Catalytic upgrading of bio‐oil: Hydrodeoxygenation study of acetone as molecule model of ketones
Bibliographic record
Abstract
Abstract The complexity of composition of bio‐oil from biomass makes it difficult to produce upgraded bio‐oil via hydrodeoxygenation. In this paper, acetone is thus considered as a model compound of the ketones family abundant in pyrolysis bio‐oil. Results showed that high conversion rates of acetone between 86.6% and 91.9% were observed with the use of HZSM‐5, 5% Ni 2 P/HZSM‐5, and 10% Ni2P/HZSM‐5 catalysts. In most cases, CO 2 , C 2 H 6 , C 3 H 6 , and C 3 H 8 were the dominant non‐condensable gas products. For liquid phase, the selectivity was evaluated for different catalysts relative to ethanol, acetaldehyde, and aromatic hydrocarbons. A lower temperature favoured the formation of acetaldehyde and methyl isobutyl ketone with the 5% Ni 2 P/HZSM‐5 catalyst, while higher temperatures increased the proportion of aromatic hydrocarbons. The principal influencing parameters of acetone HDO were temperature and contact time followed by reaction pressure and H 2 partial pressure. Optimal conditions give a selectivity of 49% of aromatics (benzene, toluene, and xylene) with the use of the 5% Ni 2 P/HZSM‐5 catalyst. The pathway of the main reactions of acetone HDO was also proposed. MIK and aromatic hydrocarbons were formed by a multiple step aldol condensation reaction of acetone molecules followed by further hydrogenation.
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 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 | 0.000 |
| 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) | 0.000 | 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".