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Combination Of High Ph SCC And Near Neutral Ph SCC Models Using Bayesian Networks

Picture for Combination Of High Ph SCC And Near Neutral Ph SCC Models Using Bayesian Networks
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Environmentally Assisted Cracking (EAC) of gas transmission lines constitute about 2.6% of the total number of significant incidents recorded in the U.S. Pipeline and Hazardous Materials Administration (PHMSA) database [1]. For the hydrocarbon liquid pipelines, the EAC-related incidents constitute about 1%. Although Stress Corrosion Cracking (SCC) incidents are a relatively small percentage of significant incidents, it is important to predict the location and rate of growth of SCC because of the potential for catastrophic consequences from the growth of undetected cracks.

Product Number: 51322-17851-SG
Author: Francois Ayello, Guanlan Liu, Narasi Sridhar, Ramgopal Thodla
Publication Date: 2022
Industries: Cathodic Protection , Corrosion Monitoring and Control , Coatings and Linings , Coatings
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Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it may remain
undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stresses, and the electrolyte chemistry beneath the disbonded coating. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, data uncertainties make the prediction of SCC even more challenging. The complex interactions of various seemingly unrelated parameters and varying mechanisms has been addressed using Bayesian network models. Two Bayesian network models have been created to predict both high pH and near neutral pH crack growth rates. This publication presents a new SCC model that combine the previous high pH and near-neutral pH SCC models.

Stress corrosion cracking (SCC) continues to be a safety concern, mainly because it may remain
undetected before a major pipeline failure occurs. SCC processes involve complex interactions between metallurgy, stresses, and the electrolyte chemistry beneath the disbonded coating. For these reasons, assessing SCC failure probability at any given location on a pipeline is difficult. In addition, data uncertainties make the prediction of SCC even more challenging. The complex interactions of various seemingly unrelated parameters and varying mechanisms has been addressed using Bayesian network models. Two Bayesian network models have been created to predict both high pH and near neutral pH crack growth rates. This publication presents a new SCC model that combine the previous high pH and near-neutral pH SCC models.

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Product Number: 51322-18080-SG
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Product Number: 51322-18164-SG
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