The contribution of neural networks to solve corrosion related problems

This paper summarizes the results of various attempts to implement a neural network for solving corrosion problems. The first activity was aimed to develop a model able to predict crevice corrosion of stainless steel and related alloys in chloride containing media from long-term exposure tests. Second, the preliminary evaluation of a neural network approach for rapid prediction of naphthenic acid corrosion performance (NAC) of carbon and stainless steels in a crude oil distillation unit will be described. In this work, the neural network was trained on the basis of experimental data from laboratory experience. Finally, non-deterministic models based on artificial neural network (ANN) were developed to predict the corrosion rate of carbon steel in CO2 environment by elaborating laboratory and field data. NN models were developed and tested using, as an input, pattern physico-chemical variables typically met in empiric and mechanistic models as well as parameters apparently not involved in the corrosion phenomenon. Results confirmed the validity of the NN approach