Modeling and forecasting of depletion of additives in car engine oils using attenuated total reflectance fast transform infrared spectroscopy

On average, additives make up to 7% of a typical lubricant base. Commonly, they are blended with lube oils to enhance specific features thereby improving their qualities. Ultimately, additives participate in the performance of car engine oils. Using an analytical tool, attenuated total reflectance fast transform infrared spectroscopy, various grades of car engine oils, at different mileages, were analyzed. Sulfate oxidation and wear were found to trigger chemical processes which, in the long run, cause lubricant degradation while carbonyl oxidation was observed to occur only at a slow rate. Based upon data obtained from infrared spectra and using a curve fitting technique, mathematical equations predicting the theoretical rates of chemical change due to the aforementioned processes were examined. Additivedepletions were found to obey exponential regression rather than polynomial. Moreover, breakpoint (breakpoint is used here to denote the initiation of deterioration of additives) and critical mileage (critical mileage defines the distance at which the lubricant is chemically unusable) of both samples were determined.