Valorization of peanut wastes into a catalyst in production of biodiesel
Bibliographic record
Abstract
To offer an innovative way to valorize industrial crop waste into the diverse types of biofuels, the thermochemical process of peanut waste (PW) was investigated. In particular, this study laid a great stress on the use of PW-derived biochar as a cheap catalytic material in the production of biodiesel. Specifically, biochar derived from the pyrolysis of PW was used as a catalytic and porous medium for biodiesel production to enhance reaction kinetics and lower reaction temperature, compared to conventional methods. Two PW-derived biochars produced at 600°C (PWB-600) and 700°C (PWB-700) were effective on the transesterification of soybean oil, showing higher than 95 wt% of biodiesel yield after 1 minute of transesterification reaction at ≥210°C. As a comparison, a commercially used reaction, alkali-catalyzed transesterification, was conducted at 60°C with a KOH catalyst. Biodiesel yield from the alkali-catalyzed reaction was less than 90 wt% even after 6 hours of reaction. Given that the biochar formation process results in the generation of pyrolytic gases and oils, both pyrolysates at different temperatures were also monitored. Pyrolytic gases included syngas and C1-2 hydrocarbons, whereas pyrolytic oils consisted of phenolic compounds that can be used as intermediates for the synthesis of value-added chemicals. Thus, the results confirmed that the thermochemical upgrading of PW produces value-added industrial chemicals (pyrolytic gases and oils) and biochars that are highly active for the biodiesel production process.
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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) | 0.000 | 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 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".