ANN MODELLING OF CORROSION INHIBITION OF MILD STEEL IN MARINE ENVIRONMENT USING EPOXY-NICKEL OXIDE NANOCOMPOSITE COATINGS

The corrosion inhibition of mild steel in 3.5 wt. % NaCl in the absence and presence of epoxy coatings containing varying concentration of Nickel oxide nanoparticles was studied using the gravimetric technique within the operating temperature range of 30 to 60oC. A predictive model based on the Artificial Neural Network (ANN) was developed to study the relationship between the input variables (exposure time, inhibitor concentration and Temperature) and output variables (Corrosion Rate and Inhibition Efficiency). The ANN model was based on the Multilayer Perceptron algorithm with input layer comprising of 23 units and 1 hidden layer consisting of 3 units. Corrosion test data obtained from 80 experimental runs were successfully modelled using ANN with minimal errors. 56 cases corresponding to 70% of test data were used for training the network and 24 cases corresponding to 30% of test data was used for testing the efficacy of the network.