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,...
| Main Authors: | , , , , , |
|---|---|
| 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 |
| _version_ | 1796970174736236544 |
|---|---|
| author | Pendashteh, Ali Reza Ahmadun, Fakhru'l-Razi Chaibakhsh, Naz Abdullah, Luqman Chuah Madaeni, Sayed Siavash Zainal Abidin, Zurina |
| 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 |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T07:54:09Z |
| format | Article |
| id | upm.eprints-22509 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T07:54:09Z |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/22509/1/Modeling%20of%20membrane%20bioreactor%20treating%20hypersaline%20oily%20wastewater%20by%20artificial%20neural%20network.pdf |
| work_keys_str_mv | AT pendashtehalireza modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork AT ahmadunfakhrulrazi modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork AT chaibakhshnaz modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork AT abdullahluqmanchuah modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork AT madaenisayedsiavash modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork AT zainalabidinzurina modelingofmembranebioreactortreatinghypersalineoilywastewaterbyartificialneuralnetwork |