(670d) Study the Degradation and Adsorption Processes of Organic Matters from Domestic Wastewater Using Chemically Prepared and Green Synthesized Nano Zero-Valent Iron | AIChE

(670d) Study the Degradation and Adsorption Processes of Organic Matters from Domestic Wastewater Using Chemically Prepared and Green Synthesized Nano Zero-Valent Iron

Authors 

Peters, R. W. - Presenter, 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)
Karam, A., Nile University
Advanced oxidation processes (AOPs) using chemically prepared and green synthesized nano zero-valent iron (nZVI) has proved to be effective in removing organic contaminants. The green synthesized nano iron (GT-nZVI) was prepared by using extracted black tea reducing agent. The prepared nZVI particles were characterized using X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and Energy Dispersive X-ray Analysis (EDAX). The main purpose of this study is to compare between chemically prepared nZVI and GT-nZVI in the biological oxygen demand (BOD) removal efficiency from domestic wastewater. Different nonlinear adsorption and kinetic models were used to describe the mechanism of organic matter adsorption onto differently prepared nano iron particles. The effects of nZVI and GT-nZVI on BOD removal were studied at different pH, adsorbent dose, contact time, stirring rate, and BOD concentrations. The results indicated that nZVI and GT-nZVI are effective in the removal of BOD from wastewater, where the maximum removal efficiencies of 86.4% and 82.5% were achieved for 300 mg/L BOD, respectively. The adsorption and kinetic data of BOD removal using chemically prepared nZVI fitted well to the Freundlich and Avrami models, respectively. On the other hand, the adsorption data for BOD removal using GT-nZVI fitted well to both Toth and Langmuir adsorption models, while the kinetic data fitted well to Avrami model. Artificial neural network (ANN) analysis adequately predicted BOD removal (r2 > 90%) for differently prepared nano iron particles.

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