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

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.