The Influence of the Components of the 06ХН28МДТ Alloy (Analogue of AISI904L Steel) and the Parameters of the Model Chloride-Containing Recycled Water of Enterprises on Its Pitting Resistance

A. V. Dzhus$^{1}$, O. E. Narivskyi$^{2}$, S. A. Subbotin$^{1}$, T. V. Pulina$^{1}$, G. V. Snizhnoi$^{1}$, S. D. Leoshchenko$^{1}$

$^{1}$Национальный университет «Запорожская политехника», ул. Жуковского, 64, 69063 Запорожье, Украина
$^{2}$LLC ‘Ukrspetsmash’, 7 Haharina Str., UA-71100 Berdiansk, Ukraine

Получена: 13.07.20223; окончательный вариант - 25.08.2023. Скачать: PDF

Two mathematical models, which describe the dependence of critical pitting temperatures of 06ХН28МДТ alloy (analogue of AISI904L steel) in model recycled water with pH 4–8 and chloride concentration from 350 up to 600 mg/l on chemical composition and structure, are developed. They are based on multivariate regressions with pairwise combinations of features and a three-layer neural network of direct signal. Applying the developed mathematical models, it is found that the critical pitting temperatures of the 06ХН28МДТ alloy increase with an increase in the pH of model recycled water, the content of Cr, Mo, Cu, the volume of titanium nitrides within it, and a decrease in the medium austenite-grain diameter, the content of nickel within the standard, and chlorides in the media. At the same time, the analysis of the developed mathematical model, which is based on multivariate regressions with paired combinations of features (alloy and media parameters), reveals that the content of Cr and Ni in the alloy in combination with the medium austenite-grain diameter most significantly affect its pitting resistance in model recycled waters, and an effect of the Cr content in combination with pH and chloride concentration in the media is somewhat lower, but much higher than an effect of the Mo and Cu content, and the volume of titanium nitrides in combination with the media parameters. The developed mathematical model, which is based on a three-layer neural network of direct signal propagation, is recommended for predicting the pitting resistance of heat exchangers made of 06ХН28МДТ alloy or AISI904L steel when operating in recycled water. In addition, the developed mathematical model, based on multivariate regressions with paired combinations of features (alloy and media parameters), is recommended for selecting the optimal melts of this alloy or steel, which are most resistant to pitting in recycled water.

Ключевые слова: pitting resistance, heat exchanger, recycled water, structural heterogeneity, pitting resistance prediction.

URL: https://mfint.imp.kiev.ua/ru/abstract/v46/i04/0371.html

PACS: 68.47.Gh, 81.05.Ni, 81.40.Np, 81.65.Kn, 82.45.Bb, 88.30.Nn


ЦИТИРОВАННАЯ ЛИТЕРАТУРА
  1. Tekhnicheskie Usloviya. TU 3.17.–00217417-024-97. Teploobmenniki Plastinchastyye Razbornyye [Technical Specifications TU 3.17.–00217417-024-97. Plate-Like Heat Exchangers] (PrAT Zavod ‘Pavlogradkhimmash’: 1998) (in Russian).
  2. G. Ya. Vorob’eva, Korrozionnaya Stoykost’ Materialov v Agressivnykh Sredakh Khimicheskikh Proizvodstv [Corrosion Resistance of Materials in Aggressive Environments of Chemical Industries] (Moskva: Khimiya: 1985) (in Russian).
  3. O. E. Narivs’kyi, Mater. Sci., 41: 122 (2005). Crossref
  4. O. E. Narivs’kyi, Mater. Sci., 43: 124 (2007). Crossref
  5. V. A. Kachanov and D. G. Nikitin, Issledovanie Sklonnosti Stali 12X18H10T k Tochechno-Yazvennoy i Shchelevoy Korrozii v Neytral’nykh Sredakh Primenitel’no k Razbornym Plastinchatym Teploobmennikam [Study of 12X18H10T Steel Susceptibility to Pitting and Crevice Corrosion in Neutral Media as Applied to Plate Like Heat Exchangers] (Kharkiv: UkrNIIhimmash: 1985) (in Russian).
  6. A. Narivskiy, R. Atchibayev, A. Muradov, K. Mukashevand Y. Yar-Mukhamedov, Int. Multidisciplinary Sci. Conf. Surveying Geology and Mining Ecology Management—SGEM, p. 267 (2018).
  7. A. Narivskiy, G. Yar-Mukhamedova, E. Temirgaliyeva, M. Mukhtarova, and Y. Yar-Mukhamedov, Int. Multidisciplinary Sci. Conf. Surveying Geology and Mining Ecology Management—SGEM, vol. 1, p. 63 (2016).
  8. GOST 9.912-89. Metody Uskorennykh Ispytaniy na Stoykost’ k Pittingovoy Korrozii [GOST 9.912-89. Accelerated Pitting Resistance Test Methods] (Izdatel’stvo Standartov: 1989) (in Russian).
  9. O. E. Narivs’kyi and S. B. Belikov, Mater. Sci., 44: 573 (2008). Crossref
  10. D. A. Frudman, Statistical Models: Theory and Practice (Cambridge: Cambridge University Press: 2005).
  11. S. O. Haykin, Neutral Networks and Learning Machines (London: Pearson: 2008).
  12. J. Nacedal and S. Wright, Numeral Optimisation (New York: Springer-Verlag: 2006).
  13. O. E. Narivskyi, S. A. Subbotin, T. V. Pulina, and M. S. Khoma, Mater. Sci., 58: 41 (2022). Crossref
  14. O. E. Narivskyi, S. B. Belikov, S. A. Subbotin, and T. V. Pulina, Mater. Sci.. 57: 291 (2021). Crossref
  15. O. Narivs’kyi, R. Atchibayev, A. Kemelzhanova, G. Yar-Mukhamedova, G. Snizhnoy, S. Subbotin, and A. Beisebayeva, Eurasian Chem.-Tech. J., 24, No. 4: 295 (2022). Crossref
  16. P. C. Pistorius and G. T. Burstein, Corrosion Sci., 33, Iss. 12: 1885 (1992). Crossref
  17. P. C. Pistorious and G. T. Burstein, Corrosion Sci., 36, Iss. 3: 525 (1994). Crossref
  18. J.-O. Nilsson, Mater. Sci. Technol., 8, Iss. 8: 685 (1992). Crossref
  19. N. Bastos, S. M. Tavares, Francis Dalard, and R. P. Nogueira, Scripta Mater., 57: 913 (2007). Crossref
  20. R. J. Brigham and E. W. Tozer, Corrosion, 29: 33 (1973). Crossref
  21. J. M. Salinas-Bravo and R. C. Newman, Corrosion Sci., 36: 67 (1994). Crossref
  22. Y. Tsutsumi, A. Nishikata, and T. Tsuru, Corrosion Sci., 49: 1394 (2007). Crossref
  23. P. C. Pistorious and G. T. Burstein, Corrosion Sci., 36, Iss. 3: 525 (1994). Crossref
  24. L. Martinez, B. Melki, G. Berthome, B. Baroux, and F. Perez, Surf. Coat. Technol., 201, Iss. 3–4: 1671 (2006). Crossref
  25. C. Alonso, M. Castellote, and C. Andrade, Electrochem. Acta, 47: 3469 (2002). Crossref
  26. W. S. Li and J. L. Luo, Corrosion Sci., 44: 1695 (2002). Crossref
  27. J. H. Wang, C. C. Su and Z. Szklarska-Smialowska, Corrosion, 44, Iss. 10: 732 (1988). Crossref
  28. O. E. Narivs’kyy, Mater. Sci., 43: 256 (2007). Crossref
  29. J. O. Park, S. Matsch, and H. Böhni, J. Electrochem. Soc., 149: 34 (2002). Crossref
  30. U. M. Ehrnsten, J. Likonen, L. I. Carpen, and O. A. Varjonen, Mater. Characterization, 36, Iss. 4–5: 279 (1996). Crossref