Prediction method for liquid level fluctuation in continuous casting mold based on GA-RF

HE Yibo, ZHANG Bingqian, ZHOU Hualun, ZHANG Tao, WANG Liyong, LI Yihong

Continuous Casting ›› 2025, Vol. 44 ›› Issue (1) : 30-35.

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Continuous Casting ›› 2025, Vol. 44 ›› Issue (1) : 30-35. DOI: 10.13228/j.boyuan.issn1005-4006.20240084
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Prediction method for liquid level fluctuation in continuous casting mold based on GA-RF

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Abstract

In the critical process of metallurgical production, the continuous casting is closely linked to the quality of the cast slab products. Among various parameters, the liquid level fluctuation in the mold is one of the most crucial during the continuous casting process. Therefore, accurate monitoring of the liquid level fluctuation inside the mold is particularly critical. An artificial intelligence model aimed at real-time accurate prediction of the mold liquid level fluctuations was introduced. The model employs a genetic algorithm (GA) optimized random forest (RF) technique, referred to as the GA-RF model. The model optimizes the parameters of random forest network through genetic algorithm, aiming at finding the optimal parameters of the model and obtaining the model with the best prediction performance. Experimental results demonstrate that the GA-RF predictive model achieves a mean absolute error (MAE) of 0.534 and a mean squared error (MSE) of 0.73, with a high prediction success rate of 96%. Compared with CNN model, BP model and SVM model, it is found that GA-RF model MAE and MSE are superior to other models, confirming the model’s high precision and its ability to meet the stringent requirements of practical production applications. Furthermore, through sensitivity analysis, the influence of different production parameters on the model is also discussed.

Key words

continuous casting mold / liquid level fluctuation / random forest / feature selection

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HE Yibo, ZHANG Bingqian, ZHOU Hualun, et al. Prediction method for liquid level fluctuation in continuous casting mold based on GA-RF[J]. Continuous Casting, 2025, 44(1): 30-35 https://doi.org/10.13228/j.boyuan.issn1005-4006.20240084

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