Reduction of organic matter from municipal wastewater at low cost using green synthesis nano iron extracted from black tea: Artificial intelligence with regression analysis - Ahmed S. Mahmoud; Rabie S. Farag; Maha M. Elshfai (Egyptian Journal of Petroleum)
Nanotechnology especially green synthesis nanoparticles is the modern technology for the adsorption and degradation a wide range of wastewater contaminants. The prepared Green Synthesis nano Zero Valent Iron (GT-nZVI) extracted from soft black tea was characterized using XRD, SEM, and EDAX analysis. This study explores different nonlinear adsorption and kinetic models that can describe the adsorption mechanism of organic matter represented in COD and BOD onto GT-nZVI. The effect of GT-nZVI on COD and BOD removal were studied at different pH, adsorbent dose, contact time, stirring rate, and concentrations. The results indicated that GT-nZVI is effective in the removal of COD and BOD from wastewater, where the removal efficiencies of 87.9 and 100% were achieved for 600 ± 15.0 and 100 ± 11.8 mg/L COD, respectively, and 91.3 and 100% for 365 and 60 mg/L BOD, respectively. Also, GT-nZVI is a highly effective material for wastewater contaminants removal and pass the Egyptian Ministerial Resolution No. 92 of 2013 limits with Operating and Maintenance Cost 0.440 $ USD. The adsorption isotherm data of COD and BOD fitted well to Freundlich and khan, respectively, and Pseudo Second Order kinetic model. Artificial neural network importance data agree with the result of response surface methodology in simulating the adsorption of organic matter onto GT-nZVI indicating the most significant coverable is adsorbent dose. Finally, this study appropriates using GT-nZVI in highly salted municipal wastewater rather than traditionally activated sludge treatment techniques. Keywords: COD and BOD removal, nZVI, GT-nZVI, Artificial neural networks (ANN), Regression analysis, Isotherm and kinetic studies, Cost estimation