Abstract
In this study, it was aimed to remove heavy metal copper from aqueous solutions by using MWCNT-OH, which is a multi-walled carbon nanotube. Modelling and optimization were performed using the Response Surface Method (RSM) and Artificial Neural Networks (ANN). Model equations were derived by both methods. ANOVA analyses were performed with RSM to determine the significance of the parameters on removal efficiency and adsorption capacity. Contour graphs showing the binary parameter interactions were obtained. Optimization was carried out to obtain the maximum removal efficiency and maximum adsorption capacity using both RSM and ANN. Using RSM and ANN, the maximum copper removal efficiencies were obtained at 45.1% and 39.1%, while the maximum adsorption capacities were found to be 16.7 mg/g and 17.12 mg/g, respectively. In addition, test experiments and modelling methods were compared, revealing that the modelling capability of ANN was superior to that of RSM.
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