Study of Canadian FCC Feeds from Various Origins and Treatments. 1. Ranking of Feedstocks Based on Feed Quality and Product Distribution
Bibliographic record
Abstract
Accurate ranking of fluid catalytic cracking (FCC) feeds will help oil producers price their commodities. The value of an FCC feed depends on its properties, which, in turn, contribute toward producing high-value products with good qualities. Feed ranking based on general analyses is the simplest way to determine the best feeds but can be misleading for some. In this study, the authors propose a feed grading method with consideration of only concentrations of gasoline precursors, total nitrogen, and microcarbon residue (MCR), assuming that gasoline is the most desirable product. By assigning the merit and discount values of the three elements, the relative gasoline yield of a feed can be calculated and its apparent feed rank established. The method was tested on 10 feeds that were catalytically cracked in both a microactivity test (MAT) unit and a riser pilot plant. With some exceptions, the apparent feed ranks indicated an agreeable order with those based on individual high- and low-value MAT product yields at both constant test severity (catalyst/oil (C/O) ratio of 7) and conversion (65 wt %). The apparent feed ranks were also validated with the exact feed ranks determined by comparing the individual MAT yields at an achievably high conversion, using the maximum gasoline yields as a guide. The two feed ranks also showed good conformity, with respect to the sequence. Verification of apparent feed ranks against riser pilot plant yields in this study was difficult, because of limited test data for comparison. MAT yields of the 10 feeds compared better with riser pilot plant yields at the same conversion than with yields at the same C/O ratio. Among the six cracked products, dry gas gave the best comparison, in terms of absolute yields, whereas coke showed the worst results.
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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 | 0.000 |
| 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) | 0.000 | 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".