Wastewater treatment using nano bimetallic iron/copper, adsorption isotherm, kinetic studies, and artificial intelligence neural networks

Industrial effluents introduce a huge amount of organic load into municipal wastewater. Organic contaminants such as carbohydrates, starch, and fats increase the COD levels to unallowable limits to be discharged into sewage systems. This study successfully prepared Fe/Cu NPs by using the drop by drop method and characterized them using XRD, SEM, and EDAX analysis. The effect of operating experiments was studied at different pH, NP doses, operating times, stirring rates, and initial COD concentrations. The removal efficiencies were between 100 and 69% and between 100 and 800 mg/L for initial COD concentrations at pH 7, NP dose 0.6 g/L, 15 min, and 150 rpm. The adsorption isotherms were studied by applying nonlinear equations of Redlich-Peterson, Hill, Sips, Khan, Toth, Koble-Corrigan, Jovanovic, Freundlich, and Langmuir models. The obtained results indicated that Fe/Cu NPs accept both Koble–Corrigan and Freundlich models with the lowest ∑ = 1.3304. The kinetic analysis was carried by practicing nonlinear equations of pseudo first order (PFO), pseudo second order (PSO), and Elovich, Avrami, and intraparticle kinetic models. The obtained results indicated that Fe/Cu NPs accept the Avrami mechanism with the lowest ∑ = 0.046. The ANNs were trained 28 times and tested 8 times using network structure 6–3-1 indicating that the most significant operating parameter is the effect of concentration 100% followed by the effect of dose 61.1% with a small deviation between the predictive and actual results. RSM relations indicating the positive linear effect of the independent variable “pH,” “dose,” “time,” and “initial concentration” with p-value   0.05.