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Title:
Artificial neural network model for turbulence promoter-assisted crossflow microfiltration of particulate suspensions
Author: Liu, Yuanfa1,2; He, Gaohong1; Tan, Ming1,3; Nie, Fei1; Li, Baojun1
Source: DESALINATION
Issued Date: 2014-04-01
Volume: 338, Issue:1, Pages:57-64
Keyword: Artificial neural network ; Genetic algorithm ; Turbulence promoter ; Fouling ; Flux improvement efficiency
DOI: 10.1016/j.desal.2014.01.015
DOC Type: Article
English Abstract: 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.
WOS Headings: Science & Technology ; Technology ; Physical Sciences
WOS Subject: Engineering, Chemical ; Water Resources
WOS Subject Extended: Engineering ; Water Resources
WOS Keyword Plus: GENETIC ALGORITHM ; CERAMIC MEMBRANES ; PERMEATE FLUX ; OPTIMIZATION ; PERFORMANCE ; PREDICTION ; ULTRAFILTRATION ; INSERTS ; DECLINE ; BAFFLE
Indexed Type: SCI
Language: 英语
WOS ID: WOS:000335544600008
Citation statistics:
Content Type: 期刊论文
URI: http://ir.qibebt.ac.cn/handle/337004/1546
Appears in Collections:膜分离与催化团队_期刊论文

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description.institution: 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

Recommended Citation:
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.
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谭明
2014-03-01 09:56
http://www.sciencedirect.com/science/article/pii/S0011916414000319
 
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