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Page 1
Development of an ensemble of machine learning algorithms to model aerobic granular sludge reactors.
Water Res. 2021 Feb 1;189:116657. doi: 10.1016/j.watres.2020.116657. Epub 2020 Nov 19.
Water Res. 2021.
PMID: 33248333
Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks.
Zaghloul MS, Hamza RA, Iorhemen OT, Tay JH.
Zaghloul MS, et al.
Sci Total Environ. 2018 Dec 15;645:449-459. doi: 10.1016/j.scitotenv.2018.07.140. Epub 2018 Jul 18.
Sci Total Environ. 2018.
PMID: 30025244
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Impact of food-to-microorganisms ratio on the stability of aerobic granular sludge treating high-strength organic wastewater.
Hamza RA, Sheng Z, Iorhemen OT, Zaghloul MS, Tay JH.
Hamza RA, et al. Among authors: zaghloul ms.
Water Res. 2018 Dec 15;147:287-298. doi: 10.1016/j.watres.2018.09.061. Epub 2018 Oct 6.
Water Res. 2018.
PMID: 30317038
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Aerobic granular sludge membrane bioreactor (AGMBR): Extracellular polymeric substances (EPS) analysis.
Iorhemen OT, Hamza RA, Zaghloul MS, Tay JH.
Iorhemen OT, et al. Among authors: zaghloul ms.
Water Res. 2019 Jun 1;156:305-314. doi: 10.1016/j.watres.2019.03.020. Epub 2019 Mar 19.
Water Res. 2019.
PMID: 30927626
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Optimization of organics to nutrients (COD:N:P) ratio for aerobic granular sludge treating high-strength organic wastewater.
Hamza RA, Zaghloul MS, Iorhemen OT, Sheng Z, Tay JH.
Hamza RA, et al. Among authors: zaghloul ms.
Sci Total Environ. 2019 Feb 10;650(Pt 2):3168-3179. doi: 10.1016/j.scitotenv.2018.10.026. Epub 2018 Oct 4.
Sci Total Environ. 2019.
PMID: 30373093
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