Operational Dependence of Galvanized Steel Corrosion Rate on Its Structural Weight Loss and Immersion-Point pH in Sea Water Environment

C. I. Nwoye^1, , E. C. Chinwuko², I. E. Nwosu³, W. C. Onyia⁴, N. I. Amalu⁵ and P. C. Nwosu⁶

¹Department of Metallurgical and Materials Engineering, Nnamdi Azikiwe University, Awka, Nigeria

²Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria

³Department of Environmental Technology, Federal University of Technology, Owerri, Nigeria

⁴Department of Metallurgical and Materials Engineering, Enugu State University of Science & Technology Enugu, Nigeria

⁵Project Development Institute Enugu, Nigeria

⁶Department of Mechanical Engineering, Federal Polytechnic, Nekede, Nigeria

Cite this paper:
C. I. Nwoye, E. C. Chinwuko, I. E. Nwosu, W. C. Onyia, N. I. Amalu and P. C. Nwosu. Operational Dependence of Galvanized Steel Corrosion Rate on Its Structural Weight Loss and Immersion-Point pH in Sea Water Environment. American Journal of Mining and Metallurgy. 2014; 2(4):81-87. doi: 10.12691/ajmm-2-4-4

Abstract

The operational dependence of galvanized steel corrosion rate on its structural weight loss and immersion-point pH (pH of stagnant sea water trapped in holes and grooves of galvanized steel made structures or equipment) in sea water environment was studied. SEM analysis of the surface structure of the corroded steel revealed that the adherent and compact nature of the white rust layers absorbed on the zinc surface affected the level of corrosion attacks on the zinc and invariably on the steel structure. The corrosion rate of the galvanized steel decreased with increase in the steel weight loss and immersion-point pH. Formation and presence of (ZnOH)₂ in corrosion medium retarded the corrosion process because of its alkaline nature. A two-factorial model was derived, validated and used for the predictive evaluation of the galvanized steel corrosion rate. The validity of the model was rooted on the core model expression ζ + 5 x 10- 5 lnɤ + 6.166 x 10-5 = - 1.5 x 10- 5 ϑ2 + 0.0001ϑ where both sides of the expression are correspondingly approximately equal. The standard errors incurred in predicting the corrosion rate for each value of the weight loss & immersion-point pH considered as obtained from experiment, derived model and regression model-predicted results were 1.516 x 10-7, 5.415 x 10-7 and 2.423 x 10-9 & 1.39 x 10-7, 4.529 x 10-7 and 2.548 x 10-8 % respectively. Deviational analysis indicates that the derived model operates most viably and reliably within a deviation range of 0-15.38% from experimental results. This translated into about 84% operational confidence and response level for the derived model as well as 0.84 reliability response coefficient of the corrosion rate to the collective operational contributions of weight loss and immersion-point pH in the sea environment.

Keywords:
galvanized steel corrosion rate immersion-point ph weight loss sea water environment

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References:

[1]	Engineering Bulletin (Evapco). (2009). White Rust on Galvanized Sheet, No. #34A,
[2]	Zhang, X., Russo, S. L., Miotello, A., Guzman, L., Cattaruzza, E., Bonora, P.L. and Benedetti, L. (2001). Surf. Coat. Technol., 141:187-193.
[3]	Volovitch, P., Allely, C., Ogle, K. (2009). Corros. Sci., 5: 1251-1562.
[4]	Boshkov, N., Petrov, K., Vitkova, S., Nemska, S., and Raichevsky, G. (2002). Surf. Coat. Technol., 157: 171-178.
[5]	Oritz, Z. I., Diaz-Arista, P., Meas, Y., Ortega-Borges, R. and Trejo, G. (2009). Corros. Sci., 51: 2703-2715.
[6]	Zapponi, M., Perez, T., Ramos, C., and Saragovi, C. (2005). Corros. Sci., 47: 923-936.
[7]	Azmat, N. S., Ralston, K. D., Muddle, B. C., and Cole, I. S. (2011). Corros. Sci., 53: 1604-1615.
[8]	Yadav, A. P., Nishikata A., and Tsuru, T. (2004). Corros. Sci., 46: 169-181.
[9]	Tsuru, T., (2010). Corros. Eng. 59: 321-329.
[10]	El-Sayed M. Sherif, A. A. Almajid, B. Bairamov, A. K., and Eissa, A. (2012). Int. J. Electrochem. Sci., 7: 2796-2810
[11]	Lin, B. L., Lu, J. T., and Kong, G. (2008). Corros. Sci., 4: 962-967.
[12]	Bajat, J.B., Stankovic, S., Jokic, B. M., Stevanovic, S. I., (2010). Surf. Coat. Technol., 204: 2745-2753.
[13]	Kartsonakis, I. A., Balaskas, A. C., Koumoulos, E. P., Charitidis, C. A., and Kordas, G. C., (2012). Corros. Sci., 57: 30-41.
[14]	Zhang, X. G. (2005). Corrosion of zinc and zinc alloys, Corrosion: Materials, ASM Handbook, p. 402-406.
[15]	Hamlaoui,Y., Tifouti, L., Pedraza, F.(2010). Corros. Sci., 52: 1883-1888.
[16]	Nwoye, C. I. (2008). C-NIKBRAN Data Analytical Memory (Software).