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:	M. Gopi Kiran, Raja Das, Shishir Kumar Behera, Kannan Pakshirajan, Gopal Das
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	cod removal heavy metal removal modelling neural network rotating biological contactor Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/259571340ea14576b7d63859406a6075
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spelling	oai:doaj.org-article:259571340ea14576b7d63859406a60752021-11-06T07:18:00ZModelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network1606-97491607-079810.2166/ws.2020.304https://doaj.org/article/259571340ea14576b7d63859406a60752021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/1895https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798The 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.;M. Gopi KiranRaja DasShishir Kumar BeheraKannan PakshirajanGopal DasIWA Publishingarticlecod removalheavy metal removalmodellingneural networkrotating biological contactorWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 1895-1912 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	cod removal heavy metal removal modelling neural network rotating biological contactor Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	cod removal heavy metal removal modelling neural network rotating biological contactor Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 M. Gopi Kiran Raja Das Shishir Kumar Behera Kannan Pakshirajan Gopal Das Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
description	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.;
format	article
author	M. Gopi Kiran Raja Das Shishir Kumar Behera Kannan Pakshirajan Gopal Das
author_facet	M. Gopi Kiran Raja Das Shishir Kumar Behera Kannan Pakshirajan Gopal Das
author_sort	M. Gopi Kiran
title	Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
title_short	Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
title_full	Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
title_fullStr	Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
title_full_unstemmed	Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
title_sort	modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/259571340ea14576b7d63859406a6075
work_keys_str_mv	AT mgopikiran modellingarotatingbiologicalcontactortreatingheavymetalcontaminatedwastewaterusingartificialneuralnetwork AT rajadas modellingarotatingbiologicalcontactortreatingheavymetalcontaminatedwastewaterusingartificialneuralnetwork AT shishirkumarbehera modellingarotatingbiologicalcontactortreatingheavymetalcontaminatedwastewaterusingartificialneuralnetwork AT kannanpakshirajan modellingarotatingbiologicalcontactortreatingheavymetalcontaminatedwastewaterusingartificialneuralnetwork AT gopaldas modellingarotatingbiologicalcontactortreatingheavymetalcontaminatedwastewaterusingartificialneuralnetwork
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Modelling a rotating biological contactor treating heavy metal contaminated wastewater using artificial neural network

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