Intrinsic gasification rate of oil sands fluid coke with carbon dioxide and steam
Bibliographic record
Abstract
Petroleum coke is a byproduct of oil sands upgrading which can be utilized as a low‐cost feedstock for further value‐added operations such as gasification. The design and scale up of gasifiers for oil sands coke demands reliable data on the intrinsic reaction rates, which are currently missing from the literature. The intrinsic gasification rates for char‐CO2, and char‐H2O reactions were determined for Canadian oil sands fluid coke. Four different specific surface area measurement techniques were used to normalize the specific reaction rate calculated from the weight loss profiles of TGA: N2‐BET surface area, two micro‐porosity measurement techniques based on Density Functional Theory (DFT) and Dubinin‐Radushkevich (DR) models, and finally active surface area (ASA) measured by CO2 chemisorption at different levels of conversion (char conversion). The specific reaction rates, calculated at different levels of conversion were divided by different surface areas, in order to find the specific surface area that results in the best reduction in the variability of reaction rate, r(X), by using surface area as a regressor variable. Overall, ASA was found to be the best regressor for deriving the intrinsic rates. Surface areas based on the N2‐BET technique were also proven to be a better choice for normalizing the specific rates compared to the ones based on DFT and DR models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".