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Computational Modelling of the Corrosion Behaviors of Mild Steel in Acidic Medium with Chlomaena ordorata Leaves Extract as Corrosion Inhibitor

Authors: Chima OM

DOI Info: http://doi.org/10.5281/zenodo.5048341

ABSTRACT

The development of models for predicting the corrosion behavior of mild steel under corrosion inhibition from Chlomaena ordorata leaves extract was carried out in this work. The inhibitor’s concentration and immersion time were varied within the ranges of 0.4 – 0.8 g/l and 1 - 7 days respectively to determine the corresponding responses- (weight loss, corrosion rate and efficiency of the inhibitor) using weight loss method. The microstructures of the samples were examined using scanning electron microscope. The empirical models were developed and the predicted optimal settings for obtaining desired condition for mitigating corrosion rate was determined using response surface methodology. The response optimizer tool predicted the respective optimal values of 275.8 mg, 1.2 mgcm-2h-1 and 89.98% for weight loss, corrosion rate and inhibition efficiency at the optimal factors of 0.8 g/l and 3.18 days for concentration of inhibitor and immersion time respectively. The concentration of inhibitor displayed an inverse relationship with weight loss and corrosion rate and a direct relationship with inhibition efficiency, while immersion time showed a nonlinear relationship with the responses. Strong correlation was observed between the experimental and the model predicted results which confirms the soundness of the model. Hence, it was concluded that RSM model is a useful tool for accurate prediction of corrosion behavior of mild steel.


Affiliations: Department of Mechanical Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, P.M.B 7267, Umahia, Abia State, Nigeria.
Keywords: Extract, Concentration, Variables, Corrosion Rate, Inhibitor, Model
Published date: 2021/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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