IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i14p3633-d384481.html
   My bibliography  Save this article

Assessment of Material Durability of Steam Pipelines Based on Statistical Analysis of Strength Properties—Selected Models

Author

Listed:
  • Jarosław Piątkowski

    (Department of Material Science, Silesian University of Technology, 40-019 Katowice, Poland)

  • Bożena Gajdzik

    (Department of Industrial Informatics, Silesian University of Technology, 40-019 Katowice, Poland)

  • Aleksander Mesjasz

    (Group Tauron, 43-603 Jaworzono, Poland)

Abstract

The paper presents a research method concerning the application of statistical prognostic models for assessment of material durability and operational reliability of steel for steam pipelines, whose operation has exceeded the working time of 100,000 h. Decisions on the admission of long-lived materials to work for power industry results from extensive diagnostic examinations are based on the results of tests of mechanical properties, microstructure degradation, and corrosion processes. Considering the economic reasons and available data published in diagnostic reports, the determination of failure-free operating time of steam pipelines is based on the results of static tensile tests—tensile strength ( R m ); conventional yield point ( R p ); elongation ( A ) and Vickers hardness ( V ), correlated with the operating time and the media type (fresh steam and secondarily super-heated steam) for the most sensitive element of a pipeline, namely the elbow. The results of changes in strength properties during operation are presented in the form of graphs of the analyzed material feature vs. operating time in the range from zero hours (for a new material) to 300,000 h, taking into account the impact of random and systematic disturbances within the adopted tolerance limits. It has been found that because of the R 2 factor and significance level in the t -Student test for regression and correlation coefficients, exponential, hyperbolic and quadratic models are best fitted to empirical points. Based on the tensile strength results ( R m ), it has been found that the forecast time of the steam pipeline ranges from 193,400 to 258,300 h. Taking the yield strength ( R p ) into account, it has been ascertained that the time ranges from 225,000 to 293,000 h, and for the working time forecast of steam pipelines based on Vickers hardness results, it ranges from 192,100 to 246,800 h.

Suggested Citation

  • Jarosław Piątkowski & Bożena Gajdzik & Aleksander Mesjasz, 2020. "Assessment of Material Durability of Steam Pipelines Based on Statistical Analysis of Strength Properties—Selected Models," Energies, MDPI, vol. 13(14), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3633-:d:384481
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/14/3633/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/14/3633/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhong, Wei & Feng, Hongcui & Wang, Xuguang & Wu, Dingfei & Xue, Minghua & Wang, Jian, 2015. "Online hydraulic calculation and operation optimization of industrial steam heating networks considering heat dissipation in pipes," Energy, Elsevier, vol. 87(C), pages 566-577.
    2. McCarthy, Ryan W. & Ogden, Joan M. & Sperling, Daniel, 2007. "Assessing reliability in energy supply systems," Energy Policy, Elsevier, vol. 35(4), pages 2151-2162, April.
    3. Balakrishnan, N. & Kateri, M., 2008. "On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2971-2975, December.
    4. Rusin, Andrzej & Bieniek, Michał, 2017. "Maintenance planning of power plant elements based on avoided risk value," Energy, Elsevier, vol. 134(C), pages 672-680.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2023. "Assessment of Energy and Heat Consumption Trends and Forecasting in the Small Consumer Sector in Poland Based on Historical Data," Resources, MDPI, vol. 12(9), pages 1-33, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcus Johnen & Stefan Bedbur & Udo Kamps, 2020. "A note on multiple roots of a likelihood equation for Weibull sequential order statistics," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(4), pages 519-525, May.
    2. Benjamin Laumen & Erhard Cramer, 2021. "k‐step stage life testing," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(2), pages 203-233, May.
    3. Wang, Hai & Wang, Haiying & Zhu, Tong & Deng, Wanli, 2017. "A novel model for steam transportation considering drainage loss in pipeline networks," Applied Energy, Elsevier, vol. 188(C), pages 178-189.
    4. Martyna Tomala & Andrzej Rusin & Adam Wojaczek, 2020. "Risk-Based Planning of Diagnostic Testing of Turbines Operating with Increased Flexibility," Energies, MDPI, vol. 13(13), pages 1-16, July.
    5. Holley, Cameron & Lecavalier, Emma, 2017. "Energy governance, energy security and environmental sustainability: A case study from Hong Kong," Energy Policy, Elsevier, vol. 108(C), pages 379-389.
    6. Zhao, Chunfu & Chen, Bin, 2014. "China’s oil security from the supply chain perspective: A review," Applied Energy, Elsevier, vol. 136(C), pages 269-279.
    7. Zhou, Suyang & Chen, Jinyi & Gu, Wei & Fang, Xin & Yuan, Xiaodong, 2023. "An adaptive space-step simulation approach for steam heating network considering condensate loss," Energy, Elsevier, vol. 263(PA).
    8. Franki, Vladimir & Višković, Alfredo, 2021. "Multi-criteria decision support: A case study of Southeast Europe power systems," Utilities Policy, Elsevier, vol. 73(C).
    9. Francesco Corman & Sander Kraijema & Milinko Godjevac & Gabriel Lodewijks, 2017. "Optimizing preventive maintenance policy: A data-driven application for a light rail braking system," Journal of Risk and Reliability, , vol. 231(5), pages 534-545, October.
    10. Joseph Y. J. Chow & Hamid R. Sayarshad, 2016. "Reference Policies for Non-myopic Sequential Network Design and Timing Problems," Networks and Spatial Economics, Springer, vol. 16(4), pages 1183-1209, December.
    11. Benjamin Laumen & Erhard Cramer, 2019. "Stage life testing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(8), pages 632-647, December.
    12. Pareek, Bhuvanesh & Kundu, Debasis & Kumar, Sumit, 2009. "On progressively censored competing risks data for Weibull distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4083-4094, October.
    13. Radovanović, Mirjana & Filipović, Sanja & Pavlović, Dejan, 2017. "Energy security measurement – A sustainable approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1020-1032.
    14. Lin, Boqiang & Raza, Muhammad Yousaf, 2020. "Analysis of energy security indicators and CO2 emissions. A case from a developing economy," Energy, Elsevier, vol. 200(C).
    15. Veldhuis, Anton Johannes & Leach, Matthew & Yang, Aidong, 2018. "The impact of increased decentralised generation on the reliability of an existing electricity network," Applied Energy, Elsevier, vol. 215(C), pages 479-502.
    16. Senderov, S.M. & Vorobev, S.V., 2020. "Approaches to the identification of critical facilities and critical combinations of facilities in the gas industry in terms of its operability," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    17. Essam A. Ahmed, 2017. "Estimation and prediction for the generalized inverted exponential distribution based on progressively first-failure-censored data with application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(9), pages 1576-1608, July.
    18. Wang, Liang & Tripathi, Yogesh Mani & Lodhi, Chandrakant & Zuo, Xuanjia, 2022. "Inference for constant-stress Weibull competing risks model under generalized progressive hybrid censoring," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 70-83.
    19. Bucher, Axel & Segers, Johan, 2015. "Maximum likelihood estimation for the Frechet distribution based on block maxima extracted from a time series," LIDAM Discussion Papers ISBA 2015023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    20. Lin, Xiaojie & Liu, Sibin & Lu, Shuowei & Li, Zhongbo & Zhou, Yi & Yu, Zitao & Zhong, Wei, 2020. "A study on operation control of urban centralized heating system based on cyber-physical systems," Energy, Elsevier, vol. 191(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3633-:d:384481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.