Production of bio‐crude from forestry waste by hydro‐liquefaction in sub‐/super‐critical methanol
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Hydro‐liquefaction of a woody biomass (birch powder) in sub‐/super‐critical methanol without and with catalysts was investigated with an autoclave reactor at temperatures of 473–673 K and an initial pressure of hydrogen varying from 2.0 to 10.0 MPa. The liquid products were separated into water soluble oil and heavy oil (as bio‐crude) by extraction with water and acetone. Without catalyst, the yields of heavy oil and water soluble oil were in the ranges of 2.4–25.5 wt % and 1.2–17.0 wt %, respectively, depending strongly on reaction temperature, reaction time, and initial pressure of hydrogen. The optimum temperature for the production of heavy oil and water soluble oil was found to be at around 623 K, whereas a longer residence time and a lower initial H 2 pressure were found to be favorite conditions for the oil production. Addition of a basic catalyst, such as NaOH, K 2 CO 3 , and Rb 2 CO 3 , could significantly promote biomass conversion and increase yields of oily products in the treatments at temperatures less than 573 K. The yield of heavy oil attained about 30 wt % for the liquefaction operation in the presence of 5 wt % Rb 2 CO 3 at 573 K and 2 MPa of H 2 for 60 min. The obtained heavy oil products consisted of a high concentration of phenol derivatives, esters, and benzene derivatives, and they also contained a higher concentration of carbon, a much lower concentration of oxygen, and a significantly increased heating value (>30 MJ/kg) when compared with the raw woody biomass. © 2009 American Institute of Chemical Engineers AIChE J, 2009
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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 it