Artificial neural network model to predict cold filter plugging point of blended diesel fuels

Diesel fuel blending is an indispensable process in the diesel fuel producing process. It will benefit greatly the refineries to increase their profits if a mathematic model is developed to accurately estimate CFPP instead of substantial experiments. In this article, a back propagation artificial neural network model is established to predict CFPP of the blended diesel fuels, using input parameters of kinematics viscosity, density, refractivity intercept, CFPP and weight percentages of constituent diesel fuels. This model can give satisfactory predicting results for unknown diesel fuel samples either without PPD or with PPD and has been tested by practical industrial applications of produce blended diesel fuels. The mean predicting errors for the unknown samples without PPD are about 1.3 °C and about 2.5 °C for unknown samples with PPD.