Abstract
Abstract 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
Heavy metals are released into the environment from different industries, such as metallurgy, tanneries, mining, electroplating industries, etc. (Kikot et al )
On the other hand, attached growth bioreactors, viz. Packed Bed Bioreactor was employed for the treatment of acid mine drainage containing a variety of heavy metals (Dev et al ), and Rotating Biological Contactors (RBCs) for heavy metal removal from synthetic wastewater (Kiran et al a)
The objectives of this study were formulated as follows: (i) to create an MLP, by varying the internal network parameters, that would predict the HMRE in the RBC used for heavy metal removal from synthetic wastewater (Kiran et al a), (ii) testing the developed model with data that was not presented to the artificial neural networks (ANNs) during training, (iii) to perform sensitivity analysis, analyze and determine the most influencing input parameters (HRT and inlet heavy metal concentrations) for each output (HMRE and CODRE), and (iv) to emphasize the application of ANNs for control of bioreactor input parameters and to address the potential advantages of ANNs for predicting bioreactor performance
Summary
Heavy metals are released into the environment from different industries, such as metallurgy, tanneries, mining, electroplating industries, etc. (Kikot et al ). Heavy metals such as Cu, Cd, and Pb discharged from industrial wastewater are toxic at high concentration and, pose serious risk to both human health and the environment. Removal of heavy metals from wastewater before their discharge into the environment is mandatory (Kiran et al ). Different kinds of bioreactor systems have been employed to treat heavy metals present in wastewater. Wastewater containing heavy metals (e.g. Cd, Cu, Cr, Zn, Pb and Ni) were treated in suspended growth bioreactors, viz. Continuously Stirred Tank Reactors (Gola et al ), and Membrane Bioreactor employed for the treatment of textile industry wastewater containing chromium (VI). On the other hand, attached growth bioreactors, viz. Packed Bed Bioreactor was employed for the treatment of acid mine drainage containing a variety of heavy metals (Dev et al ), and Rotating Biological Contactors (RBCs) for heavy metal removal from synthetic wastewater (Kiran et al a)
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