Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks
To mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5759394 |
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| author | M. Venkata Ratnam Manikkampatti Palanisamy Murugesan Srikanth Komarabathina S. Samraj Mohammedsani Abdulkadir Muktar Abdu Kalifa |
| author_facet | M. Venkata Ratnam Manikkampatti Palanisamy Murugesan Srikanth Komarabathina S. Samraj Mohammedsani Abdulkadir Muktar Abdu Kalifa |
| author_sort | M. Venkata Ratnam |
| collection | DOAJ |
| description | To mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV-irradiated nano-graphene oxide (UV/n-GO) was developed and studied for methylene blue (MB) adsorption. Furthermore, the batch adsorption studies were modelled using response surface modelling (RSM) and artificial neural networks (ANNs). Investigations employing FTIR, XRD, and SEM were carried out to characterize the adsorbent. The best MB removal of 95.81% was obtained at a pH of 6, a dose of 0.4 g/L, an MB concentration of 25 mg/L, and a period of 40 min. This was accomplished with a desirability score of 0.853. A three-layer backpropagation network with an ideal structure of 4-4-1 was used to create an ANN model. The R2 and MSE values determined by comparing the modelled data with the experimental data were 0.9572 and 0.00012, respectively. The % MB removal predicted by ANN was 94.76%. The kinetics of adsorption corresponded well with the pseudo-second-order model (R2 > 0.97). According to correlation coefficients, the order of adsorption isotherm models is Redlich–Peterson > Temkin > Langmuir > Freundlich. Thermodynamic investigations show that MB adsorption was both spontaneous and endothermic. |
| format | Article |
| id | doaj-art-209fb424890b4d3aba912d902e36d103 |
| institution | Kabale University |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-209fb424890b4d3aba912d902e36d1032025-02-03T06:12:59ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/5759394Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural NetworksM. Venkata Ratnam0Manikkampatti Palanisamy Murugesan1Srikanth Komarabathina2S. Samraj3Mohammedsani Abdulkadir4Muktar Abdu Kalifa5Department of Chemical EngineeringDepartment of Food TechnologyDepartment of Chemical EngineeringDepartment of Chemical EngineeringDepartment of Chemical EngineeringDepartment of Chemical EngineeringTo mitigate the negative effects of pollution produced by the growing levels of pollutants in the environment, research and development of novel and more effective materials for the treatment of pollutants originating from a variety of industrial sources should be prioritized. In this research, a UV-irradiated nano-graphene oxide (UV/n-GO) was developed and studied for methylene blue (MB) adsorption. Furthermore, the batch adsorption studies were modelled using response surface modelling (RSM) and artificial neural networks (ANNs). Investigations employing FTIR, XRD, and SEM were carried out to characterize the adsorbent. The best MB removal of 95.81% was obtained at a pH of 6, a dose of 0.4 g/L, an MB concentration of 25 mg/L, and a period of 40 min. This was accomplished with a desirability score of 0.853. A three-layer backpropagation network with an ideal structure of 4-4-1 was used to create an ANN model. The R2 and MSE values determined by comparing the modelled data with the experimental data were 0.9572 and 0.00012, respectively. The % MB removal predicted by ANN was 94.76%. The kinetics of adsorption corresponded well with the pseudo-second-order model (R2 > 0.97). According to correlation coefficients, the order of adsorption isotherm models is Redlich–Peterson > Temkin > Langmuir > Freundlich. Thermodynamic investigations show that MB adsorption was both spontaneous and endothermic.http://dx.doi.org/10.1155/2022/5759394 |
| spellingShingle | M. Venkata Ratnam Manikkampatti Palanisamy Murugesan Srikanth Komarabathina S. Samraj Mohammedsani Abdulkadir Muktar Abdu Kalifa Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks International Journal of Chemical Engineering |
| title | Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks |
| title_full | Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks |
| title_fullStr | Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks |
| title_full_unstemmed | Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks |
| title_short | Methylene Blue Adsorption BY UV-Treated Graphene Oxide Nanoparticles (UV/n-GO): Modeling and Optimization Using Response Surface Methodology and Artificial Neural Networks |
| title_sort | methylene blue adsorption by uv treated graphene oxide nanoparticles uv n go modeling and optimization using response surface methodology and artificial neural networks |
| url | http://dx.doi.org/10.1155/2022/5759394 |
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