Bibliographic record
Abstract
As a rich and important resource in Canada, heavy oil has the disadvantage of being transportable by pipeline because of its high viscosity. It would be of great importance to upgrade the heavy oil for potential transportation and usages. Instead of the conventional hydrogen used in hydrocracking, this thesis focused on the heavy oil upgrading by methane. In this thesis, various catalysts have been developed for the upgrading. Detailed physical and chemical properties of several types of heavy oil and their upgraded products were well characterized such as viscosity, density, and total acid value, etc. A good performance and simple version of catalyst was optimized to be 1 wt% Ag-5 wt% Mo-10 wt% Ce/HZSM-5. After the upgrading, it was confirmed that the viscosity of some heavy oil could be considerably lowered to less than 300 cP to meet the requirements for pipeline transportation. In addition, a very difficult raw feed of oil mud was also included for the upgrading in this thesis, which proved this upgrading approach by using methane can be expanded to other heavy oil feeds. Furthermore, octylbenzene was used as a model compound to run the upgrading reaction to further understand the reaction mechanism. This thesis proved that our optimized catalyst could generally upgrade heavy oil at mild conditions together with methane instead of hydrogen. It showed potential industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
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".