Effect of temporal variability of operating parameters in corrosion modelling for natural gas pipelines subject to uniform corrosion

This article proposes a new framework to estimate the degradation level in oil and gas pipelines corroded by internal pitting when operational conditions change over time. Despite the fact that the operational conditions of a pipeline change at various times, this change has not been addressed in the current available pipeline corrosion degradation models. In this framework, a hierarchical Bayesian method and augmented particle filtering are used for data fusion to address this issue. This framework is applied on a case study and the results are compared with the estimations of a state of the art pitting corrosion degradation model.

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“…where G(:) denotes the gamma function. The expectation and variance of the gamma process are given in equations (17) and (18), respectively…”

Section: Hb Methodsmentioning

confidence: 99%

“…According to equation (17), the shape parameter of a gamma process reflects the average trend of the random quantity as a function of time. Therefore, by selecting an appropriate form for the shape parameter of a gamma process, it can model degradation processes with increasing, decreasing, or constant degradation rates.…”

Section: Hb Methodsmentioning

confidence: 99%

A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management

Heidary

Groth

2019

Structural Health Monitoring

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“…Most common environmental parameters that are related to the pipeline corrosion include temperature ( Temp ), total pressure ( P ), partial pressure of CO 2 ( pCO 2 ), partial pressure of H 2 S ( pH 2 S ), flow velocity ( V ), pH value ( pH ), chloride ion ( Cl − ) concentration, and sulphate ion ( SO 4 2− ) concentration, and the presence of solids (

). The environmental parameters are stochastic in nature; therefore, in order to take temporal variability into account, we used a Poisson Square Wave Process (PSWP) [ 19 , 20 ] to model them and generate different time-varying environmental parameters. In a PSWP, the number of pulses (

) within a period of time (

follows a Poisson distribution with the probability mass function being:

where

is the mean occurrence rate per unit time (= 1 per day in this paper)

follows the exponential distribution with the mean duration to be

.…”

Section: The Test Benchmentioning

confidence: 99%

“…The uniform corrosion rate at Stage II (

) is represented as:

where

, and

are the fluxes of H 2 S, CO 2 , and H + in mol/(m 2 s). For more model details, readers are referred to [ 20 ].…”

Section: The Test Benchmentioning

confidence: 99%

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Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

Mahmoodzadeh

Droguett

et al. 2020

Sensors

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Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.

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A novel methodology based on the reflected L(0,1) guided wave for quantitative detection of corrosion-induced wall thickness loss in continuous pipes

Liu

Feng

2023

J Civil Struct Health Monit

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Effect of temporal variability of operating parameters in corrosion modelling for natural gas pipelines subject to uniform corrosion

Cited by 15 publications

References 23 publications

A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management

A hybrid model of internal pitting corrosion degradation under changing operational conditions for pipeline integrity management

Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

A novel methodology based on the reflected L(0,1) guided wave for quantitative detection of corrosion-induced wall thickness loss in continuous pipes

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