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 (Chemical Engineering Research and Design, 2022)
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.
Keywords: Adsorption mechanism; Artificial neural network modeling; Contaminated water; Fluoride adsorptions; Fluoride removal; Mactra veneriformis shell; Mechanism studies; Optimization studies; Portlandite; Response-surface methodology; Artificial neural networks; Calcination; Fluoride adsorption; Mactra veneriformis shells; Portlandite; Response surface methodology