Study of Corrosion Inhibition Potentials of Eichhornia crassipes Leaves Extract on Mild Steel in Acidic Medium using Artificial Neural Network

A Ashwe,K K Ikpambese

doi:10.46912/napas.22

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.

Highlights

The study of corrosion involves the study of the chemical, physical, metallurgical, and mechanical properties of materials as it is a synergistic phenomenon in which the environment is as important as the materials involved
The established Artificial Neural Network (ANN) model has been used to study the corrosion behaviour of mild steel in H2SO4 inhibited by Eichhornia crassipe leaves extract
Potentials of Eichhornia crassipe as corrosion inhibitor for mild steel in acidic media was established while procedure for modeling the influence of relevant factors such as specimen dimension, inhibitor conditions, and operating conditions using artificial neural network has been suggested

Summary

Introduction

The study of corrosion involves the study of the chemical, physical, metallurgical, and mechanical properties of materials as it is a synergistic phenomenon in which the environment is as important as the materials involved. Computer modeling techniques can handle the study of complex systems such as corrosion and are appropriate and powerful tools to study the mechanism without compromising accuracy. The use of ANN has grown in popularity during the last few years. The reason for this is that neural networks represent a novel and modern approach that can provide solutions to problems for which conventional mathematics, algorithms and methodologies cannot. These problems are usually very complex and some of the Nigerian Journal of Pure & Applied Sciences Vol 6, 2014. These problems are usually very complex and some of the Nigerian Journal of Pure & Applied Sciences Vol 6, 2014. 159 mechanisms involved have not been fully network consists of three mathematical understood by researchers

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Results

Conclusion