Assessment of the Pitting Corrosion Degradation Lifetime: Case Study of Boiler Tubes

[+] Author and Article Information
Lida Naseh Moghanlou

Sahand University of Technology Tabriz, East Azarbayjan, Iran

Mohammad Pourgol-Mohammad

Sahand University of Technology Tabriz, East Azarbayjan, Iran

1Corresponding author.

ASME doi:10.1115/1.4036064 History: Received January 28, 2016; Revised February 17, 2017


Corrosion degradation is a common problem for boiler tubes in power plants, resulting in unscheduled plant shut down. In this research, degradation of the corrosion is investigated for a boiler tubes with the corrosion lifetime estimated. A special focus is made on the corrosion failures, the important failure modes and mechanisms for the metallic boiler tubes via Failure Modes and Effect Analysis (FMEA) method, evaluating the pitting corrosion as the most common failure mode in the tubes. Majority of the available approaches estimate lifetime of pitting corrosion by deterministic approaches, in which the results are valid only for limited conditions. In order to improve deficiencies of available models, a stochastic method is proposed here to study the corrosion life. The temporal behavior of metal degradation is analyzed in different conditions through the developed approach and a proper degradation model is selected. Uncertainty intervals/distributions are determined for some of the model parameters. The deterministic model is converted to a probabilistic model by taking to account the variability of the uncertain input parameters. The model is simulated using Monte Carlo method via simple sampling. The result of the life estimation is updated by the Bayesian framework using Monte Carlo Markov Chain. Finally, for the element that is subjected to the pitting corrosion degradation, the life distribution is obtained. Modeling results shows that pitting corrosion has stochastic behavior with lognormal distribution as proper fit for the pitting corrosion behavior. In order to validate the results, the estimations were compared with the power plant field failure data.

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