Huang, Rongjie, Ma, Yaoshuai, Li, Hao, Sun, Chunya, Liu, Jun, Zhang, Shuai ORCID: https://orcid.org/0000-0002-9796-058X, Wang, Haoqi and Hao, Bing (2023) Operation parameters multi-objective optimization method of large vertical mill based on CFD-DPM. Advanced Powder Technology, 34 (6):104014. pp. 1-14. ISSN 0921-8831 (Print), 1568-5527 (Online) (doi:10.1016/j.apt.2023.104014)

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Abstract

The association mechanism between the main operation parameters and multi-physical fields of the large-scale vertical mill system is unclear, which leads to the difficulty in optimizing operation parameters to improve the performance of large vertical mill systems. To investigate the mechanism of multi-physical field coupling in the operation of the large vertical mill, the numerical simulation method is constructed by coupled CFD-DPM model to calculate the finished product quality, the simulation results were in good agreement with the actual operation results. Based on the Kriging surrogate model, a multi-objective optimization framework for large vertical mills is proposed. Finally, the multi-objective optimization design of LGM large vertical mills is carried out. Combined with CFD-DPM coupling method is developed, design variables and output responses are determined. The Kriging method is used for correlation analysis. The multi-objective optimization function was established. The NSGA-II. optimization algorithm was used to update the surrogate model and obtain the optimal solution, and the optimized operating parameters increased the vertical mill yield by 5.34% and the specific surface area by 9.07%. The maximum relative error between the simulated value and the optimized value is 2.02% through numerical calculation, which verifies the superiority of the optimization method of large vertical mill for performance improvement.

Item Type:	Article
Uncontrolled Keywords:	particle-fluid system; numerical simulation; kriging; CFD-DPM coupling method; Rosin-Rammler distribution
Subjects:	S Agriculture > S Agriculture (General) T Technology > TJ Mechanical engineering and machinery T Technology > TP Chemical technology
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Systems Management & Strategy Faculty of Business > Networks and Urban Systems Centre (NUSC) Greenwich Business School > Networks and Urban Systems Centre (NUSC)
Last Modified:	02 Dec 2024 15:56
URI:	http://gala.gre.ac.uk/id/eprint/41358

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