Prediction of copper corrosion by machine learning and conventional methods in neem biodiesel with ecofriendly black pepper extract

Abstract

Copper is extensively utilized across many industries due to its outstanding attributes, nonetheless, undergoes corrosion in biodiesel. In view of this menace, eco-friendly black pepper extract was investigated as a corrosion inhibitor for copper in neem biodiesel. Weight loss measurement of copper in neem biodiesel was performed and the resulting values were utilized to calculate inhibition efficiency, corrosion rate, and thermodynamic parameters. A quadratic regression machine learning algorithm was employed to analyze copper corrosion at the operational temperature of the engine. Additionally, FTIR and SEM analyses were performed to identify the functional groups of the inhibitor responsible for corrosion inhibition through adsorption and to examine the surface morphology of the metal respectively. A corrosion inhibition efficiency of 98.0% for copper in neem biodiesel was achieved at room temperature, and the inhibitor demonstrated significant effectiveness at the operating temperature of engine nozzles. Carbonyl and amine groups of the inhibitor adsorbed on the metal formed a barrier against biodiesel enabling corrosion protection. This value-added biodiesel presents a viable alternative fuel option that protects engine components and storage tanks from corrosion.