Rapid prediction of flow and concentration fields in solid–liquid suspensions of slurry electrolysis tanks

Tingting Lu; Kang Li; Hongliang Zhao; Wei Wang; Zhenhao Zhou; Xiaoyi Cai; Fengqin Liu

doi:10.1007/s12613-024-2826-7

Volume 31 Issue 9

Sep. 2024

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Article Navigation > International Journal of Minerals, Metallurgy and Materials > 2024 > 31(9): 2006-2016

Tingting Lu, Kang Li, Hongliang Zhao, Wei Wang, Zhenhao Zhou, Xiaoyi Cai, and Fengqin Liu, Rapid prediction of flow and concentration fields in solid–liquid suspensions of slurry electrolysis tanks, Int. J. Miner. Metall. Mater., 31(2024), No. 9, pp. 2006-2016. https://doi.org/10.1007/s12613-024-2826-7

Cite this article as:

Tingting Lu, Kang Li, Hongliang Zhao, Wei Wang, Zhenhao Zhou, Xiaoyi Cai, and Fengqin Liu, Rapid prediction of flow and concentration fields in solid–liquid suspensions of slurry electrolysis tanks, Int. J. Miner. Metall. Mater., 31(2024), No. 9, pp. 2006-2016. https://doi.org/10.1007/s12613-024-2826-7

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Research Article

Rapid prediction of flow and concentration fields in solid–liquid suspensions of slurry electrolysis tanks

+ Author Affiliations

Corresponding author:
Hongliang Zhao E-mail: zhaohl@ustb.edu.cn
Received: 8 December 2023; Revised: 9 January 2024; Accepted: 11 January 2024; Available online: 12 January 2024

Abstract

Abstract

Slurry electrolysis (SE), as a hydrometallurgical process, has the characteristic of a multitank series connection, which leads to various stirring conditions and a complex solid suspension state. The computational fluid dynamics (CFD), which requires high computing resources, and a combination with machine learning was proposed to construct a rapid prediction model for the liquid flow and solid concentration fields in a SE tank. Through scientific selection of calculation samples via orthogonal experiments, a comprehensive dataset covering a wide range of conditions was established while effectively reducing the number of simulations and providing reasonable weights for each factor. Then, a prediction model of the SE tank was constructed using the K-nearest neighbor algorithm. The results show that with the increase in levels of orthogonal experiments, the prediction accuracy of the model improved remarkably. The model established with four factors and nine levels can accurately predict the flow and concentration fields, and the regression coefficients of average velocity and solid concentration were 0.926 and 0.937, respectively. Compared with traditional CFD, the response time of field information prediction in this model was reduced from 75 h to 20 s, which solves the problem of serious lag in CFD applied alone to actual production and meets real-time production control requirements.
- slurry electrolysis;
- solid–liquid suspension;
- computational fluid dynamics;
- K-nearest neighbor algorithm;
- rapid prediction

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References

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