Modeling of External Metal Loss for Corroded Buried Pipeline

A statistical predictive model to estimate the time dependence of metal loss (ML) for buried pipelines has been developed considering the physical and chemical properties of soil. The parameters for this model include pH, chloride content, caliphate content (SO), sulfide content, organic content (OR...

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Main Authors:	Siti Rabeah, Othman, Nordin, Yahaya, Norhazilan, Md Noor, Lim, Kar Sing, Zardasti, Libriati, Ahmad Safuan, A. Rashid
Format:	Article
Language:	English
Published:	ASME 2017
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/6/fkasa-2017-othman-Modeling%20of%20External%20Metal%20Loss1.pdf

id	ump-18760
recordtype	eprints
spelling	ump-187602018-01-11T07:14:20Z http://umpir.ump.edu.my/id/eprint/18760/ Modeling of External Metal Loss for Corroded Buried Pipeline Siti Rabeah, Othman Nordin, Yahaya Norhazilan, Md Noor Lim, Kar Sing Zardasti, Libriati Ahmad Safuan, A. Rashid T Technology (General) TA Engineering (General). Civil engineering (General) A statistical predictive model to estimate the time dependence of metal loss (ML) for buried pipelines has been developed considering the physical and chemical properties of soil. The parameters for this model include pH, chloride content, caliphate content (SO), sulfide content, organic content (ORG), resistivity (RE), moisture content (WC), clay content (CC), plasticity index (PI), and particle size distribution. The power law-based time dependence of the ML was modeled as P = ktv, where t is the time exposure, k is the metal loss coefficient, and v is the corrosion growth pattern. The results were analyzed using statistical methods such as exploratory data analysis (EDA), single linear regression (SLR), principal component analysis (PCA), and multiple linear regression (MLR). The model revealed that chloride (CL), resistivity (RE), organic content (ORG), moisture content (WC), and pH were the most influential variables on k, while caliphate content (SO), plasticity index (PI), and clay content (CC) appear to be influential toward v. The predictive corrosion model based on data from a real site has yielded a reasonable prediction of metal mass loss, with an R2 score of 0.89. This research has introduced innovative ways to model the corrosion growth for an underground pipeline environment using measured metal loss from multiple pipeline installation sites. The model enables predictions of potential metal mass loss and hence the level of soil corrosivity for Malaysia. ASME 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/18760/6/fkasa-2017-othman-Modeling%20of%20External%20Metal%20Loss1.pdf Siti Rabeah, Othman and Nordin, Yahaya and Norhazilan, Md Noor and Lim, Kar Sing and Zardasti, Libriati and Ahmad Safuan, A. Rashid (2017) Modeling of External Metal Loss for Corroded Buried Pipeline. Journal of Pressure Vessel Technology, 139 (3). 031702. ISSN 0094-9930 (print); 1528-8978 (online) http://dx.doi.org/10.1115/1.4035463 doi: 10.1115/1.4035463
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) Siti Rabeah, Othman Nordin, Yahaya Norhazilan, Md Noor Lim, Kar Sing Zardasti, Libriati Ahmad Safuan, A. Rashid Modeling of External Metal Loss for Corroded Buried Pipeline
description	A statistical predictive model to estimate the time dependence of metal loss (ML) for buried pipelines has been developed considering the physical and chemical properties of soil. The parameters for this model include pH, chloride content, caliphate content (SO), sulfide content, organic content (ORG), resistivity (RE), moisture content (WC), clay content (CC), plasticity index (PI), and particle size distribution. The power law-based time dependence of the ML was modeled as P = ktv, where t is the time exposure, k is the metal loss coefficient, and v is the corrosion growth pattern. The results were analyzed using statistical methods such as exploratory data analysis (EDA), single linear regression (SLR), principal component analysis (PCA), and multiple linear regression (MLR). The model revealed that chloride (CL), resistivity (RE), organic content (ORG), moisture content (WC), and pH were the most influential variables on k, while caliphate content (SO), plasticity index (PI), and clay content (CC) appear to be influential toward v. The predictive corrosion model based on data from a real site has yielded a reasonable prediction of metal mass loss, with an R2 score of 0.89. This research has introduced innovative ways to model the corrosion growth for an underground pipeline environment using measured metal loss from multiple pipeline installation sites. The model enables predictions of potential metal mass loss and hence the level of soil corrosivity for Malaysia.
format	Article
author	Siti Rabeah, Othman Nordin, Yahaya Norhazilan, Md Noor Lim, Kar Sing Zardasti, Libriati Ahmad Safuan, A. Rashid
author_facet	Siti Rabeah, Othman Nordin, Yahaya Norhazilan, Md Noor Lim, Kar Sing Zardasti, Libriati Ahmad Safuan, A. Rashid
author_sort	Siti Rabeah, Othman
title	Modeling of External Metal Loss for Corroded Buried Pipeline
title_short	Modeling of External Metal Loss for Corroded Buried Pipeline
title_full	Modeling of External Metal Loss for Corroded Buried Pipeline
title_fullStr	Modeling of External Metal Loss for Corroded Buried Pipeline
title_full_unstemmed	Modeling of External Metal Loss for Corroded Buried Pipeline
title_sort	modeling of external metal loss for corroded buried pipeline
publisher	ASME
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/ http://umpir.ump.edu.my/id/eprint/18760/6/fkasa-2017-othman-Modeling%20of%20External%20Metal%20Loss1.pdf
first_indexed	2023-09-18T22:26:45Z
last_indexed	2023-09-18T22:26:45Z
_version_	1777416017143660544

Modeling of External Metal Loss for Corroded Buried Pipeline

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