Browsing by Author "Nedelchev A."

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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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    Feed Variability Effect on Performance of a Commercial Residue Hydrocracker
    (2025-11-01) Stratiev D.; Dinkov R.; Shiskova I.; Nedelchev A.; Kolev I.; Argirov G.; Sotirov S.; Sotirova E.; Bureva V.; Atanassov K.; Yordanov D.; Nenov S.; Stratiev D.
    Feed quality has been found to be related to both reactivity and sediment formation propensity in the residue hydrocracking process defining the conversion level. In this research, unlike other investigations, which examine hydrocrackability of individual vacuum residues, 529 mixtures of 33 vacuum residues were investigated for their hydrocrackability in a commercial H-Oil ebullated bed reactor unit. Intercriteria and regression analyses, together with singular value decomposition (SVD) and deep learning neural network techniques were employed to analyze data and model the vacuum residue conversion in the H-Oil unit. It was found that SVD model provided the best fit of H-Oil conversion training data (standard error of 0.95 wt.%). However, due to overfitting, the SVD model failed to predict H-Oil conversion on unseen data (standard error of 5.1 wt.%). The deep learning neural network exhibited standard error for all data (training, validation and testing) of 1.99 wt.%, while for the test data it was 2.35 wt.%. The linear regression model showed a standard error of 3.9 wt.% for the training data and 7.5 wt.% for the test data. Eleven properties of the vacuum residue (density, microcarbon residue, sulfur, nitrogen, saturate, aromatic, resin, C5-asphaltene, C7-asphaltene, Na, and Ni+V content) seem to be sufficiently informative for the purposes of modeling and predicting H-Oil conversion, thus enabling the assessment of the suitability of a given vacuum residue to be used as a feedstock for the H-Oil process. The best predicting model was found to be the deep learning neural network, which can be used for the purpose of the crude selection process.