Validation of a predictive model for corrosion inhibition of API 5L X60 steel in chloride solution

Abstract

A model was previously developed to predict the corrosion inhibition efficiency of steel in chloride solutions using genetic function approximation model. The model considers the influence of steel surface, aggressive solution, surface area, volume, highest occupied and lowest unoccupied molecular orbital of the studied inhibitors and the binding energy between the steel surface and the corrosion inhibitors. The model was based on experiments carried out in the laboratory for 28 amino acids and descriptors derived from molecular dynamics simulations and quantum mechanical calculations of these inhibitors. This paper aims to develop a cross-discipline, fully integrated, quantitative model to predict the corrosion inhibition efficiency of amino acid and its related compounds (compounds that contain both amino and carboxylic groups). Experimental corrosion inhibition efficiencies have been calculated using polarization resistance measurements, potentiodynamic polarization and electrochemical impedance spectroscopy. The predicted corrosion inhibition efficiencies are compared with the experimentally determined efficiencies. The inhibition efficiencies calculated using the suggested corrosion inhibition model show good agreement with the calculated experimental corrosion inhibition efficiencies. Three new amino acid candidates are used to validate the corrosion inhibition model including N-acetylcysteine, S-methyl-l-cysteine and L-ornithine monohydrochloride.