Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN
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Published:2023-08-02
Issue:8
Volume:11
Page:2328
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ISSN:2227-9717
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Container-title:Processes
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language:en
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Short-container-title:Processes
Author:
Palichev Georgi Nikolov1, Stratiev Dicho12ORCID, Sotirov Sotir3, Sotirova Evdokia3, Nenov Svetoslav4, Shishkova Ivelina2ORCID, Dinkov Rosen2, Atanassov Krassimir13ORCID, Ribagin Simeon13, Stratiev Danail Dichev1, Pilev Dimitar4, Yordanov Dobromir3ORCID
Affiliation:
1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Georgi Bonchev 105, 1113 Sofia, Bulgaria 2. LUKOIL Neftohim Burgas, 8104 Burgas, Bulgaria 3. Intelligent Systems Laboratory, Department Industrial Technologies and Management, University Prof. Dr. Assen Zlatarov, Professor Yakimov 1, 8010 Burgas, Bulgaria 4. Department of Mathematics, University of Chemical Technology and Metallurgy, Kliment Ohridski 8, 1756 Sofia, Bulgaria
Abstract
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.
Funder
Asen Zlatarov University, Burgas
Subject
Process Chemistry and Technology,Chemical Engineering (miscellaneous),Bioengineering
Reference62 articles.
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