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Corrosion Engineering, Science and Technology
The International Journal of Corrosion Processes and Corrosion Control
Volume 58, 2023 - Issue 8
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Research Articles

Artificial neural network modelling to predict the efficiency of aluminium sacrificial anode

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Pages 747-754 | Received 10 Jun 2023, Accepted 23 Aug 2023, Published online: 01 Sep 2023

References

  • Muazu A, Yaro SA. J Miner Mater Charact Eng. 2011;10:185–198.
  • Xia Z, Zhang W, Yang X, et al. Influence of Sn, Cd, and Si addition on the electrochemical performance of Al–Zn–In sacrificial anodes. Mater Corros. 2020;71:585–592. doi:10.1002/maco.201911336
  • Ma J, Wen J. Corrosion analysis of Al–Zn–In–Mg–Ti–Mn sacrificial anode alloy. J Alloys Compd. 2010;496:110–115. doi:10.1016/j.jallcom.2010.02.174
  • Vijayarani K, Devan MV, Natarajan R. Effects of addition of alkaline earth metal-beryllium on the dissolution behaviour of aluminium-zinc alloy. Port Electrochimica Acta. 2017;35:91–102. doi:10.4152/pea.201702091
  • Hao QG, Wen JB, He JG, et al. Effects of gallium addition on the microstructure and electrochemical properties of Al-Zn-Bi series alloy anode. Mater Sci Forum (Trans Tech Publ). 2011;686:107–112.
  • Xi Y, Jia M, Zhang J, et al. Evaluating the performance of aluminum sacrificial anodes with different concentration of gallium in artificial sea water. Coatings. 2022;12:53. doi:10.3390/coatings12010053
  • He J-G, Wen J-B, Li X-D, et al. Influence of Ga and Bi on electrochemical performance of Al-Zn-Sn sacrificial anodes. Trans Nonferrous Met Soc China. 2011;21:1580–1586. doi:10.1016/S1003-6326(11)60900-X
  • Ma J, Wen J. The effects of lanthanum on microstructure and electrochemical properties of Al–Zn–In based sacrificial anode alloys. Corros Sci. 2009;51:2115–2119. doi:10.1016/j.corsci.2009.05.039
  • Kenny ED, Paredes RSC, de Lacerda LA, et al. Artificial neural network corrosion modeling for metals in an equatorial climate. Corros Sci. 2009;51:2266–2278. doi:10.1016/j.corsci.2009.06.004
  • Dudziak T, Gajewski P, Śnieżyński B, et al. Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800 °C for 3000 h. Corros Sci. 2018;133:94–111. doi:10.1016/j.corsci.2018.01.013
  • Kim H-S, Park S-J, Seo S-M, et al. Regression analysis of high-temperature oxidation of Ni-based superalloys using artificial neural network. Corros Sci. 2021;180:109207. doi:10.1016/j.corsci.2020.109207
  • Kamrunnahar M, Urquidi-Macdonald M. Prediction of corrosion behaviour of alloy 22 using neural network as a data mining tool. Corros Sci. 2011;53:961–967. doi:10.1016/j.corsci.2010.11.028
  • Hu Q, Liu Y, Zhang T, et al. Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network. J Mater Sci Technol. 2019;35:168–175. doi:10.1016/j.jmst.2018.06.017
  • Oluwole O, Idusuyi N. (2012).
  • Murthy YI. Neural Network Models for the Half-Cell Potential of Reinforced Slabs with Magnesium Sacrificial Anodes Subjected to Chloride Ingress. J Soft Comput Civ Eng. 2023;8(1):85–106.
  • Bhattacharya SK, Sahara R, Narushima T. Predicting the parabolic rate constants of high-temperature oxidation of Ti alloys using machine learning. Oxid Met. 2020;94:205–218. doi:10.1007/s11085-020-09986-3
  • Shibli SMA, Gireesh VS. Activation of aluminium alloy sacrificial anodes by selenium. Corros Sci. 2005;47:2091–2097. doi:10.1016/j.corsci.2004.09.010
  • Sun H, Liu L, Li Y, et al. The performance of Al–Zn–In–Mg–Ti sacrificial anode in simulated deep water environment. Corros Sci. 2013;77:77–87. doi:10.1016/j.corsci.2013.07.029
  • Li W, Yan Y, Chen G, et al. The effect of temperature and dissolved oxygen concentration on the electrochemical behavior of Al-Zn-inbased anodes. Procedia Eng. 2011;12:27–34. doi:10.1016/j.proeng.2011.05.006
  • Ferdian D, Yudha RD, Ario WM, et al. Efficiency and characterization study of low voltage current sacrificial anode on Al-Zn-Cu and Al-Zn-Si alloy. IOP Conf. Ser. Mater. Sci. Eng. IOP Publishing; 2019. p. 012047.
  • Farooq A, Hamza M, Ahmed Q, et al. Evaluating the performance of zinc and aluminum sacrificial anodes in artificial seawater. Electrochim Acta. 2019;314:135–141. doi:10.1016/j.electacta.2019.05.067
  • Karaminezhaad M, Jafari AH, Sarrafi A, et al. Influence of bismuth on electrochemical behavior of sacrificial aluminum anode. Anti-Corros Methods Mater. 2006;53(2):102–109.
  • Ma J, Wen J, Li X, et al. Influence of Mg and Ti on the microstructure and electrochemical performance of aluminum alloy sacrificial anodes. Rare Met. 2009;28:187–192. doi:10.1007/s12598-009-0037-z
  • Wang SS, Liang CH, Huang NB. Influence of Si content on the characteristics of Al-Zn-In-Mg-Ti sacrificial anode. Adv Mater Res (Trans Tech Publ). 2014;936:1963–1968.
  • Wen J, He J, Lu X. Influence of silicon on the corrosion behaviour of Al–Zn–In–Mg–Ti sacrificial anode. Corros Sci. 2011;53:3861–3865. doi:10.1016/j.corsci.2011.07.039
  • Worasaen K, Mungsantisuk P. Influence of Ti on the electrochemical behavior of Al-Zn-In-Si sacrificial anodes. Key Eng Mater (Trans Tech Publ). 2017;728:15–19. doi:10.4028/www.scientific.net/KEM.728.15
  • Saeri MR, Keyvani A. Optimization of manganese and magnesium contents in As-cast aluminum-zinc-indium alloy as sacrificial anode. J Mater Sci Technol. 2011;27:785–792. doi:10.1016/S1005-0302(11)60143-6
  • Muazu A, Aliyu YS, Abdulwahab M, et al. Sacrificial anode stability and polarization potential variation in a ternary Al-xZn-xMg alloy in a seawater-marine environment. J Mar Sci Appl. 2016;15:208–213. doi:10.1007/s11804-016-1356-8
  • Jingling MA, Jiuba W, Gengxin LI, et al. The corrosion behaviour of Al–Zn–In–Mg–Ti alloy in NaCl solution. Corros Sci. 2010;52:534–539. doi:10.1016/j.corsci.2009.10.010
  • Zhang X, Du C, Liu Z, et al. The influence of temperature and dissolved oxygen on the electrochemical nature of Al–Zn–In–Ga galvanic anode. Surf Topogr Metrol Prop. 2021;9:035054. doi:10.1088/2051-672X/ac28a6
  • Gu B, Sung Y. Enhanced reinforcement learning method combining one-hot encoding-based vectors for CNN-based alternative high-level decisions. Appl Sci. 2021;11:1291. doi:10.3390/app11031291
  • Mukherjee S, Chakraborty M. A decentralized algorithm for large scale Min-Max problems. 2020 59th IEEE Conf. Decis. Control CDC (2020). p. 2967–2972.
  • Pedregosa F, Varoquaux G, Gramfort A, et al.. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
  • Otchere DA, Arbi Ganat TO, Gholami R, et al. Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: comparative analysis of ANN and SVM models. J Pet Sci Eng. 2021;200:108182. doi:10.1016/j.petrol.2020.108182
  • Bisgin H, Bera T, Ding H, et al. Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles. Sci Rep. 2018;8:6532. doi:10.1038/s41598-018-24926-7
  • Jha NK, Ghodsi Z, Garg S, et al. Deepreduce: ReLU reduction for fast private inference. Proc. 38th Int. Conf. Mach. Learn. (PMLR, 2021); 2023 Jul 21. p. 4839–4849. https://proceedings.mlr.press/v139/jha21a.html
  • Tang J, Zheng L, Han C, et al. Anal Methods Accid Res. 2020;27:100123.
  • Wang H, Du M, Liang H, et al. Study on Al-Zn-In alloy as sacrificial anodes in seawater environment. J Ocean Univ China. 2019;18:889–895. doi:10.1007/s11802-019-3788-7

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