Data Mining Model for Prediction Effect of Corrosion Inhibition

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Abstract

Electrochemical impedance Nyquist tests have become a common technique to study corrosion inhibition behavior of steel. Methionine has been investigated as corrosion inhibitor for carbon steel (C-steel) in 1 M HCl solution using electrochemical impedance spectroscopy (EIS). Based on these experimental tests, the efficiency of the inhibitor increases with increase in the inhibitor concentration and decreases with increase in temperature. In this paper, a model based on neural networks is presented in order to obtain predictions of imaginary impedance based on the real part of the impedance as a function of inhibitor concentration and temperature. For the network, the learning algorithm, the hyperbolic tangent sigmoid transfer function, and the linear transfer function were used. The results based on correlation coefficient and root-mean-square show the utility of this tool to predict impedance values without requiring the use of EIS tests.

Keywords

Neural network Corrosion inhibitor Electrochemical test 

Notes

Acknowledgements

The authors would like to acknowledge to their corresponding universities. We are grateful to the National Iranian Oil Company (NIOC) and Iranian Offshore Oil Company (IOOC) for financial support.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Iranian Offshore Oil Company, National Iranian Oil CompanyLavanIran
  2. 2.Isfahan University of Medical SciencesIsfahanIran

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