Aromatization reaction of Shandong catalytic gasoline and liquefied petroleum gas compound
Bibliographic record
Abstract
By using Shandong catalytic gasoline and liquefied petroleum gas as feedstock,the effects of reaction temperature,weight hour space velocity (WHSV),catalyst to oil weight ratio and different ratios of catalyst on the product distribution of aromatization were researched in a confined fluidized bed reactor in orthogonal experiments. And the composition of aromatized gas and liquid was analyzed in detail. The experimental results show that Shandong catalytic gasoline's light oil yield in the optimizing condition is 92.2%,the aromatics yield in light oil is 40.6%,the olefin amount is less than 15%,the off-gas and coke's amount is less than 2%,and a small amount of vapor added can improve the light oil yield; Shandong liquefied petroleum gas's light oil yield is 41.5%,its aromatics and olefin amount are 48.6% and 28.8% respectively,and off-gas and coke yield is 4.0%. When Shandong catalytic gasoline is refined together with liquefied petroleum gas,the light oil yield and aromatics amount in light oil are less than those in Shandong catalytic gasoline refined only. Liquefied petroleum gas addition is not beneficial to aromatization,and the aromatization effect for entering Shandong catalytic gasoline after entering Shandong liquefied petroleum gas is better than that entering Shandong catalytic gasoline and liquefied petroleum gas together or entering liquefied petroleum gas after entering Shandong catalytic gasoline.
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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 | 0.000 |
| 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".