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Feedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized CNTs

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dc.citedby9
dc.contributor.authorIbrahim R.K.en_US
dc.contributor.authorFiyadh S.S.en_US
dc.contributor.authorAlSaadi M.A.en_US
dc.contributor.authorHin L.S.en_US
dc.contributor.authorMohd N.S.en_US
dc.contributor.authorIbrahim S.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57188832586en_US
dc.contributor.authorid57197765961en_US
dc.contributor.authorid57216181014en_US
dc.contributor.authorid57201523473en_US
dc.contributor.authorid57192892703en_US
dc.contributor.authorid7202480735en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T08:13:41Z
dc.date.available2023-05-29T08:13:41Z
dc.date.issued2020
dc.descriptioncarbon 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 Purificationen_US
dc.description.abstractIn 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/).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1511
dc.identifier.doi10.3390/molecules25071511
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85082774469
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85082774469&doi=10.3390%2fmolecules25071511&partnerID=40&md5=10c536a69d4bd750af23ca2a38d6cb32
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25745
dc.identifier.volume25
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleMolecules
dc.titleFeedforward artificial neural network-based model for predicting the removal of phenolic compounds from water by using deep eutectic solvent-functionalized CNTsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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