Upgrading of Heavy Crude Oil with W-Zr Catalyst
Bibliographic record
Abstract
The main problem of new crude oil reserves is the incipient increase of heavy crude oils in the American continent, i.e. USA, Mexico, Canada and Venezuela. These types of crude oils require several treatments before refining. One of these treatments can be hydrocracking. In this petroleum refining process, it is possible to modify the heavy crude oils to light crude oils. In this paper, we try to use hydrocracking to improve the quality of raw heavy crude oil, through some chemical transformations C-H binding rupture using a catalyst containing active metals such as tungsten and zirconium (W-Zr). After the crude oil was hydrocracked in presence of this novel bimetallic catalyst, the hydrocracked products showed lower content of asphaltenes, resins, sulfur and nitrogen. Also positive changes in the viscosity of crude oil measured as a decreasing of this value were observed. The American Petroleum Institute (API) gravity was significantly increased 6 units. Consequently, all these changes confirmed that the upgrading of the heavy crude oil was successful.
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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".