Ethylene production from diluted bioethanol solutions
Bibliographic record
Abstract
Abstract Ethylene production by ethanol dehydration has been investigated over a set of zeolites with BEA structure in acidic form (HAlBEA). The effect on Ni addition (up to 0.03 g/g (3 mass%)) is explored as a means to improve ethylene selectivity limiting the formation of heavier byproducts. The reaction temperature has been varied between 300 and 500 °C and the effect of cofeeding water has been also explored. Both an increase of the operating temperature and of the water/ethanol ratio can improve stability of coking and thus catalyst life. The use of diluted bioethanol solutions was shown to be beneficial for catalyst stability. The effect of possible impurities present in less purified raw materials was also checked by testing two diluted second generation bioethanol solutions (0.5 and 0.9 g/g (50 and 90 mass%)). A first reactor layout and the integration with the bioethanol purification strategy are also proposed, including two purification options which are much less energy demanding than traditional rectification.
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".