Experimental study and statistical analysis of hydrogen yield in methane steam reforming over a hydroxyapatite-supported nickel catalyst
Bibliographic record
Abstract
This study aimed to investigate the use of hydroxyapatite (HAp) as a catalyst support for the methane steam reforming (MSR) process. Catalysts were prepared via incipient wetness impregnation using nickel as the active metal and characterized by N 2 physisorption analysis, X-ray diffraction, H 2 temperature-programmed reduction, and transmission electron microscopy. The effects of four key parameters in hydrogen production—nickel loading (Ni wt%), inlet steam-to-methane (H 2 O/CH 4 ) ratio, space velocity (SV), and temperature (T)—were evaluated via a statistical analysis. Thermodynamic calculations were also used to compare the catalyst activity results with the equilibrium conditions. The findings confirmed the effectiveness and stability of HAp as a support, exhibiting high thermal stability, coke resistance, and a mesoporous structure with an average specific surface area of 65 m 2 ·g −1 . The calcined Ni/HAp catalyst mainly comprised crystalline NiO and displayed a higher surface area of 54 m 2 ·g −1 and superior dispersion at loadings of 5 and 10 wt% Ni compared to 15 wt% Ni. The temperature, SV, and inlet reactant ratio significantly influenced the process. A long-term stability test carried out under optimal conditions over 98 h demonstrated the consistent activity and stability of the 10 wt%Ni/HAp catalyst, maintaining a high methane conversion of 99 % and yielding 90 % hydrogen, 80 % carbon monoxide, and 20 % carbon dioxide, with no observed carbon deposition on the catalyst surface. • Ni/HAp catalysts exhibited high performance near equilibrium. • 5 wt% and 10 wt% Ni/HAp showed excellent stability, surface area, and dispersion. • Statistical analysis highlighted key factors for hydrogen yield optimization. • No carbon formation was observed during 98 hours of stability testing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".