Specific Gravity, RVP, Octane Number, and Saturates, Olefins, and Aromatics Fractional Composition of Gasoline and Petroleum Fractions by Neural Network Algorithms

摘要

An accurate method for estimation of the properties of petroleum fractions based on their ASTM D86 distillation profile is developed using a set of conventional feed forward single layer neural networks algorithms. Results on 362 gasoline and petroleum fuels show that the proposed method outperforms other alternatives in terms of accuracy, simplicity, and generality with overall correlation coefficients 0.995 for research octane number, 0.994 for motor octane number, 0.995 for Reid vapor pressure, 0.9996 for specific gravity, and 0.994 for saturates 0.992 for olefins and 0.998 for aromatics fractional compositions. The model is suitable for incorporation into ASTM D86 test apparatus to predict the properties of petroleum fractions in a single test, resulting in savings in terms of space, time, energy, and cost.