Prediction of Zinc Consumption as Sacrificial Anode in Cathodic Protection of Steel in Sea Water Using Artificial Neural Network

Abstract

Corrosion has gained special attention due to its significance, when predicting corrosion rates. However, the complexity and variability makes it hard to model its effects. This study evaluates the usefulness of Artificial Neural Networks (ANN) to predict the corrosion rate as a function of several factors which have been related in previous studies to the protectiveness of low carbon steel in sea water, i.e. Temperature, Flow rate, pH, and time. Results showed that neural networks are a powerful tool and that the validity of the results is closely linked to the amount of data available and the experience and knowledge that accompany the analysis. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 0.09% and Standard Deviation (S.D) 0.46%