Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks
Main Article Content
Abstract
The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.
Article Details
Section
How to Cite
References
• Abd El Haleem, S. M. et al., 2010. Environmental factors affecting the corrosion behavior of reinforcing steel II. Role of some anions in the initiation and inhibition of pitting corrosion of steel in Ca(OH)2 solutions’, Corrosion Science, 52(2), pp. 292–302. DOI: 10.1016/j.corsci.2009.09.004.
• ASTM,G48,.2005 Standard Test Methods for Pitting and Crevice Corrosion Resistance of Stainless Steels and Related Alloys by the Use of Ferric Chloride Solution, Annual Book of ASTM Standards, ASTM International.
• ASTM,G46,.2005 Standard Guide for Examination and Evaluation of Pitting Corrosion 1 , Annual Book of ASTM Standards.
• Bhandari, J., Khan, F., Abbassi, R., Garaniya, V., Ojeda, R., 2015. Modeling of Pitting Corrosion in Marine and Offshore Steel Structures-A Technical Review. Journal of Loss Prevention in the Process Industries, 37, 39–62. DOI:10.1016/j.jlp.2015.06.008.
• Bhandari J., Lau S., Abbassi R., Garaniya V., Ojeda R., Lisson D., and Khan. F., 2017. Accelerated Pitting Corrosion Test of 304 Stainless Steel using ASTM G48 Experimental Investigation and Concomitant Challenges, Loss Prevention in the Process Industries, DOI: 10.1016/j.jlp.2017.02.025.
• Boucherit, M. N. and Tebib, D., 2005. A study of carbon steels in basic pitting environments, Anti-Corrosion Methods and Materials, 52(6), pp. 365–370. DOI: 10.1108/00035590510624703.
• Boucherit, M. N. et al. 2019. Modeling input data interactions for the optimization of artificial neural networks used in the prediction of pitting corrosion, Anti-Corrosion Methods and Materials, 66(4), pp. 369–378. DOI: 10.1108/ACMM-07-2018-1976.
• Choi, Y.-S., Shim, J.-J., and Kim, J.-G., 2005 Effects of Cr, Cu, Ni and Ca on the Corrosion Behavior of Low Carbon Steel in Synthetic Tap Water. J. Alloy Comp. 391, 162–169. DOI:10.1016/j.jallcom.2004.07.081
• Codaro, E. N. et al., 2002. An image processing method for morphology characterization and pitting corrosion evaluation, Materials Science and Engineering A, 334(1–2), pp. 298–306. DOI: 10.1016/S0921-5093(01)01892-5.
• Ghidini, T., and Dalle Donne, C., 2009. Fatigue Life Predictions Using Fracture Mechanics Methods. Eng. Fracture Mech., 76 (1), 134–148. DOI:10.1016/ j.engfracmech.2008.07.008.
• Jiménez-Come, M. J. et al., 2015. Characterization of pitting corrosion of stainless steel using artificial neural networks, Materials and Corrosion, 66(10), pp. 1084–1091. DOI: 10.1002/maco.201408173.
• Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Network 2, 359–366.
• Khadom, A.A., Hassan, A.F., Abod, B.M., 2015. Evaluation of environmentally friendly inhibitor for galvanic corrosion of steel–copper couple in petroleum wastewater. Process Safety and Environmental Protection 98, 93-101.
• Kh. Hussein, 2015. Application of Box-Behnken Method Based ANN-GA to Prediction of wt % of Doping Elements for Incoloy 800H Coated Coated by Aluminizing-Chromizing, Journal of Engineering, 21(9).
• Kolawole, S. K., Kolawole, F. O., Enegela, O. P., Adewoye, O. O., Soboyejo, A. B. O., and Soboyejo, W. O., 2016. Pitting Corrosion of a Low Carbon Steel in Corrosive Environments: Experiments and Models. Amr 1132, 349–365. DOI:10.4028/www.scientific.net.
• Krzemień, A., Więckol-Ryk, A., Smoliński, A., Koteras, A., Więcław-Solny, L., 2016 Assessing the risk of corrosion in amine-based CO2 capture process. Journal of Loss Prevention in the Process Industries 43, 189-197.
• Li, W.-f., Zhou, Y.-j., and Xue, Y., 2012. Corrosion Behavior of 110S Tube Steel in Environments of High H2S and CO2 Content, J. Iron Steel Res. Int. 19, 59–6, DOI:10.1016/S1006-706X(13)60033-3.
• M.E.A. BenSeghier et al., 2021. Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines, Process Safety and Environmental Protection, 147, 818-833
• M. Mohammad, H., J. Hammadi, N., and M. Lafta, R. 2012. Prediction of Pitting Corrosion Characteristics using Artificial Neural Networks, International Journal of Computer.
• Qu Z., Tang D., Wang Z., Li X., Chen H., and Lv Y., 2021. Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods. Frontiers in Materials. Volume 8. Article 733813. DOI: 10.3389/fmats.2021.733813. www.frontiersin.org.
• Shakir, I. K. et al., 2018. Pitting Corrosion Behavior of 304 SS and 316 SS Alloys in Aqueous Chloride and Bromide Solutions, Journal of Engineering, 24(1), pp. 53–69.
• Szklarska-Smialowska, Z., 1986 Pitting corrosion of metals. National Association of Corrosion Engineers.
• Tang, Y. et al., 2019 Effect of surface roughness on pitting corrosion of 2205 duplex stainless steel investigated by electrochemical noise measurements, Materials, 12(5). DOI: 10.3390/ma12050738