TY - JOUR

T1 - Weibull distribution optimization for piping risk calculation due to uniform corrosion using Monte Carlo method

AU - Hartoyo, Fernanda

AU - Irianti, Gabriella Pasya

AU - Fatriansyah, Jaka Fajar

AU - Ovelia, Hanna

AU - Mas'ud, Imam Abdillah

AU - Digita, Farhan Rama

AU - Fauzi, Andrian

AU - Anis, Muhammad

N1 - Funding Information:
The work reported in this paper was supported through grant Program Pendanaan Perancangan dan Pengembangan Purwarupa (P5) 2022 , Number: PKS-140/UN2.INV/HKP.05/2022.
Publisher Copyright:
© 2023

PY - 2023

Y1 - 2023

N2 - Pipes are the main component in the oil and gas industries, which need serious attention due to the high risk of piping system failure. The failure of the piping system leads to severe consequences since it causes substantial material losses. The Risk-Based Inspection can minimize failure by using the Monte Carlo simulation to calculate the failure probabilities of the component. A normal distribution is generally used in Monte Carlo simulation for Random Variable Generator. However, the generated data may be biased, i.e., an error sampling, so the resulting data becomes inaccurate. In the normal distribution, the result of the data produces an overestimation data because the result of the data can be negative on the corrosion rate. Therefore, another method is needed for determining random variables where the generated data is not biased, so the accuracy of the results of the Risk-Based Inspection increases. The Weibull method can reduce the biased data generated from the normal distribution.

AB - Pipes are the main component in the oil and gas industries, which need serious attention due to the high risk of piping system failure. The failure of the piping system leads to severe consequences since it causes substantial material losses. The Risk-Based Inspection can minimize failure by using the Monte Carlo simulation to calculate the failure probabilities of the component. A normal distribution is generally used in Monte Carlo simulation for Random Variable Generator. However, the generated data may be biased, i.e., an error sampling, so the resulting data becomes inaccurate. In the normal distribution, the result of the data produces an overestimation data because the result of the data can be negative on the corrosion rate. Therefore, another method is needed for determining random variables where the generated data is not biased, so the accuracy of the results of the Risk-Based Inspection increases. The Weibull method can reduce the biased data generated from the normal distribution.

KW - Failure

KW - Monte Carlo

KW - Pipeline

KW - Risk-based

KW - Weibull

UR - http://www.scopus.com/inward/record.url?scp=85149805234&partnerID=8YFLogxK

U2 - 10.1016/j.matpr.2023.02.312

DO - 10.1016/j.matpr.2023.02.312

M3 - Article

AN - SCOPUS:85149805234

SN - 2214-7853

VL - 80

SP - 1650

EP - 1655

JO - Materials Today: Proceedings

JF - Materials Today: Proceedings

ER -