Development And Application Research Of Gasoline Main Macroscopic Properties Model Based On Gasoline Molecular Composition

The physical properties of gasoline are related to the molecular composition of gasoline.In oil refineries,gasoline blending is an important part of the production of gasoline.The diversification of blending components makes it difficult to predict the blending of gasoline properties.The blending of components can change gasoline properties.In this paper,a model for predicting macroscopic physical properties based on the molecular composition of gasoline is established.The improved prediction model is based on the Ghosh octane model.The molecular composition and blending ratio of the components predict the octane number of the blended gasoline product research method.The improved octane prediction model 1 and model 2 are used to predict straight blended naphtha,reformed oil,catalytically cracked gasoline and other blending components(the measured research octane number are 39.6 ? 109).The standard errors of the improved octane prediction models are small.For the gasoline blending process,six blended gasoline products with different octane number are predicted.After considering the repeatability and reproducibility errors of the experiment in the actual blending process,the standard errors of the improved octane prediction model are small.For gasoline blending components and blended gasoline products,the improved octane prediction model 2 has better prediction accuracy than model 1.Based on the established improved physical property prediction model,taking profit maximization as the optimization goal,under some constraints,a gasoline blending optimization model based on the molecular composition of gasoline is established.The improved gasoline blending property prediction models are independent on the quantity,type or property of the blended components,and have robust prediction accuracy for both different gasoline blending components and blended gasoline products,which can provide reference for blending optimization and actual production.