Elucidating the reaction kinetics of hydrogen generation via ethanol steam reforming using a nickel-based catalyst
Bibliographic record
Abstract
Abstract The continuous rise in CO2 emissions from fossil fuel consumption has intensified the search for alternative clean energy sources. Hydrogen produced from renewable sources like ethanol offers a promising alternative to fossil fuels, mitigating CO2 emissions. This study investigates the kinetics of hydrogen production via ethanol steam reforming using a nickel-based catalyst, specifically the Ar-401 catalyst. Characterization techniques, including scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy, transmission electron microscopy, Brunauer–Emmett–Teller method, temperature-programmed reduction, and powder X-ray diffraction, were used to analyze the catalyst properties. Under optimal conditions of 973 K, atmospheric pressure, and a steam-to-ethanol ratio of 9, we achieved 100% ethanol conversion, 74.8% hydrogen selectivity, and 85% hydrogen yield. Kinetic experiments were conducted under kinetically controlled conditions, examining the effects of temperature (473–673 K) and weight hourly space velocity ranging from 1 to 15 (g·h/mol). A power law kinetic model was developed, yielding an activation energy of 11.17 kJ/mol and a reaction order of 0.46, with an absolute average deviation of 3.23% between predicted and experimental rates. This study provides key insights into the reaction mechanisms and highlights the effectiveness of the nickel-based catalyst, providing valuable insights for the design of efficient chemical reactors for sustainable hydrogen production.
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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".