Browsing by Author "Stratiev D.D."
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Item 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.Item 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.Item 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.Item Crude slate, FCC slurry oil, recycle, and operating conditions effects on H-Oil® product quality(2021-06-01) Stratiev D.S.; Shishkova I.K.; Dinkov R.K.; Petrov I.P.; Kolev I.V.; Yordanov D.; Sotirov S.; Sotirova E.N.; Atanassova V.K.; Ribagin S.; Atanassov K.T.; Stratiev D.D.; Nenov S.This paper evaluates the influence of crude oil (vacuum residue) properties, the processing of fluid catalytic cracking slurry oil, and recycle of hydrocracked vacuum residue diluted with fluid catalytic cracking heavy cycle oil, and the operating conditions of the H-Oil vacuum residue hydroc-racking on the quality of the H-Oil liquid products. 36 cases of operation of a commercial H-Oil® ebullated bed hydrocracker were studied at different feed composition, and different operating con-ditions. Intercriteria analysis was employed to define the statistically meaningful relations between 135 parameters including operating conditions, feed and products characteristics. Correlations and regression equations which related the H-Oil® mixed feed quality and the operating conditions (reaction temperature, and reaction time (throughput)) to the liquid H-Oil® products quality were developed. The developed equations can be used to find the optimal performance of the whole refinery considering that the H-Oil liquid products are part of the feed for the units: fluid catalytic cracking, hydrotreating, road pavement bitumen, and blending.Item 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.Item 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%.Item 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.Item 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.Item Industrial Investigation of the Combined Action of Vacuum Residue Hydrocracking and Vacuum Gas Oil Catalytic Cracking While Processing Different Feeds and Operating under Distinct Conditions(2023-11-01) Stratiev D.; Toteva V.; Shishkova I.; Nenov S.; Pilev D.; Atanassov K.; Bureva V.; Vasilev S.; Stratiev D.D.Ebullated bed vacuum residue hydrocracking and fluid catalytic cracking (FCC) are among the most profitable processes in modern refining. Their optimal performance is vital for petroleum refining profitability. That is why a better understanding of their combined action and the interrelations between these two heavy oil conversion processes in a real-world refinery could provide valuable information for further performance optimization. Nine distinct petroleum crudes belonging to the extra light, light, and medium petroleum crude types were processed in the LUKOIL Neftohim Burgas refinery to study the combined performance of two processes: FCC of vacuum gas oil and ebullated bed vacuum residue H-Oil hydrocracking. The operating conditions along with the characterization data of the feeds and products of both processes were evaluated through the employment of intercriteria analysis to define the variables with statistically significant relationships. Maple 2023 Academic Edition mathematics software was used to develop models to predict the vacuum residue conversion level under different operating conditions. The plug flow reactor model with an activation energy of 215 kJ/mol and a reaction order of 1.59 was found to provide the highest accuracy of vacuum residue conversion, with an average absolute deviation of 2.2%. H-Oil yields were found to correlate with the vacuum residue conversion level and the content of FCC slurry oil (SLO), the recycling of partially blended fuel oil, a material boiling point below 360 °C, and the vacuum gas oil (VGO) in the H-Oil feed. FCC conversion was found to depend on the H-Oil VGO content in the FCC feed and the content of FCC SLO in the H-Oil feed.Item 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.Item 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.