基于大数据挖掘的连铸结晶器传热独立变化规律

doi:10.11900/0412.1961.2021.00340

金属学报

2023, Vol. 59

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

研究论文

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基于大数据挖掘的连铸结晶器传热独立变化规律

彭治强¹^,², 柳前¹^,², 郭东伟¹^,², 曾子航¹^,², 曹江海¹^,², 侯自兵¹^,²(

)

^1.重庆大学材料科学与工程学院重庆 400044
^2.重庆大学钒钛冶金及新材料重庆市重点实验室重庆 400044

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

引用本文:

彭治强, 柳前, 郭东伟, 曾子航, 曹江海, 侯自兵. 基于大数据挖掘的连铸结晶器传热独立变化规律[J]. 金属学报, 2023, 59(10): 1389-1400.
Zhiqiang PENG, Qian LIU, Dongwei GUO, Zihang ZENG, Jianghai CAO, Zibing HOU. Independent Change Law of Mold Heat Transfer in Continuous Casting Based on Big Data Mining[J]. Acta Metall Sin, 2023, 59(10): 1389-1400.

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摘要:

针对连铸板坯表面纵裂纹，基于连铸生产过程参数，利用大数据挖掘方法，提出了一种获取现场不同参数对结晶器传热单独影响的新方法，即生产参数独立影响(IPI)法。IPI方法包含数据预处理、交叉相关程度计算、主要相关参数检验、数据筛选和独立影响分析等5个环节，以实现从交互相关的连铸参数中找到参数的主要相关参数，从而分析各连铸参数对结晶器传热的独立影响规律。结果表明，除拉速、过热度、板坯宽度、结晶器锥度、结晶器总水流量等常规影响因素外，结晶器振动频率、结晶器液位、水口插入深度、塞棒位置以及不同位置吹Ar流量等均对结晶器热流有不同程度的影响。塞棒吹Ar流量、塞棒位置和水口插入深度均对结晶器传热有正向促进作用，而水口吹Ar流量、结晶器振动频率、结晶器总水流量主要表现为负向抑制作用。另外，对于水口吹Ar流量和结晶器总水流量而言，存在结晶器热流最大的拐点值，分别为3.5 L/min和8250~8750 L/min。

关键词 ：结晶器传热, 连铸, 纵裂纹, 低碳钢, 独立变化规律, 大数据挖掘

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

收稿日期: 2021-08-16

ZTFLH:

TF701.3

基金资助:国家自然科学基金项目(52274318)

通讯作者: 侯自兵，houzibing@cqu.edu.cn，主要从事凝固组织与偏析特征及精细化控制研究

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

作者简介: 彭治强，男，1996年生，博士生

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https://www.ams.org.cn/CN/10.11900/0412.1961.2021.00340 或 https://www.ams.org.cn/CN/Y2023/V59/I10/1389

图1 生产大数据的独立参数影响(IPI)法计算流程图

图2 各连铸参数数据的分位数-分位数(Q-Q)图

图3 各连铸操作参数Spearman相关系数热图

表1 各连铸参数的主要相关参数

表2 结晶器各面平均热流Spearman相关系数

图4 拉速与结晶器热流的关系

图5 过热度与结晶器热流的关系

图6 总水流量、板坯宽度、结晶器锥度、水口插入深度、结晶器液位及振动频率与结晶器热流之间的关系

图7 塞棒位置、塞棒吹Ar流量及水口吹Ar流量、保护吹Ar流量与结晶器热流之间的关系

表3 连铸各参数与结晶器热流Spearman相关系数(按相关系数绝对值大小由高往低排序)

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