Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models

The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation...

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Autores principales:	B. L. Dinesha, Sharanagouda Hiregoudar, Udaykumar Nidoni, K. T. Ramappa, Anilkumar Dandekar, M. V. Ravi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	dairy industry optimization modelling nano-adsorbents wastewater treatment Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/644cd4cf6e044e24aa4cf30f9eb137b3
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spelling	oai:doaj.org-article:644cd4cf6e044e24aa4cf30f9eb137b32021-11-06T10:51:10ZComparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models0273-12231996-973210.2166/wst.2021.035https://doaj.org/article/644cd4cf6e044e24aa4cf30f9eb137b32021-03-01T00:00:00Zhttp://wst.iwaponline.com/content/83/5/1250https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using scanning electron microscope with elemental detection sensor (SEM-EDS) and atomic force microscope (AFM). Maximum %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial biological oxygen demand (BOD) and chemical oxygen demand (COD) concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of sum of square errors (SSE), root mean square error (RMSE), R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.B. L. DineshaSharanagouda HiregoudarUdaykumar NidoniK. T. RamappaAnilkumar DandekarM. V. RaviIWA Publishingarticledairy industryoptimizationmodellingnano-adsorbentswastewater treatmentEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 83, Iss 5, Pp 1250-1264 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dairy industry optimization modelling nano-adsorbents wastewater treatment Environmental technology. Sanitary engineering TD1-1066
spellingShingle	dairy industry optimization modelling nano-adsorbents wastewater treatment Environmental technology. Sanitary engineering TD1-1066 B. L. Dinesha Sharanagouda Hiregoudar Udaykumar Nidoni K. T. Ramappa Anilkumar Dandekar M. V. Ravi Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
description	The present investigation was focused to compare chitosan based nano-adsorbents (CZnO and CTiO2) for efficient treatment of dairy industry wastewater using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models. The nano-adsorbents were synthesized using chemical precipitation method and characterized by using scanning electron microscope with elemental detection sensor (SEM-EDS) and atomic force microscope (AFM). Maximum %RBOD (96.71 and 87.56%) and %RCOD (90.48 and 82.10%) for CZnO and CTiO2 nano-adsorbents were obtained at adsorbent dosage of 1.25 mg/L, initial biological oxygen demand (BOD) and chemical oxygen demand (COD) concentration of 100 and 200 mg/L, pH of 7.0 and 2.00, contact time of 100 and 60 min, respectively. The results obtained for both the nano-adsorbents were subject to RSM and ANN models for determination of goodness of fit in terms of sum of square errors (SSE), root mean square error (RMSE), R2 and Adj. R2, respectively. The well trained ANN model was found superior over RSM in prediction of the treatment effect. Hence, the developed CZnO and CTiO2 nano-adsorbents could be effectively used for dairy industry wastewater treatment.
format	article
author	B. L. Dinesha Sharanagouda Hiregoudar Udaykumar Nidoni K. T. Ramappa Anilkumar Dandekar M. V. Ravi
author_facet	B. L. Dinesha Sharanagouda Hiregoudar Udaykumar Nidoni K. T. Ramappa Anilkumar Dandekar M. V. Ravi
author_sort	B. L. Dinesha
title	Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
title_short	Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
title_full	Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
title_fullStr	Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
title_full_unstemmed	Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
title_sort	comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/644cd4cf6e044e24aa4cf30f9eb137b3
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Comparison of chitosan based nano-adsorbents for dairy industry wastewater treatment through response surface methodology and artificial neural network models

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