Prognostic Model for Corrosion-Inhibition of Mild Steel in Hydrochloric Acid by Crushed Leaves of Voacanga Africana
International Journal of Computational and Theoretical Chemistry
Volume 5, Issue 3, May 2017, Pages: 30-41
Received: Jun. 2, 2017; Accepted: Jun. 15, 2017; Published: Jul. 26, 2017
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Charles Nwachukwu Anyakwo, Department of Materials and Metallurgical Engineering, Federal University of Technology, Owerri, Nigeria
Agha Inya Ndukwe, Department of Metallurgical Engineering Technology, Akanu Ibiam Federal Polytechnic Unwana, Afikpo, Nigeria
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The weight-loss technique was used to study the inhibition of the corrosion of mild steel in 0.7M, 1.2M and 2.2M HCl by thoroughly crushed fresh-leaves of Voacanga Africana. The corrosion rate was observed to increase with increase in the concentration of acid. The maximum inhibition efficiency of 69.80% was obtained when thoroughly crushed leaves of Voacanga Africana was added at 15g per litre of 0.7M HCl with a corresponding decrease in corrosion rate from to The prediction of the corrosion rate by the artificial neural network gave a minimal error and was closer to the experimental corrosion-rate value in comparison with the prediction by multiple regression. Upon the variation of temperature between 298K and 358K, the activation energy obtained for the corrosion of mild steel in the blank solution of 0.7M HCl was 20,908.68J while the addition of Voacanga Africana’s crushed leaves at 15g per litre of 0.7M HCl increased the activation energy to 26,710.26J. The corrosion inhibition of mild steel in hydrochloric acid by the addition of the crushed plant-leaves is in agreement with the Langmuir adsorption isotherm with R2 = 0.992.
Voacanga Africana, Crushed Plant-Leaves, Hydrochloric Acid, Artificial Neural Network, Multiple Regression, Inhibition Efficiency, Corrosion Rate, Mild Steel
To cite this article
Charles Nwachukwu Anyakwo, Agha Inya Ndukwe, Prognostic Model for Corrosion-Inhibition of Mild Steel in Hydrochloric Acid by Crushed Leaves of Voacanga Africana, International Journal of Computational and Theoretical Chemistry. Vol. 5, No. 3, 2017, pp. 30-41. doi: 10.11648/j.ijctc.20170503.12
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