Hydrogen Production by Steam Reforming of Vegetable Oils Using Nickel-Based Catalysts
Bibliographic record
Abstract
Vegetable oils and fats are a renewable resource derived from biomass that can contribute to reduce the net emission of CO 2 into the atmosphere if used to produce hydrogen for fuel-cell-based energy systems. In this paper, we present the results of the steam reforming of several vegetable oils with three different nickel-based commercial catalysts (ICI 46-1, ICI 46-4, and UCI G90C) and two research catalysts (UdeS and HT). The experiments were performed in an isothermal fixed-bed tubular reactor at steam-to-carbon ( S / C ) ratios of 9, 6, and 3 and temperatures between 500 and 630 °C. High space velocities of 0.76−1.90 mol carbon /(g cat h) were used so that conversions of the feed would be incomplete. Hydrogen productions were from 0.3 to 7.5 mol H 2 /(g Ni h) depending on the operating conditions. The HT catalyst, which was prepared from a hydrotalcite-like precursor, seems promising for steam reforming vegetable oils because of its very high activity per gram of catalyst. Results for the steam reforming of sunflower, rapeseed, corn, and soybean oils at the same catalyst temperature and S / C ratio show that oil conversion to gases and hydrogen yields do not depend on the type of vegetable oil. This indicates that the process might be suitable for producing hydrogen from residual oils and fats from food processing, for which process economics are more favorable.
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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".