“Petroleum Gas Oil−Ethanol” Blends Used as Feeds: Increased Production of Ethylene and Propylene over Catalytic Steam-Cracking (CSC) Hybrid Catalysts. Different Behavior of Methanol in Blends with Petroleum Gas Oil
Bibliographic record
Abstract
Hybrid catalysts that contain Zn−Pd-based cocatalyst show a higher and more positive sensitivity to ethanol than catalysts that contain supported Ni−Ru cocatalyst. In fact, with the former catalysts, the use of “gas oil−ethanol” blends significantly increases the product yields of light olefins and particularly ethylene. This appears to be actually a good approach for the partial replacement of petroleum feedstocks by bioderived chemicals (particularly, bioethanol). Another advantage of the CSC process is that it can make use of simply concentrated ethanol in aqueous solution as obtained by enzymatic conversion of biomass. This is maybe the first example of the beneficial effect of bioethanol on the performance of the CSC catalysts, suggesting that the integration of a small “biorefinery” to a petrochemical production plant is now possible. On the other hand, over our hybrid catalysts methanol used as coreactant behaves very differently from ethanol. In fact, while ethanol undergoes predominantly dehydration into ethylene, methanol predominantly intervenes directly in the hydrocarbon pool, keeping the product propylene-to-ethylene ratio almost constant and higher than 1.5.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".