Combined Hydrotreating and Fluid Catalytic Cracking Processing for the Conversion of Inferior Coker Gas Oil: Effect on Nitrogen Compounds and Condensed Aromatics
Bibliographic record
Abstract
Inferior coker gas oil (ICGO) derived from Venezuelan vacuum residue delayed coking is difficult to process using fluid catalytic cracking (FCC) or hydrocracking (HDC). The high content of nitrogen and condensed aromatics leads to major coking and readily deactivates the acid catalyst. In this work, a sequence of hydrotreating (HDT) and FCC processing is used to effectively convert ICGO to a high-value light oil product. The results show a higher overall conversion and a significant increase in the yield of gasoline compared to FCC processing. Molecular level characterization of the nitrogen compounds and condensed aromatics before and after HDT confirms that the nitrogen content and the 2+-ring aromatic content decreased, whereas the single-ring aromatics increased. The nitrogen compounds were mainly N 1, N 1 O 1, N 1 O 2, and N 1 S 1 class species in basic nitrogen and N 1, N 1 O 1, N 1 O 2, N 2, and N 2 O 1 class species in non-basic nitrogen. Moreover, the double bond equivalent of these species shifted to lower values. The decrease in the nitrogen compounds with a high heteroatom content reduces coking on the FCC catalyst. Subsequently, FCC unit performance and conversion to light oil increased. Moreover, the decrease in the size of N 1 class compounds and the ease of their cracking following HDT improved the performance of the FCC unit. Partial saturation of condensed aromatics following HDT also made it easier to crack these compounds.
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) | 0.001 | 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".