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.