期刊
POWDER TECHNOLOGY
卷 319, 期 -, 页码 279-288出版社
ELSEVIER
DOI: 10.1016/j.powtec.2017.06.064
关键词
Hydrocyclone; Computational fluid dynamics; Flow pattern; Equation model; Feed flow rate
资金
- National Natural Science Foundation of China [51474054, 51504054]
- Fund of State Key Laboratory of Mineral Processing [BGRIMM-KJSKL-2015-04]
- Fundamental Research Funds for the Central Universities [N150101001, N150104005]
Existing design equations can hardly provide reliable guidance in hydrocyclone designs and give explanations for the selected optimum range. Therefore, a new method combined flow patterns and equation models (FP-EM) for hydrocyclone designs is proposed in this paper. The feed flow rate is used as a parameter to evaluate the FP-EM method. Results show that under a lower feed flow rate, the inner flow field is extremely unsteady, which is characterized by higher turbulence intensity and an unsteady air core. When the flowrate becomes extremely large, the value of the exponent n for the tangential velocity inside the free vortex region decreases. Furthermore, before flow field achieving stability, the separation accuracy is extremely low. In the optimum range, the separation efficiency and the accuracy increases with the increase of the feed flow rate, while the cut size decreases. Thus, for the 950 mm hydrocyclone used in this study, the lower limit (15 L/min) can be determined by the flow field stability and the upper limit (55 L/min) is largely ascertained by energy consumption. Three power-type fitting equations are deduced in the optimum range to quantify the relationships between separation indexes (split ratio, pressure drop and cut size) and the flowrate. Correlation coefficients (R-2 > 0.97) of three equations verify that equation models can predict the separation performance successfully. Therefore, all above results confirm that the FP-EM method is capable of providing an optimum range, quantifying the relationships between separation indexes and the feed parameter, and predicting separation performances. (C) 2017 Elsevier B.V. All rights reserved.
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