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Research and Application of a Rolling Gap Prediction Model in Continuous Casting

School of Mechanical Engineering, Xi’an Jiaotong University, 28 West Xianning Road, Xi’an 710049, China
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Metals 2019, 9(3), 380; https://doi.org/10.3390/met9030380
Received: 6 March 2019 / Revised: 23 March 2019 / Accepted: 23 March 2019 / Published: 25 March 2019
(This article belongs to the Special Issue Continuous Casting)
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Abstract

Control of the roll gap of the caster segment is one of the key parameters for ensuring the quality of a slab in continuous casting. In order to improve the precision and timeliness of the roll gap value control, we proposed a rolling gap value prediction (RGVP) method based on the continuous casting process parameters. The process parameters collected from the continuous casting production site were first dimension-reduced using principal component analysis (PCA); 15 process parameters were chosen for reduction. Second, a support vector machine (SVM) model using particle swarm optimization (PSO) was proposed to optimize the parameters and perform roll gap prediction. The experimental results and practical application of the models has indicated that the method proposed in this paper provides a new approach for the prediction of roll gap value. View Full-Text
Keywords: multi-source information fusion; data stream; continuous casting; roll gap value; prediction; global optimization; support vector regression multi-source information fusion; data stream; continuous casting; roll gap value; prediction; global optimization; support vector regression
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Lei, Z.; Su, W. Research and Application of a Rolling Gap Prediction Model in Continuous Casting. Metals 2019, 9, 380.

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