Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment. (June 2016)
- Record Type:
- Journal Article
- Title:
- Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment. (June 2016)
- Main Title:
- Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment
- Authors:
- Giwa, A.
Daer, S.
Ahmed, I.
Marpu, P.R.
Hasan, S.W. - Abstract:
- Highlights: Membrane bioprocess is hybridized with electrokinetics for wastewater treatment. 99, 99, and 98% removal efficiency of COD, PO4 3− -P, and NH4 + -N is achieved. Modeling of electrically enhanced MBR using artificial neural networks (ANNs). Abstract: In this work, a new configuration of an electrically-enhanced membrane bioreactor has been introduced to treat medium strength wastewater at Masdar City, Abu Dhabi, United Arab Emirates (UAE). The integrated setup enhanced the reduction of wastewater contaminant concentrations. The investigated components in this study were chemical oxygen demand (COD), orthophosphates (PO4 3− -P) and ammonium (NH4 + -N). The percentages of COD, PO4 3− -P, and NH4 + -N removal obtained were 98, 99, and 98%, respectively. Variation in environmental compositions such as mixed liquor dissolved oxygen (DO), volatile suspended solids (MLVSS), pH, and electrical conductivity influenced the effluent concentration of wastewater components. Artificial neural networks (ANNs) based ensemble model was used to model the experimental findings of COD, PO4 3− -P and NH4 + -N removal given the initial mixed liquor compositions. Comparison between the model results and experimental data set gave high correlation coefficients for COD ( r = 0.9942), PO4 3− -P ( r = 0.9998) and NH4 + -N ( r = 0.9955).
- Is Part Of:
- Journal of water process engineering. Volume 11(2016)
- Journal:
- Journal of water process engineering
- Issue:
- Volume 11(2016)
- Issue Display:
- Volume 11, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 11
- Issue:
- 2016
- Issue Sort Value:
- 2016-0011-2016-0000
- Page Start:
- 88
- Page End:
- 97
- Publication Date:
- 2016-06
- Subjects:
- Wastewater -- Electric field -- Membrane -- Artificial neural networks -- Modeling
Water-supply engineering -- Periodicals
Saline water conversion -- Periodicals
Seawater -- Distillation -- Periodicals
Sanitary engineering -- Periodicals
Sewage -- Purification -- Periodicals
627 - Journal URLs:
- http://www.sciencedirect.com/ ↗
- DOI:
- 10.1016/j.jwpe.2016.03.011 ↗
- Languages:
- English
- ISSNs:
- 2214-7144
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 1253.xml