Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes
This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene unde...
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Nature Research
2024
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| author | Mijwel A.-A.S. Ahmed A.N. Afan H.A. Alayan H.M. Sherif M. Elshafie A. |
| author2 | 58664907300 |
| author_facet | 58664907300 Mijwel A.-A.S. Ahmed A.N. Afan H.A. Alayan H.M. Sherif M. Elshafie A. |
| author_sort | Mijwel A.-A.S. |
| building | UNITEN Library |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550��C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 � 10�4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater. � 2023, Springer Nature Limited. |
| format | Article |
| id | my.uniten.dspace-33920 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2024 |
| publisher | Nature Research |
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| spelling | my.uniten.dspace-339202024-10-14T11:17:26Z Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes Mijwel A.-A.S. Ahmed A.N. Afan H.A. Alayan H.M. Sherif M. Elshafie A. 58664907300 57214837520 56436626600 57200752209 7005414714 16068189400 This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550��C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 � 10�4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater. � 2023, Springer Nature Limited. Final 2024-10-14T03:17:26Z 2024-10-14T03:17:26Z 2023 Article 10.1038/s41598-023-45032-3 2-s2.0-85174945353 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174945353&doi=10.1038%2fs41598-023-45032-3&partnerID=40&md5=3f12a82c4fd0a3f061cb3cac8f673b24 https://irepository.uniten.edu.my/handle/123456789/33920 13 1 18260 All Open Access Gold Open Access Green Open Access Nature Research Scopus |
| spellingShingle | Mijwel A.-A.S. Ahmed A.N. Afan H.A. Alayan H.M. Sherif M. Elshafie A. Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title | Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title_full | Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title_fullStr | Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title_full_unstemmed | Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title_short | Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| title_sort | artificial intelligence models for methylene blue removal using functionalized carbon nanotubes |
| url_provider | http://dspace.uniten.edu.my/ |