Hydrogen production by methanol‐steam reforming using NiMoCu/γ‐alumina trimetallic catalysts
Bibliographic record
Abstract
Abstract Recent attention has focused on steam reforming (SR) of methanol to produce high‐purity hydrogen for ‘clean’ energy applications. Methanol (as a hydrogen carrier) is a renewable and easily accessible energy source that can be produced from biomass and natural gas; the advantages are its availability, high energy density, relative low cost, and easy storage and transportation. In the hydrogen production processes, catalysts play a very critical role for increasing the hydrogen yield and purity as well as reducing by‐products. In this study, the thermodynamic parameters of the methanol‐SR reaction were evaluated. Several NiMoCu/γ‐alumina trimetallic catalysts with different compositions were prepared by a wet impregnation method. The hydrogen production efficiency of the catalysts was evaluated for the methanol‐SR reaction. Thermodynamic studies of the reaction showed that methanol‐SR was an exothermic and spontaneous reaction for all catalysts investigated. Thermodynamic considerations revealed that a catalyst with a composition of 7 wt% Cu, 0.2 wt% Mo and 0.2 wt% Ni was the most reactive catalyst with an enthalpy of − 663.102 kcal. Experimental results showed that the optimum reaction occurred at 548 K and pressure of 3 bar. At these conditions, the production of undesirable by‐products such as CO and CO2 was negligible. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.001 | 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".