Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction
The removal of Reactive Black 5 dye in an aqueous solution by electrocoagulation (EC) as well as addition of flocculant was investigated. The effect of operational parameters, i.e. current density, treatment time, solution conductivity and polymer dosage, was investigated. Two models, namely the art...
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| Format: | Article |
| Language: | English |
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IWA Publishing
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23060/ http://psasir.upm.edu.my/id/eprint/23060/1/Optimisation%20of%20reactive%20dye%20removal%20by%20sequential%20electrocoagulation%E2%80%93flocculation%20method%20comparing%20ANN%20and%20RSM%20prediction.pdf |
| _version_ | 1848844653433978880 |
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| author | Mobarekeh, Mohsen Nourouzi Chuah, Teong Guan Choong, Thomas Shean Yaw |
| author_facet | Mobarekeh, Mohsen Nourouzi Chuah, Teong Guan Choong, Thomas Shean Yaw |
| author_sort | Mobarekeh, Mohsen Nourouzi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The removal of Reactive Black 5 dye in an aqueous solution by electrocoagulation (EC) as well as addition of flocculant was investigated. The effect of operational parameters, i.e. current density, treatment time, solution conductivity and polymer dosage, was investigated. Two models, namely the artificial neural network (ANN) and the response surface method (RSM), were used to model the effect of independent variables on percentage of dye removal. The findings of this work showed that current density, treatment time and dosage of polymer had the most significant effect on percentage of dye removal (p<0.001). In addition, interaction between time and current density, time and dosage of polymer, current density and dosage of polymer also significantly affected the percentage of dye removal (p=0.034, 0.003 and 0.024, respectively). It was shown that both the ANN and RSM models were able to predict well the experimental results (R2>0.8). |
| first_indexed | 2025-11-15T08:34:20Z |
| format | Article |
| id | upm-23060 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:34:20Z |
| publishDate | 2011 |
| publisher | IWA Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-230602018-05-25T01:21:46Z http://psasir.upm.edu.my/id/eprint/23060/ Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction Mobarekeh, Mohsen Nourouzi Chuah, Teong Guan Choong, Thomas Shean Yaw The removal of Reactive Black 5 dye in an aqueous solution by electrocoagulation (EC) as well as addition of flocculant was investigated. The effect of operational parameters, i.e. current density, treatment time, solution conductivity and polymer dosage, was investigated. Two models, namely the artificial neural network (ANN) and the response surface method (RSM), were used to model the effect of independent variables on percentage of dye removal. The findings of this work showed that current density, treatment time and dosage of polymer had the most significant effect on percentage of dye removal (p<0.001). In addition, interaction between time and current density, time and dosage of polymer, current density and dosage of polymer also significantly affected the percentage of dye removal (p=0.034, 0.003 and 0.024, respectively). It was shown that both the ANN and RSM models were able to predict well the experimental results (R2>0.8). IWA Publishing 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23060/1/Optimisation%20of%20reactive%20dye%20removal%20by%20sequential%20electrocoagulation%E2%80%93flocculation%20method%20comparing%20ANN%20and%20RSM%20prediction.pdf Mobarekeh, Mohsen Nourouzi and Chuah, Teong Guan and Choong, Thomas Shean Yaw (2011) Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction. Water Science and Technology, 63 (5). pp. 985-994. ISSN 0273-1223 10.2166/wst.2011.280 |
| spellingShingle | Mobarekeh, Mohsen Nourouzi Chuah, Teong Guan Choong, Thomas Shean Yaw Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title_full | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title_fullStr | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title_full_unstemmed | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title_short | Optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ANN and RSM prediction |
| title_sort | optimisation of reactive dye removal by sequential electrocoagulation–flocculation method: comparing ann and rsm prediction |
| url | http://psasir.upm.edu.my/id/eprint/23060/ http://psasir.upm.edu.my/id/eprint/23060/ http://psasir.upm.edu.my/id/eprint/23060/1/Optimisation%20of%20reactive%20dye%20removal%20by%20sequential%20electrocoagulation%E2%80%93flocculation%20method%20comparing%20ANN%20and%20RSM%20prediction.pdf |