Physico-Chemical Properties of Bio-Oils from Pyrolysis of Lignocellulosic Biomass with High and Slow Heating Rate
Bibliographic record
Abstract
Bio-oil is a major product of biomass pyrolysis that could potentially be used in motor engines, boilers, furnaces and turbines for heat and power. Upon catalytic upgrading, bio-oils can be used as transportation fuels due to enhancement of their fuel properties. In this study, bio-oils produced from lignocellulosic biomasses such as wheat straw, timothy grass and pinewood were estimated through slow and high heating rate pyrolysis at 450 °C. The slow heating rate (2 °C/min) pyrolysis resulted in low bio-oil yields and high amount of biochars, whereas the high heating rate (450 °C/min) pyrolysis produced significant amount of bio-oils with reduced biochar yields. The physico-chemical and compositional analyses of bio-oils were achieved through carbon-hydrogen-nitrogen-sulfur (CHNS) studies, calorific value, Fourier transform-infra red (FT-IR) spectroscopy, gas chromatography-mass spectrometry (GC-MS), electrospray ionization-mass spectrometry (ESI-MS) and nuclear magnetic resonance (NMR) spectroscopy. The yields of bio-oils produced from the three biomasses were 40-48 wt.% through high heating rate pyrolysis and 18-24 wt.% through slow heating rate pyrolysis. The chemical components identified in bio-oils were classified into five major groups such as organic acids, aldehydes, ketones, alcohols and phenols. The percent intensities of hydrogen and carbon containing species were calculated from 1H and 13C-NMR. The study on bio-oils from herbaceous and woody biomasses revealed their potentials for fossil fuel substitution and bio-chemical production.
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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".