Bio-Oil Steam Reforming over a Mining Residue Functionalized with Ni as Catalyst: Ni-UGSO
Bibliographic record
Abstract
Bio-oil reforming is considered for syngas or H2 production. In this work, we studied the steam reforming (SR) of two raw bio-oils without adding external steam, using a recently-developed catalyst, Ni-UGSO. Experiments were performed at temperature (T) = 750–850 °C and weight hourly space velocity (WHSV) = 1.7–7.1 g/gcat/h to assess C conversion (XC) and product yields. The results show that, in all conditions and with both bio-oils tested, the catalyst is stable for the entire duration of the tests (~500 min) even when some C deposition occurred and that only at the highest WHSV tested there is a slight deactivation. In all tests, catalytic activity remained constant after a first, short, transient state, which corresponded to catalyst activation. The highest yields and conversions, with Y H 2 , Y CO and XC of 94%, 84% and 100%, respectively, were observed at temperatures above 800 °C and WHSV = 1.7 g/gcat/h. The amount of H2O in the bio-oils had a non-negligible effect on catalyst activity, impacting Y H 2 , Y CO and XC values. It was observed that, above a critical amount of H2O, the catalyst was not fully activated. However, higher H2O content led to the reduction of C deposits as well as lower Y H 2 and Y CO and, through the water-gas-shift reaction, to higher Y CO 2 (CO2 selectivity). Fresh and spent catalysts were analyzed by physisorption (BET), X-ray diffraction, scanning electron microscopy and thermogravimetric analysis: the results reveal that, during the oils’ SR reaction, the initial spinel (Ni-Fe-Mg-Al) structures decreased over time-on-stream (TOS), while metallic Ni, Fe and their alloy phases appeared. Although significant sintering was observed in used catalysts, especially at high H2O/C ratio, the catalyst’s specific surface generally increased; the latter was attributed to the presence of nanometric metallic Ni and Ni-Fe alloy particles formed by reduction reactions. A small amount of C (4%) was formed at low H2O/C.
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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".