Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Authors

  • Wei Yuan
  • Liang Zhang
  • Hao Ren

DOI:

https://doi.org/10.54097/1th80z38

Keywords:

Improved support vector machine, Submarine oil and gas pipelines, Pipeline internal corrosion, Machine learning, Finite element method.

Abstract

In this study, the failure pressure of submarine oil and gas pipelines was predicted by using five commonly used machine learning models and derivative models. Firstly, an efficient local time-intensive finite element algorithm is constructed and used to generate a machine learning database. Secondly, in order to improve the accuracy of machine learning algorithm, the frontier optimization algorithm is improved to form a new predictive regression model IAEO-SVM model. To compare the accuracy of commonly used machine learning models and IAEO-SVM models, a comprehensive evaluation standard consisting of k-fold cross-validation and three statistical indicators was used. Finally, the influence of seawater depth and geometric factors of corrosion defects on the blasting pressure of submarine oil and gas pipelines was comprehensively analyzed through the high-dimensional surface built by the IAEO-SVM model. The IAEO-SVM model shows superior stability and accuracy compared to the comparison model, as demonstrated by its MSE’ of 0.0482, R2’ of 0.9982, MAE’ of 0.1295, and SD of 0.2198. The high-dimensional surface obtained through inversion shows a linear relationship between seawater depth and failure pressure. Meanwhile, the width of corrosion defects has a significant impact on failure pressure, accounting for up to 23% and thus cannot be overlooked.

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References

G. Teran, S. Capula-Colindres, J.C. Velazquez, M.J. Fernandez-Cueto, D. Angeles-Herrera, H. Herrera-Hernandez, Failure Pressure Estimations for Pipes with Combined Corrosion Defects on the External Surface: A Comparative Study, International Journal of Electrochemical Science, (2017) 10152-10176.

C. Bao-ping, Z. Yan-ping, Y. Xiao-bing, G. Chun-tan, L. Yong-hong, C. Guo-ming, L. Zeng-kai, J.I. Ren-jie, A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study, CHINA OCEAN ENGINEERING, (2020) 597-607.

M. Pourahmadi, M. Saybani, Reliability analysis with corrosion defects in submarine pipeline case study: Oil pipeline in Ab-khark island., OCEAN ENGINEERING, (2022).

L. Zhang, Y. Gao, Improvement of BP neural network model and its application in predicting external corrosion rate of submarine pipeline, Journal of Safety and Environment, (2022). https://doi.org/10.13637/j.issn.1009-6094.2022.1608.

J.A. Gao, P.A. Yang, X.A.L.D. Li, J.A. Zhou, J.A. Liu, Analytical prediction of failure pressure for pipeline with long corrosion defect., OCEAN ENGINEERING, (2019) 106497.

M. Abyani, M.R. Bahaari, A new approach for finite element based reliability evaluation of offshore corroded pipelines., International Journal of Pressure Vessels & Piping, (2021) 104449.

M. Sun, H. Zhao, X. Li, J. Liu, Z. Xu, A new evaluation method for burst pressure of pipeline with colonies of circumferentially aligned defects., OCEAN ENGINEERING, (2021) 108628.

Y. Chen, F. Hou, S. Dong, L. Guo, T. Xia, G. He, Reliability evaluation of corroded pipeline under combined loadings based on back propagation neural network method, OCEAN ENGINEERING, (2022) 111910.

B. Ma, J. Shuai, J. Wang, K. Han, Analysis on the Latest Assessment Criteria of ASME B31G-2009 for the Remaining Strength of Corroded Pipelines, Journal of Failure Analysis and Prevention, (2011) 666-671.

R. Zhou, X. Gu, X. Luo, Residual strength prediction of X80 steel pipelines containing group corrosion defects, OCEAN ENGINEERING, (2023) 114077.

B.M.B. Ma, J.S.J. Shuai, D.L.D. Liu, K.X.K. Xu, Assessment on failure pressure of high strength pipeline with corrosion defects, ENGINEERING FAILURE ANALYSIS, (2013) 209-219.

W. Xu, C.B. Li, J. Choung, J. Lee, Corroded pipeline failure analysis using artificial neural network scheme, ADVANCES IN ENGINEERING SOFTWARE, (2017) 255-266.

K.Y. Kang, 2022. Research on Residual Strength of Corrosion Defect Pressure Piping Based on ANSYS. Shenyang University of Chemical Technology. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD-202301&filename=1022749221.nh

S.J. Zhao, S.L. Ma, M.C. Wang, Super Parameter Optimization of ELM by Artificial Ecosystem-Based Optimization with Crowding Forward-Backward and Backtracking Tips. Control and Decision, (2022) https://kns.cn-ki.net/kcms/detail/21.1124.TP.20220301.0948.005.html

D. Oh, J. Race, S.O.A.B. Koo, Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network, Journal of Marine Science and Engineering, (2020) 766.

R.C.C. Silva, J.N.C. Guerreiro, A.F.D. Loula, A study of pipe interacting corrosion defects using the FEM and neural networks, ADVANCES IN ENGINEERING SOFTWARE, (2007) 868-875.

V. Chauhan, Advances in interaction rules for corrosion defects in pipelines. In: Proceedings of the International Gas Research Conference, (2004). Vancouver, Canada.

Y. Xü, A. Fenerci, O.A. Øiseth, T. Moan, Efficient prediction of wind and wave induced long-term extreme load effects of floating suspension bridges using artificial neural networks and support vector machines(Article), OCEAN ENGINEERING, (2020) 107888.

A.J. Smola, B. Scholkopf, A tutorial on support vector regression, STATISTICS AND COMPUTING, (2004) 199-222.

A.M. Andrew, An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods, KYBERNETES, (2001) 103-115.

C.J. Evans, T.F. Miller, Failure prediction of pressure vessels using finite element analysis(Article), Journal of Pressure Vessel Technology, Transactions of the ASME, (2015) 51206.

M. Abyani, M.R. Bahaari, M. Zarrin, M. Nasseri, Predicting failure pressure of the corroded offshore pipelines using an efficient finite element based algorithm and machine learning techniques., OCEAN ENGINEERING, (2022) 111382.

S. Sain, The Nature of Statistical Learning Theory, TECHNOMETRICS, (1996) 409.

W. Zhao, L. Wang, Z. Zhang, Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm, Neural Computing and Applications, (2020) 9383-9425.

H.R. Tizhoosh, Opposition-based learning: A new scheme for machine intelligence, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), Vienna, Austria, 2005.

G. Louppe, UNDERSTANDING RANDOM FORESTS: FROM THEORY TO PRACTICE, University of Liège, 2014.

Y. Mahmutoglu, K. Turk, Positioning of leakages in underwater natural gas pipelines for time-varying multipath environment., OCEAN ENGINEERING, (2020).

X.A. Liu, M.A. Xia, D.A. Bolati, J.A. Liu, Q.A. Zheng, H.A.H.C. Zhang, An ANN‐based failure pressure prediction method for buried high‐strength pipes with stray current corrosion defect., Energy Science & Engineering, (2020) 248-259.

B.M. Wilamowski, H. Yu, Improved Computation for Levenberg-Marquardt Training., IEEE transactions on neural networks, (2010) 930-937.

J. Li, Y. Wu, Improved Sparrow Search Algorithm with the Extreme Learning Machine and Its Application for Prediction, NEURAL PROCESSING LETTERS, (2022) 4189-4209. https://doi.org/10.1007/s11063-022-10804-x

S. Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems(Article), Neural Computing and Applications, (2016) 1053-1073.

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Published

26-03-2024

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Section

Articles

How to Cite

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects. (2024). Academic Journal of Science and Technology, 10(1), 118-130. https://doi.org/10.54097/1th80z38

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