Comparison between Non-linear and ANN Models for Prediction of Corrosion Inhibition Efficiency of Mild Steel
الملخص
Abstract: QSAR can assist in the quick and low-cost identification of new corrosion inhibitor compounds. Early research revealed a connection between a molecule's quantum parameters and inhibition efficiency (IE). A possible nonlinear equation between experimental inhibition efficiencies (IEexp) of corrosion inhibitors and some quantum parameters was sought for some Triazole derivatives in previous study. This work aims to compare nonlinear model from previous study and artificial neural network (ANN) used in this study to predict inhibition efficiency. The investigation shows that ANN is more efficient and accurate than nonlinear model to predict inhibition efficiencies, where correlation coefficient R (between IEexp and predicted inhibition efficiency (IEpred)) increased from 0.95 to 0.99 and mean square error decreased from 4.0×10-3 to 6.1×10-5respectively.
الكلمات المفتاحية:
الانجليزية
كيفية الاقتباس
RAMZI JALGHAM. (2024). Comparison between Non-linear and ANN Models for Prediction of Corrosion Inhibition Efficiency of Mild Steel . مجلة جامعة بني وليد للعلوم الإنسانية والتطبيقية, 7(5), 62-73. https://doi.org/10.58916/jhas.v7i6.576
إصدار
القسم
Article
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