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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| التنسيق: | مقال |
| اللغة: | English English |
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Multidisciplinary Digital Publishing Institute (MDPI)
2020
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| الوصول للمادة أونلاين: | 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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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 |
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| url_provider |
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| 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 |
| _version_ |
1684653067935416320 |
| score |
13.251813 |