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Artificial neural network model for turbulence promoter-assisted crossflow microfiltration of particulate suspensions
Liu, Yuanfa1,2; He, Gaohong1; Tan, Ming1,3; Nie, Fei1; Li, Baojun1
2014-04-01
发表期刊DESALINATION
卷号338期号:1页码:57-64
摘要In this study, an artificial neural network (ANN) model for the turbulence promoter-assisted crossflow microfiltration (CFMF) process was successfully established, in which the inlet velocity, transmembrane pressure (TMP) and feed concentration were taken as inputs, and the flux improvement efficiency (FIE) by turbulence promoter was taken as output. Using the trained ANN model, the FIE can be predicted under CFMF operation conditions that are not included in the training database. It reveals that the FIE first increases and then decreases with increasing either TMP or inlet velocity, and increases with increasing feed concentration. Among three input variables, TMP has the most important effect on the FIE. The optimization of MP operation conditions was largely dependent on the feed concentration. The high FIE can be obtained by exerting both high inlet velocity (>0.7 m/s) and low TMP ( <30 kPa) at a relatively low feed concentration ( <1 g/L), and both high inlet velocity (>0.7 m/s) and high IMP (>70 kPa) at a relatively high feed concentration (>8 g/L). This study provides a useful guide for the applications of turbulence promoter in CFMF processes. (C) 2014 Elsevier B.V. All rights reserved.
文章类型Article
关键词Artificial Neural Network Genetic Algorithm Turbulence Promoter Fouling Flux Improvement Efficiency
WOS标题词Science & Technology ; Technology ; Physical Sciences
DOI10.1016/j.desal.2014.01.015
关键词[WOS]GENETIC ALGORITHM ; CERAMIC MEMBRANES ; PERMEATE FLUX ; OPTIMIZATION ; PERFORMANCE ; PREDICTION ; ULTRAFILTRATION ; INSERTS ; DECLINE ; BAFFLE
收录类别SCI
语种英语
WOS研究方向Engineering ; Water Resources
WOS类目Engineering, Chemical ; Water Resources
WOS记录号WOS:000335544600008
引用统计
被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.qibebt.ac.cn/handle/337004/1546
专题膜分离与催化团队
作者单位1.Dalian Univ Technol, Sch Chem Engn, R&D Ctr Membrane Sci & Technol, State Key Lab Fine Chem, Dalian 116012, Peoples R China
2.Dalian Polytech Univ, Sch Text & Mat Engn, Dalian, Peoples R China
3.Chinese Acad Sci, Qingdao Inst Bioenergy & Bioproc Technol, Key Lab Biobased Mat, Qingdao 266101, Peoples R China
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Liu, Yuanfa,He, Gaohong,Tan, Ming,et al. Artificial neural network model for turbulence promoter-assisted crossflow microfiltration of particulate suspensions[J]. DESALINATION,2014,338(1):57-64.
APA Liu, Yuanfa,He, Gaohong,Tan, Ming,Nie, Fei,&Li, Baojun.(2014).Artificial neural network model for turbulence promoter-assisted crossflow microfiltration of particulate suspensions.DESALINATION,338(1),57-64.
MLA Liu, Yuanfa,et al."Artificial neural network model for turbulence promoter-assisted crossflow microfiltration of particulate suspensions".DESALINATION 338.1(2014):57-64.
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谭明
2014-03-01 09:56
http://www.sciencedirect.com/science/article/pii/S0011916414000319
 

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