Study of a Crude Oil Desalting Plant of the National Iranian South Oil Company in Gachsaran by Using Artificial Neural Networks

Authors: H. Kiani, S. Moradi, B. Soltani Soulgani, S. Mousavian

Abstract:

Desalting/dehydration plants (DDP) are often installed in crude oil production units in order to remove water-soluble salts from an oil stream. In order to optimize this process, desalting unit should be modeled. In this research, artificial neural network is used to model efficiency of desalting unit as a function of input parameter. The result of this research shows that the mentioned model has good agreement with experimental data.

Keywords: Desalting unit, Crude oil, Neural Networks, Simulation, Recovery, Separation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1089573

References:

[1] Leila Vafajoo, Kamran Ganjian, Moslem Fattahi, "Influence of key parameters on crude oil desalting: An experimental and theoretical study”, Journal of Petroleum Science and Engineering 90–91 (2012) 107–111.
[2] Arnold, K., Stewart, M., 2008. Surface Production Operations: Design of Oil Handling Systems and Facilities, Third Edition Elsevier, Amsterdam 351–456.
[3] K. Mahdi, R. Gheshlaghi, G. Zahedi, A. Lohi, "Characterization and modeling of a crude oil desalting plant by a statistically designed approach”, Journal of Petroleum Science and Engineering 61 (2008) 116–123.
[4] Bartley, D., 1982. Heavy crude stocks pose desalting problems. Oil Gas J. 80 (5), 117–124.
[5] Burris, D.R., 1978. How to design an efficient desalting system. World Oil 186 (7), 150–156.
[6] Anon, A., 1983. Static mixer improves desalting efficiency. Oil Gas J. 81 (42), 128–129.
[7] Agar, 2000. Agar's solution to desalting systems. Agar group. http://www.agarcorp.com, ApptNote3.htm System 3.
[8] Al-Otaibi, M., 2004. Modelling and optimizing of crude oil desalting process. Ph.D. Thesis, Loughborough University, Leicestershire, England.
[9] Al-Otaibi, M., Elkamel, A., Nassehi, V., Abdul-Wahab, S.A., 2005. A computational intelligence based approach for the analysis and optimization of a crude oil desalting and dehydration process. Energy & Fuels 19 (6), 2526–2534.
[10] U. R. Chaudhuri, D. Ghosh,” Modeling & Simulation of a Crude Petroleum Desalter using Artificial Neural Network”, Petroleum Science and Technology, 27:1233–1250, 2009.
[11] Hoskins J. C., and Himmelblau, D. M. (1988). Artificial neural network models of knowledge representation in chemical engineering. Comp and Chem Eng. 12:881–890.
[12] Mavrovountotis, M. L. (1990). Artificial Intelligence in Process Engineering. Fault Detection and Diagnosis using Artificial Neural Network. College Park, MD: Academic Press.