A prototype of textile wastewater treatment using coagulation and adsorption by Fe/Cu nanoparticles: Techno-economic and scaling-up studies

• Published in: Nanomaterials and nanotechnology Vol. 11; p. 184798042110411
• Main Authors: Mahmoud, Ahmed S, Mostafa, Mohamed K, Peters, Robert W
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 2021; Hindawi Limited
• Subjects: Adsorption; Artificial neural networks; Bimetals; Biochemical oxygen demand; Chemical oxygen demand; Coagulants; Coagulation; Color; Copper; Dosage; Flocculation; Iron; Mathematical models; Nanoparticles; Parameter sensitivity; Prototypes; Room temperature; Sensitivity analysis; Wastewater treatment; Water reuse; filtration; adsorption; economical evaluation; scale up and continuous feed; degradation; real textile wastewater; coagulation; nano-bimetallic Fe/Cu
• ISSN: 1847-9804; 1847-9804
• DOI: 10.1177/18479804211041181

This study aims to investigate the efficiency of a pilot prototype system comprising coagulation/flocculation, filtration, and nano-bimetallic iron/copper (Fe/Cu) degradation and adsorption units for the removal of chemical oxygen demand (COD), biological oxygen demand (BOD), color, total nitrogen (TN), total phosphorus (TP), and TSS from real textile wastewater. The total removal efficiencies of the system were 96, 98, 82, 69, 88, and 97%, respectively, using 0.5 g/L ferric chlorides as a coagulant under an optimum adsorption condition of pH 6.0, nano-dosage 1.4 g/L, contact time 80 min, and stirring rate 250 r/min at room temperature. Adsorption isotherms indicated that the removal of COD and TP obeys both Koble–Corrigan and Freundlich adsorption models, removal of color obeys both Koble–Corrigan and Hill adsorption models, and removal of TN and TSS obeys Koble–Corrigan and Khan models, respectively. Avrami kinetic models adequately describe the adsorption data for COD, BOD, TN, and TSS, while pseudo-second-order and intraparticle models described the removal mechanism of color and TSS, respectively. An artificial neural network (ANN) with r2-value exceeding 0.98 is accurate and can be used with confidence in predicting removal efficiencies of the targeted parameters. Sensitivity analysis results showed that the initial concentration was the most influential parameter for TSS removal with relative importance greater than 25%, while the bimetallic Fe/Cu dosage was the most influential factor for all other studied parameters with relative importance greater than 40%. The total treatment cost of the proposed system per m3 after scaling up was found to be US$4.5 for reuse of the treated water for the irrigation of forest trees.