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- In relation to this article, we declare that there is no conflict of interest.
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Received March 7, 2013
Accepted September 4, 2013
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
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Modeling of corrosion reaction data in inhibited acid environment using regressions and artificial neural networks
Department of Chemical Engineering, College of Engineering, Diyala University, Baquba City 32001, Diyala Governorate, Iraq
aneesdr@gmail.com
Korean Journal of Chemical Engineering, December 2013, 30(12), 2197-2204(8), 10.1007/s11814-013-0170-0
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Abstract
This paper reports the results of mass loss measurements in the corrosion inhibition of mild steel in different concentrations of H3PO4 in the temperature range 30-60 ℃ using potassium iodide as an inhibitor. The present work is focused on determining the optimum mathematical equation and the ANN architecture in order to gain good prediction properties. Three mathematical equations and three ANN architectures are suggested. Computer aided program was used for developing these models. The results show that the polynomial mathematical equation and multi-layer perception are able to accurately predict the measured data with high correlation coefficients.
References
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Khadom AA, Yaro AS, Kadhum AH, J. Chilean Chem.Soc., 55, 150 (2010)
Khadom AA, Yaro AS, Altaie AS, Kadhum AH, Portugalia Electrochem. Acta., 27, 699 (2009)
Musa AY, Kadhum AH, Mohamad AB, Takriff MS, Daud AR, Kamarudin SK, Corros. Sci., 52, 526 (2010)
Khadom AA, Yaro AS, Altaie AS, Kadhum AH, J. Appl.Sci., 9, 2457 (2009)
Tatlier M, Cigizoglu HK, Erdem-Senatalar A, Comput. Chem. Eng., 30(1), 137 (2005)
Dua V, Comput. Chem. Eng., 35(3), 545 (2011)
Rashidi AM, J. Mater. Sci. Technol., 28, 1071 (2012)
Song K, Xing J, Dong Q, Liu P, Tian B, Cao X, Mater. Des., 26, 337 (2005)
Moral H, Aksoy A, Golcay CF, Comput. Chem. Eng., 32(10), 2471 (2008)
Fahmi I, Cremaschi S, Comput. Chem. Eng., 46, 105 (2012)
Ahmad AL, Azid IA, Yusof AR, Seetharamu KN, Comput. Chem. Eng., 28(12), 2709 (2004)
Sousa SV, Martins FG, Alvim-Ferraz MC, Pereira MC, Environ. Model. Software., 22, 97 (2007)
Mathur PB, Vasudevan T, Corrosion., 38, 171 (1982)
Khadom AA , Yaro AS, Kadhum AH, J. Taiwan Inst. Chem.Eng., 41, 126 (2010)
Yaro AS, Al-Jendeel H, Khadom AA, Desalination, 270(1-3), 193 (2011)
Obot IB, Obi-Egbedi NO, Corros. Sci., 52, 198 (2010)
Kosari A, Momeni M, Parvizi R, Zakeri M, Moayed MH, Davoodi A, Eshghi H, Corros. Sci., 53, 3058 (2011)
Poornima T, Nayak J, Nityananda Shetty A, Corros. Sci., 53, 3688 (2011)
Birbilis N, Cavanaugh MK, Sudholz AD, Zhu SM, Easton MA, Gibson MA, Corros. Sci., 53, 168 (2011)
Zhang Z, Friedrich K, Compos. Sci. Technol., 63, 2029 (2003)
Platt JA, Neural Comput., 3, 213 (1991)
Haykin S, Neural networks: A comprehensive foundation, Macmillan Publishing, New York (1994)
