Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

International Conference on Chemical and Bioprocess Engineering

地球科学;化学;生物科学

Ebrahimi, Sh.^1 ; Kalhor, E.G.^1 ; Nabavi, S.R.^3 ; Alamiparvin, L.^2 ; Pogaku, R.^1

Faculty of Engineering, Universiti Malaysia Sabah, Sabah, Kota Kinabalu, Malaysia^1

Department of Chemistry, Faculty of Science, Tabriz Branch, Islamic Azad University, Tabriz, Iran^2

Department of Applied Chemistry, University of Mazandaran, Babolsar, Iran^3

关键词: Correlation coefficient;    Corrosion inhibition efficiency;    Environmental conditions;    Molecular descriptors;    Multiple linear regression method;    Non-linear model;    Output variables;    Structural descriptors;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/36/1/012011/pdf
DOI  :  10.1088/1755-1315/36/1/012011

学科分类：生物科学（综合）

来源: IOP

【 摘 要 】

In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.