FCC coprocessing oil sands heavy gas oil and canola oil. 3. Some cracking characteristics
Bibliographic record
Abstract
Coprocessing of bitumen-derived feeds and biomass through a fluid catalytic cracking (FCC) route has the potential to assist in the reduction of fuel and petroleum product carbon footprints while meeting government regulatory requirements on renewable transportation fuels. This approach is desirable because green house gas (GHG) emissions for producing renewable biofuels are significantly lower than those for fossil fuels, and coprocessing can be executed using existing refining infrastructure to save capital cost. The present study investigates the specific FCC performances of pure heavy gas oil (HGO) derived from oil sands synthetic crude, and a mixture of 15 v% canola oil in HGO using a commercial equilibrium catalyst under typical FCC conditions. Cracking experiments were performed using a bench-scale Advanced Cracking Evaluation (ACE) unit at fixed weight hourly space velocity (WHSV) of 8 h−1, 490–530 °C, and catalyst/oil ratios of 4–12 g/g. This work focuses on some cracking phenomena resulting from the presence of oxygen in the blend—a lower heat requirement for cracking due to the exothermic water formation, which also entails lower hydrogen yield at a given severity. The distribution of feed oxygen in gaseous and liquid products, the mitigation in GHG emissions, and the technological and economical advantages of the coprocessing option are also discussed.
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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".