Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN

• Published in: Processes 2023-08, Vol.11 (8), p.2328
• Main Authors: Palichev, Georgi Nikolov, Stratiev, Dicho, Sotirov, Sotir, Sotirova, Evdokia, Nenov, Svetoslav, Shishkova, Ivelina, Dinkov, Rosen, Atanassov, Krassimir, Ribagin, Simeon, Stratiev, Danail Dichev, Pilev, Dimitar, Yordanov, Dobromir
• Format: Article
• Language: English
• Subjects: Accuracy; Algebra; Analysis; Artificial neural networks; Asphaltenes; Boiling; Boiling points; Composition; Computer algebra; Correlation; Crude oil; Datasets; Density; Dilution; Equipment and supplies; Fiber optics; Flocculation; Fluids; Fractions; Heuristic methods; Hydrocarbons; Iterative methods; Molecular weight; Neural networks; Petroleum; Predictions; Refractivity; Specific gravity; Support vector machines; Transport properties; Viscosity
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr11082328

The refractive index is an important physical property that is used to estimate the structural characteristics, thermodynamic, and transport properties of petroleum fluids, and to determine the onset of asphaltene flocculation. Unfortunately, the refractive index of opaque petroleum fluids cannot be measured unless special experimental techniques or dilution is used. For that reason, empirical correlations, and metaheuristic models were developed to predict the refractive index of petroleum fluids based on density, boiling point, and SARA fraction composition. The capability of these methods to accurately predict refractive index is discussed in this research with the aim of contrasting the empirical correlations with the artificial neural network modelling approach. Three data sets consisting of specific gravity and boiling point of 254 petroleum fractions, individual hydrocarbons, and hetero-compounds (Set 1); specific gravity and molecular weight of 136 crude oils (Set 2); and specific gravity, molecular weight, and SARA composition data of 102 crude oils (Set 3) were used to test eight empirical correlations available in the literature to predict the refractive index. Additionally, three new empirical correlations and three artificial neural network (ANN) models were developed for the three data sets using computer algebra system Maple, NLPSolve with Modified Newton Iterative Method, and Matlab. For Set 1, the most accurate refractive index prediction was achieved by the ANN model, with %AAD of 0.26% followed by the new developed correlation for Set 1 with %AAD of 0.37%. The best literature empirical correlation found for Set 1 was that of Riazi and Daubert (1987), which had %AAD of 0.40%. For Set 2, the best performers were the models of ANN, and the new developed correlation of Set 2 with %AAD of refractive index prediction was 0.21%, and 0.22%, respectively. For Set 3, the ANN model exhibited %AAD of refractive index prediction of 0.156% followed by the newly developed correlation for Set 3 with %AAD of 0.163%, while the empirical correlations of Fan et al. (2002) and Chamkalani (2012) displayed %AAD of 0.584 and 0.552%, respectively.