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题名Hydraulic modeling of slag cover surface in top-blown molten bath smelting processes assisted by machine learning
作者
发表日期2024-10-01
发表期刊Physics of Fluids
ISSN/eISSN1070-6631
卷号36期号:10
摘要

Variation of the slag cover surface (SCS) in the oxygen-enriched top-blown molten bath smelting process is critical for the smelting efficiency of a complex Cu-S concentrate. However, capturing these variation characteristics is difficult because of the high temperature inside the molten bath and the dynamic complexity of the smelting process. In this work, machine learning (i.e., U-net algorithm and support vector machine) is combined with a skillful hydraulic model (i.e., gas-liquid two-phase top-blown agitated vessel) and an experimental measurement strategy to quantitatively explore the variation characteristics of the SCS in an oxygen-enriched top-blown molten bath smelting process. Results showed that a minimum of 30 images, with the smallest size being 900 × 600 pixels, was sufficient for the training process. The data accuracy of the training procedure ranged from 93.20% to 96.23% for identifying the SCS at the laboratory scale. The highest average height of 2.23 cm for the SCS occurred under the operational condition, with a flow rate of 160 L/h, a liquid temperature of 60 °C, and a liquid depth of 0.4 m. The chaotic systems of SCS in industry were deterministic. It was found that the proposed strategy could be used to accurately identify the variation characteristics of the SCS in the gas-liquid two-phase top-blown agitated vessel. The variation of the SCS in the industrial process could be roughly grasped by magnifying the height of the SCS obtained from the experimental data in the laboratory. Quantification of the variation characteristics of the SCS is useful to increase the smelting efficiency of the oxygen-enriched top-blown molten bath smelting process. This also provides insights for multiphase measurements in other studies related to efficient utilization of complex Cu-S concentrates.

DOI10.1063/5.0225560
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语种英语English
Scopus入藏号2-s2.0-85205890484
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文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11965
专题理工科技学院
通讯作者Xiao, Qingtai
作者单位
1.State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, Yunnan, 650093, China
2.Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, Yunnan, 650093, China
3.Research Center for Mathematics, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong, 519087, China
4.Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Beijing Normal University, Hong Kong Baptist University United International College, Zhuhai, Guangdong, 519087, China
5.Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, 78249-0634, United States
推荐引用方式
GB/T 7714
Yang, Kai,Yu, Bo,Pan, Jianxinet al. Hydraulic modeling of slag cover surface in top-blown molten bath smelting processes assisted by machine learning[J]. Physics of Fluids, 2024, 36(10).
APA Yang, Kai, Yu, Bo, Pan, Jianxin, Wang, Min, Wang, Hua, & Xiao, Qingtai. (2024). Hydraulic modeling of slag cover surface in top-blown molten bath smelting processes assisted by machine learning. Physics of Fluids, 36(10).
MLA Yang, Kai,et al."Hydraulic modeling of slag cover surface in top-blown molten bath smelting processes assisted by machine learning". Physics of Fluids 36.10(2024).
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