The complex refractive index of crude oils determined by the combined Brewster–transmission method

Lai, Qi; Xie, Yong‐Rong; Wang, Min; Wang, Chengan; Sun, Kewei; Tan, Jianyu

doi:10.1016/j.infrared.2020.103515

Cited by 6 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where ρ is density at 20 • C, g/cm 3 , and RI is refractive index at 20 • C. Equation ( 5) presents the correlation from Yarranton et al [30] which predicted the refractive index of petroleum fluids and individual hydrocarbons, published in 2015. Yarranton et al utilizes data from petroleum fluids, including saturate, aromatic, resin, and asphaltene (SARA) fractions that have an RI between 1.3326 and 1.897.…”

Section: Methodsmentioning

confidence: 99%

“…The refractive index of petroleum fluids is an important characterization parameter that provides information about the composition and identification of petroleum fluids [1][2][3][4]. This, along with the molecular weight, can be used to predict the average number of aromatic rings in the petroleum fluids [5], the volumetric properties, i.e., thermal expansivity and isothermal compressibility for nonpolar hydrocarbon systems [6]; critical temperature, critical pressure, and acentric factor for nonpolar hydrocarbons [7][8][9]; and viscosity of nonpolar mixtures [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%