Partial Upgrading of Extra-Heavy Crude Oils via Fixed Bed Aquaprocessing Using Two Catalysts with Different Pore Size Distributions
Bibliographic record
Abstract
Partial upgrading of heavy oil represents a great alternative to cope with current crude oil low prices, in particular if the chosen path is catalytic, considerably reducing energy usage, i.e. emissions, and replacing hydrogen with an inexpensive and abundant source of it such as steam. The convenient yield to desirable stable products makes Aquaprocessing an attractive process to reduce upgrading costs. This thesis is focused on the comparison of the results obtained with an in-house formulated catalyst called CAT-J20 with results obtained using a previous less macroporous version of this catalyst (CAT-J15). CAT-J15 support presents a smaller average pore width size of 8.2 nm when compared with the improved solid having 18.1 nm. The higher pore size catalyst allows better results when upgrading a SAGD produced Canadian oil (JACOS) and steam produced Mexican oil (Samaria), the former being more naphthenic and the latter more aromatic. Results obtained with both feedstocks demonstrated that CAT-J20 presents a great activity running JACOS but presents moderate results with Samaria due to its molecular complexity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".