Mild Hydrothermal Liquefaction of High Water Content Agricultural Residue for Bio-Crude Oil Production: A Parametric Study
Bibliographic record
Abstract
Depleting petroleum reserves together with the associated environmental concerns have intensified the exploration of alternatives to petroleum. Wet food processing wastes present promising bioresources for liquid fuel production via hydrothermal liquefaction (HTL) followed by additional upgrading. In this study, tomato plant waste (TPW) was utilized as a feedstock for the production of bio-crude oils via HTL at medium-temperature (220–280 °C) in water or a water–ethanol (17/3, v/v) medium in a 600 mL autoclave reactor. Effects of various operating parameters, such as catalysts (H2SO4 or KOH), reaction time (15–60 min) and reaction temperature (220–280 °C) on product yields were investigated. This study showed that a high yield (45.1 wt%) of bio-crude oil was achieved from HTL of TPW in water–ethanol medium at 250 °C in the presence of acid catalyst H2SO4. The oil, gas and solid residue (SR) products were analyzed for their chemical and structural properties.
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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".