Modeling and Experimental Prediction of Wastewater Treatment Efficiency in Oil Refineries Using Activated Sludge Process

Document Type : Original Paper

Authors

1 Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran

2 Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran

Abstract

In this study, activated sludge process for wastewater treatment in a refinery was investigated. For such purpose, a laboratory scale rig was built. The effect of several parameters such as temperature, residence time, effect of Leca (filling-in percentage of the reactor by Leca) and UV radiation on COD removal efficiency were experimentally examined. Maximum COD removal efficiency was obtained to be 94% after final testing. An artificial neural network (ANN) was applied to evaluate the effect of operational parameters on the efficiency as the next step. A two-layered ANN provided the best results, using Levenberg–Marquardt back propagation learning algorithm (trainLM) in which tansig and purelin used as transfer functions in the hidden and output layers. Furthermore, the application of three neurons in the hidden layer caused to gratify network training while overfitting was hindered. ANN model, provided a good estimation for correlation coefficient and the mean square error (MSE) which calculated 0.997  and 0.5 × 10-3 respectively.

Keywords


[1] Bosch, H., Klcerebezem, G.J. and Mars, P. (1976). “Activated carbon from activated sludge.”Water Pou.Cont Fed, Vol. 48, pp. 551-651.
[2] Zittwitz, M., Gerhardt, M. and Ringpfeil, M. “Practical experience from commercical in-Situ bioremediation in cases of cable insulating oil and Tri- / perchloroethyleneBioprct GmbH.” RudowerChaussee 29, D-12489 Berlin, Germany.
[3] Tellez, G.T., Nirmalakhandan, N. and Gardea-Torresdey, J.L. (2002). “Performance evaluation of an activated sludge system for removing petroleum hydrocarbons from oil field produced water.” Adv. Environ. Res, Vol. 6, pp. 455–470.
[4] Freire, D.D.C., Cammarota, M.C. and Sant’Anna, G.L. (2001). “Biological treatment of oil field wastewater in a sequencing batch reactor.”Environ. Technol., Vol. 22, pp. 1125–1135.
[5] Palmer, L.L., Beyer, A.H. and Stock, J. (1981). “Biological oxidation of dissolved compounds in oilfield produced water by a field pilot biodisk.” J. Petrol. Technol., Vol. 33, pp. 1136–1140.
[6] Zhao, X., Wang, Y., Ye, Z., Borthwick, A.G.L. and Ni, J. (2006). “Oil field wastewater treatment in biological aerated filter by immobilized microorganisms.” Process Biochem., Vol. 41, pp. 1475–1483.
[7] Jackson, L.M. and Myers, J.E. (2003). “Design and construction of pilot wetlands for produced-water treatment.” in: SPE Annual Technical Conference and Exhibition Denver, Colorado, USA, 5–8 October.
[8] Hommel, R.K. (1990). “Formation and physiological role of biosurfactants produced by hydrocarbon-utilizing microorganisms.”Biodegradation Vol. 1, pp. 107–119.
[9] Gallagher, J.R. “Anaerobic biological treatment of produced water.” available at: http://www.energystorm.us/Anaerobic Biological Treatment of Produced Water-r 54822.html, (2001).
[10] Gurden, C. and Cramwinckel, J. (2000). “Application of reedbed technology in production water management.” in: SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Stavanger, Norway, 26–28 June.
[11] Al Mahruki, A. and Alloway, B. (2006). “The use of reed-bed technology for treating oil production waters in the sultanate of Oman.” in: SPE International Health, Safety & Environment Conference, Abu Dhabi, UAE, 2–4 April.
[12] Metcalf and Eddy Inc. (2003). Wastewater Eengineering, disposal &resue, 4th edition, McGraw-Hill Pub. Co., New York.
[13] junkins, R., Deeny, K. and Eckoff, Th. (1983). The Activated Sludge Process: fundamentals of operation, Ann Arbor, Mich.Science.
[14] Lenore, S. C., Arnold, E.G. and Andrew, D.E. (1999). Standard Methods for the Examination of Water and Wastewater, 20th edition, American Public Health Association, Washington, D.C, USA.
[15] Fausett, L. (1994). Fundamentals of Neural Network, New Jersey, Prentice Hall.
[16] Aleboyeh, A., Kasiri, M. B., Olya, M. E. and Aleboyeh, H. (2008).“Prediction of azo dye decolorization by UV/H2O2 using artificial neural networks.”Dyes and Pigments, Vol. 77, pp. 288–294.
[17] Lim, F. (1994). Neural Networks in Computer Intelligence, McGraw-Hill International Series in Computer Science.