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Feasibility of fluoride removal using calcined Mactra veneriformis shells: Adsorption mechanism and optimization study using RSM and ANN
  • Choi, Moon Yeong ;
  • Kang, Jin Kyu ;
  • Lee, Chang Gu ;
  • Park, Seong Jik
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Publication Year
2022-12-01
Publisher
Institution of Chemical Engineers
Citation
Chemical Engineering Research and Design, Vol.188, pp.1042-1053
Keyword
Artificial neural networksCalcinationFluoride adsorptionMactra veneriformis shellsPortlanditeResponse surface methodology
Mesh Keyword
Adsorption mechanismArtificial neural network modelingContaminated waterFluoride adsorptionsFluoride removalMactra veneriformis shellMechanism studiesOptimization studiesPortlanditeResponse-surface methodology
All Science Classification Codes (ASJC)
Chemistry (all)Chemical Engineering (all)
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.
ISSN
0263-8762
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/33040
DOI
https://doi.org/10.1016/j.cherd.2022.10.031
Fulltext

Type
Article
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) [grant number 2020R1C1C1008982 ].
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Lee, Chang-Gu 이창구
Department of Environmental and Safety Engineering
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