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

Fernanda Hartoyo, Gabriella Pasya Irianti, Jaka Fajar Fatriansyah, Hanna Ovelia, Imam Abdillah Mas'ud, Farhan Rama Digita, Andrian Fauzi, Muhammad Anis

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1650-1655
Number of pages6
JournalMaterials Today: Proceedings
Volume80
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Failure
  • Monte Carlo
  • Pipeline
  • Risk-based
  • Weibull

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