Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique

The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0...

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Autores principales:	Ayad A.H. Faisal, Zahraa Saud Nassir
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2016
Materias:	Neural network Adsorption Olive pips Modeling Equilibrium Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/f5011f7373d047eaabdfc11fe13fd3d8
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spelling	oai:doaj.org-article:f5011f7373d047eaabdfc11fe13fd3d82021-12-02T05:27:12ZModeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique1818-11712312-0789https://doaj.org/article/f5011f7373d047eaabdfc11fe13fd3d82016-09-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/303https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively. Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlation equal to 0.99798. The sensitivity analysis for outputs of ANN signified that the relative importance of initial pH equal to 38 % and it is the influential parameter in the treatment process, followed by initial concentration, agitation speed, biosorbent dosage, time and temperature Ayad A.H. FaisalZahraa Saud NassirAl-Khwarizmi College of Engineering – University of BaghdadarticleNeural networkAdsorptionOlive pipsModelingEquilibriumChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 12, Iss 3 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neural network Adsorption Olive pips Modeling Equilibrium Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Neural network Adsorption Olive pips Modeling Equilibrium Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Ayad A.H. Faisal Zahraa Saud Nassir Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
description	The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively. Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlation equal to 0.99798. The sensitivity analysis for outputs of ANN signified that the relative importance of initial pH equal to 38 % and it is the influential parameter in the treatment process, followed by initial concentration, agitation speed, biosorbent dosage, time and temperature
format	article
author	Ayad A.H. Faisal Zahraa Saud Nassir
author_facet	Ayad A.H. Faisal Zahraa Saud Nassir
author_sort	Ayad A.H. Faisal
title	Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
title_short	Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
title_full	Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
title_fullStr	Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
title_full_unstemmed	Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique
title_sort	modeling the removal of cadmium ions from aqueous solutions onto olive pips using neural network technique
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2016
url	https://doaj.org/article/f5011f7373d047eaabdfc11fe13fd3d8
work_keys_str_mv	AT ayadahfaisal modelingtheremovalofcadmiumionsfromaqueoussolutionsontoolivepipsusingneuralnetworktechnique AT zahraasaudnassir modelingtheremovalofcadmiumionsfromaqueoussolutionsontoolivepipsusingneuralnetworktechnique
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Modeling the removal of Cadmium Ions from Aqueous Solutions onto Olive Pips Using Neural Network Technique

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