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Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining

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成果类型:
期刊论文
作者:
Huang, Ao;Huo, Yanzhu;Yang, Juan*;Gu, Huazhi;Li, Guangqiang
通讯作者:
Yang, Juan
作者机构:
[Gu, Huazhi; Li, Guangqiang; Huo, Yanzhu; Huang, Ao] Wuhan Univ Sci & Technol, State Key Lab Refractories & Met, Wuhan 430081, Peoples R China.
[Yang, Juan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Yang, Juan] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
slag;data mining;viscosity;big data;predictive modeling
期刊:
Minerals
ISSN:
2075-163X
年:
2020
卷:
10
期:
3
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [U1860205, U1908227]
机构署名:
本校为通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low...

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