Production of Heavy Oils with High Caloric Values by Direct Liquefaction of Woody Biomass in Sub/Near-critical Water
Bibliographic record
Abstract
Direct liquefaction of a woody biomass (Jack pine sawdust) in sub/near-critical water without and with catalysts (alkaline earth and iron ions) has been investigated at temperatures of 280–380 °C. Heavy oils with a high caloric value of 30–35 MJ/kg (much greater than that of the crude wood sample used) were obtained, along with water soluble oils with a caloric value of 19–25 MJ/kg. The yields of heavy oil and total oil products tended to maximize in the temperature range of 280–340 °C for all the liquefaction operations regardless of the presence of a catalyst or the type of catalyst. All the catalysts tested, i.e., Ca(OH) 2, Ba(OH) 2, and FeSO 4, were found effective for enhancing the formation of heavy oil products at 280–340 °C, while they significantly promoted the formation of gas and water at >340 °C. The yield of heavy oil in the operation at 300 °C for 30 min was improved significantly from around 30% without catalyst to greater than 45% by Ba(OH) 2 . The maximum yield of total oil products reached 51% in the operation without catalyst, while it increased to about 65% with Ca(OH) 2 at 300 °C. The GC/MS measurements for the heavy oil products revealed that the oils contain mainly carboxylic acids, phenolic compounds and derivatives, and long-chain alkanes.
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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) | 0.001 | 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".