Independent Change Law of Mold Heat Transfer in Continuous Casting Based on Big Data Mining

doi:10.11900/0412.1961.2021.00340

Acta Metall Sin

2023, Vol. 59

Issue (10): 1389-1400 DOI: 10.11900/0412.1961.2021.00340

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Independent Change Law of Mold Heat Transfer in Continuous Casting Based on Big Data Mining

PENG Zhiqiang¹^,², LIU Qian¹^,², GUO Dongwei¹^,², ZENG Zihang¹^,², CAO Jianghai¹^,², HOU Zibing¹^,²(

)

^1.College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
^2.Chongqing Key Laboratory of Vanadium-Titanium Metallurgy and New Materials, Chongqing University, Chongqing 400044, China

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PENG Zhiqiang, LIU Qian, GUO Dongwei, ZENG Zihang, CAO Jianghai, HOU Zibing. Independent Change Law of Mold Heat Transfer in Continuous Casting Based on Big Data Mining. Acta Metall Sin, 2023, 59(10): 1389-1400.

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Abstract

Mold heat transfer has an essential influence on the initial formation of surface longitudinal cracks in slabs, and control over various process parameters in continuous casting is very important for achieving the desired qualified product. A study on the influence of parameters on mold heat transfer helps determine the law of mold heat transfer and realize fine control of the parameters. However, the cross-correlation between continuous casting parameters makes it difficult to identify the independent influence of each parameter on the mold heat flux. Based on the parameters in the production process of continuous casting using big data mining and analytics, a new method for obtaining the independent effect of each parameter on the mold heat transfer is proposed, namely the new method for independent process influence (IPI). The five links included in the IPI method can be calculated in turn: data preprocessing, cross-correlation level calculation, main related parameters testing, data filtering, and independent influence analysis. The results showed that, in addition to the conventional influence of the casting speed, superheat, slab width, mold taper, and total water flow on mold heat flux, the oscillation frequency, mold level, immersion depth of nozzle, stopper position, and argon blowing flow rate at different positions affected mold heat flux to a certain extent. The argon blowing flow rate of the stopper, stopper position, and immersion depth of the nozzle positively influence the mold heat flux. Conversely, the argon blowing flow rate of the nozzle, oscillation frequency, and total water flow rate have a negative influence. In addition, for the argon blowing flow rate in the nozzle and total water flow rate, an inflection point is reached after achieving the maximum values of 3.5 L/min and 8250-8750 L/min, respectively. This research can provide a new reference and basis for the systematic mechanism analysis of the heat transfer process and formation of longitudinal surface cracks in continuous casting and services for the fine control of high-quality steel production on site.

Key words: mold heat transfer continuous casting surface longitudinal crack low carbon steel independent change law big data mining

Received: 16 August 2021

ZTFLH:

TF701.3

Fund: National Natural Science Foundation of China(52274318)

Corresponding Authors: HOU Zibing, associate professor, Tel: 13628489073, E-mail: houzibing@cqu.edu.cn

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	PENG Zhiqiang
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	HOU Zibing

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https://www.ams.org.cn/EN/10.11900/0412.1961.2021.00340 OR https://www.ams.org.cn/EN/Y2023/V59/I10/1389

Fig.1 Calculation flowchart of the method for independent process influence (IPI) with big data in production process

Fig.2 Quantile-quantile (Q-Q) plots of each continuous casting parameter
(a) casting speed (b) immersion depth of nozzle (c) mold level
(d) oscillation frequency (e) slab width (f) position of stopper
(g) argon blowing flow rate in stopper (Q_stopper)
(h) argon blowing flow rate in nozzle (Q_nozzle)
(i) argon blowing flow rate in sliding plate (Q_shield)
(j) superheat (k) total water flow rate (l) mold taper

Fig.3 Heat map of the Spearman correlation coefficients between different continuous casting parameters

Table 1 Main related parameters to each continuous casting parameter

Table 2 Spearman correlation coefficients of average heat flux of different mold faces

Fig.4 Relationship between casting speed and mold heat flux (R²—fitting coefficient)

Fig.5 Relationship between superheat and mold heat flux

Fig.6 Relationships between total water flow rate (a), slab width (b), mold taper (c), immersion depth of nozzle (d), mold level (e), mold oscillation frequency (f) and mold heat flux

Fig.7 Relationships between position of stopper (a), Q_stopper (b), Q_nozzle (c), Q_shield (d) and mold heat flux

Table 3 Spearman correlation coefficients (ρ)between continuous casting parameters and mold heat flux, and their absolute values |ρ|

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