A neural network approach to the prediction of diesel fuel lubricity

The continuous sulfur reduction in diesel fuel has resulted in poor fuel lubricity and engine pump failure, a fact that led to the development of a number of methods that measure the actual fuel lubricity level. However, lubricity measurement is costly and time consuming, and a number of predictive models have been developed in the past, based mainly on various fuel properties. In the present paper, a black box modeling approach is proposed, where the lubricity is approximated by a radial basis function (RBF) neural network that uses other fuel properties as inputs. The HFRR apparatus was used for lubricity measurements. In the present model, the variables used included the diesel fuel conductivity, density, kinematic viscosity at 40 °C, sulfur content and 90% distillation point, which produced the smallest error in the validation data.