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The development of predictive correlations for the critical properties of hydrocarbons and petroleum fractions is reported. This equation is a function of two input parameters, which can be measured for both pure compounds and unknown mixtures. Moreover, an available data bank was used to develop the correlations using optimisation by genetics algorithms. In order to improve the accuracy of the proposed correlation, about 80% of the available data bank was used to develop the correlations using optimisation and the other 20% are used to validate the proposed correlations. The results of the proposed correlations are compared to others recommended in the literature that have had large acceptance in the oil industry. The comparison results indicate that the proposed model is both simple to use and more accurate than the most common correlations for characterising pure compounds and petroleum fractions. Furthermore, the proposed correlations obtain interesting results, which are in good agreement with available literature data. The average absolute error (AAE, %) and squared correlation coefficient (R 2 ) of the obtained models over all experimental data are 2.91% and 0.98 for T c , 7.12% and 0.95, for P c , and 3.31% and 0.98 for V c , respectively.

Developing correlations for critical properties prediction of pure hydrocarbons and Algerian petroleum fraction