Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique
In this study, Pb2+ removal from wastewater using multiwall carbon nanotubes (MWCNTs) was investigated. XRD, SEM-EDX, BET, and FTIR were employed for MWCNT characterization. The effects of various parameters, including the solution pH, adsorbent dosage, initial concentration of Pb2+, and contact tim...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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Taylor and Francis Ltd.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38612/1/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes.pdf http://umpir.ump.edu.my/id/eprint/38612/2/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes%20using%20artificial_ABS.pdf |
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| author | Al Jadir, Thaer Alardhi, Saja Mohsen Al-Sheikh, Farooq Jaber, Alaa Abdulhady Kadhim, Wafaa Abdul Mohd Hasbi, Ab. Rahim |
| author_facet | Al Jadir, Thaer Alardhi, Saja Mohsen Al-Sheikh, Farooq Jaber, Alaa Abdulhady Kadhim, Wafaa Abdul Mohd Hasbi, Ab. Rahim |
| author_sort | Al Jadir, Thaer |
| collection | UMP |
| description | In this study, Pb2+ removal from wastewater using multiwall carbon nanotubes (MWCNTs) was investigated. XRD, SEM-EDX, BET, and FTIR were employed for MWCNT characterization. The effects of various parameters, including the solution pH, adsorbent dosage, initial concentration of Pb2+, and contact time, on the Pb2+ removal from wastewater were investigated experimentally. Furthermore, the nonlinear relationship among the parameters was predicted using an artificial neural network (ANN) approach. The Levenberg–Marquardt training algorithm showed the best training performance, with a mean-square error of 2.200× 10−5 and an R2 of 0.998. Combining the ANN models and Monte Carlo simulation, Pb2+ removal efficiency of 99.8% was obtained under the optimum conditions (pH of 10, MWCNT dosage of 0.05 g, contact duration of 60 min, and Pb2+ concentration of 100 mg/L). The high removal efficiency can be attributed to the available adsorption sites (active sites). The results of the reusability of MWCNTs showed that the adsorption efficiency was higher than 90%. Thus, MWCNTs have great potential for recycling and managing Pb2+ from wastewater. |
| first_indexed | 2024-03-06T13:09:02Z |
| format | Article |
| id | UMPir38612 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:09:02Z |
| publishDate | 2023 |
| publisher | Taylor and Francis Ltd. |
| record_format | dspace |
| spelling | UMPir386122023-09-13T01:58:55Z http://umpir.ump.edu.my/id/eprint/38612/ Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique Al Jadir, Thaer Alardhi, Saja Mohsen Al-Sheikh, Farooq Jaber, Alaa Abdulhady Kadhim, Wafaa Abdul Mohd Hasbi, Ab. Rahim HD28 Management. Industrial Management QD Chemistry In this study, Pb2+ removal from wastewater using multiwall carbon nanotubes (MWCNTs) was investigated. XRD, SEM-EDX, BET, and FTIR were employed for MWCNT characterization. The effects of various parameters, including the solution pH, adsorbent dosage, initial concentration of Pb2+, and contact time, on the Pb2+ removal from wastewater were investigated experimentally. Furthermore, the nonlinear relationship among the parameters was predicted using an artificial neural network (ANN) approach. The Levenberg–Marquardt training algorithm showed the best training performance, with a mean-square error of 2.200× 10−5 and an R2 of 0.998. Combining the ANN models and Monte Carlo simulation, Pb2+ removal efficiency of 99.8% was obtained under the optimum conditions (pH of 10, MWCNT dosage of 0.05 g, contact duration of 60 min, and Pb2+ concentration of 100 mg/L). The high removal efficiency can be attributed to the available adsorption sites (active sites). The results of the reusability of MWCNTs showed that the adsorption efficiency was higher than 90%. Thus, MWCNTs have great potential for recycling and managing Pb2+ from wastewater. Taylor and Francis Ltd. 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38612/1/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes.pdf pdf en http://umpir.ump.edu.my/id/eprint/38612/2/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes%20using%20artificial_ABS.pdf Al Jadir, Thaer and Alardhi, Saja Mohsen and Al-Sheikh, Farooq and Jaber, Alaa Abdulhady and Kadhim, Wafaa Abdul and Mohd Hasbi, Ab. Rahim (2023) Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique. Chemical Engineering Communications, 210 (10). pp. 1642-1658. ISSN 0098-6445. (Published) https://doi.org/10.1080/00986445.2022.2129622 https://doi.org/10.1080/00986445.2022.2129622 |
| spellingShingle | HD28 Management. Industrial Management QD Chemistry Al Jadir, Thaer Alardhi, Saja Mohsen Al-Sheikh, Farooq Jaber, Alaa Abdulhady Kadhim, Wafaa Abdul Mohd Hasbi, Ab. Rahim Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title | Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title_full | Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title_fullStr | Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title_full_unstemmed | Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title_short | Modeling of lead (II) ion adsorption on multiwall carbon nanotubes using artificial neural network and Monte Carlo technique |
| title_sort | modeling of lead ii ion adsorption on multiwall carbon nanotubes using artificial neural network and monte carlo technique |
| topic | HD28 Management. Industrial Management QD Chemistry |
| url | http://umpir.ump.edu.my/id/eprint/38612/1/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes.pdf http://umpir.ump.edu.my/id/eprint/38612/2/Modeling%20of%20lead%20%28II%29%20ion%20adsorption%20on%20multiwall%20carbon%20nanotubes%20using%20artificial_ABS.pdf |
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