文献类型：期刊文献

英文题名：Numerical modelling and multi-objective optimization of the novel hydrocyclone for ultra-fine particles classification

作者：Ye, Junxiang[1];Xu, Yanxia[1];Song, Xingfu[1];Yu, Jianguo[1]

机构：[1]East China Univ Sci & Technol, Sch Resources & Environm Engn, Natl Engn Res Ctr Integrated Utilizat Salt Lake R, Shanghai 200237, Peoples R China

年份：2019

卷号：207

起止页码：1072

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20193007221731);WOS:【SCI-EXPANDED(收录号:WOS:000481644300089)】；

基金：The funds for this research were provided by the National Natural Science Foundation of China [51504098].

语种：英文

外文关键词：Novel hydrocyclone; Computational fluid dynamics; Support vector machine; Radial basis function neural network; Multi-objective optimization

摘要：A novel hydrocyclone with arc inlet and convex cone is proposed to obtain the higher particles classification sharpness than the conventional design. The classification of the micron scale manganese dioxide particle is taken as a study case, and various methods are used to perform the modelling and optimization of the novel geometrical structure. Two performance indexes have been taken into consideration in the multi-objective optimization which are the classification sharpness, S-s and total pressure drop, Delta P. The numerical experiments designed via response surface methodology are carried out using computation fluid dynamics simulations of Eulerian-Eulerian strategy. The obtained data sets are utilized for modelling the performance indexes by support vector machine (SVM) and radial basis function neural network (RBFNN) approaches. The optimal structure with the classification sharpness of 0.956 is searched using the genetic algorithm (GA) in contrast to the conventional design of 0.849. Flow field analysis illustrates that the extended radial space, strengthened centrifugal field and inlet pre-classification effect of the optimal design improve the classification sharpness. However, the narrower inlet cross area leads to a higher pressure drop. The Pareto front of the multi-objective optimization is obtained using the NSGA-II algorithm to provide alternatives for the optimal performance of the novel hydrocyclone. (C) 2019 Elsevier Ltd. All rights reserved.