Sorption enhanced steam methane reforming by <scp>Ni</scp>/<scp>CaO</scp>/mayenite combined systems: Overview of experimental results from <scp>E</scp>uropean research project <scp>ASCENT</scp>
Bibliographic record
Abstract
Abstract Work Package 4 (WP4) of the European research project ASCENT developed combined sorbent‐catalyst materials (CSCM) for sorption enhanced steam methane reforming (SESMR), based on nickel (Ni) and calcium oxide (CaO). This work summarizes the whole experimental study carried out in ASCENT WP4 on Ni/CaO/mayenite systems obtained from wet mixing and wet impregnation synthesis methods. Effects from Ni precursor (Ni (CH3COO)2 · 4H2O or Ni(NO3)2 · 6H2O), Ni load (from 3 wt%‐10 wt%), and free CaO load (from 0 wt%‐54 wt%) were investigated for 26 materials by means of characterizations and reforming reactivity tests in a packed‐bed microreactor (650°C, 1 atm). Thanks to comparative analyses of the results, evidence emerged about the detrimental influence of low Ni/CaO ratio on the reforming catalytic activity of solid inventories, made of CSCM or even of the raw mixing of CaO‐mayenite and Ni‐mayenite particles. Catalytic materials were active towards reforming only when derived from Ni(NO3)2 · 6H2O. Based on this, the best CSCM (with the lowest free CaO content and the highest Ni load from nickel nitrate) was chosen to further study its industrial applicability by multicycle SESMR/sorbent‐regeneration tests in a bench‐scale packed‐bed rig and attrition tests according to ASTM D5757‐11. The CSCM was stable and active for 200 cycles with regenerations in N2 at 850°C, while a progressive loss of its activity occurred with regenerations in CO2 at 925°C as the cycle number increased due to Ni sintering. Its performance in the attrition tests was comparable to that of calcined dolomite.
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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) | 0.001 | 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) | 0.002 | 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".