Quantitative estimation of corrosion rate in 3C steels under seawater environment

An artificial neural network method is proposed to correlate the relationship between the corrosion rate of 3C steels with seawater environment factors. The predictions with the unseen test data are in good agreement with experimental values. Further, the developed model used to simulate the combined effect of environmental factors (temperature, dissolved oxygen, salinity, pH values, and oxidation-reduction potential) on the corrosion rate. 3D mappings remarkably reveal the complex interrelationship between the input environmental parameters on the output corrosion rate. The quantitative estimation of corrosion by virtual addition/subtraction of environmental factors individually to a hypothetical system helps to understand the impact of each parameter.