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

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.

Keywords

slurry electrolysis / solid-liquid suspension / computational fluid dynamics / K-nearest neighbor algorithm / rapid prediction

References

[[1]] Wang CY, Qiu DF, Yin F, Wang HY, Chen YQ. Slurry electrolysis of ocean polymetallic nodule. Trans. Nonferrous Met. Soc. China, 2010, 20(Supplement 1): s60,

[[2]] Zhang YL, Wang CY, Ma BZ, Jie XW, Xing P. Extracting antimony from high arsenic and gold-containing stibnite ore using slurry electrolysis. Hydrometallurgy, 2019, 186: 284,

[[3]] Zhang YJ, Yang XW, Deng LH, Yao G. Leaching mechanism of sulfide ores in slurry electrolysis. Trans. Nonferrous Met. Soc. China, 2000, 10(1): 105

[[4]] Zhang YL, Qiu DF, Wang CY, et al.. Anodic process of stibnite in slurry electrolysis: The direct collision oxidation. Chin. J. Chem. Eng., 2022, 41: 466,

[[5]] Zhang YL, Yao ZC, Jie XW, Ma BZ, Wang CY, Chen YQ. Anodic process of stibnite in slurry electrolysis: Indirect electro-oxidation. JOM, 2023, 75(5): 1551,

[[6]] Lu TT, Shen H, Na GY, et al.. CFD simulation of suspension characteristics in a stirred tank for slurry electrolysis. Metall. Mater. Trans. B, 2022, 53(3): 1747,

[[7]] Lu TT, Li K, Liu FQ, et al.. Numerical simulation of solid-liquid suspension in a slurry electrolysis stirred tank. Chem. Eng. Res. Des., 2023, 189: 371,

[[8]] F.Q. Liu, T. Lu, K. Li, C.M. Xie, and H.L. Zhao, Computational fluid dynamics study on the effect of stirring parameters on solid-liquid suspension in the slurry electrolysis square tank, Adv. Powder Technol., 34(2023), No. 5, art. No. 104016.

[[9]] Gu MQ, Xu AJ, Yuan F, He XM, Cui ZF. An improved CBR model using time-series data for predicting the end-point of a converter. ISIJ Int., 2021, 61(10): 2564,

[[10]] He F, Zhang LY. Prediction model of end-point phosphorus content in BOF steelmaking process based on PCA and BP neural network. J. Process. Control, 2018, 66: 51,

[[11]] K. Feng, A.J. Xu, D.F. He, and H.B. Wang, An improved CBR model based on mechanistic model similarity for predicting end phosphorus content in dephosphorization converter, Steel Res. Int., 89(2018), No. 6, art. No. 1800063.

[[12]] Zhang RH, Yang J. State of the art in applications of machine learning in steelmaking process modeling. Int. J. Miner. Metall. Mater., 2023, 30(11): 2055,

[[13]] Pan GF, Wang FY, Shang CL, et al.. Advances in machine learning- and artificial intelligence-assisted material design of steels. Int. J. Miner. Metall. Mater., 2023, 30(6): 1003,

[[14]] Qin ZJ, Wang Z, Wang YQ, et al.. Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning. Mater. Res. Lett., 2021, 9(1): 32,

[[15]] Xia Z, Zhao F, Liu XH, Xie JX. Prediction of interface structure and properties of Cu-Al composites assisted by machine learning. Chin. J. Nonferrous Met., 2023, 33(1): 88

[[16]] C. Gebhardt, T. Trimborn, F. Weber, A. Bezold, C. Broeckmann, and M. Herty, Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship, Mech. Mater., 151(2020), art. No. 103625.

[[17]] Marcato A, Boccardo G, Marchisio D. From computational fluid dynamics to structure interpretation via neural networks: An application to flow and transport in porous media. Ind. Eng. Chem. Res., 2022, 61(24): 8530,

[[18]] T. Yasuda, S. Ookawara, S. Yoshikawa, and H. Matsumoto, Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency, Chem. Eng. J., 453(2023), art. No. 139540.

[[19]] Modaresi F, Araghinejad S, Ebrahimi K. A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resour. Manage., 2018, 32(1): 243,

[[20]] N.S. Altman, An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat., 46(1992), No. 3, art. No. 175.

[[21]] Panchigar D, Kar K, Shukla S, Mathew RM, Chadha U, Selvaraj SK. Machine learning-based CFD simulations: A review, models, open threats, and future tactics. Neural Comput. Appl., 2022, 34(24): 21677,

[[22]] Gao YL, Gao F. Edited AdaBoost by weighted kNN. Neurocomputing, 2010, 73(16–18): 3079,

[[23]] Kamarol SKA, Jaward MH, Kälviäinen H, Parkkinen J, Parthiban R. Joint facial expression recognition and intensity estimation based on weighted votes of image sequences. Pattern Recognit. Lett., 2017, 92: 25,

[[24]] J. Mohammadpour, S. Husain, F. Salehi, and A. Lee, Machine learning regression-CFD models for the nanofluid heat transfer of a microchannel heat sink with double synthetic jets, Int. Commun. Heat Mass Transf., 130(2022), art. No. 105808.

[[25]] Wei XL, Xue BJ, Zhao Q. Optimization design of the stability for the plunger assembly of oil pumps based on multitarget orthogonal test design. J. Hebei Univ. Eng. Nat. Sci. Ed., 2010, 27(3): 95