Kinetic Modeling of Bitumen Hydroprocessing at In-Reservoir Conditions Employing Ultradispersed Catalysts
Bibliographic record
Abstract
Heavy oil and bitumen’s low American Petroleum Institute (API) gravity and high viscosity make the economics of their industrialization difficult. Therefore, new recovery techniques must be developed to enhance these materials. Ultradispersed catalytic hydroprocessing of heavy oil and bitumen has been proposed as one of these novel techniques and has been tested in laboratories and pilot plants. In this work, a kinetic model for ultradispersed catalytic hydroprocessing of bitumen is proposed. Kinetic parameters were estimated from experimental results obtained in a tubular pilot plant reactor. The predicted products composition was in good agreement with experimental values with average absolute errors of less than 7%. Additionally, experimental liquid products viscosity and residue conversions followed an exponential correlation. This correlation, in combination with the results from the kinetic modeling, was employed to create a computational program that calculates products distribution from bitumen hydroprocessing and provides reaction conditions to achieve a specific liquid products viscosity.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".