Prediction of Corrosion Inhibitor Efficiency of Some Aromatic Hydrazides and Schiff Bases Compounds by Using Artificial Neural Network

Authors

  • Hanan A. Al-Hazam Department of Chemistry, College of Science, University of Basrah

DOI:

https://doi.org/10.3329/jsr.v2i1.2757

Keywords:

Neural network, Corrosion inhibitor efficiency.

Abstract

Artificial neural networks are used for evaluating the corrosion inhibitor efficiency of some aromatic hydrazides and Schiff bases compounds. The nodes of neural network input layer represent the quantum parameters, total negative charge (TNC) on molecule, energy of highest occupied molecular orbital (E Homo), energy of lowest unoccupied molecular orbital (E Lomo), dipole moment (μ), total energy (TE), molecular volume (V), dipolar-polarizability factor (Π) and inhibitor  concentration (C). The neural network output is the corrosion inhibitor efficiency (E) for the mentioned compounds. The training and testing of the developed network are based on a database of 31 published experimental tests obtained by weight loss. The neural network predictions for corrosion inhibitor efficiency are more reliable than prediction using other conventional theoretical methods such as AM1, PM3, Mindo, and Mindo-3.

 

Key words: Neural network; Corrosion inhibitor efficiency.

 

© 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved

DOI: 10.3329/jsr.v2i1.2757                 J. Sci. Res. 2 (1), 108-113  (2010)

 

Downloads

Download data is not yet available.
Abstract
576
PDF
511

Author Biography

Hanan A. Al-Hazam, Department of Chemistry, College of Science, University of Basrah

Dr. Hanan A. Al. Hazam Department of Chemistry, College of Science, University of Basrah, Basrah, Iraq

Downloads

Published

2009-12-29

How to Cite

Al-Hazam, H. A. (2009). Prediction of Corrosion Inhibitor Efficiency of Some Aromatic Hydrazides and Schiff Bases Compounds by Using Artificial Neural Network. Journal of Scientific Research, 2(1), 108–113. https://doi.org/10.3329/jsr.v2i1.2757

Issue

Section

Section B: Chemical and Biological Sciences