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Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater

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dc.citedby3
dc.contributor.authorKhan M.S.J.en_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorAlkhadher S.A.A.en_US
dc.contributor.authorBasri H.en_US
dc.contributor.authorZawawi M.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid57214778682en_US
dc.contributor.authorid35070506500en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid56405495700en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid39162217600en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2025-03-03T07:41:49Z
dc.date.available2025-03-03T07:41:49Z
dc.date.issued2024
dc.description.abstractTo maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machine learning techniques was employed in order to make an estimation of the performance of reduction catalysis in the context of ecologically detrimental nitrophenols and azo dyes contaminants. The catalyst utilized in the experiment was Ag@CMC, which proved to be highly effective in eliminating various contaminants found in water, like 4-nitrophenol (4-NP). The experiments were carefully conducted at various time intervals, and the machine learning procedures used in this study were all employed to forecast catalytic performance. The evaluation of the performance of such algorithms were done by means of Mean Absolute Error. The noteworthy findings of this research indicated that the ADAM and LSTM algorithm exhibited the most favourable performance in the case of toxic compounds i.e. 4-NP. Moreover, the Ag@CMC catalyst demonstrated an impressive reduction efficiency of 98 % against nitrophenol in just 8 min. Thus, based on these compelling results, it can be concluded that Ag@CMC works as a highly effective catalyst for practical applications in real-world scenarios. ? 2024 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo134701
dc.identifier.doi10.1016/j.ijbiomac.2024.134701
dc.identifier.scopus2-s2.0-85201860374
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85201860374&doi=10.1016%2fj.ijbiomac.2024.134701&partnerID=40&md5=cd04ecc2d1a2aa249f9063016a7b785c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36289
dc.identifier.volume278
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Biological Macromolecules
dc.subjectAlgorithms
dc.subjectAzo Compounds
dc.subjectCatalysis
dc.subjectColoring Agents
dc.subjectMachine Learning
dc.subjectNitrophenols
dc.subjectSilver
dc.subjectWastewater
dc.subjectWater Pollutants, Chemical
dc.subjectWater Purification
dc.subjectWastewater treatment
dc.subjectazo dye
dc.subjectnitrophenol
dc.subject4-nitrophenol
dc.subjectazo compound
dc.subjectcoloring agent
dc.subjectsilver
dc.subject% reductions
dc.subject4-Nitrophenol
dc.subjectAquatic waste
dc.subjectAzo-dyes
dc.subjectCatalytic performance
dc.subjectDye reduction
dc.subjectMachine-learning
dc.subjectNitrophenols
dc.subject]+ catalyst
dc.subjectadaptive moment estimation
dc.subjectArticle
dc.subjectcatalysis
dc.subjectcatalyst
dc.subjectelectromagnetic radiation
dc.subjectelectron
dc.subjectenergy resource
dc.subjectlearning algorithm
dc.subjectlong short term memory network
dc.subjectmachine learning
dc.subjectmean absolute error
dc.subjectpollutant
dc.subjectrecurrent neural network
dc.subjecttime series analysis
dc.subjectwaste water management
dc.subjectwastewater
dc.subjectwater pollutant
dc.subjectalgorithm
dc.subjectcatalysis
dc.subjectchemistry
dc.subjectisolation and purification
dc.subjectprocedures
dc.subjectwater management
dc.subjectwater pollutant
dc.subjectAzo dyes
dc.titleMachine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewateren_US
dc.typeArticleen_US
dspace.entity.typePublication
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