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dc.contributor.authorOh, Juryeon-
dc.contributor.authorPhark, Chuntak-
dc.contributor.authorJung, Seungho-
dc.contributor.authorLee, Jechan-
dc.contributor.authorChoi, Kwon Young-
dc.contributor.authorLee, Chang-Gu-
dc.contributor.authorPark, Seong-Jik-
dc.contributor.authorLee, Sang-Hyup-
dc.date.accessioned2024-01-19T18:04:03Z-
dc.date.available2024-01-19T18:04:03Z-
dc.date.created2022-01-25-
dc.date.issued2020-02-
dc.identifier.issn1944-3994-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/118988-
dc.description.abstractPhosphate, which can cause eutrophication of the aquatic environment, can be effectively removed by an adsorption process using calcium-containing adsorbents. In this study, Ca-carbon foam was prepared by a simple method that entailed addition of Ca during the manufacturing process of carbon foam, and the phosphate removal characteristics of the prepared foam were analyzed using kinetic, equilibrium isotherm, and artificial neural network (ANN) models. The phosphate adsorption capacity decreased with an increase in the solution pH from 3.05 to 6.99. Furthermore, phosphate adsorption increased with increasing time, and the kinetic data were found to be well described by a pseudo-second-order model. The Freundlich isotherm provided the best fit of the equilibrium data, which indicates multilayer adsorption of phosphate on the Ca-carbon foam. The phosphate adsorption capacity as a function of three operational parameters-solution pH, initial phosphate concentration, and time-was well predicted by an ANN model (R-2 > 0.993), and the optimal ANN for the process of phosphate adsorption on Ca-carbon foam had a 3-20-1 structure with 20 hidden layers. The solution pH was the most influential operational parameter out of all the examined parameters. The results of these analyses are expected to be useful in designing the process of phosphate adsorption from aqueous solutions.-
dc.languageEnglish-
dc.publisherDESALINATION PUBL-
dc.titleAdsorption of phosphate on Ca-carbon foam: batch experiments and model evaluation-
dc.typeArticle-
dc.identifier.doi10.5004/dwt.2020.24940-
dc.description.journalClass1-
dc.identifier.bibliographicCitationDESALINATION AND WATER TREATMENT, v.178, pp.101 - 109-
dc.citation.titleDESALINATION AND WATER TREATMENT-
dc.citation.volume178-
dc.citation.startPage101-
dc.citation.endPage109-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid000544998600010-
dc.identifier.scopusid2-s2.0-85098800977-
dc.relation.journalWebOfScienceCategoryEngineering, Chemical-
dc.relation.journalWebOfScienceCategoryWater Resources-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaWater Resources-
dc.type.docTypeArticle-
dc.subject.keywordPlusPLATING WASTE-WATER-
dc.subject.keywordPlusAQUEOUS-SOLUTION-
dc.subject.keywordPlusMETHYLENE-BLUE-
dc.subject.keywordPlusMETAL-IONS-
dc.subject.keywordPlusREMOVAL-
dc.subject.keywordPlusEQUILIBRIUM-
dc.subject.keywordPlusKINETICS-
dc.subject.keywordPlusRECOVERY-
dc.subject.keywordPlusSORPTION-
dc.subject.keywordPlusBIOCHAR-
dc.subject.keywordAuthorPhosphate-
dc.subject.keywordAuthorCarbon foam-
dc.subject.keywordAuthorKinetic adsorption model-
dc.subject.keywordAuthorEquilibrium isotherm-
dc.subject.keywordAuthorArtificial neural network-
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KIST Article > 2020
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