General corrosion vulnerability assessment using a Bayesian belief network model incorporating experimental corrosion data for X60 pipe steel

Tesfamariam, Solomon; Woldesellasse, Haile; Xu, Ming; Asselin, Edouard

doi:10.1016/j.jpse.2021.08.003

Cited by 14 publications

(2 citation statements)

References 31 publications

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“…To accurately model pipeline decay in real-world operation, factors outside those discussed above should be considered. External factors such as human, geotechnical, and natural hazards are all known to pose a risk to pipeline integrity [57]. External corrosion related to environmental conditions is known to be one of the most significant factors governing pipe life.…”

Section: Recirculation Testsmentioning

confidence: 99%

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Wood¹,

Cook²

2023

Advances in Slurry Technology

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The transmission of particle-bearing liquids in pipes has motivated continuing research into erosion mechanisms and the distribution of erosion rates over wetted surfaces. This chapter covers these initiatives with particular reference to erosion-corrosion modelling within bends and straight sections of cylindrical pipes manufactured in a variety of materials and transporting a variety of liquids. Erosion-corrosion modelling techniques such as submerged slurry jets and rotating cylinder electrodes have been used to study factors influencing material degradation. Improvements in computational fluid dynamics (CFD), such as the development of a moving deforming mesh (MDM) have improved the accuracy of CFD models in predicting pipe wall erosion rates. Combined discrete phase tracking approaches such as the CFD-DPM-DEM (discrete phase-discrete element model) have helped improve computational efficiency. Wall impact erosion models are calibrated using laboratory scale tests. Validation of CFD models using full-scale test data is rare, meaning their accuracy is still largely unreported. Material testing has helped to identify the resilience of prospective pipeline materials to erosion-corrosion, while modifications to internal geometry and pipe section have shown potential to improve erosion-corrosion resistance.

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Section: Recirculation Testsmentioning

confidence: 99%

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Wood¹,

Cook²

2023

Advances in Slurry Technology

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“…Soil corrosion has become the largest cause of pipeline corrosion and perforation, endangering the safe operation of oil and gas pipelines. 2 The salt level of the saline-alkali soil in the northwest of the country, which is more than four times that of saltwater, is exceptionally high and extremely corrosive. The majority of coating corrosion research is done in a 3.5% NaCl solution system; however, the corrosion of saline-alkali soil in the northwest cannot be overlooked.…”

Section: Introductionmentioning

confidence: 99%

Preparation and corrosion resistance of superhydrophobic Ni–Co–Al₂O₃ coating on X100 steel

et al. 2023

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Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

Woldesellasse,

Tesfamariam

2024

Can J Chem Eng

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Corrosion poses a great risk to the integrity of oil and gas pipelines, leading to substantial investments in corrosion control and management. Several studies have been conducted on accurately estimating the maximum pitting depth in oil and gas pipelines using available field data. Some of the frequently employed machine learning techniques include artificial neural networks, random forests, fuzzy logic, Bayesian belief networks, and support vector machines. Despite the ability of machine learning methods to address a variety of problems, traditional machine learning methods have evident limitations, such as overfitting, which can diminish the model's generalization capabilities. Additionally, traditional machine learning models that provide point estimations are incapable of addressing uncertainties. In the current study, a Bayesian neural network is proposed to include uncertainty in estimating the corrosion defect of a pipeline exposed to external pitting corrosion. The results are then incorporated into a Bayesian belief network for evaluating the probability of failure and its corresponding consequences in terms of social impact, thus forming a comprehensive risk assessment framework. The results of the Bayesian neural network are validated using field data and achieved a testing accuracy of 90%. The framework of the study offers a powerful decision‐making tool for the integrity management of pipelines against external corrosion.

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General corrosion vulnerability assessment using a Bayesian belief network model incorporating experimental corrosion data for X60 pipe steel

Cited by 14 publications

References 31 publications

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Preparation and corrosion resistance of superhydrophobic Ni–Co–Al₂O₃ coating on X100 steel

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

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General corrosion vulnerability assessment using a Bayesian belief network model incorporating experimental corrosion data for X60 pipe steel

Cited by 14 publications

References 31 publications

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Erosion-Corrosion in Pipe Flows of Particle-Laden Liquids

Preparation and corrosion resistance of superhydrophobic Ni–Co–Al2O3 coating on X100 steel

Risk assessment of gas pipeline using an integrated Bayesian belief network and GIS: Using Bayesian neural networks for external pitting corrosion modelling

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Preparation and corrosion resistance of superhydrophobic Ni–Co–Al₂O₃ coating on X100 steel