Removal of Pb² from water using the carbon nanotube-g-poly[(sodium methacrylate)-co- 2-(methacryloyloxy)ethyl acetoacetate]: experimental investigation and modeling.

Mazumder, Mohammad Abu Jafar; Chowdhury, Imran Rahman; Chowdhury, Shakhawat; Al-Ahmed, Amir

A solid polymer, poly[(sodium methacrylate)-co-2-(methacryloyloxy)ethyl acetoacetate], p(MAA-co-MEAA) was synthesized and then grafted onto carbon nanotubes to prepare poly(MAA-co-MEAA)-grafted carbon nanotubes [CNT-g-p(MAA-co-MEAA)]. NMR, TGA, and FT-IR characterized the synthesized polymers and adsorbents. SEM-EDX was used to investigate the surface characteristics of the adsorbents. Pb2 was removed from the aqueous solution using the CNT-g-p(MAA-co-MEAA). A batch adsorption experiment was performed at different Pb2 concentrations (1, 10, 25, 50 mg/L), pH (4 and 6.75), temperature (25 and 35 °C), and contact periods (1, 5, 20, 60, and 1440 min) to study the adsorption kinetics and isotherm. The adsorbent dose of 2.5 g/L could effectively lower the initial Pb2 concentration of 1000 to 2 ppb. The maximum adsorption capacity of the adsorbent was found to be 1178 mg/g. In addition, the adsorbents have been shown to effectively reduce the coexisting metal ion concentrations from industrial wastewater, which indicated the potential of the proposed adsorbent in removing metal ions from coexisting metals containing wastewater. To predict the adsorption efficiency of Pb2 , various linear, non-linear, and neural network models were established. An additional data set, not incorporated in model training, was used to validate the models. A number of models showed excellent performance with R2 in the range of 0.89–0.98. In model validation studies, the correlation coefficients (r) ranged from 0.94 to 0.99. The novel adsorbent and models will most likely aid in the development of a robust treatment technique for removing Pb2 ions from water and wastewater.

METHACRYLATES; ETHYL acetoacetate; LEAD removal (Sewage purification); ADSORPTION kinetics; WATER use; ADSORPTION isotherms; ARTIFICIAL neural networks

Environmental Science & Pollution Research, 2022, Vol 29, Issue 36, p54432

0944-1344

Academic Journal

10.1007/s11356-022-19585-1