Octane number prediction for gasoline blends

Artificial Neural Network (ANN) models have been developed to determine the Research Octane Number (RON) of gasoline blends produced in a Greek refinery. The developed ANN models use as input variables the volumetric content of seven most commonly used fractions in the gasoline production and their respective RON numbers. The model parameters (ANN weights) are presented such that the model can be easily implemented by the reader. The predicting ability of the models, in the multi-dimensional space determined by the input variables, was thoroughly examined in order to assess their robustness. Based on the developed ANN models, the effect of each gasoline constituent on the formation of the blend RON value, was revealed.