A database of 140 diesel fuels having cetane numbers in the range of 10–70 points; densities at 15 °C; and distillation characteristics according to ASTM D-86 T 10%, T 50%, and T 90% was used to develop new procedures for predicting diesel cetane numbers by application of the least-squares method (LSM) using MAPLE software and an artificial neural network (ANN) using MATLAB. The existing standard methods of determining cetane-index values, ASTM D-976 and ASTM D-4737, which are correlations of the cetane number, confirmed the earlier conclusions that these methods predict the cetane number with a large variation. The four-variable ASTM D-4737 method was found to better approximate the diesel cetane number than the two-variable ASTM D-976 method. The developed four cetane-index models (one LSM and three ANN models) were found to better approximate the middle-distillate cetane numbers. Between 4% and 5% of the selected database of 140 middle distillates were samples with differences between their measured cetane numbers and the cetane-index values predicted by the four new procedures was higher than the specified reproducibility limit in the standard for measuring cetane number, ASTM D-613. In contrast, the cetane-index values calculated in accordance with standards ASTM D-976 and ASTM D-4737 demonstrated that 18% and 16% of the selected database of 140 middle distillates, respectively, were samples with differences between their measured cetane numbers and predicted cetane-index values higher than the specified reproducibility limit in standard ASTM D-613. The ASTM D-4737 method, LSM, and three ANN models were tested against 22 middle distillates not included in the database of 140 diesel fuels. The LSM cetane index showed the best cetane-number prediction capability among all of the models tested.

Twenty-two crude oils around the world, from which 19 are processed in the LUKOIL Neftohim Burgas (LNB) refinery, were characterized in the LNB research laboratory by measuring 67 properties. These 22 crude oils included light low sulfur, light sulfur, intermediate low sulfur, intermediate sulfur, intermediate high sulfur, heavy high sulfur, and extra heavy extra high sulfur crudes. A new mathematical approachthe intercriteria analysiswas employed to study the relations between the petroleum properties. It was found that the petroleum properties, density, and sulfur content, along with the crude oil simulated distillation, seem to be capable of providing the same information as that from the full assay of a crude oil. Crude oils containing insoluble asphaltenes (self-incompatible oils) were found to have a high content of low aromaticity naphtha and kerosene. It was found that the asphaltene solubility correlated with the asphaltene hydrogen content. The oil solubility power was found to correlate with the oil saturate content. The oil colloidal stability seems to be controlled by the following rule: "like dissolves like". The higher the aromaticity of the asphaltenes, the higher the aromaticity of the oil is required to keep the asphaltenes in solution. The processing of blends of oils which are incompatible or nearly incompatible may deteriorate the performance of the dewatering and desalting in the refinery, which consequently may damage the equipment due to accelerated corrosion, entailed by salt deposition. The processing of blends of oils, which are incompatible, not always can be related to an increased fouling.

Intercriteria analysis (ICA) is a new method, which is based on the concepts of index matrices and intuitionistic fuzzy sets, aiming at detection of possible correlations between pairs of criteria, expressed as coefficients of the positive and negative consonance between each pair of criteria. Here, the proposed method is applied to study the behavior of one type of neural networks, the modular neural networks (MNN), that combine several simple neural models for simplifying a solution to a complex problem. They are a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent data. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters during the implementation of the MNN. On the other hand, a high number of neurons can slow down the learning process, which is not desired. In this paper, we propose a method for removing part of the inputs and, hence, the neurons, which in addition leads to a decrease of the error between the desired goal value and the real value obtained on the output of the MNN. In the research work reported here the authors have applied the ICA method to the data from real datasets with measurements of crude oil probes, glass, and iris plant. The method can also be used to assess the independence of data with good results.

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This work presents characterization data and viscosity of 34 secondary vacuum gas oils (H-Oil gas oils, visbreaker gas oils, and fluid catalytic cracking slurry oils) with aromatic content reaching up to 100 wt.%. Inter-criteria analysis was employed to define the secondary VGO characteristic parameters which have an effect on viscosity. Seven published empirical models to predict viscosity of the secondary vacuum gas oils were examined for their prediction ability. The empirical model of Aboul-Seud and Moharam was found to have the lowest error of prediction. A modification of Aboul-Seoud and Moharam model by separating the power terms accounting for the effects of specific gravity and average boiling point improves the accuracy of viscosity prediction. It was discovered that the relation of slope of viscosity decrease with temperature enhancement for the secondary vacuum gas oil is not a constant. This slope increases with the average boiling point and the specific gravity augmentation, a fact that has not been discussed before.

The exactitude of petroleum fluid molecular weight correlations affects significantly the precision of petroleum engineering calculations and can make process design and trouble-shooting inaccurate. Some of the methods in the literature to predict petroleum fluid molecular weight are used in commercial software process simulators. According to statements made in the literature, the correlations of Lee–Kesler and Twu are the most used in petroleum engineering, and the other methods do not exhibit any significant advantages over the Lee–Kesler and Twu correlations. In order to verify which of the proposed in the literature correlations are the most appropriate for petroleum fluids with molecular weight variation between 70 and 1685 g/mol, 430 data points for boiling point, specific gravity, and molecular weight of petroleum fluids and individual hydrocarbons were extracted from 17 literature sources. Besides the existing correlations in the literature, two different techniques, nonlinear regression and artificial neural network (ANN), were employed to model the molecular weight of the 430 petroleum fluid samples. It was found that the ANN model demonstrated the best accuracy of prediction with a relative standard error (RSE) of 7.2%, followed by the newly developed nonlinear regression correlation with an RSE of 10.9%. The best available molecular weight correlations in the literature were those of API (RSE = 12.4%), Goosens (RSE = 13.9%); and Riazi and Daubert (RSE = 15.2%). The well known molecular weight correlations of Lee–Kesler, and Twu, for the data set of 430 data points, exhibited RSEs of 26.5, and 30.3% respectively.

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

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