Removal of Organic Nitrogen Compounds in LCO Reduce Hydrodesulfurization Severity
Bibliographic record
Abstract
Abstract The United States and Canada have set targets to reduce diesel sulphur from 500 to 15 ppm by the year 2006 and 2007, respectively. Better understanding the effects of feed matrix on sulphur removal by hydrodesulphurization (HDS) could guide refineries to select the right feed or feed pre-treatment options for their existing HDS units and achieve the required sulphur level at minimum cost. To this end, the influence of nitrogen compounds on the HDS activities of substituted dibenzothiophenes in light cycle oil has been studied over a NiMo/Al2O3 commercial catalyst using five light cycle oil feeds with different concentrations of organic nitrogen compounds. Experiments were conducted under conditions close to industrial HDS processes. Our work revealed that sulphur compounds could be removed under less severe reaction conditions if organic nitrogen compounds in LCO were reduced through adsorption by a silica column prior to HDS. The results also demonstrated that organic nitrogen compounds had more of a inhibition effect on sulphur removal by the hydrogenation pathway than by the hydrogenolysis pathway. Several approaches have been considered by refineries to meet the 15 ppm sulphur diesel specification, such as using more active catalysts, increasing catalyst volume, reducing cycle length and reducing the feedstock end point. The most refractory sulphur compounds such as 4 and 6 alkyl-substituted dibenzothiophenes become major reaction barriers when sulphur level has to be reduced. Since those sulphur compounds are removed predominately by the hydrogenation pathway, close attention should be paid to how to increase the hydrogenation activity. Commercialized Ni-Mo catalysts cannot meet the ultra-low sulphur requirement without increasing HDS severity. New active catalysts under development are mostly not entering the market before 2006. All these facts make the nitrogen removal by feed pre-treatment an attractive alternative to achieve the ultra-low sulphur goal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".