Modeling the Flux Decline during Protein Microfiltration: A Comparison between Feed-Forward Back Propagation and Radial Basis Function Neural Networks

Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) has been studied. A hydrophobic PES (polyethersulfone) membrane with an average pore diameter of 0.2 µm was used in all experiments. The experiments were carried out to investigate the effect...

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Bibliographic Details
Published in:Separation science and technology 2013-06, Vol.48 (9), p.1324-1330
Main Authors: Ghandehari, Sara, Montazer-Rahmati, Mohammad Mehdi, Asghari, Morteza
Format: Article
Language:English
Subjects:
Ambient temperature
Artificial neural networks
Back propagation networks
back-propagation
Bovine serum albumin
Cross flow
cross-flow microfiltration
Flow velocity
Fluctuations
Hydrophobicity
Mathematical models
Membranes
Microfiltration
Modelling
Neural networks
Parameters
Polyethersulfones
Pore size
Proteins
Radial basis function
radial basis function network
Rejection
Serum
Serum albumin
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Summary:Flux decline under various operating parameters in cross-flow microfiltration of BSA (bovine serum albumin) has been studied. A hydrophobic PES (polyethersulfone) membrane with an average pore diameter of 0.2 µm was used in all experiments. The experiments were carried out to investigate the effect of protein solution concentration and pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and membrane pore size on the flux decline trend and membrane rejection at constant trans-membrane pressure and ambient temperature. Subsequently, the experimental data, as a relatively large data set, have been subjected to a modeling study using both feed-forward back-propagation (BP) and radial basis function (RBF) artificial neural network (ANN) models. It is shown that through appropriate selection of parameters, it is possible to model the process accurately. Furthermore, it is concluded that the prediction capacity of RBFNN is superior to the BPNN, especially in the case of membrane rejection prediction.
ISSN:0149-6395
1520-5754
DOI:10.1080/01496395.2012.736914