Advances in Upgrading Process of Petroleum Residue: A Review
Bibliographic record
Abstract
Due to the depletion of light crude oil reserves, heavy crude oil and residues are the alternatives to meet ?the ?increasing global demand for light oil products. Heavy crude oil and residues are characterized by the presence of heavy ?hydrocarbon ?compounds which contain high levels of impurities such as metals, nitrogen, and sulfur-containing compounds. ?Methods of ?upgrading are required to increase refining efficiencies and to obtain high-quality products.? Upgrading processes can be categorized into three categories; ?carbon rejection processes, hydrogen addition processes, and a combination of the two. The catalyst can be used with any of these processes for better improvement. Many types of research have been carried out to develop a high-performance process which is stable, high commercial products yield, and low solids formation. In this work, recent advances on petroleum residues upgrading with catalyst, solvents, and thermal cracking were reviewed. Advantages and disadvantages of each process were discussed along with conditions and main features. Nanoparticles catalysts and supercritical fluids based-processes are the trends of upgrading due to the excellent performance of these processes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".