Modeling and Optimization of Photocatalytic Degradation Process of 4-Chlorophenol using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)


Modeling and Optimization of Photocatalytic Degradation Process of 4-Chlorophenol using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

P.S. Patel, V. Gandhi, M.P. Shah, T.S. Natarajan, K. Natarajan, R.J. Tayade

Present study focuses on modeling and optimization of photocatalytic degradation of 4-Chlorophenol (4-CP) using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Titanium nanotube synthesized by hydrothermal method were used and characterized using various physico-chemical and electronic techniques. The influence of operational parameters was investigated by employing face centred experimental design.. Response surface model was developed and its significance was evaluated by an ANOVA study. This process was also modeled by a novel approach of ANN. A two layer feed forward neural network with back propagation algorithm was employed. Architecture of ANN was optimised based on fractional factorial design. Optimum parameters were found to be eight hidden layer neurons, tansig transfer function in the hidden as well as output layer. Optimum conditions were found using the response surface model with initial concentration of 40 mg/L, catalyst dose of 184.84 mg/L, and initial pH of 3.94. From the ANN model, the optimum conditions were found to be with initial concentration of 42 mg/L, catalyst dose of 212.51 mg/L, and initial pH of 3.38. These optimum conditions were experimentally verified and reasonably good agreement was found between predicted and experimental conditions.

Photocatalytic Degradation, 4-Chlorophenol (4-CP), Response Surface Methodology (RSM), Artificial Neural Network (ANN)

Published online 2/25/2018, 28 pages


Part of Photocatalytic Nanomaterials for Environmental Applications

[1] S. Ahmed, M.G. Rasul, W.N. Martens, R. Brown, M.A. Hashib, Heterogeneous photocatalytic degradation of phenols in wastewater: A review on current status and developments, Desalination 261 (2010) 3-18.
[2] K. Natarajan, P. Singh, H.C. Bajaj, R.J. Tayade, Facile synthesis of TiO2/ZnFe2O4 nanocomposite by sol-gel auto combustion method for superior visible light photocatalytic efficiency, Korean Journal of Chemical Engineering, 33 (2016) 1788-1798.
[3] W.K. Jo, G.T. Park, R. J. Tayade, Synergetic effect of adsorption on degradation of malachite green dye under blue LED irradiation using spiral-shaped photocatalytic reactor, J. Chem. Technol. Biotechnol. 90 (2015) 2280-2289.
[4] M.B. Kasiri, H. Aleboyeh, A. Aleboyeh, Modeling and optimization of heterogeneous photo-fenton process with response surface methodology and artificial neural network, Environ. Sci. Technol. 42 (2008) 7970–7975.
[5] G.E.P. Box, K.B. Wilson, On the experimental attainment of optimum conditions, Journal of the Royal Statistical Society 13 (1951) 1–45.
[6] D.C. Montgomery, Design and analysis of experiments, 5th ed., New York: Wiley (2001).
[7] R.H. Myers, D.C. Montgomery, Response surface methodology: Process and product optimization using designed experiments, 2nd ed., New York: Wiley (2002).
[8] Y.R. Smith, A. Kar, V.R. Subramaniam, Investigation of physicochemical parameters that influence photocatalytic degradation of Methyl Orange over TiO2 nanotubes, Ind. Eng. Chem. Res. 48 (2007) 10268-10276.
[9] V.A. Sakkas, A.Md. Islam, C. Stalikas, T.A. Albanis, Photocatalytic degradation using design of experiments: A review and example of the Congo red degradation, J. Haz. Mat. 175 (2010) 33-44.
[10] M.S. Secula, G.D. Suditu, I. Poulios, C. Cojocaru, I. Cretescu, Response surface optimization of the photocatalytic decolorization of a simulated dyestuff effluent, Chem. Eng. J. 141 (2008) 18–26.
[11] C. Liyana-Pathirana, F. Shahidi, Optimization of extraction of phenolic compounds from wheat using response surface methodology, Food Chemistry 93 (2005) 47-56.
[12] Aleboyeh, N. Daneshvar, M.B. Kasiri, Optimization of C. I. Acid Red 14 azo dye removal by electrocoagulation batch process with response surface methodology, Chem. Eng. Process. 47 (2008) 827–832.
[13] M. Gfrerer, E. Lankmayr, Screening, optimization and validation of microwave-assisted extraction for the determination of persistent organochlorine pesticides, Analytica Chimica. Acta. 533 (2005) 203–211.
[14] D.M. Himmelblau, Accounts of experiences in the application of artificial neural networks in chemical engineering, Ind. Eng. Chem. Res. 47 (2008) 5782-5796.
[15] L. Jin, P. Nikiforuk, M. Gupta, Direct adaptive output tracking control using multilayered neural networks, IEEE Proc, 140 (1993) 393–398.
[16] B. Zhao, Y. Su, Artificial neural network-based modelling of pressure drop coefficient for cyclone separators, Chemical Engineering Research and Design 88 (2010) 606–613.
[17] K. Elsayed, C. Lacor, Modeling analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches, Powder Technology 212 (2011) 115–133.
[18] J.A. Burns, G.M. Whitesides, Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition, Chem. Rev. 93 (1993) 2583-2601.
[19] H.M. Cartwright, Applications of artificial intelligence in chemistry, Oxford University Press: Oxford, U.K., 1993.
[20] T. Aoyama, Y. Suzuki, H. Ichikawa, Neural networks applied to structure-activity relationships, J. Med. Chem. 33 (1990) 905-908.
[21] N. Quis, T. Sejnowski, Predicting the secondary structure of globular proteins using neural networks models. J. Mol. Biol. 202 (1998) 865-884.
[22] R. Baratti, G. Vacca, A. Servida, Neural networks modeling of distillation column. Hydrocarbon Process. (1995) 35-38.
[23] M.L. Thompson, M.A. Kramer, Modeling chemical process using prior knowledge and neural networks. AIChE J. 40 (1994) 1328-1340.
[24] J.W. Prasad, S.S. Bhagwat, Simple neural network models for prediction of physical properties of organic compounds. Chem. Eng. Technol. 25 (2002) 1041-1046.;2-5
[25] D. Howard, M. Beale, M. Hagan, Neural network toolbox for use with MATLAB® user’s guide, Version 5, Mathworks Inc, 2006.
[26] A.R. Khataee, Photocatalytic removal of C.I. Basic Red 46 on immobilized TiO2 nanoparticles: Artificial neural network modelling, Environ. Techno. 8 (2009) 1155-1168.
[27] A.R. Khataee, O. Mirzajani, UV/peroxydisulfate oxidation of C. I. Basic Blue 3: Modeling of key factors by artificial neural network, Desalination 251 (2010) 64–69.
[28] D. Salari, N. Daneshvar, F. Aghazadeh, A.R. Khataee, Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H2O2 process, J. Haz. Mat. B. 125 (2005) 205-210.
[29] S. Gob, E. Oliveros, S.H. Bossmann, A.M. Braun, R. Guardani, C.A.O. Nascimento, Modeling the kinetics of a photochemical water treatment process by means of artifical neual networks, Chem. Eng. Process. 38 (1999) 373–382.
[30] J.E.F. Moraes, F.H. Quina, C.A.O. Nascimeto, D.N. Silva, O. Chiavone-Filho, Treatment of saline wastewater contaminated with hydrocarbons by the photo-fenton process, Environ. Sci. Technol. 38 (2004) 1183–1187.
[31] V.K. Pareek, M.P. Brungs, A.A. Adesina, R. Sharma, Artificial neural network modeling of a multiphase photodegradation system, J. Photochem. Photobiol. A 149 (2002) 139–146.
[32] Aleboyeh, M.B. Kasiri, M.E. Olya, H. Aleboyeh, Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks, Dyes and Pigments 77 (2008) 288–294.
[33] Duran, J.M. Monteagudo, M. Mohedano, Neural networks simulation of photo-Fenton degradation of Reactive Blue 4. Appl. Catal., B: Environ. 65 (2006) 127–134.
[34] G. Benoit, Degradation of chlorophenols by ozone and light, Fresenius Environ. Bull. 3 (1994) 331–338.
[35] M.S. Goswami, R.P. Singh, Kinetics of chlorophenol degradation by benzoate-induced culture of Rhodococcus erythropolis M1, World J. Microbiol. Biotechnol. 18 (2002) 779–783.
[36] M. Hugul, I.Boz, R. Apak, Photocatalytic decomposition of 4-CP over oxide catalysts, J. Haz. Mat. B 64 (1999) 313–322.
[37] T. Poznyak, J. Vivero, Degradation of aqueous phenol and chlorinated phenols by ozone, Ozone: Sci. and Eng. (2005) 447-458.
[38] M.H. Priya, G. Madras, Kinetics of photocatalytic degradation of chlorophenol, nitrophenol, and their mixtures, Ind. Eng. Chem. Res. 45 (2006) 482-486.
[39] N. Venkatachalam, M. Palanichamy, V. Murugesan, Sol–gel preparation and characterization of alkaline earth metal doped nano TiO2: Efficient photocatalytic degradation of 4-chlorophenol, J. Mol. Cat. A: Chem. 273 (2007) 177–185.
[40] R.J. Tayade, D.L. Key, Synthesis and characterization of titanium dioxide nanotubes for photocatalytic degradation of aqueous nitrobenzene in the presence of sunlight, Materials Science Forum 657 (2010). 62-74