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DC Field | Value | Language |
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dc.contributor.author | Choi, Moon Yeong | - |
dc.contributor.author | Kang, Jin Kyu | - |
dc.contributor.author | Lee, Chang Gu | - |
dc.contributor.author | Park, Seong Jik | - |
dc.date.issued | 2022-12-01 | - |
dc.identifier.issn | 0263-8762 | - |
dc.identifier.uri | https://dspace.ajou.ac.kr/dev/handle/2018.oak/33040 | - |
dc.description.abstract | In this study, Mactra veneriformis shells (MVS), a seafood by-product with high Ca content, was assessed as an adsorbent for fluoride removal from contaminated water. MVS was calcined at various temperatures (100–900 °C), and MVS calcined at 800 and 900 °C (MVS-800 and MVS-900) had the highest adsorption capacity. The high fluoride adsorption of MVS-800 and MVS-900 originated from the conversion of CaCO3 present in the raw MVS to CaO and Ca(OH)2 by calcination at high temperatures. The kinetic and equilibrium adsorption of fluoride by MVS-800 were accurately described by the pseudo-second-order and Langmuir models, respectively. The maximum fluoride adsorption capacity was 244.61 mg/g, which is comparable to that of other adsorbents reported in the literature. The enthalpy and entropy of adsorption were 7.42 kJ/mol and 56.48 J/mol‧K, respectively, and the Gibbs free energy was negative at all reaction temperatures. The interactive effects of pH, reaction time, dosage, and temperature and the optimal values for fluoride removal by MVS-800 were explored using response surface methodology (RSM) and artificial neural networks (ANN). The RSM results demonstrated that reaction time, dosage, and temperature significantly influenced fluoride removal; however, pH was an insignificant term. The accuracy of the ANN model (R2 = 0.9932) for predicting fluoride removal was higher than that of RSM (R2 = 0.9347). The optimal fluoride removal at a dosage of 3.3 g/L under optimized conditions (pH 5; reaction time 9 h; temperature 35 °C) was predicted to be 98.5% by the ANN model. | - |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) [grant number 2020R1C1C1008982 ]. | - |
dc.language.iso | eng | - |
dc.publisher | Institution of Chemical Engineers | - |
dc.subject.mesh | Adsorption mechanism | - |
dc.subject.mesh | Artificial neural network modeling | - |
dc.subject.mesh | Contaminated water | - |
dc.subject.mesh | Fluoride adsorptions | - |
dc.subject.mesh | Fluoride removal | - |
dc.subject.mesh | Mactra veneriformis shell | - |
dc.subject.mesh | Mechanism studies | - |
dc.subject.mesh | Optimization studies | - |
dc.subject.mesh | Portlandite | - |
dc.subject.mesh | Response-surface methodology | - |
dc.title | Feasibility of fluoride removal using calcined Mactra veneriformis shells: Adsorption mechanism and optimization study using RSM and ANN | - |
dc.type | Article | - |
dc.citation.endPage | 1053 | - |
dc.citation.startPage | 1042 | - |
dc.citation.title | Chemical Engineering Research and Design | - |
dc.citation.volume | 188 | - |
dc.identifier.bibliographicCitation | Chemical Engineering Research and Design, Vol.188, pp.1042-1053 | - |
dc.identifier.doi | 10.1016/j.cherd.2022.10.031 | - |
dc.identifier.scopusid | 2-s2.0-85141526885 | - |
dc.identifier.url | http://www.elsevier.com/wps/find/journaldescription.cws_home/713871/description#description | - |
dc.subject.keyword | Artificial neural networks | - |
dc.subject.keyword | Calcination | - |
dc.subject.keyword | Fluoride adsorption | - |
dc.subject.keyword | Mactra veneriformis shells | - |
dc.subject.keyword | Portlandite | - |
dc.subject.keyword | Response surface methodology | - |
dc.description.isoa | false | - |
dc.subject.subarea | Chemistry (all) | - |
dc.subject.subarea | Chemical Engineering (all) | - |
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