Bakır biyosorpsiyon işlemine Yapay Sinir Ağı (ANN) yaklaşımı

Year 2016, Volume: 20 Issue: 3, 433 - 440, 01.12.2016

Deniz Bingöl , Erdal Kılıç , Merve Hercan

https://doi.org/10.16984/saufenbilder.25723

Cited By: 3

Abstract

Bu makale, çörek otu kullanılarak bakır biyosorpsiyon işleminin değerlendirilmesi için yapay sinir ağı (ANN) modelinin kullanımını göstermektedir. Deneysel değişkenler (sıcaklık, biyosorbent kütlesi, başlangıç bakır derişimi, başlangıç pH’ı) çıkış olarak, herhangi bir zamanda adsorplanan bakır miktarını tahmin etmek için kurulan sinir ağında girdi olarak kullanılmıştır. Ağ tahmini ve ilgili deneysel veriler arasındaki yüksek R²-değerleri, eğitim ve test veri setleri için sırasıyla 0,89 ve 0,93, yapay nöron ağını kullanarak biyosorpsiyon işleminin modellemede yeterli bir yöntem olduğunu kanıtlamaktadır. Gibbs serbest enerji (ΔG°), entalpi (AH°) ve sorpsiyonun entropi değişimi (ΔS°) gibi termodinamik parametreler de değerlendirildi. Biyosorpsiyon işleminin gerçekte kendiliğinden, istemli ve ekzotermik olduğu bulunmuştur. Bakır iyonunun denge sorpsiyonu, Langmuir denklemine göre belirlenmiştir ve 293 K’de 16,13 mg/g olarak bulunmuştur. Model sonuçları ve deneysel veriler arasındaki karşılalaştırma çörek otu kullanılarak bakırın giderilebileceğini göstermektedir.

Keywords

yapay sinir ağı (ANN), çörek otu, bakır, biyosorpsiyon

Artificial Neural Network (ANN) approach to copper biosorption process

Abstract

This paper demonstrates use of artificial neural network (ANN) model for the evaluation of copper biosorption process using black cumin. The experimental variables (temperature, biosorbent mass, initial copper concentration, initial pH) were used as the input to the constructed neural network to predict the adsorbed amounts of copper at any time as the output. The high R²-values, 0.89 and 0.93 for training and testing data sets, respectively; between the network prediction and the corresponding experimental data prove that modeling the biosorption process using artificial neuron network is a satisfactory method. Thermodynamic parameters such as Gibbs free energy (ΔG°), the enthalpy (ΔH°) and the entropy change of sorption (ΔS°) were also evaluated. It was found that the biosorption process was spontaneous, favorable and exothermic in nature. The equilibrium sorption of copper ions was determined from the Langmuir equation and found to be 16.13 mg/g at 293 K. A comparison between the model results and experimental data showed that the ANN model is able to predict the removal of copper using black cumin.

Keywords

artificial neural network (ANN), black cumin, copper, biosorption

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