Karbon nanotüpler kullanılarak sulardan bakır gideriminin YYY ve YSA ile modelleme ve optimizasyonu

Abstract

Bu çalışmada çok duvarlı karbon nanotüplerden olan MWCNT-OH kullanılarak ağır metallerden bakırın sulu çözeltilerden giderimi hedeflenmiştir. Çalışmada modelleme ve optimizasyon için Yanıt Yüzey Yöntemi (YYY) ile Yapay Sinir Ağları (YSA) kullanılmıştır. Her iki yöntemle model denklemleri türetilmiştir. YYY ile ANOVA analizi yapılarak parametrelerin giderim verimi ve adsorpsiyon kapasitesi üzerindeki anlamlılıklarını belirlenmiştir. İkili parametre etkileşimlerinin görüldüğü contour grafikler elde edilmiştir. YYY ve YSA ile maksimum giderim verimi ve maksimum adsorpsiyon kapasitesini elde etmek amacıyla optimizasyon yapılmıştır. RSM ve YSA kullanılarak, maksimum bakır giderim verimleri %45,1 ve %39,1 olarak elde edilirken, maksimum adsorpsiyon kapasiteleri sırasıyla 16,7 mg/g ve 17,12 mg/g olarak bulunmuştur. Ayrıca test deneyleri ile modelleme yöntemleri karşılaştırılmıştır. YSA’nın modelleme kabiliyetinin YYY’ye göre daha iyi olduğu görülmüştür.

Keywords

• Bakır
• karbon nanotüpler
• adsorpsiyon
• yanıt yüzey metodolojisi
• yapay sinir ağları

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

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.

Keywords

• Copper
• carbon nanotubes
• adsorption
• response surface methodology
• artificial neural networks

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