Effects of Nitrogen and Aromatics on Hydrodesulfurization of Light Cycle Oil Predicted by a System Dynamics Model
Bibliographic record
Abstract
The production of ultralow-sulfur diesel from light cycle oil (LCO) by hydrotreatment is promising. Unfortunately, hydrodesulfurization (HDS) of LCO is seriously inhibited by the coexistence of organic nitrogen and aromatic compounds during the hydrotreating process. It is of great interest to understand this inhibition confidently by simulations. In this study, the inhibition effects from both organonitrogen and aromatic compounds are simulated by a proposed system dynamics (SD) model for the first time. The axial profiles of the concentrations of organic sulfur, nitrogen, and aromatics in the hydrotreating reactor are predicted by the SD model. Impact factors for nitrogen and aromatic compounds respectively are used to characterize their inhibition effects. Studies were also conducted on the variation of the impact factors along the axial position of the hydrotreator. The simulation results of HDS, with consideration of the nitrogen and aromatic inhibition effects, are compared with published experimental data. The influence of the operating conditions such as inlet system temperature and liquid hourly space velocity on the impact factors is also investigated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".