2021
DOI: 10.1016/j.jece.2021.106406
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A critical review of biomass kinetics and membrane filtration models for membrane bioreactor systems

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Cited by 19 publications
(4 citation statements)
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“…The presence of soluble microbial products (SMPs) has been recognized as one of the main factors leading to membrane fouling [ 58 ]. In order to improve the understanding and the representation of the individual foulants, starting with the late 1980s, mathematical models have been developed to include the contribution of the SMPs into the MBR bioprocesses either individually [ 59 , 60 ] or as an extension of the ASMs [ 61 , 62 , 63 ].…”
Section: Model-based Anti-biofouling Strategiesmentioning
confidence: 99%
See 1 more Smart Citation
“…The presence of soluble microbial products (SMPs) has been recognized as one of the main factors leading to membrane fouling [ 58 ]. In order to improve the understanding and the representation of the individual foulants, starting with the late 1980s, mathematical models have been developed to include the contribution of the SMPs into the MBR bioprocesses either individually [ 59 , 60 ] or as an extension of the ASMs [ 61 , 62 , 63 ].…”
Section: Model-based Anti-biofouling Strategiesmentioning
confidence: 99%
“…These new representations enhance the performance of the ASMs in the modelling of the biomass kinetics inside the MBR without the need for calibration with experimental data. However, the addition of SMP components and other variables generally results in complex models with many variables which can make their validation difficult due to over-parameterization [ 58 ].…”
Section: Model-based Anti-biofouling Strategiesmentioning
confidence: 99%
“…Process modeling in membrane systems is based on membrane filtration. The focus of modeling is on the physical processes that occur after filtration, such as concentration polarization and membrane fouling (Hamedi et al 2021). Membrane filtration models are mostly realized in a mechanistic way using Darcy's law and the concept of series resistance.…”
Section: Introductionmentioning
confidence: 99%
“…Kinetic information on specific growth rates, lag phases, biomass yields, and substrate consumption rates under different environmental conditions helps construct mathematical models that optimize and predict bioprocess outcomes [ 5 ]. In the food industry, selection of appropriate models depends on the characteristics of the microorganism and the food matrix, and their accuracy should be validated with experimental data [ 6 , 7 ].…”
Section: Introductionmentioning
confidence: 99%