Artificial Neural Network (ANN) approach to copper biosorption process

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 (?G°), entalpi (AH°) ve sorpsiyonun entropi değişimi (?S°) 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.

Bakır biyosorpsiyon işlemine Yapay Sinir Ağı (ANN) yaklaşımı

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 (?G°), the enthalpy (?H°) and the entropy change of sorption (?S°) 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.

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"Table of Regulated Drinking Water Contaminants". [Online]. Available: http://water.epa.gov/drink/contaminants/basicinf ormation/copper.cfm. [Erişildi: 1 Nisan 2011].
A. Özer, G. Gürbüz, A. Çalimli, B. K. Körbahti, "Biosorption of copper(II) ions on Enteromorpha prolifera: Application of response surface methodology (RSM)", Chemical Engineering Journal, vol.146, pp. 377-387, 2009.
P. King, P. Srinivas, Y. P. Kumar, V. S. R. K. Prasad, "Sorption of copper(II) ion from aqueous solution by Tectona grandis l.f. (teak leaves powder)", Journal of Hazardous Materials, vol.136, pp. 560-566, 2006.
Y. P. Kumar, P. King, V. S. R. K. Prasad, "Equilibrium and kinetic studies for the biosorption system of copper(II) ion from aqueous solution using Tectona grandis L.f. leaves powder", Journal of Hazardous Materials, vol.137, pp. 1211-1217, 2006.
Y. P. Kumar, P. King, V. S. R. K. Prasad., "Removal of copper from aqueous solution using Ulva fasciata sp.-A marine green algae", Journal of Hazardous Materials, vol.137, pp. 373, 2006.
C. Cojocaru, M. Diaconu, I. Cretescu, J. Savic, V. Vasic, "Biosorption of copper(II) ions from aqua solutions using dried yeast biomass". Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol.335, pp. 181-188,
H. Chen, G. Dai, J. Zhao, A. Zhong, J. Wu, H. Yan, "Removal of copper(II) ions by a biosorbent-Cinnamomum powder", Journal of Hazardous Materials, vol.177, pp. 228-236, 2010. camphora leaves
A. Çelekli, M. Yavuzatmaca, H. Bozkurt, "An eco-friendly process: Predictive modelling of copper adsorption from aqueous solution on Spirulina platensis", Journal of Hazardous Materials, vol.173, pp. 123-129, 2010.
A. N. Kosasih, J. Febrianto, J. Sunarso, Y. H. Ju, N. Indraswati, S. Ismadji, "Sequestering of Cu(II) from aqueous solution using cassava peel (Manihot esculenta)", Journal of Hazardous Materials, vol.180, pp. 366-374, 2010.
W. M. Ibrahim, "Biosorption of heavy metal ions from aqueous solution by red macroalgae", Journal of Hazardous Materials, vol.192, pp. 1835, 2011.
B. Kiran, K. Thanasekaran, "Copper biosorption on Lyngbya putealis: Application of response surface methodology (RSM)", International Biodeterioration & Biodegradation, vol.65, pp. 845, 2011. S. P. Kumar, S.
P. S. Kumar, S. Ramalingam, V. Sathyaselvabala, S. D. Kirupha, S. Sivanesan, "Removal of copper(II) ions from aqueous solution by adsorption using cashew nut shell", Desalination, vol.266, pp. 63-71, 2011.
S. Lu, S. W. Gibb, "Copper removal from wastewater using spent-grain as biosorbent", Bioresource Technology, vol.99, pp. 1509-1517, 2008.
K. S. Tong, M. J. Kassim, A. Azraa, "Adsorption of copper ion from its aqueous solution by a novel biosorbent Uncaria gambir: Equilibrium, kinetics, and thermodynamic studies", Chemical Engineering Journal, vol.170, pp. 145-153, 2011.
S. M. El-Said, M. B. S. Alamri, S. El-Barak AliBin, O. Alsogair, "Adsorptive removal of Arsenite as (III) and Arsenate as (V) heavy metals from waste water using Nigella sativa L.", Asian Journal of Scientific Research vol.2, pp. 96-104, 2009.
D. Bingöl, M. Hercan, S. Elevli, E. Kılıç, "Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin", Bioresource Technology, vol.112, pp. 11-115, 2012.
K. H. Chu, "Prediction of two-metal biosorption equilibria using a neural network", European Journal of Mineral Processing and Environmental Protection, vol.3, no.1, pp. 119- 127, 2003.
K. Yetilmezsoy, S. Demirel, "Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells", Journal of Hazardous Materials, vol.153, pp. 1288-1300, 2008.
K. V. Kumar, K. Porkodi, "Modelling the solid- liquid adsorption processes using artificial neural networks rained by pseudo second order kinetics", Chemical Engineering Journal, vol.148, pp. 20-25, 2009.
A. Çelekli, F. Geyik, "Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria", Bioresource Technology, vol.102, pp. 5634-5638, 2011.
B. Das, N. K. Mondal, "Calcareous Soil as a New Adsorbent to remove lead from aqueous solution: Equilibrium, kinetic and thermodynamic study", Universal Journal of Environmental Research and Technology, vol.1, no.4, pp. 515-530, 2011.
A. R. Khataee, G. Dehghan, M. Zarei, A. Ebadi, M. Pourhassan, "Neural network modeling of biotreatment of triphenylmethane dye solution by a gren macroalgae", Chemical Engineering Research and Design, vol.89, pp. 172-178, 2011.
S. D. Balkin, D. K. J. Lin, "A neural network approach to response surface methodology", Communications in Statistics - Theory and Methods, vol.29, pp. 2215-2227, 2000.
S. Haykin, "Neural networks: a comprehensive foundation", Second Edition, Prentice Hall International, Inc., 1999.
A. Ghaffar, "Removal of lead(II) ions from aqueous solution under different physicochemical conditions using various sorbents", Arabian Journal for Science and Engineering, vol.33, no.1a, pp. 55-61, 2008.
M. Hercan, "Çörek Otu (Nigella Sativa L.) ile Ağır Metallerin Uzaklaştırılması", Kocaeli Üniversitesi, Fen Bilimleri Enstitüsü, Kimya Bölümü, Yüksek Lisans Tezi, 2012.
A. R. Khataee, M. B. Kasiri, Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis", Journal of Molecular Catalysis A: Chemical, vol.331, pp. 86-100, 2010.