Exploring copper corrosion inhibition in value added neem biodiesel: Evaluation by traditional and machine learning approaches

Abstract

Neem seed biodiesel is an eco-friendly fuel, however, to minimize its corrosion on copper metal used in engine parts, ethyl acetate extract of Syzygium aromaticum flowers was tested as an inhibitor. Copper corrosion evaluation in biodiesel revealed an inhibition efficiency of 97.96 %. Corrosion at the operating temperature of diesel engine was predicted by machine learning - quadratic regression. Thermodynamics and spectral evaluations bring to light the adsorption of inhibitors on the metal surface for inhibiting the corrosion of copper metal immersed in biodiesel. Surface morphological studies revealed the protective nature of the copper metal corrosion. Quadratic Surface Vector Machine (SVM) algorithm classified the corrosion classes as low, medium, and high with an overall accuracy value of 94.6 %. The percentage of non-corroded area of copper metal specimen in biodiesel medium with the said inhibitor as estimated with quadratic SVM is 97.193 %. The research findings suggest that the ethyl acetate extract of S. aromaticum flowers has the potential to perform as an effective corrosion inhibitor for copper in biodiesel medium and revealed the potential of quadratic SVM machine learning algorithm in corrosion estimation and classification.