Optimization of microwave-assisted hydrothermal pretreatment and its effect on pyrolytic oil quality obtained by an auger reactor
Bibliographic record
Abstract
Microwave-assisted hydrothermal (MHT) treatment of biomass has received significant attention owing to energy efficiency during internal energy transfer and the additional benefits to the hydrochar produced in terms of physicochemical composition. Therefore, this study proposes the combination of MHT pretreatment with the fast pyrolysis process, to evaluate and optimize the effect of this treatment on the quality of the hydrochar and, consequently, on the quality of the bio-oil. The optimization of MHT treatment using black spruce was carried out, followed by fast pyrolysis of the hydrochar produced under optimal conditions in an auger reactor at 550 °C to obtain a high-quality bio-oil. As a result, the pretreated biomass showed on the one hand a significant decrease in the ash content by 58% and 43% in the content of the extractives. While on the other hand, the obtained hydrochar showed an increase in the availability of cellulose by 18.5% as a consequence of the reduction in the content of hemicellulose. Accordingly, hydrochar showed an increase in thermal stability during pyrolysis and it produced a higher total bio-oil yield, increasing by 24%. Most importantly, the oil obtained showed a 35% reduction in moisture content. Chemical composition of the oil was qualitatively examined through GC-MS analysis. It was observed that the bio-oil showed a dramatic increase in the relative content of levoglucosan, by 127%. A bio-oil with the characteristics obtained would be a suitable candidate for use in boilers for heating purposes or chemical extraction.
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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) | 0.001 | 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".