Hydrogen impact on transmission pipeline risk: probabilistic analysis of failure causes

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 110825 - 110825

Published: Jan. 1, 2025

Language: Английский

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Measuring node importance in air transportation systems: On the quality of complex network estimations

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 240, P. 109596 - 109596

Published: Aug. 25, 2023

Language: Английский

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Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 238, P. 109486 - 109486

Published: June 30, 2023

Language: Английский

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Consequence assessment of gas pipeline failure caused by external pitting corrosion using an integrated Bayesian belief network and GIS model: Application with Alberta pipeline

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 240, P. 109573 - 109573

Published: Aug. 17, 2023

Language: Английский

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Reliability-based maintenance optimization of long-distance oil and gas transmission pipeline networks

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 249, P. 110236 - 110236

Published: May 22, 2024

Language: Английский

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Tensile capacity degradation of randomly corroded strands based on a refined numerical model

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: unknown, P. 110512 - 110512

Published: Sept. 1, 2024

Language: Английский

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A Bayesian network‐based susceptibility assessment model for oil and gas pipelines suffering under‐deposit corrosion

The Canadian Journal of Chemical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: March 11, 2024

Abstract Oil and gas pipelines are exposed to harsh operating conditions that facilitate their susceptibility complex corrosion mechanisms. This affects integrity results in failure with associated consequences. Capturing these phenomena requires a robust approach. study proposes the application of dynamic probabilistic model capture key influential factors contribute under‐deposit (UDC) mechanism oil pipelines. The Bayesian network assesses pipeline's (degradation rate) UDC, capturing parametric dependencies. predicted rates input data for propagation prediction. Three semi‐empirical models used comparative assessment establish degree given prevalent parameters. proposed approach is tested on an offshore pipeline, impact parameters predicted. result shows percentage increase degradation rate by 18.7%, 33.2%, 35.8%, 63.4%, respectively, various interaction scenarios. present offers adaptive technique would provide early warning guide pipeline aid management assets suffering from deposit corrosion.

Language: Английский

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Residual strength prediction of corroded pipelines based on physics-informed machine learning and domain generalization

npj Materials Degradation, Journal Year: 2025, Volume and Issue: 9(1)

Published: Feb. 14, 2025

Language: Английский

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Study on the pitting corrosion behavior of X65 steel in supercritical and dense-phase CO2 based on in-situ electrochemical noise measurement

Process Safety and Environmental Protection, Journal Year: 2025, Volume and Issue: unknown, P. 107060 - 107060

Published: March 1, 2025

Language: Английский

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A Bayesian approach to assess under-deposit corrosion in oil and gas pipelines

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 176, P. 489 - 505

Published: June 15, 2023

Language: Английский

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