DSpace Repository :: Browsing by Author "Sotirova E."

Browsing by Author "Sotirova E."

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Commercial Ebullated Bed Vacuum Residue Hydrocracking Performance Improvement during Processing Difficult Feeds
(2023-03-01) Georgiev B.E.; Stratiev D.S.; Argirov G.S.; Nedelchev A.; Dinkov R.; Shishkova I.K.; Ivanov M.; Atanassov K.; Ribagin S.; Nikolov Palichev G.; Nenov S.; Sotirov S.; Sotirova E.; Pilev D.; Stratiev D.D.
The Urals and Siberian vacuum residues are considered difficult to process in the ebullated bed hydrocracking because of their increased tendency to form sediments. Their achievable conversion rate reported in the literature is 60%. Intercriteria analysis was used to assess data from a commercial vacuum residue hydrocracker during processing blends from three vacuum residues: Urals, Siberian Light, and Basra Heavy. The analysis revealed that the main contributors to conversion enhancement is hydrodemetallization (HDM) and the first reactor ΔT augmentation. The increase of HDM from 40 to 98% and the first reactor ΔT (ΔT(R1)) from 49 to 91 °C were associated with a vacuum residue conversion enhancement of 62.0 to 82.7 wt.%. The developed nonlinear regression prediction of conversion from HDM and ΔT(R1) suggests a bigger influence of ΔT(R1) enhancement on conversion augmentation than the HDM increase. The intercriteria analysis evaluation revealed that the higher first reactor ΔT suppresses the sediment formation rate to a greater extent than the higher HDM. During processing Basrah Heavy vacuum residue, a reduction in hydrodeasphaltization (HDAs) from 73.6 to 55.2% and HDM from 88 to 81% was observed. It was confirmed that HDM and HDAs are interrelated. It was found that the attainment of conversion of 80 wt.% and higher during processing Urals and Siberian Light vacuum residues is possible when the HDM is about 90% and LHSV ≤ 0.19 h−1.
Comparison of empirical models to predict viscosity of secondary vacuum gas oils
(2021-08-01) Stratiev D.S.; Nenov S.; Shishkova I.K.; Dinkov R.K.; Zlatanov K.; Yordanov D.; Sotirov S.; Sotirova E.; Atanassova V.; Atanassov K.; Stratiev D.D.; Todorova‐yankova L.
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 charac-teristic 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 modifica-tion 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.
Correlations of HTSD to TBP and Bulk Properties to Saturate Content of a Wide Variety of Crude Oils
(2023-02-01) Stratiev D.; Dinkov R.; Tavlieva M.; Shishkova I.; Nikolov Palichev G.; Ribagin S.; Atanassov K.; Stratiev D.D.; Nenov S.; Pilev D.; Sotirov S.; Sotirova E.; Simeonov S.; Boyadzhieva V.
Forty-eight crude oils with variations in specific gravity (0.782 ≤ SG ≤ 1.002), sulphur content (0.03 ≤ S ≤ 5.6 wt.%), saturate content (23.5 ≤ Sat. ≤ 92.9 wt.%), asphaltene content (0.1 ≤ As ≤ 22.2 wt.%), and vacuum residue content (1.4 ≤ VR ≤ 60.7 wt.%) were characterized with HTSD, TBP, and SARA analyses. A modified SARA analysis of petroleum that allows for the attainment of a mass balance ≥97 wt.% for light crude oils was proposed, a procedure for the simulation of petroleum TBP curves from HTSD data using nonlinear regression and Riazi’s distribution model was developed, and a new correlation to predict petroleum saturate content from specific gravity and pour point with an average absolute deviation of 2.5 wt.%, maximum absolute deviation of 6.6 wt.%, and bias of 0.01 wt.% was developed. Intercriteria analysis was employed to evaluate the presence of statistically meaningful relations between the different petroleum properties and to evaluate the extent of similarity between the studied petroleum crudes. It was found that the extent of similarity between the crude oils based on HTSD analysis data could be discerned from data on the Kw characterization factor of narrow crude oil fractions. The results from this study showed that contrary to the generally accepted concept of the constant Kw characterization factor, the Kw factors of narrow fractions differ from that of crude oil. Moreover, the distributions of Kw factors of the different crudes were different.
Different nonlinear regression techniques and sensitivity analysis as tools to optimize oil viscosity modeling
(2021-10-01) Stratiev D.; Nenov S.; Nedanovski D.; Shishkova I.; Dinkov R.; Stratiev D.D.; Stratiev D.D.; Sotirov S.; Sotirova E.; Atanassova V.; Atanassov K.; Yordanov D.; Angelova N.A.; Ribagin S.; Todorova-Yankova L.
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.
Empirical Modeling of Viscosities and Softening Points of Straight-Run Vacuum Residues from Different Origins and of Hydrocracked Unconverted Vacuum Residues Obtained in Different Conversions
(2022-03-01) Stratiev D.; Nenov S.; Nedanovski D.; Shishkova I.; Dinkov R.; Stratiev D.D.; Stratiev D.D.; Sotirov S.; Sotirova E.; Atanassova V.; Ribagin S.; Atanassov K.; Yordanov D.; Angelova N.A.; Todorova-Yankova L.
The use of hydrocracked and straight-run vacuum residues in the production of road pavement bitumen requires a good understanding of how the viscosity and softening point can be modeled and controlled. Scientific reports on modeling of these rheological properties for hydroc-racked and straight-run vacuum residues are scarce. For that reason, 30 straight-run vacuum residues and 33 hydrocracked vacuum residues obtained in a conversion range of 55–93% were investigated, and the characterization data were employed for modeling purposes. An intercriteria analysis was applied to investigate the statistically meaningful relations between the studied vacuum residue properties. It revealed that the straight-run and hydrocracked vacuum residues were completely different, and therefore their viscosity and softening point should be separately modeled. Through the use of nonlinear regression by applying CAS Maple and NLPSolve with the modified Newton iterative method and the vacuum residue bulk properties the viscosity and softening point were modeled. It was found that the straight-run vacuum residue viscosity was best modeled from the molecular weight and specific gravity, whereas the softening point was found to be best modeled from the molecular weight and C7-asphaltene content. The hydrocracked vacuum residue viscosity and softening point were modeled from a single property: the Conradson carbon content. The vacuum residue viscosity models developed in this work were found to allow prediction of the asphaltene content from the molecular weight and specific gravity with an average absolute relative error of 20.9%, which was lower of that of the model of Samie and Mortaheb (Fuel. 2021, 305, 121609)—32.6%.
Empirical models to characterize the structural and physiochemical properties of vacuum gas oils with different saturate contents
(2021-07-01) Stratiev D.S.; Shishkova I.K.; Dinkov R.K.; Petrov I.P.; Kolev I.V.; Yordanov D.; Sotirov S.; Sotirova E.; Atanassova V.; Ribagin S.; Atanassov K.; Stratiev D.D.; Nenov S.; Todorova‐yankova L.; Zlatanov K.
Inter‐criteria analysis was employed in VGO samples having a saturate content between 0.8 and 93.1 wt.% to define the statistically significant relations between physicochemical properties, empirical structural models and vacuum gas oil compositional information. The use of a logistic function and employment of a non‐linear least squares method along with the aromatic ring index allowed for our newly developed correlation to accurately predict the saturate content of VGOs. The empirical models developed in this study can be used not only for obtaining the valuable structural information necessary to predict the behavior of VGOs in the conversion processes but can also be utilized to detect incorrectly performed SARA analyses. This work confirms the possibility of predicting the contents of VGO compounds from physicochemical properties and empirical models.
Evaluation of the different compatibility indices to model and predict oil colloidal stability and its relation to crude oil desalting
(2021-08-01) Shishkova I.K.; Stratiev D.S.; Tavlieva M.P.; Dinkov R.K.; Yordanov D.; Sotirov S.; Sotirova E.; Atanassova V.; Ribagin S.; Atanassov K.; Stratiev D.D.; Todorova-Yankova L.; Nenov S.
Thirty crude oils, belonging to light, medium, heavy, and extra heavy, light sulfur, and high sulfur have been characterized and compatibility indices defined. Nine crude oil compatibility indices have been employed to evaluate the compatibility of crude blends from the thirty individual crude oils. Intercriteria analysis revealed the relations between the different compatibility indices, and the different petroleum properties. Tetra-plot was employed to model crude blend compatibility. The ratio of solubility blending number to insolubility number was found to best describe the desalting efficiency, and therefore could be considered as the compatible index that best models the crude oil blend compatibility. Density of crude oil and the n-heptane dilution test seem to be sufficient to model, and predict the compatibility of crude blends.
Predicting Petroleum SARA Composition from Density, Sulfur Content, Flash Point, and Simulated Distillation Data Using Regression and Artificial Neural Network Techniques
(2024-08-01) Shiskova I.; Stratiev D.; Sotirov S.; Sotirova E.; Dinkov R.; Kolev I.; Stratiev D.D.; Nenov S.; Ribagin S.; Atanassov K.; Yordanov D.; van den Berg F.
The saturate, aromatic, resin, and asphaltene content in petroleum (SARA composition) provides valuable information about the chemical nature of oils, oil compatibility, colloidal stability, fouling potential, and other important aspects in petroleum chemistry and processing. For that reason, SARA composition data are important for petroleum engineering research and practice. Unfortunately, the results of SARA composition measurements reported by diverse laboratories are frequently very dissimilar and the development of a method to assign SARA composition from oil bulk properties is a question that deserves attention. Petroleum fluids with great variability of SARA composition were employed in this study to model their SARA fraction contents from their density, flash point, sulfur content, and simulated distillation characteristics. Three data mining techniques: intercriteria analysis, regression, and artificial neural networks (ANNs) were applied. It was found that the ANN models predicted with higher accuracy the contents of resins and asphaltenes, whereas the non-linear regression model predicted most accurately the saturate fraction content but with an accuracy that was lower than that reported in the literature regarding uncertainty of measurement. The aromatic content was poorly predicted by all investigated techniques, although the prediction of aromatic content was within the uncertainty of measurement. The performed study suggests that as well as the investigated properties, additional characteristics need to be explored to account for complex petroleum chemistry in order to improve the accuracy of SARA composition prognosis.
Prediction of Molecular Weight of Petroleum Fluids by Empirical Correlations and Artificial Neuron Networks
(2023-02-01) Stratiev D.; Sotirov S.; Sotirova E.; Nenov S.; Dinkov R.; Shishkova I.; Kolev I.V.; Yordanov D.; Vasilev S.; Atanassov K.; Simeonov S.; Palichev G.N.
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.
Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN
(2023-08-01) Palichev G.N.; Stratiev D.; Sotirov S.; Sotirova E.; Nenov S.; Shishkova I.; Dinkov R.; Atanassov K.; Ribagin S.; Stratiev D.D.; Pilev D.; Yordanov D.
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.
Role of Catalyst in Optimizing Fluid Catalytic Cracking Performance during Cracking of H-Oil-Derived Gas Oils
(2021-03-23) Stratiev D.; Shishkova I.; Ivanov M.; Dinkov R.; Georgiev B.; Argirov G.; Atanassova V.; Vassilev P.; Atanassov K.; Yordanov D.; Popov A.; Padovani A.; Hartmann U.; Brandt S.; Nenov S.; Sotirov S.; Sotirova E.
Three H-Oil gas oils, heavy atmospheric gas oil (HAGO), light vacuum gas oil (LVGO), heavy vacuum gas oil (HVGO), and two their blends with hydrotreated straight run vacuum gas oils (HTSRVGOs) were cracked on two high unit cell size (UCS) lower porosity commercial catalysts and two low UCS higher porosity commercial catalysts. The cracking experiments were performed in an advanced cracking evaluation fluid catalytic cracking (FCC) laboratory unit at 527 °C, 30 s catalyst time on stream, and catalyst-to-oil (CTO) variation between 3.5 and 7.5 wt/wt The two high UCS lower porosity catalysts were more active and more coke selective. However, the difference between conversion of the more active high UCS lower porosity and low UCS higher porosity catalysts at 7.5 wt/wt CTO decreased in the order 10% (HAGO) > 9% (LVGO) > 6% (HVGO) > 4% (80% HTSRVGO/20% H-Oil VGO). Therefore, the catalyst performance is feedstock-dependent. The four studied catalysts along with a blend of one of them with 2% ZSM-5 were examined in a commercially revamped UOP FCC VSS unit. The lower UCS higher porosity catalysts exhibited operation at a higher CTO ratio achieving a similar conversion level with more active higher UCS lower porosity catalysts. However, the higher UCS lower porosity catalysts made 0.67% Δcoke that was higher than the maximum acceptable limit of 0.64% for this particular commercial FCC unit (FCCU), which required excluding the HVGO from the FCC feed blend. The catalyst system containing ZSM-5 increased the LPG yield but did not have an impact on gasoline octane. It was found that the predominant factor that controls refinery profitability related to the FCCU performance is the FCC slurry oil (bottoms) yield.
Roles of Catalysts and Feedstock in Optimizing the Performance of Heavy Fraction Conversion Processes: Fluid Catalytic Cracking and Ebullated Bed Vacuum Residue Hydrocracking
(2024-09-01) Stratiev D.; Shishkova I.; Argirov G.; Dinkov R.; Ivanov M.; Sotirov S.; Sotirova E.; Bureva V.; Nenov S.; Atanassov K.; Stratiev D.; Vasilev S.
Petroleum refining has been, is still, and is expected to remain in the next decades the main source of energy required to drive transport for mankind. The demand for automotive and aviation fuels has urged refiners to search for ways to extract more light oil products per barrel of crude oil. The heavy oil conversion processes of ebullated bed vacuum residue hydrocracking (EBVRHC) and fluid catalytic cracking (FCC) can assist refiners in their aim to produce more transportation fuels and feeds for petrochemistry from a ton of petroleum. However, a good understanding of the roles of feed quality and catalyst characteristics is needed to optimize the performance of both heavy oil conversion processes. Three knowledge discovery database techniques—intercriteria and regression analyses, and artificial neural networks—were used to evaluate the performance of commercial FCC and EBVRHC in processing 19 different heavy oils. Seven diverse FCC catalysts were assessed using a cascade and parallel fresh catalyst addition system in an EBVRHC unit. It was found that the vacuum residue conversion in the EBVRHC depended on feed reactivity, which, calculated on the basis of pilot plant tests, varied by 16.4%; the content of vacuum residue (VR) in the mixed EBVRHC unit feed (each 10% fluctuation in VR content leads to an alteration in VR conversion of 1.6%); the reaction temperature (a 1 °C deviation in reaction temperature is associated with a 0.8% shift in VR conversion); and the liquid hourly space velocity (0.01 h-1 change of LHSV leads to 0.85% conversion alteration). The vacuum gas oil conversion in the FCC unit was determined to correlate with feed crackability, which, calculated on the basis of pilot plant tests, varied by 8.2%, and the catalyst ΔCoke (each 0.03% ΔCoke increase reduces FCC conversion by 1%), which was unveiled to depend on FCC feed density and equilibrium FCC micro-activity. The developed correlations can be used to optimize the performance of FCC and EBVRHC units by selecting the appropriate feed slate and catalyst.

Browsing by Author "Sotirova E."

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