Prognosis the Erosion-Corrosion Rates for Slurry Seawater Flow in Steel Pipeline Using Neural System

Abstract:

The work of this paper proposes a new technique for estimating and predicting erosion corrosion rate for laminar and turbulent flow in pipes. The technique depends on the neural networks Artificial Intelligent algorithms. Based on experimental results, which was applied to A 106 carbon-steel pipes, the networks are trained. Four velocities (Reynolds numbers) are used for laminar and four for turbulent regimes. The experiments also used seawater containment three different concentrations of sand. For each experiment the iron losses were measured six times in three hours’ time interval. The proposed estimating/predicting system managed to find values between the readings as well as predict the behavior of the erosion corrosion rate for extra three hours. The estimated/predicted results have been developed to find the transient zone between the Laminar and Turbulent flow regimes and investigating its effects on the erosion-corrosion behavior.

• Periodical: Advanced Materials Research (Volumes 1025-1026)
• Pages: 355-360
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1025-1026.355
• Online since: September 2014
• Authors: Ahmed El-Shenawy*, Mohamed Shehadeh
• Keywords: Erosion-Corrosion, Neural Network (NN), Sand-Seawater

References

[1] Petroleum Technology Alliance Canada: Research Priorities (Upstream Conventional Oil & Gas), Canada (2001).

[2] Macdonald D.D.: Corrosion of Steam Cycle Materials, Chapter 9, ASME Handbook on Water Technology for Thermal Power Systems. ASME, New York (1989).

[3] Y. Li, G.T. Burstein and I.M. Hutchings: Wear Vol. 181-183 (1995), p.70.

[4] M.M. Stack and N. Pungwiwat: Tribol. Int. Vol. 35 (2002), p.651.

[5] E.V. Hatzikos, L. Anastasakis, N. Bassiliades and I. Vlahavas: Applying neural networks with active neurons to sea-water quality measurements. 2nd Int Scientific Conf. on Computer Science, Plamenka Borovska, Sofoklis Schristofordis. IEEE computer Socity. Bulgaria section (2005).

[6] S. Mohamed, A.I. Shahata, E.S. Mohamed and O. Ahmed: Int. J. Appl. Engin. Res. Vol. 8, No. 11 (2013), p.1217.

[7] W. You and Y. Liu: Predicting the Corrosion Rates of Steels in Sea Water Using Artificial Neural Network, Fourth International Conference on Natural Computation. ( ICNC '08), Vol. 1, pp.18-20 (2008).

DOI: 10.1109/icnc.2008.481

[8] A. Sarrafan, S. Hamid, Z. Amir, A. Akbar and K. Abolghassem Zabihollah: J. Mech. Sci. Tech. Vol. 26, No. 4 (2012), p.1179.

[9] R. Guo, C. Q, Zhang, C.H. and M.P. Paidoussis: J. Fluids Struc. Vol. 26, No. 5 (2010), p.793.

[10] F. Culkin: The Major Constituents of Seawater, Chemical Oceanography, Vol. 1 (1965), p.121.

[11] Z. Ahmad, T.P. Ha and R.A.M. Noor: IIUM Eng. J. Vol. 9, No. 1 (2008), p.19.