Abstract

In spite of the huge success of computational chemistry in corrosion studies, most of the ongoing research on the inhibition of preferential weldcorrosion is restricted to laboratory work. In the present study, a nondeterministic artificial intelligence model is proposed, the aim being to more accurately predict the occurrence of corrosion in the Heat Affected Zone (HAZ), which is most exposed to corrosion risk. The prediction of corrosion rates has become an important challenge for the engineering community. For industry, one of the more important aspects of corrosion is the HAZ for welded carbon steel in CO₂ environments. Nowadays, data from various sources (e.g., temperature and velocity), for both inhibited and non-inhibited CO₂ solutions, can be fed into neural networks, allowing them to be used for data processing. An artificial neural network is proposed for the prediction of corrosion in the HAZ.A phenomenal outcome for the prediction of corrosion in the HAZ was proposed with the learning ability of an artificial neural network using software, through which training of 406 sets of data using the Leven Berg-Marquardt algorithm were obtained from experimental data. The training sets were developed for three levels of corrosion (mild, moderate and severe) through the Artificial Neural Network (ANN) and resulted in a trend which took the form of an incremental parabolic curve. This study presents an artificial neural network model which simulates the complex and nonlinear atmospheric corrosion process observed in experimental data. The correlation statistics (R) in the ANN proved to be 90% accurate. The test results were validated to confirm the efficacy of the developed ANN model for prediction of corrosion rate and good performance was observed. The interactions between the inputs were estimated by performing a sensitivity analysis based on the developed model. Since the model results from this research showed good agreement with experimentally obtained corrosion rates, it could now be widely applied in corrosion studies.

Keywords:

Artificial Neural Networks (ANN), corrosion , prediction,

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