Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: Experiments and modelling |
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Authors: | Saba Golshan Shandi Faramarz Doulati Ardejani Fereydoun Sharifi |
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Affiliation: | 1.School of Mining, College of Engineering, University of Tehran, Tehran, Iran;2.Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran |
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Abstract: | Lignocellulosic materials can be used as biosorbent for refinement of the wastewaters when they are available in large quantities. Many studies were conducted to uptake Cu (II) ion from aqueous solutions. In this paper, the biosorption efficiency of Cu (II) ions from a synthetic aqueous solution was investigated using Gundelia tournefortii (GT), without any pre-treatment. Fourier transform infrared spectroscopy, scanning electron microscopy and determining the point of zero charge were employed to characterise the biosorbent. Batch experiments were performed to study the influence of pH, biosorbent dosage, contact time, temperature and initial Cu (II) concentration on Cu (II) removal. The biosorption isotherms were investigated using the Langmuir, Freundlich, Temkin and D-R isotherm models. The findings show that the biosorption isotherm was better fitted by the Langmuir equation and the maximum adsorption capacity of GT was found to be 38.7597 mg·g-1. The kinetics data were analysed by pseudo-first order, pseudo-second order, and intra-particle diffusion equations. The results indicate that the pseudosecond-order model was found to explain the adsorption kinetics most effectively. The values of thermodynamic parameters including Gibbs free energy (△G°), enthalpy (△H°), and entropy (△S°) demonstrate that the biosorption process was exothermic and spontaneous. The multiple nonlinear regression (MnLR) and artificial neural network (ANN) analyses were applied for the prediction of biosorption capacity. A relationship between the predicted and observed data was obtained and the results show that the MnLR and ANN models provided successful predictions. |
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Keywords: | Corresponding author Adsorption Batch experiment Kinetics Isotherms Thermodynamic Multiple nonlinear regression Artificial neural network |
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