Article ; Online: Predictive modeling based on artificial neural networks for membrane fouling in a large pilot-scale anaerobic membrane bioreactor for treating real municipal wastewater.
The Science of the total environment
2023 Volume 912, Page(s) 169164
Abstract: Membrane fouling is the primary obstacle to applying anaerobic membrane bioreactors (AnMBRs) in municipal wastewater treatment. This issue holds critical significance as efficient wastewater treatment serves as a cornerstone for achieving environmental ... ...
Abstract | Membrane fouling is the primary obstacle to applying anaerobic membrane bioreactors (AnMBRs) in municipal wastewater treatment. This issue holds critical significance as efficient wastewater treatment serves as a cornerstone for achieving environmental sustainability. This study uses machine learning to predict membrane fouling, taking advantage of rapid computational and algorithmic advances. Based on the 525-day operation data of a large pilot-scale AnMBR for treating real municipal wastewater, regression prediction was realized using multilayer perceptron (MLP) and long short-term memory (LSTM) artificial neural networks under substantial variations in operating conditions. The models involved employing the organic loading rate, suspended solids concentration, protein concentration in extracellular polymeric substance (EPS |
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MeSH term(s) | Wastewater ; Waste Disposal, Fluid ; Anaerobiosis ; Extracellular Polymeric Substance Matrix ; Membranes, Artificial ; Bioreactors ; Sewage |
Chemical Substances | Wastewater ; Membranes, Artificial ; Sewage |
Language | English |
Publishing date | 2023-12-10 |
Publishing country | Netherlands |
Document type | Journal Article |
ZDB-ID | 121506-1 |
ISSN | 1879-1026 ; 0048-9697 |
ISSN (online) | 1879-1026 |
ISSN | 0048-9697 |
DOI | 10.1016/j.scitotenv.2023.169164 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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