Risk-based evaluation of pitting corrosion in process facilities

Shekari, Elahe (2017) Risk-based evaluation of pitting corrosion in process facilities. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Abstract

Pitting is one of the most challenging forms of corrosion to study and model due to complex pit behavior. Pitting can occur in different engineering alloys and can lead to catastrophic consequences. Pits are usually latent or difficult-to-detect and resulting degradation often causes in-service failure of process equipment. Therefore, the ability to predict pit behavior is key to design and maintenance of assets. In particular, pitting corrosion is a significant challenge in marine environments and offshore operations due to remoteness of operations and hidden damage under insulations. Thus, the ability to assess risk and estimate remaining life of assets affected by pitting corrosion is necessary for timely maintenance and safe operation of assets. This thesis proposes a methodology to assess and dynamically update the risk of pressurized components affected by pitting corrosion. To take into consideration the time-dependent growth of pits, the application of non-homogenous Markov process is proposed to model the maximum pit depth. The integration of the developed maximum pit model into a pressureresistance model is proposed to predict the failure probability of affected components. An economic consequence analysis model is developed to estimate both business and accidental losses due to failure of the affected component. Then, risk is estimated by integrating models developed for probability of failure and associated consequences. The application of Bayesian analysis is proposed to update estimated risk as new inspection data gets available and also as economic condition of the process evolves. This work also proposes a risk management strategy including corrosion prevention, control and monitoring measures to make effective decision related to pitting corrosion. The application of the proposed methods is demonstrated using different case studies.

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