Modelling and optimization of copper removal from water using carbon nanotubes with RSM and ANN

Author:

ÇALGAN Elif1ORCID,OZMETİN Elif1ORCID

Affiliation:

1. BALIKESİR ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ÇEVRE MÜHENDİSLİĞİ BÖLÜMÜ, ÇEVRE MÜHENDİSLİĞİ PR.

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.

Publisher

Balikesir Universitesi Fen Bilimleri Enstitusu Dergisi

Subject

General Engineering

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