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- W3034008871 abstract "New methods to predict membrane fouling in membrane bioreactors (MBRs) used for wastewater treatment (in worldwide plants treating all kind of influents) are required in order to optimize MBRs operation. Artificial intelligence (AI) was progressively considered for that purpose and showed great potential. In this study, several parameters including pH, DO, COD, MLSS, MLVSS, TN, NO3-N, NH4-N, TP and Alkalinity in the different parts of the anoxic-aerobic MBR system were used as the input variables of the AI. As a result, MLSS, COD, pH and DO could not be linked to membrane fouling as the performances carried out by the AI were not satisfying (from R 2 = 0.169 for DO to less than 0.70 for COD). The combination of TNin - TNeff, TPin - TPan and Nitratembr - Nitrateeff was linked with the membrane fouling prediction (i.e., performance with R 2 = 0.850). The AI model could be a potential method to predict membrane fouling in wastewater treatment by membrane technology." @default.
- W3034008871 created "2020-06-12" @default.
- W3034008871 creator A5032083849 @default.
- W3034008871 creator A5069846182 @default.
- W3034008871 date "2020-01-28" @default.
- W3034008871 modified "2023-10-03" @default.
- W3034008871 title "Artificial Intelligence Model for Forecasting of Membrane Fouling in Wastewater Treatment by Membrane Technology" @default.
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- W3034008871 doi "https://doi.org/10.1002/9781119536260.ch9" @default.
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