Failure pressure prediction of pipeline with single corrosion defect using artificial neural network

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Mar 31, 2020
Kiu Toh Chin Thibankumar Arumugam Saravanan Karuppanan Mark Ovinis

Abstract

This paper describes the development and application of artificial neural network (ANN) to predict the failure pressure of single corrosion affected pipes subjected to internal pressure only. The development of the ANN model is based on the results of 71 sets of full-scale burst test data of pipe grades ranging from API 5L X42 to X100. The ANN model was developed using MATLAB’s Neural Network Toolbox with 1 hidden layer and 30 neurons. Before further deployment, the developed ANN model was compared against the training data and it produced a coefficient of determination of 0.99. The developed ANN model was further tested against a set of failure pressure data of API 5L X52 and X80 grade corroded pipes. Results revealed that the developed ANN model is able to predict the failure pressure with good margins of error (within 15%). Furthermore, the developed ANN model was used to determine the failure trends when corrosion defect length and depth were varied. Results from this failure trend analysis revealed that corrosion defect depth is the most significant parameter when it comes to corroded pipeline failure.

How to Cite

1.
Chin KT, Arumugam T, Karuppanan S, Ovinis M. Failure pressure prediction of pipeline with single corrosion defect using artificial neural network. PST [Internet]. 2020Mar.31 [cited 2020Jul.8];4(1(3):10-7. Available from: https://pipeline-science.com/index.php/PST/article/view/112

Downloads

Download data is not yet available.
Abstract 50 | PDF file Downloads 51

##plugins.themes.bootstrap3.article.details##

Keywords

pipe corrosion, single defect, internal pressure, artificial neural network

References
[1] L. Dai, D. Wang, T. Wang, Q. Feng, and X. Yang, “Analysis and comparison of long-distance pipeline failures,” Journal of Petroleum Engineering, vol. 2017, pp. 1–7, 2017.
[2] M. H. Mohd, B. J. Lee, Y. Cui, and J. K. Paik, “Residual strength of corroded subsea pipelines subject to combined internal pressure and bending moment,” Ships and Offshore Structures, pp. 1–11, 2015.
[3] S. M. Renato, L. D. G. Helder, M. B. A. Silvana, B. W. Ramiro, B. Nadege, R. M. L. Paulo, and Q. A. Edmundo, “Comparative studies for failure pressure prediction of corroded pipelines,” Engineering Failure Analysis, vol. 81, pp.178-192, 2017.
[4] A. D. M. Ferreira, A. H. T. Souza, R. B. Willmersdorf, S. M. B. Afonso and P. R. M. Lyra, “A general procedure to model and to analyze pipelines with real defects caused by corrosion measured in situ,” Rio Pipeline Conference and Exposition 2009, Rio de Janeiro, pp. 1–8, 2009.
[5] R. S. Motta, S. M. B. Afonso, R. B. Willmersdorf, P. R. M. Lyra and E.Q. Andrade, “Automatic Modeling and Analysis of Pipelines with Colonies of Corrosion Defects,” MECOM-CILAMCE, Buenos Aires, 2010.
[6] B31G ANSI/ASME (American National Standards Institute/American Society for Mechanical Engineers), Manual for Determining the Remaining Strength of Corroded Pipelines, 1991.
[7] DNV, Recommended Practice DNV RP-F101 Corroded Pipelines, Det Norske Veritas, Norway, 1999.
[8] C. Stolte, D. Tang and P. Hanrahan, “Query, Analysis, and Visualization of Hierarchically Structured Data Using Polaris,” Proc. ACM SIGKDD 2002.
[9] H. Jiawei and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann, 2011.
[10] J. L. Berral-Garcia, “A Quick View on Current Techniques and Machine Learning Algorithms for Big Data Analytics,” ICTON, 2016.
[11] X. Wu, X. Zhu, G. Wu and W. Ding, “Data mining with big data,” IEEE Transaction On Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107, 2014.
[12] R. Silva, J. Guerreiro, and A. Loula, “A study of pipe interacting corrosion defects using the FEM and neural networks,” Advances in Engineering Software, vol. 38, no. 11, pp. 868-875, 2007.
[13] G. De Masi, R. Vichi, M. Gentile, R. Bruschi, and G. Gabetta, “A neural network predictive model of pipeline internal corrosion profile,” IEEE SIMS, 2014.
[14] X. Xia, J. Nie, C. Davies, W. Tang, S. Xu, and N. Birbilis, “An artificial neural network for predicting corrosion rate and hardness of magnesium alloys,” Materials & Design, vol. 90, pp. 1034-1043, 2016.
[15] M. S. El-Abbasy, A. Senouci, T. Zayed, F. Mirahadi, and L. Parvizsedghy, “Artificial neural network models for predicting condition of offshore oil and gas pipelines,” Automation in Construction, vol. 45, pp. 50-65, 2014.
[16] Z. Zangenehmadar and O. Moselhi, “Assessment of remaining useful life of pipelines using different artificial neural networks models,” Journal of Performance of Constructed Facilities, vol. 30, no. 5, p. 04016032, 2016.
[17] M. M. Aydın, M. S. Yıldırım, O. Karpuz, and K. Ghasemlou, “Modeling of driver lane choice behavior with artificial neural networks (ANN) and linear regression (LR) analysis on deformed roads,” Computer Science & Engineering: An International Journal, vol. 4, no. 1, pp. 47–57, 2014.
[18] A. Cosham, P. Hopkins, and K. Macdonald, “Best practice for the assessment of defects in pipelines – Corrosion,” Engineering Failure Analysis, vol. 14, no. 7, pp. 1245–1265, 2007.
[19] O.H. Bjornoy, O. Rengard and S. Fredheim, “Residual strength of dented pipelines, DNV test results,” 10th International Offshore and Polar Engineering Conference Seattle, USA; May 28– June 2, 2000.
[20] D. Mok, R. Pick, and A. J. M. P. Glover, “Behavior of line pipe with long external corrosion,” Materials Performance, vol. 29, no. 5, 1990.
[21] A. C. Benjamin, R. D. Vieira, J. L. F. Freire, and J. T. P. D. Castro, “Burst tests on pipeline with long external corrosion,” Volume 2: Integrity and Corrosion; Offshore Issues; Pipeline Automation and Measurement; Rotating Equipment, 2000.
[22] D. S. Cronin, K. A. Roberts, and R. J. Pick, “Assessment of long corrosion grooves in line pipe,” Volume 1: Regulations, Codes, and Standards; Current Issues; Materials; Corrosion and Integrity, 1996.
[23] D. B. Noronha, A. C. Benjamin, and E. Q. D. Andrade, “Finite element models for the prediction of the failure pressure of pipelines with long corrosion defects,” 4th International Pipeline Conference, Parts A and B, 2002.
[24] D. Mok, R. Pick, A. Glover, and R. Hoff, “Bursting of line pipe with long external corrosion,” International Journal of Pressure Vessels and Piping, vol. 46, no.
2, pp. 195–216, 1991.
[25] J. Freire, R. Vieira, J. Castro, and A. Benjamin, “Part 3: Burst tests of pipeline with extensive longitudinal metal loss,” Experimental Techniques, vol. 30, no. 6,
pp. 60–65, 2006.
[26] A. C. Benjamin, J. L. F. Freire, R. D. Vieira, J. L. C. Diniz, and E. Q. D. Andrade, “Burst tests on pipeline containing interacting corrosion defects,” 24th International Conference on Offshore Mechanics and Arctic Engineering: Volume 3, 2005.
[27] M. Besel, S. Zimmermann, C. Kalwa, Köppe Theo, and A. Liessem, “Corrosion assessment method validation for high-grade line pipe,” 2010 8th International Pipeline Conference, Volume 4, 2010.
[28] J. Choi, B. Goo, J. Kim, Y. Kim, and W. Kim, “Development of limit load solutions for corroded gas pipelines,” International Journal of Pressure Vessels and Piping, vol. 80, no. 2, pp. 121–128, 2003.
[29] T. Arumugam “Residual strength analysis of corroded pipeline subjected to internal pressure and compressive stress”, M.S. thesis, Mech. Eng. Dept,
Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia, 2019.
Section
Original Work