Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)

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 s...

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Main Authors: Khan, Taimur, Abd Manan, Teh Sabariah, Hasnain Isa, Mohamed, A. J. Ghanim, Abdulnoor, Beddu, Salmia, Jusoh, Hisyam, Iqbal, Muhammad Shahid, Ayele, Gebiaw T, Jami, Mohammed Saedi
Format: Article
Language:English
English
Published: Multidisciplinary Digital Publishing Institute (MDPI) 2020
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Online Access:http://irep.iium.edu.my/81775/1/molecules-25-03263.pdf
http://irep.iium.edu.my/81775/7/81775_Modeling%20of%20Cu%28II%29%20adsorption%20from%20an%20aqueous_scopus.pdf
http://irep.iium.edu.my/81775/
https://www.mdpi.com/1420-3049/25/14/3263
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spelling my.iium.irep.817752020-11-25T01:43:37Z http://irep.iium.edu.my/81775/ Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann) Khan, Taimur Abd Manan, Teh Sabariah Hasnain Isa, Mohamed A. J. Ghanim, Abdulnoor Beddu, Salmia Jusoh, Hisyam Iqbal, Muhammad Shahid Ayele, Gebiaw T Jami, Mohammed Saedi TP155 Chemical engineering 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. Multidisciplinary Digital Publishing Institute (MDPI) 2020-07-17 Article PeerReviewed application/pdf en http://irep.iium.edu.my/81775/1/molecules-25-03263.pdf application/pdf en http://irep.iium.edu.my/81775/7/81775_Modeling%20of%20Cu%28II%29%20adsorption%20from%20an%20aqueous_scopus.pdf Khan, Taimur and Abd Manan, Teh Sabariah and Hasnain Isa, Mohamed and A. J. Ghanim, Abdulnoor and Beddu, Salmia and Jusoh, Hisyam and Iqbal, Muhammad Shahid and Ayele, Gebiaw T and Jami, Mohammed Saedi (2020) Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann). Molecules, 25 (3263). pp. 1-15. ISSN 1420-3049 https://www.mdpi.com/1420-3049/25/14/3263 10.3390/molecules25143263
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TP155 Chemical engineering
spellingShingle TP155 Chemical engineering
Khan, Taimur
Abd Manan, Teh Sabariah
Hasnain Isa, Mohamed
A. J. Ghanim, Abdulnoor
Beddu, Salmia
Jusoh, Hisyam
Iqbal, Muhammad Shahid
Ayele, Gebiaw T
Jami, Mohammed Saedi
Modeling of cu(ii) adsorption from an aqueous solution using an artificial neural network (ann)
description 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.
format Article
author Khan, Taimur
Abd Manan, Teh Sabariah
Hasnain Isa, Mohamed
A. J. Ghanim, Abdulnoor
Beddu, Salmia
Jusoh, Hisyam
Iqbal, Muhammad Shahid
Ayele, Gebiaw T
Jami, Mohammed Saedi
author_facet Khan, Taimur
Abd Manan, Teh Sabariah
Hasnain Isa, Mohamed
A. J. Ghanim, Abdulnoor
Beddu, Salmia
Jusoh, Hisyam
Iqbal, Muhammad Shahid
Ayele, Gebiaw T
Jami, Mohammed Saedi
author_sort Khan, Taimur
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 Multidisciplinary Digital Publishing Institute (MDPI)
publishDate 2020
url http://irep.iium.edu.my/81775/1/molecules-25-03263.pdf
http://irep.iium.edu.my/81775/7/81775_Modeling%20of%20Cu%28II%29%20adsorption%20from%20an%20aqueous_scopus.pdf
http://irep.iium.edu.my/81775/
https://www.mdpi.com/1420-3049/25/14/3263
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