Bibliographic record
Abstract
Summary Previously, nuclear-magnetic-resonance (NMR) carbon-type-analysis data were used to develop a mathematical model of mild thermal conversion (visbreaking) of Athabasca bitumen (Chan et al. 2006). In that work, the major reaction pathways followed during visbreaking were identified. This approach is being extended in the current work to model the visbreaking behaviour of five different oils from different geographical locations around the world. This paper shows the correlation of residue conversion with the contents of different carbon types for five heavy oils from four continents. During visbreaking runs, operators intend to maximize process yields. This is achieved through increasing process severity by raising temperature. However, if the temperature is too high, coke forms. This maximum temperature varies with different crude oils; therefore, as refinery feedstock composition changes, so does the onset of coking temperature. Coke is a hydrocarbon material that has low hydrogen content and is insoluble in the oil. Consequently, this precipitates in the reactor, eventually causing an unscheduled unit shutdown. We have found that contents of specific carbon types in the feed oils correlate with coke formation. This correlation allows prediction of the quantities of coke that will form under the chosen visbreaking (mild thermal) conditions and the "maximum" quantities of coke that would form under coking (severe thermal) conditions.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".