casting technology has become the most internationally competitive key technology [1]. Precise mold level monitoring is regarded as the key to improving continuous casting production quality, as shown in Figure 1 [2,3,4] It is an important source of reference data for casting speed control, Metals 2019, 9, 458; doi:10.3390/met9040458 www.mdpi.com/journal/metals. This paper advanced mold level signal method to prepare data input for future level proposes an advanced molddenoising level signal denoising methodaccurate to prepare accurate data inputmold for future prediction, realize the purpose of purpose predictive and greatlyand reduce thereduce occurrence of accidents mold level prediction, realize the of control, predictive control, greatly the occurrence of affecting quality and safety in the continuous casting production process.
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