Pipeline Leak Detection in Two Phase Flow Based on Fluctuation Pressure Difference and Artificial Neural Network (ANN)

Abstract:

Pipe network was an important part of the fluid transport infrastructure. On the other hand, the pipeline leak detection in two-phase flow using the flow and pressure parameters is very rarely studied. A system on the basis of the Artificial Neural Network (ANN) was proposed for detecting the pipeline leak for the two-phase plug flow by using the pressure difference measurement. In the present research, water-air mixture flows in pipe horizontal of 24 mm inner diameter. Artificial pipeline leak was modeled with the leak of solenoid valve on the bottom and top of pipe. Differential Pressure Transducer (DPT) was placed after the leak position and connected by the high-speed data acquisition. The fluctuations of the pressure difference signals were recorded as a time series of random data. The data of the combinations of the input flow rate, the pressure difference can be used to identify the pipeline leak in two-phase flow plug by using ANN. The results demonstrated a very good ability to the pipeline leak on two-phase flow.

• Periodical: Applied Mechanics and Materials (Volume 493)
• Pages: 186-191
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.493.186
• Online since: January 2014
• Authors: Budi Santoso, Indarto Indarto, Deendarlianto Deendarlianto
• Keywords: Artificial Neural Network (ANN), Fluctuations Pressure Difference, Pipeline Leak, Two-Phase Flow

References

[1] Scott, S. L., dan Barrufet, M. A. Worldwide Assessment of Industry Leak Detection Capabilitties for Single & Multiphase Pipelines. Project report to Minerals Management Service. (2003).

[2] Vítkovský, J. P., Lambert, M. F., Simpson A. R., dan Liggett, J. A. Experimental Observation And Analysis Of Inverse Transients For Pipeline Leak Detection. Journal Of Water Resources Planning And Management. Vol. November/December, 2007, pp.519-530.

DOI: 10.1061/(asce)0733-9496(2007)133:6(519)

[3] Wu M., dan Wang W. Application of wavelet to detect pipeline leak point. In Proceedings of the sixth International Conference on Intelligent Systems Design and Applications, (2006).

DOI: 10.1109/isda.2006.253711

[4] Feng, J., Zhang, H., dan Liu, D. Applications of fuzzy decision-making in pipeline leak localization. In Proceedings of IEEE International Conference on Fuzzy Systems. Vol. 2, 2004, p.599–603.

DOI: 10.1109/fuzzy.2004.1375464

[5] Hu R., Ye H., Wang G., dan. Lu C. Leak detection in pipelines based on PCA. Proceedings of the 8th IEEE International Conference on Control, Automation, Robotics and Vision. Vol. 3, 2004, p.1985–(1989).

DOI: 10.1109/icarcv.2004.1469466

[6] Emara-Shabaik, H. E., Khulief, A. E., dan Hussaini, I. A non-linear multiple-model state estimation scheme for pipeline leak detection and isolation. Journal of Systems and Control Engineering. Proc Instn Mech Engrs Vol. 216 part I, 2002, p.497.

DOI: 10.1177/095965180221600605

[7] Verde C., Visairo N., dan Gentil S. Two leaks isolation in a pipeline by transient response. Advances in Water Resources, Vol. 30, 2007, p.1711–1721.

DOI: 10.1016/j.advwatres.2007.01.001

[8] Silva, H., Marooka C. K., Guilherme, I. R., Fonseca, T. C., dan Mendes, J. R.P. Leak Detection In Petroleum Pipelines Using A Fuzzy System. Journal of Petroleum Science and Engineering. Vol. 49, 2005, p.223– 238.

DOI: 10.1016/j.petrol.2005.05.004

[9] Belsito S., Lombardi P., Andreussi P., and Banerjee S. Leak Detection in Liquefied Gas Pipelines by Artificial Neural Networks. AIChE Journal. Vol. 44, No. 12, 1998, pp.2675-2688.

DOI: 10.1002/aic.690441209

[10] Liu, N., dan Zhao Y. Application of Wavelet Packet and Support Vector Machine to Leak Detection in Pipeline. International Colloquium on Computing, Communication, Control, and Management, 2008, pp.66-69.

DOI: 10.1109/cccm.2008.346

[11] Gajbhiye, R. N., Kam, S. I., dan Hawkins. Leak Detection in Subsea Pipeline: A Mechanistic modeling approach with fixed pressure boundaries. Proceeding of offshore thecnology conference. Houston, Texas 5-8 May (2008).

DOI: 10.4043/19347-ms

[12] Kam S. I., 2010. Mechanistic modeling of pipeline leak detection at fixed inlet rate. Journal of Petroleum Science and Engineering. Vol. 70, 2008, pp.145-156.

DOI: 10.1016/j.petrol.2009.09.008

[13] Barradas, I., Luis E. Garza, Ruben Morales-Menendez, dan Adriana Vargas-Mart´ınez. Leaks Detection in a Pipeline Using Artificial Neural Networks, Springer, 2009, p.637–644.

DOI: 10.1007/978-3-642-10268-4_75

[14] Aguiar, F. G. The use of artificial neural networks for pattern detection of leaks in pipelines. MSc. Dissertation in Mechanical Engineering, Escola de Engenharia de São Carlos – Universidade de São Paulo, (2010).

DOI: 10.11606/9786587156095

[15] Kim, S., Yoo, W., dan Kang, I., 2001. Detection of leakage point via frequency analysis of a pipeline flow. KSME International Journal. Vol. 15, No. 2, pp.232-238.

[16] Guo, X., dan Yang, K. Pure frequency domain analysis for detecting pipeline leaks. Proceedings of 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS. October 20-23 2008, Hohai University, Nanjing, China, (2008).

DOI: 10.1007/978-3-540-89465-0

[17] Ghazali, M. F., Beck, S. B. M., Shucksmith, J. D., Boxall, J. B., dan Staszewski, W. J. Comparative study of instantaneous frequency based methods for leak detection in pipeline networks, Mechanical Systems and Signal Processing. Vol. 29, 2012, p.187.

DOI: 10.1016/j.ymssp.2011.10.011

[18] Santoso B., Indarto, Deendarlianto dan Thomas S. Widodo. Pipeline Leak Detection in Two Phase Flow Plug by Using Artificial Neural Network (ANN). Proceeding of Mechanical Engineering National Seminar XI (SNTTM XI) & Thermofluid IV Gadjah Mada University (UGM), Yogyakarta, 16-17 October (2012).

DOI: 10.4028/www.scientific.net/amm.493.186