Optimization of hardness removal using response surface methodology from wastewater containing high boron by Bigadiç clinoptilolite

Çalgan, Elif; Özmetin, Elif

doi:10.5004/dwt.2019.24907

DWT 2019

DOI: 10.5004/dwt.2019.24907

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Optimization of hardness removal using response surface methodology from wastewater containing high boron by Bigadiç clinoptilolite

Elif Çalgan¹,

Elif Özmetin²

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2024

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

ÇALGAN,

OZMETİN

2024

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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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. With MWCNT-OH, 45.1 % removal efficiency and 16.7 mg/g adsorption capacity were achieved. 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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Modelling and optimization of copper removal from water using carbon nanotubes with RSM and ANN

ÇALGAN,

OZMETİN

2024

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Optimization of hardness removal using response surface methodology from wastewater containing high boron by Bigadiç clinoptilolite

Cited by 1 publication

References 0 publications

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

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

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