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Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

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dc.citedby5
dc.contributor.authorMijwel A.-A.S.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAfan H.A.en_US
dc.contributor.authorAlayan H.M.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid58664907300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56436626600en_US
dc.contributor.authorid57200752209en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:17:26Z
dc.date.available2024-10-14T03:17:26Z
dc.date.issued2023
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo18260
dc.identifier.doi10.1038/s41598-023-45032-3
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85174945353
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174945353&doi=10.1038%2fs41598-023-45032-3&partnerID=40&md5=3f12a82c4fd0a3f061cb3cac8f673b24
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/33920
dc.identifier.volume13
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.relation.ispartofGreen Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleArtificial intelligence models for methylene blue removal using functionalized carbon nanotubesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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