Feedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized CNTs
carbon nanotube; phenol derivative; solvent; adsorption; algorithm; chemistry; kinetics; theoretical model; water management; water pollutant; Adsorption; Algorithms; Kinetics; Models, Theoretical; Nanotubes, Carbon; Neural Networks, Computer; Phenols; Solvents; Water Pollutants, Chemical; Water Pur...
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my.uniten.dspace-257452023-05-29T16:13:42Z Feedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized CNTs Ibrahim R.K. Fiyadh S.S. AlSaadi M.A. Hin L.S. Mohd N.S. Ibrahim S. Afan H.A. Fai C.M. Ahmed A.N. Elshafie A. 57188832586 57197765961 57216181014 57201523473 57192892703 7202480735 56436626600 57214146115 57214837520 16068189400 carbon nanotube; phenol derivative; solvent; adsorption; algorithm; chemistry; kinetics; theoretical model; water management; water pollutant; Adsorption; Algorithms; Kinetics; Models, Theoretical; Nanotubes, Carbon; Neural Networks, Computer; Phenols; Solvents; Water Pollutants, Chemical; Water Purification In 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 (R2) 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?5. 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 R2 of 0.99. � 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Final 2023-05-29T08:13:41Z 2023-05-29T08:13:41Z 2020 Article 10.3390/molecules25071511 2-s2.0-85082774469 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082774469&doi=10.3390%2fmolecules25071511&partnerID=40&md5=10c536a69d4bd750af23ca2a38d6cb32 https://irepository.uniten.edu.my/handle/123456789/25745 25 7 1511 All Open Access, Gold, Green MDPI AG Scopus |
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carbon nanotube; phenol derivative; solvent; adsorption; algorithm; chemistry; kinetics; theoretical model; water management; water pollutant; Adsorption; Algorithms; Kinetics; Models, Theoretical; Nanotubes, Carbon; Neural Networks, Computer; Phenols; Solvents; Water Pollutants, Chemical; Water Purification |
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57188832586 Ibrahim R.K. Fiyadh S.S. AlSaadi M.A. Hin L.S. Mohd N.S. Ibrahim S. Afan H.A. Fai C.M. Ahmed A.N. Elshafie A. |
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| author |
Ibrahim R.K. Fiyadh S.S. AlSaadi M.A. Hin L.S. Mohd N.S. Ibrahim S. Afan H.A. Fai C.M. Ahmed A.N. Elshafie A. |
| spellingShingle |
Ibrahim R.K. Fiyadh S.S. AlSaadi M.A. Hin L.S. Mohd N.S. Ibrahim S. Afan H.A. Fai C.M. Ahmed A.N. Elshafie A. Feedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized CNTs |
| author_sort |
Ibrahim R.K. |
| title |
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_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_sort |
feedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized cnts |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806425788292005888 |