A Comparison of Neural Network and Partial Least Squares Approaches in Correlating Base Oil Composition to Lubricant Performance in Gasoline Engine Tests and Industrial Oil Applications

Since the base oil component of engine oils, driveline fluids and industrial lubricants typically exceeds 80 wt. % of the formulation, the complex chemical composition of base oils is a critical parameter in defining the ultimate performance of the finished products into which they are blended. Using both statistical and Neural Network methods, we have correlated the relative distribution of molecular types such as aromatics, naphthenes, paraffins and certain sulfur-containing species to lubricant performance in the ASTM Sequence IIIE and VE gasoline engine tests as well as the ASTM D-943 test which measures the long-term oxidative stability of industrial oils. For all cases, the “modeling” procedures enable approximately 20 input variables (compositional parameters, VI, aniline point) to be used to predict the output ratings of the respective test procedures.