Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities

Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater...

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Published inEnvironmental science and pollution research international Vol. 24; no. 29; pp. 22885 - 22913
Main Authors Schmitt, Félix, Do, Khac-Uan
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Springer Nature B.V
Springer Verlag
Subjects
Aquatic Pollution
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Beverage industry
Biofouling - prevention & control
Bioreactors
Bioreactors - microbiology
Computer Simulation
Control methods
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Filtration
Fouling
Fouling control
Influents
Mathematical models
membrane bioreactors
Membrane processes
Membrane resistance
Membranes
Membranes, Artificial
methodology
Neural networks
Neural Networks (Computer)
Physics
prediction
Review Article
Waste Water - chemistry
Waste Water Technology
wastewater
Wastewater treatment
Wastewater treatment plants
Water Management
Water Pollution Control
Water Purification - methods
Fouling control
Membrane bioreactor
Microfiltration
Artificial neural network
Wastewater
Membrane fouling
Transmembrane pressure
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Abstract Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.
AbstractList Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.
Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane resistance to filtration, such as the wastewater characteristics, the mixed liquor constituents, or the operational conditions, for example. Worldwide applications of MBRs in wastewater treatment plants treating all kinds of influents require new methods to predict membrane fouling and thus optimize operating MBRs. That is why new models capable of simulating membrane fouling phenomenon were progressively developed, using mainly a mathematical or numerical approach. Faced with the limits of such models, artificial neural networks (ANNs) were progressively considered to predict membrane fouling in MBRs and showed great potential. This review summarizes fouling control methods used in MBRs and models built in order to predict membrane fouling. A critical study of the application of ANNs in the prediction of membrane fouling in MBRs was carried out with the aim of presenting the bottlenecks associated with this method and the possibilities for further investigation on the subject.
Author Schmitt, Félix
Do, Khac-Uan
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  surname: Do
  fullname: Do, Khac-Uan
  email: uan.dokhac@hust.edu.vn
  organization: School of Environmental Science and Technology, Hanoi University of Science and Technology
BackLink https://www.ncbi.nlm.nih.gov/pubmed/28871555$$D View this record in MEDLINE/PubMed
https://hal.science/hal-04516548$$DView record in HAL
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Thu Jul 10 18:55:36 EDT 2025
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Thu Apr 24 22:53:23 EDT 2025
Tue Jul 01 01:11:25 EDT 2025
Fri Feb 21 02:34:25 EST 2025
IsPeerReviewed true
IsScholarly true
Issue 29
Keywords Fouling control
Membrane bioreactor
Microfiltration
Artificial neural network
Wastewater
Membrane fouling
Transmembrane pressure
Language English
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Snippet Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane...
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SubjectTerms Aquatic Pollution
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Beverage industry
Biofouling - prevention & control
Bioreactors
Bioreactors - microbiology
Computer Simulation
Control methods
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Filtration
Fouling
Fouling control
Influents
Mathematical models
membrane bioreactors
Membrane processes
Membrane resistance
Membranes
Membranes, Artificial
methodology
Neural networks
Neural Networks (Computer)
Physics
prediction
Review Article
Waste Water - chemistry
Waste Water Technology
wastewater
Wastewater treatment
Wastewater treatment plants
Water Management
Water Pollution Control
Water Purification - methods
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Title Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities
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