Browsing by Author "Pilev D."

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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.
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    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.
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    CYBER-PHYSICAL SECURITY THROUGH FACIAL RECOGNITION AND SENSOR DATA ANALYSIS
    (2024-01-01) Atanasov I.; Pilev D.
    The digital age has brought tremendous opportunities for innovation and efficiency. However, it has also exposed businesses, governments, and individuals to a range of cyber threats, such as data breaches, network attacks, ransomware, malicious insiders, and identity theft. This requires the implementation of robust cybersecurity measures to safeguard sensitive information and ensure the uninterrupted operation of all critical IT systems. This paper aims to provide a facial recognition security system for cyber-physical security that incorporates a neural network and intelligent algorithms to assess the severity level of security breaches. The system also includes alarms with severity levels ranging from 1 (low severity) to 4 (critical), based on facial recognition and data from carbon dioxide and temperature sensors. In the event of a security breach, an incident response plan is presented. The proposed system is applicable to offices, workspaces, server rooms, data centers and other areas where information is stored, to enhance physical security and protect against cybersecurity threats.
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    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.
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    Intercriteria Analysis to Diagnose the Reasons for Increased Fouling in a Commercial Ebullated Bed Vacuum Residue Hydrocracker
    (2022-01-01) Stratiev D.; Shishkova I.; Dinkov R.; Kolev I.; Argirov G.; Ivanov V.; Ribagin S.; Atanassova V.; Atanassov K.; Stratiev D.; Nenov S.; Pilev D.; Yordanov D.
    The intercriteria analysis developed on the base of intuitionistic fuzziness and index matrices was applied to evaluate processing data of the LUKOIL Neftohim Burgas H-Oil ebullated bed vacuum residue hydrocracker with the aim of revealing the reasons for increased fouling registered during the 3rd cycle of the H-Oil hydrocracker. It was found that when the ratio of the δT of the 1st reactor to the δT of the 2nd reactor gets lower than 2.0, an excessive H-Oil equipment fouling occurs. The fouling was also found to be favored by processing of lower Conradson carbon content vacuum residual oils and increased throughput and depressed by increasing the dosage of the HCAT nanodispersed catalyst. The fouling in the atmospheric tower bottom section is facilitated by a lower aromatic content in the atmospheric tower bottom product. The addition of FCC slurry oil not only increases aromatic content but also dissolves some of the asphaltenes in the atmospheric residual hydrocracked oil and decreases its colloidal instability index. The fouling in the vacuum tower bottom section is facilitated by a higher saturate content in the VTB. Surprisingly, it was found that the asphaltene content in the VTB depresses the fouling rate. No relation was found of the sediment content in the hydrocracked residual oils measured by hot filtration tests and by the centrifuge method to the equipment fouling of the H-Oil hydrocracker.
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    MULTIMODAL SYSTEM FOR FACIAL EMOTION RECOGNITION BASED ON DEEP LEARNING
    (2024-01-01) Atanassov A.; Pilev D.; Tomova F.
    Emotions are one of the main ways of communication between people and of expressing attitudes towards objects, products, services, etc. They are divided to verbal and non-verbal classes. Human speech and intonation belong to the first class, and to the second (non-verbal) facial and body emotions, known as body language. The subject of this report is the development of multimodal deep learning system intended to recognize facial and body emotions and their relationship with the scene (weather) in which they occur. It is based on three deep learning neural networks (DNN) each one for recognition of facial emotion, body emotion and weather. Combining their results, we improve significantly the final facial emotion recognition (FER) results.
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    PHENOLIC COMPOUNDS IN PEELS, SEEDS, MARCS AND WINES FROM MAVRUD GRAPES
    (2020-01-01) Tochev D.; Karsheva M.; Pilev D.; Cayla L.; Masson G.; Mihaylova G.
    The aim of this work was to determine the amounts of different classes of phenolic compounds in ethanolic extracts from red grape marc and its components, peels and seeds, and to compare with wine musts, and red wine of the same variety of grapes. The results showed that the red grape marc was rich in polyphenolic compounds. Grape seeds demonstrated the highest contents of proanthocyanidines, (a type of flavonoid with high antioxidant activity).
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    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.