Mathematical Regression and Artificial Neural Network for Prediction of Corrosion Inhibition Process of Steel in Acidic Media

Khadom, Anees A.; Mahdi, Mustafa S.; Mahood, Hameed B.

doi:10.1007/s40735-020-00390-7

Cited by 19 publications

(1 citation statement)

References 28 publications

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“…Linear (M 1 , Eq.1), polynomial (with and without interaction) (M 2 and M 3 , Eq. 2 and Eq.3), exponential growth (M 4 , Eq.4), and Piecwise linear with breakpoint models (M 5 , Eq.5) were used [10].…”

Section: Methodsmentioning

confidence: 99%

Statistical and Artificial Neural Network Analysis for Corrosion of Mild Steel in Hydrochloric Acid in Presence of Eco-Friendly Inhibitor

Khadom

Fadhil

Ahmed

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In present work, the inhibition performance of the Portulaca Grandiflora Leaf Extract (PGL), as environmental-friendly corrosion inhibitor, for low-carbon steel in 0.5 M hydrochloric acid solution at variable inhibitor concentrations and temperatures is evaluated by mass loss technique. The dependent variable was corrosion rate, while the independent variables were inhibitor concentration and temperatures. Several mathematical and artificial neural network (ANN) models have been suggested. A computer aided program is used during regression and estimation processes. Several models were used. Polynomial – individual effect, polynomial – interaction effect, linear effect, exponential growth, and piecewise regression models were estimated. Results show that the Piecewise regression model was the best one with high correlation coefficient (R2) equal to 0.9994. For ANN studies, Linear Model (LM), Radial Basis Function (RBF), and Multi-Layer Perceptron (MLP) were evaluated. The data were divided into training and testing. MLP of two inputs, multi-hidden layers, and one output (2:2-8-1:1) was the highly accurate artificial neural network model ANN with a high correlation coefficient (R2=0.9826). The effect of temperature was lower than the effect of PGL concentration as shown by mathematical and ANN analysis.

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Section: Methodsmentioning

confidence: 99%