Modelling bioprocesses and membrane fouling in membrane bioreactor (MBR): A review towards finding an integrated model framework-Reference-Cited by-同舟云学术

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Modelling bioprocesses and membrane fouling in membrane bioreactor (MBR): A review towards finding an integrated model framework

Published:2012-10 Issue: Volume:122 Page:119-129
ISSN:0960-8524
Container-title:Bioresource Technology
language:en
Short-container-title:Bioresource Technology

Author:

Zuthi M.F.R.,Ngo H.H.,Guo W.S.

Publisher

Elsevier BV

Subject

Waste Management and Disposal,Renewable Energy, Sustainability and the Environment,General Medicine,Environmental Engineering,Bioengineering

Reference73 articles.

1. Modeling of extracellular polymeric substances and soluble microbial products production in a submerged MBR at various SRTs;Ahn;Water Sci. Technol.,2006

2. Integrated model of the production of soluble microbial products (SMP) and extracellular polymeric substances (EPS) in anaerobic chemostats during transient conditions;Aquino;Biochem. Eng. J.,2008

3. Characterization of foulants in conventional and simultaneous nitrification and denitrification membrane bioreactors;Arabi;Sep. Purif. Technol.,2009

4. Implications of the task group model I. the effect of the initial substrate concentration;Artan;Water Res.,1990

5. Mathematical modelling of aerobic membrane bioreactor (MBR) using activated sludge model no. 1(ASM1);Baek;J. Ind. Eng. Chem.,2009

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1. Modelling biofilm development: The importance of considering the link between EPS distribution, detachment mechanisms and physical properties;Water Research;2024-02

2. Biological processes modelling for MBR systems: A review of the state-of-the-art focusing on SMP and EPS;Water Research;2023-08

3. Machine Learning Implementation in Membrane Bioreactor Systems: Progress, Challenges, and Future Perspectives: A Review;Environments;2023-07-19

4. Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques;Membranes;2023-02-09

5. Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction;Bioresource Technology;2023-02

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