This study aims to explore the efficiency of an agro waste material for the remediation of Pb(II) contaminated water. A factorial design approach is adopted to optimize removal efficiency and to study the interaction between effective variables. A face-centered Draper-Lin composite design predicted 100% removal efficiency at optimum variables; pH 8, initial concentration of Pb(II) ion 12mg/L, sorbent dose 200mg and agitation time 110 min. Regration coefficient (R2 = 99.9%) of a plot of the predicted versus the observed values and p value (>0.05) confirms the applicability of the predicted model. Langmuir and Dubinin-Radushkevich (D-R) isotherm models were applicable to sorption data with the Langmuir sorption capacity of 21.61š0.78 mg/g. The energy of sorption was found to be 13.62š0.32 kJ/mol expected for ion-exchange or chemisorption nature of sorption process. Characterization of Grewia seed suggested a possible contribution of carboxyl and hydroxyl groups in the process of biosorption. The present study shows that Grewia seeds can be used effectively for the remediation of Pb(II) contaminated water.
In this research, the batch removal of Pb2+ ions from wastewater and aqueous solution with the use o two different modified algae Gracilaria corticata (red algae) and Sargassum glaucescens (brown algae) was examined. The experiment was performed in a batch system and the effect of the pH solution; initial concentration and contact time on biosorption by both biomasses were investigated and compared. When we used S. glaucescens as a biosorbent, the optima conditions of pH, Pb2+ concentration and equilibrium time were at 5, 200 mg/L and 70 min, in the range of 95.6% removal. When G. corticata was used for this process, pH 3, 15 mg/L pb2+ concentration and 50 min contact time, resulted in the maximum removal (86.4%). The equilibrium adsorption data are fitted to the Frundlich and Langmuir isotherm model, by S. glaucescens and G. corticata, respectively. The pb2+ uptake by both biosorbent was best described by the second-order rate model.
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