High-Pressure Catalytic Ethanol Reforming for Enhanced Hydrogen Production Using Efficient and Stable Nickel-Based Catalysts
Bibliographic record
Abstract
The urgent need to address the climate crisis demands a swift transition from fossil fuels to renewable energy. Clean hydrogen, produced through ethanol steam reforming (ESR), offers a viable solution. Traditional ESR operates at atmospheric pressure, requiring costly separation and compression of hydrogen. High-pressure ESR, however, improves hydrogen purification, streamlines processes like pressure swing adsorption, and reduces operational costs while enhancing energy efficiency. High-pressure ESR also enables compact reactor designs, minimizing equipment size and land use by compressing reactants into smaller volumes. This study evaluates two nickel-based commercial catalysts, AR-401 and NGPR-2, under high-pressure ESR conditions. Key parameters, including reaction temperature, steam-to-ethanol ratio, and weight hourly space velocity, were optimized. At 30 bars, 700 °C, and a steam-to-ethanol ratio of 9, both catalysts demonstrated complete ethanol conversion, with hydrogen selectivity of 65–70% and yields of 4–4.5 moles of H2 per mole of ethanol. Raising the temperature to 850 °C improved hydrogen selectivity to 74% and yielded 5.2 moles of H2 per mole. High-pressure ESR using renewable ethanol provides a scalable, efficient pathway for hydrogen production, supporting sustainable energy solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".