Fast Pyrolysis of Cellulose, Hemicellulose, and Lignin: Effect of Operating Temperature on Bio-oil Yield and Composition and Insights into the Intrinsic Pyrolysis Chemistry
Bibliographic record
Abstract
Fast pyrolysis of biomass produces bio-oil as a dominant product. However, the yield and composition of bio-oil are governed by numerous pyrolysis reactions which are difficult to understand because of the multiphase decomposition phenomena with convoluted chemistry and transport effects at millisecond time scales. In this work, thin-film pyrolysis experiments of biopolymers present in the biomass (i.e., cellulose (∼50 μm), hemicellulose (using xylan as a model biopolymer, ∼12 μm), and lignin (∼10 μm)) were performed over 200–550 °C, to investigate underlying thermal decomposition reactions, based on the product distribution obtained under reaction-controlled operating conditions. Experimental yields of non-condensable gases, bio-oil, and char at different operating temperatures and in the absence of transport limitations were obtained for each biopolymer. Cellulose- and xylan-derived bio-oil comprised of anhydrosugars, furans, and light oxygenates, in addition to pyrans in cellulosic bio-oil and phenols in xylan-derived bio-oil. Lignin pyrolysis bio-oil contained methoxyphenols, phenolic aldehydes/ketones, low-molecular-weight phenols, and light oxygenates. With an increase in the operating temperature, the anhydrosugars, furans (especially HMF and furfural), and pyrans of cellulosic and xylan bio-oils showed further degradation to form light oxygenates and furanic compounds. In the case of lignin, monolignols, initially formed at lower temperatures, further reacted to form low-molecular-weight phenols and light oxygenates with an increase in the operating temperature. In addition, based on the change in bio-oil yield and composition with temperatures, a reaction network/map was proposed for designing the molecular simulation studies of pyrolysis chemistry and developing detailed and accurate kinetics necessary for the bottom-up design of a pyrolysis reactor.
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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) | 0.001 | 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".