The performance characteristics of mineral base oils depend largely on their physiochemical properties. These properties in turn are dependent on the type and relative amount of different hydrocarbons present in the system. The physical properties of some molecules are known to be quite different when they are in a mixture and their influence on bulk physical properties of base oils can vary considerably. The evaluation of these properties can involve long procedures, considerable manpower, a large sample and a costly laboratory infrastructure. A rapid method was developed using quantitative 13C NMR derived structural information on a set of Group 1 type base oils. The molecular level characterization is considered more accurate, requiring less time and test sample to study base oil properties. If such structural data are correlated suitably with the bulk physical properties, they can be used as reliable tools for predictive models. A best subset multi-component regression analysis of the NMR data generated on several base oils are used to develop correlations to predict base oil properties such as API gravity, pour point, aniline point and viscosity. The method and some results are reported.
All Science Journal Classification (ASJC) codes
- Mechanics of Materials
- Mechanical Engineering
- Surfaces and Interfaces
- Surfaces, Coatings and Films