Effect of Initiative Additives on Hydro-Thermal Cracking of Heavy Oils and Model Compound
Bibliographic record
Abstract
The hydro-thermal cracking of heavy oils, such as Canadian oil sand bitumen and Arabian heavy vacuum residue, as well as their model compound, was performed over sulfided Ni/Al 2 O 3 and NiMo/Al 2 O 3 catalysts under 663−703 K and 5.0−8.0 MPa of hydrogen pressure in a batch autoclave reactor. According to the reaction mechanism of hydro-thermal cracking, some free radical initiators, such as di- tert -butyl-peroxide (DTBP), sulfur, etc., were added into the feed to generate free radicals at lower temperature, and some initiators did obviously show a promotional effect on the conversion of hydrocarbons. The reaction mechanisms of hydro-thermal cracking as well as the enhancing effect of initiators were studied by a probe reaction with 1-phenyldodecane as a model compound under the conditions of hydro-thermal cracking. The hydro-thermal cracking of hydrocarbons proceeded via a free-radical mechanism and hydrogenating quench. The initiators might easily generate free radicals under the reaction temperature, these radicals might abstract H from hydrocarbon molecules and reasonably initiated the chain reactions, therefore, promoted the conversion of hydrocarbons even at lower reaction temperature. The reaction temperature could be lowered by the addition of a free radical initiator, while keeping the same conversion level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".