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.

本研究旨在研究由混凝/絮凝、过滤和纳米双金属铁/铜 (Fe/Cu) 降解和吸附单元组成的中试原型系统的效率，用于去除化学需氧量 (COD)、生物需氧量 ( BOD)、颜色、总氮 (TN)、总磷 (TP) 和来自真实纺织废水的 TSS。以0.5 g/L氯化铁为混凝剂,在pH 6.0,纳米用量1.4 g/L的最佳吸附条件下,体系的总去除效率分别为96、98、82、69、88和97% , 接触时间 80 min, 室温下搅拌速率 250 r/min。吸附等温线表明 COD 和 TP 的去除符合 Koble-Corrigan 和 Freundlich 吸附模型，颜色的去除符合 Koble-Corrigan 和 Hill 吸附模型，TN 和 TSS 的去除分别遵循 Koble-Corrigan 和 Khan 模型。Avrami 动力学模型充分描述了 COD、BOD、TN 和 TSS 的吸附数据，而伪二级和粒子内模型分别描述了颜色和 TSS 的去除机制。人工神经网络 (ANN)超过0.98的r ^{2 -}值是准确的并且可以自信地用于预测目标参数的去除效率。灵敏度分析结果表明，初始浓度是对 TSS 去除影响最大的参数，相对重要性大于 25%，而双金属 Fe/Cu 用量是所有其他研究参数的最大影响因素，相对重要性大于 40%。每平方米所提出的系统的总治疗费用³扩大后，被认为是美国对处理后的水再利用4.5 $林木的灌溉。