4.6 Article

Adsorptive removal of Safranin-O dye from aqueous medium using coconut coir and its acid-treated forms: Adsorption study, scale-up design, MPR and GA-ANN modeling

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SUSTAINABLE CHEMISTRY AND PHARMACY
卷 19, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.scp.2021.100374

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Adsorption; Bioadsorbent; Coconut coir; Safranin-O dye; GA-ANN

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Coconut coir has been identified as an effective adsorbent for the removal of safranin-O dye from aqueous solution, with acid-treated coconut coir showing the highest adsorption capacity. The adsorption process is optimized by adjusting physicochemical conditions, and Langmuir and Tempkin models are found to best fit the isotherm and kinetic study. The thermodynamic parameters indicate that the adsorption process is viable, spontaneous, and exothermic.
Coconut coir (Cocos nucifera L.), particle size 300-850 tint, has been identified as an adsorbent for safranin-O dye removal from aqueous solution. Bioadsorption efficiency is improved by modifying untreated coconut coir (UCC) with 1 N phosphoric acid (PCC) and 1 N sulphuric acid (SCC). The acid treatment enhances the surface area of adsorbents and accelerates more dye uptake. The adsorption process is optimized by varying the physicochemical conditions like initial pH, adsorbent amount, contact time, initial dye concentration, and temperatures. The adsorption process's optimum pH is 4, 6, and 6, respectively, using UCC, PCC, and SCC adsorbents. In contrast, more than 98% of dye removal has been observed at the lower concentration of dyes up to 200 mg/L at 303 K. Maximum dye removal is possible at 75 mg/L of dye concentration. UCC, PCC, and SCC adsorbents' adsorption capacity is 80.32 mg/g, 96.81 mg/g, and 89.53 mg/g, respectively, at 303 K temperature. Langmuir and Tempkin model and the pseudo-second-order model are the best-fitted models for isotherm and kinetic study. Thermodynamic parameters indicate the adsorption process is viable, spontaneous, exothermic. 75% glacial acetic acid is the most potent solvent for safranin-O dye extraction from dye loaded biomass. The functional groups and different interactions are identified to establish the adsorption mechanism. The PCC adsorbent has been used for scale-up design. The multiple polynomial regression (MPR) successfully predicts the dye removal efficiency for individual adsorbents. The modeling of the Genetic Algorithm has also been done successfully.

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