Modelling density of pure and binary mixtures of normal alkanes: Comparison of hybrid soft computing techniques, gene expression programming, and equations of state

Determining the properties of hydrocarbons, especially density, is one of the most important measures in the oil and gas industry. In this study, robust artificial intelligence techniques have been investigated to predict the density of pure and binary mixtures of normal alkanes (between C1 and C44). Since there are various conditions in oil and gas reservoirs, it has been tried to study the properties of different hydrocarbons in a wide range of temperature (up to 522 K) and pressure (up to 275 MPa). An extensive databank, including 2143 and 985 data points for pure and binary mixtures of normal alkanes, respectively, was extracted from the literature. The crow search algorithm (CSA), firefly algorithm (FFA), grey wolf optimization (GWO), and wind-driven optimization (WDO) algorithms were utilized to improve the learning process of least square support vector machine (LSSVM) and radial basis function neural network (RBFNN) models, which were developed to predict the density of hydrocarbons. Also, gene expression programming (GEP) correlations were presented using complex mathematical calculations to estimate the density of hydrocarbons. The results obtained from the models were also compared with seven equations of state (EOSs). The obtained results showed that the predictions of the proposed techniques are in a great match with the experimental data. By performing a comparison on the models' outcomes, LSSVM-GWO and RBFNN-CSA were found to be the most accurate models for pure and binary mixtures of normal alkanes with overall average absolute percent relative error (AAPRE) values of 0.0622% and 0.0098%, respectively. It is noteworthy that the GEP correlations with the AAPRE values of 0.1955% and 0.3525% for pure and binary mixtures of normal alkanes, respectively, have a high accuracy compared to the equations of state and are suitable practical correlations for estimating the density of hydrocarbons.

Automated summary

In this study, robust artificial intelligence techniques were investigated to predict the density of normal alkanes. The models developed showed great match with experimental data, with LSSVM-GWO and RBFNN-CSA identified as the most accurate models for predicting the density of pure and binary mixtures of normal alkanes. The gene expression programming correlations also showed high accuracy compared to equations of state, making them suitable for practical density estimation of hydrocarbons.