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Experimental studies and artificial neural network modeling of hydrogen sulfide removal from wastewater by calcium-modified coconut shell based activated carbon

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dc.contributor.authorHabeeb O.A.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorAbdullah T.A.R.B.T.en_US
dc.contributor.authorKanthasamy R.en_US
dc.contributor.authorYunus R.B.M.en_US
dc.contributor.authorid57194114981en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid57222568824en_US
dc.contributor.authorid56070146400en_US
dc.contributor.authorid14720494400en_US
dc.date.accessioned2023-05-29T09:12:02Z
dc.date.available2023-05-29T09:12:02Z
dc.date.issued2021
dc.description.abstractIn this study, activated carbon-based adsorbent was prepared from eggshells and coconut shells. The effects of contact time, initial H2S concentration, and the calcium impregnated coconut shell activated carbon (Ca-CSAC) adsorption dosage on the hydrogen sulphide (H2S) removal efficiency and adsorption capacity were investigated. The batch adsorption data obtained from the experimental runs were employed to fit an artificial neural network (ANN) model. An initial optimization was performed to obtain the most suitable number of hidden neurons for training and validation of the ANN. The optimization results show that 16 hidden neurons was the most appropriate choice. The trained ANN was adequately validated and tested with coefficients of determination (R2) of 0.99 and 0.95, respectively. The ANN was found to be a robust tool for modeling of H2S removal efficiency by and adsorption capacity on Ca-CSAC under different process conditions. � 2021, Prince of Songkla University. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage104
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85103267295
dc.identifier.spage96
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103267295&partnerID=40&md5=5c6cba97cf46b709d39f62c62a8388a0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26564
dc.identifier.volume43
dc.publisherPrince of Songkla Universityen_US
dc.sourceScopus
dc.sourcetitleSongklanakarin Journal of Science and Technology
dc.titleExperimental studies and artificial neural network modeling of hydrogen sulfide removal from wastewater by calcium-modified coconut shell based activated carbonen_US
dc.typeArticleen_US
dspace.entity.typePublication
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