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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
Source PublicationDESALINATION
Volume338Issue:1Pages:57-64
AbstractIn 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.
SubtypeArticle
KeywordArtificial Neural Network Genetic Algorithm Turbulence Promoter Fouling Flux Improvement Efficiency
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1016/j.desal.2014.01.015
WOS KeywordGENETIC ALGORITHM ; CERAMIC MEMBRANES ; PERMEATE FLUX ; OPTIMIZATION ; PERFORMANCE ; PREDICTION ; ULTRAFILTRATION ; INSERTS ; DECLINE ; BAFFLE
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Water Resources
WOS SubjectEngineering, Chemical ; Water Resources
WOS IDWOS:000335544600008
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qibebt.ac.cn/handle/337004/1546
Collection膜分离与催化团队
Affiliation1.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
Recommended Citation
GB/T 7714
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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All comments (1)
谭明
2014-03-01 09:56
http://www.sciencedirect.com/science/article/pii/S0011916414000319
 

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