Multiple regression modelling of mineral base oil biodegradability based on their physical properties and overall chemical composition.

A set of 38 mineral base oils was characterized by a number of chemical (i.e., overall chemical composition) and physical parameters used routinely in industry. Their primary biodegradability was evaluated using the CEC L-33-A-93 test. Multiple (stepwise) linear regression (MLR) analyses were performed to describe the relationships between the biodegradability values and the chemical or physical properties of oils. Chemical, physical, and both types of parameters were successively used as independent variables. Using chemical descriptors as variables, a four-variable model equation was obtained that explained only 68.2% (adjusted R-squared statistic=68.2%) of the variability in biodegradability. The fitting was improved by using either the physical or the whole parameters as variables. MLR analyses led to three-descriptor model equations involving kinematic viscosity (as log), Noack volatility (as log) and either the viscosity index (pure physical model) or the paraffinic carbon percentage (mixed chemical-physical model). These two models displayed very similar adjusted R-squared statistics, of approximately 91%. Their predicting ability was verified using 25 additional base oils or oil blends. For 80% of oils on a total of 63, the absolute percentage error on biodegradability predicted by either model was lower than 20%. Kinematic viscosity was by far the most influential parameter in the two models.