Abstract
Prediction of corrosion behavior of steel in acidic environments is an essential step towards optimizing the design
of equipment in any industrial setting. An artificial neural network (ANN) may be used as a reliable modeling
method for simulating and predicting the corrosion behaviour. The present study has been conducted to
investigate the corrosion inhibition potentials of Eichhornia crassipes (water hyacinth) leaves extract for mild steel
in acidic media and to establish an appropriate ANN model for predicting corrosion behavior of mild steel in H 2 SO 4
inhibited by Eichhornia crassipes. The experimental procedure employed weight loss method for corrosion rate
measurements. Results have shown that Eichhornia crassipes is an effective inhibitor for corrosion inhibition of
mild steel in acidic medium. A Levenberg-Marquardt (LM) ANN with single hidden layer having five neurons was
employed to simulate the corrosion behaviour. The neural network was trained using the experimental corrosion
database. Finally, validity of the proposed model was tested using standard statistical parameters. Results indicate
that the trained ANN model is robust for predicting corrosion behaviour of mild steel in acidic media.
Keywords: Artificial neural networks (ANNs); corrosion rate, simulation, corrosion behaviour.