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Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network

A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563,...

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Main Authors: Pendashteh, Ali Reza, Ahmadun, Fakhru'l-Razi, Chaibakhsh, Naz, Abdullah, Luqman Chuah, Madaeni, Sayed Siavash, Zainal Abidin, Zurina
Format: Article
Language:English
Published: Elsevier 2011
Online Access:http://psasir.upm.edu.my/id/eprint/22509/1/Modeling%20of%20membrane%20bioreactor%20treating%20hypersaline%20oily%20wastewater%20by%20artificial%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/22509/
http://www.sciencedirect.com/science/article/pii/S0304389411006911
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spelling my.upm.eprints.225092015-10-09T08:33:50Z http://psasir.upm.edu.my/id/eprint/22509/ Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network Pendashteh, Ali Reza Ahmadun, Fakhru'l-Razi Chaibakhsh, Naz Abdullah, Luqman Chuah Madaeni, Sayed Siavash Zainal Abidin, Zurina A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits. Elsevier 2011-08 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/22509/1/Modeling%20of%20membrane%20bioreactor%20treating%20hypersaline%20oily%20wastewater%20by%20artificial%20neural%20network.pdf Pendashteh, Ali Reza and Ahmadun, Fakhru'l-Razi and Chaibakhsh, Naz and Abdullah, Luqman Chuah and Madaeni, Sayed Siavash and Zainal Abidin, Zurina (2011) Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network. Journal of Hazardous Materials, 192 (2). pp. 568-575. ISSN 0304-3894; ESSN: 1873-3336 http://www.sciencedirect.com/science/article/pii/S0304389411006911 10.1016/j.jhazmat.2011.05.052
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000 mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372 kg COD/(m3 day)) and cyclic time (12, 24, and 48 h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44 kg COD/(m3 day), TDS of 78,000 mg/L and reaction time (RT) of 40 h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100 mg/L and met the discharge limits.
format Article
author Pendashteh, Ali Reza
Ahmadun, Fakhru'l-Razi
Chaibakhsh, Naz
Abdullah, Luqman Chuah
Madaeni, Sayed Siavash
Zainal Abidin, Zurina
spellingShingle Pendashteh, Ali Reza
Ahmadun, Fakhru'l-Razi
Chaibakhsh, Naz
Abdullah, Luqman Chuah
Madaeni, Sayed Siavash
Zainal Abidin, Zurina
Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
author_facet Pendashteh, Ali Reza
Ahmadun, Fakhru'l-Razi
Chaibakhsh, Naz
Abdullah, Luqman Chuah
Madaeni, Sayed Siavash
Zainal Abidin, Zurina
author_sort Pendashteh, Ali Reza
title Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
title_short Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
title_full Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
title_fullStr Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
title_full_unstemmed Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
title_sort modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network
publisher Elsevier
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/22509/1/Modeling%20of%20membrane%20bioreactor%20treating%20hypersaline%20oily%20wastewater%20by%20artificial%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/22509/
http://www.sciencedirect.com/science/article/pii/S0304389411006911
_version_ 1643827846754336768
score 13.211869

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