Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs
<b> </b>In<b> </b>the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for th...
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| Format: | Article |
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MDPI AG
2020-03-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/25/7/1511 |
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| author | Rusul Khaleel Ibrahim Seef Saadi Fiyadh Mohammed Abdulhakim AlSaadi Lai Sai Hin Nuruol Syuhadaa Mohd Shaliza Ibrahim Haitham Abdulmohsin Afan Chow Ming Fai Ali Najah Ahmed Ahmed Elshafie |
| author_facet | Rusul Khaleel Ibrahim Seef Saadi Fiyadh Mohammed Abdulhakim AlSaadi Lai Sai Hin Nuruol Syuhadaa Mohd Shaliza Ibrahim Haitham Abdulmohsin Afan Chow Ming Fai Ali Najah Ahmed Ahmed Elshafie |
| author_sort | Rusul Khaleel Ibrahim |
| collection | DOAJ |
| description | <b> </b>In<b> </b>the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R<sup>2</sup>) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10<sup>−5</sup>. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R<sup>2</sup> of 0.99. |
| first_indexed | 2024-12-11T02:09:35Z |
| format | Article |
| id | doaj.art-d8702b9b47b7497c800d179d14c5db0f |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-11T02:09:35Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-d8702b9b47b7497c800d179d14c5db0f2022-12-22T01:24:18ZengMDPI AGMolecules1420-30492020-03-01257151110.3390/molecules25071511molecules25071511Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTsRusul Khaleel Ibrahim0Seef Saadi Fiyadh1Mohammed Abdulhakim AlSaadi2Lai Sai Hin3Nuruol Syuhadaa Mohd4Shaliza Ibrahim5Haitham Abdulmohsin Afan6Chow Ming Fai7Ali Najah Ahmed8Ahmed Elshafie9Department of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, MalaysiaNanotechnology & Catalysis Research Centre, University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Materials Science and Metallurgy, University of Nizwa, Birkat Al Mawz 616, OmanDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, MalaysiaInstitute of Ocean and Earth Sciences (IOES), University of Malaya, Kuala Lumpur 50603, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, MalaysiaInstitute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Selangor 43000, MalaysiaInstitute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor 43000, MalaysiaDepartment of Civil Engineering, Faculty of Engineering, University Malaya, Kuala Lumpur 50603, Malaysia<b> </b>In<b> </b>the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R<sup>2</sup>) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10<sup>−5</sup>. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R<sup>2</sup> of 0.99.https://www.mdpi.com/1420-3049/25/7/1511water qualitydeep eutectic solventscarbon nanotubesfeedforward back propagation neural networkadsorption |
| spellingShingle | Rusul Khaleel Ibrahim Seef Saadi Fiyadh Mohammed Abdulhakim AlSaadi Lai Sai Hin Nuruol Syuhadaa Mohd Shaliza Ibrahim Haitham Abdulmohsin Afan Chow Ming Fai Ali Najah Ahmed Ahmed Elshafie Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs Molecules water quality deep eutectic solvents carbon nanotubes feedforward back propagation neural network adsorption |
| title | Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs |
| title_full | Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs |
| title_fullStr | Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs |
| title_full_unstemmed | Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs |
| title_short | Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs |
| title_sort | feedforward artificial neural network based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent functionalized cnts |
| topic | water quality deep eutectic solvents carbon nanotubes feedforward back propagation neural network adsorption |
| url | https://www.mdpi.com/1420-3049/25/7/1511 |
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