Fuel Lubricity: Statistical Analysis of Literature Data 2000-01-1917
A number of laboratory-scale test methods are available to predict the effects of fuel lubricity on injection system wear. Anecdotal evidence exists to indicate that these methods produce poor correlation with pump wear, particularly for fuels that contain lubricity additives. The issue is further complicated by variations in the lubricity requirements of full-scale equipment and the test methodologies used to evaluate the pumps. However, the cost of performing full-scale equipment testing severely limits the quantity of data available for validation of the laboratory procedures at any single location. In the present study, the technical literature was reviewed and all previously published data was combined to form a single database of 175 pump stand results. This volume of data allows far more accurate statistical analysis than is possible with tests performed at a single location. The results indicate differences in the effectiveness of the standardized laboratory-scale methods. The High Frequency Reciprocating Rig (HFRR) produced much lower correlation with pump wear than did the Scuffing Load Ball on Cylinder Lubricity Evaluator (SLBOCLE), with HFRR tests performed at 60°C being even less accurate than those performed at 25°C. Correlation was also lower for fuels that contain lubricity additives as opposed to neat fuels. Multi-variable regression analysis of the data indicates that correlation with injection equipment is improved by combining the results from different laboratory-scale test procedures using simple mathematical formulae. The squared correlation (R2) of laboratory-scale wear tests with pump wear rating is unlikely to greatly exceed 77%, due to the inherent variability of the pump data. Combination of three or more laboratory tests using the equations derived from the multi-variable regression analysis allowed this maximum value to be achieved. As a result, it is hoped that the equations derived in the present paper may become more widely used to better predict full-scale pump wear.
