(592d) Green Synthesis of Nano Iron Carbide: Preparation, Characterization and Application for Removal of Phosphate from Aqueous Solutions

Peters, R. W., University of Alabama at Birmingham
Farag, R., Faculty of Science, Al-Azhar University
El-Shafei, D. M. M., Housing and Building National Research Center
Mahmoud, A. S., Housing and Building National Research Center
Mostafa, M. K., Badr University in Cairo (BUC)
Nanoparticles, especially iron nanoparticles, have proved to be effective in wastewater treatment due to large surface area and reactivity. The main purpose of this study is to prepare and characterize low cost iron nanoparticles that are produced by reduction of iron cations using extracted phenolic compound contained in black tea to produce carbide compounds. The prepared iron carbide nanoparticles were used to treat phosphate in aqueous solution. The iron carbide was characterized using XRD, SEM and EDAX. The effect of iron carbide on phosphate removal was studied at different contact times, absorbent doses, pH, stirring rates, and initial phosphate concentrations. The results indicated that iron carbide is effective in the removal of phosphate, where a removal efficiency of 82% was achieved for 5 mg/L phosphate solution after 30 minutes of contact time using a dose of 0.6 mg/L at pH 3 and stirring rate 250 rpm. The removal efficiency of phosphate was recorded in the range of 66% to 92% for initial phosphate concentrations of 10 and 1 mg/L, respectively, at the same environmental conditions. The equilibrium isotherm of phosphate was determined using Freundlich, Langmuir, Jovanovich, Koble–Corrigan, Toth, Hill, Khan, Redlich and Sips adsorption models and the adsorption data of phosphate fitted well to the Hill isotherm with the lowest Chi-squared error of 0.0637. Artificial Neural Network (ANN) with a structure of 5 - 2 - 1 was used to predict the phosphate removal efficiency. The proposed ANN was found to be effective in simulating the performance of iron carbide for phosphate removal, where the sum of squares error was recorded as 0.33 and 0.623 for testing and training, respectively while the relative error was recorded as 0.044 and 0.043, respectively. The obtained ANN results from testing and training indicated that the relation between predictive value and residual was between -4% and +6%. The response surface methodology (RSM) showed also a good relationship between the experimental factors and the phosphate removal efficiencies with r2 of 0.76, adjusted r2 of 0.73 and a standard error of the estimate of 3.45.


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