Production of bio-oil and biochar from digestate via pyrolysis in a mechanical fluidized bed reactor
Bibliographic record
Abstract
The rising pressure to reduce greenhouse gas emissions calls for effective strategies to valorize organic waste streams. This study investigates the potential of slow pyrolysis to convert digestate, a byproduct of anaerobic digestion, into biochar and bio-oil using a mechanically stirred fluidized bed reactor. Experiments were done at 400, 450, 500, and 550 °C, with an additional run at 500 °C which employed a catalyst bed composed of process-derived biochar. As the temperature increased from 400 to 500 °C, the biochar yield decreased from 68 % to 50 %, while the bio-oil yield increased from 21 % to 30 %. The presence of the biochar bed further reduced biochar formation by approximately 5 %, enhancing vapor production. GC-MS analysis revealed that the bio-oil was primarily composed of carbonyl compounds, sterols, alcohols, and phenolic derivatives. These results demonstrate the influence of temperature and a biochar catalyst on product distribution and composition. Overall, the study supports pyrolysis as a viable pathway for digestate valorization and sustainable carbon recovery, contributing to emissions mitigation and improved resource management.
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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".