Catalytic fast pyrolysis of biomass
Bibliographic record
Abstract
Selected catalysts were screened or use in fast pyrolysis of biomass to upgrade the quality and quantity of bio-oil products. The screening was carried out using a Py-GC/MS microscale reactor followed by catalytic pyrolysis experiment on a bench scale fluidized bed reactor system with a scale o 150 g/hr biomass throughput. Cassava rhizome was used as the main biomass feedstock with selected catalysts including ZSM-5, Al-MCM-41, Al-MSU-F, copper chromite, proprietary commercial catalysts Criterion-534, and MI-575 as well as biomass ash. The mass balance closures for the catalytic pyrolysis experiment in the bench scale reactor were ≥ 95%. The presence of catalysts led to changes in product distribution especially in the organic fractions of the liquid bio-oil. Almost all the catalysts led to a decrease in organic yield accompanied with an increase of reaction water, secondary solid, and gases suggesting that the primary pyrolysis vapor has gone through severe dehydration and cracking reactions over the catalysts. Based on the volumetric gas composition, Criterion-534 favored production of hydrogen whereas ZSM-5 favored the production of olefins. According to the molecular weight distribution results of the bio-oil produced, most of the catalysts studied have the potential or improving bio-oils viscosity. This is an abstract of a paper presented at the 8th World Congress of Chemical Engineering (Montreal, Quebec, Canada 8/23-27/2009).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".