Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor.
Artificial neural network
Interfacial energy
Membrane bioreactor
Membrane fouling
Wastewater treatment
Journal
Bioresource technology
ISSN: 1873-2976
Titre abrégé: Bioresour Technol
Pays: England
ID NLM: 9889523
Informations de publication
Date de publication:
Dec 2019
Dec 2019
Historique:
received:
04
08
2019
revised:
30
08
2019
accepted:
02
09
2019
pubmed:
11
9
2019
medline:
11
10
2019
entrez:
11
9
2019
Statut:
ppublish
Résumé
Efficient quantification of interfacial energy related with membrane fouling represents the primary interest in membrane bioreactors (MBRs) as interfacial energy determines foulant layer formation. In this study, radial basis function (RBF) artificial neural networks (ANNs) with five related factors as input variables were applied to quantify interfacial energy with randomly rough membrane surface. It was found that, RBF ANNs could well capture the complex non-linear relationships between the related factors and interfacial energy. RBF ANN quantification showed high regression coefficient and accuracy, suggesting its high capacity to quantify interfacial energy. Compared to at least one-week time consumption of the advanced extensive Derjaguin-Landau-Verwey-Overbeek (XDLVO) approach, quantification by RBF ANNs only took several seconds for a same case, indicating the high efficiency of RBF ANNs. Moreover, the abilities of RBF ANNs can be further improved. The robust RBF ANNs proposed paved a new way to study membrane fouling in MBRs.
Identifiants
pubmed: 31505391
pii: S0960-8524(19)31333-1
doi: 10.1016/j.biortech.2019.122103
pii:
doi:
Substances chimiques
Membranes, Artificial
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
122103Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.