0
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.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Schweitzer, P. A. , 2009, Fundamentals of Corrosion: Mechanisms, Causes, and Preventative Methods, CRC Press, Boca Raton, FL. [CrossRef]
Miracle, D. B. , and Donaldson, S. L. , 2001, ASM Handbook, Vol. 21, ASM International, Materials Park, OH. [PubMed] [PubMed]
Valor, A. , Caleyo, F. , Alfonso, L. , Rivas, D. , and Hallen, J. M. , 2007, “ Stochastic Modeling of Pitting Corrosion: A New Model for Initiation and Growth of Multiple Corrosion Pits,” Corros. Sci., 49(2), pp. 559–579. [CrossRef]
Caleyo, F. , Velázquez, J. C. , Valor, A. , and Hallen, J. M. , 2009, “ Probability Distribution of Pitting Corrosion Depth and Rate in Underground Pipelines: A Monte Carlo Study,” Corros. Sci., 51(9), pp. 1925–1934. [CrossRef]
Caleyo, F. , Velázquez, J. C. , Valor, A. , and Hallen, J. M. , 2009, “ Markov Chain Modelling of Pitting Corrosion in Underground Pipelines,” Corros. Sci., 51(9), pp. 2197–2207. [CrossRef]
Valor, A. , Caleyo, F. , Rivas, D. , and Hallen, J. M. , 2010, “ Stochastic Approach to Pitting-Corrosion-Extreme Modelling in Low-Carbon Steel,” Corros. Sci., 52(3), pp. 910–915. [CrossRef]
Murer, N. , and Buchheit, R. , 2013, “ Stochastic Modeling of Pitting Corrosion in Aluminum Alloys,” Corros. Sci., 69, pp. 139–148. [CrossRef]
Svintradze, D. V. , and Pidaparti, R. M. , 2010, “ A Theoretical Model for Metal Corrosion Degradation,” Int. J. Corros., 2010, p. 279540.
Omdahl, T. P. , 1988, Reliability, Availability, and Maintainability (RAM) Dictionary, ASQC Quality Press, Milwaukee, WI.
Dhillon, B. S. , 2002, Design Reliability: Fundamentals and Applications, CRC Press, Boca Raton, FL.
Naseh-Moghanlou, L. , 2014, “ Analysis and Management the Failures of Thermal Power Plant Boiler,” Sahand University of Technology, Tabriz, Iran.
ASME, 1998, “ ASME Boiler and Pressure Vessel Code,” Sec. I, American Society of Mechanical Engineers, New York.
MathWorks, 2013, “ Matlab,” Mathworks, Natick, MA, accessed 1994–2017, www.mathworks.com/products/matlab
Ivanov, K. L. , and Lukzen, N. N. , 2004, “ A Novel Method for Calculating Rate Constants of Diffusion-Influenced Reactions,” J. Chem. Phys., 121(11), pp. 5109–5114. [CrossRef] [PubMed]
Meier, M. , 2004, “ Electrical Resistivity as a Function of Temperature,” Department of Chemical Engineering and Materials Science, University of California, Davis, CA.
Levine, I. N. , 2008, Physical Chemistry, McGraw-Hill, New York.
Infoplease, 2004, “ Periodic Table, 2000–2016,” FEN Learning, Boston, MA.
Albert, B. , Guy, B. , and Damidot, D. , 2006, “ Water Chemical Potential: A Key Parameter to Determine the Thermodynamic Stability of Some Hydrated Cement Phases in Concrete?,” Cem. Concr. Res., 36(5), pp. 783–790. [CrossRef]
Nowacki, K. , and Kasprzyk, W. , 2010, “ The Sound Velocity in an Alloy Steel at High-Temperature Conditions,” Int. J. Thermophys., 31(1), pp. 103–112. [CrossRef]
Rajevac, V. , 2004, “ Lattice Dynamics in Hydrogenated Austenitic Stainless Steels and in the Superionic Conductor Cu2-dSe [Cu 2-Delta Se],” Technische Universitat Darmstadt, Darmstadt, Hesse, Germany.
Scully, J. C. , 1978, The Fundamentals of Corrosion, Pergamon Press, Oxford, UK. [PubMed] [PubMed]
Shukla, M. , and Padial, N. , 1973, “ A Calculation of the Debye Characteristic Temperature of Cubic Crystals,” Rev. Bras. Fis., 3(1), pp. 39–45.
Ternes, M. , Lutz1, C. P. , Hirjibehedin, C. F. , Giessibl, F. J. , and Heinrich, A. J. , 2008, “ The Force Needed to Move an Atom on a Surface,” Science, 319(5866), pp. 1066–1069. [CrossRef] [PubMed]
GE Infrastructure, 2004, “ Sound Speeds and Pipe Size Data,” GE Infrastructure Sensing Inc., Billerica, MA, accessed 2004, http://www.instrumart.com/assets/ge-sound-speeds-and-pipe-size-data.pdf
Fontana, M. G. , 2005, Corrosion Engineering, Tata McGraw-Hill Education, New York.
Technical Office, 2009, “ Maintenance Report 13871009,” Tabriz Thermal Power Plant, Tabriz, Iran.
Modarres, M. , Kaminskiy, M. P. , and Krivtsov, V. , 2009, Reliability Engineering and Risk Analysis: A Practical Guide, CRC Press, Boca Raton, FL.
Mathwave, 2013, “ EasyFit: Distribution Fitting Made Easy,” Mathwave Technologies, Spokane, WA, accessed 2004–2017, http://www.mathwave.com/easyfit-distribution-fitting.html
Kelly, D. , and Smith, C. , 2011, Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook, Springer Science & Business Media, London. [CrossRef]
Olshausen, B. A. , 2004, “ Bayesian Probability Theory,” University of California at Berkeley, Berkeley, CA.
OpenBUGS Foundation, 2013, “ OpenBUGS,” OpenBUGS Foundation.

Figures

Grahic Jump Location
Fig. 1

Overall step for the proposed methodology

Grahic Jump Location
Fig. 2

Schematic corrosion metal degradation subject to electrolyte solution [8]

Grahic Jump Location
Fig. 3

Tube thickness and critical thickness

Grahic Jump Location
Fig. 4

Element of modeling

Grahic Jump Location
Fig. 5

Schematic pitting corrosion chemical reaction [2]

Grahic Jump Location
Fig. 6

Time profile of the pit radii

Grahic Jump Location
Fig. 7

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

Grahic Jump Location
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)

Grahic Jump Location
Fig. 9

Comparing histograms of Weibull, Gumball, and lognormal

Grahic Jump Location
Fig. 10

Goodness-of-fit result for lifetime

Grahic Jump Location
Fig. 11

Lifetime distribution of pitting corrosion

Grahic Jump Location
Fig. 12

Graphical Bayesian theory [30]

Grahic Jump Location
Fig. 13

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

Grahic Jump Location
Fig. 14

Prior and posterior distribution of the pitting corrosion lifetime

Grahic Jump Location
Fig. 15

Updated distribution of the pitting corrosion lifetime

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In