Improving the Accuracy of Electrochemical Experiment Data for Artificial Intelligence–Based Carbon Steel Corrosion Analysis
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 15, Issue 3
Abstract
Carbon steel is an essential material for constructing pipelines in different industries, but serious casualties and significant economic loss could occur due to the corrosion of the pipeline steel. The artificial intelligence–based method has been shown successful in evaluating the corrosion problem in pipelines. The accuracy of the corrosion data used in the artificial intelligence–based method is crucial. The data can be collected from field tests and laboratory experiments. Compared with field tests, the corrosion data obtained through laboratory experiments, such as electrochemical measurements, are more comprehensive, but the results are often inaccurate due to the nonuniform surface state. This study aims to propose a method to improve the accuracy of electrochemical experiment data by modified the surface morphology. To achieve the objective, a technique was designed to prepare the uniform morphology. Moreover, different electrochemical measurements, texture analysis, micromorphological characteristics, and chemical composition analysis were performed under the nonuniform and uniform surface conditions in this work. It was found that the addition of was effective in preparing a homogenous electrode surface while almost having no additional impact on the corrosion process. The results proved that the surface morphology of the carbon steel electrode significantly influenced the results of the open circuit potential and potentiodynamic polarization. A uniform surface could improve the accuracy of electrochemical measurements.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by Zhejiang Provincial Natural Science Foundation (No. LQ22E040003).
References
Abdel-Gaber, A. M., B. A. Abd-El-Nabey, I. M. Sidahmed, A. M. El-Zayady, and M. Saadawy. 2006. “Kinetics and thermodynamics of aluminium dissolution in 1.0 M sulphuric acid containing chloride ions.” Mater. Chem. Phys. 98 (2–3): 291–297. https://doi.org/10.1016/j.matchemphys.2005.09.023.
Abed, T. M., M. E. Torbaghan, A. Hojjati, C. D. F. Rogers, and D. N. Chapman. 2020. “Experimental investigation into the effects of cast-iron pipe corrosion on GPR detection performance in clay soils.” J. Pipeline Syst. Eng. Pract. 11 (4): 04020040. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000491.
Aditiyawarman, T., J. W. Soedarsono, A. P. S. Kaban, R. Riastuti, and H. Rahmadani. 2023. “The study of artificial intelligent in risk-based inspection assessment and screening: A study case of inline inspection.” J. Risk Uncertainty Eng. Syst. Part B: Mech. Eng. 9 (1): 011204. https://doi.org/10.1115/1.4054969.
Ashari, R., A. Eslami, and M. Shamanian. 2019. “Corrosion and electrochemical conditions of pipeline steel under tape coating disbondments: Effect of disbondment gap size and morphology.” J. Pipeline Syst. Eng. Pract. 11 (1): 04019051. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000413.
Chapagain, A., D. Acharya, A. K. Das, K. Chhetri, H. B. Oli, and A. P. Yadav. 2022. “Alkaloid of Rhynchostylis retusa as green inhibitor for mild steel corrosion in 1 M H2SO4 solution.” Electrochem 3 (2): 211–224. https://doi.org/10.3390/electrochem3020013.
Córdoba-Torres, P., T. J. Mesquita, O. Devos, B. Tribollet, V. Roche, and R. P. Nogueira. 2012. “On the intrinsic coupling between constant-phase element parameters and Q in electrochemical impedance spectroscopy.” Electrochim. Acta 72 (Jun): 172–178. https://doi.org/10.1016/j.electacta.2012.04.020.
Curioni, M., F. Scenini, T. Monetta, and F. Bellucci. 2015. “Correlation between electrochemical impedance measurements and corrosion rate of magnesium investigated by real-time hydrogen measurement and optical imaging.” Electrochim. Acta 166 (Jun): 372–384. https://doi.org/10.1016/j.electacta.2015.03.050.
Daniel, E. F., J. Dong, X. Li, I.-I. N. Etim, I. I. Udoh, R. Ma, L. Chen, and C. Wang. 2022. “Corrosion behaviour of carbon steel fasteners in neutral chloride solution.” Acta Metall. Sin. Engl. Lett. 35 (4): 563–576. https://doi.org/10.1007/s40195-021-01284-4.
El Mouaden, K., D. S. Chauhan, M. A. Quraishi, and L. Bazzi. 2020. “Thiocarbohydrazide-crosslinked chitosan as a bioinspired corrosion inhibitor for protection of stainless steel in 3.5% NaCl.” Sustainable Chem. Pharm. 15 (Mar): 100213. https://doi.org/10.1016/j.scp.2020.100213.
Fazal, B. R., T. Becker, B. Kinsella, and K. Lepkova. 2022. “A review of plant extracts as green corrosion inhibitors for corrosion of carbon steel.” NPJ Mater. Degrad. 6 (1): 14. https://doi.org/10.1038/s41529-021-00201-5.
Feliciano, F. F., F. R. Leta, and F. B. Mainier. 2015. “Texture digital analysis for corrosion monitoring.” Corros. Sci. 93 (Apr): 138–147. https://doi.org/10.1016/j.corsci.2015.01.017.
Fu, J., J. Xue, Y. Wang, Z. Liu, and C. Shan. 2018. “Malware visualization for fine-grained classification.” IEEE Access 6 (Feb): 14510–14523. https://doi.org/10.1109/ACCESS.2018.2805301.
Gateman, S. M., O. Gharbi, M. Turmine, and V. Vivier. 2021. “Measuring changes in wettability and surface area during micro droplet corrosion measurements.” Electrochim. Acta 399 (Dec): 139402. https://doi.org/10.1016/j.electacta.2021.139402.
Gomma, G. K. 1998. “Mechanism of corrosion behaviour of carbon steel in tartaric and malic acid in the presence of Fe2+ ion.” Mater. Chem. Phys. 52 (3): 200–206. https://doi.org/10.1016/S0254-0584(97)02046-4.
Hoshi, Y., T. Oda, I. Shitanda, and M. Itagaki. 2017. “Communication-real-time surface observation of copper during anodic polarization with channel flow double electrode.” J. Electrochem. Soc. 164 (7): 450–452. https://doi.org/10.1149/2.0071709jes.
Kazemi, M., S. Ahangarani, M. Esmailian, and A. Shanaghi. 2022. “Investigating the corrosion performance of Ti-6Al-4V biomaterial alloy with hydroxyapatite coating by artificial neural network.” Mater. Sci. Eng. B 278 (Apr): 115644. https://doi.org/10.1016/j.mseb.2022.115644.
Kelly, R. G., J. R. Scully, D. W. Shoesmith, and R. G. Buchheit. 2002. Electrochemical techniques in corrosion science and engineering. New York: Marcel Dekker.
Kim, S. K., I. J. Park, D. Y. Lee, and J. G. Kim. 2013. “Influence of surface roughness on the electrochemical behavior of carbon steel.” J. Appl. Electrochem. 43 (5): 507–514. https://doi.org/10.1007/s10800-013-0534-5.
Li, X., D. Zhang, Z. Liu, Z. Li, C. Du, and C. Dong. 2015. “Share corrosion data.” Nature 527 (7579): 441–442. https://doi.org/10.1038/527441a.
Li, Y., and Y. F. Cheng. 2016. “Effect of surface finishing on early-stage corrosion of a carbon steel studied by electrochemical and atomic force microscope characterizations.” Appl. Surf. Sci. 366 (Mar): 95–103. https://doi.org/10.1016/j.apsusc.2016.01.081.
Liu, L., Q. Fu, C. Peng, B. Wei, Q. Qin, L. Gao, Y. Bai, J. Xu, and C. Sun. 2022. “Effect of glutaraldehyde as a biocide against the microbiologically influenced corrosion of X80 steel pipeline.” J. Pipeline Syst. Eng. Pract. 13 (3): 04022014. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000650.
Ma, S., Y. Du, S. Wang, and Y. Su. 2023. “Application of machine learning in material corrosion research.” Corros. Rev. 41 (4): 417–426. https://doi.org/10.1515/corrrev-2022-0089.
Newman, J., and K. E. Thomas-Alyea. 2004. Electrochemical systems. Berkeley, CA: Wiley.
Rathod, M. R., S. K. Rajappa, B. M. Praveen, and D. K. Bharath. 2021. “Investigation of Dolichandra unguis-cati leaves extract as a corrosion inhibitor for mild steel in acid medium.” Curr. Res. Green Sustainable Chem. 4 (Jan): 100113. https://doi.org/10.1016/j.crgsc.2021.100113.
Sarac, B., et al. 2021. “Transition metal-based high entropy alloy microfiber electrodes: Corrosion behavior and hydrogen activity.” Corros. Sci. 193 (Dec): 109880. https://doi.org/10.1016/j.corsci.2021.109880.
Saraswat, V., and M. Yadav. 2021. “Improved corrosion resistant performance of mild steel under acid environment by novel carbon dots as green corrosion inhibitor.” Colloids Surf. 627 (Oct): 127172. https://doi.org/10.1016/j.colsurfa.2021.127172.
Seghier, M. E. A. B., D. Höche, and M. Zheludkevich. 2022. “Prediction of the internal corrosion rate for oil and gas pipeline: Implementation of ensemble learning techniques.” J. Nat. Gas Sci. Eng. 99 (Mar): 104425. https://doi.org/10.1016/j.jngse.2022.104425.
Wang, W., Y. Zhou, B. Liang, B. Wang, M. Zhang, and S. Tan. 2022. “Electrochemical behavior and corrosion rate prediction study of alloy 690.” Int. J. Adv. Nucl. Reactor Des. Technol. 4 (4): 171–176. https://doi.org/10.1016/j.jandt.2022.11.001.
Wei, B., Q. Qin, Q. Fu, Y. Bai, J. Xu, C. Yu, C. Sun, and W. Ke. 2020. “X80 steel corrosion induced by alternating current in water-saturated acidic soil.” Corrosion 76 (3): 248–267. https://doi.org/10.5006/3418.
Woldesellasse, H., and S. Tesfamariam. 2021. “Handling incomplete and missing data in corrosion pit measurement database using imputation methods: Model development using artificial neural network.” J. Pipeline Syst. Eng. Pract. 12 (3): 04021033. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000572.
Xia, D.-H., et al. 2022. “Electrochemical measurements used for assessment of corrosion and protection of metallic materials in the field: A critical review.” J. Mater. Sci. Technol. 112 (Jun): 151–183. https://doi.org/10.1016/j.jmst.2021.11.004.
Xu, Y., Q. Zhou, L. Liu, Q. Zhang, S. Song, and Y. Huang. 2020. “Exploring the corrosion performances of carbon steel in flowing natural sea water and synthetic sea waters.” Corros. Eng. Sci. Technol. 55 (7): 579–588. https://doi.org/10.1080/1478422X.2020.1765476.
Xue, F., X. Wei, J. Dong, C. Wang, and W. Ke. 2019. “Effect of chloride ion on corrosion behavior of low carbon steel in 0.1 M NaHCO3 solution with different dissolved oxygen concentrations.” J. Mater. Sci. Technol. 35 (4): 596–603. https://doi.org/10.1016/j.jmst.2018.10.001.
Zhang, T., W. Liu, L. Chen, B. Dong, W. Yang, Y. Fan, and Y. Zhao. 2021. “On how the corrosion behavior and the functions of Cu, Ni and Mo of the weathering steel in environments with different NaCl concentrations.” Corros. Sci. 192 (Nov): 109851. https://doi.org/10.1016/j.corsci.2021.109851.
Zhang, X. L., Z. H. Jiang, Z. P. Yao, Y. Song, and Z. D. Wu. 2009. “Effects of scan rate on the potentiodynamic polarization curve obtained to determine the Tafel slopes and corrosion current density.” Corros. Sci. 51 (3): 581–587. https://doi.org/10.1016/j.corsci.2008.12.005.
Zhu, M., B. Zhao, Y. Yuan, S. Guo, and G. Wei. 2021. “Study on corrosion behavior and mechanism of CoCrFeMnNi HEA interfered by AC current in simulated alkaline soil environment.” J. Electroanal. Chem. 882 (Feb): 115026. https://doi.org/10.1016/j.jelechem.2021.115026.
Zhu, Y., X.-M. Qian, Z.-Y. Liu, P. Huang, and M.-Q. Yuan. 2015. “Analysis and assessment of the Qingdao crude oil vapor explosion accident: Lessons learnt.” J. Loss Prev. Process Ind. 33 (Jan): 289–303. https://doi.org/10.1016/j.jlp.2015.01.004.
Zou, Y., J. Wang, Q. Bai, L. L. Zhang, X. Peng, and X. F. Kong. 2012. “Potential distribution characteristics of mild steel in seawater.” Corros. Sci. 57 (Apr): 202–208. https://doi.org/10.1016/j.corsci.2011.12.017.
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Published In
Journal of Pipeline Systems Engineering and Practice
Volume 15 • Issue 3 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 1, 2023
Accepted: Mar 7, 2024
Published online: May 30, 2024
Published in print: Aug 1, 2024
Discussion open until: Oct 30, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Carbon fibers
- Computer programming
- Computing in civil engineering
- Corrosion
- Deterioration
- Electrokinetics
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Fibers
- Field tests
- Infrastructure
- Materials characterization
- Materials engineering
- Pipe materials
- Pipeline systems
- Pipes
- Steel pipes
- Tests (by type)
- Waste management
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