Thermal and catalytic upgrading of a biomass‐derived oil in a dual reaction system
Bibliographic record
Abstract
Abstract The thermal and catalytic upgrsding of bio‐oil to liquid fuels was studied at atmospheric pressure in a dual reactor system over HZSM‐5, silica‐alumina and a mixed catalyst containing HZSM‐5 and silica‐alumina. This bio‐oil was produced by the rapid thermal processing of the maple wood. In this work, the intent was to improve the catalyst life. Therefore, the first reactor containing no catalyst facilitated thermal cracking of blo‐oil whereas the second reactor containing the desired catalyst upgraded the thermally cracked products. The effects of process variables such as reaction temperature (350°C to 410°C), space velocity (1.8 to 7.2 h−1) and catalyst type on the amounts and quality of organic liquid product (OLP) were investigated, In the case of HZSM‐5 catalyst, the yield of OLP was maximum at 27.2 wt% whereas the selectivity for aromatic hydrocarbons was maximum at 83 wt%. The selectivities towards aromatics and aliphatic hydrocarbons were highest for mixed and silica‐alumina catalysts, respectively. In all catalyst cases, maximum OLP was produced at an optimum reaction temperature of 370°C in both reactors, and at higher space velocity. The gaseous product consisted of CO and CO2, and C1‐C6 hydrocarbons, which amounted to about 20 to 30 wt% of bio‐oil. The catalysts were deactivated due to coking and were regenerated to achieve their original activity.
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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".