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Research Papers

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

[+] Author and Article Information
Lida Naseh Moghanlou, Mohammad Pourgol-Mohammad

Mechanical Engineering Department,
Sahand University of Technology,
Tabriz 51355-1996, East Azarbaijan, Iran

Manuscript received January 28, 2016; final manuscript received February 17, 2017; published online June 13, 2017. Assoc. Editor: Jeremy M. Gernand.

ASME J. Risk Uncertainty Part B 3(4), 041002 (Jun 13, 2017) (7 pages) Paper No: RISK-16-1031; 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 an unscheduled plant shutdown. In this research, degradation of the corrosion is investigated for boiler tubes by estimating the corrosion lifetime. A special focus is made on the corrosion failures, important failure modes, and mechanisms for the metallic boiler tubes via failure modes and effect analysis (FMEA) method, thereby evaluating the pitting corrosion as the most common failure mode in the tubes. Majority of the available approaches estimates lifetime of the 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 the 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 into 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. The modeling results show that the 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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References

Figures

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Fig. 1

Overall step for the proposed methodology

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Fig. 2

Schematic corrosion metal degradation subject to electrolyte solution [8]

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Fig. 3

Tube thickness and critical thickness

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Fig. 4

Element of modeling

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Fig. 5

Schematic pitting corrosion chemical reaction [2]

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Fig. 6

Time profile of the pit radii

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Fig. 7

Comparison of the modeling result with the field performance of the economizer lifetime

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Fig. 8

Uncertainty distribution of metal properties (a)–(d) and temperature of the economizer (e): (a) Debye temperature (K), (b) free energy per atom in metal (J), (c) resistivity ( Ω m), (d) sound velocity (m/s), and (e) temperature (K)

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Fig. 10

Goodness-of-fit result for lifetime

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Fig. 11

Lifetime distribution of pitting corrosion

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Fig. 9

Comparing histograms of Weibull, Gumball, and lognormal

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Fig. 12

Graphical Bayesian theory [30]

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Fig. 13

Lognormal distribution parameters: (a) mean (μ) and (b) standard deviation (σ)

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Fig. 14

Prior and posterior distribution of the pitting corrosion lifetime

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Fig. 15

Updated distribution of the pitting corrosion lifetime

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