Research Papers: Pipeline Systems

Modeling of External Metal Loss for Corroded Buried Pipeline

[+] Author and Article Information
Siti Rabeah Othman

Faculty of Civil Engineering,
Universiti Teknologi Malaysia (UTM),
UTM Skudai,
Johor 81310, Malaysia
e-mail: srabeah@yahoo.com

Nordin Yahaya

Faculty of Civil Engineering,
Universiti Teknologi Malaysia (UTM),
UTM Skudai,
Johor 81310, Malaysia
e-mail: nordiny@utm.my

Norhazilan Md Noor

Faculty of Civil Engineering,
Universiti Teknologi Malaysia (UTM),
UTM Skudai,
Johor 81310, Malaysia
e-mail: norhazilan@utm.my

Lim Kar Sing

Faculty of Civil Engineering and
Earth Resources,
Universiti Malaysia Pahang,
Lebuhraya Tun Razak, Gambang,
Kuantan, Pahang 26300, Malaysia
e-mail: limkarsing@ump.edu.my

Libriati Zardasti

Faculty of Civil Engineering,
Universiti Teknologi Malaysia (UTM),
UTM Skudai,
Johor 81310, Malaysia
e-mail: libriati@utm.my

Ahmad Safuan A Rashid

Faculty of Civil Engineering,
Universiti Teknologi Malaysia (UTM),
UTM Skudai,
Johor 81310, Malaysia
e-mail: ahmadsafuan@utm.my

1Corresponding author.

Manuscript received August 13, 2016; final manuscript received December 7, 2016; published online February 3, 2017. Assoc. Editor: Xian-Kui Zhu.

J. Pressure Vessel Technol 139(3), 031702 (Feb 03, 2017) (12 pages) Paper No: PVT-16-1135; doi: 10.1115/1.4035463 History: Received August 13, 2016; Revised December 07, 2016

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.

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Grahic Jump Location
Fig. 1

Research methodology

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Fig. 2

Position of steel coupons at 0.5 m and 1.0 m prior to backfilling

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Fig. 3

Preliminary analysis of part 1

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Fig. 4

Model development of part 2

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Fig. 5

Metal mass loss (ML) plotted against time (median data)

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Fig. 6

PCA of k coefficient

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Fig. 7

PCA of v coefficient

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Fig. 8

Actual mass loss versus predicted model




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