Residual strength hybrid prediction of hydrogen-blended natural gas pipelines based on FEM-FC-BP model

Shulin Li, Yan Yang, Bensheng Huang and Yanlin Jia

Energy, 2025, vol. 321, issue C

Abstract: Utilizing existing natural gas pipelines for hydrogen transportation is deemed crucial in developing the hydrogen energy industry, thus necessitating urgent safety evaluations. However, current experimental research on hydrogen-blended natural gas pipelines confronts challenges like high cost and risk, making obtaining relevant experimental data difficult. In addition, the oil and gas pipeline field research also faces the problem of limited data features. To address these problems, this paper organically integrates the finite element method (FEM), feature crossing (FC), and backpropagation (BP) neural network, and proposes a residual strength hybrid prediction model of hydrogen-blended natural gas pipelines based on FEM-FC-BP. The model first uses the FEM to simulate a data set. Then, the FC is introduced to reasonably enhance the data feature. Finally, the optimal BP neural network is trained using the five-fold cross-validation method. Experiments on API SPEC 5L X52 pipeline steel show that the proposed FEM-FC-BP hybrid prediction model can effectively predict the residual strength of hydrogen-blended natural gas pipelines, in which FEM can effectively overcome the difficulty of data acquisition, and FC can reasonably enhance the data feature to improve the accuracy of the model. This research provides valuable references for the integrity management of hydrogen-blended natural gas pipelines.

Keywords: Hydrogen-blended natural gas; Finite element method; Feature crossing; BP neural network; Residual strength (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225011053
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:321:y:2025:i:c:s0360544225011053

DOI: 10.1016/j.energy.2025.135463

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().