Modelling of a vacuum residue hydrocracking in an industrial slurry phase reactor
Bibliographic record
Abstract
Abstract The upgradation of the bottom of the barrel has gained much interest across the refineries due to severe environmental rules, limitations of conventional oil reserves, and its flexibility to produce light end products which benefit end users. Slurry phase hydrocracking is one of the growing technologies to fulfil the increasing demand for light cut. Modelling of an industrial slurry phase reactor (SPR) for vacuum residue hydrocracking using different kinetic models is proposed. The axial dispersion model (ADM) is used for modelling an industrial SPR. The mathematical model of the reactors is incorporated for the three different lump kinetic models. This study deals with the continuous stirred tank reactor (CSTR) and SPR modelling, followed by industrial SPR modelling. The small lab‐scale reactor models are validated with the experimental data reported in the literature. The study's objective was to investigate the one‐dimensional and two‐dimensional concentration dynamics of each lump along the axial and radial positions of industrial SPR. The vacuum residue conversion into the light fractions was obtained by more than 73% in industrial SPR. Also, the yield of vacuum gas oil and resins were evaluated as 49% and 63%, respectively. The sensitivity analysis was performed to explain the effect of process variables. The optimum range was found as a length of 15 m, liquid hourly space velocity (LHSV) of 0.2 h −1 , 1% catalyst concentration, and 420°C reaction temperature to enhance the throughput of the reactor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".