A Comparative Study on Thermochemical Valorization Routes for Spent Coffee Grounds
Bibliographic record
Abstract
Extracting oil from spent coffee grounds (SCG) for biodiesel production has recently attracted much research interest. Large amounts of organic solvents are involved for oil extraction and biodiesel synthesis. Hydrothermal liquefaction (HTL) is an emerging thermochemical technology with great potential for biocrude production from a broad range of feedstocks. This study attempted to compare two SCG valorization options: route 1, oil extraction for biodiesel production followed by HTL of defatted SCG; and route 2, direct HTL of raw SCG. The microwave-assisted extraction conditions were optimized to effectively remove oil from SCG using response surface methodology. Under optimal conditions, an SCG oil yield of 8.4 wt.% could be achieved. HTL of defatted SCG generated less biocrude (18.9 wt.%) than that of raw SCG (28.1 wt.%). The biochemical compositions of resultant biocrudes were largely different from each other. The life cycle assessment was conducted on each of the SCG valorization routes and showed that the greenhouse gas emissions from direct HTL of raw SCG were only 35% of the other valorization route, suggesting that direct HTL is a favorable valorization route for SCG within this study’s scope.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".