Yapay Sinir Ağı Yaklaşımı ile Atık Portakal Kabuğundan Elde Edilen Grafen Benzeri Gözenekli Karbon Üzerinde Arsenik (V) Biyosorpsiyonunun Modellenmesi

Öz

Özellikle arsenikle kirlenmiş içme suyundan kaynaklanan ana tehditler, çevresel ve sosyal olarak önemli bir sorun olarak ortaya çıkmaktadır. Burada, atık portakal kabuğundan (OP) üretilmiş grafen benzeri gözenekli karbonun (GPC) arsenik (V) (As (V)) biyosorpsiyon performansı deneysel olarak incelenmiş ve biyosorpsiyon prosesini modellemek için yapay bir sinir ağı (YSA) yaklaşımı kullanılmıştır. Biyosorpsiyon prosesini optimize etmek için başlangıç pH’ı (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 10.0), başlangıç As (V) konsantrasyonu (5.0, 25.0, 50.0, 100.0, 250.0, 500.0 ve 750.0 mg.L-1), biyosorbent dozu (1.0, 2.0, 3.0, 4.0 and 5.0 g.L-1) ve temas süresi (0– 120.0 dakika) parametreleri incelenmiştir. Yüksek spesifik yüzey alanine (985 m2.g-1) ve gözenek hacmine (1.04 cm3.g-1) sahip GPC biyosorbentleri, 6.0 başlangıç pH değerinde, 2.0 g.L-1 biyosorbent dozunda ve 100 mg.L-1 başlangıç As(V) konsantrasyonunda, % 88.2 giderim verimi (denge biyosorpsiyon kapasitesi; 46.5 mg.g-1) ile üstün biyosorpsiyon kapasitesi göstermişlerdir. Bu çalışmada, GPC'lerin Ar (V) biyosorpsiyon performansının modellemesinde üç katmanlı bir YSA modeli geliştirilmiştir. YSA modelini doğrulamak için elde edilen deneysel veriler, YSA modelinde test verileri olarak kullanıldı. YSA modeli, Levengberg-Marquardt (LMA) algoritması, çıktı katmanında lineer transfer fonksiyonu (purelin) ve 12 nöronlu gizli katmanda tanjant sigma transfer fonksiyonu (tansig) ile gerçekleştirildi. Sonuçlar, deneysel verilerin YSA temelli verilerle uyum içerisinde olduğunu ve YSA yaklaşımının hedef değişkeni tahmin etmedeki yüksek doğruluğunu ortaya koydu. Geliştirilen YSA modeli, GPC tarafından As (V) biyosorpsiyon işleminin pilot ölçekli kullanımı için işlem koşullarının optimizasyonu için yararlıdır.

Anahtar Kelimeler

• Tarımsal Atık
• Arsenik(V) giderimi
• Yapay Sinir Ağları
• Biyosorpsiyon
• Modelleme
• Portakal Kabuğu

Modeling of Biosorption of Arsenic (V) On Waste Orange Peel Derived Graphene-Like Porous Carbon by Artificial Neural Network Approach

Abstract

The main threats from heavy metals specifically arsenic-contaminated drinking water have been emerging as an environmental and social crucial issue. Herein, the arsenic (V) (As(V)) biosorption performance of waste orange peel (OP) driven-graphene-like porous carbon (GPC) was investigated experimentally and an artificial neural network (ANN) approach was used to model the biosorption process. The initial pH (2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, and 10.0), initial As(V) concentration (25.0, 50.0, 100.0, 250.0, 500.0 and 750.0 mg.L-1), biosorbent dosage (1.0, 2.0, 3.0, 4.0 and 5.0 g.L-1), and contact time (0– 120.0 min) were investigated to optimize the biosorption process. The as-synthesized GPC biosorbents with a high specific surface area (985 m2.g-1) and pore volume (1.04 cm3.g-1) offered superior removal efficiency as 88.2% (equilibrium uptake capacity of 46.5 mg.g-1) at initial pH 6.0, initial As(V) concentration 100 mg.L-1, and biosorbent dosage 2.0 g.L-1. A three-layer ANN model was developed to forecast the Ar(V) biosorption performance of GPCs. Several experimental data points were considered as test data to validate the ANN model. The ANN model was performed with the Levengberg-Marquardt algorithm (LMA), linear transfer function (purelin) at the output layer, and a tangent sigmoid transfer function (tansig) in the hidden layer with 12 neurons. The values of coefficient of determination and mean squared error were calculated to be 0.9858 and 0.0014, respectively. The results revealed that the experimental data were in accordance with ANN-driven data as well as reveling the high accuracy of the ANN approach in estimating the target variable. The developed ANN model is useful for the optimization of process conditions for pilot-scale utilization of As(V) biosorption process by GPC.

Keywords

• Agricultural Waste
• Arsenic(V) removal
• Artificial Neural Network (ANN)
• Biosorption
• Modeling
• Orange Peel

Kaynakça

1. Abid, M., Niazi, N. K., Bibi, I., Farooqi, A., Ok, Y. S., Kunhikrishnan, A., ... & Arshad, M. (2016). Arsenic (V) biosorption by charred orange peel in aqueous environments. International journal of phytoremediation, 18(5), 442-449.
2. Aghav, R. M., Kumar, S., & Mukherjee, S. N. (2011). Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents. Journal of hazardous materials, 188(1-3), 67-77.
3. Almasri, D. A., Rhadfi, T., Atieh, M. A., McKay, G., & Ahzi, S. (2018). High performance hydroxyiron modified montmorillonite nanoclay adsorbent for arsenite removal. Chemical engineering journal, 335, 1-12.
4. Asfaram, A., Ghaedi, M., Azqhandi, M. A., Goudarzi, A., & Dastkhoon, M. (2016). Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye. RSC advances, 6(46), 40502-40516.
5. Beale, M. H., Hagan, M. T., & Demuth, H. B. (2012). Neural network toolbox™ user’s guide. In R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, www. mathworks. com.
6. Chandana, L., Krushnamurty, K., Suryakala, D., & Subrahmanyam, C. H. (2020). Low-cost adsorbent derived from the coconut shell for the removal of hexavalent chromium from aqueous medium. Materials Today: Proceedings, 26, 44-51.
7. Chattopadhyay, A., Singh, A. P., Singh, S. K., Barman, A., Patra, A., Mondal, B. P., & Banerjee, K. (2020). Spatial variability of arsenic in Indo-Gangetic basin of Varanasi and its cancer risk assessment. Chemosphere, 238, 124623.
8. Chow, H., Chen, H., Ng, T., Myrdal, P., & Yalkowsky, S. H. (1995). Using backpropagation networks for the estimation of aqueous activity coefficients of aromatic organic compounds. Journal of chemical information and computer sciences, 35(4), 723-728.

9. Çelebi, H. (2020). Recovery of detox tea wastes: Usage as a lignocellulosic adsorbent in Cr6+ adsorption. Journal of Environmental Chemical Engineering, 104310.
10. Das, S. K., Das, A. R., & Guha, A. K. (2007). A study on the adsorption mechanism of mercury on Aspergillus versicolor biomass. Environmental science & technology, 41(24), 8281-8287.
11. Dutta, S., Parsons, S. A., Bhattacharjee, C., Bandhyopadhyay, S., & Datta, S. (2010). Development of an artificial neural network model for adsorption and photocatalysis of reactive dye on TiO2 surface. Expert Systems with Applications, 37(12), 8634-8638.
12. Ebrahimi, B., Mohammadiazar, S., & Ardalan, S. (2019). New modified carbon based solid phase extraction sorbent prepared from wild cherry stone as natural raw material for the pre-concentration and determination of trace amounts of copper in food samples. Microchemical Journal, 147, 666-673.
13. Elemen, S., Kumbasar, E. P. A., & Yapar, S. (2012). Modeling the adsorption of textile dye on organoclay using an artificial neural network. Dyes and Pigments, 95(1), 102-111.
14. Fawzy, M., Nasr, M., Nagy, H., & Helmi, S. (2018). Artificial intelligence and regression analysis for Cd (II) ion biosorption from aqueous solution by Gossypium barbadense waste. Environmental Science and Pollution Research, 25(6), 5875-5888.
15. Ghaedi, M., Hosaininia, R., Ghaedi, A. M., Vafaei, A., & Taghizadeh, F. (2014a). Adaptive neuro-fuzzy inference system model for adsorption of 1, 3, 4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 131, 606-614.
16. Ghaedi, M., Ghaedi, A. M., Abdi, F., Roosta, M., Sahraei, R., & Daneshfar, A. (2014b). Principal component analysis-artificial neural network and genetic algorithm optimization for removal of reactive orange 12 by copper sulfide nanoparticles-activated carbon. Journal of Industrial and Engineering Chemistry, 20(3), 787-795.
17. Guo, Y., Tan, C., Sun, J., Li, W., Zhang, J., & Zhao, C. (2020). Porous activated carbons derived from waste sugarcane bagasse for CO2 adsorption. Chemical Engineering Journal, 381, 122736.
18. He, C., Lin, H., Dai, L., Qiu, R., Tang, Y., Wang, Y., ... & Ok, Y. S. (2020). Waste shrimp shell-derived hydrochar as an emergent material for methyl orange removal in aqueous solutions. Environment international, 134, 105340.
19. Irem, S., Islam, E., Mahmood Khan, Q., Anwar ul Haq, M., & Jamal Hashmat, A. (2017). Adsorption of arsenic from drinking water using natural orange waste: kinetics and fluidized bed column studies. Water Science and Technology: Water Supply, 17(4), 1149-1159.
20. Khaskheli, M. I., Memon, S. Q., Siyal, A. N., & Khuhawar, M. Y. (2011). Use of orange peel waste for arsenic remediation of drinking water. Waste and Biomass Valorization, 2(4), 423.
21. Karaman, C., Aktas, Z., Bayram, E., Karaman, O., & Kızıl, Ç. (2020). Correlation Between the Molecular Structure of Reducing Agent and pH of Graphene Oxide Dispersion On the Formation of 3D-Graphene Networks. ECS Journal of Solid State Science and Technology.
22. Kodal, Süheyla Pınar, and Zümriye Aksu. "Cationic surfactant-modified biosorption of anionic dyes by dried Rhizopus arrhizus." Environmental technology 38, no. 20 (2017): 2551-2561.
23. Liang, S., Guo, X., Feng, N., & Tian, Q. (2009). Application of orange peel xanthate for the adsorption of Pb2+ from aqueous solutions. Journal of Hazardous Materials, 170(1), 425-429.
24. Lu, D., Cao, Q., Li, X., Cao, X., Luo, F., & Shao, W. (2009). Kinetics and equilibrium of Cu (II) adsorption onto chemically modified orange peel cellulose biosorbents. Hydrometallurgy, 95(1-2), 145-152.
25. Ma, J., Li, T., Liu, Y., Cai, T., Wei, Y., Dong, W., & Chen, H. (2019). Rice husk derived double network hydrogel as efficient adsorbent for Pb (II), Cu (II) and Cd (II) removal in individual and multicomponent systems. Bioresource technology, 290, 121793.
26. Meng, Q., Qin, K., Ma, L., He, C., Liu, E., He, F., ... & Zhao, N. (2017). N-doped porous carbon nanofibers/porous silver network hybrid for high-rate supercapacitor electrode. ACS applied materials & interfaces, 9(36), 30832-30839.
27. Molga, E. J., & Westerterp, K. R. (1997). Neural network based model of the kinetics of catalytic hydrogenation reactions. In Studies in Surface Science and Catalysis (Vol. 109, pp. 379-388). Elsevier.
28. Mustafa, Y. A., Jaid, G. M., Alwared, A. I., & Ebrahim, M. (2014). The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP. Environmental Science and Pollution Research, 21(12), 7530-7537.
29. Naik, A. D., & Bhagwat, S. S. (2005). Optimization of an artificial neural network for modeling protein solubility. Journal of Chemical & Engineering Data, 50(2), 460-467.
30. Nia, R. H., Ghaedi, M., & Ghaedi, A. M. (2014). Modeling of reactive orange 12 (RO 12) adsorption onto gold nanoparticle-activated carbon using artificial neural network optimization based on an imperialist competitive algorithm. Journal of Molecular Liquids, 195, 219-229.
31. Omwene, P. I., Çelen, M., Öncel, M. S., & Kobya, M. (2019). Arsenic removal from naturally arsenic contaminated ground water by packed-bed electrocoagulator using Al and Fe scrap anodes. Process Safety and Environmental Protection, 121, 20-31.
32. Pathak, P. D., Mandavgane, S. A., & Kulkarni, B. D. (2016). Characterizing fruit and vegetable peels as bioadsorbents. Current Science, 2114-2123.
33. Rahaman, M. S., Basu, A., & Islam, M. R. (2008). The removal of As (III) and As (V) from aqueous solutions by waste materials. Bioresource technology, 99(8), 2815-2823.
34. Rozman, U., Kalčíková, G., Marolt, G., Skalar, T., & Gotvajn, A. Ž. (2020). Potential of waste fungal biomass for lead and cadmium removal: Characterization, biosorption kinetic and isotherm studies. Environmental Technology & Innovation, 100742.
35. Su, T., Guan, X., Tang, Y., Gu, G., & Wang, J. (2010). Predicting competitive adsorption behavior of major toxic anionic elements onto activated alumina: A speciation-based approach. Journal of hazardous materials, 176(1-3), 466-472.
36. Tran, T. H., Le, A. H., Pham, T. H., Nguyen, D. T., Chang, S. W., Chung, W. J., & Nguyen, D. D. (2020). Adsorption isotherms and kinetic modeling of methylene blue dye onto a carbonaceous hydrochar adsorbent derived from coffee husk waste. Science of The Total Environment, 725, 138325.
37. World Health Organization (WHO). (2011). Guidelines for drinking-water quality. WHO chronicle, 38(4), pp 186.