Bibliographic record
Abstract
Abstract The selective removal of nitrogen-containing compounds from oil and oil fractions is of interest because of the potential deleterious impact of such compounds on products and processes. Problems caused by nitrogen-containing compounds include gum formation, acid catalyst inhibition and deactivation, acid?base pair-related corrosion, and metal complexation. A brief overview of the classes of nitrogen compounds found in oil is provided. The review of processes to remove nitrogen from oil emphasizes studies that investigated denitrogenation of industrial feedstocks, such as refinery fractions, heavy oils, and bitumens. The main topics covered are hydrotreating, liquid?liquid phase partitioning, solvent deasphalting, adsorption, chemical conversion followed by separation, and microbial conversion. Chemical conversion processes include oxidative denitrogenation, N-alkylation, complexation with metal salts, and conversion in high-temperature water. There are many processes for denitrogenation by separation of the nitrogen-rich products from oil without removing the nitrogen group from the nitrogen-containing compounds. As a consequence, most of these processes are viable mainly for removal of nitrogen from low-nitrogen-content oils, typically with <0.1 wt % N. At present, hydrodenitrogenation appears to be the only industrially viable process for nitrogen removal from oils with high nitrogen content.
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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".