Modeling inhibition efficiency of mango leaf extract for corrosion control of mild steel in HCl solution: Response Surface Methodology and Artificial Neural Network

Abstract

This study advanced the establishment of natural plant-based inhibitors for corrosion prevention procedures. It entails modelling the efficiency of leaf extract for mild steel corrosion control in HCl solution. The mango leaf extract are characterize to ascertain its molecules/molecular structures using gas chromatography-mass spectrophotometer (GCMS). The efficiency undergo modeling using response surface methodology (RSM) and artificial neural network (ANN). Critical phenomena of the inhibitor’s bio-molecules in the HCl solution and interfacial transition between the molecules and mild steel’s surface are examine using Langmuir, Frumkin, Temkin and Flory-Huggins adsorption isotherms. The results showed that 2-hydroxycyclopentadecanone (C15H28O2), 4-hepten-3-one (C8H14O), benzenemethanol (C9H12O), and 2,7-dimethyloct-7-en-5-yn-4-yl ester (C14H20O4 are the predominant molecular constituents (of higher inhibitive properties) in the mango leaf extract. The highest efficiency of 91.42% is obtain at an inhibition concentration of 0.6 g/L, temperature of 318 K and immersion time of 16 hours. Efficiency of the extract are model by optimization tools of RSM and ANN. Based on statistical analyses (correlation coefficient, RMSE and standard error of prediction), ANN performed better than RSM in the prediction of inhibition efficiency of the extract. Interfacial transition between the extract’s molecules and the mild steel surface established. The bio-molecular constituents inhibited the corrosion by process of adsorption.