Pyrolysis bio-oil as a renewable fuel and source of chemicals: its production, characterization and stability
Bibliographic record
Abstract
Bio-oil is a liquid fuel that can be produced from various lignocellulosic feedstocks via fast pyrolysis. It is a complex mixture comprised of hundreds of highly oxygenated organic compounds originating from lignin and carbohydrates and is recognized as a clean renewable bio-fuel, an attractive alternative to fossil fuels. It can be easily transported and used directly in boilers and modified turbines or upgraded/fractionated for drop in fuels or chemical production. Proper bio-oil characterization is important in optimizing the pyrolysis process, bio-oil upgrading and utilization, and its stabilization for long-term storage. With this in mind, research has been undertaken to develop better techniques to rapidly profile the composition of whole bio-oil samples, and an accelerated aging study performed to determine why bio-oil is unstable upon storage. Pyrolysis-GC/MS and TLC-FID were used as tools to differentiate bio-oils of different lignocellulosic biomasses, and among thermal-cracking (upgrading) fractions. Results showed that birch bio-oil had high syringol derivatives compared to pine and barley straw bio-oils which had higher guaiacol and non-methoxy-phenolic compounds, respectively, compared with birch bio-oil. TLC-FID was successful in bio-oil differentiation, showing diagnostic chromatographic profile differences. Direct infusion-ESI-ion trap MS and ESI-ion trap MS2 were successfully used in the analysis of forest-residue bio-oil and reference bio-oils from cellulose and hardwood lignin dissolved in methanol:water. NH4Cl can be used as a dopant to distinguish carbohydrate-derived products from other bio-oil components. NaOH and NaCl dopants resulted in the highest intensity peaks in negative ion mode and positive mode, respectively. Tandem MS, that is, ESI-Ion Trap MS2 was a successful tool for the confirmation of individual target ions such as levoglucosan and cellobiosan and for structural insight into lignin products. In accelerated aging (at 80 °C for 1, 3 and 7 days) studies, the physical and chemical properties of bio-oil from ash wood (produced from a pilot-scale auger pyrolyzer) and birch wood (lab-scale pyrolyzer) were monitored in order to identify the factors responsible for bio-oil instability. Water content, viscosity, and decomposition temperature (by TGA) increased for both bio-oil samples with aging. Chemical analysis showed reduction in amount of most of the bio-oil components as aging progressed, typically for are olefins and aldehydes. The oils remained a single phase throughout until the 7th day.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".