Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
The performance of a continuously operated laboratory-scale rotating biological contactor (RBC) was assessed for the removal of heavy metals viz. Cu(II), Cd(II) and Pb(II) from synthetic wastewater using artificial neural networks (ANNs). The RBC was inoculated with Sulfate Reducing Bacteria consort...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IWA Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/259571340ea14576b7d63859406a6075 |
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| Sumario: | The performance of a continuously operated laboratory-scale rotating biological contactor (RBC) was assessed for the removal of heavy metals viz. Cu(II), Cd(II) and Pb(II) from synthetic wastewater using artificial neural networks (ANNs). The RBC was inoculated with Sulfate Reducing Bacteria consortium (predominantly Desulfovibrio species), and the performance was evaluated at different hydraulic retention times (HRTs) and inlet heavy metal concentrations. A feed-forward back-propagation neural network model was developed using 90 data sets obtained over a period of three months, to predict the removal of heavy metal (HMRE) and COD (CODRE). The predictive capability of the model was evaluated in terms of the coefficient of determination (R) and mean absolute percentage error between the model fitted and actual experimental data, whereas sensitivity analysis was performed on the input parameters by determining the absolute average sensitivity (AAS) values. The higher AAS value of the HRT compared with that of inlet heavy metal concentration suggested that the change of HRT has a significant influence on HMRE and CODRE. Overall, the results obtained from this study demonstrated that ANNs can efficiently predict RBC behaviour with regard to heavy metal and COD removal characteristics under the prevailing operational conditions. HIGHLIGHTS
Development of a feed-forward back propagation neural network model for an RBC.;
Prediction of heavy metal and COD removal.;
Evaluation of predictive capability in terms of coefficient of determination and mean absolute percentage error.;
Sensitivity analysis by determining the absolute average sensitivity values.; |
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