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.
The control of the roll gap of the segment is one of the key links to ensure the quality of cast billet. In this paper, the big data in traditional continuous casting production operations is studied through in-depth experimental comparative analysis of linear and nonlinear dimension reduction method. The method is suitable for continuous casting to obtain the data of the dimension reduction. The method of principal component analysis is improved by using standardized data increment method. A faster and more efficient method of dimension reduction is obtained when the unrelated data, training time and reconstruction error are removed. Actual data simulation results show that this method is more efficient and suitable for continuous casting than any other dimension reduction method.
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