The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process
The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicill...
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| Format: | Citation Index Journal |
| Language: | English |
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Elsevier
2010
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| Online Access: | http://scholars.utp.edu.my/id/eprint/2289/ http://scholars.utp.edu.my/id/eprint/2289/1/The_Use_of_Artificial_Neural_Network_%28ANN%29_for_Modeling_of_COD_Removal_from_Antibiotic_Aqueous_Solution_by_the_Fenton_Process.pdf |
| _version_ | 1848659224808128512 |
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| author | Elmolla, E. S. Chaudhuri, M. Eltoukhy, M. M. |
| author_facet | Elmolla, E. S. Chaudhuri, M. Eltoukhy, M. M. |
| author_sort | Elmolla, E. S. |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | The study examined the implementation of artificial neural network (ANN) for the prediction and simulation
of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicillin and cloxacillin in aqueous solution in terms of COD removal. The configuration of the backpropagation neural network giving the smallest mean square error (MSE) was three-layer ANN with tangent sigmoid transfer function (tansig) at hidden layer with 14 neurons, linear transfer function (purelin) at output layer and
Levenberg–Marquardt backpropagation training algorithm (LMA). ANN predicted results are very close to the experimental results with correlation coefficient (R2) of 0.997 and MSE 0.000376. The sensitivity analysis showed that all studied variables (reaction time, H2O2/COD molar ratio, H2O2/Fe2+ molar ratio, pH and antibiotics concentration) have strong effect on antibiotic degradation in terms of COD removal. In addition, H2O2/Fe2+ molar ratio is the most influential parameter with relative importance of 25.8%. The results showed that neural network modeling could effectively predict and simulate the behavior of
the Fenton process. |
| first_indexed | 2025-11-13T07:27:02Z |
| format | Citation Index Journal |
| id | oai:scholars.utp.edu.my:2289 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:27:02Z |
| publishDate | 2010 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:22892017-01-19T08:24:41Z http://scholars.utp.edu.my/id/eprint/2289/ The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process Elmolla, E. S. Chaudhuri, M. Eltoukhy, M. M. TD Environmental technology. Sanitary engineering The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicillin and cloxacillin in aqueous solution in terms of COD removal. The configuration of the backpropagation neural network giving the smallest mean square error (MSE) was three-layer ANN with tangent sigmoid transfer function (tansig) at hidden layer with 14 neurons, linear transfer function (purelin) at output layer and Levenberg–Marquardt backpropagation training algorithm (LMA). ANN predicted results are very close to the experimental results with correlation coefficient (R2) of 0.997 and MSE 0.000376. The sensitivity analysis showed that all studied variables (reaction time, H2O2/COD molar ratio, H2O2/Fe2+ molar ratio, pH and antibiotics concentration) have strong effect on antibiotic degradation in terms of COD removal. In addition, H2O2/Fe2+ molar ratio is the most influential parameter with relative importance of 25.8%. The results showed that neural network modeling could effectively predict and simulate the behavior of the Fenton process. Elsevier 2010 Citation Index Journal PeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/2289/1/The_Use_of_Artificial_Neural_Network_%28ANN%29_for_Modeling_of_COD_Removal_from_Antibiotic_Aqueous_Solution_by_the_Fenton_Process.pdf Elmolla, E. S. and Chaudhuri, M. and Eltoukhy, M. M. (2010) The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process. [Citation Index Journal] |
| spellingShingle | TD Environmental technology. Sanitary engineering Elmolla, E. S. Chaudhuri, M. Eltoukhy, M. M. The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title | The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title_full | The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title_fullStr | The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title_full_unstemmed | The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title_short | The Use of Artificial Neural Network (ANN) for Modeling of COD Removal from Antibiotic Aqueous Solution by the Fenton Process |
| title_sort | use of artificial neural network (ann) for modeling of cod removal from antibiotic aqueous solution by the fenton process |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://scholars.utp.edu.my/id/eprint/2289/ http://scholars.utp.edu.my/id/eprint/2289/1/The_Use_of_Artificial_Neural_Network_%28ANN%29_for_Modeling_of_COD_Removal_from_Antibiotic_Aqueous_Solution_by_the_Fenton_Process.pdf |