FCC Study of Canadian Heavy Gas Oils Comparisons of Product Yields and Qualities between Reactors
Bibliographic record
Abstract
Several series of cracking tests in a comprehensive study were conducted on separate occasions involving all or parts of ten Canadian vacuum gas oils (VGOs) and two catalysts with bottoms-cracking or octane-barrel capability.VGOs were cracked in fixed- and/or fluid-bed microactivity test (MAT) units, in an Advanced Cracking Evaluation (ACE)unit, and in a modified ARCO riser reactor. Individual yields of gas, liquid, and coke from the MATs at 55, 65, 70, and 81 wt% conversion levels were compared with their respective pilot plant data. Good linear correlations could be established between MAT and riser yields except for liquefied petroleum gas (LPG) and light cycle oil (LCO). At a given conversion,correlations existed among the fixed- and fluid-bed MAT units and the ACE for each product yield. Liquid products from the fixed or fluid-bed MAT were analyzed for hydrocarbon types, sulfur, nitrogen and density, most of which showed good agreement with those obtained from the riser study. When cracking Canadian oil-sands-derived VGOs, the bottomscracking catalyst containing a large-pore active matrix was found to be more suitable than the octane-barrel catalyst with smaller pores to produce higher yields of valuable distillates, but with less superior qualities (in terms of sulfur and nitrogen contents). The advantages of hydrotreating some poor feeds to improve product yields and qualities were demonstrated and 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".