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ZnO/Mg-Al Layered Double Hydroxides as a Photocatalytic Bleaching of Methylene Orange - A Black Box Modeling by Artificial Neural Network

Department of Applied Chemistry, Faculty of Chemistry, Urmia University, Urmia, Iran, Islamic Republic of

Received: 28 Jun 2016; Published: 11 Oct 2016.
Open Access Copyright (c) 2016 by Authors, Published by BCREC Group under

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The paper reports the development of ZnO-MgAl layered double hydroxides as an adsorbent-photo catalyst to remove the dye pollutants from aqueous solution and the experiments of a photocatalytic study were designed and modeled by response surface methodology (RSM) and artificial neural network (ANN). The co-precipitation and urea methods were used to synthesize the ZnO-MgAl layered double hydroxides and FT-IR, XRD and SEM analysis were done for characterization of the catalyst.The performance of the ANN model was determined and showed the efficiency of the model in comparison to the RSM method to predict the percentage of dye removal accurately with a determination coefficient (R2) of 0.968. The optimized conditions were obtained as follows: 600 oC, 120 min, 0.05 g and 20 ppm for the calcination temperature, irradiation time, catalyst amount and dye pollutant concentration, respectively. 

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Keywords: layered double hydroxide; photo catalyst; artificial neural network; response surface methodology; nano composite

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  1. Le Mire, G.J., Heinig Jr, C.F., Le Mire, E.A (1985). System and method for water purification. Google Patents
  2. Al-Rasheed, R.A. (2005). Water treatment by heterogeneous photocatalysis an overview. In 4th SWCC acquired Experience Symposium held in Jedda
  3. Byrne, J.A., Fernandez-Ibanez, P.A., Dunlop, P.S., Alrousan, D., Hamilton, J.W. (2011). Photocatalytic enhancement for solar disinfection of water: a review. Int. J. Photoeng., 2011: 1-12
  4. Antonopoulou, M., Konstantinou, I. (2013). Optimization and Modeling of the Photocatalytic Degradation of the Insect Repellent DEET in Aqueous TiO2 Suspensions. CLEAN – Soil, Air, Water, 41: 593-600
  5. Zhao, J., Yang, X. (2003). Photocatalytic oxidation for indoor air purification: a literature review. Building and Environment, 38: 645-654
  6. Mozia, S., Tomaszewska, M., Morawski, A.W. (2005). Photocatalytic degradation of azo-dye Acid Red 18. Desalination, 185: 449-456
  7. Kitsiou, V., Filippidis, N., Mantzavinos, D., Poulios, I. (2009). Heterogeneous and homogeneous photocatalytic degradation of the insecticide imidacloprid in aqueous solutions. Applied Catalysis B: Environmental, 86: 27-35
  8. Bubacz, K., Kusiak-Nejman, E., Tryba, B., Morawski, A.W. (2013). Investigation of OH radicals formation on the surface of TiO2/N photocatalyst at the presence of terephthalic acid solution. Estimation of optimal conditions. Journal of Photochemistry and Photobiology A: Chemistry, 261: 7-11
  9. Jiang, W., Joens, J.A., Dionysiou, D.D., O'Shea, K.E. (2013). Optimization of photocatalytic performance of TiO2 coated glass microspheres using response surface methodology and the application for degradation of dimethyl phthalate. Journal of Photochemistry and Photobiology A: Chemistry, 262: 7-13
  10. Saien, J., Soleymani, A.R., Bayat, H. (2012). Modeling Fentonic advanced oxidation process decolorization of Direct Red 16 using artificial neural network technique. Desalination and Water Treatment, 40: 174-182
  11. Daraei, H., Maleki, A., Mahvi, A.H., Zandsalimi, Y., Alaei, L., Gharibi, F. (2014). Synthesis of ZnO nano-sono-catalyst for degradation of reactive dye focusing on energy consumption: operational parameters influence, modeling, and optimization. Desalination and Water Treatment, 52: 6745-6755
  12. Yang, L., Fan, B., Cui, X., Shi, X., Li, R. (2015). Solvent-free aerobic oxidation of ethylbenzene over Mn-containing silylated MgAl layered double hydroxides. Journal of Industrial and Engineering Chemistry, 21: 689-695
  13. Sun, X., Imai, T., Sekine, M., Higuchi, T., Yamamoto, K., Kanno, A., Nakazono, S. (2014). Adsorption of phosphate using calcined Mg3–Fe layered double hydroxides in a fixed-bed column study. Journal of Industrial and Engineering Chemistry, 20: 3623-3630
  14. Andreozzi, R., Caprio, V., Insola, A., Marotta, R. (1999). Advanced oxidation processes (AOP) for water purification and recovery. Catalysis Today, 53: 51-59
  15. Shan, R.-r., Yan, L.-g., Yang, Y.-m., Yang, K., Yu, S.-j., Yu, H.-q., Zhu, B.-c., Du, B. (2015). Highly efficient removal of three red dyes by adsorption onto Mg–Al-layered double hydroxide. Journal of Industrial and Engineering Chemistry, 21: 561-568
  16. Nitoi, I., Oancea, P., Raileanu, M., Crisan, M., Constantin, L., Cristea, I. (2015). UV–VIS photocatalytic degradation of nitrobenzene from water using heavy metal doped titania. Journal of Industrial and Engineering Chemistry, 21: 677-682
  17. Nezamzadeh-Ejhieh, A., Khodabakhshi-Chermahini, F. (2014). Incorporated ZnO onto nano clinoptilolite particles as the active centers in the photodegradation of phenylhydrazine. Journal of Industrial and Engineering Chemistry, 20: 695-704
  18. Cho, I.-H., Zoh, K.-D. (2007). Photocatalytic degradation of azo dye (Reactive Red 120) in TiO2/UV system: Optimization and modeling using a response surface methodology (RSM) based on the central composite design. Dyes and Pigments, 75: 533-543
  19. Ayodele, B.V., Cheng, C.K. (2015). Modelling and optimization of syngas production from methane dry reforming over ceria-supported cobalt catalyst using artificial neural networks and Box–Behnken design. Journal of Industrial and Engineering Chemistry, 32: 246-258
  20. Badrnezhad, R., Mirza, B. (2014). Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach. Journal of Industrial and Engineering Chemistry, 20: 528-543
  21. Shabanzadeh, P., Yusof, R., Shameli, K. (2015). Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites. Journal of Industrial and Engineering Chemistry, 24: 42-50
  22. Khajeh, M., Kaykhaii, M., Sharafi, A. (2013). Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples. Journal of Industrial and Engineering Chemistry, 19: 1624-1630
  23. Shojaeimehr, T., Rahimpour, F., Khadivi, M.A., Sadeghi, M. (2014). A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA). Journal of Industrial and Engineering Chemistry, 20: 870-880
  24. Istadi, I., Anggoro, D.D., Buchori, L., Utami, I., Solikhah, R. (2012). Process parameters optimization of potential SO42-/ZnO acid catalyst for heterogeneous transesterification of vegetable oil to biodiesel. Bulletin of Chemical Reaction Engineering and Catalysis, 7 (2): 150-157
  25. Istadi, I., Amin, N.A.S. (2005). A hybrid numerical approach for multi-responses optimization of process parameters and catalyst compositions in CO2 OCM process over CaO-MnO/CeO2 catalyst. Chemical Engineering Journal, 106(3): 213-227
  26. Hafizi, A., Ahmadpour, A., Koolivand-Salooki, M., Heravi, M.M., Bamoharram, F.F. (2013). Comparison of RSM and ANN for the investigation of linear alkylbenzene synthesis over H14[NaP5W30O110]/SiO2 catalyst. Journal of Industrial and Engineering Chemistry, 19: 1981-1989
  27. Khataee, A.R., Zarei, M., Moradkhannejhad, L. (2010). Application of response surface methodology for optimization of azo dye removal by oxalate catalyzed photoelectro-Fenton process using carbon nanotube-PTFE cathode. Desalination, 258: 112-119
  28. Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M., Kowalski, S.M. (2004). Response surface methodology: a retrospective and literature survey. Journal of Quality Technology, 36: 53
  29. Khataee, A.R., Zarei, M., Moradkhannejhad, L. (2010). Application of response surface methodology for optimization of azo dye removal by oxalate catalyzed photoelectro-Fenton process using carbon nanotube-PTFE cathode. Desalination, 258:112-119
  30. Bezerra, M.A., Santelli, R.E., Oliveira, E.P., Villar, L.S., Escaleira, L.A. (2008). Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta, 76: 965-977
  31. Shao, P., Jiang, S., Ying, Y. (2007). Optimization of molecular distillation for recovery of tocopherol from rapeseed oil deodorizer distillate using response surface and artificial neural network models. Food and Bioproducts Processing, 85: 85-92
  32. Kempthorne, O. (1952). The design and analysis of experiments……….
  33. Corma, A., Serra, J.M., Serna, P., Valero, S., Argente, E., Botti, V. (2005). Optimisation of olefin epoxidation catalysts with the application of high-throughput and genetic algorithms assisted by artificial neural networks (softcomputing techniques). Journal of Catalysis, 229: 513-524
  34. Bas, D., Boyaci, I.H. (2007). Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. Journal of Food Engineering, 78: 846-854
  35. Huitao, L., Ketai, W., Hongping, X., Xingguo, C., Zhide, H. (2002). Application of experimental design and artificial neural networks to separation and determination of active components in traditional Chinese medicinal preparations by capillary electrophoresis. Chromatographia, 55: 579-583
  36. Frías‐García, S., Sánchez, M.J., Rodríguez‐Delgado, M.Á. (2004). Optimization of the separation of a group of triazine herbicides by micellar capillary electrophoresis using experimental design and artificial neural networks. Electrophoresis, 25: 1042-1050
  37. Boti, V.I., Sakkas, V.A., Albanis, T.A. (2009). An experimental design approach employing artificial neural networks for the determination of potential endocrine disruptors in food using matrix solid-phase dispersion. Journal of Chromatography A, 1216: 1296-1304
  38. Papadopoulos, V.D., Beligiannis, G.N., Hela, D.G. (2011). Combining experimental design and artificial neural networks for the determination of chlorinated compounds in fish using matrix solid-phase dispersion. Applied Soft Computing, 11: 5155-5164
  39. Agatonovic-Kustrin, S., Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis, 22: 717-727
  40. Desai, K.M., Survase, S.A., Saudagar, P.S., Lele, S.S., Singhal, R.S. (2008) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: Case study of fermentative production of scleroglucan. Biochemical Engineering Journal, 41: 266-273
  41. Yetilmezsoy, K., Demirel, S. (2008). 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, 153: 1288-1300
  42. Basha, C.A., Saravanathamizhan, R., Manokaran, P., Kannadasan, T., Lee, C.W. (2012). Photoelectrocatalytic Oxidation of Textile Dye Effluent: Modeling Using Response Surface Methodology. Industrial & Engineering Chemistry Research, 51: 2846-2854
  43. Kasiri, M.B., Aleboyeh, H., Aleboyeh, A. (2008). Modeling and Optimization of Heterogeneous Photo-Fenton Process with Response Surface Methodology and Artificial Neural Networks. Environmental Science & Technology, 42: 7970-7975
  44. Goel, J., Kadirvelu, K., Rajagopal, C., Garg, V.K. (2005) Removal of Lead(II) from Aqueous Solution by Adsorption on Carbon Aerogel Using a Response Surface Methodological Approach. Industrial & Engineering Chemistry Research, 44: 1987-1994
  45. Ghaffari-Moghaddam, M., Yekke-Ghasemi, Z., Khajeh, M., Rakhshanipour, M., Yasin, Y. (2014) Application of response surface methodology in enzymatic synthesis: A review. Russian Journal of Bioorganic Chemistry, 40: 252-262
  46. Smits, J., Melssen, W., Buydens, L., Kateman, G. (1994) Using artificial neural networks for solving chemical problems: Part I. Multi-layer feed-forward networks. Chemometrics and Intelligent Laboratory Systems, 22:165-189
  47. Wang, S.-C. (2003). Artificial neural network. In Interdisciplinary Computing in Java Programming; Springer
  48. Baş, D., Boyacı, İ.H. (2007) Modeling and optimization II: Comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction. J. Food Eng. 78: 846-854
  49. Kocjančič, R., Zupan, J. (1997). Application of a Feed-Forward Artificial Neural Network as a Mapping Device. J. Chem. Inform. Comput. Sci., 37: 985-989
  50. Istadi, I., Amin, N.A.S. (2006). Hybrid artificial neural network-genetic algorithm technique for modeling and optimization of plasma reactor. Industrial & Engineering Chemistry Research. 45(20): 6655-6664

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