Adaptive neuro-fuzzy inference system for forecasting corrosion rates of automotive parts in biodiesel environment

Abstract

It is precarious to scrutinize the impacts of operational parameters on corrosion when choosing materials for the green diesel and automotive industries. This was the original study to showcase an optimization stratagem for abating corrosion rates (CRs) of automotive parts (APs) explicitly copper and brass in a biodiesel environment, adopting novel Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS).To model CRs, the RSM and ANFIS were utilized. The mechanical properties of APs were inspected, explicitly their hardness number and tensile strength, as well as their outward morphologies. The optimal CRs for copper and brass were 0.01656 mpy and 0.008189 mpy at a B 3.91 biodiesel/diesel blend and 240.9-h exposure. The ANFIS model had a higher coefficient of determination and lower values of root mean squared errors (RMSE), mean average error (MAE), and average absolute deviation (AAD) when compared to the RSM model; this authenticates the ANFIS model's superiority for predicting CRs of copper and brass. The tensile strength of brass was greater than that of copper, while the latter had a higher hardness number. The information, model, and correlations can assist APS in mitigating and slaving over for the corrosiveness of APs while utilizing green diesel.

Keywords

• Response surface methodology (RSM)
• Adaptive neuro-fuzzy inference (ANFIS)
• Corrosion
• Copper
• Brass
• Biodiesel