Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

copper; sulfur; water; adsorption; algorithm; chemistry; diffusion; kinetics; molecular model; solution and solubility; thermodynamics; water pollutant; Adsorption; Algorithms; Copper; Diffusion; Kinetics; Models, Molecular; Neural Networks, Computer; Solutions; Sulfur; Thermodynamics; Water; Water...

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Main Authors:	Khan T., Manan T.S.B., Isa M.H., Ghanim A.A.J., Beddu S., Jusoh H., Iqbal M.S., Ayele G.T., Jami M.S.
Other Authors:	54991181500
Format:	Article
Published:	MDPI AG 2023
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Institution:	Universiti Tenaga Nasional

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spelling	my.uniten.dspace-253982023-05-29T16:08:56Z Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN) Khan T. Manan T.S.B. Isa M.H. Ghanim A.A.J. Beddu S. Jusoh H. Iqbal M.S. Ayele G.T. Jami M.S. 54991181500 57219650719 12808940900 57210192561 55812080500 54779965400 57683949300 57079976900 36949955800 copper; sulfur; water; adsorption; algorithm; chemistry; diffusion; kinetics; molecular model; solution and solubility; thermodynamics; water pollutant; Adsorption; Algorithms; Copper; Diffusion; Kinetics; Models, Molecular; Neural Networks, Computer; Solutions; Sulfur; Thermodynamics; Water; Water Pollutants, Chemical This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher-Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater. � 2020 by the authors. Final 2023-05-29T08:08:56Z 2023-05-29T08:08:56Z 2020 Article 10.3390/molecules25143263 2-s2.0-85088679425 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088679425&doi=10.3390%2fmolecules25143263&partnerID=40&md5=c2fea54fb167ec8dec531894b22f8991 https://irepository.uniten.edu.my/handle/123456789/25398 25 14 3263 All Open Access, Gold, Green MDPI AG Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	copper; sulfur; water; adsorption; algorithm; chemistry; diffusion; kinetics; molecular model; solution and solubility; thermodynamics; water pollutant; Adsorption; Algorithms; Copper; Diffusion; Kinetics; Models, Molecular; Neural Networks, Computer; Solutions; Sulfur; Thermodynamics; Water; Water Pollutants, Chemical
author2	54991181500
author_facet	54991181500 Khan T. Manan T.S.B. Isa M.H. Ghanim A.A.J. Beddu S. Jusoh H. Iqbal M.S. Ayele G.T. Jami M.S.
format	Article
author	Khan T. Manan T.S.B. Isa M.H. Ghanim A.A.J. Beddu S. Jusoh H. Iqbal M.S. Ayele G.T. Jami M.S.
spellingShingle	Khan T. Manan T.S.B. Isa M.H. Ghanim A.A.J. Beddu S. Jusoh H. Iqbal M.S. Ayele G.T. Jami M.S. Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
author_sort	Khan T.
title	Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
title_short	Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
title_full	Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
title_fullStr	Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
title_full_unstemmed	Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)
title_sort	modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)
publisher	MDPI AG
publishDate	2023
_version_	1806426043746091008

Modeling of Cu(II) adsorption from an aqueous solution using an Artificial Neural Network (ANN)

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