Microwave catalytic pyrolysis of solid digestate for high quality bio-oil and biochar
Bibliographic record
Abstract
Microwave-assisted catalytic pyrolysis (MACP) of solid digestate (SD) into value-added products presents a promising solution for waste SD. Both types of catalysts and reactor temperature critically influence the properties of MACP products. This study systematically investigated pyrolysis of SD mixed with different catalysts (K 3 PO 4 , natural zeolite, and mixture of K 3 PO 4 and natural zeolite) at various pyrolysis temperatures (300, 400, and 500 ℃) for bio-oil and biochar production. The results showed that higher temperatures led to reduced bio-oil and biochar yields, favoring gas production. The bio-oil derived from SD with 20 wt% K 3 PO 4 and 20 wt% natural zeolite at 500 ℃ exhibited the largest fraction of aromatic hydrocarbons, reaching 92.43 % and 91.56 %, respectively. Catalytic pyroysis resulted in reduction in bio-oil acidity. Biochar specific surface area is influenced by both heating rate and temperature, with the highest surface area (207 m 2 /g), pore volume (0.2244 cm³/g), and a more regular pore structure being obtained at 500 ℃ and 66.1 °C/min with 20 wt% K 3 PO 4 . This work demonstrated the feasibility of upgrading waste SD into value-added chemicals, materials, and energy-rich fuels by MACP. Notably, SD with 20 wt% K 3 PO 4 at 500 ℃ represents the optimal operating condition for both bio-oil and biochar production. • Catalytic pyrolysis resulted in a reduction in bio-oil acidity. • K 3 PO 4 catalytic pyrolysis yielded more aromatic hydrocarbons in bio-oil. • Biochar specific surface area is influenced by both heating rate and temperature. • Highest surface area (207 m 2 /g) was obtained at 500 ℃ with 20 wt% K 3 PO 4 .
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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".