Improved bio-oil yield and quality through fast pyrolysis and fractional condensation concepts
Bibliographic record
Abstract
Fast pyrolysis is a thermochemical process capable of producing biofuels that can replace fossil fuels. Pyrolytic oil can be used for electricity production, heating and chemical extraction (Fu et al., 2017; Kalargaris et al., 2017). However, the quality of bio-oil is limited by its low chemical stability associated with aging, low calorific value, high water content, high viscosity and high acidity (Alvarez-Chavez et al., 2019; Carpenter et al., 2014). Therefore, it is necessary to improve the quality of bio-oil before its use in our daily life. This study provides the evaluation of the effect of the operating conditions of a pyrolyzer reactor and its condensing system on the quality and yield of bio-oil. Response surface analysis was applied to optimize the quality and yield of bio-oil using four operational variables of the pyrolyzer. As a result, statistical models corresponding to the studied responses were obtained.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".