Investigation of spent caustic wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor

Document Type: Research note

Authors

1 Faculty of Chemical, Gas and Petroleum Engineering, Semnan University, Semnan, Iran

2 Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran

Abstract

In this research, photocatalytic degradation method has been introduced to clean up Spent Caustic of Olefin units of petrochemical industries (neutralized Spent Caustic by means of sulfuric acid) in the next step, adaptable method and effective parameters in the process performance have been investigated. Chemical oxygen demand (COD) was measured by the commercial zinc oxide that synthesized with precipitation synthesis method in a two-shell photoreactor. The percent of reduction of COD in the photocatalytic process was modeled using Box–Behnken design and artificial neural network techniques. It was concluded that the ANN was a more accurate method than the design of experiment. The effect of important parameters including oxidant dosage, aeration rate, pH, and catalyst loading was investigated. The results showed that all of the parameters, except pH, had positive effects on increasing COD removal. According to the obtained results, adsorption and photolysis phenomena had a negligible effect on COD removal.

Keywords

Main Subjects


[1]      Wang, G. S., Liau, H., Chen, H. W. and Yang, H. C., “Characteristics of natural organic matter degradation in water by UV/ H2O2 treatment”, Environmental Technology, 27, 277 (2006).

[2]      Ellis, C. E., “Wet air oxidation of refinery spent caustic”, Environmental Progress, 17 (1), 28 (2000).

[3]      Tania Mara, S. and Carlos, “Wet air oxidation of refinery spent caustic: A refinery case study”, NPRA Conference, San Antonio, Texas, (2000).

[4]      Sheu, S. H. and Weng, H. S., “Treatment of olefin plant spent caustic by combination of neutralization and fenton reaction”, Water Research, 35 (8), 2017 (2001).

[5]      Rodriguez, N., Hansen, H. K., Nunez, P. and Guzman, J., “Spent caustic oxidation using electro-generated Fenton's reagent in a batch reactor”, Journal of Environmental Science and Health, Part A, 43 (8), 952 (2008).

[6]      Nunez, P., Hansen, H. K., Rodriguez, N., Guzman, J. and Gutierrez, C., “Electrochemical generation of Fenton's reagent to treat spent caustic wastewater”, Separation Science and Technology, 44 (10), 2223 (2008).

[7]      Yu, Z. Z., Sun, D. Z., Li, C. H., Shi, P. F., Duan, X. D., Sun, G. R. and Liu, J. X., “UV-catalytic treatment of spent caustic from ethane plant with hydrogen peroxide and ozone oxidation”, Journal of Environmental Science, 16 (2), 272 (2004).

[8]      Hawari, A., Ramadan, H., Abu-Reesh, I. and Ouederni, M., “A comparative study of the treatment of ethylene plant spent caustic by neutralization and classical and advanced oxidation”, Journal of Environmental Management, 151, 105 (2015).

[9]      Abdulah, S. S., Hassan, M. A., Noor, Z. Z. and Aris, A., “Optimization of photo-fenton oxidation of sulfidic spent caustic by using response surface methodology”, Journal of Environmental Science, 25 (4), 231 (2011).

[10]  Chen, C., “Wet air oxidation and catalytic wet air oxidation for refinery spent caustic degradation”, J. Chem. Soc. Pak., 35 (2), 121 (2013).

[11]  Alaiezadeh, M., “Spent caustic wastewater treatment with electrical coagulation method”, The 1st International Conference Oil, Gas, Petrochemical and Power Plant, (2015).

[12]  Montgomery, D. C., “Design and analysis of experiments”, 6th Edition, John Wiley & Sons, (2013).

[13]  Behjoomanesh, M., Keyhani, M., Ganji-Azad, E., Izadmehr, M. and Riahi, S., “Assessment of total oil production in gas-lift process of wells using Box–Behnken design of experiments in comparison with traditional approach”, Journal of Natural Gas Science and Engineering, 27, 1455 (2015).

[14]  Fox, R. J., Elgart, D. and Davis, S. C., “Bayesian credible intervals for response surface optima”, Journal of Statistical Planning and Inference, 139, 2498 (2009).

[15]  Khataee, A. R. and Kasiri, M. B., “Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis”, J. Mol. Catal., A: Chem., 331, 86 (2010).

[16]  Sakkas, V. A., Calza, P., Medana, C., Villioti, A. E., Baiocchi, C., Pelizzetti, E. and Albanis, T., “Heterogeneous photocatalytic degradation of the pharmaceutical agent salbutamol in aqueous titanium dioxide suspensions”, Applied Catalysis, B: Environmental, 77, 135 (2007).

[17]  Sleiman, M., Vildozo, D., Ferronato, C. and Chovelon, J. M., “Photocatalytic degradation of azo dye Metanil Yellow: Optimization and kinetic modeling using a chemometric approach”, Applied Catalysis, B: Environmental, 77, 1 (2007).

[18]  Secula, M. S., Suditu, G. D., Poulios, I., Cojocaru, C. and Cretescu, I., “Response surface optimization of the photocatalytic decolorization of a simulated dyestuff effluent”, Chem. Eng. J., 141, 18 (2008).

[19]  Keramati, N., Nasernejad, B. and Fallah, N., “Photocatalytic degradation of styrene in aqueous solution: Central composite design optimization”, JDST, 35, 1543 (2014).

[20]  Zak, A. K., Wang, H. Z., Yousefi, R., Golsheikh, A. M. and Ren, Z. F., “Sonochemical synthesis of hierarchical ZnO nanostructures”, Ultrasonics sonochemistry, 20 (1), 395 (2013).

[21]   Rivera‐Utrilla, J., Bautista‐Toledo, I., Ferro‐García, M. A. and Moreno‐Castilla, C., “Activated carbon surface modifications by adsorption of bacteria and their effect on aqueous lead adsorption”, Journal of Chemical Technology and Biotechnology, 76, 1209 (2001).

[22]  Evans, M., Optimisation of manufacturing processes: A response surface approach, Maney Pub., (2003).

[23]  Nazzal, S. and Khan, M. A., “Response surface methodology for the optimization of ubiquinone self-nano emulsified drug delivery system”, AAPS Pharm Sci. Tech., 3, 23 (2002).

[24]  Ranjan, D., Mishra, D. and Hasan, S. H., “Bioadsorption of arsenic: An artificial neural networks and response surface methodological approach”, Ind. Eng. Chem. Res., 50, 9852 (2011).

[25]  Nelofer, R., Ramanan, R. N., Rahman, R. N. Z. R. A., Basri, M. and Ariff, A. B., “Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21”, Journal of Ind. Microbiol. Biotechnol., 39, 243 (2012).

[26]  Konstantinou, I. K. and Albanis, T. A., “Photocatalytic transformation of pesticides in aqueous titanium dioxide suspensions using artificial and solar light: Intermediates and degradation pathways”, Applied Catalysis, B: Environmental, 42, 319 (2003).

[27]  Konstantinou, I. K. and Albanis, T. A., “ -assisted photocatalytic degradation of azo dyes in aqueous solution: kinetic and mechanistic investigations: A review”, Applied Catalysis, B: Environmental, 49, 1 (2004).

[28]  Antonopoulou, M. and Konstantinou, I., “Photocatalytic degradation of pentachlorophenol by visible light Ν-F-  in the presence of oxalate ions: Optimization, modeling and scavenging studies”, Environmental Science and Pollution Research, 22, 9438 (2015).

[29]  Aisien, F. A., Amenaghawon, N. A. and Ekpenisi, E. F., “Photocatalytic decolourisation of industrial wastewater from a soft drink company”, JEAS, 9, 11 (2013).

[30]  Ranjan, D., Mishra, D. and Hasan, S. H., “Bioadsorption of arsenic: An artificial neural networks and response surface methodological approach”, Ind. Eng. Chem. Res., 50, 9852 (2011).