Application of Artificial Neural Network (ANN) for Modeling of DOC Removal from Antibiotics Aqueous Solution by Fenton Process
The study examined the implementation of a three layer backpropagation artificial neural network (ANN) for prediction and simulation of amoxicillin, ampicillin and cloxacillin degradation using Fenton process in terms of dissolved organic carbon (DOC). Neural Network Toolbox V4.0 of MATLAB was used...
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| Format: | Conference or Workshop Item |
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2009
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| Online Access: | http://eprints.utp.edu.my/2303/ http://eprints.utp.edu.my/2303/1/Application_of_Artificial_Neural_Network_%28ANN%29_for_Modeling_of_DOC_Removal_from_Antibiotics_Aqueous_Solution_by_Fenton_Process.pdf |
| Summary: | The study examined the implementation of a three layer backpropagation artificial neural network (ANN) for prediction and simulation of amoxicillin, ampicillin and cloxacillin degradation using Fenton process in terms of dissolved organic carbon (DOC). Neural Network Toolbox V4.0 of MATLAB was used for prediction and simulation of the data. The configuration of the backpropagation neural network giving the smallest MSE was a 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 0.988. The sensitivity analysis confirmed that all studied variables (reaction time, H2O2/COD molar ratio, H2O2/Fe2+ molar ratio, pH and antibiotics concentration) have strong effect on antibiotics degradation. The results confirmed that neural network modeling could effectively predict and simulate the behavior of the Fenton process for antibiotics degradation. |
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