Effect of Hydrotreating Conditions on the Conversion of Residual Fraction and Microcarbon Residue Present in Oil Sands Derived Heavy Gas Oil
Bibliographic record
Abstract
Oil sands derived heavy crude oil also known as synthetic crude is emerging as an alternative source of feedstock to the refiners for producing transportation fuels.The presence of very high levels of sulfur and nitrogen in these heavy gas oils needs to be reduced before they are used for further processing. This at present is accomplished by hydrotreating these stocks over Ni−Mo based alumina catalysts at high temperature and pressure. During hydrotreatment of oil sand derived heavy gas oil, residual fraction (500 °C+) is converted into more valuable gasoline and middle distillate range products. The effect of different process variables on the conversion of residual fraction (500 °C+) of heavy gas oil has been studied in a trickle bed reactor using 5 mL of alumina supported Ni−Mo based commercial catalyst. The process variables studied were temperature (365−415 °C), liquid hourly space velocity (0.5−1.9 h - 1 ), pressure (65−88 bar), and hydrogen/heavy gas oil volumetric ratio (400−1000 mL/mL). The effect of all these variables on the conversion of microcarbon residue (MCR) has also been investigated. It was found that significant reduction in the residual fraction and MCR content of the heavy gas oil can be achieved by selecting appropriate hydrotreatment conditions. The kinetics of the removal of residual fraction and MCR content as well as the generation of more valuable distillate products has also been studied in this work. Power law models having order of reaction equal to 2.0 can be used to describe the kinetics of the removal of residual fraction and the MCR content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".