Conversion of Biomass-Derived Tars in a Fluidized Catalytic Post-Gasification Process
Bibliographic record
Abstract
The present study deals with the development, characterization, and performance of a Ni-based catalyst over a ceria-doped alumina support as a post-gasification step, in the conversion of biomass-derived tars. The catalysts were prepared using the incipient wetness technique and characterized chemically and physically using NH3-TPD, CO2-TPD, H2-TPR, XRD, Pyridine-FTIR, N2 physisorption, and H2-Pulse Chemisorption. It was observed that the 5 wt% CeO2 reduced the strong and very strong acid sites of the alumina support and helped with the dispersion of nickel. It was noticed that the nickel crystallite sizes and metal dispersion remained unchanged as the nickel loading increased. The performance of the catalysts was studied in a mini-fluidized CREC Riser Simulator at different temperatures and reaction times. The selected tar surrogate was 2-methoxy-4-methylphenol, given its functional group similarities with lignin-derived tars. A H2/CO2 gas blend was used to emulate the syngas at post-gasification conditions. The obtained tar surrogate conversion was higher than 75%, regardless of the reaction conditions. Furthermore, the catalysts used in this research provided an enhancement in the syngas product composition when compared to that observed in the thermal experiments. The presence of hydrocarbons greater than CH4 (C1+) was reduced at 525 °C, from 96 ± 3% with no catalyst, to 85 ± 2% with catalyst and steam, to 68 ± 4% with catalyst and steam-H2/CO2. Thus, the catalyst that we developed promoted tar cracking, tar reforming, and water-gas shift reactions, with a H2/CO ratio higher than 3.8, providing a syngas suitable for alcohol synthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".