Using neural networks for corrosion inhibition efficiency prediction during corrosion of steel in chloride solutions

Abstract

In spite of the huge success that has been attributed to the use of computational chemistry in corrosion studies, most of the ongoing research on the inhibition potential of organic inhibitors is restricted to laboratory work. The quantitative structure inhibition (activity) relationship (QSAR) approach is an effective method that can be used together with experimental techniques to predict inhibitor candidates for corrosion processes. The study has demonstrated that the neural network can effectively generalize correct responses that only broadly resemble the data in the training set. The neural network can now be put to use with the actual data, this involves feeding the neural network with several quantum chemical descriptors as dipole moment, highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbital energy, energy gap, molecular area and volume. The neural network will produce almost instantaneous results of corrosion inhibition efficiency. © 2013 by ESG.