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Evaluation of Supercritical Carbon Dioxide Corrosion by High Temperature Oxidation Experiments and Machine Learning Models

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Abstract

Corrosion behaviors of ferritic-martensitic, austenitic steels, and Ni-based alloys were examined in high-temperature CO2 environments. Aside from the conventional evaluation, we further used machine learning based on experimental data and an existing SCO2 database, from which contributing factors influencing the SCO2 corrosion were quantified and the correlation between the CO2 and SCO2 corrosion was revealed. Among the tested alloys, Ni-based alloys revealed most exceptional corrosion resistance at 500 °C to 800 °C. The random forest model learning the SCO2 database suggested that the most important factor was the material type, followed in sequence by temperature, exposure time, Cr content, flow rate, and pressure. In the ferritic-martensitic steel, we observed a strong correlation between the CO2 evaluation data and the linear regression model. The machine learning models (linear regression, decision tree, and random forest) revealed a relatively weak correlation in the austenitic steel, but a relatively strong correlation in the Ni-based alloy.

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References

  1. V. Dostal, M.J. Driscoll, and P. Hejzlar: A Supercritical Carbon Dioxide Cycle for next Generation Nuclear Reactors (MIT-ANP-TR-100), 2004.

  2. H.J. Lee, H. Kim, and C. Jang: Corros. Sci. Technol., 2014, vol. 13, pp. 41–47.

    Article  Google Scholar 

  3. S.A. Wright, T.M. Conboy, and G.E. Rochau: in 2011 University Turbine Systems Research Workshop, 2011.

  4. K. Brun, P. Friedman, and R. Dennis: Fundamentals and Applications of Supercritical Caron Dioxide (SCO2) Based Power Cycles, Woodhead Publishing, Sawston, 2017.

    Google Scholar 

  5. A. Moisseytsev and J.J. Sienicki: Performance Improvement Options for the Supercritical Carbon Dioxide Brayton Cycle (ANL-GenIV-103), 2007.

  6. Y.I. Chang, P.J. Finck, and C. Grandy: Advanced Burner Test Reactor Preconceptual Design Report (ANL-ABR-1), 2006.

  7. Y. Kato, T. Nitawaki, and Y. Muto: Nucl. Eng. Des., 2004, vol. 230, pp. 195–207.

    Article  CAS  Google Scholar 

  8. C.H. Oh, T. Lillo, W. Windes, T. Totemeier, B. Ward, R. Moore, and R. Barner: Development of a Supercritical Carbon Dioxide Brayton Cycle: Improving VHTR Efficiency and Testing Material Compatibility-Final Report (INL/EXT-06-01271), 2006.

  9. Y.S. Kim, H. Chae, W.C. Kim, J.C. Jeong, H. Kim, J.-G. Kim, and S.Y. Lee: Corros. Sci. Technol., 2020, vol. 19, pp. 122–30.

    Google Scholar 

  10. J. Cho, H. Chae, H. Kim, J.-G. Kim, W.C. Kim, J.C. Jeong, and S.Y. Lee: Corros. Sci. Technol., 2020, vol. 19, pp. 196–202.

    Google Scholar 

  11. H. Lee, H. Chae, J. Cho, W.C. Kim, J.C. Jeong, H. Kim, J.-G. Kim, and S.Y. Lee: Corros. Sci. Technol., 2020, vol. 19, pp. 189–95.

    Google Scholar 

  12. H. Chae, H. Wang, M. Hong, W.C. Kim, J.-G. Kim, H. Kim, and S.Y. Lee: Met. Mater. Int., 2019, vol. 26, pp. 989–97.

    Article  CAS  Google Scholar 

  13. M. Hong, H. Chae, W.C. Kim, J.-G. Kim, H. Kim, and S.Y. Lee: Met. Mater. Int., 2019, vol. 25, pp. 1191–201.

    Article  CAS  Google Scholar 

  14. L.-F. He, P. Roman, B. Leng, K. Sridharan, M. Anderson, and T.R. Allen: Corros. Sci., 2014, vol. 82, pp. 67–76.

    Article  CAS  Google Scholar 

  15. J. Mahaffey, A. Kalra, M. Anderson, and K. Sridharn: in The 4th International Symposium - Supercritical CO2 Power cycles, Pittsburgh, Pennsylvania, 2014.

  16. B.A. Pint and K.A. Unocic: JOM., 2018, vol. 70, pp. 1511–19.

    Article  CAS  Google Scholar 

  17. B.A. Pint, J. Lehmusto, M.J. Lance, and J.R. Keiser: Mater. Corros., 2019, vol. 70, pp. 1400–09.

    Article  CAS  Google Scholar 

  18. J. Lehmusto, J.M. Kurley, M.J. Lance, J.R. Keiser, and B.A. Pint: Oxid. Met., 2020, vol. 94, pp. 95–111.

    Article  CAS  Google Scholar 

  19. B.A. Pint and J.R. Keiser: in The 4th International Symposium—Supercritical CO2 power cycles, Pittsburgh, Pennsylvania, 2014, pp. 1–13.

  20. B.A. Pint, R.G. Brese, and J.R. Keiser: in NACE—Int. Corros. Conf. Ser., 2018, vol. 2018-April, pp. 1–13.

  21. Y. Zhang, X. Pang, S. Qu, X. Li, and K. Gao: Corros. Sci., 2012, vol. 59, pp. 186–97.

    Article  CAS  Google Scholar 

  22. G.A. Zhang and Y.F. Cheng: Corros. Sci., 2009, vol. 51, pp. 87–94.

    Article  CAS  Google Scholar 

  23. X.P. Guo and Y. Tomoe: Corros. Sci., 1999, vol. 41, pp. 1391–402.

    Article  CAS  Google Scholar 

  24. G.A. Zhang and Y.F. Cheng: Corros. Sci., 2010, vol. 52, pp. 2716–24.

    Article  CAS  Google Scholar 

  25. J.K. Heuer and J.F. Stubbins: Corros. Sci., 1999, vol. 41, pp. 1231–43.

    Article  CAS  Google Scholar 

  26. B. Wang, M. Du, J. Zhang, and C.J. Gao: Corros. Sci., 2011, vol. 53, pp. 353–61.

    Article  CAS  Google Scholar 

  27. G.A. Zhang and Y.F. Cheng: Electrochim. Acta., 2011, vol. 56, pp. 1676–85.

    Article  CAS  Google Scholar 

  28. K. Gao, F. Yu, X. Pang, G. Zhang, L. Qiao, W. Chu, and M. Lu: Corros. Sci., 2008, vol. 50, pp. 2796–803.

    Article  CAS  Google Scholar 

  29. G.X. Zhao, M. Zheng, X.H. Lv, X.H. Dong, and H.L. Li: Met. Mater. Int., 2005, vol. 11, pp. 135–40.

    Article  CAS  Google Scholar 

  30. T. Thankachan, K. Soorya Prakash, V. Kavimani, and S.R. Silambarasan: Met. Mater. Int., 2021, vol. 27, pp. 220–34.

    Article  CAS  Google Scholar 

  31. D. Hong, S. Kwon, and C. Yim: Met. Mater. Int., 2021, vol. 27, pp. 298–305.

    Article  CAS  Google Scholar 

  32. S.K. Bhattacharya, R. Sahara, and T. Narushima: Oxid. Met., 2020, vol. 94, pp. 205–18.

    Article  CAS  Google Scholar 

  33. Y. Diao, L. Yan, and K. Gao: Mater. Des., 2021, vol. 198, art. no. 109326.

    Article  CAS  Google Scholar 

  34. Y. Zhi, D. Fu, D. Zhang, T. Yang, and X. Li: Metals (Basel)., 2019, vol. 9, art. no. 383.

    Article  Google Scholar 

  35. Z. Pei, D. Zhang, Y. Zhi, T. Yang, L. **, D. Fu, X. Cheng, H.A. Terryn, J.M.C. Mol, and X. Li: Corros. Sci., 2020, vol. 170, art. no. 108697.

    Article  CAS  Google Scholar 

  36. G.G. Lee, E.H. Lee, S.W. Kim, K.M. Kim, and D.J. Kim: Corros. Sci.Technol., 2019, vol. 18, pp. 61–71.

    Google Scholar 

  37. T. Furukawa, Y. Inagaki, and M. Aritomi: Prog. Nucl. Energy., 2011, vol. 53, pp. 1050–55.

    Article  CAS  Google Scholar 

  38. M.W. Dunlevy: Massachusetts Institute of Technology, 2007.

  39. J.P. Gibbs: Massachusetts Institute of Technology, 2010.

  40. H. Saari, C. Parks, R. Petrusenko, B. Maybee, and K. Zanganeh: in The 4th International Symposium—Supercritical CO2 Power cycles, Pittsburgh, Pennsylvania, 2014.

  41. F. Rouillard, F. Charten, and G. Moine: Corrosion., 2011, vol. 67, pp. 095001–7.

    Article  Google Scholar 

  42. J.Y. Lim, T.J. Mckrell, G. Eastwick, and R.G. Ballinger: in CORROSION 2008, NACE International, 2008.

  43. L. Tan, M. Anderson, D. Taylor, and T.R. Allen: Corros. Sci., 2011, vol. 53, pp. 3273–80.

    Article  CAS  Google Scholar 

  44. V. Firouzdor, K. Sridharan, G. Cao, M. Anderson, and T.R. Allen: Corros. Sci., 2013, vol. 69, pp. 281–91.

    Article  CAS  Google Scholar 

  45. G. Cao, V. Firouzdor, K. Sridharan, M. Anderson, and T.R. Allen: Corros. Sci., 2012, vol. 60, pp. 246–55.

    Article  CAS  Google Scholar 

  46. T. Furukawa, Y. Inagaki, and M. Aritomi: J. Power Energy Syst., 2010, vol. 4, pp. 252–61.

    Article  Google Scholar 

  47. J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek, L. Zagar, J. Zbontar, M. Zitnik, and B. Zupan: J. Mach. Learn. Res., 2013, vol. 14, pp. 2349–53.

    Google Scholar 

  48. D.A. Freedman: Statistical Models: Theory and Practice, Cambridge University Press, Cambridge, 2009.

    Book  Google Scholar 

  49. B. Kamiński, M. Jakubczyk, and P. Szufel: Cent. Eur. J. Oper. Res., 2018, vol. 26, pp. 135–59.

    Article  Google Scholar 

  50. L. Breiman: Mach. Learn., 2001, vol. 45, pp. 5–32.

    Article  Google Scholar 

  51. A. Celisse: Ann. Stat., 2014, vol. 42, pp. 1879–910.

    Article  Google Scholar 

  52. L.I. Lin: Biomatrics., 1989, vol. 45, pp. 255–68.

    Article  CAS  Google Scholar 

  53. H. Chae, S. Seo, Y.C. Jung, and S.Y. Lee: KEPCO J. Electr. Power Energy., 2015, vol. 1, pp. 109–13.

    Article  Google Scholar 

  54. H. Chae, S. Seo, Y.C. Jung, and S.Y. Lee: Korean J. Mater. Res., 2017, vol. 27, pp. 552–56.

    Article  CAS  Google Scholar 

  55. D. Chaliampalias, G. Vourlias, E. Pavlidou, and K. Chrissafis: J. Therm. Anal. Calorim., 2013, vol. 113, pp. 1309–15.

    Article  CAS  Google Scholar 

  56. V. Deodeshmukh and B.A. Pint: Long-Term Performance of High Temperature Alloys in Oxidizing Environments and Supercritical CO2, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2019.

  57. Y.-S. Choi and S. Nešić: in CORROSION 2009, NACE International, 2009.

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Acknowledgments

This work was supported by the National Research Foundation (NRF) Grant funded by the Korean Government (2021R1A4A1031494) and also supported by the Korea Institute for Advancement of Technology (KIAT) Grant funded by the Korean Government (MOTIE) (P0002019, The Competency Development Program for Industry Specialist). EWH appreciates the financial support from the “Center for the Semiconductor Technology Research” of the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, also supported in part by the Ministry of Science and Technology (MOST), Taiwan, under Grant MOST 109-2634-F-009-029, 108-2221-E-009-131-MY4 and Industrial Technology Research Institute (ITRI) Program 109A502.

Author Contributions

HC: methodology, formal analysis, writing-original draft, visualization. SS: investigation, methodology. YCJ: conceptualization, methodology. E-WH: validation, writing-review & editing. JJ: validation, writing-review & editing. JHH: validation, writing-review & editing. SYL: methodology, validation, writing-original draft, supervision, project administration, funding acquisition.

Data Availability

The data that support the findings of this study are available upon request.

Conflict of interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Materials Science and Engineering, Chungnam National University, Daejeon, 34134, Republic of Korea

    Hobyung Chae, Sukho Seo, Jun Hyun Han & Soo Yeol Lee

  2. Korea Electric Power Research Institute, Daejeon, 34056, Republic of Korea

    Yong Chan Jung

  3. Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 30013, Taiwan

    E-Wen Huang

  4. Department of Materials Science and Engineering, Indian Institute of Technology, New Delhi, 110016, India

    Jayant Jain

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  1. Hobyung Chae
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Chae, H., Seo, S., Jung, Y.C. et al. Evaluation of Supercritical Carbon Dioxide Corrosion by High Temperature Oxidation Experiments and Machine Learning Models. Metall Mater Trans A 53, 2614–2626 (2022). https://doi.org/10.1007/s11661-022-06691-5

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